Dataset:\\n return self.reduce(\\n duck_array_ops.min,\\n dim=dim,\\n skipna=skipna,\\n numeric_only=False,\\n keep_attrs=keep_attrs,\\n **kwargs,\\n )\",\n \"def find_min_distance():\\n return np.argmin(d)\",\n \"def nanargmin(a, axis=None):\\n y = array(a, subok=True)\\n if not issubclass(y.dtype.type, _nx.integer):\\n y[isnan(a)] = _nx.inf\\n return y.argmin(axis)\",\n \"def min(self, fn=lambda x: x):\\n return _(min(*self._, key=fn))\",\n \"def MinX(*args, **kwargs):\\n return _gdi_.DC_MinX(*args, **kwargs)\",\n \"def minimum(self, start, end):\\n return self.foldl1(start, end, min)\",\n \"def min(self):\\n if 0 in type(self).flatten_shape(self.shape):\\n raise ValueError(\\\"zero-size array has no minimum\\\")\\n if self.isscalar():\\n return self.defval\\n # If not all blocks are set, then the tensor has an element of defval\\n # somewhere.\\n m = np.inf if self.is_full() else self.defval\\n for v in self.sects.values():\\n try:\\n m = min(m, np.min(v))\\n except ValueError:\\n # This block was zero-size, and has no elements.\\n pass\\n return m\",\n \"def _null_min(a, b):\\n if a is None:\\n return b\\n if b is None:\\n return a\\n return min(a, b)\",\n \"def min_pool(inputs, window_shape, strides=None, padding=\\\"VALID\\\"):\\n return pool(inputs, jnp.inf, lax.min, window_shape, strides, padding)\",\n \"def arg_nonzero_min(a):\\n\\n if not a:\\n return\\n\\n min_ix, min_v = None, None\\n # find the starting value (should be nonzero)\\n for i, e in enumerate(a):\\n if e != 0:\\n min_ix = i\\n min_v = e\\n if not min_ix:\\n print('Warning: all zero')\\n return np.inf, np.inf\\n\\n # search for the smallest nonzero\\n for i, e in enumerate(a):\\n if e < min_v and e != 0:\\n min_v = e\\n min_ix = i\\n\\n return min_v, min_ix\",\n \"def min(\\n self,\\n dim: Dims = None,\\n *,\\n skipna: bool | None = None,\\n keep_attrs: bool | None = None,\\n **kwargs: Any,\\n ) -> DataArray:\\n if (\\n flox_available\\n and OPTIONS[\\\"use_flox\\\"]\\n and contains_only_chunked_or_numpy(self._obj)\\n ):\\n return self._flox_reduce(\\n func=\\\"min\\\",\\n dim=dim,\\n skipna=skipna,\\n # fill_value=fill_value,\\n keep_attrs=keep_attrs,\\n **kwargs,\\n )\\n else:\\n return self.reduce(\\n duck_array_ops.min,\\n dim=dim,\\n skipna=skipna,\\n keep_attrs=keep_attrs,\\n **kwargs,\\n )\",\n \"def min(\\n self,\\n dim: Dims = None,\\n *,\\n skipna: bool | None = None,\\n keep_attrs: bool | None = None,\\n **kwargs: Any,\\n ) -> DataArray:\\n if (\\n flox_available\\n and OPTIONS[\\\"use_flox\\\"]\\n and contains_only_chunked_or_numpy(self._obj)\\n ):\\n return self._flox_reduce(\\n func=\\\"min\\\",\\n dim=dim,\\n skipna=skipna,\\n # fill_value=fill_value,\\n keep_attrs=keep_attrs,\\n **kwargs,\\n )\\n else:\\n return self.reduce(\\n duck_array_ops.min,\\n dim=dim,\\n skipna=skipna,\\n keep_attrs=keep_attrs,\\n **kwargs,\\n )\",\n \"def expanding_min_1d_nb(a, minp=1):\\n out = np.empty_like(a, dtype=np.float_)\\n minv = a[0]\\n cnt = 0\\n for i in range(a.shape[0]):\\n if np.isnan(minv) or a[i] < minv:\\n minv = a[i]\\n if ~np.isnan(a[i]):\\n cnt += 1\\n if cnt < minp:\\n out[i] = np.nan\\n else:\\n out[i] = minv\\n return out\",\n \"def get_min(criterion, max_card, elements):\\n if max_card <= 0:\\n raise ValueError(\\n \\\"max_card: \\\" + str(max_card) + \\\"\\\\n\\\" +\\n \\\"The maximum cardinal cannot be null nor negative!\\\"\\n )\\n\\n minima = []\\n currentMin = 1000000000\\n for e in elements:\\n if 0 < e.cardinal <= max_card:\\n if len(minima) == 0:\\n currentMin = criterion(e)\\n minima.append((e, currentMin))\\n else:\\n newCandidate = criterion(e)\\n if newCandidate == currentMin:\\n minima.append((e, currentMin))\\n elif newCandidate < currentMin:\\n minima = []\\n currentMin = newCandidate\\n minima.append((e, currentMin))\\n return minima\",\n \"def _call_min(vecObj):\\n res = vecObj.min()\\n return res\",\n \"def min_(*args, **kwargs):\\n ...\",\n \"def convert_min(node, **kwargs):\\n name, input_nodes, attrs = get_inputs(node, kwargs)\\n\\n mx_axis = attrs.get(\\\"axis\\\", None)\\n axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None\\n\\n keepdims = get_boolean_attribute_value(attrs, \\\"keepdims\\\")\\n\\n if axes is not None:\\n node = onnx.helper.make_node(\\n 'ReduceMin',\\n inputs=input_nodes,\\n outputs=[name],\\n axes=axes,\\n keepdims=keepdims,\\n name=name\\n )\\n\\n return [node]\\n else:\\n node = onnx.helper.make_node(\\n 'ReduceMin',\\n inputs=input_nodes,\\n outputs=[name],\\n keepdims=keepdims,\\n name=name\\n )\\n\\n return [node]\",\n \"def reduce_min(data, axis=None, keepdims=False):\\n\\n return reduce_min_max_common.reduce_min_max(data, axis=axis, keepdims=keepdims, method=\\\"min\\\")\",\n \"def argmin(seq, fn):\\n best = seq[0]; best_score = fn(best)\\n for x in seq:\\n x_score = fn(x)\\n if x_score < best_score:\\n best, best_score = x, x_score\\n return best\",\n \"def argmin(seq, fn):\\n best = seq[0]; best_score = fn(best)\\n for x in seq:\\n x_score = fn(x)\\n if x_score < best_score:\\n best, best_score = x, x_score\\n return best\",\n \"def masked_minimum(data, mask, dim=1):\\n axis_maximums = math_ops.reduce_max(data, dim, keepdims=True)\\n masked_minimums = math_ops.reduce_min(\\n math_ops.multiply(data - axis_maximums, mask), dim,\\n keepdims=True) + axis_maximums\\n return masked_minimums\",\n \"def getMin(listOfVCFs, compareFun, numMins = 1):\\n # \\\"\\\"\\\"Returns the numMin keys with smallest values in the list\\\"\\\"\\\"\\n return min(listOfVCFs, key = compareFun)\",\n \"def min_scalar_prod(x, y):\\n x = sorted(x) # make copies\\n y = sorted(y) # to save arguments\\n return sum(x[i] * y[-i - 1] for i in range(len(x)))\",\n \"def min(self, values):\\n return self.aggregate(values, \\\"min\\\")\",\n \"def magma_isamin(n, dx, incx, queue):\\n\\n return _libmagma.magma_isamin(n, int(dx), incx, queue)\",\n \"def argmin(seq, fn):\\n best = seq[0]; best_score = fn(best)\\n for x in seq:\\n x_score = fn(x)\\n if x_score < best_score:\\n best, best_score = x, x_score\\n return best\",\n \"def min(self, elim=None, out=None):\\n if (elim is None):\\n elim = self.v\\n return self.__opReduce2(self.v & elim,np.min, out=out)\",\n \"def min(self):\\n mins = self.client.map(_call_min, self.vecDask, pure=False)\\n min_val = np.inf\\n for future, result in daskD.as_completed(mins, with_results=True):\\n if result < min_val:\\n min_val = result\\n return min_val\",\n \"def find_workload_with_min_eff_cost_by_idling_scipy(\\n w: types.WorkloadSpace, workload_mat: types.WorkloadMatrix,\\n cost_per_buffer: types.StateSpace, method: str = 'interior-point') \\\\\\n -> Optional[types.WorkloadSpace]:\\n res = linprog(c=cost_per_buffer, A_ub=-workload_mat, b_ub=-w, bounds=(0, None),\\n method=method)\\n if res.success:\\n x = res.x[:, None]\\n w_star = workload_mat @ x\\n else:\\n w_star = None\\n return w_star\",\n \"def min_reduce_nb(col, a, *args):\\n return np.nanmin(a)\",\n \"def argmin2(self, cvars=None, ctuple=None):\\n if (cvars is None):\\n return self.v.ind2sub(self.t.argmin())\\n ax = tuple(map(lambda x:ctuple[cvars.index(x)] if x in cvars else slice(None) ,self.v))\\n return self.v.ind2sub(self.t[ax].argmin())\",\n \"def argmin(self, array):\\n minvalue = inf\\n minindex = None\\n for i in range(len(array)):\\n if array[i] < minvalue:\\n minvalue = array[i]\\n minindex = i\\n return minvalue, minindex\",\n \"def min(self):\\n return self._summarize(DataFrameCpu._cmin)\",\n \"def min(self):\\n if self._mesh.is_1d():\\n ind = 1\\n elif self._mesh.is_2d():\\n ind = 2\\n else:\\n if self._logger:\\n self._logger.error(\\\"mesh dimension not implemented\\\")\\n raise NotImplementedError(\\\"mesh dimension not implemented\\\")\\n\\n def __map(m):\\n return m[ind]\\n\\n return self.data.map(\\n __map\\n ).min()\",\n \"def auxminf1(x):\\n \\n# Sum over data points\\n f = 0.0\\n for m_ind in range(cfg.ntrain):\\n f += auxmin_f1_part_i(x,m_ind) \\n \\n return f\",\n \"def _find_min_pair(pandas_matrix):\\n numpy_matrix = pandas_matrix.values\\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\\n min_col_idx = mins[0][0]\\n min_row_idx = mins[1][0]\\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \\n pandas_matrix.columns[min_row_idx])\\n\\n return (min_col, min_row)\",\n \"def _findMin(p, A):\\n\\n m=(-1, (0,0))\\n for p0 in A:\\n dist = np.linalg.norm(p0-np.array(p))\\n if m[0]==-1 or m[0]>dist:\\n m = (dist, p0)\\n \\n return tuple(m[1])\",\n \"def localMin0(R, L, W):\\n fo = costFunction(R, W)\\n vacantL = vacantPoint(L)\\n beta = None\\n q = None\\n\\n while True:\\n fmin = fo\\n\\n for alpha in range(0, len(R)):\\n for p in range(0, len(vacantL)):\\n TxpR = transpositionMatrix(R, vacantL, alpha, p)\\n ftrial = costFunction(TxpR, W)\\n if ftrial < fmin:\\n fmin = ftrial\\n beta = alpha\\n q = p\\n\\n if (beta != None) and (q != None):\\n TaqR = transpositionMatrix(R, vacantL, beta, q)\\n vacantL[q] = R[beta].copy()\\n R = TaqR.copy()\\n beta = None\\n q = None\\n\\n if fmin <= fo:\\n return fmin, R\",\n \"def min(self, numeric_only=None):\\n assert numeric_only == None\\n return self._lift(\\\"min\\\")\",\n \"def find_min(ls):\\n\\n if len(ls) == 1:\\n return ls[0]\\n elif len(ls) == 2:\\n return ls[0] if ls[0] < ls[1] else ls[1]\\n else:\\n mid = len(ls) // 2\\n m1 = find_min(ls[0:mid])\\n m2 = find_min(ls[mid:])\\n return m1 if m1 < m2 else m2\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6745464","0.6651574","0.6647899","0.64386827","0.6284703","0.6036429","0.6033137","0.5970848","0.5958427","0.5948367","0.5947503","0.59440386","0.59381294","0.5921218","0.59118783","0.5897402","0.5858605","0.5849506","0.58406216","0.5815177","0.5813334","0.580042","0.5780769","0.5776347","0.5771414","0.5746883","0.57168865","0.570722","0.5706416","0.57015264","0.56944436","0.56883717","0.5687363","0.5677604","0.5663252","0.5641195","0.5628933","0.56262743","0.5626167","0.5607744","0.55943644","0.5591628","0.55726874","0.55726874","0.5567892","0.5564642","0.5558497","0.55452824","0.5540589","0.5540086","0.55381745","0.5536753","0.55302197","0.5529999","0.55107224","0.549543","0.5493311","0.5489365","0.54865366","0.54734933","0.5453156","0.5448254","0.5446694","0.5445967","0.541405","0.54092896","0.5408699","0.5407686","0.5404132","0.5397567","0.5388802","0.5388802","0.53832066","0.5375078","0.53571045","0.5354303","0.5351121","0.5347383","0.53445745","0.53445745","0.53380376","0.53259766","0.53074706","0.5304742","0.5296926","0.5276803","0.5267605","0.5261811","0.52534705","0.5245532","0.5242939","0.5236853","0.5234936","0.52235776","0.5222826","0.5220293","0.52153367","0.52062285","0.5203046","0.51987857"],"string":"[\n \"0.6745464\",\n \"0.6651574\",\n \"0.6647899\",\n \"0.64386827\",\n \"0.6284703\",\n \"0.6036429\",\n \"0.6033137\",\n \"0.5970848\",\n \"0.5958427\",\n \"0.5948367\",\n \"0.5947503\",\n \"0.59440386\",\n \"0.59381294\",\n \"0.5921218\",\n \"0.59118783\",\n \"0.5897402\",\n \"0.5858605\",\n \"0.5849506\",\n \"0.58406216\",\n \"0.5815177\",\n \"0.5813334\",\n \"0.580042\",\n \"0.5780769\",\n \"0.5776347\",\n \"0.5771414\",\n \"0.5746883\",\n \"0.57168865\",\n \"0.570722\",\n \"0.5706416\",\n \"0.57015264\",\n \"0.56944436\",\n \"0.56883717\",\n \"0.5687363\",\n \"0.5677604\",\n \"0.5663252\",\n \"0.5641195\",\n \"0.5628933\",\n \"0.56262743\",\n \"0.5626167\",\n \"0.5607744\",\n \"0.55943644\",\n \"0.5591628\",\n \"0.55726874\",\n \"0.55726874\",\n \"0.5567892\",\n \"0.5564642\",\n \"0.5558497\",\n \"0.55452824\",\n \"0.5540589\",\n \"0.5540086\",\n \"0.55381745\",\n \"0.5536753\",\n \"0.55302197\",\n \"0.5529999\",\n \"0.55107224\",\n \"0.549543\",\n \"0.5493311\",\n \"0.5489365\",\n \"0.54865366\",\n \"0.54734933\",\n \"0.5453156\",\n \"0.5448254\",\n \"0.5446694\",\n \"0.5445967\",\n \"0.541405\",\n \"0.54092896\",\n \"0.5408699\",\n \"0.5407686\",\n \"0.5404132\",\n \"0.5397567\",\n \"0.5388802\",\n \"0.5388802\",\n \"0.53832066\",\n \"0.5375078\",\n \"0.53571045\",\n \"0.5354303\",\n \"0.5351121\",\n \"0.5347383\",\n \"0.53445745\",\n \"0.53445745\",\n \"0.53380376\",\n \"0.53259766\",\n \"0.53074706\",\n \"0.5304742\",\n \"0.5296926\",\n \"0.5276803\",\n \"0.5267605\",\n \"0.5261811\",\n \"0.52534705\",\n \"0.5245532\",\n \"0.5242939\",\n \"0.5236853\",\n \"0.5234936\",\n \"0.52235776\",\n \"0.5222826\",\n \"0.5220293\",\n \"0.52153367\",\n \"0.52062285\",\n \"0.5203046\",\n \"0.51987857\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7239349"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14984,"cells":{"query":{"kind":"string","value":"Computes the elementwise maximum of some matrices."},"document":{"kind":"string","value":"def sim_max(sim_mats):\n return np.array(sim_mats).max(axis=0)"},"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 three_array_max(array_list: List[np.ndarray]) -> np.ndarray:\n temp = np.maximum(array_list[0], array_list[1])\n all_maxs = np.maximum(temp, array_list[2])\n\n return all_maxs","def d_max(x, y):\n axis = np.argmax(x.shape)\n return np.max(np.array([x, y]), axis=axis)","def max(x, reduce_instance_dims=True, name=None): # pylint: disable=redefined-builtin\n return _numeric_combine(x, np.max, reduce_instance_dims, name)","def maximum(x):\n return np.maximum(x, 0)","def compare_max(values, weights):\n return np.max(values.numpy())","def max(*x, na_rm: bool = False) -> Any:\n fun = numpy.nanmax if na_rm else numpy.max\n x = Collection(*x) # flatten\n return fun(x)","def max_decode(M):\r\n return scipy.array([ f.val.argmax() for f in M])","def find_max_product(mtx):\n max_prod = 0\n for row_num in range(20):\n vert = 0\n diag = 0\n anti_diag = 0\n horiz = horiz_max(mtx[row_num])\n if row_num < len(mtx) - 3:\n vert = vert_max(mtx[row_num], mtx[row_num + 1],\n mtx[row_num + 2], mtx[row_num + 3])\n diag = diag_max(mtx[row_num], mtx[row_num + 1],\n mtx[row_num + 2], mtx[row_num + 3])\n anti_diag = anti_diag_max(mtx[row_num], mtx[row_num + 1],\n mtx[row_num + 2], mtx[row_num + 3])\n max_prod = max(max_prod, horiz, vert, diag, anti_diag)\n return max_prod","def max(x, axis=None, keepdims=False):\r\n\r\n # We have a choice of implementing this call with the\r\n # CAReduce op or the MaxAndArgmax op.\r\n\r\n # MaxAndArgmax supports grad and Rop, so we prefer to use that.\r\n # CAReduce is faster, but optimizations will replace MaxAndArgmax[0]\r\n # with CAReduce at compile time, so at this stage the important\r\n # thing is supporting all user interface features, not speed.\r\n # Some cases can be implemented only with CAReduce.\r\n\r\n # We thus prefer to use MaxAndArgmax, if possible. It does not\r\n # support all axis arguments, so we may need to fall back to CAReduce.\r\n\r\n try:\r\n out = max_and_argmax(x, axis)[0]\r\n except Exception:\r\n out = CAReduce(scal.maximum, axis)(x)\r\n\r\n if keepdims:\r\n out = makeKeepDims(x, out, axis)\r\n return out","def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\n\n return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out,\n mask=mask, shift_x=shift_x, shift_y=shift_y)","def compute_largest_eigenvalue(A, num_simulations):\n b_k = power_iteration(A, num_simulations)\n return b_k.dot(A).dot(b_k) / (b_k.dot(b_k))","def find_matrix_max(matrix):\n\n max_val = 0.0\n max_i = 0\n max_j = 0\n\n for i in matrix.keys():\n try:\n kvp = max(matrix[i].iteritems(), key=itemgetter(1))\n except ValueError:\n continue\n \n # Maybe I should store the max value with the array, and then always \n # store the previous largest, and when i insert or delete...\n \n if kvp[1] > max_val:\n max_val = kvp[1]\n max_i = i\n max_j = kvp[0]\n\n return (max_i, max_j, max_val)","def maximum(lhs, rhs):\n return _make.maximum(lhs, rhs)","def argmax(module, x, axes=None):\n return module.argmax(x, axes)","def max_dim(elements, coordinates):\n atom_vdw_vertical = np.matrix(\n [[atomic_vdw_radius[i.upper()]] for i in elements])\n atom_vdw_horizontal = np.matrix(\n [atomic_vdw_radius[i.upper()] for i in elements])\n dist_matrix = euclidean_distances(coordinates, coordinates)\n vdw_matrix = atom_vdw_vertical + atom_vdw_horizontal\n re_dist_matrix = dist_matrix + vdw_matrix\n final_matrix = np.triu(re_dist_matrix)\n i1, i2 = np.unravel_index(final_matrix.argmax(), final_matrix.shape)\n maxdim = final_matrix[i1, i2]\n return i1, i2, maxdim","def structured_maximum(x, y):\r\n # see decorator for function body\r","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 pmax(\n *x: Iterable,\n na_rm: bool = False\n) -> Iterable[float]:\n maxlen = max(map(length_of, x))\n x = (recycle_value(elem, maxlen) for elem in x)\n return Array([max(elem, na_rm=na_rm) for elem in zip(*x)])","def relu(arr):\n return column_bind(zeros(len(arr)), arr).max(axis=1)","def mrv_max1(f, g, exps, x):\n u, b = f.union(g, exps)\n return mrv_max3(f, g.do_subs(exps), g, f.do_subs(exps),\n u, b, x)","def MaxMatrix(m):\n max = 0\n index = [0,1]\n for i in m:\n for j in i:\n if j > max:\n max = j\n index = [m.index(i),i.index(j)]\n return index","def max(x, y):\n x[:] = np.maximum(x[:], y[:])\n return x","def scalar_max(self, dst, src0, src1):\n return self._scalar_binary_func('max', dst, src0, src1)","def argmax(a, *args, **kwargs):\n warn('The function argmax is deprecated from JAMS. Use module pyjams.',\n category=DeprecationWarning)\n if isinstance(a, np.ma.MaskedArray):\n return np.ma.argmax(a, *args, **kwargs)\n elif isinstance(a, np.ndarray):\n return np.argmax(a, *args, **kwargs)\n else:\n return _argmax(a)","def max_and_argmax(a, axis=None, keepdims=False):\r\n\r\n out, argout = _max_and_argmax(a, axis)\r\n\r\n if keepdims:\r\n out = makeKeepDims(a, out, axis)\r\n argout = makeKeepDims(a, argout, axis)\r\n return [out, argout]","def compute_maximisation( self, X, Z, O ):\n\n raise NotImplementedError","def arglexmax(keys, multi=False):\n # Handle keys in reverse order to be consistent with np.lexsort\n reverse_keys = keys[::-1]\n arr = reverse_keys[0]\n breakers = reverse_keys[1:]\n # Look for the maximum value in the first array, and continue using new\n # arrays until a unique maximum index is found.\n _cand_idxs = np.where(arr == arr.max())[0]\n if len(_cand_idxs) > 1:\n for breaker in breakers:\n vals = breaker[_cand_idxs]\n _cand_idxs = _cand_idxs[vals == vals.max()]\n if len(_cand_idxs) == 1:\n break\n # If multiple maximum values are found then either\n # return them all or return an arbitrary one.\n return _cand_idxs if multi else _cand_idxs[0]","def max_op(*substrate_index_arrays):\n result = numpy.max(\n numpy.stack([x.flatten() for x in substrate_index_arrays]) *\n self.species_substrate_suitability_index_array, axis=0)\n result = result.reshape(substrate_index_arrays[0].shape)\n result[substrate_index_arrays[0] == _INDEX_NODATA] = _INDEX_NODATA\n return result","def maximum_inplace(a, b):","def maxi(a, b):\n return max(a, b)","def argmax(x):\n def op(a, b):\n comp = (a[1] > b[1])\n return comp.if_else(a[0], b[0]), comp.if_else(a[1], b[1])\n return tree_reduce(op, enumerate(x))[0]","def argmax(x):\n def op(a, b):\n comp = (a[1] > b[1])\n return comp.if_else(a[0], b[0]), comp.if_else(a[1], b[1])\n return tree_reduce(op, enumerate(x))[0]","def r_max(nxs):\n largest = None\n for i,e in enumerate(nxs):\n if type(e) == type([]):\n val = r_max(e)\n else:\n val = e\n\n if i == 0 or val > largest:\n largest = val\n\n return largest","def scale_matrix_by_max(A):\n # Section 1: Find the max value of the matrix\n max = 0\n for row in A:\n for col in row:\n if abs(col) > max:\n max = col\n\n # Section 2: Create a copy of the matrix A\n new = copy_matrix(A)\n rows = len(new)\n cols = len(new[0])\n\n # Section 3: Reduce each value of copied matrix by max value\n for i in range(rows):\n for j in range(cols):\n new[i][j] = new[i][j] / max\n\n return new","def maxProduct(data):\n maxval = float('-inf')\n for i in range(len(data)):\n for j in range(i+1, len(data)):\n if maxval < data[i]*data[j]:\n maxval = data[i]*data[j]\n a,b = (data[i],data[j])\n return tuple([a,b])","def maximum(x, y):\r\n # see decorator for function body\r","def max_pairwise_product_linear(array):\n\n if len(array) <= 1:\n return 0\n\n two_biggest_values = [0, 0]\n\n for element in array:\n if element > two_biggest_values[0]:\n two_biggest_values[0] = element\n elif element > two_biggest_values[1]:\n two_biggest_values[1] = element\n\n return two_biggest_values[0] * two_biggest_values[1]","def argmax(fn,over):\n return max([(arg,fn(arg)) for arg in over],key=lambda v: v[1])[0]","def argmax(self, values):\n return self.aggregate(values, \"argmax\")","def max(self, axis=None, keepdims=False, out=None):\n return np.maximum.reduce(self, out=out, axis=axis, keepdims=keepdims)","def local_max_and_argmax(node):\r\n if node.op == T._max_and_argmax:\r\n if len(node.outputs[1].clients) == 0:\r\n #MaxAndArgmax support variable axis,\r\n #but CAReduce support only constant axis.\r\n if node.inputs[1].data is None:\r\n axis = None\r\n else:\r\n try:\r\n axis = get_scalar_constant_value(node.inputs[1])\r\n except NotScalarConstantError:\r\n return False\r\n\r\n new = CAReduce(scal.maximum, axis)(node.inputs[0])\r\n return [new, None]","def softmax(x):\n orig_shape = x.shape\n\n if len(x.shape) > 1:\n # Matrix\n tmp = np.max(x, axis=1)\n x -= tmp.reshape((x.shape[0], 1))\n x = np.exp(x)\n tmp = np.sum(x, axis=1)\n x /= tmp.reshape((x.shape[0], 1))\n else:\n # Vector\n tmp = np.max(x)\n x -= tmp\n x = np.exp(x)\n tmp = np.sum(x)\n x /= tmp\n\n assert x.shape == orig_shape\n return x","def r_max(nxs):\n largest = None\n first_time = True\n for e in nxs:\n if type(e) == type([]):\n val = r_max(e)\n else:\n val = e\n\n if first_time or val > largest:\n largest = val\n first_time = False\n\n return largest","def get_max(criterion, max_card, elements):\n if max_card <= 0:\n raise ValueError(\n \"max_card: \" + str(max_card) + \"\\n\" +\n \"The maximum cardinal cannot be null nor negative!\"\n )\n\n maxima = []\n currentMax = 0\n for e in elements:\n if 0 < e.cardinal <= max_card:\n if len(maxima) == 0:\n currentMax = criterion(e)\n maxima.append((e, currentMax))\n else:\n newCandidate = criterion(e)\n if newCandidate == currentMax:\n maxima.append((e, currentMax))\n elif newCandidate > currentMax:\n maxima = []\n currentMax = newCandidate\n maxima.append((e, currentMax))\n return maxima","def compute_largest_product(grid):\n row_largest = compute_largest_row_product(grid)\n column_largest = compute_largest_column_product(grid)\n diagonal1_largest = compute_largest_diagonal1_product(grid)\n diagonal2_largest = compute_largest_diagonal2_product(grid)\n\n my_list = [row_largest , column_largest , diagonal1_largest , diagonal2_largest]\n return max(my_list)","def np_max(arr, axis):\n assert arr.ndim == 2\n assert axis in [0, 1]\n if axis == 0:\n result = np.empty(arr.shape[1])\n for i in range(len(result)):\n result[i] = np.max(arr[:, i])\n else:\n result = np.empty(arr.shape[0])\n for i in range(len(result)):\n result[i] = np.max(arr[i, :])\n return result","def max(self):\n max_i = np.nanargmax(self.ys)\n return self.xs[max_i], self.ys[max_i]","def get_max(bij, exploration, bij_bool):\n\n#\tbij[bij_bool] = -sys.maxint - 1\n\n\tm = bij.argmax()\n\tc = np.unravel_index(m, bij.shape)\n\t#c = np.unravel_index(bij.argmax(), bij.shape)\n\n############################## A MODIFIER EVENTUELLEMENT #################\n#\tb = bij[bij_bool]\n#\tm = b.argmax()\n#\tind = np.unravel_index(m, b.shape)\n#\tc = np.where(bij == b[ind])\n#\tc = (c[0][0], c[1][0])\n#\tprint('mMAXx', bij[c])\n\treturn (c)","def maximum(some_list):\n return max(some_list)","def max(self, elim=None, out=None):\n if (elim is None):\n elim = self.v\n return self.__opReduce2(self.v & elim,np.max, out=out)","def _call_maximum(vecObj, vec2):\n res = vecObj.maximum(vec2)\n return res","def extract_max_value(h: np.ndarray):\n return np.argmax(h, axis=1)","def max(scores):\n return __builtin__.max(scores) if len(scores) else 0","def test_maximax():\n f = np.asarray([\n [0.99, 1.0, 0.5],\n [0.69, 0.6, 0.6]])\n R = common_metrics.maximax(f, maximise=True)\n expected = np.asarray(\n [1.0, 0.69])\n assert np.allclose(R, expected)\n R = common_metrics.maximax(f, maximise=False)\n expected = np.asarray(\n [-0.5, -0.6])\n assert np.allclose(R, expected)","def grid_channelwise_max(grid_):\n return np.max(np.max(np.max(grid_, axis=0), axis=0), axis=0)","def softmax(a,c):\n\n assert(np.shape(a) == np.shape(c))\n return np.multiply(np.sign(a) , np.maximum(np.absolute(a) - c, np.zeros(np.shape(a))))","def maximum(x1, x2, *args, **kwargs):\n raise NotImplementedError","def max(self, axis=0, **kwargs) -> \"Dataset\":\n return self.aggregate(axis=axis, func=np.max, **kwargs)","def batch_maximum(imstack):\n maxpos = np.zeros((imstack.shape[0], 2))\n for i in range(imstack.shape[0]):\n if np.isnan(imstack[i, 0, 0]):\n maxpos[i, 0] = np.nan\n maxpos[i, 1] = np.nan\n else:\n ind = np.unravel_index(\n np.argmax(np.squeeze(imstack[i]), axis=None),\n np.squeeze(imstack[i]).shape,\n )\n maxpos[i, 0] = ind[1]\n maxpos[i, 1] = ind[0]\n return maxpos","def max_ij(f, K):\n i_best, j_best, m_value_min = min_ij(lambda i, j: (-1) * f(i, j), K)\n return i_best, j_best, - m_value_min","def _metric_max_over_ground_truths(metric_fn: Callable[[str, str], Tensor], prediction: str, ground_truths: List[str]) ->Tensor:\n return max(metric_fn(prediction, truth) for truth in ground_truths)","def max_pairwise_product_brute_force(array):\n\n if len(array) <= 1:\n return 0\n\n max_product = 0\n\n for i in range(len(array)):\n for j in range(len(array)):\n if i != j:\n if array[i] * array[j] > max_product:\n max_product = array[i] * array[j]\n\n return max_product","def max_val_rec_2d(arr):\n return max_val_rec([max_val_rec(each_list) for each_list in arr])","def max(self):\n if 0 in type(self).flatten_shape(self.shape):\n raise ValueError(\"zero-size array has no maximum\")\n if self.isscalar():\n return self.defval\n # If not all blocks are set, then the tensor has an element of defval\n # somewhere.\n m = -np.inf if self.is_full() else self.defval\n for v in self.sects.values():\n try:\n m = max(m, np.max(v))\n except ValueError:\n # This block was zero-size, and has no elements.\n pass\n return m","def masked_maximum(data, mask, dim=1):\n axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)\n masked_maximums = math_ops.reduce_max(\n math_ops.multiply(data - axis_minimums, mask), dim,\n keepdims=True) + axis_minimums\n return masked_maximums","def max(self):\n max = 0\n a = self.array_form\n for i in xrange(len(a)):\n if a[i] != i and a[i] > max:\n max = a[i]\n return max","def argmax(x1, axis=None, out=None):\n\n x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)\n if x1_desc:\n if axis is not None:\n pass\n elif out is not None:\n pass\n else:\n result_obj = dpnp_argmax(x1_desc).get_pyobj()\n result = dpnp.convert_single_elem_array_to_scalar(result_obj)\n\n return result\n\n return call_origin(numpy.argmax, x1, axis, out)","def test_max_back(a, num_axes, keepdims):\n if num_axes == 0:\n axes = None\n else:\n axes = np.random.choice(range(0, a.ndim), size=min(num_axes, a.ndim), replace=False)\n axes = tuple(sorted(axes))\n\n # single global maximum\n if axes is None or axes == tuple(range(a.ndim)):\n index = tuple(np.random.choice(i.flat) for i in np.indices(a.shape))\n a[index] = a.max() + 1\n\n grad = np.zeros_like(a)\n grad[index] = 1\n\n a = Tensor(a)\n out = a.max(axis=axes, keepdims=keepdims)\n out.backward()\n assert np.allclose(grad, a.grad)\n return None\n\n # explicitly place maxima within tensor\n static_axes = tuple(sorted(set(range(a.ndim)) - set(axes)))\n static_shape = tuple(a.shape[i] for i in static_axes)\n red_shapes = tuple(a.shape[i] for i in axes)\n sorter = np.argsort(static_axes + axes)\n\n # generate indices to span static axes\n static_indices = tuple(i for i in np.indices(static_shape))\n\n # generate random index-runs along reduction axes\n choose_indices = tuple(np.random.choice(range(i), size=static_indices[0].shape) for i in red_shapes)\n\n # create index tuple the selects random runs along reduction axes\n static_indices += choose_indices\n indices = []\n for i in sorter:\n indices.append(static_indices[i])\n indices = tuple(indices)\n\n # place extrema\n a[indices] = a.max() + np.random.rand(*indices[0].shape)\n a = Tensor(a)\n out = a.max(axis=axes)\n\n # break degeneracy amongst grad values\n tmp = np.arange(1, out.data.size+1).reshape(out.shape)\n out2 = out * tmp\n out2.backward()\n\n grad = np.zeros_like(a.data)\n grad[indices] = np.arange(1, out.data.size+1).reshape(out.shape)\n\n assert np.allclose(grad, a.grad)","def largest(*args):\r\n if len(args) == 2:\r\n a, b = args\r\n return switch(a > b, a, b)\r\n else:\r\n return max(stack(*args), axis=0)","def maxnorm(self, priors=None):\n def maxnorm_f(x): return x.max(axis=0)\n return self.utility(maxnorm_f, priors)","def softmax(x):\n orig_shape = x.shape\n\n if len(x.shape) > 1:\n # Matrix\n exp_minmax = lambda x: np.exp(x - np.max(x))\n denom = lambda x: 1.0 / np.sum(x)\n x = np.apply_along_axis(exp_minmax, 1, x)\n denominator = np.apply_along_axis(denom, 1, x)\n\n if len(denominator.shape) == 1:\n denominator = denominator.reshape((denominator.shape[0], 1))\n\n x = x * denominator\n else:\n # Vector\n x_max = np.max(x)\n x = x - x_max\n numerator = np.exp(x)\n denominator = 1.0 / np.sum(numerator)\n x = numerator.dot(denominator)\n\n assert x.shape == orig_shape\n return x","def compute_largest_column_product(grid):\n max_product = 0\n for column in range(len(grid)):\n for row in range(len(grid) - 3):\n current_product = 1\n for j in range(4):\n current_product *= grid[row + j][column]\n if current_product > max_product:\n max_product = current_product\n return max_product","def argmax(self, values: pdarray) -> Tuple[groupable, pdarray]:\n k, v = self.aggregate(values, \"argmax\")\n return k, cast(pdarray, v)","def _mul(*args):\n\treturn functools.reduce(numpy.dot, args)","def zmax(self):\n # Extract parameters\n pzs = self.params[0]\n return max([pz.zmax for pz in pzs])","def zmax(self):\n # Extract parameters\n pzs = self.params[0]\n return max([pz.zmax for pz in pzs])","def maxElem(A):\n n = len(A)\n AMax = 0.0\n for i in range(n):\n for j in range(i+1,n):\n if abs(A[i,j]) >= AMax:\n AMax = abs(A[i,j])\n k = i;l = j\n return AMax, k, l","def max_pairwise_product_fast(numbers):\n num_list = numbers.copy()\n max_num_1 = max(num_list)\n num_list.remove(max_num_1)\n max_num_2 = max(num_list)\n ans = max_num_1*max_num_2\n return ans","def argmax(func, seq):\n def compare(a1, b1):\n if a1[0] > b1[0]:\n return a1\n return b1\n # using a generator expression here should save memory\n objs = ((func(val), val) for val in seq)\n return reduce(compare, objs)[1]","def max_over(fn,over):\n return max([fn(arg) for arg in over])","def maximize(self,x0,method='fmin',**kwargs):\n return self._optimize(x0,'max',method,**kwargs)","def max_array(func: Callable[[Tuple], np.ndarray], state: Tuple[Union[int, float]]) \\\n -> Tuple[float, Tuple[int], np.ndarray]:\n arr = func((state,)).ravel()\n max_index = np.argmax(arr)\n return (arr[max_index], (max_index,), arr)","def max_pairwise_product(numbers):\n n = len(numbers)\n max_product = 0\n for first in range(n):\n for second in range(first + 1, n):\n max_product = max(max_product,\n numbers[first] * numbers[second])\n\n return max_product","def get_maximum_value(dataset):\n d = [int(i) for i in dataset if i.isdigit()]\n op = [o for o in dataset if o in ['*', '-', '+']]\n n = len(d)\n d.insert(0, None)\n op.insert(0, None)\n m = [[0 for x in range(n+1)] for y in range(n+1)]\n M = [[0 for x in range(n+1)] for y in range(n+1)]\n for i in range(1, n+1):\n m[i][i] = d[i]\n M[i][i] = d[i]\n for s in range(1, n):\n for i in range(1, n-s+1):\n j = i + s\n m[i][j], M[i][j] = min_and_max(i, j, op, m, M)\n return M[1][n]","def hmax(f, h=1, Bc=None):\n\n if Bc is None: Bc = secross()\n g = subm(f,h)\n y = infrec(g,f,Bc);\n return y","def arrmax(a):\n try:\n return a.max()\n except AttributeError:\n # not a NumPy array\n if isinstance(a, collections.Sequence):\n return max(a) # does not work for nested sequences\n elif isinstance(a, numbers.Number):\n return a\n else:\n raise TypeError('arrmax of %s not supported' % type(a))","def arrmax(a):\n try:\n return a.max()\n except AttributeError:\n # not a NumPy array\n if isinstance(a, collections.Sequence):\n return max(a) # does not work for nested sequences\n elif isinstance(a, numbers.Number):\n return a\n else:\n raise TypeError('arrmax of %s not supported' % type(a))","def max(tensor, axis=None):\n raise NotImplementedError","def max(self):\n return numpy.ma.max(self.data)","def softmax(oSums):\n result = np.zeros(shape=[len(oSums)], dtype=np.float32)\n m = max(oSums)\n divisor = 0.0\n for k in range(len(oSums)):\n divisor += math.exp(oSums[k] - m)\n for k in range(len(result)):\n result[k] = math.exp(oSums[k] - m) / divisor\n return result","def ReLU(z):\n return np.max([z, np.zeros(z.shape)], axis=0)","def max_apply(x): \n if len(x) == 1:\n return x[0]\n else:\n return x[1]","def findMax2d(x):\n m, n = x.shape \n x_ = x.ravel()\n idx = np.argmax(x_)\n i = idx // n \n j = idx % n \n return i, j","def highest_product(arr):\n\n product = 1\n\n for i in range(3):\n # find the max value in the list, get the index, pop it, and mulitply\n product *= arr.pop(arr.index(max(arr)))\n\n return product","def nanmax(a, axis=None):\n y = array(a, subok=True)\n if not issubclass(y.dtype.type, _nx.integer):\n y[isnan(a)] = -_nx.inf\n return y.max(axis)","def arrmax(a):\n # could set arrmax = amax in scipy if scipy is installed\n try:\n return max(a.flat) # use Python's list min\n except AttributeError:\n # not a NumPy array\n if isinstance(a, collections.Sequence):\n return max(a)\n elif isinstance(a, numbers.Number):\n return a\n else:\n raise TypeError('arrmax of %s not supported' % type(a))","def _call_max(vecObj):\n res = vecObj.max()\n return res","def _multiple_values_max(self, maps, threshold):\r\n max_val = np.zeros((maps.shape[0], maps.shape[1]), dtype=np.float)\r\n for i in range(maps.shape[1]):\r\n cmin = np.min(maps[:,i])\r\n cmax = np.max(maps[:,i])\r\n limit = cmax - (cmax - cmin) * threshold[i]\r\n min_mask = maps[:,i] <= limit\r\n max_mask = maps[:,i] > limit\r\n # for an abundance map the delta is around [-1..1],\r\n # but it can be outside this interval, it's something\r\n # to test\r\n # a guard with a -10 value maybe ok.\r\n rmin = min_mask * -10\r\n max_val[:,i] = max_mask * maps[:,i] + rmin\r\n max_vec = np.max(max_val, axis=1)\r\n max_mask = max_vec > -10\r\n argmax = np.argmax(max_val, axis=1)\r\n return (argmax + 1) * max_mask","def find_max(centroids):\n \n max_sim = 0.0\n max_i = 0\n max_j = 0\n length = len(centroids)\n\n for i in xrange(0, length):\n for j in xrange(i + 1, length):\n curr_sim = similarity(centroids[i], centroids[j])\n if curr_sim > max_sim:\n max_sim = curr_sim\n max_i = i\n max_j = j\n\n return (max_i, max_j, max_sim)","def max_abs_weights(x,\n *,\n axis = None):\n abs_max_x = jnp.max(jnp.abs(x), axis=axis, keepdims=True)\n return abs_max_x"],"string":"[\n \"def three_array_max(array_list: List[np.ndarray]) -> np.ndarray:\\n temp = np.maximum(array_list[0], array_list[1])\\n all_maxs = np.maximum(temp, array_list[2])\\n\\n return all_maxs\",\n \"def d_max(x, y):\\n axis = np.argmax(x.shape)\\n return np.max(np.array([x, y]), axis=axis)\",\n \"def max(x, reduce_instance_dims=True, name=None): # pylint: disable=redefined-builtin\\n return _numeric_combine(x, np.max, reduce_instance_dims, name)\",\n \"def maximum(x):\\n return np.maximum(x, 0)\",\n \"def compare_max(values, weights):\\n return np.max(values.numpy())\",\n \"def max(*x, na_rm: bool = False) -> Any:\\n fun = numpy.nanmax if na_rm else numpy.max\\n x = Collection(*x) # flatten\\n return fun(x)\",\n \"def max_decode(M):\\r\\n return scipy.array([ f.val.argmax() for f in M])\",\n \"def find_max_product(mtx):\\n max_prod = 0\\n for row_num in range(20):\\n vert = 0\\n diag = 0\\n anti_diag = 0\\n horiz = horiz_max(mtx[row_num])\\n if row_num < len(mtx) - 3:\\n vert = vert_max(mtx[row_num], mtx[row_num + 1],\\n mtx[row_num + 2], mtx[row_num + 3])\\n diag = diag_max(mtx[row_num], mtx[row_num + 1],\\n mtx[row_num + 2], mtx[row_num + 3])\\n anti_diag = anti_diag_max(mtx[row_num], mtx[row_num + 1],\\n mtx[row_num + 2], mtx[row_num + 3])\\n max_prod = max(max_prod, horiz, vert, diag, anti_diag)\\n return max_prod\",\n \"def max(x, axis=None, keepdims=False):\\r\\n\\r\\n # We have a choice of implementing this call with the\\r\\n # CAReduce op or the MaxAndArgmax op.\\r\\n\\r\\n # MaxAndArgmax supports grad and Rop, so we prefer to use that.\\r\\n # CAReduce is faster, but optimizations will replace MaxAndArgmax[0]\\r\\n # with CAReduce at compile time, so at this stage the important\\r\\n # thing is supporting all user interface features, not speed.\\r\\n # Some cases can be implemented only with CAReduce.\\r\\n\\r\\n # We thus prefer to use MaxAndArgmax, if possible. It does not\\r\\n # support all axis arguments, so we may need to fall back to CAReduce.\\r\\n\\r\\n try:\\r\\n out = max_and_argmax(x, axis)[0]\\r\\n except Exception:\\r\\n out = CAReduce(scal.maximum, axis)(x)\\r\\n\\r\\n if keepdims:\\r\\n out = makeKeepDims(x, out, axis)\\r\\n return out\",\n \"def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\\n\\n return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out,\\n mask=mask, shift_x=shift_x, shift_y=shift_y)\",\n \"def compute_largest_eigenvalue(A, num_simulations):\\n b_k = power_iteration(A, num_simulations)\\n return b_k.dot(A).dot(b_k) / (b_k.dot(b_k))\",\n \"def find_matrix_max(matrix):\\n\\n max_val = 0.0\\n max_i = 0\\n max_j = 0\\n\\n for i in matrix.keys():\\n try:\\n kvp = max(matrix[i].iteritems(), key=itemgetter(1))\\n except ValueError:\\n continue\\n \\n # Maybe I should store the max value with the array, and then always \\n # store the previous largest, and when i insert or delete...\\n \\n if kvp[1] > max_val:\\n max_val = kvp[1]\\n max_i = i\\n max_j = kvp[0]\\n\\n return (max_i, max_j, max_val)\",\n \"def maximum(lhs, rhs):\\n return _make.maximum(lhs, rhs)\",\n \"def argmax(module, x, axes=None):\\n return module.argmax(x, axes)\",\n \"def max_dim(elements, coordinates):\\n atom_vdw_vertical = np.matrix(\\n [[atomic_vdw_radius[i.upper()]] for i in elements])\\n atom_vdw_horizontal = np.matrix(\\n [atomic_vdw_radius[i.upper()] for i in elements])\\n dist_matrix = euclidean_distances(coordinates, coordinates)\\n vdw_matrix = atom_vdw_vertical + atom_vdw_horizontal\\n re_dist_matrix = dist_matrix + vdw_matrix\\n final_matrix = np.triu(re_dist_matrix)\\n i1, i2 = np.unravel_index(final_matrix.argmax(), final_matrix.shape)\\n maxdim = final_matrix[i1, i2]\\n return i1, i2, maxdim\",\n \"def structured_maximum(x, y):\\r\\n # see decorator for function body\\r\",\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 pmax(\\n *x: Iterable,\\n na_rm: bool = False\\n) -> Iterable[float]:\\n maxlen = max(map(length_of, x))\\n x = (recycle_value(elem, maxlen) for elem in x)\\n return Array([max(elem, na_rm=na_rm) for elem in zip(*x)])\",\n \"def relu(arr):\\n return column_bind(zeros(len(arr)), arr).max(axis=1)\",\n \"def mrv_max1(f, g, exps, x):\\n u, b = f.union(g, exps)\\n return mrv_max3(f, g.do_subs(exps), g, f.do_subs(exps),\\n u, b, x)\",\n \"def MaxMatrix(m):\\n max = 0\\n index = [0,1]\\n for i in m:\\n for j in i:\\n if j > max:\\n max = j\\n index = [m.index(i),i.index(j)]\\n return index\",\n \"def max(x, y):\\n x[:] = np.maximum(x[:], y[:])\\n return x\",\n \"def scalar_max(self, dst, src0, src1):\\n return self._scalar_binary_func('max', dst, src0, src1)\",\n \"def argmax(a, *args, **kwargs):\\n warn('The function argmax is deprecated from JAMS. Use module pyjams.',\\n category=DeprecationWarning)\\n if isinstance(a, np.ma.MaskedArray):\\n return np.ma.argmax(a, *args, **kwargs)\\n elif isinstance(a, np.ndarray):\\n return np.argmax(a, *args, **kwargs)\\n else:\\n return _argmax(a)\",\n \"def max_and_argmax(a, axis=None, keepdims=False):\\r\\n\\r\\n out, argout = _max_and_argmax(a, axis)\\r\\n\\r\\n if keepdims:\\r\\n out = makeKeepDims(a, out, axis)\\r\\n argout = makeKeepDims(a, argout, axis)\\r\\n return [out, argout]\",\n \"def compute_maximisation( self, X, Z, O ):\\n\\n raise NotImplementedError\",\n \"def arglexmax(keys, multi=False):\\n # Handle keys in reverse order to be consistent with np.lexsort\\n reverse_keys = keys[::-1]\\n arr = reverse_keys[0]\\n breakers = reverse_keys[1:]\\n # Look for the maximum value in the first array, and continue using new\\n # arrays until a unique maximum index is found.\\n _cand_idxs = np.where(arr == arr.max())[0]\\n if len(_cand_idxs) > 1:\\n for breaker in breakers:\\n vals = breaker[_cand_idxs]\\n _cand_idxs = _cand_idxs[vals == vals.max()]\\n if len(_cand_idxs) == 1:\\n break\\n # If multiple maximum values are found then either\\n # return them all or return an arbitrary one.\\n return _cand_idxs if multi else _cand_idxs[0]\",\n \"def max_op(*substrate_index_arrays):\\n result = numpy.max(\\n numpy.stack([x.flatten() for x in substrate_index_arrays]) *\\n self.species_substrate_suitability_index_array, axis=0)\\n result = result.reshape(substrate_index_arrays[0].shape)\\n result[substrate_index_arrays[0] == _INDEX_NODATA] = _INDEX_NODATA\\n return result\",\n \"def maximum_inplace(a, b):\",\n \"def maxi(a, b):\\n return max(a, b)\",\n \"def argmax(x):\\n def op(a, b):\\n comp = (a[1] > b[1])\\n return comp.if_else(a[0], b[0]), comp.if_else(a[1], b[1])\\n return tree_reduce(op, enumerate(x))[0]\",\n \"def argmax(x):\\n def op(a, b):\\n comp = (a[1] > b[1])\\n return comp.if_else(a[0], b[0]), comp.if_else(a[1], b[1])\\n return tree_reduce(op, enumerate(x))[0]\",\n \"def r_max(nxs):\\n largest = None\\n for i,e in enumerate(nxs):\\n if type(e) == type([]):\\n val = r_max(e)\\n else:\\n val = e\\n\\n if i == 0 or val > largest:\\n largest = val\\n\\n return largest\",\n \"def scale_matrix_by_max(A):\\n # Section 1: Find the max value of the matrix\\n max = 0\\n for row in A:\\n for col in row:\\n if abs(col) > max:\\n max = col\\n\\n # Section 2: Create a copy of the matrix A\\n new = copy_matrix(A)\\n rows = len(new)\\n cols = len(new[0])\\n\\n # Section 3: Reduce each value of copied matrix by max value\\n for i in range(rows):\\n for j in range(cols):\\n new[i][j] = new[i][j] / max\\n\\n return new\",\n \"def maxProduct(data):\\n maxval = float('-inf')\\n for i in range(len(data)):\\n for j in range(i+1, len(data)):\\n if maxval < data[i]*data[j]:\\n maxval = data[i]*data[j]\\n a,b = (data[i],data[j])\\n return tuple([a,b])\",\n \"def maximum(x, y):\\r\\n # see decorator for function body\\r\",\n \"def max_pairwise_product_linear(array):\\n\\n if len(array) <= 1:\\n return 0\\n\\n two_biggest_values = [0, 0]\\n\\n for element in array:\\n if element > two_biggest_values[0]:\\n two_biggest_values[0] = element\\n elif element > two_biggest_values[1]:\\n two_biggest_values[1] = element\\n\\n return two_biggest_values[0] * two_biggest_values[1]\",\n \"def argmax(fn,over):\\n return max([(arg,fn(arg)) for arg in over],key=lambda v: v[1])[0]\",\n \"def argmax(self, values):\\n return self.aggregate(values, \\\"argmax\\\")\",\n \"def max(self, axis=None, keepdims=False, out=None):\\n return np.maximum.reduce(self, out=out, axis=axis, keepdims=keepdims)\",\n \"def local_max_and_argmax(node):\\r\\n if node.op == T._max_and_argmax:\\r\\n if len(node.outputs[1].clients) == 0:\\r\\n #MaxAndArgmax support variable axis,\\r\\n #but CAReduce support only constant axis.\\r\\n if node.inputs[1].data is None:\\r\\n axis = None\\r\\n else:\\r\\n try:\\r\\n axis = get_scalar_constant_value(node.inputs[1])\\r\\n except NotScalarConstantError:\\r\\n return False\\r\\n\\r\\n new = CAReduce(scal.maximum, axis)(node.inputs[0])\\r\\n return [new, None]\",\n \"def softmax(x):\\n orig_shape = x.shape\\n\\n if len(x.shape) > 1:\\n # Matrix\\n tmp = np.max(x, axis=1)\\n x -= tmp.reshape((x.shape[0], 1))\\n x = np.exp(x)\\n tmp = np.sum(x, axis=1)\\n x /= tmp.reshape((x.shape[0], 1))\\n else:\\n # Vector\\n tmp = np.max(x)\\n x -= tmp\\n x = np.exp(x)\\n tmp = np.sum(x)\\n x /= tmp\\n\\n assert x.shape == orig_shape\\n return x\",\n \"def r_max(nxs):\\n largest = None\\n first_time = True\\n for e in nxs:\\n if type(e) == type([]):\\n val = r_max(e)\\n else:\\n val = e\\n\\n if first_time or val > largest:\\n largest = val\\n first_time = False\\n\\n return largest\",\n \"def get_max(criterion, max_card, elements):\\n if max_card <= 0:\\n raise ValueError(\\n \\\"max_card: \\\" + str(max_card) + \\\"\\\\n\\\" +\\n \\\"The maximum cardinal cannot be null nor negative!\\\"\\n )\\n\\n maxima = []\\n currentMax = 0\\n for e in elements:\\n if 0 < e.cardinal <= max_card:\\n if len(maxima) == 0:\\n currentMax = criterion(e)\\n maxima.append((e, currentMax))\\n else:\\n newCandidate = criterion(e)\\n if newCandidate == currentMax:\\n maxima.append((e, currentMax))\\n elif newCandidate > currentMax:\\n maxima = []\\n currentMax = newCandidate\\n maxima.append((e, currentMax))\\n return maxima\",\n \"def compute_largest_product(grid):\\n row_largest = compute_largest_row_product(grid)\\n column_largest = compute_largest_column_product(grid)\\n diagonal1_largest = compute_largest_diagonal1_product(grid)\\n diagonal2_largest = compute_largest_diagonal2_product(grid)\\n\\n my_list = [row_largest , column_largest , diagonal1_largest , diagonal2_largest]\\n return max(my_list)\",\n \"def np_max(arr, axis):\\n assert arr.ndim == 2\\n assert axis in [0, 1]\\n if axis == 0:\\n result = np.empty(arr.shape[1])\\n for i in range(len(result)):\\n result[i] = np.max(arr[:, i])\\n else:\\n result = np.empty(arr.shape[0])\\n for i in range(len(result)):\\n result[i] = np.max(arr[i, :])\\n return result\",\n \"def max(self):\\n max_i = np.nanargmax(self.ys)\\n return self.xs[max_i], self.ys[max_i]\",\n \"def get_max(bij, exploration, bij_bool):\\n\\n#\\tbij[bij_bool] = -sys.maxint - 1\\n\\n\\tm = bij.argmax()\\n\\tc = np.unravel_index(m, bij.shape)\\n\\t#c = np.unravel_index(bij.argmax(), bij.shape)\\n\\n############################## A MODIFIER EVENTUELLEMENT #################\\n#\\tb = bij[bij_bool]\\n#\\tm = b.argmax()\\n#\\tind = np.unravel_index(m, b.shape)\\n#\\tc = np.where(bij == b[ind])\\n#\\tc = (c[0][0], c[1][0])\\n#\\tprint('mMAXx', bij[c])\\n\\treturn (c)\",\n \"def maximum(some_list):\\n return max(some_list)\",\n \"def max(self, elim=None, out=None):\\n if (elim is None):\\n elim = self.v\\n return self.__opReduce2(self.v & elim,np.max, out=out)\",\n \"def _call_maximum(vecObj, vec2):\\n res = vecObj.maximum(vec2)\\n return res\",\n \"def extract_max_value(h: np.ndarray):\\n return np.argmax(h, axis=1)\",\n \"def max(scores):\\n return __builtin__.max(scores) if len(scores) else 0\",\n \"def test_maximax():\\n f = np.asarray([\\n [0.99, 1.0, 0.5],\\n [0.69, 0.6, 0.6]])\\n R = common_metrics.maximax(f, maximise=True)\\n expected = np.asarray(\\n [1.0, 0.69])\\n assert np.allclose(R, expected)\\n R = common_metrics.maximax(f, maximise=False)\\n expected = np.asarray(\\n [-0.5, -0.6])\\n assert np.allclose(R, expected)\",\n \"def grid_channelwise_max(grid_):\\n return np.max(np.max(np.max(grid_, axis=0), axis=0), axis=0)\",\n \"def softmax(a,c):\\n\\n assert(np.shape(a) == np.shape(c))\\n return np.multiply(np.sign(a) , np.maximum(np.absolute(a) - c, np.zeros(np.shape(a))))\",\n \"def maximum(x1, x2, *args, **kwargs):\\n raise NotImplementedError\",\n \"def max(self, axis=0, **kwargs) -> \\\"Dataset\\\":\\n return self.aggregate(axis=axis, func=np.max, **kwargs)\",\n \"def batch_maximum(imstack):\\n maxpos = np.zeros((imstack.shape[0], 2))\\n for i in range(imstack.shape[0]):\\n if np.isnan(imstack[i, 0, 0]):\\n maxpos[i, 0] = np.nan\\n maxpos[i, 1] = np.nan\\n else:\\n ind = np.unravel_index(\\n np.argmax(np.squeeze(imstack[i]), axis=None),\\n np.squeeze(imstack[i]).shape,\\n )\\n maxpos[i, 0] = ind[1]\\n maxpos[i, 1] = ind[0]\\n return maxpos\",\n \"def max_ij(f, K):\\n i_best, j_best, m_value_min = min_ij(lambda i, j: (-1) * f(i, j), K)\\n return i_best, j_best, - m_value_min\",\n \"def _metric_max_over_ground_truths(metric_fn: Callable[[str, str], Tensor], prediction: str, ground_truths: List[str]) ->Tensor:\\n return max(metric_fn(prediction, truth) for truth in ground_truths)\",\n \"def max_pairwise_product_brute_force(array):\\n\\n if len(array) <= 1:\\n return 0\\n\\n max_product = 0\\n\\n for i in range(len(array)):\\n for j in range(len(array)):\\n if i != j:\\n if array[i] * array[j] > max_product:\\n max_product = array[i] * array[j]\\n\\n return max_product\",\n \"def max_val_rec_2d(arr):\\n return max_val_rec([max_val_rec(each_list) for each_list in arr])\",\n \"def max(self):\\n if 0 in type(self).flatten_shape(self.shape):\\n raise ValueError(\\\"zero-size array has no maximum\\\")\\n if self.isscalar():\\n return self.defval\\n # If not all blocks are set, then the tensor has an element of defval\\n # somewhere.\\n m = -np.inf if self.is_full() else self.defval\\n for v in self.sects.values():\\n try:\\n m = max(m, np.max(v))\\n except ValueError:\\n # This block was zero-size, and has no elements.\\n pass\\n return m\",\n \"def masked_maximum(data, mask, dim=1):\\n axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)\\n masked_maximums = math_ops.reduce_max(\\n math_ops.multiply(data - axis_minimums, mask), dim,\\n keepdims=True) + axis_minimums\\n return masked_maximums\",\n \"def max(self):\\n max = 0\\n a = self.array_form\\n for i in xrange(len(a)):\\n if a[i] != i and a[i] > max:\\n max = a[i]\\n return max\",\n \"def argmax(x1, axis=None, out=None):\\n\\n x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)\\n if x1_desc:\\n if axis is not None:\\n pass\\n elif out is not None:\\n pass\\n else:\\n result_obj = dpnp_argmax(x1_desc).get_pyobj()\\n result = dpnp.convert_single_elem_array_to_scalar(result_obj)\\n\\n return result\\n\\n return call_origin(numpy.argmax, x1, axis, out)\",\n \"def test_max_back(a, num_axes, keepdims):\\n if num_axes == 0:\\n axes = None\\n else:\\n axes = np.random.choice(range(0, a.ndim), size=min(num_axes, a.ndim), replace=False)\\n axes = tuple(sorted(axes))\\n\\n # single global maximum\\n if axes is None or axes == tuple(range(a.ndim)):\\n index = tuple(np.random.choice(i.flat) for i in np.indices(a.shape))\\n a[index] = a.max() + 1\\n\\n grad = np.zeros_like(a)\\n grad[index] = 1\\n\\n a = Tensor(a)\\n out = a.max(axis=axes, keepdims=keepdims)\\n out.backward()\\n assert np.allclose(grad, a.grad)\\n return None\\n\\n # explicitly place maxima within tensor\\n static_axes = tuple(sorted(set(range(a.ndim)) - set(axes)))\\n static_shape = tuple(a.shape[i] for i in static_axes)\\n red_shapes = tuple(a.shape[i] for i in axes)\\n sorter = np.argsort(static_axes + axes)\\n\\n # generate indices to span static axes\\n static_indices = tuple(i for i in np.indices(static_shape))\\n\\n # generate random index-runs along reduction axes\\n choose_indices = tuple(np.random.choice(range(i), size=static_indices[0].shape) for i in red_shapes)\\n\\n # create index tuple the selects random runs along reduction axes\\n static_indices += choose_indices\\n indices = []\\n for i in sorter:\\n indices.append(static_indices[i])\\n indices = tuple(indices)\\n\\n # place extrema\\n a[indices] = a.max() + np.random.rand(*indices[0].shape)\\n a = Tensor(a)\\n out = a.max(axis=axes)\\n\\n # break degeneracy amongst grad values\\n tmp = np.arange(1, out.data.size+1).reshape(out.shape)\\n out2 = out * tmp\\n out2.backward()\\n\\n grad = np.zeros_like(a.data)\\n grad[indices] = np.arange(1, out.data.size+1).reshape(out.shape)\\n\\n assert np.allclose(grad, a.grad)\",\n \"def largest(*args):\\r\\n if len(args) == 2:\\r\\n a, b = args\\r\\n return switch(a > b, a, b)\\r\\n else:\\r\\n return max(stack(*args), axis=0)\",\n \"def maxnorm(self, priors=None):\\n def maxnorm_f(x): return x.max(axis=0)\\n return self.utility(maxnorm_f, priors)\",\n \"def softmax(x):\\n orig_shape = x.shape\\n\\n if len(x.shape) > 1:\\n # Matrix\\n exp_minmax = lambda x: np.exp(x - np.max(x))\\n denom = lambda x: 1.0 / np.sum(x)\\n x = np.apply_along_axis(exp_minmax, 1, x)\\n denominator = np.apply_along_axis(denom, 1, x)\\n\\n if len(denominator.shape) == 1:\\n denominator = denominator.reshape((denominator.shape[0], 1))\\n\\n x = x * denominator\\n else:\\n # Vector\\n x_max = np.max(x)\\n x = x - x_max\\n numerator = np.exp(x)\\n denominator = 1.0 / np.sum(numerator)\\n x = numerator.dot(denominator)\\n\\n assert x.shape == orig_shape\\n return x\",\n \"def compute_largest_column_product(grid):\\n max_product = 0\\n for column in range(len(grid)):\\n for row in range(len(grid) - 3):\\n current_product = 1\\n for j in range(4):\\n current_product *= grid[row + j][column]\\n if current_product > max_product:\\n max_product = current_product\\n return max_product\",\n \"def argmax(self, values: pdarray) -> Tuple[groupable, pdarray]:\\n k, v = self.aggregate(values, \\\"argmax\\\")\\n return k, cast(pdarray, v)\",\n \"def _mul(*args):\\n\\treturn functools.reduce(numpy.dot, args)\",\n \"def zmax(self):\\n # Extract parameters\\n pzs = self.params[0]\\n return max([pz.zmax for pz in pzs])\",\n \"def zmax(self):\\n # Extract parameters\\n pzs = self.params[0]\\n return max([pz.zmax for pz in pzs])\",\n \"def maxElem(A):\\n n = len(A)\\n AMax = 0.0\\n for i in range(n):\\n for j in range(i+1,n):\\n if abs(A[i,j]) >= AMax:\\n AMax = abs(A[i,j])\\n k = i;l = j\\n return AMax, k, l\",\n \"def max_pairwise_product_fast(numbers):\\n num_list = numbers.copy()\\n max_num_1 = max(num_list)\\n num_list.remove(max_num_1)\\n max_num_2 = max(num_list)\\n ans = max_num_1*max_num_2\\n return ans\",\n \"def argmax(func, seq):\\n def compare(a1, b1):\\n if a1[0] > b1[0]:\\n return a1\\n return b1\\n # using a generator expression here should save memory\\n objs = ((func(val), val) for val in seq)\\n return reduce(compare, objs)[1]\",\n \"def max_over(fn,over):\\n return max([fn(arg) for arg in over])\",\n \"def maximize(self,x0,method='fmin',**kwargs):\\n return self._optimize(x0,'max',method,**kwargs)\",\n \"def max_array(func: Callable[[Tuple], np.ndarray], state: Tuple[Union[int, float]]) \\\\\\n -> Tuple[float, Tuple[int], np.ndarray]:\\n arr = func((state,)).ravel()\\n max_index = np.argmax(arr)\\n return (arr[max_index], (max_index,), arr)\",\n \"def max_pairwise_product(numbers):\\n n = len(numbers)\\n max_product = 0\\n for first in range(n):\\n for second in range(first + 1, n):\\n max_product = max(max_product,\\n numbers[first] * numbers[second])\\n\\n return max_product\",\n \"def get_maximum_value(dataset):\\n d = [int(i) for i in dataset if i.isdigit()]\\n op = [o for o in dataset if o in ['*', '-', '+']]\\n n = len(d)\\n d.insert(0, None)\\n op.insert(0, None)\\n m = [[0 for x in range(n+1)] for y in range(n+1)]\\n M = [[0 for x in range(n+1)] for y in range(n+1)]\\n for i in range(1, n+1):\\n m[i][i] = d[i]\\n M[i][i] = d[i]\\n for s in range(1, n):\\n for i in range(1, n-s+1):\\n j = i + s\\n m[i][j], M[i][j] = min_and_max(i, j, op, m, M)\\n return M[1][n]\",\n \"def hmax(f, h=1, Bc=None):\\n\\n if Bc is None: Bc = secross()\\n g = subm(f,h)\\n y = infrec(g,f,Bc);\\n return y\",\n \"def arrmax(a):\\n try:\\n return a.max()\\n except AttributeError:\\n # not a NumPy array\\n if isinstance(a, collections.Sequence):\\n return max(a) # does not work for nested sequences\\n elif isinstance(a, numbers.Number):\\n return a\\n else:\\n raise TypeError('arrmax of %s not supported' % type(a))\",\n \"def arrmax(a):\\n try:\\n return a.max()\\n except AttributeError:\\n # not a NumPy array\\n if isinstance(a, collections.Sequence):\\n return max(a) # does not work for nested sequences\\n elif isinstance(a, numbers.Number):\\n return a\\n else:\\n raise TypeError('arrmax of %s not supported' % type(a))\",\n \"def max(tensor, axis=None):\\n raise NotImplementedError\",\n \"def max(self):\\n return numpy.ma.max(self.data)\",\n \"def softmax(oSums):\\n result = np.zeros(shape=[len(oSums)], dtype=np.float32)\\n m = max(oSums)\\n divisor = 0.0\\n for k in range(len(oSums)):\\n divisor += math.exp(oSums[k] - m)\\n for k in range(len(result)):\\n result[k] = math.exp(oSums[k] - m) / divisor\\n return result\",\n \"def ReLU(z):\\n return np.max([z, np.zeros(z.shape)], axis=0)\",\n \"def max_apply(x): \\n if len(x) == 1:\\n return x[0]\\n else:\\n return x[1]\",\n \"def findMax2d(x):\\n m, n = x.shape \\n x_ = x.ravel()\\n idx = np.argmax(x_)\\n i = idx // n \\n j = idx % n \\n return i, j\",\n \"def highest_product(arr):\\n\\n product = 1\\n\\n for i in range(3):\\n # find the max value in the list, get the index, pop it, and mulitply\\n product *= arr.pop(arr.index(max(arr)))\\n\\n return product\",\n \"def nanmax(a, axis=None):\\n y = array(a, subok=True)\\n if not issubclass(y.dtype.type, _nx.integer):\\n y[isnan(a)] = -_nx.inf\\n return y.max(axis)\",\n \"def arrmax(a):\\n # could set arrmax = amax in scipy if scipy is installed\\n try:\\n return max(a.flat) # use Python's list min\\n except AttributeError:\\n # not a NumPy array\\n if isinstance(a, collections.Sequence):\\n return max(a)\\n elif isinstance(a, numbers.Number):\\n return a\\n else:\\n raise TypeError('arrmax of %s not supported' % type(a))\",\n \"def _call_max(vecObj):\\n res = vecObj.max()\\n return res\",\n \"def _multiple_values_max(self, maps, threshold):\\r\\n max_val = np.zeros((maps.shape[0], maps.shape[1]), dtype=np.float)\\r\\n for i in range(maps.shape[1]):\\r\\n cmin = np.min(maps[:,i])\\r\\n cmax = np.max(maps[:,i])\\r\\n limit = cmax - (cmax - cmin) * threshold[i]\\r\\n min_mask = maps[:,i] <= limit\\r\\n max_mask = maps[:,i] > limit\\r\\n # for an abundance map the delta is around [-1..1],\\r\\n # but it can be outside this interval, it's something\\r\\n # to test\\r\\n # a guard with a -10 value maybe ok.\\r\\n rmin = min_mask * -10\\r\\n max_val[:,i] = max_mask * maps[:,i] + rmin\\r\\n max_vec = np.max(max_val, axis=1)\\r\\n max_mask = max_vec > -10\\r\\n argmax = np.argmax(max_val, axis=1)\\r\\n return (argmax + 1) * max_mask\",\n \"def find_max(centroids):\\n \\n max_sim = 0.0\\n max_i = 0\\n max_j = 0\\n length = len(centroids)\\n\\n for i in xrange(0, length):\\n for j in xrange(i + 1, length):\\n curr_sim = similarity(centroids[i], centroids[j])\\n if curr_sim > max_sim:\\n max_sim = curr_sim\\n max_i = i\\n max_j = j\\n\\n return (max_i, max_j, max_sim)\",\n \"def max_abs_weights(x,\\n *,\\n axis = None):\\n abs_max_x = jnp.max(jnp.abs(x), axis=axis, keepdims=True)\\n return abs_max_x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6566944","0.6431522","0.63984","0.6369676","0.6349694","0.6326043","0.6279134","0.6229197","0.6208424","0.61817044","0.61411893","0.60957426","0.60940033","0.6092088","0.60329014","0.6031537","0.60274714","0.6022276","0.60205376","0.6020099","0.5998723","0.59805614","0.5967194","0.59653574","0.59646475","0.5962682","0.5953714","0.5928523","0.59127104","0.589588","0.5889035","0.5889035","0.5878276","0.5857597","0.5853045","0.5852265","0.58227915","0.58184695","0.58146054","0.5813447","0.57603395","0.57597196","0.57491565","0.5745431","0.57359046","0.573237","0.5728822","0.5727834","0.5712465","0.5706454","0.57033837","0.56779367","0.5651676","0.56460965","0.5642402","0.5636118","0.56345284","0.5634226","0.56235266","0.55995613","0.5597091","0.5586038","0.558091","0.5580449","0.55704796","0.5559455","0.5557173","0.5538738","0.553324","0.5525905","0.55249393","0.5518928","0.5515279","0.5509808","0.5507624","0.5507624","0.5507026","0.55050266","0.5503994","0.5503443","0.5500355","0.5497242","0.5482993","0.5475726","0.5466146","0.5461653","0.5461653","0.5461469","0.54596","0.5442801","0.5437634","0.54374486","0.5437357","0.543486","0.5432863","0.5432403","0.54307383","0.54252636","0.54190993","0.5418082"],"string":"[\n \"0.6566944\",\n \"0.6431522\",\n \"0.63984\",\n \"0.6369676\",\n \"0.6349694\",\n \"0.6326043\",\n \"0.6279134\",\n \"0.6229197\",\n \"0.6208424\",\n \"0.61817044\",\n \"0.61411893\",\n \"0.60957426\",\n \"0.60940033\",\n \"0.6092088\",\n \"0.60329014\",\n \"0.6031537\",\n \"0.60274714\",\n \"0.6022276\",\n \"0.60205376\",\n \"0.6020099\",\n \"0.5998723\",\n \"0.59805614\",\n \"0.5967194\",\n \"0.59653574\",\n \"0.59646475\",\n \"0.5962682\",\n \"0.5953714\",\n \"0.5928523\",\n \"0.59127104\",\n \"0.589588\",\n \"0.5889035\",\n \"0.5889035\",\n \"0.5878276\",\n \"0.5857597\",\n \"0.5853045\",\n \"0.5852265\",\n \"0.58227915\",\n \"0.58184695\",\n \"0.58146054\",\n \"0.5813447\",\n \"0.57603395\",\n \"0.57597196\",\n \"0.57491565\",\n \"0.5745431\",\n \"0.57359046\",\n \"0.573237\",\n \"0.5728822\",\n \"0.5727834\",\n \"0.5712465\",\n \"0.5706454\",\n \"0.57033837\",\n \"0.56779367\",\n \"0.5651676\",\n \"0.56460965\",\n \"0.5642402\",\n \"0.5636118\",\n \"0.56345284\",\n \"0.5634226\",\n \"0.56235266\",\n \"0.55995613\",\n \"0.5597091\",\n \"0.5586038\",\n \"0.558091\",\n \"0.5580449\",\n \"0.55704796\",\n \"0.5559455\",\n \"0.5557173\",\n \"0.5538738\",\n \"0.553324\",\n \"0.5525905\",\n \"0.55249393\",\n \"0.5518928\",\n \"0.5515279\",\n \"0.5509808\",\n \"0.5507624\",\n \"0.5507624\",\n \"0.5507026\",\n \"0.55050266\",\n \"0.5503994\",\n \"0.5503443\",\n \"0.5500355\",\n \"0.5497242\",\n \"0.5482993\",\n \"0.5475726\",\n \"0.5466146\",\n \"0.5461653\",\n \"0.5461653\",\n \"0.5461469\",\n \"0.54596\",\n \"0.5442801\",\n \"0.5437634\",\n \"0.54374486\",\n \"0.5437357\",\n \"0.543486\",\n \"0.5432863\",\n \"0.5432403\",\n \"0.54307383\",\n \"0.54252636\",\n \"0.54190993\",\n \"0.5418082\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69111717"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14985,"cells":{"query":{"kind":"string","value":"Supports an additional argument ``jsonp_callback``. If specified, the JSON serialized data string will be wrapped in parenthesis and prefixed by the value of ``jsonp_callback``."},"document":{"kind":"string","value":"def __init__(self, *args, **kwargs):\r\n self.jsonp_callback = kwargs.pop('jsonp_callback', None)\r\n super(JsonResponse, self).__init__(*args, **kwargs)"},"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 makeJSONP(callback, data):\n response_data = callback\n response_data += '('\n response_data += json_dumps(data)\n response_data += ');'\n return response_data","def to_jsonp(self, data, options=None):\r\n options = options or {}\r\n json = self.to_json(data, options)\r\n json = json.replace(u'\\u2028', u'\\\\u2028').replace(u'\\u2029', u'\\\\u2029')\r\n return u'%s(%s)' % (options['callback'], json)","def _get_jsonp(self):\n jsonp = self.environ['tiddlyweb.query'].get('callback', [None])[0]\n if jsonp:\n return '%s(' % jsonp, ')'\n else:\n return '', ''","def support_jsonp(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n callback = request.args.get('callback', False)\n if callback:\n content = str(callback) + '(' + str(f().data) + ')'\n return app.response_class(content, mimetype='application/json')\n else:\n return f(*args, **kwargs)\n return decorated_function","def support_jsonp(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n # callback = request.args.get('callback', False) # GET only\n callback = request.values.get('callback', False) # A CombinedMultiDict with the contents of both form and args.\n if callback:\n # content = str(callback) + '(' + str(f(*args,**kwargs).data) + ')'\n # returns dump_json output instead of Dictionary\n content = str(callback) + '(' + str(f(*args,**kwargs)) + ')'\n return current_app.response_class(content, mimetype=JAVASCRIPT_MIMETYPE)\n else:\n return f(*args, **kwargs)\n return decorated_function","def auto_jsonp(f):\n def new(*arg, **kw):\n callback = request.GET.get('callback')\n result_data = f(*arg, **kw)\n if callback and isinstance(result_data, dict):\n # We only do JSONP for dicts\n response.headers['Content-type'] = 'text/javascript'\n return makeJSONP(callback, result_data)\n # otherwise, we just return as usual\n return result_data\n\n return new","def support_jsonp(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n callback = request.args.get('callback', False)\n if callback:\n content = str(callback) + '(' + str(f(*args,**kwargs).data) + ')'\n return current_app.response_class(content, mimetype='application/javascript')\n else:\n return f(*args, **kwargs)\n return decorated_function","def jsonp(func):\n @wraps(func)\n def decorated_function(*args, **kwargs):\n callback = request.args.get('callback', False)\n if callback:\n data = str(func(*args, **kwargs).data)\n content = str(callback) + '(' + data + ')'\n mimetype = 'application/javascript'\n return current_app.response_class(content, mimetype=mimetype)\n else:\n return func(*args, **kwargs)\n return decorated_function","def jsonp(f):\n from functools import wraps\n\n @wraps(f)\n def jsonp_wrapper(request, *args, **kwargs):\n resp = f(request, *args, **kwargs)\n if resp.status_code != 200:\n return resp\n\n callback = None\n if 'callback' in request.GET:\n callback = request.GET['callback']\n elif 'jsonp' in request.GET:\n callback = request.GET['jsonp']\n else:\n return resp\n\n resp['Content-Type'] = 'application/javascript; charset=utf-8'\n resp.content = \"%s(%s);\" % (callback, resp.content)\n return resp\n\n return jsonp_wrapper","def jsonp(func):\n @wraps(func)\n def decorated_function(*args, **kwargs):\n callback = request.args.get('callback', False)\n if callback:\n data = str(jsonify(func(*args, **kwargs)).data)\n content = str(callback) + '(' + data + ')'\n mimetype = 'application/javascript'\n return current_app.response_class(content, mimetype=mimetype)\n else:\n return func(*args, **kwargs)\n return decorated_function","def jsonpify(f):\r\n @wraps(f)\r\n def decorated_function(*args, **kwargs):\r\n callback = request.args.get('callback', False)\r\n if callback:\r\n content = str(callback) + '(' + str(f(*args, **kwargs).data) + ')'\r\n return current_app.response_class(content,\r\n mimetype='application/javascript')\r\n else:\r\n return f(*args, **kwargs)\r\n return decorated_function","def support_jsonp(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n callback = request.args.get('callback', False)\n if callback:\n content = str(callback) + '(' + str(f(*args,**kwargs)) + ')'\n return current_app.response_class(content, mimetype='application/javascript')\n else:\n return f(*args, **kwargs)\n return decorated_function","def test_jsonpify(self):\r\n res = self.app.get('/api/app/1?callback=mycallback')\r\n err_msg = \"mycallback should be included in the response\"\r\n assert \"mycallback\" in res.data, err_msg\r\n err_msg = \"Status code should be 200\"\r\n assert res.status_code == 200, err_msg","def _serialize(self, request, data, format, options=None):\n options = options or {}\n\n if 'text/javascript' in format:\n # get JSONP callback name. default to \"callback\"\n callback = request.GET.get('callback', 'callback')\n\n if not is_valid_jsonp_callback_value(callback):\n raise BadRequest('JSONP callback name is invalid.')\n\n options['callback'] = callback\n\n return self._meta.serializer.serialize(data, format, options)","def jsonp2json(self, jsonp):\n if len(jsonp) > 0:\n json_ = jsonp[ jsonp.index(\"(\")+1 : jsonp.rindex(\")\") ]\n json_ = json.loads(json_)\n else:\n json_ = None\n return json_","def serialize(self, content):\r\n content = super(JSONPTemplateEmitter, self).serialize(content)\r\n callback = self.request.GET.get('callback', 'callback')\r\n return '%s(%s)' % (callback, content)","def render_to_response(self, context, **response_kwargs):\n json = self.convert_context_to_json(context)\n # If callback is specified, serve as JSONP\n callback = self.request.GET.get('callback', None)\n if callback:\n response_kwargs['content_type'] = 'application/javascript'\n json = \"%s(%s);\" % (callback, json)\n return self.response_class(json, **response_kwargs)","def json_response(func):\n def decorator(request, *args, **kwargs):\n objects = func(request, *args, **kwargs)\n if isinstance(objects, HttpResponse):\n return objects\n try:\n data = simplejson.dumps(objects)\n if 'callback' in request.GET:\n data = '%s(%s);' % (request.GET['callback'], data)\n except:\n data = simplejson.dumps(str(objects))\n if 'just_the_json_plz' in kwargs:\n return data\n if 'just_the_data_plz' in kwargs:\n return objects\n if 'callback' in request.GET or 'callback' in request.POST:\n #jsonp\n return HttpResponse(data, \"text/javascript\")\n else:\n #json\n return HttpResponse(data, \"application/json\")\n return decorator","def json_response(func):\n def decorator(request, *args, **kwargs):\n objects = func(request, *args, **kwargs)\n if isinstance(objects, HttpResponse):\n return objects\n try:\n data = simplejson.dumps(objects)\n if 'callback' in request.REQUEST:\n # a jsonp response!\n data = '%s(%s);' % (request.REQUEST['callback'], data)\n return HttpResponse(data, \"text/javascript\")\n except:\n data = simplejson.dumps(str(objects))\n return HttpResponse(data, \"application/json\")\n return decorator","def json_response(func):\n def decorator(request, *args, **kwargs):\n objects = func(request, *args, **kwargs)\n if isinstance(objects, HttpResponse):\n return objects\n\n data = json.dumps(objects)\n if 'callback' in request:\n # a jsonp response!\n data = '%s(%s);' % (request['callback'], data)\n return HttpResponse(data, \"text/javascript\")\n\n return HttpResponse(data, \"application/json\")\n return decorator","def test_json_callback_validation(self):\n response = self.client.get(reverse('search'), {\n 'q': 'bookmarks',\n 'a': '1',\n 'format': 'json',\n 'callback': 'callback',\n }, follow=True)\n eq_(response.status_code, 200)\n eq_(response['Content-Type'], 'application/x-javascript')","def json_response(func):\n\n def decorator(request, *args, **kwargs):\n objects = func(request, *args, **kwargs)\n if isinstance(objects, HttpResponse):\n return objects\n try:\n data = simplejson.dumps(objects)\n if 'callback' in request.REQUEST:\n # a jsonp response!\n data = '%s(%s);' % (request.REQUEST['callback'], data)\n return HttpResponse(data, \"text/javascript\")\n except:\n data = simplejson.dumps(str(objects))\n return HttpResponse(data, \"application/json\")\n\n return decorator","def send_callback_json_message(value, *args, **kwargs):\r\n\r\n if value:\r\n kwargs['result'] = value\r\n\r\n send_json_message(args[0], args[1], **kwargs)\r\n\r\n return value","def is_valid_jsonp_callback_value(value):\r\n\r\n for identifier in value.split(u'.'):\r\n while '[' in identifier:\r\n if not has_valid_array_index(identifier):\r\n return False\r\n identifier = replace_array_index(u'', identifier)\r\n if not is_valid_javascript_identifier(identifier):\r\n return False\r\n\r\n return True","def json_response(func):\n def decorator(request, *args, **kwargs):\n objects = func(request, *args, **kwargs)\n if isinstance(objects, HttpResponse):\n return objects\n try:\n data = json.dumps(objects, default=json_serialize)\n if 'callback' in request.REQUEST:\n # a jsonp response!\n data = '%s(%s);' % (request.REQUEST['callback'], data)\n return HttpResponse(data, \"text/javascript\")\n except Exception as e:\n print (e)\n data = json.dumps(str(objects))\n return HttpResponse(data, \"application/json\")\n return decorator","def _format_data(self, data):\n return json.dumps(data)","def json_data_obj(str_data):\n return '\"data\":' + str_data + '}'","def test_get_stories_jsonp(mock_get, api):\n\n\tresponse_text = \"\"\"homeTopStoriesCallback({\"status\": \"OK\", \"section\": \"home\", \"results\": [{\"section\": \"home\", \"title\": \"test_title\"}]})\"\"\"\n\n\tmock_get.return_value = Mock(text=response_text)\n\n\tjavascript_func = api.get_stories(\"home\", \"jsonp\")\n\n\ttry:\n\t\tassert isinstance(javascript_func, basestring)\n\texcept NameError:\n\t\tassert isinstance(javascript_func, str)\n\n\tassert \"homeTopStoriesCallback\" in javascript_func","def encode(value, *args, **kwargs):\n # By default encode using a compact format.\n kwargs.setdefault('separators', (',', ':'))\n # JSON permits but does not require forward slashes to be escaped.\n # This is useful when json data is emitted in a <script> tag\n # in HTML, as it prevents </script> tags from prematurely terminating\n # the javascript. Some json libraries do this escaping by default,\n # although python's standard library does not, so we do it here.\n # See: http://goo.gl/WsXwv\n return _json.dumps(value, *args, **kwargs).replace(\"</\", \"<\\\\/\")","def handle_json(self, source, data):\n method, args = json.loads(data)\n try:\n result = self.call(source, method, *args)\n except Exception as exc:\n result = str(exc)\n\n return json.dumps(result)","def serialize_data(data: Any) -> str:\n return json.dumps(data, separators=(\",\", \":\"))","def _encode(self, data, format: str = None) -> bytes:\n if isinstance(data, bytes):\n return data\n if format == 'url':\n return (urllib.parse.urlencode(data)).encode('utf-8')\n if format is None:\n return (json.dumps(data)).encode('utf-8')\n elif format == 'json':\n return (json.dumps(data)).encode('utf-8')\n else:\n msg = f\"received format = {format}.\\nUse 'json' or 'url'.\\n 'json' is default.\"\n raise NotImplementedError(msg)","def get_json_callback_parameter_name(self):\n return self.request.GET.get(self.get_json_callback_parameter(), '')","def format_json(data, **addl_kwargs):\n return json.dumps(\n data, indent=2, sort_keys=True, separators=(',', ': '), **addl_kwargs\n )","def get_json_string(self, **kwargs):\n ...","def json(data):\n if isinstance(data, dict):\n data = ujson.encode(data)\n uid = str(uuid.uuid4())\n display(HTML('<div id=\"{0}\" style=\"height: 600px; width:100%;\"></div>'.format(uid)))\n display(Javascript(\"\"\"\n require([\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\"], function() {\n document.getElementById('%s').appendChild(renderjson(%s))\n });\n \"\"\" % (uid, data)))","def kafka_serializer(data):\n return json.dumps(data).encode('utf-8')","def json_escape(context, value):\n\n return json.dumps(value).strip('\"')","def json_encode(data):\n return json.dumps(data, cls=JSONEncoder)","def json(self, data):\n import json\n data = json.dumps(data)\n return data","def json_http_response(data):\r\n return HttpResponse(json.dumps(data), content_type=\"application/json\")","def escape_javascript(value):\n return cgi.escape(json.dumps(value))","def json(data, *args, **kwargs):\n return HttpResponseBehaviour(JsonResponse, data, *args, **kwargs)","def _callback(self, data: list):\n self.data = data","def jsonify(obj, status=200, headers=None):\n jsondata = json.dumps(obj, cls=JSONEncoder)\n\n if 'callback' in request.args:\n jsondata = '%s(%s)' % (request.args.get('callback'), jsondata)\n\n return Response(jsondata, headers=headers, status=status, mimetype='application/json')","def json_encode(data, pretty=False):\n # type: (Union[Dict,List],Optional[bool]) -> str\n if pretty:\n return json_encode_pretty(data)\n return json.dumps(data, separators=(\",\", \":\"))","def respond(self,result):\n callback = self.request.get('callback')\n self.response.headers['Content-Type'] = 'application/json'\n #self.response.headers['Content-Type'] = '%s; charset=%s' % (config.CONTENT_TYPE, config.CHARSET)\n self.response.headers['Access-Control-Allow-Origin'] = '*'\n self.response.headers['Access-Control-Allow-Methods'] = 'GET, POST, PUT, DELETE, OPTIONS, PATCH, HEAD'\n self.response.headers['Access-Control-Allow-Headers'] = 'Origin, Content-Type, X-Requested-With'\n self.response.headers['Access-Control-Allow-Credentials'] = 'True'\n\n #Add a handler to automatically convert datetimes to ISO 8601 strings. \n dthandler = lambda obj: obj.isoformat() if isinstance(obj, datetime.datetime) else None\n if callback:\n content = str(callback) + '(' + json.dumps(result,default=dthandler) + ')'\n return self.response.out.write(content)\n \n return self.response.out.write(json.dumps(result,default=dthandler))","def format_data(self, params):\n return json.dumps(params)","def json_dumps(value, **kwargs):\n kwargs.setdefault('ensure_ascii', False)\n return json.dumps(value, **kwargs)","def request_callback(request):\n headers = {'content-type': \"application/json\",\n 'cache-control': \"no-cache\"}\n return (200, headers, json.dumps(response_data))","def __call__(self, value, system): \n request = system.get('request') \n if request is not None: \n if not hasattr(request, 'response_content_type'): \n request.response_content_type = 'application/json'\n request.response.headerlist.append(('Access-Control-Allow-Origin', '*'))\n return customjson.dumps(value)","def _format_json(s):\n return json.dumps({\n 'url': u'%s%s' % (BASE_URL, s.get_absolute_url()),\n 'content': s.content,\n 'lexer': s.lexer,\n })","def callback_method(value):\n assert value == 'With callback'","def callback_to_facebook(url):\n return url.replace(settings.FACEBOOK_CALLBACK_HOST + settings.FACEBOOK_CALLBACK_PATH,\n \"http://apps.facebook.com/%s/\" % settings.FACEBOOK_APP_NAME)","def test_callback(self):\n data = b\"data\"\n extra = b\"extra\"\n\n literal = imap4.LiteralString(len(data), self.deferred)\n\n for c in iterbytes(data):\n literal.write(c)\n\n literal.callback(b\"extra\")\n\n result = self.successResultOf(self.deferred)\n self.assertEqual(result, (data, extra))","def json_encode(py_value, pretty=True):\n encoder = PRETTY_JSON_ENCODER if pretty else COMPACT_JSON_ENCODER\n return encoder.encode(py_value)","def json_out(self, data):\n\t\treturn json.dumps(data)","def json_encode(value):\r\n # JSON permits but does not require forward slashes to be escaped.\r\n # This is useful when json data is emitted in a <script> tag\r\n # in HTML, as it prevents </script> tags from prematurely terminating\r\n # the javscript. Some json libraries do this escaping by default,\r\n # although python's standard library does not, so we do it here.\r\n # http://stackoverflow.com/questions/1580647/json-why-are-forward-slashes-escaped\r\n return json.dumps(value).replace(\"</\", \"<\\\\/\")","def _encode_post_dict(self, post_dict):\r\n return json.dumps({\r\n k: v.encode('utf-8') if v is not None else v\r\n for k, v in post_dict.items()\r\n })","def _encode_data(self, data, **kwargs):\n encoder = JSONEncoder(**kwargs) if kwargs else self._json_encoder\n return encoder.encode(data)","def encode(data):\n return json_mod.dumps(data).encode(\"utf-8\")","def jsonify(func, *args, **kwargs): \n adict = func(*args, **kwargs)\n if not isinstance(adict, dict):\n return adict\n \n \n #: getting updates from session and database\n \n updates = list(session['callback_updates']) \n updates.extend(models.CallbackUpdate.dump())\n \n if updates:\n if not adict.get('type') == 'composite':\n adict = beans._wrap('composite', [adict]) \n \n adict['result'].extend(updates)\n \n json = simplejson.dumps(adict)\n response = make_response(json) \n response.headers['Content-Type'] = 'application/json'\n session['callback_updates'] = []\n db.session.commit() \n return response","def set_callback_url(self, callback_url):\n self.callback_url = callback_url","def callback(ch, method, properties, body):\n record = json.loads(body.decode()) # decode binary string to dict\n pprint(record)","def __dumps(*args, **kwargs):\n indent = None\n if (current_app.config.get('JSONIFY_PRETTYPRINT_REGULAR', False)\n and not request.is_xhr):\n indent = 2\n return json.dumps(args[0] if len(args) is 1 else dict(*args, **kwargs),\n indent=indent)","def render(self, data, accepted_media_type=None, renderer_context=None):\n if data is None:\n return b''\n\n renderer_context = renderer_context or {}\n indent = self.get_indent(accepted_media_type, renderer_context)\n\n if indent is None:\n separators = SHORT_SEPARATORS if self.compact else LONG_SEPARATORS\n else:\n separators = INDENT_SEPARATORS\n\n ret = json.dumps(\n data, cls=self.encoder_class,\n indent=indent, ensure_ascii=self.ensure_ascii,\n allow_nan=not self.strict, separators=separators\n )\n\n # We always fully escape \\u2028 and \\u2029 to ensure we output JSON\n # that is a strict javascript subset.\n # See: http://timelessrepo.com/json-isnt-a-javascript-subset\n ret = ret.replace('\\u2028', '\\\\u2028').replace('\\u2029', '\\\\u2029')\n return ret.encode()","def addGETdata(url, data):\n return url + '?' + urllib.urlencode(data)","def json(data, strip_str_floats=False):\n x = simplejson.dumps(data, separators=(',', ':'))\n if strip_str_floats:\n return mark_safe(FLOAT_AS_STRING_RE.sub(r\"\\1\", x))\n else:\n return mark_safe(x)","def get_previewdata(cls, pydata):\n return JsonContentType.dumps(pydata)","def convert_context_to_json(self, context):\n name = self.get_json_callback_parameter_name()\n context = self.inject_debug_info(context)\n obj = self.json_encoder.encode(context)\n if name:\n return \"%s(%s)\" % (name, obj)\n return obj","def tiddler_as(self, tiddler):\n prefix, suffix = self._get_jsonp()\n return prefix + JSON.tiddler_as(self, tiddler) + suffix","def getJSON(self):\n text = super().getJSON() + f', \"exchange\": \"{self.__exchange}\"'\n text += f', \"market pair\": \"{self.__market_pairs}\"'\n text += f', \"interval\": \"{self.__interval}\"}}'\n return text","def json_encode(value):\n return json.dumps(value)","def __init__(self):\r\n self.apiroot = 'http://ws.spotify.com/'\r\n self.add_filter(self.use_json)","def safe_json(value):\n return json.dumps(value).replace('</', '<\\\\/') # Fix injection of closing markup in strings","def package_data(data_dict):\r\n return json.dumps(data_dict)","def custom_shadow_callback_get(self, payload, response_status, token):\n self._callbackresponses.update({token: {\"payload\": json.loads(payload), \"responseStatus\": response_status}})","def json_data(self, data=None):\n if data is None:\n data = {}\n data.update(self.default_data)\n return json.dumps(data)","def pretty_format_json(data):\n return json.dumps(data, ensure_ascii=False, indent=4, cls=RssantJSONEncoder)","def to_html(self, data, options=None):\r\n options = options or {}\r\n return 'Sorry, not implemented yet. Please append \"?format=json\" to your URL.'","def process_data(request, response, resource):\n response.set_header('Access-Control-Allow-Origin', '*')","def postjson_request_(req_url, data):\n print(\"Requesting: %s\" % req_url)\n request = Request(req_url, data=data.encode('ascii'))\n request.add_header('Content-Type', 'application/json')\n response = urlopen(request)\n out = response.read().decode('utf8') # cos python3 is kind of stupid http://stackoverflow.com/questions/6862770/python-3-let-json-object-accept-bytes-or-let-urlopen-output-strings\n return out","def do_post_parse_json(self, *args, **kwargs): # real signature unknown\n pass","def JsonEncode(py_value):\n return JSON_ENCODER.encode(py_value)","def json_encode_pretty(data):\n # type: (Union[Dict,List]) -> str\n return json.dumps(data, indent=2, separators=(\",\", \": \"))","def prep_json_for_BQ_callback(self, key):\n # add _ if first character in key is numeric\n if key[0].isnumeric():\n key = \"_\" + key\n # replace \".\" or \" \" with \"_\"\n key = re.sub(r\"[. ]\",\"_\",key)\n # replace non-alphanumeric, non-\"_\" characters with nothing\n key = re.sub(r\"[^a-zA-Z0-9_]\",\"\",key)\n return key","def output_json(data, code, headers=None):\n #data[\"timestamp\"] = datetime.now()\n return jsonify(data)","def serialize_json(json_data):\n return json.dumps(json_data).encode(\"utf-8\")","def _encode_url(data: str) -> str:\n return urllib.parse.quote(data, safe=\"\")","def test_validate_callback_http_pgturl(self):\n with self.assertRaises(InvalidProxyCallback):\n ProxyGrantingTicket.objects.validate_callback('http://www.example.com/', 'http://www.example.com/',\n self.pgtid, self.pgtiou)","def uglify(data):\n\treturn json.dumps(data, separators=(',', ':'))","def format_heatmap_answer(self, heatmapData, maximum, radius=None):\n jsonp = \"heatmap_request_callback({\\r\\n\"\n jsonp += \" datacount: {0},\\r\\n\".format(len(heatmapData))\n jsonp += \" max : {0},\\r\\n\".format(maximum)\n if radius:\n jsonp += \" radius : {0},\\r\\n\".format(radius)\n jsonp += \" datapoints: [\"\n points = []\n for lat, lon, count in heatmapData:\n points.append(\"{0:.7f},{1:.7f},{2:.1f}\".format(lat, lon, count))\n jsonp += \",\".join(points)\n jsonp += \"]\\r\\n\"\n jsonp += \"})\"\n return jsonp","def _jsonify(data: dict):\n j = data.pop('json', None)\n if isinstance(j, dict):\n return j\n if j is None:\n for k, v in data.items():\n if isinstance(v, datetime) or isinstance(v, date):\n data[k] = arrow.get(v).isoformat()\n\n # Create json from kwargs\n j = json.dumps(data)\n return json.loads(j)","def create_followup_event_data(facebook_id: str):\n data = {\n \"followupEvent\": {\n \"name\": \"custom_event\",\n \"data\": {\n \"user_id\": facebook_id,\n \"ip_address\": ip + \":\" + str(port)\n }\n }\n }\n\n return data","def json_decorator(f):\n def decorator(*args, **kwargs):\n return jsonify(f(*args, **kwargs))\n return decorator","def post_data():\n return json.loads('{\"success\":true, \"message\":\"Data created (but not really)\" }')","def wrap_javascript_in_anonfct(js_data):\n return \"(function(){\"+js_data+\"})()\"","def insert_callback(self, chain, value):","def transmit(self, location, args):\r\n args = dict(args)\r\n args['utf8'] = '\\u2713'\r\n\r\n data = json.dumps(args)\r\n\r\n\r\n req = urlrequest.Request(self.host + location, data, self.headers)\r\n\r\n if self.timeout:\r\n response = urlrequest.urlopen(req, None, self.timeout)\r\n else:\r\n response = urlrequest.urlopen(req)\r\n\r\n code = response.code\r\n data = response.fp.read()\r\n\r\n log.info(\"Response status: %s\\nResponse data: \\n%s\\n\" % (code, data))","def json_response(*args, **kwargs):\n data = stringify(*args, **kwargs)\n return Response(data, mimetype='application/json')"],"string":"[\n \"def makeJSONP(callback, data):\\n response_data = callback\\n response_data += '('\\n response_data += json_dumps(data)\\n response_data += ');'\\n return response_data\",\n \"def to_jsonp(self, data, options=None):\\r\\n options = options or {}\\r\\n json = self.to_json(data, options)\\r\\n json = json.replace(u'\\\\u2028', u'\\\\\\\\u2028').replace(u'\\\\u2029', u'\\\\\\\\u2029')\\r\\n return u'%s(%s)' % (options['callback'], json)\",\n \"def _get_jsonp(self):\\n jsonp = self.environ['tiddlyweb.query'].get('callback', [None])[0]\\n if jsonp:\\n return '%s(' % jsonp, ')'\\n else:\\n return '', ''\",\n \"def support_jsonp(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n callback = request.args.get('callback', False)\\n if callback:\\n content = str(callback) + '(' + str(f().data) + ')'\\n return app.response_class(content, mimetype='application/json')\\n else:\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def support_jsonp(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n # callback = request.args.get('callback', False) # GET only\\n callback = request.values.get('callback', False) # A CombinedMultiDict with the contents of both form and args.\\n if callback:\\n # content = str(callback) + '(' + str(f(*args,**kwargs).data) + ')'\\n # returns dump_json output instead of Dictionary\\n content = str(callback) + '(' + str(f(*args,**kwargs)) + ')'\\n return current_app.response_class(content, mimetype=JAVASCRIPT_MIMETYPE)\\n else:\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def auto_jsonp(f):\\n def new(*arg, **kw):\\n callback = request.GET.get('callback')\\n result_data = f(*arg, **kw)\\n if callback and isinstance(result_data, dict):\\n # We only do JSONP for dicts\\n response.headers['Content-type'] = 'text/javascript'\\n return makeJSONP(callback, result_data)\\n # otherwise, we just return as usual\\n return result_data\\n\\n return new\",\n \"def support_jsonp(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n callback = request.args.get('callback', False)\\n if callback:\\n content = str(callback) + '(' + str(f(*args,**kwargs).data) + ')'\\n return current_app.response_class(content, mimetype='application/javascript')\\n else:\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def jsonp(func):\\n @wraps(func)\\n def decorated_function(*args, **kwargs):\\n callback = request.args.get('callback', False)\\n if callback:\\n data = str(func(*args, **kwargs).data)\\n content = str(callback) + '(' + data + ')'\\n mimetype = 'application/javascript'\\n return current_app.response_class(content, mimetype=mimetype)\\n else:\\n return func(*args, **kwargs)\\n return decorated_function\",\n \"def jsonp(f):\\n from functools import wraps\\n\\n @wraps(f)\\n def jsonp_wrapper(request, *args, **kwargs):\\n resp = f(request, *args, **kwargs)\\n if resp.status_code != 200:\\n return resp\\n\\n callback = None\\n if 'callback' in request.GET:\\n callback = request.GET['callback']\\n elif 'jsonp' in request.GET:\\n callback = request.GET['jsonp']\\n else:\\n return resp\\n\\n resp['Content-Type'] = 'application/javascript; charset=utf-8'\\n resp.content = \\\"%s(%s);\\\" % (callback, resp.content)\\n return resp\\n\\n return jsonp_wrapper\",\n \"def jsonp(func):\\n @wraps(func)\\n def decorated_function(*args, **kwargs):\\n callback = request.args.get('callback', False)\\n if callback:\\n data = str(jsonify(func(*args, **kwargs)).data)\\n content = str(callback) + '(' + data + ')'\\n mimetype = 'application/javascript'\\n return current_app.response_class(content, mimetype=mimetype)\\n else:\\n return func(*args, **kwargs)\\n return decorated_function\",\n \"def jsonpify(f):\\r\\n @wraps(f)\\r\\n def decorated_function(*args, **kwargs):\\r\\n callback = request.args.get('callback', False)\\r\\n if callback:\\r\\n content = str(callback) + '(' + str(f(*args, **kwargs).data) + ')'\\r\\n return current_app.response_class(content,\\r\\n mimetype='application/javascript')\\r\\n else:\\r\\n return f(*args, **kwargs)\\r\\n return decorated_function\",\n \"def support_jsonp(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n callback = request.args.get('callback', False)\\n if callback:\\n content = str(callback) + '(' + str(f(*args,**kwargs)) + ')'\\n return current_app.response_class(content, mimetype='application/javascript')\\n else:\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def test_jsonpify(self):\\r\\n res = self.app.get('/api/app/1?callback=mycallback')\\r\\n err_msg = \\\"mycallback should be included in the response\\\"\\r\\n assert \\\"mycallback\\\" in res.data, err_msg\\r\\n err_msg = \\\"Status code should be 200\\\"\\r\\n assert res.status_code == 200, err_msg\",\n \"def _serialize(self, request, data, format, options=None):\\n options = options or {}\\n\\n if 'text/javascript' in format:\\n # get JSONP callback name. default to \\\"callback\\\"\\n callback = request.GET.get('callback', 'callback')\\n\\n if not is_valid_jsonp_callback_value(callback):\\n raise BadRequest('JSONP callback name is invalid.')\\n\\n options['callback'] = callback\\n\\n return self._meta.serializer.serialize(data, format, options)\",\n \"def jsonp2json(self, jsonp):\\n if len(jsonp) > 0:\\n json_ = jsonp[ jsonp.index(\\\"(\\\")+1 : jsonp.rindex(\\\")\\\") ]\\n json_ = json.loads(json_)\\n else:\\n json_ = None\\n return json_\",\n \"def serialize(self, content):\\r\\n content = super(JSONPTemplateEmitter, self).serialize(content)\\r\\n callback = self.request.GET.get('callback', 'callback')\\r\\n return '%s(%s)' % (callback, content)\",\n \"def render_to_response(self, context, **response_kwargs):\\n json = self.convert_context_to_json(context)\\n # If callback is specified, serve as JSONP\\n callback = self.request.GET.get('callback', None)\\n if callback:\\n response_kwargs['content_type'] = 'application/javascript'\\n json = \\\"%s(%s);\\\" % (callback, json)\\n return self.response_class(json, **response_kwargs)\",\n \"def json_response(func):\\n def decorator(request, *args, **kwargs):\\n objects = func(request, *args, **kwargs)\\n if isinstance(objects, HttpResponse):\\n return objects\\n try:\\n data = simplejson.dumps(objects)\\n if 'callback' in request.GET:\\n data = '%s(%s);' % (request.GET['callback'], data)\\n except:\\n data = simplejson.dumps(str(objects))\\n if 'just_the_json_plz' in kwargs:\\n return data\\n if 'just_the_data_plz' in kwargs:\\n return objects\\n if 'callback' in request.GET or 'callback' in request.POST:\\n #jsonp\\n return HttpResponse(data, \\\"text/javascript\\\")\\n else:\\n #json\\n return HttpResponse(data, \\\"application/json\\\")\\n return decorator\",\n \"def json_response(func):\\n def decorator(request, *args, **kwargs):\\n objects = func(request, *args, **kwargs)\\n if isinstance(objects, HttpResponse):\\n return objects\\n try:\\n data = simplejson.dumps(objects)\\n if 'callback' in request.REQUEST:\\n # a jsonp response!\\n data = '%s(%s);' % (request.REQUEST['callback'], data)\\n return HttpResponse(data, \\\"text/javascript\\\")\\n except:\\n data = simplejson.dumps(str(objects))\\n return HttpResponse(data, \\\"application/json\\\")\\n return decorator\",\n \"def json_response(func):\\n def decorator(request, *args, **kwargs):\\n objects = func(request, *args, **kwargs)\\n if isinstance(objects, HttpResponse):\\n return objects\\n\\n data = json.dumps(objects)\\n if 'callback' in request:\\n # a jsonp response!\\n data = '%s(%s);' % (request['callback'], data)\\n return HttpResponse(data, \\\"text/javascript\\\")\\n\\n return HttpResponse(data, \\\"application/json\\\")\\n return decorator\",\n \"def test_json_callback_validation(self):\\n response = self.client.get(reverse('search'), {\\n 'q': 'bookmarks',\\n 'a': '1',\\n 'format': 'json',\\n 'callback': 'callback',\\n }, follow=True)\\n eq_(response.status_code, 200)\\n eq_(response['Content-Type'], 'application/x-javascript')\",\n \"def json_response(func):\\n\\n def decorator(request, *args, **kwargs):\\n objects = func(request, *args, **kwargs)\\n if isinstance(objects, HttpResponse):\\n return objects\\n try:\\n data = simplejson.dumps(objects)\\n if 'callback' in request.REQUEST:\\n # a jsonp response!\\n data = '%s(%s);' % (request.REQUEST['callback'], data)\\n return HttpResponse(data, \\\"text/javascript\\\")\\n except:\\n data = simplejson.dumps(str(objects))\\n return HttpResponse(data, \\\"application/json\\\")\\n\\n return decorator\",\n \"def send_callback_json_message(value, *args, **kwargs):\\r\\n\\r\\n if value:\\r\\n kwargs['result'] = value\\r\\n\\r\\n send_json_message(args[0], args[1], **kwargs)\\r\\n\\r\\n return value\",\n \"def is_valid_jsonp_callback_value(value):\\r\\n\\r\\n for identifier in value.split(u'.'):\\r\\n while '[' in identifier:\\r\\n if not has_valid_array_index(identifier):\\r\\n return False\\r\\n identifier = replace_array_index(u'', identifier)\\r\\n if not is_valid_javascript_identifier(identifier):\\r\\n return False\\r\\n\\r\\n return True\",\n \"def json_response(func):\\n def decorator(request, *args, **kwargs):\\n objects = func(request, *args, **kwargs)\\n if isinstance(objects, HttpResponse):\\n return objects\\n try:\\n data = json.dumps(objects, default=json_serialize)\\n if 'callback' in request.REQUEST:\\n # a jsonp response!\\n data = '%s(%s);' % (request.REQUEST['callback'], data)\\n return HttpResponse(data, \\\"text/javascript\\\")\\n except Exception as e:\\n print (e)\\n data = json.dumps(str(objects))\\n return HttpResponse(data, \\\"application/json\\\")\\n return decorator\",\n \"def _format_data(self, data):\\n return json.dumps(data)\",\n \"def json_data_obj(str_data):\\n return '\\\"data\\\":' + str_data + '}'\",\n \"def test_get_stories_jsonp(mock_get, api):\\n\\n\\tresponse_text = \\\"\\\"\\\"homeTopStoriesCallback({\\\"status\\\": \\\"OK\\\", \\\"section\\\": \\\"home\\\", \\\"results\\\": [{\\\"section\\\": \\\"home\\\", \\\"title\\\": \\\"test_title\\\"}]})\\\"\\\"\\\"\\n\\n\\tmock_get.return_value = Mock(text=response_text)\\n\\n\\tjavascript_func = api.get_stories(\\\"home\\\", \\\"jsonp\\\")\\n\\n\\ttry:\\n\\t\\tassert isinstance(javascript_func, basestring)\\n\\texcept NameError:\\n\\t\\tassert isinstance(javascript_func, str)\\n\\n\\tassert \\\"homeTopStoriesCallback\\\" in javascript_func\",\n \"def encode(value, *args, **kwargs):\\n # By default encode using a compact format.\\n kwargs.setdefault('separators', (',', ':'))\\n # JSON permits but does not require forward slashes to be escaped.\\n # This is useful when json data is emitted in a <script> tag\\n # in HTML, as it prevents </script> tags from prematurely terminating\\n # the javascript. Some json libraries do this escaping by default,\\n # although python's standard library does not, so we do it here.\\n # See: http://goo.gl/WsXwv\\n return _json.dumps(value, *args, **kwargs).replace(\\\"</\\\", \\\"<\\\\\\\\/\\\")\",\n \"def handle_json(self, source, data):\\n method, args = json.loads(data)\\n try:\\n result = self.call(source, method, *args)\\n except Exception as exc:\\n result = str(exc)\\n\\n return json.dumps(result)\",\n \"def serialize_data(data: Any) -> str:\\n return json.dumps(data, separators=(\\\",\\\", \\\":\\\"))\",\n \"def _encode(self, data, format: str = None) -> bytes:\\n if isinstance(data, bytes):\\n return data\\n if format == 'url':\\n return (urllib.parse.urlencode(data)).encode('utf-8')\\n if format is None:\\n return (json.dumps(data)).encode('utf-8')\\n elif format == 'json':\\n return (json.dumps(data)).encode('utf-8')\\n else:\\n msg = f\\\"received format = {format}.\\\\nUse 'json' or 'url'.\\\\n 'json' is default.\\\"\\n raise NotImplementedError(msg)\",\n \"def get_json_callback_parameter_name(self):\\n return self.request.GET.get(self.get_json_callback_parameter(), '')\",\n \"def format_json(data, **addl_kwargs):\\n return json.dumps(\\n data, indent=2, sort_keys=True, separators=(',', ': '), **addl_kwargs\\n )\",\n \"def get_json_string(self, **kwargs):\\n ...\",\n \"def json(data):\\n if isinstance(data, dict):\\n data = ujson.encode(data)\\n uid = str(uuid.uuid4())\\n display(HTML('<div id=\\\"{0}\\\" style=\\\"height: 600px; width:100%;\\\"></div>'.format(uid)))\\n display(Javascript(\\\"\\\"\\\"\\n require([\\\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\\\"], function() {\\n document.getElementById('%s').appendChild(renderjson(%s))\\n });\\n \\\"\\\"\\\" % (uid, data)))\",\n \"def kafka_serializer(data):\\n return json.dumps(data).encode('utf-8')\",\n \"def json_escape(context, value):\\n\\n return json.dumps(value).strip('\\\"')\",\n \"def json_encode(data):\\n return json.dumps(data, cls=JSONEncoder)\",\n \"def json(self, data):\\n import json\\n data = json.dumps(data)\\n return data\",\n \"def json_http_response(data):\\r\\n return HttpResponse(json.dumps(data), content_type=\\\"application/json\\\")\",\n \"def escape_javascript(value):\\n return cgi.escape(json.dumps(value))\",\n \"def json(data, *args, **kwargs):\\n return HttpResponseBehaviour(JsonResponse, data, *args, **kwargs)\",\n \"def _callback(self, data: list):\\n self.data = data\",\n \"def jsonify(obj, status=200, headers=None):\\n jsondata = json.dumps(obj, cls=JSONEncoder)\\n\\n if 'callback' in request.args:\\n jsondata = '%s(%s)' % (request.args.get('callback'), jsondata)\\n\\n return Response(jsondata, headers=headers, status=status, mimetype='application/json')\",\n \"def json_encode(data, pretty=False):\\n # type: (Union[Dict,List],Optional[bool]) -> str\\n if pretty:\\n return json_encode_pretty(data)\\n return json.dumps(data, separators=(\\\",\\\", \\\":\\\"))\",\n \"def respond(self,result):\\n callback = self.request.get('callback')\\n self.response.headers['Content-Type'] = 'application/json'\\n #self.response.headers['Content-Type'] = '%s; charset=%s' % (config.CONTENT_TYPE, config.CHARSET)\\n self.response.headers['Access-Control-Allow-Origin'] = '*'\\n self.response.headers['Access-Control-Allow-Methods'] = 'GET, POST, PUT, DELETE, OPTIONS, PATCH, HEAD'\\n self.response.headers['Access-Control-Allow-Headers'] = 'Origin, Content-Type, X-Requested-With'\\n self.response.headers['Access-Control-Allow-Credentials'] = 'True'\\n\\n #Add a handler to automatically convert datetimes to ISO 8601 strings. \\n dthandler = lambda obj: obj.isoformat() if isinstance(obj, datetime.datetime) else None\\n if callback:\\n content = str(callback) + '(' + json.dumps(result,default=dthandler) + ')'\\n return self.response.out.write(content)\\n \\n return self.response.out.write(json.dumps(result,default=dthandler))\",\n \"def format_data(self, params):\\n return json.dumps(params)\",\n \"def json_dumps(value, **kwargs):\\n kwargs.setdefault('ensure_ascii', False)\\n return json.dumps(value, **kwargs)\",\n \"def request_callback(request):\\n headers = {'content-type': \\\"application/json\\\",\\n 'cache-control': \\\"no-cache\\\"}\\n return (200, headers, json.dumps(response_data))\",\n \"def __call__(self, value, system): \\n request = system.get('request') \\n if request is not None: \\n if not hasattr(request, 'response_content_type'): \\n request.response_content_type = 'application/json'\\n request.response.headerlist.append(('Access-Control-Allow-Origin', '*'))\\n return customjson.dumps(value)\",\n \"def _format_json(s):\\n return json.dumps({\\n 'url': u'%s%s' % (BASE_URL, s.get_absolute_url()),\\n 'content': s.content,\\n 'lexer': s.lexer,\\n })\",\n \"def callback_method(value):\\n assert value == 'With callback'\",\n \"def callback_to_facebook(url):\\n return url.replace(settings.FACEBOOK_CALLBACK_HOST + settings.FACEBOOK_CALLBACK_PATH,\\n \\\"http://apps.facebook.com/%s/\\\" % settings.FACEBOOK_APP_NAME)\",\n \"def test_callback(self):\\n data = b\\\"data\\\"\\n extra = b\\\"extra\\\"\\n\\n literal = imap4.LiteralString(len(data), self.deferred)\\n\\n for c in iterbytes(data):\\n literal.write(c)\\n\\n literal.callback(b\\\"extra\\\")\\n\\n result = self.successResultOf(self.deferred)\\n self.assertEqual(result, (data, extra))\",\n \"def json_encode(py_value, pretty=True):\\n encoder = PRETTY_JSON_ENCODER if pretty else COMPACT_JSON_ENCODER\\n return encoder.encode(py_value)\",\n \"def json_out(self, data):\\n\\t\\treturn json.dumps(data)\",\n \"def json_encode(value):\\r\\n # JSON permits but does not require forward slashes to be escaped.\\r\\n # This is useful when json data is emitted in a <script> tag\\r\\n # in HTML, as it prevents </script> tags from prematurely terminating\\r\\n # the javscript. Some json libraries do this escaping by default,\\r\\n # although python's standard library does not, so we do it here.\\r\\n # http://stackoverflow.com/questions/1580647/json-why-are-forward-slashes-escaped\\r\\n return json.dumps(value).replace(\\\"</\\\", \\\"<\\\\\\\\/\\\")\",\n \"def _encode_post_dict(self, post_dict):\\r\\n return json.dumps({\\r\\n k: v.encode('utf-8') if v is not None else v\\r\\n for k, v in post_dict.items()\\r\\n })\",\n \"def _encode_data(self, data, **kwargs):\\n encoder = JSONEncoder(**kwargs) if kwargs else self._json_encoder\\n return encoder.encode(data)\",\n \"def encode(data):\\n return json_mod.dumps(data).encode(\\\"utf-8\\\")\",\n \"def jsonify(func, *args, **kwargs): \\n adict = func(*args, **kwargs)\\n if not isinstance(adict, dict):\\n return adict\\n \\n \\n #: getting updates from session and database\\n \\n updates = list(session['callback_updates']) \\n updates.extend(models.CallbackUpdate.dump())\\n \\n if updates:\\n if not adict.get('type') == 'composite':\\n adict = beans._wrap('composite', [adict]) \\n \\n adict['result'].extend(updates)\\n \\n json = simplejson.dumps(adict)\\n response = make_response(json) \\n response.headers['Content-Type'] = 'application/json'\\n session['callback_updates'] = []\\n db.session.commit() \\n return response\",\n \"def set_callback_url(self, callback_url):\\n self.callback_url = callback_url\",\n \"def callback(ch, method, properties, body):\\n record = json.loads(body.decode()) # decode binary string to dict\\n pprint(record)\",\n \"def __dumps(*args, **kwargs):\\n indent = None\\n if (current_app.config.get('JSONIFY_PRETTYPRINT_REGULAR', False)\\n and not request.is_xhr):\\n indent = 2\\n return json.dumps(args[0] if len(args) is 1 else dict(*args, **kwargs),\\n indent=indent)\",\n \"def render(self, data, accepted_media_type=None, renderer_context=None):\\n if data is None:\\n return b''\\n\\n renderer_context = renderer_context or {}\\n indent = self.get_indent(accepted_media_type, renderer_context)\\n\\n if indent is None:\\n separators = SHORT_SEPARATORS if self.compact else LONG_SEPARATORS\\n else:\\n separators = INDENT_SEPARATORS\\n\\n ret = json.dumps(\\n data, cls=self.encoder_class,\\n indent=indent, ensure_ascii=self.ensure_ascii,\\n allow_nan=not self.strict, separators=separators\\n )\\n\\n # We always fully escape \\\\u2028 and \\\\u2029 to ensure we output JSON\\n # that is a strict javascript subset.\\n # See: http://timelessrepo.com/json-isnt-a-javascript-subset\\n ret = ret.replace('\\\\u2028', '\\\\\\\\u2028').replace('\\\\u2029', '\\\\\\\\u2029')\\n return ret.encode()\",\n \"def addGETdata(url, data):\\n return url + '?' + urllib.urlencode(data)\",\n \"def json(data, strip_str_floats=False):\\n x = simplejson.dumps(data, separators=(',', ':'))\\n if strip_str_floats:\\n return mark_safe(FLOAT_AS_STRING_RE.sub(r\\\"\\\\1\\\", x))\\n else:\\n return mark_safe(x)\",\n \"def get_previewdata(cls, pydata):\\n return JsonContentType.dumps(pydata)\",\n \"def convert_context_to_json(self, context):\\n name = self.get_json_callback_parameter_name()\\n context = self.inject_debug_info(context)\\n obj = self.json_encoder.encode(context)\\n if name:\\n return \\\"%s(%s)\\\" % (name, obj)\\n return obj\",\n \"def tiddler_as(self, tiddler):\\n prefix, suffix = self._get_jsonp()\\n return prefix + JSON.tiddler_as(self, tiddler) + suffix\",\n \"def getJSON(self):\\n text = super().getJSON() + f', \\\"exchange\\\": \\\"{self.__exchange}\\\"'\\n text += f', \\\"market pair\\\": \\\"{self.__market_pairs}\\\"'\\n text += f', \\\"interval\\\": \\\"{self.__interval}\\\"}}'\\n return text\",\n \"def json_encode(value):\\n return json.dumps(value)\",\n \"def __init__(self):\\r\\n self.apiroot = 'http://ws.spotify.com/'\\r\\n self.add_filter(self.use_json)\",\n \"def safe_json(value):\\n return json.dumps(value).replace('</', '<\\\\\\\\/') # Fix injection of closing markup in strings\",\n \"def package_data(data_dict):\\r\\n return json.dumps(data_dict)\",\n \"def custom_shadow_callback_get(self, payload, response_status, token):\\n self._callbackresponses.update({token: {\\\"payload\\\": json.loads(payload), \\\"responseStatus\\\": response_status}})\",\n \"def json_data(self, data=None):\\n if data is None:\\n data = {}\\n data.update(self.default_data)\\n return json.dumps(data)\",\n \"def pretty_format_json(data):\\n return json.dumps(data, ensure_ascii=False, indent=4, cls=RssantJSONEncoder)\",\n \"def to_html(self, data, options=None):\\r\\n options = options or {}\\r\\n return 'Sorry, not implemented yet. Please append \\\"?format=json\\\" to your URL.'\",\n \"def process_data(request, response, resource):\\n response.set_header('Access-Control-Allow-Origin', '*')\",\n \"def postjson_request_(req_url, data):\\n print(\\\"Requesting: %s\\\" % req_url)\\n request = Request(req_url, data=data.encode('ascii'))\\n request.add_header('Content-Type', 'application/json')\\n response = urlopen(request)\\n out = response.read().decode('utf8') # cos python3 is kind of stupid http://stackoverflow.com/questions/6862770/python-3-let-json-object-accept-bytes-or-let-urlopen-output-strings\\n return out\",\n \"def do_post_parse_json(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def JsonEncode(py_value):\\n return JSON_ENCODER.encode(py_value)\",\n \"def json_encode_pretty(data):\\n # type: (Union[Dict,List]) -> str\\n return json.dumps(data, indent=2, separators=(\\\",\\\", \\\": \\\"))\",\n \"def prep_json_for_BQ_callback(self, key):\\n # add _ if first character in key is numeric\\n if key[0].isnumeric():\\n key = \\\"_\\\" + key\\n # replace \\\".\\\" or \\\" \\\" with \\\"_\\\"\\n key = re.sub(r\\\"[. ]\\\",\\\"_\\\",key)\\n # replace non-alphanumeric, non-\\\"_\\\" characters with nothing\\n key = re.sub(r\\\"[^a-zA-Z0-9_]\\\",\\\"\\\",key)\\n return key\",\n \"def output_json(data, code, headers=None):\\n #data[\\\"timestamp\\\"] = datetime.now()\\n return jsonify(data)\",\n \"def serialize_json(json_data):\\n return json.dumps(json_data).encode(\\\"utf-8\\\")\",\n \"def _encode_url(data: str) -> str:\\n return urllib.parse.quote(data, safe=\\\"\\\")\",\n \"def test_validate_callback_http_pgturl(self):\\n with self.assertRaises(InvalidProxyCallback):\\n ProxyGrantingTicket.objects.validate_callback('http://www.example.com/', 'http://www.example.com/',\\n self.pgtid, self.pgtiou)\",\n \"def uglify(data):\\n\\treturn json.dumps(data, separators=(',', ':'))\",\n \"def format_heatmap_answer(self, heatmapData, maximum, radius=None):\\n jsonp = \\\"heatmap_request_callback({\\\\r\\\\n\\\"\\n jsonp += \\\" datacount: {0},\\\\r\\\\n\\\".format(len(heatmapData))\\n jsonp += \\\" max : {0},\\\\r\\\\n\\\".format(maximum)\\n if radius:\\n jsonp += \\\" radius : {0},\\\\r\\\\n\\\".format(radius)\\n jsonp += \\\" datapoints: [\\\"\\n points = []\\n for lat, lon, count in heatmapData:\\n points.append(\\\"{0:.7f},{1:.7f},{2:.1f}\\\".format(lat, lon, count))\\n jsonp += \\\",\\\".join(points)\\n jsonp += \\\"]\\\\r\\\\n\\\"\\n jsonp += \\\"})\\\"\\n return jsonp\",\n \"def _jsonify(data: dict):\\n j = data.pop('json', None)\\n if isinstance(j, dict):\\n return j\\n if j is None:\\n for k, v in data.items():\\n if isinstance(v, datetime) or isinstance(v, date):\\n data[k] = arrow.get(v).isoformat()\\n\\n # Create json from kwargs\\n j = json.dumps(data)\\n return json.loads(j)\",\n \"def create_followup_event_data(facebook_id: str):\\n data = {\\n \\\"followupEvent\\\": {\\n \\\"name\\\": \\\"custom_event\\\",\\n \\\"data\\\": {\\n \\\"user_id\\\": facebook_id,\\n \\\"ip_address\\\": ip + \\\":\\\" + str(port)\\n }\\n }\\n }\\n\\n return data\",\n \"def json_decorator(f):\\n def decorator(*args, **kwargs):\\n return jsonify(f(*args, **kwargs))\\n return decorator\",\n \"def post_data():\\n return json.loads('{\\\"success\\\":true, \\\"message\\\":\\\"Data created (but not really)\\\" }')\",\n \"def wrap_javascript_in_anonfct(js_data):\\n return \\\"(function(){\\\"+js_data+\\\"})()\\\"\",\n \"def insert_callback(self, chain, value):\",\n \"def transmit(self, location, args):\\r\\n args = dict(args)\\r\\n args['utf8'] = '\\\\u2713'\\r\\n\\r\\n data = json.dumps(args)\\r\\n\\r\\n\\r\\n req = urlrequest.Request(self.host + location, data, self.headers)\\r\\n\\r\\n if self.timeout:\\r\\n response = urlrequest.urlopen(req, None, self.timeout)\\r\\n else:\\r\\n response = urlrequest.urlopen(req)\\r\\n\\r\\n code = response.code\\r\\n data = response.fp.read()\\r\\n\\r\\n log.info(\\\"Response status: %s\\\\nResponse data: \\\\n%s\\\\n\\\" % (code, data))\",\n \"def json_response(*args, **kwargs):\\n data = stringify(*args, **kwargs)\\n return Response(data, mimetype='application/json')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77660763","0.77004063","0.71376044","0.7129176","0.7104028","0.7059136","0.69853944","0.69456315","0.6932959","0.68902475","0.68627375","0.678367","0.62437904","0.6236117","0.6163715","0.57675","0.5750369","0.5558858","0.5427558","0.5421836","0.5384548","0.5358065","0.5321329","0.53101325","0.5298061","0.5274883","0.52740085","0.5225193","0.50782144","0.5002192","0.50002104","0.4986775","0.49044937","0.49040315","0.48985025","0.48787197","0.48358303","0.48089027","0.48043078","0.4780301","0.47749278","0.4767144","0.47528684","0.47460413","0.47413042","0.47407535","0.47357392","0.473084","0.47141752","0.46834722","0.46826082","0.46753368","0.46598575","0.4641281","0.46359736","0.46185815","0.4617685","0.4608647","0.46074378","0.45959672","0.459504","0.4590471","0.45852718","0.45788443","0.45715943","0.45564386","0.45456177","0.45423022","0.45384687","0.45324448","0.45160255","0.44932744","0.44782296","0.4475197","0.4467716","0.44495484","0.4444254","0.4436904","0.4422195","0.44075975","0.43931454","0.43890166","0.43724662","0.43671203","0.436238","0.43614113","0.43598273","0.4349205","0.43467948","0.4344525","0.43399698","0.43364647","0.43328452","0.4331445","0.4326845","0.4325925","0.43256682","0.43254775","0.43095067","0.43073627"],"string":"[\n \"0.77660763\",\n \"0.77004063\",\n \"0.71376044\",\n \"0.7129176\",\n \"0.7104028\",\n \"0.7059136\",\n \"0.69853944\",\n \"0.69456315\",\n \"0.6932959\",\n \"0.68902475\",\n \"0.68627375\",\n \"0.678367\",\n \"0.62437904\",\n \"0.6236117\",\n \"0.6163715\",\n \"0.57675\",\n \"0.5750369\",\n \"0.5558858\",\n \"0.5427558\",\n \"0.5421836\",\n \"0.5384548\",\n \"0.5358065\",\n \"0.5321329\",\n \"0.53101325\",\n \"0.5298061\",\n \"0.5274883\",\n \"0.52740085\",\n \"0.5225193\",\n \"0.50782144\",\n \"0.5002192\",\n \"0.50002104\",\n \"0.4986775\",\n \"0.49044937\",\n \"0.49040315\",\n \"0.48985025\",\n \"0.48787197\",\n \"0.48358303\",\n \"0.48089027\",\n \"0.48043078\",\n \"0.4780301\",\n \"0.47749278\",\n \"0.4767144\",\n \"0.47528684\",\n \"0.47460413\",\n \"0.47413042\",\n \"0.47407535\",\n \"0.47357392\",\n \"0.473084\",\n \"0.47141752\",\n \"0.46834722\",\n \"0.46826082\",\n \"0.46753368\",\n \"0.46598575\",\n \"0.4641281\",\n \"0.46359736\",\n \"0.46185815\",\n \"0.4617685\",\n \"0.4608647\",\n \"0.46074378\",\n \"0.45959672\",\n \"0.459504\",\n \"0.4590471\",\n \"0.45852718\",\n \"0.45788443\",\n \"0.45715943\",\n \"0.45564386\",\n \"0.45456177\",\n \"0.45423022\",\n \"0.45384687\",\n \"0.45324448\",\n \"0.45160255\",\n \"0.44932744\",\n \"0.44782296\",\n \"0.4475197\",\n \"0.4467716\",\n \"0.44495484\",\n \"0.4444254\",\n \"0.4436904\",\n \"0.4422195\",\n \"0.44075975\",\n \"0.43931454\",\n \"0.43890166\",\n \"0.43724662\",\n \"0.43671203\",\n \"0.436238\",\n \"0.43614113\",\n \"0.43598273\",\n \"0.4349205\",\n \"0.43467948\",\n \"0.4344525\",\n \"0.43399698\",\n \"0.43364647\",\n \"0.43328452\",\n \"0.4331445\",\n \"0.4326845\",\n \"0.4325925\",\n \"0.43256682\",\n \"0.43254775\",\n \"0.43095067\",\n \"0.43073627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6793149"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":14986,"cells":{"query":{"kind":"string","value":"Takes in an Image message and identifies the locations of the three dumbbells"},"document":{"kind":"string","value":"def identify_dbs(image):\n locations = {\"red\": Point(), \"green\": Point(), \"blue\": Point()}\n masks = {\"red\": [], \"green\": [], \"blue\": []}\n\n bridge = cv_bridge.CvBridge()\n image = bridge.imgmsg_to_cv2(image, \"bgr8\")\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n # upper and lower bounds for red\n # using python 3 bgr [0,0,188] = hsv [0, 255, 188]\n lower_red = numpy.array([0, 100, 100]) \n upper_red = numpy.array([10, 255, 255])\n masks[\"red\"] = cv2.inRange(hsv, lower_red, upper_red)\n\n # upper and lower bounds for green\n # using python 3 bgr [0,175,0] = hsv [60, 255, 175]\n lower_green = numpy.array([50, 100, 100]) \n upper_green = numpy.array([70, 255, 255])\n masks[\"green\"] = cv2.inRange(hsv, lower_green, upper_green)\n\n # upper and lower bounds for blue\n # using python 3 bgr [176, 0, 17] = hsv [123, 255, 176]\n lower_blue = numpy.array([113, 100, 100])\n upper_blue = numpy.array([133, 255, 255])\n masks[\"blue\"] = cv2.inRange(hsv, lower_blue, upper_blue)\n\n x, y, w, h = 0, 0, image.shape[1]//3, image.shape[0]\n\n for color, mask in masks.items():\n pixels = {\"left\": 0, \"middle\": 0, \"right\": 0}\n \n # define section of image to use for left, middle and right\n left = mask[y:y+h, x:x+w]\n middle = mask[y:y+h, x+w:x+w+w]\n right = mask[y:y+h, x+w+w:x+3*w]\n\n # count the number of pixels in each section\n pixels[\"left\"] = cv2.countNonZero(left)\n pixels[\"middle\"] = cv2.countNonZero(middle)\n pixels[\"right\"] = cv2.countNonZero(right)\n location = max(pixels, key=pixels.get)\n\n # map the relative position of the db (left, middle, right) to the correct Point()\n locations[color] = db_locations[location]\n \n return locations"},"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 belt(image):\n\n # Belt Detector\n x, y = circular_detector(image, 70, 80)\n\n return x, y","def describe(image):\n needle = cv2.imread(image, 0)\n orb = cv2.ORB()\n keypoints, description = orb.detectAndCompute(needle, None)\n print(keypoints)\n print(description)\n return keypoints, description","def identify_blocks(images):\n locations = {1: Point(), 2: Point(), 3: Point()}\n blocks = {\"left\": 0, \"middle\": 0, \"right\": 0}\n pipeline = keras_ocr.pipeline.Pipeline()\n cv2_images = []\n\n for image in images:\n bridge = cv_bridge.CvBridge()\n cv2_images.append(bridge.imgmsg_to_cv2(image, \"bgr8\"))\n\n predictions = pipeline.recognize(cv2_images)\n rospy.loginfo(predictions)\n\n blocks[\"left\"] = int(predictions[0][0][0])\n blocks[\"middle\"] = int(predictions[1][0][0])\n blocks[\"right\"] = int(predictions[2][0][0])\n\n for position, block in blocks.items():\n locations[block] = block_locations[position]\n \n return locations","def _get_image_location(self):\n\t\timagePointer = self.labels['^IMAGE'].split()\n\t\tif len(imagePointer) == 1:\n\t\t\trecordBytes = int(self.labels['RECORD_BYTES'])\n\t\t\timageLocation = (int(imagePointer[0]) - 1) * recordBytes\n\t\telif len(imagePointer) == 2:\n\t\t\tunits = imagePointer[1]\n\t\t\tif not units == '<BYTES>':\n\t\t\t\terrorMessage = (\"Expected <BYTES> image pointer units but found %s\") % (units)\n\t\t\t\traise ValueError, (errorMessage)\n\t\t\telse:\n\t\t\t\timageLocation = int(imagePointer[0])\n\t\telse:\n\t\t\terrorMessage = (\"^IMAGE contains extra information\") % (imageSampleType)\n\t\t\traise ValueError(errorMessage)\n\t\treturn imageLocation","def identify_image(im):\n score_cures = np.mean(im[1025:1065, 1130:1180, 0])\n score_ingredients = np.mean(im[1025:1065, 675:720, 0])\n if score_cures < 177.5:\n return 'cures'\n if score_ingredients < 177.5:\n return 'ingredients'\n else:\n return 'other'","def _get_positions(self, image):\n\t\tH, W, _ = image.shape\n\t\tpos_list = self.apply_detection(image)\n\t\tdetections = {}\n\t\thasDetection = False\n\t\tfor i, L in enumerate(pos_list):\n\t\t\ttext, coordinates = L[0], L[1]\n\t\t\tfor x, y, w, h in coordinates:\n\t\t\t\tif x < 0 or y < 0 or x + w > W or \\\n\t\t\t\t y + h > H or w <= 1 or h <= 1:\n\t\t\t\t\tcontinue\n\t\t\t\t# add the detection to the dict for tracking\n\t\t\t\tif text == 'face' or text == 'super woman':\n\t\t\t\t\tself.detection_index[self.num_detect] = (x, y, w, h, self.num_save, text, -1)\n\t\t\t\telse:\n\t\t\t\t\tself.detection_index[self.num_detect] = (x, y, w, h, self.num_save, text, -2)\n\t\t\t\tdetections[self.num_detect] = (x, y, w, h)\n\t\t\t\tself.num_detect += 1\n\t\t\t\thasDetection = True\n\t\tif hasDetection:\n\t\t\tself.detection_frames[self.num_save] = detections\n\t\tself.num_save +=1","def face_coords(img, model):\n (h, w) = img.shape[:2]\n blob = cv2.dnn.blobFromImage(cv2.resize(img, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))\n\n model.setInput(blob)\n detections = model.forward()\n\n box = detections[0, 0, 0, 3:7] * np.array([w, h, w, h])\n (startX, startY, endX, endY) = box.astype(\"int\")\n\n return (startX, startY, endX, endY)","def process_image(self, msg):\n self.cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding=\"bgr8\")\n self.edge_detected = cv2.Canny(self.cv_image,self.minVal,self.maxVal)\n if cv2.__version__.startswith('3.'):\n _, self.contours,_ = cv2.findContours(self.edge_detected, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n else:\n self.contours,_ = cv2.findContours(self.edge_detected, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n self.contour_image = cv2.drawContours(self.cv_image, self.contours, -1, (0,255,0), 3)\n for i in range(len(self.contours)):\n temp = self.dp(self.contours[i], 20)\n self.res.append(len(temp))\n if len(temp) == 7:\n for i in range(0,len(temp)-1,2):\n cv2.line(self.contour_image, (temp[i][0],temp[i][1]),(temp[i+1][0], temp[i+1][1]), (0,0,255), 5)\n if len(temp) == 5:\n for i in range(0,len(temp)-1,2):\n cv2.line(self.contour_image, (temp[i][0],temp[i][1]),(temp[i+1][0], temp[i+1][1]), (255,0,0), 5)","def image(self, state):\n return state['positions']","def debug_3_locations( img, gt_location, yolo_location, rolo_location):\n img_cp = img.copy()\n for i in range(3): # b-g-r channels\n if i== 0: location= gt_location; color= (0, 0, 255) # red for gt\n elif i ==1: location= yolo_location; color= (255, 0, 0) # blue for yolo\n elif i ==2: location= rolo_location; color= (0, 255, 0) # green for rolo\n x = max(int(location[0]), 0)\n y = max(int(location[1]), 0)\n w = max(int(location[2]), 0)\n h = max(int(location[3]), 0)\n # if i== 2: cv2.rectangle(img_cp,(x-w//2, y-h//2),(x+w//2,y+h//2), color, 2)\n # elif i== 0 or i == 1: cv2.rectangle(img_cp,(x,y),(x+w,y+h), color, 2)\n cv2.rectangle(img_cp,(x-w/2,y-h/2),(x+w/2,y+h/2), color, 2)\n # cv2.imshow('3 locations',img_cp)\n # cv2.waitKey(100)\n return img_cp","def M12Nut(image):\n kernel = np.ones((5, 5), np.uint8)\n image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=4)\n\n parameters = cv2.SimpleBlobDetector_Params()\n detector = cv2.SimpleBlobDetector_create(parameters=parameters)\n keypoints = detector.detect(image)\n new_image = cv2.drawKeypoints(image, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n for i in range(len(keypoints)):\n print(\"Keypoint: \", keypoints[i].pt)\n cv2.imshow(\"Keypoints\", new_image)\n cv2.waitKey(1000)\n cv2.destroyAllWindows()\n x, y = keypoints[0].pt\n\n return x, y","def particle_images (sim,frame_id) :\n # get positions of all particles: define first the atom selection, then jump to\n # the user-requested trajectory frame, get the box dimensions (currently works\n # only for orthorhombic boxes, then calculate the image indices\n atoms = sim.u.select_atoms ('all')\n ts = sim.u.trajectory[frame_id]\n L = ts.dimensions[:3]\n pos = atoms.positions + L/2.\n return pos//L","def get_symbol_images_and_positions(im):\n gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n _, threshold = cv2.threshold(gray, 100, 255, 0)\n threshold = 255 - threshold\n # show(threshold)\n contours, hierarchy = cv2.findContours(threshold, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n contours = [cv2.approxPolyDP(contour, 8, True) for contour in contours]\n contours = [c for c in contours if c.shape[0] == 4 and cv2.isContourConvex(c)]\n contours = sorted(contours, key=cv2.contourArea)\n contour = contours[-1]\n\n offset_x, offset_y, _, _ = cv2.boundingRect(contour)\n symbols_im = trim_to_contour_bounding_box(im, contour)\n half_height = symbols_im.shape[0] / 2\n half_width = symbols_im.shape[1] / 2\n symbols = (\n symbols_im[:half_height, :half_width],\n symbols_im[:half_height, half_width:],\n symbols_im[half_height:, :half_width],\n symbols_im[half_height:, half_width:],\n\n )\n symbols = (_process_button_im(symbol_im) for symbol_im in symbols)\n\n positions = (\n (offset_x + half_width / 2, offset_y + half_height / 2),\n (offset_x + half_width * 3 / 2, offset_y + half_height / 2),\n (offset_x + half_width / 2, offset_y + half_height * 3 / 2),\n (offset_x + half_width * 3 / 2, offset_y + half_height * 3 / 2),\n )\n\n return symbols, positions","def bbxes_data(img):\n _, _, stats, centr = cv2.connectedComponentsWithStats(img)\n # (xCentr, yCentr, area, width, height, xStart, xEnd , yStart, yEnd )\n return sorted([(cent[0], cent[1], stat[4], stat[2], stat[3], stat[0], stat[0] + stat[2], stat[1], stat[1] + stat[3])\n for cent, stat in zip(centr[1:], stats[1:])])","def detect_face(self, img):\n # Fetch face location from the frame with 128 encoding of face landmarks\n curr_face_loc, name_list, info_list = load_encode_loc(img, self.kwn_names,\n self.kwn_encoding,\n self.status_list, self.since_list)\n print('Current value is ', curr_face_loc, name_list)\n face_list = []\n face_area = []\n print('face loc', curr_face_loc)\n if len(curr_face_loc):\n\n for (top, right, bottom, left), name in zip(curr_face_loc, name_list):\n print(top, right, bottom, left)\n cv2.rectangle(img, (top, right), (bottom, left), (0, 255, 2), 2)\n\n w = right - left\n h = bottom - top\n cx = left + w // 2\n cy = top + h // 2\n area = w * h\n\n for idx, info in enumerate(info_list):\n cv2.putText(img, info, (bottom, int(left * idx * 0.2)),\n cv2.FONT_HERSHEY_COMPLEX, 1,\n (0, 0, 255), 1)\n\n face_list.append([cx, cy])\n face_area.append(area)\n\n i = face_area.index(max(face_area))\n\n return img, [face_list[i], face_area[i]]\n\n else:\n return img, [[0, 0], 0]","def process_image(im):\r\n h, _, _ = im.shape\r\n im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\r\n \r\n # Divide the picture into 3 regions\r\n l1 = int(0.65*h)\r\n l2 = int(0.77*h)\r\n im1 = im_gray[:l1,:]\r\n im2 = im_gray[l1+1:l2,:]\r\n im3 = im_gray[l2+1:,:]\r\n \r\n # Extract 4 pictures\r\n pics = extract_4_pics(im, im1)\r\n \r\n # Extract the word size\r\n word_size = extract_word_size(im2)\r\n \r\n # Extract the letters\r\n letters = extract_letters(im3)\r\n \r\n print 'word size =', word_size\r\n print 'letters =', letters\r\n for i, pic in enumerate(pics):\r\n imsave(str(i) + '.png', pic)\r\n\r\n return word_size, letters, pics","def get_image_characteristics(self):\r\n self.image_height, self.image_width, self.image_channels = self.image.shape\r\n\r\n # Estimate the cell size to be around a ninth of the width of the screenshot area\r\n self.cell_size = int(self.image_width / 9) | 1\r\n\r\n # Cell size should be at most a ninth of the width and at least a twentieth of the width of the screenshot\r\n # Since a typical grid is 9x9, so it should be at most a ninth of the image width, and it shouldn't be too small\r\n self.min_cell_size = int(self.image_width / 20 * self.image_width / 20)\r\n self.max_cell_size = int(self.image_width / 9 * self.image_width / 9)","def depth_range_from_img(img, inspect):\n x0, y0, x1, y1 = TEXT_BBOX\n crop_fn = lambda x: x[x0:x1,y0:y1]\n\n if isinstance(img, str):\n img = io.imread(img)\n\n # Get string of text, look for possible floats\n chars = pytesseract.image_to_string(crop_fn(img), config=TESSERACT_CONFIG)\n numbers = [truncate(number,2) for number in re.findall('\\d+\\.\\d+', chars)]\n\n if len(numbers) < 2:\n if inspect:\n print('Less than two floats found, filling in 0\\'s')\n print(chars)\n plt.imshow(crop_fn(img))\n plt.show()\n return (0.0, 0.0)\n else:\n # Return last two possible floats\n return (float(numbers[-2]), float(numbers[-1]))","def get_element_coordinates(path_to_image):\n return pyautogui.center(pyautogui.locateOnScreen(path_to_image, confidence=0.9))","def test():\n\n fname='./MedData/Lung-PET-CT-Dx/Lung_Dx-A0164/04-12-2010-PET01PTheadlung Adult-08984/8.000000-Thorax 1.0 B31f-52757/1-001.dcm' \n \n ds=pydicom.dcmread(fname)\n # print(ds.pixel_array.shape)\n print(ds.pixel_array[1])\n plt.figure(figsize=(10,10))\n plt.imshow(ds.pixel_array, cmap=plt.cm.bone)\n plt.show()","def image_handler(self, bot, update):\n text = update.message.text\n if text.startswith('/recon'):\n bot.sendMessage(chat_id=update.message.chat_id, text='*Object recognition*\\nSend me an image',\n parse_mode=ParseMode.MARKDOWN)\n return 10\n elif text.startswith('/faces'):\n bot.sendMessage(chat_id=update.message.chat_id, text='*Face recognition*\\nSend me an image',\n parse_mode=ParseMode.MARKDOWN)\n return 11","def message_received(self, message):\n\n body_lxml = message.body_lxml()\n for image in body_lxml.iterfind(\".//img[@src]\"):\n if image.attrib['src'] == self.no_devil_banana_url:\n logger.debug(\":nodb: found in {0}'s message {1}\".format(\n message.user_name, message.id\n ))\n with self.last_lock:\n self.last_nodb_message = message.id\n elif image.attrib['src'] in self.devil_banana_urls:\n if message.user_name == self.connector.username:\n # ignore my own devil banana messages\n return\n logger.debug(\"devil banana {2} found in {0}'s message {1}\".format(\n message.user_name, message.id, image.attrib['src']\n ))\n with self.last_lock:\n self.last_banana_message_due_to_edit = False\n self.last_banana_message = message.id","def paintings_detection(query_image, mask):\n\n image = cv2.imread(query_image)\n\n image_width = mask.shape[0]\n image_height = mask.shape[1]\n x_box_1, y_box_1, w_box_1, h_box_1, x_box_2, y_box_2, w_box_2, h_box_2 = 0, 0, 0, 0, 0, 0, 0, 0, \n\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n\n for cnt in contours:\n x, y, w, h = cv2.boundingRect(cnt)\n \n if (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 == 0):\n x_box_1, y_box_1, w_box_1, h_box_1 = x, y, w, h\n elif (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 != 0):\n x_box_2, y_box_2, w_box_2, h_box_2 = x, y, w, h\n\n if x_box_2 == 0:\n x_value_to_split = 0\n else:\n x_value_to_split = (x_box_1 + w_box_1/2 + x_box_2 + w_box_2/2) / 2\n\n\n return(x_value_to_split)","def cover(img: Image, message: bitarray) -> Image:\n width, height = img.size\n check_image_width(width, RGB_PIXEL, message)\n pixels = img.load()\n\n row = random.randint(0, height) # Randomly chooses row.\n i = 0 # Tracks hidden bits\n\n # If Image consist of 8-bit pixels\n if img.mode == \"P\":\n offset = generate_offset(width, P_PIXEL, message)\n for x in range(offset, width):\n p = pixels[x,row]\n if i < len(message):\n p = modify_byte(p, message[i])\n i += 1\n pixels[x,row] = p\n generate_key(row, message, offset * P_PIXEL)\n # If Image consists of 3x8-bit pixels\n elif img.mode == \"RGB\":\n offset = generate_offset(width, RGB_PIXEL, message)\n for x in range(offset, width):\n r, g, b = pixels[x,row]\n if i < len(message):\n r = modify_byte(r, message[i])\n i += 1\n if i < len(message):\n g = modify_byte(g, message[i])\n i += 1\n if i < len(message):\n b = modify_byte(b, message[i])\n i += 1\n pixels[x,row] = (r, g, b)\n generate_key(row, message, offset * RGB_PIXEL)\n # If Image consists of 4x8-bits pixels\n elif img.mode == \"RGBA\":\n offset = generate_offset(width, RGBA_PIXEL, message)\n for x in range(offset, width):\n r, g, b, a = pixels[x,row]\n if i < len(message):\n r = modify_byte(r, message[i])\n i += 1\n if i < len(message):\n g = modify_byte(g, message[i])\n i += 1\n if i < len(message):\n b = modify_byte(b, message[i])\n i += 1\n if i < len(message):\n a = modify_byte(a, message[i])\n i += 1\n pixels[x,row] = (r, g, b, a)\n generate_key(row, message, offset * RGBA_PIXEL)\n\n return img","def dimension_finder(self, text_header):\r\n dimension = re.search('\\d+x\\d+',text_header).group()\r\n dimension1 = re.findall('\\d+',dimension)[0]\r\n dimension1 = float(dimension1)\r\n dimension2 = re.findall('\\d+',dimension)[1]\r\n dimension2 = float(dimension2)\r\n return (dimension1,dimension2)","def checkImages(self):\r\n\r\n self.leftImage, self.rightImage, res = self.receiver.getImageData()\r\n\r\n return res","def draw_boxes_info(image, current_data):\n\n font_position1 = (50, 600)\n font_position2 = (50, 650)\n font_scale = .4\n font_thickness = 1\n\n locations = current_data[\"locations\"] #returns x1, y1, x2, y2\n frame_num = \"Frame Number: \" + str(current_data[\"frame_num\"])\n\n for box in locations:\n box_text = (\"Box locations are x1: {0}, y1: {1}, x2: {2}, y2: {3}\").format(box[1],box[3],box[0],box[2])\n\n cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 3)\n cv2.putText(image, box_text, font_position1, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\n font_thickness, cv2.LINE_AA)\n\n cv2.putText(image, frame_num, font_position2, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\n font_thickness, cv2.LINE_AA)\n\n return image","def test_get_click_locations(images):\n #Show the click locations for all images\n for i, image in enumerate(images):\n stack_bounds = get_stack_bounds(image)\n clicks = get_click_locations(stack_bounds)\n\n #Draw the number of points and the points at the center of their stacks\n clicks_image = cv2.putText(np.copy(image),\n 'num_stacks: {}'.format(len(clicks)),\n (10, 30),\n cv2.FONT_HERSHEY_DUPLEX,\n 0.75, (0, 0, 0), 1, cv2.LINE_AA)\n for click in clicks.values():\n clicks_image = cv2.circle(clicks_image, click, 5, (0,0,0), -1)\n\n original_and_clicks = hstack_images(image, clicks_image, False)\n cv2.imshow('original and clicks_image {}'.format(i), original_and_clicks)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","def detect_face_api(self, img):\n\n curr_face_loc, name_list, info_list = load_encode_loc(img, self.kwn_names,\n self.kwn_encoding,\n self.status_list, self.since_list)\n print('Current value is ', curr_face_loc, name_list)\n face_list = []\n face_area = []\n print('face loc', curr_face_loc)\n if len(curr_face_loc):\n\n for (top, right, bottom, left), name in zip(curr_face_loc, name_list):\n print(top, right, bottom, left)\n cv2.rectangle(img, (top, right), (bottom, left), (0, 255, 2), 2)\n\n w = right - left\n h = bottom - top\n cx = left + w // 2\n cy = top + h // 2\n area = w * h\n\n for idx, info in enumerate(info_list):\n cv2.putText(img, info, (bottom, int(left * idx * 0.2)),\n cv2.FONT_HERSHEY_COMPLEX, 1,\n (0, 0, 255), 1)\n\n face_list.append([cx, cy])\n face_area.append(area)\n\n i = face_area.index(max(face_area))\n\n return img, [face_list[i], face_area[i]]\n\n else:\n return img, [[0, 0], 0]","def process(self, image):","def get_grads(img):\n dx = ndimage.sobel(img, 0) # horizontal derivative\n dy = ndimage.sobel(img, 1) # vertical derivative\n return dx, dy","def onObjectDetected(msg):\n bridge = CvBridge()\n try:\n depthMap = bridge.imgmsg_to_cv2(msg.depthImg,\n desired_encoding=\"passthrough\")\n\n depthMap = cv2.convertScaleAbs(depthMap, alpha=100, beta=0)\n # Get the center crop of the boxed image\n startY = int(msg.box.origin_y) + int(msg.box.height//4)\n startX = int(msg.box.origin_x) + int(msg.box.width//4)\n\n crop = depthMap[startY: startY + int(msg.box.height//2),\n startX: startX + int(msg.box.width//2)]\n\n centerY = startY + int(msg.box.height//2)\n centerX = startX + int(msg.box.width//2)\n\n avg = getAverageColor(crop)\n distance = shadeToDepth(avg)\n\n print \"object: {} @{:4.3}, {}\".format(msg.className, distance,\n (centerX, centerY))\n global OBJECT_MAP\n OBJECT_MAP.addToMap(msg.className,\n centerX, centerY,\n distance)\n except CvBridgeError as e:\n print(e)","def handle_image_data(data):\n \n #Get the incoming RGB image from the Kinect\n D.image = D.bridge.imgmsg_to_cv(data, \"bgr8\")\n\n if D.created_images == False:\n #Initialize the additional images we need for processing\n ImageProcessing.initialize(D)\n D.created_images = True\n\n # Recalculate threshold image\n ImageProcessing.threshold_image(D)\n\n # Recalculate blob in main image\n ImageProcessing.find_biggest_region(D)\n\n # Check on the display of dragged section\n ImageProcessing.mouse_section(D)\n\n #Display target circle\n #ImageProcessing.target_coord(D)\n \n #Display info box on image\n ImageProcessing.draw_on_image(D)\n \n #Handle incoming key presses\n key_press = cv.WaitKey(5) & 255\n if key_press != 255:\t\t\t#Handle only if it's a real key\n check_key_press(D, key_press)\t\t#(255 = \"no key pressed\")\n\n #Update the displays:\n #Show main image in the image window\n #cv.ShowImage('Image', D.image)\n\n #Show threshold image in the threshold window 3currentThreshold = getattr(D, D.current_threshold)\n cv.ShowImage('Threshold', currentThreshold)","def findMatchesBetweenImages(image_1, image_2):\n # matches - type: list of cv2.DMath\n matches = None\n # image_1_kp - type: list of cv2.KeyPoint items.\n image_1_kp = None\n # image_1_desc - type: numpy.ndarray of numpy.uint8 values.\n image_1_desc = None\n # image_2_kp - type: list of cv2.KeyPoint items.\n image_2_kp = None\n # image_2_desc - type: numpy.ndarray of numpy.uint8 values.\n image_2_desc = None\n # WRITE YOUR CODE HERE.\n\n sift = cv2.ORB_create()\n image_1_kp, image_1_desc = sift.detectAndCompute(image_1, None)\n image_2_kp, image_2_desc = sift.detectAndCompute(image_2, None)\n\n # bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n # matches = bf.match(image_1_desc, image_2_desc)\n # matches = sorted(matches, key = lambda x:x.distance)\n # matches = matches[:10]\n\n bf = cv2.BFMatcher()\n matches = bf.knnMatch(image_1_desc, image_2_desc, k=2)\n\n # Apply ratio test\n good = []\n for m, n in matches:\n print(m.distance, n.distance, m.distance < .75*n.distance)\n if m.distance < (0.75 * n.distance):\n good.append([m])\n\n # We coded the return statement for you. You are free to modify it -- just\n # make sure the tests pass.\n print(len(good), good)\n return image_1_kp, image_2_kp, matches\n # END OF FUNCTION.","def frame_to_distance_images(frame):\n \n # distance from ridge midlines\n frame = rgb2gray(frame)\n ridges = enhance_ridges(frame)\n thresh = filters.threshold_otsu(ridges)\n prominent_ridges = ridges > 0.8*thresh\n skeleton = morphology.skeletonize(prominent_ridges)\n edge_dist = ndimage.distance_transform_edt(~skeleton)\n edge_dist = filters.gaussian(edge_dist, sigma=2)\n\n # distance from skeleton branch points (ie, ridge intersections)\n blurred_skeleton = uniform_filter(skeleton.astype(float), size=3)\n corner_im = blurred_skeleton > 4./9\n corner_dist = ndimage.distance_transform_edt(~corner_im)\n \n return edge_dist, corner_dist","def find_pixel_edges(img):\n \n transparency_cells = img.T[3].T # Ignores rbg, returns only transparency. \n # Values are either:\n # 0 (fully transparent) or \n # 1 (not at all transparent)\n right_up = np.rot90(transparency_cells)\n bottom_up = np.rot90(right_up)\n left_up = np.rot90(bottom_up)\n\n y_top = count_from_top(transparency_cells)\n x_left = count_from_top(left_up)\n y_bottom = count_from_top(bottom_up)\n x_right = count_from_top(right_up)\n #print(f'\\ty_top:{y_top}\\nx_left:{x_left}\\t\\tx_right:{x_right}\\n\\ty_bottom:{y_bottom}')\n return y_top, y_bottom, x_left, x_right","def image_received(self, image_message):\n # Convert the image message to something usable by opencv\n # http://wiki.ros.org/cv_bridge/Tutorials/ConvertingBetweenROSImagesAndOpenCVImagesPython\n # Note that mono8 and bgr8 are the two image encodings expected by most OpenCV functions.\n cv_image = self.bridge.imgmsg_to_cv2(image_message, desired_encoding=\"bgr8\")\n image_data = extract_data(cv_image)\n linear_velocity, angular_velocity = self.clf.predict(image_data)\n self.cmd_vel = Twist(linear=Vector3(x=linear_velocity), angular=Vector3(z=angular_velocity))\n rospy.loginfo(self.cmd_vel)","def _generate_dimensions(conf_dict, message):\n sq_side = math.ceil(math.sqrt(_minimal_pixel_count(conf_dict, message)))\n return (sq_side, sq_side)","def detect_helmet_coordinates(image, visualize=True):\n if visualize:\n cv2.imshow('Raw Image', image)\n\n # TODO(you) Fill this out to \"threshold\" the image to show only the color you want, display that thresholded image,\n # and figure out the coordinates of the center of the blob of pixels.\n\n return (0, 0) # (x, y) measured in pixels","def extract(self, files):\n for i in range(len(files)):\n print(files[i])\n img = cv2.imread('{}/{}'.format('{}/{}/{}'.format(DIR_2DST_Mask, self.patient, self.plan), files[i]), 0)\n\n \"\"\"\n Find the indices of array elements that are non-zero, i.e,\n find the pixels' positions that represents the respiratory\n functions (pixels in the respiratory function are brighter).\n \"\"\"\n color_pts = np.argwhere(img > 70)\n\n \"\"\"\n Sorts the pixels according to their x coordenate.\n Obs: np.argwhere inverts x and y, it's like (y, x), because of it,\n the parameter of itemgetter is 1 (to get x coordinate)\n \"\"\"\n lcolor_pts = sorted(color_pts.tolist(), key=itemgetter(1))\n\n \"\"\"\n If there is no pixel representing the respiratory function\n (i.e., lighter pixel) it creates an empty image (without any\n respiratory function)\n \"\"\"\n if len(lcolor_pts) == 0:\n diaphragmatic_lvl = np.zeros((256, 50, 3), np.uint8)\n\n cv2.imwrite('{}/{}/{}/{}'.format(\n DIR_2DST_Diaphragm, patient, plan, files[i]), diaphragmatic_lvl)\n\n # file = open(\n # '{}/{}/{}/points.txt'.format(DIR_2DST_Diaphragm, self.patient, self.plan), 'a')\n # file.write(\"{}:{}\\n\".format(files[i], []))\n # file.close()\n\n continue\n\n # Reverse the coordinates and store the result in lordered_pts list\n lordered_pts = []\n for j in range(len(lcolor_pts)):\n lordered_pts.append(lcolor_pts[j][::-1])\n\n \"\"\"\n Convert pixels coordinates into a tuples and check which column\n has pixels that corresponding to diaphragmatic level\n Obs. There are some columns that doesnt have any pixel that\n correpond to diaphragmatic level.\n \"\"\"\n # Columns that have a pixel corresponding diaphragmatic level\n lcolumn_available = []\n for j in range(len(lordered_pts)):\n lordered_pts[j] = tuple(lordered_pts[j])\n lcolumn_available.append(lordered_pts[j][0])\n lcolumn_available = list(set(lcolumn_available))\n # print(\"Ordered points: \", lordered_pts)\n # print(\"Columns available: \", lcolumn_available)\n\n \"\"\"\n If there is not enough columns to build a respiratory pattern,\n create a blank image\n \"\"\"\n if len(lcolumn_available) < 20:\n diaphragmatic_lvl = np.zeros((256, 50, 3), np.uint8)\n cv2.imwrite('{}/{}/{}/{}'.format(\n DIR_2DST_Diaphragm, patient, plan, files[i]), diaphragmatic_lvl)\n continue\n\n \"\"\"\n If there are no pixel that corresponding diaphragmatic level in the\n first column, assign to it the value of the second y coordinate\n \"\"\"\n if lcolumn_available[0] is not 0:\n y = max(\n [x for x in lordered_pts if x[0] == lcolumn_available[0]],\n key=itemgetter(1))[1]\n lordered_pts.insert(0, (0, y))\n lcolumn_available.insert(0, 0)\n\n \"\"\"\n If there are no pixel that corresponding diaphragmatic level in the\n last column, assign to it the value of the penultimate y coordinate\n available\n \"\"\"\n if lcolumn_available[-1] is not 49:\n lordered_pts.append(\n (49, lordered_pts[len(lcolumn_available)][1]))\n lcolumn_available.append(49)\n\n \"\"\"\n Get the biggest y value in each column that represents the\n diaphragmatic level\n \"\"\"\n column = 0\n lcolumn = []\n ldiaphragm_pts = []\n for j in range(50):\n # Get the column's points\n lcolumn = [x for x in lordered_pts if x[0] == column]\n # print('{}: {}'.format(j, lcolumn))\n\n if len(lcolumn) > 0:\n ldiaphragm_pts.append(\n max(lcolumn, key=itemgetter(1))) # Get the biggest y\n else:\n # Get the y value from the previous column\n lcolumn_available.insert(column, column)\n ldiaphragm_pts.append((column, ldiaphragm_pts[-1][1]))\n column += 1\n lcolumn = []\n\n # Draw diaphragmatic level\n diaphragmatic_lvl = np.zeros((256, 50, 3), np.uint8)\n j = 0\n while(j < len(lcolumn_available) - 1):\n cv2.line(\n diaphragmatic_lvl,\n ldiaphragm_pts[j], ldiaphragm_pts[j + 1],\n (0, 0, 255), 1)\n j = j + 1\n\n lcolumn_available = []\n\n print(\"Diaphragmatic's points: \", ldiaphragm_pts)\n cv2.imshow('Diaphragmatic level', diaphragmatic_lvl)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n cv2.imwrite('{}/{}/{}/{}'.format(\n DIR_2DST_Diaphragm, patient, plan, files[i]), diaphragmatic_lvl)\n\n # file = open('{}/{}/{}/points.txt'.format(DIR_2DST_Diaphragm, self.patient, self.plan), 'a')\n # file.write(\"{}:{}\\n\".format(files[i], ldiaphragm_pts))\n # file.close()\n\n # return ldiaphragm_pts","def recognize_image(self, bot, update):\n # get image\n chat_id = update.message.chat_id\n new_image = bot.get_file(update.message.photo[-1].file_id)\n new_image.download('recon.jpg')\n\n group_of_items = [i for i in range(len(docvectors))]\n num_to_select = 3\n list_of_random_items = random.sample(group_of_items, num_to_select)\n jokes = '🤔\\nIt would take a while to recognize objects on the image, Morty. Your grandfather will tell some jokes while you wait :)\\n\\n'\n for joke in list_of_random_items:\n jokes += '- ' + docvectors[joke][1] + \"\\n\\n\"\n bot.sendMessage(chat_id=update.message.chat_id, text=jokes)\n\n try:\n entities = self.detector.recon()\n if len(entities) == 1:\n k = \"Are you blind, Morty? There's \"\n else:\n k = \"Are you blind, Morty? There're \"\n\n d = {}\n for entity in entities:\n d[entity] = d.get(entity, 0) + 1\n\n blacklist = ''\n if len(entities) > 0:\n blacklist = entities[-1]\n\n for i in d:\n if i != blacklist:\n k += detector.NLG(i, d[i], False, False)\n\n if len(d) > 0:\n k += detector.NLG(blacklist, d[blacklist], True, False if len(d) == 1 else True)\n k += ' on the picture'\n else:\n k = \"I can't recognize anything, Morty. Get lost!\"\n except:\n message = \"You broke it again, Morty!\"\n bot.send_message(chat_id=update.message.chat_id, text=message)\n return -1\n # return an image with a caption\n bot.send_photo(chat_id=chat_id, photo=open('recon.jpg', 'rb'), caption=k[:200])\n return -1","def model(msg):\n url = 'https://southcentralus.api.cognitive.microsoft.com/customvision/v3.0/Prediction/\\\n eff56ac8-0f36-41d9-93a9-da19396b0f30/detect/iterations/Iteration2_ppl_focus/image'\n headers = {\n 'Prediction-Key': os.getenv('AZURE_VIS_KEY'),\n 'Content-Type': 'application/octet-stream'\n }\n r = requests.post(url=url, headers=headers, data=msg['img'])\n predictions = r.json()\n print('Number of object predictions: {}'.format(\n len(predictions['predictions'])))\n print('Frame Number:', msg['frame_num'],\n 'Image Dimensions:', np.array(Image.open(BytesIO(msg['img']))).shape)\n Coord_matrix = Bbox(predictions)\n return Coord_matrix, len(predictions['predictions'])","def decode(img):\r\n ints=[];#A list of ints that will contain all of our alpha values.\r\n width,height=img.size #Get the width and the height of my image.\r\n pixelData=ImageUtilities.getPixelList(img); #Get all of the pixels in the image and put them into a list.\r\n for y in range(height): #Iterate across the pixels from top to bottom.\r\n for x in range(width):#Iterate across out image from left to right.\r\n alpha=ImageUtilities.getAlphaFromList(img,pixelData,x,y); #Referenced the dumped contents\r\n if(alpha==255): #If the alpha of our pixel is 255....\r\n continue; #I don't want 255 values because that means that is not part of my message.\r\n ints.append(alpha); #Get the alpha value and append it to my list of ints.\r\n\r\n msg=\"\"; #Make an empty string to store our decoded message.\r\n for value in ints: #Iterate across my list of ints. (For each int in my list...)\r\n msg+=chr(value); #Convert my int to it's character value and add it back to my message.\r\n return msg; #Return my message string.\r","def who_is_it(image_path, database, model):\n \n ### START CODE HERE ### \n \n ## Step 1: Compute the target \"encoding\" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)\n encoding = img_to_encoding(image_path,model)\n \n ## Step 2: Find the closest encoding ##\n \n # Initialize \"min_dist\" to a large value, say 100 (≈1 line)\n min_dist = 100\n \n # Loop over the database dictionary's names and encodings.\n for (name, db_enc) in database.items():\n \n # Compute L2 distance between the target \"encoding\" and the current \"emb\" from the database. (≈ 1 line)\n dist = np.linalg.norm(encoding - db_enc)\n\n # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)\n if dist<min_dist:\n min_dist = dist\n identity = name\n\n ### END CODE HERE ###\n \n if min_dist > 0.5:\n print(\"Not in the database.\")\n else:\n print (\"it's \" + str(identity) + \", the distance is \" + str(min_dist))\n \n return min_dist, identity","def _raw_face_locations(img, number_of_times_to_upsample=1, model=\"hog\"):\n\tif model == \"cnn\":\n\t\treturn cnn_face_detector(img, number_of_times_to_upsample)\n\telse:\n\t\treturn face_detector(img, number_of_times_to_upsample)","def mesgBetweenRounds(self, message):\n font = UIC.Medium_Text\n y_offset = (UIC.Disp_Height * (1 - (UIC.Hand_Row_Fraction * 0.8)))\n for message_string in message:\n text_surface = font.render(message_string, True, UIC.Black)\n text_rect = text_surface.get_rect()\n text_rect.center = ((UIC.Disp_Width * 0.5), y_offset)\n y_offset = y_offset + UIC.Medium_Text_Feed\n self.display.blit(text_surface, text_rect)","def detect_fields(img):\n lines = np.array_split(img, 5, axis=1)\n fields = [np.array_split(line, 5, axis=0) for line in lines]\n return fields","def find(image):\n keypoint, description = describe(image)\n # load keypoints, descriptions from mongodb\n\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n\n best_match_size = float(\"inf\")\n best_match_index = -1\n match_index = 0\n best_matches = 0\n\n for desc in descriptions:\n matches = bf.match(desc,description)\n matches = sorted(matches, key = lambda x:x.distance)\n if len(matches) > 0:\n match_size = sum(x.distance for x in matches[:10])\n\n print \"match size is \", match_size\n if match_size < best_match_size:\n best_match_size = match_size\n best_match_index = match_index\n best_matches = matches\n\n match_index += 1\n\n needle_color = cv2.imread('needle-stripped.png')[:,:,::-1] # needle\n best_match_image = cv2.imread(\"haystack/\"+files[best_match_index])\n print \"best match is \", files[best_match_index]\n\n # Draw first 10 matches.\n outImg = cv2.imread(\"output/outImg.png\")\n match = cv2.drawMatches(needle_color,keypoint,best_match_image[:,:,::-1],keypoints[best_match_index],best_matches[-20:],outImg, flags=6)\n\n plt.imshow(match),plt.show()\n return","def _raw_face_locations(img, number_of_times_to_upsample=1):\n return face_detector(img, number_of_times_to_upsample)","def PoliqarpSubposDict(image, i, n):\n record = struct.unpack('I' * (n >> 2), image[i: (i+n)])\n\n return [(n & 0xfffff, n >> 20) for n in record]","def other_object(img):\n return img[500:570, 1000:1070]","def Hexagon(image):\n return x, y","def detect_faces(image):\n\n face_locations = face_recognition.face_locations(image)\n return face_locations","def find_shapes(im_bw, kernel, th):\n im_convolved = scipy.ndimage.filters.convolve(im_bw, kernel[::-1, ::-1])\n im_hotspots = im_convolved >= th\n bin_dy, bin_dx = kernel.shape\n radius_x = bin_dx // 2\n radius_y = bin_dy // 2\n positions = []\n open_for_interpretation = np.ones_like(im_convolved, dtype=bool)\n xmax = im_hotspots.shape[1] - 1\n ymax = im_hotspots.shape[0] - 1\n for x in range(xmax + 1):\n for y in range(ymax + 1):\n if open_for_interpretation[y, x] and im_hotspots[y, x]:\n x0 = max(0, x - radius_x)\n x1 = min(xmax, x + radius_x)\n y0 = max(0, y - radius_y)\n y1 = min(ymax, y + radius_y)\n window = im_hotspots[y0:y1, x0:x1]\n cx, cy = center_of_mass(window, x_offset=x - radius_x, y_offset=y - radius_y)\n positions.append([cx, cy])\n open_for_interpretation[y0:y1, x0:x1] = False\n return positions","def get_start_positions(img_in):\n\n def initialize_coordinates(kernel_h, kernel_w):\n \"\"\" locates positions of interest by traversing eroded image and\n saves 9 points on each area of interest to global matrix\n :param kernel_h height of kernel used for harsh erosion\n :param kernel_w width of kernel used for harsh erosion\"\"\"\n global init_coords\n\n count = 0\n y = 0\n while y < frame_height - kernel_h:\n x = 0\n while x < frame_width - kernel_w:\n locator = img[y:y+kernel_h, x:x+kernel_w, 2] > 0 + numpy.zeros((kernel_h, kernel_w))\n if numpy.any(locator):\n if count == 0:\n init_coords[count][0][0] = y - 2\n init_coords[count][0][1] = x + 2\n elif count == 1:\n init_coords[count][0][0] = y + 2\n init_coords[count][0][1] = x + 2\n elif count == 2:\n init_coords[count][0][0] = y + 2\n init_coords[count][0][1] = x + 2\n elif count == 3:\n init_coords[count][0][0] = y - 3\n init_coords[count][0][1] = x + 2\n elif count == 4:\n init_coords[count][0][0] = y + 3\n init_coords[count][0][1] = x - 5\n count += 1\n break\n x += kernel_w\n y += kernel_h\n\n # store 8 more points for each body part\n f = 1.5\n for count in range(5):\n init_coords[count][1][1] = init_coords[count][0][1] + 3*f\n init_coords[count][1][0] = init_coords[count][0][0] + 0\n init_coords[count][2][1] = init_coords[count][0][1] + 6*f\n init_coords[count][2][0] = init_coords[count][0][0] + 0\n init_coords[count][3][1] = init_coords[count][0][1] + 0\n init_coords[count][3][0] = init_coords[count][0][0] + 3*f\n init_coords[count][4][1] = init_coords[count][0][1] + 3*f\n init_coords[count][4][0] = init_coords[count][0][0] + 3*f\n init_coords[count][5][1] = init_coords[count][0][1] + 6*f\n init_coords[count][5][0] = init_coords[count][0][0] + 3*f\n init_coords[count][6][1] = init_coords[count][0][1] + 0\n init_coords[count][6][0] = init_coords[count][0][0] + 6*f\n init_coords[count][7][1] = init_coords[count][0][1] + 3*f\n init_coords[count][7][0] = init_coords[count][0][0] + 6*f\n init_coords[count][8][1] = init_coords[count][0][1] + 6*f\n init_coords[count][8][0] = init_coords[count][0][0] + 6*f\n\n limb_coords[0][0][0] = init_coords[0][5][0]\n limb_coords[0][0][1] = init_coords[0][5][1]\n limb_coords[1][0][0] = init_coords[1][5][0]\n limb_coords[1][0][1] = init_coords[1][5][1]\n limb_coords[2][0][0] = init_coords[2][5][0]\n limb_coords[2][0][1] = init_coords[2][5][1]\n limb_coords[3][0][0] = init_coords[3][5][0]\n limb_coords[3][0][1] = init_coords[3][5][1]\n limb_coords[4][0][0] = init_coords[4][5][0]\n limb_coords[4][0][1] = init_coords[4][5][1]\n\n img = img_in.copy()\n img = segment_red(img, 205, 135)\n erode(img, 14, 12)\n initialize_coordinates(14, 12)","def test_find_details(self):\n input_image = np.array([\n 0.01, 0.1, 0.2, 0.5, 0.75, 0.99\n ])\n blur_input_image = np.array([\n 0.28641213, 0.32315277, 0.3871898, 0.46174035, 0.52684723, 0.56466555\n ])\n expected_image_lower = np.array([\n -0.277, -0.224, -0.188, 0.038, 0.223, 0.425\n ])\n expected_image_upper = np.array([\n -0.276, -0.223, -0.187, 0.039, 0.224, 0.426\n ])\n output = localHDR.find_details(input_image, blur_input_image)\n self.assertTrue(np.allclose(output, expected_image_lower, atol=6e-03))\n self.assertTrue(np.allclose(output, expected_image_upper, atol=6e-03))","def _raw_face_locations(img, number_of_times_to_upsample=1, model=\"hog\"):\n if model == \"cnn\":\n cnn_face_detector = dlib.cnn_face_detection_model_v1('mmod_human_face_detector.dat')\n return cnn_face_detector(img, number_of_times_to_upsample)\n else:\n # face_detector = dlib.get_frontal_face_detector()\n return face_detector(img, number_of_times_to_upsample)","def botStack_x(self):\r\n self.x_stack=self.img.shape[2]-1\r\n \r\n self.pixmap5=self.drawPixmap(\"yz\")\r\n self.lbl5.setPixmap(self.pixmap5)\r\n self.pixmap6= self.writeEdge(\"yz\")\r\n self.lbl6.setPixmap(self.pixmap6)\r\n self.x_stack_lbl.setText(str(self.x_stack+1) + '/' + str(self.img.shape[2]))","def imageparts(msg):\n # Don't want a set here because we want to be able to process them in\n # order.\n return filter(lambda part:\n part.get_content_type().startswith('image/'),\n msg.walk())","def data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend):\n\tinfile = glob.glob(grbdir + \"/*quad_clean.evt\")[0] \n\tpredph = evt2image(infile,pre_tstart,pre_tend)\n\tgrbdph = evt2image(infile,grb_tstart,grb_tend)\n\tpostdph = evt2image(infile,post_tstart,post_tend)\n\n\tbkgddph = predph+postdph\n\n\toneD_grbdph = grbdph.flatten()\n\toneD_bkgddph = bkgddph.flatten()\n\tt_src = grb_tend - grb_tstart\n\tt_total = (pre_tend-pre_tstart)+(post_tend-post_tstart)\n\n\treturn oneD_grbdph,oneD_bkgddph,grbdph,bkgddph,t_src,t_total","def find_pixels(self):\n ref_image=Image.open('sample0000.png')\n imarray=np.array(ref_image)\n ref_image.close()\n self.number_of_pix=imarray.shape\n print self.number_of_pix\n ref_image=None\n imarray=None","def _get_image_dimensions(self):\n\t\timageWidth = int(self.labels['IMAGE']['LINE_SAMPLES'])\n\t\timageHeight = int(self.labels['IMAGE']['LINES'])\n\t\treturn imageWidth, imageHeight","def draw_on_image(D):\n \n #Set up rectangle's position within window\n lower_left_x = 20 \n lower_left_y = 42\n dx = 5\n dy = 5\n\n #Display border for rectangle\n #Border is a black rectangle under white text rectangle\n bord_upper_left = (lower_left_x-dx-3, lower_left_y-dy-20-3)\n bord_lower_right = (lower_left_x+dx+160+3, lower_left_y+dy+50+3)\n cv.Rectangle(D.image, bord_upper_left, bord_lower_right, D.black, cv.CV_FILLED)\n \n #Display white rectangle under text\n rect_upper_left = (lower_left_x-dx, lower_left_y-dy-20)\n rect_lower_right = (lower_left_x+dx+160, lower_left_y+dy+50)\n cv.Rectangle(D.image, rect_upper_left, rect_lower_right, D.white, cv.CV_FILLED)\n \n ####\n hive = \"hi!\"\n hiveStat = \"hive\"\n \n #Build Strings\n robotAssignment = (\"Assignment #: %.lf\"%R.assignment)\n robotConverged = (\"Converged: %s\"%R.converged)\n robotStatus = (\"Robot Status: \" + R.status)\n hiveCommand = (\"Hive Command: \" + hive)\n hiveStatus = (\"Hive Status: \" + hiveStat)\n \n # Position strings in a box so they won't overlap\n firstLineString = (lower_left_x,lower_left_y) \n secondLineString = (lower_left_x, lower_left_y + 20) \n thirdLineString = (lower_left_x, lower_left_y + 40)\n fourthLineString = (lower_left_x, lower_left_y + 60)\n fifthLineString = (lower_left_x, lower_left_y + 80)\n\n #Display strings in window\n cv.PutText(D.image, robotAssignment, firstLineString, D.font, cv.RGB(0,0,255)) \n cv.PutText(D.image, robotConverged, secondLineString, D.font, cv.RGB(0,0,255))\n cv.PutText(D.image, robotStatus, thirdLineString, D.font, cv.RGB(0,0,255))\n cv.PutText(D.image, hiveCommand, fourthLineString, D.font, cv.RGB(0,0,255))\n cv.PutText(D.image, hiveStatus, fifthLineString, D.font, cv.RGB(0,0,255))","def compare_images(first_img_path, second_img_path):\n img1 = Image.open(first_img_path)\n img2 = Image.open(second_img_path)\n\n diff = ImageChops.difference(img1, img2)\n print(diff.getbbox())","def computePosition(self, state):\n d = 0\n if state[5] == \"East\":\n d = 0\n elif state[5] == \"West\":\n d = 1\n elif state[5] == \"North\":\n d = 2\n else:\n d = 3\n return state[0]*64+state[1]*32+state[2]*16+state[3]*8+state[4]*4+d","def parse_3ccd_images(hdu_lst):\n if len(hdu_lst) != 4:\n raise ValueError\n\n # get CCD Binning\n tmp = hdu_lst[0].header['CCDSUM'].split()\n binx, biny = int(tmp[0]), int(tmp[1])\n # get data sect rectanle\n dataset_lst = {(2, 1): ('[7:1030,1:4096]', (6, 1030), (0, 4096)),\n (2, 2): ('[7:1030,1:2048]', (6, 1030), (0, 2048)),\n }\n datasec, (x1, x2), (y1, y2) = dataset_lst[(binx, biny)]\n # get data section\n data_lst = [hdu_lst[i+1].data[y1:y2, x1:x2] for i in range(3)\n if hdu_lst[i+1].header['DATASEC']==datasec]\n\n # get saturated masks\n mask_sat1 = data_lst[0]==65535 # for UV CCD, saturated pixels are 65535.\n mask_sat2 = data_lst[1]==0 # for green & red CCDs, saturated pixels\n mask_sat3 = data_lst[2]==0 # are 0.\n # get bad pixel masks\n #mask_bad1 = np.zeros_like(mask_sat1, dtype=np.bool)\n #mask_bad2 = np.zeros_like(mask_sat1, dtype=np.bool)\n #mask_bad3 = np.zeros_like(mask_sat1, dtype=np.bool)\n mask_bad1 = get_badpixel_mask((binx, biny), ccd=1)\n mask_bad2 = get_badpixel_mask((binx, biny), ccd=2)\n mask_bad3 = get_badpixel_mask((binx, biny), ccd=3)\n # pack masks\n mask1 = np.int16(mask_sat1)*4 + np.int16(mask_bad1)*2\n mask2 = np.int16(mask_sat2)*4 + np.int16(mask_bad2)*2\n mask3 = np.int16(mask_sat3)*4 + np.int16(mask_bad3)*2\n\n mask_lst = (mask1, mask2, mask3)\n\n # fix saturated pixels in the green and red CCDs\n data_lst[1][mask_sat2] = 65535\n data_lst[2][mask_sat3] = 65535\n\n return (data_lst, mask_lst)","def calculate_embeddings(img, _network, dets):\n \n face_descriptors = []\n \n (_, sp, facerec) = _network\n for (k, d) in enumerate(dets):\n \n print(\"Detection {}: Left: {} Top: {} Right: {} Bottom: {}\".format(\n k, d.left(), d.top(), d.right(), d.bottom()))\n # Get the landmarks/parts for the face in box d.\n shape = sp(img, d)\n \n face_descriptor = facerec.compute_face_descriptor(img, shape)\n face_descriptors.append(face_descriptor)\n \n return face_descriptors","def find_body_part(self, body_points, segmentation_image):\n print(\"start the measuring....\")\n body_parts = {}\n shoulders = self.find_shoulders_point(body_points, segmentation_image)\n abdomen = self.find_abdomen_point(body_points)\n chest = self.find_chest_point(body_points)\n knees = self.find_knee_point(body_points)\n arms = self.find_arms_point(body_points)\n elbows = self.find_elbow_point(body_points)\n ankles = self.find_ankle_point(body_points)\n head = self.find_head_point(body_points)\n\n if shoulders is not None:\n body_parts['shoulders'] = shoulders\n\n if abdomen is not None:\n body_parts['abdomen'] = abdomen\n\n if chest is not None:\n body_parts['chest'] = chest\n\n if knees is not None:\n body_parts['knees'] = knees\n\n if arms is not None:\n body_parts['arms'] = arms\n\n if elbows is not None:\n body_parts['elbows'] = elbows\n\n if ankles is not None:\n body_parts['ankles'] = ankles\n\n if head is not None:\n body_parts['head'] = head\n\n return body_parts","def detectBlocksInDepthImage(self):\n depth_range_dict = {'1':[173,178],'2':[169,172],'3':[165,169],'4':[159,163],'5':[156,158],'6':[147,155],'7':[139,146],'8':[132,138]}\n depth_frame = self.DepthFrameRaw\n rgb_frame = self.VideoFrame\n rgb_frame = cv2.resize(rgb_frame, (640,480))\n depth_frame = cv2.resize(depth_frame, (640, 480))\n np.clip(depth_frame,0,2**10 - 1,depth_frame)\n depth_frame >>= 2\n depth_frame = depth_frame.astype(np.uint8)\n filt_block = []\n for k,v in depth_range_dict.items():\n thresh = cv2.inRange(depth_frame,v[0],v[1])\n cv2.imwrite(\"/home/student/armlab-w20/log/img.jpeg\", thresh)\n _ , contours, _ = cv2.findContours(thresh, 1, 2)\n for cnt in contours:\n area = cv2.contourArea(cnt)\n if area > 400 and area < 700:\n block = []\n rect = cv2.minAreaRect(cnt)\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n color = self.determine_color(rgb_frame, box)\n org = (box[0][0], box[0][1])\n rgb_frame = cv2.putText(rgb_frame, color, org,cv2.FONT_HERSHEY_SIMPLEX , 0.5 ,(0,0,0),2, cv2.LINE_AA)\n rgb_frame = cv2.drawContours(rgb_frame,[box],0,(0,0,0),0)\n self.VideoFrame = rgb_frame\n block.append(box)\n block.append(int(k))\n block.append(color)\n filt_block.append(block)\n return filt_block","def debug_image(self, state_index: int = -1):\n image = self.make_image(state_index, channel_type=\"n\")\n return np.array([np.sum(arr) for arr in image])[3:].reshape(8, 12)","def process_payload(payload):\n\n # Convertion of payload string to image array for opencv\n ret, img = make_image(payload)#ret is 0 when conversion is successful or 1 when not\n result='Unable to detect'\n if ret == 0:\n cv2.imwrite('received.png', img)\n try:\n roi = extract_roi_2(img)\n \n result = detect(roi) \n \n #write_characters(roi)\n\n except:\n result = \"----------------\"\n # # When roi is extracted its a 2d array \n \n return result","def extract_signature(source_image):\n # read the input image\n img = source_image\n img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1] # ensure binary\n\n # connected component analysis by scikit-learn framework\n blobs = img > img.mean()\n blobs_labels = measure.label(blobs, background=1)\n # image_label_overlay = label2rgb(blobs_labels, image=img)\n\n fig, ax = plt.subplots(figsize=(10, 6))\n\n '''\n # plot the connected components (for debugging)\n ax.imshow(image_label_overlay)\n ax.set_axis_off()\n plt.tight_layout()\n plt.show()\n '''\n\n the_biggest_component = 0\n total_area = 0\n counter = 0\n average = 0.0\n for region in regionprops(blobs_labels):\n if (region.area > 10):\n total_area = total_area + region.area\n counter = counter + 1\n # print region.area # (for debugging)\n # take regions with large enough areas\n if (region.area >= 250):\n if (region.area > the_biggest_component):\n the_biggest_component = region.area\n\n average = (total_area/counter)\n print(\"the_biggest_component: \" + str(the_biggest_component))\n print(\"average: \" + str(average))\n\n # experimental-based ratio calculation, modify it for your cases\n # a4_constant is used as a threshold value to remove connected pixels\n # are smaller than a4_constant for A4 size scanned documents\n a4_constant = (((average/84.0)*250.0)+100)*1.5\n print(\"a4_constant: \" + str(a4_constant))\n\n # remove the connected pixels are smaller than a4_constant\n b = morphology.remove_small_objects(blobs_labels, a4_constant)\n # save the the pre-version which is the image is labelled with colors\n # as considering connected components\n plt.imsave('pre_version.png', b)\n\n # read the pre-version\n img = cv2.imread('pre_version.png', 0)\n # ensure binary\n img = cv2.threshold(img, 0, 255,\n cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n # save the the result\n # cv2.imwrite(\"output.png\", img)\n return img","def handleImageRunner(self) :\n tag = self.readByte()\n if tag == ord(self.imagerunnermarker1[-1]) :\n oldpos = self.pos-2\n codop = self.minfile[self.pos:self.pos+2]\n length = unpack(\">H\", self.minfile[self.pos+6:self.pos+8])[0]\n self.pos += 18\n if codop != self.imagerunnermarker2 :\n self.pos += length\n self.logdebug(\"IMAGERUNNERTAG SKIP %i AT %08x\" % (self.pos-oldpos, self.pos))\n else :\n self.pos -= 1 # Adjust position","def getTasseledCap(img):","def find_dots(img):\n # will hold all points\n coordinates = []\n # will hold only relevant points\n points = []\n # losing the side\n img[:, 475:] = 0\n # using for finding the best corners in edged image 65\n corners = cv2.goodFeaturesToTrack(img, 75, 0.085, 61)\n corners = np.int0(corners)\n for corner in corners:\n x, y = corner.ravel()\n if y > 350 or y < 10: # avoid from top and bottom\n continue\n coordinates.append((x, y))\n # sort in order to start from right to left\n sort_coordinates = sorted(coordinates)\n num_of_dot = 1\n for i in reversed(sort_coordinates):\n # when its 9, break\n if num_of_dot > 9:\n break\n points.append((i[0], i[1]))\n num_of_dot += 1\n return points","def testDetect(name = \"smokey.gif\", amount = 20):\n image = Image(name)\n print(\"Close the image window to see the transformation\")\n image.draw()\n image2 = detectEdges(image, amount)\n image2.draw()","def getLines(image):\r\n\treturn pytesseract.image_to_string(Image.open(image), lang='eng').split(\"\\n\")","def get_image_position(\n self, iop: np.ndarray\n ) -> Tuple[float, float, float]:\n try:\n x = self.header[\"Rows\"]\n y = self.header[\"Columns\"]\n raw_ipp = self.header[\"ImagePositionPatient\"]\n pixel_spacing = self.header[\"PixelSpacing\"]\n except KeyError:\n return\n raw_shape = np.array([x, y])\n\n slice_shape = raw_shape / self.size\n translation_fix = (raw_shape - slice_shape) / 2\n Q = np.fliplr(iop) * pixel_spacing\n return raw_ipp + np.dot(Q, translation_fix[:, None]).ravel()","def _raw_face_locations_batched(images, number_of_times_to_upsample=1, batch_size=128):\n\treturn cnn_face_detector(images, number_of_times_to_upsample, batch_size=batch_size)","def botStack(self):\r\n\r\n self.z_stack=self.img.shape[0]-1\r\n #self.pixmap=QtGui.QPixmap.fromImage(ImageQt.ImageQt(misc.toimage(self.img[self.z_stack]))).scaleds(500,500)\r\n self.pixmap= self.drawPixmap(\"xy\")\r\n self.lbl.setPixmap(self.pixmap)\r\n\r\n self.pixmap2=self.writeEdge(\"xy\")\r\n self.lbl2.setPixmap(self.pixmap2)\r\n self.z_stack_lbl.setText(str(self.z_stack+1) + '/' + str(self.img.shape[0]))","def get_img_coord_tuple(img):\n\n lat = convert_to_degress(get_gps_details(img)['GPSLatitude'])\n if get_gps_details(img)['GPSLatitudeRef'] == 'S':\n lat = -lat\n\n longitude = convert_to_degress(get_gps_details(img)['GPSLongitude'])\n if get_gps_details(img)['GPSLongitudeRef'] == 'W':\n longitude = -longitude\n\n return lat, longitude","def extract_dl(driving_log_path):\r\n entries = []\r\n with open(driving_log_path) as csv_file:\r\n reader = csv.reader(csv_file)\r\n for entry in reader:\r\n entries.append(entry)\r\n empty_lists = [[] for i in range(7)]\r\n center_images, left_images, right_images, steerings, throttles, brakes, speeds = empty_lists\r\n for entry in entries:\r\n center_image_path, left_image_path, right_image_path = (entry[0], entry[1], entry[2])\r\n steering = float(entry[3])\r\n throttle = float(entry[4])\r\n brake = float(entry[5])\r\n speed = float(entry[6])\r\n center_image = cv2.imread(center_image_path)\r\n left_image = cv2.imread(left_image_path)\r\n right_image = cv2.imread(right_image_path)\r\n center_images.append(center_image)\r\n left_images.append(left_image)\r\n right_images.append(right_image)\r\n steerings.append(steering)\r\n throttles.append(throttle)\r\n brakes.append(brake)\r\n speeds.append(speed)\r\n return center_images, left_images, right_images, steerings, throttles, brakes, speeds","def devMsg(self, text):\n # Preprocess text\n lines = text.splitlines()\n\n image = self.devMsgImage.copy()\n draw = ImageDraw.Draw(image)\n # Text\n x0 = 0\n y0 = -2\n for i in range(0,len(lines)):\n draw.text((x0, y0+i*7), lines[i], font=self.font, fill=255)\n self.disp.image(image.rotate(180))\n self.disp.display()\n return","def ccsn_pixel_locations(size=100):\n\n L_UV_corr = UVimage_corrected()\n [x,y,width,height] = GordonSurveyArea()\n L_UV_corr_gord = L_UV_corr[y:y+height+1, x:x+width+1]\n xpix = np.arange(0,L_UV_corr_gord.shape[1]+1,1)\n ypix = np.arange(0,L_UV_corr_gord.shape[0]+1,1)\n\n#Calculate CDF per pixel\n p_UV = L_UV_corr_gord/np.sum(L_UV_corr_gord)\n cdf_UV = np.cumsum(p_UV).reshape(p_UV.shape)\n \n#Randomly select pixels\n snrs = np.random.random(size)\n xx,yy = np.meshgrid(xpix, ypix)\n loc_x = np.zeros_like(snrs)\n loc_y = np.zeros_like(snrs)\n for i,randcdf in enumerate(snrs):\n index = zip(*np.where(cdf_UV<=randcdf))\n loc_x[i] = xx[index[-1]]\n loc_y[i] = yy[index[-1]]\n \n return (loc_x, loc_y)","def extract_rows(img):\n img = img[:, 0:img.shape[1]//2]\n mid = img.shape[0]//2\n top, bot = img[:int(1.15*mid)], img[int(0.85*mid):]\n\n return top, bot","def process_image(file_path):\n img_array = io.imread(file_path)\n detections, shapes, descriptors = detect_faces(person_database,img_array)\n\n names = []\n\n for desc in descriptors:\n name = find_match(person_database, desc)\n names.append(name)\n\n return pic_array, names, detections, shapes, descriptors","def locateImageOnScreen(ImageName):\n location = pyautogui.locateOnScreen(ImageName) \n try: \n for x in location:\n return location\n except:\n sys.exit('The image could not be found in the active screen. \\n'+'Stopping program.')","def extract_data(filename: str, directory: str) -> Dict:\n with open(filename) as f:\n lines = f.readlines()\n\n # Split data by :\n annotations = [line.replace(\" \", \"\").split(\":\") for line in lines]\n\n # Split data by ;\n for annotation in annotations:\n annotation[1] = annotation[1].split(\";\")\n\n # Loop for saving metadata into dictionary\n annot_dict = dict()\n for annotation in annotations:\n img = annotation[0]\n bbox_metadata = annotation[1]\n bbox = list()\n \n # Path to images\n img_path = os.path.join(directory, img)\n im = Image.open(img_path)\n width, height = im.size\n\n # Iterate over each bounding box\n for annot in bbox_metadata:\n \n if \"MISC_SIGNS\" == annot:\n signStatus = 'N/A'\n signTypes = \"MISC_SIGNS\"\n signPurpose = 'N/A'\n\n signBB = (-1, -1, -1, -1)\n signC = (-1, -1)\n signSize = 0\n aspectRatio = 0\n\n bbox.append({\"signStatus\": signStatus, \n \"signTypes\": signTypes, \n \"signPurpose\": signPurpose, \n \"signBB\": signBB, \n \"signC\": signC, \n \"signSize\": signSize, \n \"aspectRatio\": aspectRatio})\n elif \"\\n\" in annot:\n pass\n else:\n data = annot.split(\",\")\n \n signStatus = data[0] # signStatus\n signTypes = data[6] # signTypes\n signPurpose = data[5] # PROHIBITORY, WARNING, OTHER, INFORMATION\n tl_x, tl_y, br_x, br_y = data[3], data[4], data[1], data[2]\n \n if is_valid_decimal(tl_x):\n tl_x = float(tl_x)\n else:\n tl_x = float(cutoff_letter(tl_x))\n\n if is_valid_decimal(tl_y):\n tl_y = float(tl_y)\n else:\n tl_y = float(cutoff_letter(tl_y))\n\n if is_valid_decimal(br_x):\n br_x = float(br_x)\n else:\n br_x = float(cutoff_letter(br_x))\n\n if is_valid_decimal(br_y):\n br_y = float(br_y)\n else:\n br_y = float(cutoff_letter(br_y))\n\n if tl_x < 0:\n tl_x = 0\n elif tl_x > width:\n tl_x = width\n \n if tl_y < 0:\n tl_y = 0\n elif tl_y > height:\n tl_y = height\n \n if br_x < 0:\n br_x = 0\n elif br_x > width:\n br_x = width\n \n if br_y < 0:\n br_y = 0\n elif br_y > height:\n br_y = height\n\n signBB = (tl_x, tl_y, br_x, br_y)\n signC = (br_x + tl_x)/2, (br_y + tl_y)/2\n signSize = (br_x - tl_x) * (br_y - tl_y)\n aspectRatio = (br_x - tl_x) / (br_y - tl_y)\n\n bbox.append({\"signStatus\": signStatus, \n \"signTypes\": signTypes, \n \"signPurpose\": signPurpose, \n \"signBB\": signBB, \n \"signC\": signC, \n \"signSize\": signSize, \n \"aspectRatio\": aspectRatio})\n \n \n annot_dict[img_path] = bbox\n return annot_dict","def face_locations(img, number_of_times_to_upsample=0, model=\"hog\"):\n\tif model == \"cnn\":\n\t\treturn [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, \"cnn\")]\n\telse:\n\t\treturn [_trim_css_to_bounds(_rect_to_css(face), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, model)]","def draw_bboxes_withindex(img,boxes, uids):\n source = Image.fromarray(img)\n draw = ImageDraw.Draw(source)\n w2,h2 = (img.shape[0],img.shape[1])\n \n font = ImageFont.truetype('/usr/share/fonts/truetype/freefont/FreeSerif.ttf', 40)\n #font = ImageFont.truetype('arial.ttf', 24)\n\n\n idx = 0\n\n for b in boxes:\n xmin,ymin,xmax,ymax = b\n \n for j in range(3):\n draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline=\"red\")\n draw.text((xmin+20, ymin+70), str(uids[idx]), font = font)\n idx +=1\n return source","def _get_image_index_position(self) :\n \n return self._image_index_position","def __get_format_info_locations(self,size):\n\t\tloc = {\n\t\t\t\t0: [[8,0],[size-1,8]],\n\t\t\t\t1: [[8,1],[size-2,8]],\n\t\t\t\t2: [[8,2],[size-3,8]],\n\t\t\t\t3: [[8,3],[size-4,8]],\n\t\t\t\t4: [[8,4],[size-5,8]],\n\t\t\t\t5: [[8,5],[size-6,8]],\n\t\t\t\t6: [[8,7],[size-7,8]],\n\t\t\t\t7: [[8,8],[8,size-8]],\n\t\t\t\t8: [[7,8],[8,size-7]],\n\t\t\t\t9: [[5,8],[8,size-6]],\n\t\t\t\t10: [[4,8],[8,size-5]],\n\t\t\t\t11: [[3,8],[8,size-4]],\n\t\t\t\t12: [[2,8],[8,size-3]],\n\t\t\t\t13: [[1,8],[8,size-2]],\n\t\t\t\t14: [[0,8],[8,size-1]]\n\t\t}\n\t\treturn loc","def findPosition(self, img, handNo=0, draw=True):\n\n xList = []\n yList = []\n bbox = []\n bboxInfo =[]\n self.lmList = []\n if self.results.multi_hand_landmarks:\n myHand = self.results.multi_hand_landmarks[handNo]\n for id, lm in enumerate(myHand.landmark):\n h, w, c = img.shape\n px, py = int(lm.x * w), int(lm.y * h)\n xList.append(px)\n yList.append(py)\n self.lmList.append([px, py])\n if draw:\n cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)\n xmin, xmax = min(xList), max(xList)\n ymin, ymax = min(yList), max(yList)\n boxW, boxH = xmax - xmin, ymax - ymin\n bbox = xmin, ymin, boxW, boxH\n cx, cy = bbox[0] + (bbox[2] // 2), \\\n bbox[1] + (bbox[3] // 2)\n bboxInfo = {\"id\": id, \"bbox\": bbox,\"center\": (cx, cy)}\n\n if draw:\n cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),\n (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),\n (0, 255, 0), 2)\n\n return self.lmList, bboxInfo","def count_blood_cells(image_path):\n\n # TODO - Prebrojati crvena i bela krvna zrnca i vratiti njihov broj kao povratnu vrednost ove procedure\n \"\"\"\n White cells\n \"\"\"\n # Getting image\n white_cells_img = cv2.imread(image_path)\n gray_img = cv2.cvtColor(white_cells_img, cv2.COLOR_BGR2GRAY)\n\n # Apply median filter for smoothing\n smooth_img_white = cv2.medianBlur(gray_img, 5)\n\n # Morphological operations\n kernel = np.ones((5, 5), np.uint8)\n closing_img = cv2.morphologyEx(smooth_img_white, cv2.MORPH_CLOSE, kernel)\n\n # Adaptive threshold gaussian filter\n threshold_img = cv2.adaptiveThreshold(closing_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n cv2.THRESH_BINARY, 9, 2)\n\n # Segmentation of white cells\n circles_a = cv2.HoughCircles(threshold_img, cv2.HOUGH_GRADIENT, 1.2, 105,\n param1=50, param2=28, minRadius=2, maxRadius=28)\n\n # Getting count of white cells\n cell_count_a = []\n if circles_a is not None:\n circles_a = np.round(circles_a[0, :]).astype(\"int\")\n for (r) in circles_a:\n cell_count_a.append(r)\n # print(len(cell_count_a))\n white_blood_cell_count = len(cell_count_a)\n\n \"\"\"\n Red cells\n \"\"\"\n # Getting image\n red_cells_img = cv2.imread(image_path)\n\n # Getting red color\n red = [(150, 137, 168), (218, 209, 208)] # (lower), (upper)\n colors = [red]\n\n # Apply median filter for smoothing\n smooth_img_red = cv2.medianBlur(red_cells_img, 3)\n\n cell_count_b = 0\n output = red_cells_img.copy()\n for lower, upper in colors:\n mask = cv2.inRange(smooth_img_red, lower, upper)\n\n # Segmentation of red cells\n circles_b = cv2.HoughCircles(mask, cv2.HOUGH_GRADIENT, 1, 20, param1=15, param2=17,\n minRadius=2, maxRadius=60)\n\n # Getting count of red cells\n if circles_b is not None:\n circles_b = np.round(circles_b[0, :]).astype(\"int\")\n\n for (x, y, r) in circles_b:\n cv2.circle(output, (x, y), r, (255, 0, 255), 2)\n cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (255, 0, 255), -1)\n cell_count_b += 1\n\n # cv2.imwrite('output.png', output)\n # print(cell_count_b)\n red_blood_cell_count = cell_count_b\n\n # TODO - Odrediti da li na osnovu broja krvnih zrnaca pacijent ima leukemiju i vratiti True/False kao povratnu\n # vrednost ove procedure\n\n if (white_blood_cell_count > 2\n or\n white_blood_cell_count >= (red_blood_cell_count / 3)):\n has_leukemia = True\n else:\n has_leukemia = False\n\n return red_blood_cell_count, white_blood_cell_count, has_leukemia","def get_offsets(i):\n x = (i % 3) * (100 + PICTURE_BORDER) + PICTURE_BORDER\n if (i < 3):\n y = PICTURE_BORDER\n elif (i >= 3 and i <= 5):\n y = (100 * 1) + (2 * PICTURE_BORDER)\n else:\n y = (100 * 2) + (3 * PICTURE_BORDER)\n return (x, y)","def getDimension(data):\r\n # open image for reading in binary mode\r\n\r\n # read the 2 bytes\r\n a = data[163:165]\r\n\r\n # calculate height\r\n height = (a[0] << 8) + a[1]\r\n\r\n # next 2 bytes is width\r\n a = data[165:167]\r\n\r\n # calculate width\r\n width = (a[0] << 8) + a[1]\r\n\r\n return (width, height)","def find_letters(line_image):\r\n\r\n\tif line_image.shape[0] < 40:\r\n\t\tline_image = cv2.resize(line_image, (line_image.shape[1] * 2, line_image.shape[0] * 2))\r\n\r\n\t#binary\r\n\tret,thresh = cv2.threshold(line_image, 109, 255, cv2.THRESH_BINARY_INV)\r\n\r\n\tif cv2.__version__.startswith('3.'):\r\n\t\tim2, ctrs, hier = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n\telse:\r\n\t\t(ctrs, __) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n\t#sort contours\r\n\tsorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0], reverse=True)\r\n\r\n\t#creating objects - so we coult hold a few arguments that connected together in the same variable\r\n\r\n\tclass contur:\r\n\t\tdef __init__(self, x, y, w, h):\r\n\t\t\tself.x_start = x\r\n\t\t\tself.y_start = y\r\n\t\t\tself.x_end = x + w\r\n\t\t\tself.y_end = y + h\r\n\r\n\tletters_images = list()\r\n\tnew_ctr = list()\r\n\r\n\tfor j, ctr in enumerate(sorted_ctrs):\r\n\t\tx, y, w, h = cv2.boundingRect(ctr)\r\n\t\tc = contur(x, y, w, h)\r\n\t\tnew_ctr.append(c)\r\n\r\n\tlength = len(new_ctr)\r\n\r\n\ti = 0\r\n\twhile i < length:\r\n\t\tx, y, w, h = cv2.boundingRect(sorted_ctrs[i])\r\n\r\n\t\tif h > 3:\r\n\t\t\tcanvas = np.ones_like(line_image)\r\n\t\t\tcanvas.fill(255)\r\n\t\t\tcv2.drawContours(canvas, sorted_ctrs, i, (0, 0, 0), 3)\r\n\r\n\t\t\tif i < length - 1 and new_ctr[i].x_start >= new_ctr[i + 1].x_start and new_ctr[i].x_end <= new_ctr[i + 1].x_end:\r\n\t\t\t\tY_end_bigger = max(new_ctr[i].y_end, new_ctr[i + 1].y_end)\r\n\t\t\t\tcv2.drawContours(canvas, sorted_ctrs, i + 1, (0, 0, 0), 3)\r\n\r\n\t\t\t\tif union_left_ctr(new_ctr[i], new_ctr[i+1], canvas) == 0:\r\n\t\t\t\t\troi = canvas[y:y + h, x:x + w]\r\n\t\t\t\t\troiriginal = line_image[y:y + h, x:x + w]\r\n\t\t\t\telse:\r\n\t\t\t\t\troi = canvas[new_ctr[i + 1].y_start:Y_end_bigger, new_ctr[i + 1].x_start:new_ctr[i + 1].x_end]\r\n\t\t\t\t\troiriginal = line_image[new_ctr[i + 1].y_start:Y_end_bigger, new_ctr[i + 1].x_start:new_ctr[i + 1].x_end]\r\n\t\t\t\t\ti += 1\r\n\t\t\telse:\r\n\t\t\t\troi = canvas[y:y + h, x:x + w]\r\n\t\t\t\troiriginal = line_image[y:y + h, x:x + w]\r\n\r\n\t\t\tletter = np.pad(roiriginal, pad_width=10, mode='constant', constant_values=255)\r\n\r\n\t\t\tletters_images.append(letter)\r\n\t\ti += 1\r\n\treturn letters_images","def extract_faces(self, img, list_face_coord):\n #from the img array extract the facees\n list_faces = []\n #Go through each face coordinates and store the array\n #or clip face region in the list\n for i, coord in enumerate(list_face_coord):\n left, top, right, bottom = coord\n list_faces.append(img[top:bottom, left:right])\n return list_faces","def identify(data, image, rgb):\r\n\tdetection_method = \"hog\"\r\n\r\n\t# Locate the faces in the image, embed them\r\n\tprint(\"[INFO] recognizing faces...\")\r\n\tboxes = face_recognition.face_locations(rgb, model=detection_method)\r\n\tencodings = face_recognition.face_encodings(rgb, boxes)\r\n\r\n\t# Initialize the list of names for each face detected\r\n\tnames = []\r\n\t\r\n\t# Loop over the facial embeddings\r\n\tfor encoding in encodings:\r\n\t\tname = match_face(data,encoding)\r\n\t\t# Update the list of names\r\n\t\tnames.append(name)\r\n\t\r\n\trois = []\r\n\t# Loop over the recognized faces\r\n\tfor ((top, right, bottom, left), name) in zip(boxes, names):\r\n\t\trois.append(((top,right,bottom,left), name))\r\n\r\n\treturn rois","def parse_image_meta(meta):\n image_id = meta[:, 0]\n image_shape = meta[:, 1:4]\n window = meta[:, 4:8] # (x1, y1, x2, y2) window of image in in pixels\n active_class_ids = meta[:, 8:]\n return image_id, image_shape, window, active_class_ids"],"string":"[\n \"def belt(image):\\n\\n # Belt Detector\\n x, y = circular_detector(image, 70, 80)\\n\\n return x, y\",\n \"def describe(image):\\n needle = cv2.imread(image, 0)\\n orb = cv2.ORB()\\n keypoints, description = orb.detectAndCompute(needle, None)\\n print(keypoints)\\n print(description)\\n return keypoints, description\",\n \"def identify_blocks(images):\\n locations = {1: Point(), 2: Point(), 3: Point()}\\n blocks = {\\\"left\\\": 0, \\\"middle\\\": 0, \\\"right\\\": 0}\\n pipeline = keras_ocr.pipeline.Pipeline()\\n cv2_images = []\\n\\n for image in images:\\n bridge = cv_bridge.CvBridge()\\n cv2_images.append(bridge.imgmsg_to_cv2(image, \\\"bgr8\\\"))\\n\\n predictions = pipeline.recognize(cv2_images)\\n rospy.loginfo(predictions)\\n\\n blocks[\\\"left\\\"] = int(predictions[0][0][0])\\n blocks[\\\"middle\\\"] = int(predictions[1][0][0])\\n blocks[\\\"right\\\"] = int(predictions[2][0][0])\\n\\n for position, block in blocks.items():\\n locations[block] = block_locations[position]\\n \\n return locations\",\n \"def _get_image_location(self):\\n\\t\\timagePointer = self.labels['^IMAGE'].split()\\n\\t\\tif len(imagePointer) == 1:\\n\\t\\t\\trecordBytes = int(self.labels['RECORD_BYTES'])\\n\\t\\t\\timageLocation = (int(imagePointer[0]) - 1) * recordBytes\\n\\t\\telif len(imagePointer) == 2:\\n\\t\\t\\tunits = imagePointer[1]\\n\\t\\t\\tif not units == '<BYTES>':\\n\\t\\t\\t\\terrorMessage = (\\\"Expected <BYTES> image pointer units but found %s\\\") % (units)\\n\\t\\t\\t\\traise ValueError, (errorMessage)\\n\\t\\t\\telse:\\n\\t\\t\\t\\timageLocation = int(imagePointer[0])\\n\\t\\telse:\\n\\t\\t\\terrorMessage = (\\\"^IMAGE contains extra information\\\") % (imageSampleType)\\n\\t\\t\\traise ValueError(errorMessage)\\n\\t\\treturn imageLocation\",\n \"def identify_image(im):\\n score_cures = np.mean(im[1025:1065, 1130:1180, 0])\\n score_ingredients = np.mean(im[1025:1065, 675:720, 0])\\n if score_cures < 177.5:\\n return 'cures'\\n if score_ingredients < 177.5:\\n return 'ingredients'\\n else:\\n return 'other'\",\n \"def _get_positions(self, image):\\n\\t\\tH, W, _ = image.shape\\n\\t\\tpos_list = self.apply_detection(image)\\n\\t\\tdetections = {}\\n\\t\\thasDetection = False\\n\\t\\tfor i, L in enumerate(pos_list):\\n\\t\\t\\ttext, coordinates = L[0], L[1]\\n\\t\\t\\tfor x, y, w, h in coordinates:\\n\\t\\t\\t\\tif x < 0 or y < 0 or x + w > W or \\\\\\n\\t\\t\\t\\t y + h > H or w <= 1 or h <= 1:\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t# add the detection to the dict for tracking\\n\\t\\t\\t\\tif text == 'face' or text == 'super woman':\\n\\t\\t\\t\\t\\tself.detection_index[self.num_detect] = (x, y, w, h, self.num_save, text, -1)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.detection_index[self.num_detect] = (x, y, w, h, self.num_save, text, -2)\\n\\t\\t\\t\\tdetections[self.num_detect] = (x, y, w, h)\\n\\t\\t\\t\\tself.num_detect += 1\\n\\t\\t\\t\\thasDetection = True\\n\\t\\tif hasDetection:\\n\\t\\t\\tself.detection_frames[self.num_save] = detections\\n\\t\\tself.num_save +=1\",\n \"def face_coords(img, model):\\n (h, w) = img.shape[:2]\\n blob = cv2.dnn.blobFromImage(cv2.resize(img, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))\\n\\n model.setInput(blob)\\n detections = model.forward()\\n\\n box = detections[0, 0, 0, 3:7] * np.array([w, h, w, h])\\n (startX, startY, endX, endY) = box.astype(\\\"int\\\")\\n\\n return (startX, startY, endX, endY)\",\n \"def process_image(self, msg):\\n self.cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding=\\\"bgr8\\\")\\n self.edge_detected = cv2.Canny(self.cv_image,self.minVal,self.maxVal)\\n if cv2.__version__.startswith('3.'):\\n _, self.contours,_ = cv2.findContours(self.edge_detected, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\\n else:\\n self.contours,_ = cv2.findContours(self.edge_detected, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\\n\\n self.contour_image = cv2.drawContours(self.cv_image, self.contours, -1, (0,255,0), 3)\\n for i in range(len(self.contours)):\\n temp = self.dp(self.contours[i], 20)\\n self.res.append(len(temp))\\n if len(temp) == 7:\\n for i in range(0,len(temp)-1,2):\\n cv2.line(self.contour_image, (temp[i][0],temp[i][1]),(temp[i+1][0], temp[i+1][1]), (0,0,255), 5)\\n if len(temp) == 5:\\n for i in range(0,len(temp)-1,2):\\n cv2.line(self.contour_image, (temp[i][0],temp[i][1]),(temp[i+1][0], temp[i+1][1]), (255,0,0), 5)\",\n \"def image(self, state):\\n return state['positions']\",\n \"def debug_3_locations( img, gt_location, yolo_location, rolo_location):\\n img_cp = img.copy()\\n for i in range(3): # b-g-r channels\\n if i== 0: location= gt_location; color= (0, 0, 255) # red for gt\\n elif i ==1: location= yolo_location; color= (255, 0, 0) # blue for yolo\\n elif i ==2: location= rolo_location; color= (0, 255, 0) # green for rolo\\n x = max(int(location[0]), 0)\\n y = max(int(location[1]), 0)\\n w = max(int(location[2]), 0)\\n h = max(int(location[3]), 0)\\n # if i== 2: cv2.rectangle(img_cp,(x-w//2, y-h//2),(x+w//2,y+h//2), color, 2)\\n # elif i== 0 or i == 1: cv2.rectangle(img_cp,(x,y),(x+w,y+h), color, 2)\\n cv2.rectangle(img_cp,(x-w/2,y-h/2),(x+w/2,y+h/2), color, 2)\\n # cv2.imshow('3 locations',img_cp)\\n # cv2.waitKey(100)\\n return img_cp\",\n \"def M12Nut(image):\\n kernel = np.ones((5, 5), np.uint8)\\n image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=4)\\n\\n parameters = cv2.SimpleBlobDetector_Params()\\n detector = cv2.SimpleBlobDetector_create(parameters=parameters)\\n keypoints = detector.detect(image)\\n new_image = cv2.drawKeypoints(image, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\\n for i in range(len(keypoints)):\\n print(\\\"Keypoint: \\\", keypoints[i].pt)\\n cv2.imshow(\\\"Keypoints\\\", new_image)\\n cv2.waitKey(1000)\\n cv2.destroyAllWindows()\\n x, y = keypoints[0].pt\\n\\n return x, y\",\n \"def particle_images (sim,frame_id) :\\n # get positions of all particles: define first the atom selection, then jump to\\n # the user-requested trajectory frame, get the box dimensions (currently works\\n # only for orthorhombic boxes, then calculate the image indices\\n atoms = sim.u.select_atoms ('all')\\n ts = sim.u.trajectory[frame_id]\\n L = ts.dimensions[:3]\\n pos = atoms.positions + L/2.\\n return pos//L\",\n \"def get_symbol_images_and_positions(im):\\n gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\\n _, threshold = cv2.threshold(gray, 100, 255, 0)\\n threshold = 255 - threshold\\n # show(threshold)\\n contours, hierarchy = cv2.findContours(threshold, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\\n contours = [cv2.approxPolyDP(contour, 8, True) for contour in contours]\\n contours = [c for c in contours if c.shape[0] == 4 and cv2.isContourConvex(c)]\\n contours = sorted(contours, key=cv2.contourArea)\\n contour = contours[-1]\\n\\n offset_x, offset_y, _, _ = cv2.boundingRect(contour)\\n symbols_im = trim_to_contour_bounding_box(im, contour)\\n half_height = symbols_im.shape[0] / 2\\n half_width = symbols_im.shape[1] / 2\\n symbols = (\\n symbols_im[:half_height, :half_width],\\n symbols_im[:half_height, half_width:],\\n symbols_im[half_height:, :half_width],\\n symbols_im[half_height:, half_width:],\\n\\n )\\n symbols = (_process_button_im(symbol_im) for symbol_im in symbols)\\n\\n positions = (\\n (offset_x + half_width / 2, offset_y + half_height / 2),\\n (offset_x + half_width * 3 / 2, offset_y + half_height / 2),\\n (offset_x + half_width / 2, offset_y + half_height * 3 / 2),\\n (offset_x + half_width * 3 / 2, offset_y + half_height * 3 / 2),\\n )\\n\\n return symbols, positions\",\n \"def bbxes_data(img):\\n _, _, stats, centr = cv2.connectedComponentsWithStats(img)\\n # (xCentr, yCentr, area, width, height, xStart, xEnd , yStart, yEnd )\\n return sorted([(cent[0], cent[1], stat[4], stat[2], stat[3], stat[0], stat[0] + stat[2], stat[1], stat[1] + stat[3])\\n for cent, stat in zip(centr[1:], stats[1:])])\",\n \"def detect_face(self, img):\\n # Fetch face location from the frame with 128 encoding of face landmarks\\n curr_face_loc, name_list, info_list = load_encode_loc(img, self.kwn_names,\\n self.kwn_encoding,\\n self.status_list, self.since_list)\\n print('Current value is ', curr_face_loc, name_list)\\n face_list = []\\n face_area = []\\n print('face loc', curr_face_loc)\\n if len(curr_face_loc):\\n\\n for (top, right, bottom, left), name in zip(curr_face_loc, name_list):\\n print(top, right, bottom, left)\\n cv2.rectangle(img, (top, right), (bottom, left), (0, 255, 2), 2)\\n\\n w = right - left\\n h = bottom - top\\n cx = left + w // 2\\n cy = top + h // 2\\n area = w * h\\n\\n for idx, info in enumerate(info_list):\\n cv2.putText(img, info, (bottom, int(left * idx * 0.2)),\\n cv2.FONT_HERSHEY_COMPLEX, 1,\\n (0, 0, 255), 1)\\n\\n face_list.append([cx, cy])\\n face_area.append(area)\\n\\n i = face_area.index(max(face_area))\\n\\n return img, [face_list[i], face_area[i]]\\n\\n else:\\n return img, [[0, 0], 0]\",\n \"def process_image(im):\\r\\n h, _, _ = im.shape\\r\\n im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\\r\\n \\r\\n # Divide the picture into 3 regions\\r\\n l1 = int(0.65*h)\\r\\n l2 = int(0.77*h)\\r\\n im1 = im_gray[:l1,:]\\r\\n im2 = im_gray[l1+1:l2,:]\\r\\n im3 = im_gray[l2+1:,:]\\r\\n \\r\\n # Extract 4 pictures\\r\\n pics = extract_4_pics(im, im1)\\r\\n \\r\\n # Extract the word size\\r\\n word_size = extract_word_size(im2)\\r\\n \\r\\n # Extract the letters\\r\\n letters = extract_letters(im3)\\r\\n \\r\\n print 'word size =', word_size\\r\\n print 'letters =', letters\\r\\n for i, pic in enumerate(pics):\\r\\n imsave(str(i) + '.png', pic)\\r\\n\\r\\n return word_size, letters, pics\",\n \"def get_image_characteristics(self):\\r\\n self.image_height, self.image_width, self.image_channels = self.image.shape\\r\\n\\r\\n # Estimate the cell size to be around a ninth of the width of the screenshot area\\r\\n self.cell_size = int(self.image_width / 9) | 1\\r\\n\\r\\n # Cell size should be at most a ninth of the width and at least a twentieth of the width of the screenshot\\r\\n # Since a typical grid is 9x9, so it should be at most a ninth of the image width, and it shouldn't be too small\\r\\n self.min_cell_size = int(self.image_width / 20 * self.image_width / 20)\\r\\n self.max_cell_size = int(self.image_width / 9 * self.image_width / 9)\",\n \"def depth_range_from_img(img, inspect):\\n x0, y0, x1, y1 = TEXT_BBOX\\n crop_fn = lambda x: x[x0:x1,y0:y1]\\n\\n if isinstance(img, str):\\n img = io.imread(img)\\n\\n # Get string of text, look for possible floats\\n chars = pytesseract.image_to_string(crop_fn(img), config=TESSERACT_CONFIG)\\n numbers = [truncate(number,2) for number in re.findall('\\\\d+\\\\.\\\\d+', chars)]\\n\\n if len(numbers) < 2:\\n if inspect:\\n print('Less than two floats found, filling in 0\\\\'s')\\n print(chars)\\n plt.imshow(crop_fn(img))\\n plt.show()\\n return (0.0, 0.0)\\n else:\\n # Return last two possible floats\\n return (float(numbers[-2]), float(numbers[-1]))\",\n \"def get_element_coordinates(path_to_image):\\n return pyautogui.center(pyautogui.locateOnScreen(path_to_image, confidence=0.9))\",\n \"def test():\\n\\n fname='./MedData/Lung-PET-CT-Dx/Lung_Dx-A0164/04-12-2010-PET01PTheadlung Adult-08984/8.000000-Thorax 1.0 B31f-52757/1-001.dcm' \\n \\n ds=pydicom.dcmread(fname)\\n # print(ds.pixel_array.shape)\\n print(ds.pixel_array[1])\\n plt.figure(figsize=(10,10))\\n plt.imshow(ds.pixel_array, cmap=plt.cm.bone)\\n plt.show()\",\n \"def image_handler(self, bot, update):\\n text = update.message.text\\n if text.startswith('/recon'):\\n bot.sendMessage(chat_id=update.message.chat_id, text='*Object recognition*\\\\nSend me an image',\\n parse_mode=ParseMode.MARKDOWN)\\n return 10\\n elif text.startswith('/faces'):\\n bot.sendMessage(chat_id=update.message.chat_id, text='*Face recognition*\\\\nSend me an image',\\n parse_mode=ParseMode.MARKDOWN)\\n return 11\",\n \"def message_received(self, message):\\n\\n body_lxml = message.body_lxml()\\n for image in body_lxml.iterfind(\\\".//img[@src]\\\"):\\n if image.attrib['src'] == self.no_devil_banana_url:\\n logger.debug(\\\":nodb: found in {0}'s message {1}\\\".format(\\n message.user_name, message.id\\n ))\\n with self.last_lock:\\n self.last_nodb_message = message.id\\n elif image.attrib['src'] in self.devil_banana_urls:\\n if message.user_name == self.connector.username:\\n # ignore my own devil banana messages\\n return\\n logger.debug(\\\"devil banana {2} found in {0}'s message {1}\\\".format(\\n message.user_name, message.id, image.attrib['src']\\n ))\\n with self.last_lock:\\n self.last_banana_message_due_to_edit = False\\n self.last_banana_message = message.id\",\n \"def paintings_detection(query_image, mask):\\n\\n image = cv2.imread(query_image)\\n\\n image_width = mask.shape[0]\\n image_height = mask.shape[1]\\n x_box_1, y_box_1, w_box_1, h_box_1, x_box_2, y_box_2, w_box_2, h_box_2 = 0, 0, 0, 0, 0, 0, 0, 0, \\n\\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]\\n\\n for cnt in contours:\\n x, y, w, h = cv2.boundingRect(cnt)\\n \\n if (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 == 0):\\n x_box_1, y_box_1, w_box_1, h_box_1 = x, y, w, h\\n elif (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 != 0):\\n x_box_2, y_box_2, w_box_2, h_box_2 = x, y, w, h\\n\\n if x_box_2 == 0:\\n x_value_to_split = 0\\n else:\\n x_value_to_split = (x_box_1 + w_box_1/2 + x_box_2 + w_box_2/2) / 2\\n\\n\\n return(x_value_to_split)\",\n \"def cover(img: Image, message: bitarray) -> Image:\\n width, height = img.size\\n check_image_width(width, RGB_PIXEL, message)\\n pixels = img.load()\\n\\n row = random.randint(0, height) # Randomly chooses row.\\n i = 0 # Tracks hidden bits\\n\\n # If Image consist of 8-bit pixels\\n if img.mode == \\\"P\\\":\\n offset = generate_offset(width, P_PIXEL, message)\\n for x in range(offset, width):\\n p = pixels[x,row]\\n if i < len(message):\\n p = modify_byte(p, message[i])\\n i += 1\\n pixels[x,row] = p\\n generate_key(row, message, offset * P_PIXEL)\\n # If Image consists of 3x8-bit pixels\\n elif img.mode == \\\"RGB\\\":\\n offset = generate_offset(width, RGB_PIXEL, message)\\n for x in range(offset, width):\\n r, g, b = pixels[x,row]\\n if i < len(message):\\n r = modify_byte(r, message[i])\\n i += 1\\n if i < len(message):\\n g = modify_byte(g, message[i])\\n i += 1\\n if i < len(message):\\n b = modify_byte(b, message[i])\\n i += 1\\n pixels[x,row] = (r, g, b)\\n generate_key(row, message, offset * RGB_PIXEL)\\n # If Image consists of 4x8-bits pixels\\n elif img.mode == \\\"RGBA\\\":\\n offset = generate_offset(width, RGBA_PIXEL, message)\\n for x in range(offset, width):\\n r, g, b, a = pixels[x,row]\\n if i < len(message):\\n r = modify_byte(r, message[i])\\n i += 1\\n if i < len(message):\\n g = modify_byte(g, message[i])\\n i += 1\\n if i < len(message):\\n b = modify_byte(b, message[i])\\n i += 1\\n if i < len(message):\\n a = modify_byte(a, message[i])\\n i += 1\\n pixels[x,row] = (r, g, b, a)\\n generate_key(row, message, offset * RGBA_PIXEL)\\n\\n return img\",\n \"def dimension_finder(self, text_header):\\r\\n dimension = re.search('\\\\d+x\\\\d+',text_header).group()\\r\\n dimension1 = re.findall('\\\\d+',dimension)[0]\\r\\n dimension1 = float(dimension1)\\r\\n dimension2 = re.findall('\\\\d+',dimension)[1]\\r\\n dimension2 = float(dimension2)\\r\\n return (dimension1,dimension2)\",\n \"def checkImages(self):\\r\\n\\r\\n self.leftImage, self.rightImage, res = self.receiver.getImageData()\\r\\n\\r\\n return res\",\n \"def draw_boxes_info(image, current_data):\\n\\n font_position1 = (50, 600)\\n font_position2 = (50, 650)\\n font_scale = .4\\n font_thickness = 1\\n\\n locations = current_data[\\\"locations\\\"] #returns x1, y1, x2, y2\\n frame_num = \\\"Frame Number: \\\" + str(current_data[\\\"frame_num\\\"])\\n\\n for box in locations:\\n box_text = (\\\"Box locations are x1: {0}, y1: {1}, x2: {2}, y2: {3}\\\").format(box[1],box[3],box[0],box[2])\\n\\n cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 3)\\n cv2.putText(image, box_text, font_position1, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\\n font_thickness, cv2.LINE_AA)\\n\\n cv2.putText(image, frame_num, font_position2, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\\n font_thickness, cv2.LINE_AA)\\n\\n return image\",\n \"def test_get_click_locations(images):\\n #Show the click locations for all images\\n for i, image in enumerate(images):\\n stack_bounds = get_stack_bounds(image)\\n clicks = get_click_locations(stack_bounds)\\n\\n #Draw the number of points and the points at the center of their stacks\\n clicks_image = cv2.putText(np.copy(image),\\n 'num_stacks: {}'.format(len(clicks)),\\n (10, 30),\\n cv2.FONT_HERSHEY_DUPLEX,\\n 0.75, (0, 0, 0), 1, cv2.LINE_AA)\\n for click in clicks.values():\\n clicks_image = cv2.circle(clicks_image, click, 5, (0,0,0), -1)\\n\\n original_and_clicks = hstack_images(image, clicks_image, False)\\n cv2.imshow('original and clicks_image {}'.format(i), original_and_clicks)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def detect_face_api(self, img):\\n\\n curr_face_loc, name_list, info_list = load_encode_loc(img, self.kwn_names,\\n self.kwn_encoding,\\n self.status_list, self.since_list)\\n print('Current value is ', curr_face_loc, name_list)\\n face_list = []\\n face_area = []\\n print('face loc', curr_face_loc)\\n if len(curr_face_loc):\\n\\n for (top, right, bottom, left), name in zip(curr_face_loc, name_list):\\n print(top, right, bottom, left)\\n cv2.rectangle(img, (top, right), (bottom, left), (0, 255, 2), 2)\\n\\n w = right - left\\n h = bottom - top\\n cx = left + w // 2\\n cy = top + h // 2\\n area = w * h\\n\\n for idx, info in enumerate(info_list):\\n cv2.putText(img, info, (bottom, int(left * idx * 0.2)),\\n cv2.FONT_HERSHEY_COMPLEX, 1,\\n (0, 0, 255), 1)\\n\\n face_list.append([cx, cy])\\n face_area.append(area)\\n\\n i = face_area.index(max(face_area))\\n\\n return img, [face_list[i], face_area[i]]\\n\\n else:\\n return img, [[0, 0], 0]\",\n \"def process(self, image):\",\n \"def get_grads(img):\\n dx = ndimage.sobel(img, 0) # horizontal derivative\\n dy = ndimage.sobel(img, 1) # vertical derivative\\n return dx, dy\",\n \"def onObjectDetected(msg):\\n bridge = CvBridge()\\n try:\\n depthMap = bridge.imgmsg_to_cv2(msg.depthImg,\\n desired_encoding=\\\"passthrough\\\")\\n\\n depthMap = cv2.convertScaleAbs(depthMap, alpha=100, beta=0)\\n # Get the center crop of the boxed image\\n startY = int(msg.box.origin_y) + int(msg.box.height//4)\\n startX = int(msg.box.origin_x) + int(msg.box.width//4)\\n\\n crop = depthMap[startY: startY + int(msg.box.height//2),\\n startX: startX + int(msg.box.width//2)]\\n\\n centerY = startY + int(msg.box.height//2)\\n centerX = startX + int(msg.box.width//2)\\n\\n avg = getAverageColor(crop)\\n distance = shadeToDepth(avg)\\n\\n print \\\"object: {} @{:4.3}, {}\\\".format(msg.className, distance,\\n (centerX, centerY))\\n global OBJECT_MAP\\n OBJECT_MAP.addToMap(msg.className,\\n centerX, centerY,\\n distance)\\n except CvBridgeError as e:\\n print(e)\",\n \"def handle_image_data(data):\\n \\n #Get the incoming RGB image from the Kinect\\n D.image = D.bridge.imgmsg_to_cv(data, \\\"bgr8\\\")\\n\\n if D.created_images == False:\\n #Initialize the additional images we need for processing\\n ImageProcessing.initialize(D)\\n D.created_images = True\\n\\n # Recalculate threshold image\\n ImageProcessing.threshold_image(D)\\n\\n # Recalculate blob in main image\\n ImageProcessing.find_biggest_region(D)\\n\\n # Check on the display of dragged section\\n ImageProcessing.mouse_section(D)\\n\\n #Display target circle\\n #ImageProcessing.target_coord(D)\\n \\n #Display info box on image\\n ImageProcessing.draw_on_image(D)\\n \\n #Handle incoming key presses\\n key_press = cv.WaitKey(5) & 255\\n if key_press != 255:\\t\\t\\t#Handle only if it's a real key\\n check_key_press(D, key_press)\\t\\t#(255 = \\\"no key pressed\\\")\\n\\n #Update the displays:\\n #Show main image in the image window\\n #cv.ShowImage('Image', D.image)\\n\\n #Show threshold image in the threshold window 3currentThreshold = getattr(D, D.current_threshold)\\n cv.ShowImage('Threshold', currentThreshold)\",\n \"def findMatchesBetweenImages(image_1, image_2):\\n # matches - type: list of cv2.DMath\\n matches = None\\n # image_1_kp - type: list of cv2.KeyPoint items.\\n image_1_kp = None\\n # image_1_desc - type: numpy.ndarray of numpy.uint8 values.\\n image_1_desc = None\\n # image_2_kp - type: list of cv2.KeyPoint items.\\n image_2_kp = None\\n # image_2_desc - type: numpy.ndarray of numpy.uint8 values.\\n image_2_desc = None\\n # WRITE YOUR CODE HERE.\\n\\n sift = cv2.ORB_create()\\n image_1_kp, image_1_desc = sift.detectAndCompute(image_1, None)\\n image_2_kp, image_2_desc = sift.detectAndCompute(image_2, None)\\n\\n # bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\n # matches = bf.match(image_1_desc, image_2_desc)\\n # matches = sorted(matches, key = lambda x:x.distance)\\n # matches = matches[:10]\\n\\n bf = cv2.BFMatcher()\\n matches = bf.knnMatch(image_1_desc, image_2_desc, k=2)\\n\\n # Apply ratio test\\n good = []\\n for m, n in matches:\\n print(m.distance, n.distance, m.distance < .75*n.distance)\\n if m.distance < (0.75 * n.distance):\\n good.append([m])\\n\\n # We coded the return statement for you. You are free to modify it -- just\\n # make sure the tests pass.\\n print(len(good), good)\\n return image_1_kp, image_2_kp, matches\\n # END OF FUNCTION.\",\n \"def frame_to_distance_images(frame):\\n \\n # distance from ridge midlines\\n frame = rgb2gray(frame)\\n ridges = enhance_ridges(frame)\\n thresh = filters.threshold_otsu(ridges)\\n prominent_ridges = ridges > 0.8*thresh\\n skeleton = morphology.skeletonize(prominent_ridges)\\n edge_dist = ndimage.distance_transform_edt(~skeleton)\\n edge_dist = filters.gaussian(edge_dist, sigma=2)\\n\\n # distance from skeleton branch points (ie, ridge intersections)\\n blurred_skeleton = uniform_filter(skeleton.astype(float), size=3)\\n corner_im = blurred_skeleton > 4./9\\n corner_dist = ndimage.distance_transform_edt(~corner_im)\\n \\n return edge_dist, corner_dist\",\n \"def find_pixel_edges(img):\\n \\n transparency_cells = img.T[3].T # Ignores rbg, returns only transparency. \\n # Values are either:\\n # 0 (fully transparent) or \\n # 1 (not at all transparent)\\n right_up = np.rot90(transparency_cells)\\n bottom_up = np.rot90(right_up)\\n left_up = np.rot90(bottom_up)\\n\\n y_top = count_from_top(transparency_cells)\\n x_left = count_from_top(left_up)\\n y_bottom = count_from_top(bottom_up)\\n x_right = count_from_top(right_up)\\n #print(f'\\\\ty_top:{y_top}\\\\nx_left:{x_left}\\\\t\\\\tx_right:{x_right}\\\\n\\\\ty_bottom:{y_bottom}')\\n return y_top, y_bottom, x_left, x_right\",\n \"def image_received(self, image_message):\\n # Convert the image message to something usable by opencv\\n # http://wiki.ros.org/cv_bridge/Tutorials/ConvertingBetweenROSImagesAndOpenCVImagesPython\\n # Note that mono8 and bgr8 are the two image encodings expected by most OpenCV functions.\\n cv_image = self.bridge.imgmsg_to_cv2(image_message, desired_encoding=\\\"bgr8\\\")\\n image_data = extract_data(cv_image)\\n linear_velocity, angular_velocity = self.clf.predict(image_data)\\n self.cmd_vel = Twist(linear=Vector3(x=linear_velocity), angular=Vector3(z=angular_velocity))\\n rospy.loginfo(self.cmd_vel)\",\n \"def _generate_dimensions(conf_dict, message):\\n sq_side = math.ceil(math.sqrt(_minimal_pixel_count(conf_dict, message)))\\n return (sq_side, sq_side)\",\n \"def detect_helmet_coordinates(image, visualize=True):\\n if visualize:\\n cv2.imshow('Raw Image', image)\\n\\n # TODO(you) Fill this out to \\\"threshold\\\" the image to show only the color you want, display that thresholded image,\\n # and figure out the coordinates of the center of the blob of pixels.\\n\\n return (0, 0) # (x, y) measured in pixels\",\n \"def extract(self, files):\\n for i in range(len(files)):\\n print(files[i])\\n img = cv2.imread('{}/{}'.format('{}/{}/{}'.format(DIR_2DST_Mask, self.patient, self.plan), files[i]), 0)\\n\\n \\\"\\\"\\\"\\n Find the indices of array elements that are non-zero, i.e,\\n find the pixels' positions that represents the respiratory\\n functions (pixels in the respiratory function are brighter).\\n \\\"\\\"\\\"\\n color_pts = np.argwhere(img > 70)\\n\\n \\\"\\\"\\\"\\n Sorts the pixels according to their x coordenate.\\n Obs: np.argwhere inverts x and y, it's like (y, x), because of it,\\n the parameter of itemgetter is 1 (to get x coordinate)\\n \\\"\\\"\\\"\\n lcolor_pts = sorted(color_pts.tolist(), key=itemgetter(1))\\n\\n \\\"\\\"\\\"\\n If there is no pixel representing the respiratory function\\n (i.e., lighter pixel) it creates an empty image (without any\\n respiratory function)\\n \\\"\\\"\\\"\\n if len(lcolor_pts) == 0:\\n diaphragmatic_lvl = np.zeros((256, 50, 3), np.uint8)\\n\\n cv2.imwrite('{}/{}/{}/{}'.format(\\n DIR_2DST_Diaphragm, patient, plan, files[i]), diaphragmatic_lvl)\\n\\n # file = open(\\n # '{}/{}/{}/points.txt'.format(DIR_2DST_Diaphragm, self.patient, self.plan), 'a')\\n # file.write(\\\"{}:{}\\\\n\\\".format(files[i], []))\\n # file.close()\\n\\n continue\\n\\n # Reverse the coordinates and store the result in lordered_pts list\\n lordered_pts = []\\n for j in range(len(lcolor_pts)):\\n lordered_pts.append(lcolor_pts[j][::-1])\\n\\n \\\"\\\"\\\"\\n Convert pixels coordinates into a tuples and check which column\\n has pixels that corresponding to diaphragmatic level\\n Obs. There are some columns that doesnt have any pixel that\\n correpond to diaphragmatic level.\\n \\\"\\\"\\\"\\n # Columns that have a pixel corresponding diaphragmatic level\\n lcolumn_available = []\\n for j in range(len(lordered_pts)):\\n lordered_pts[j] = tuple(lordered_pts[j])\\n lcolumn_available.append(lordered_pts[j][0])\\n lcolumn_available = list(set(lcolumn_available))\\n # print(\\\"Ordered points: \\\", lordered_pts)\\n # print(\\\"Columns available: \\\", lcolumn_available)\\n\\n \\\"\\\"\\\"\\n If there is not enough columns to build a respiratory pattern,\\n create a blank image\\n \\\"\\\"\\\"\\n if len(lcolumn_available) < 20:\\n diaphragmatic_lvl = np.zeros((256, 50, 3), np.uint8)\\n cv2.imwrite('{}/{}/{}/{}'.format(\\n DIR_2DST_Diaphragm, patient, plan, files[i]), diaphragmatic_lvl)\\n continue\\n\\n \\\"\\\"\\\"\\n If there are no pixel that corresponding diaphragmatic level in the\\n first column, assign to it the value of the second y coordinate\\n \\\"\\\"\\\"\\n if lcolumn_available[0] is not 0:\\n y = max(\\n [x for x in lordered_pts if x[0] == lcolumn_available[0]],\\n key=itemgetter(1))[1]\\n lordered_pts.insert(0, (0, y))\\n lcolumn_available.insert(0, 0)\\n\\n \\\"\\\"\\\"\\n If there are no pixel that corresponding diaphragmatic level in the\\n last column, assign to it the value of the penultimate y coordinate\\n available\\n \\\"\\\"\\\"\\n if lcolumn_available[-1] is not 49:\\n lordered_pts.append(\\n (49, lordered_pts[len(lcolumn_available)][1]))\\n lcolumn_available.append(49)\\n\\n \\\"\\\"\\\"\\n Get the biggest y value in each column that represents the\\n diaphragmatic level\\n \\\"\\\"\\\"\\n column = 0\\n lcolumn = []\\n ldiaphragm_pts = []\\n for j in range(50):\\n # Get the column's points\\n lcolumn = [x for x in lordered_pts if x[0] == column]\\n # print('{}: {}'.format(j, lcolumn))\\n\\n if len(lcolumn) > 0:\\n ldiaphragm_pts.append(\\n max(lcolumn, key=itemgetter(1))) # Get the biggest y\\n else:\\n # Get the y value from the previous column\\n lcolumn_available.insert(column, column)\\n ldiaphragm_pts.append((column, ldiaphragm_pts[-1][1]))\\n column += 1\\n lcolumn = []\\n\\n # Draw diaphragmatic level\\n diaphragmatic_lvl = np.zeros((256, 50, 3), np.uint8)\\n j = 0\\n while(j < len(lcolumn_available) - 1):\\n cv2.line(\\n diaphragmatic_lvl,\\n ldiaphragm_pts[j], ldiaphragm_pts[j + 1],\\n (0, 0, 255), 1)\\n j = j + 1\\n\\n lcolumn_available = []\\n\\n print(\\\"Diaphragmatic's points: \\\", ldiaphragm_pts)\\n cv2.imshow('Diaphragmatic level', diaphragmatic_lvl)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\\n cv2.imwrite('{}/{}/{}/{}'.format(\\n DIR_2DST_Diaphragm, patient, plan, files[i]), diaphragmatic_lvl)\\n\\n # file = open('{}/{}/{}/points.txt'.format(DIR_2DST_Diaphragm, self.patient, self.plan), 'a')\\n # file.write(\\\"{}:{}\\\\n\\\".format(files[i], ldiaphragm_pts))\\n # file.close()\\n\\n # return ldiaphragm_pts\",\n \"def recognize_image(self, bot, update):\\n # get image\\n chat_id = update.message.chat_id\\n new_image = bot.get_file(update.message.photo[-1].file_id)\\n new_image.download('recon.jpg')\\n\\n group_of_items = [i for i in range(len(docvectors))]\\n num_to_select = 3\\n list_of_random_items = random.sample(group_of_items, num_to_select)\\n jokes = '🤔\\\\nIt would take a while to recognize objects on the image, Morty. Your grandfather will tell some jokes while you wait :)\\\\n\\\\n'\\n for joke in list_of_random_items:\\n jokes += '- ' + docvectors[joke][1] + \\\"\\\\n\\\\n\\\"\\n bot.sendMessage(chat_id=update.message.chat_id, text=jokes)\\n\\n try:\\n entities = self.detector.recon()\\n if len(entities) == 1:\\n k = \\\"Are you blind, Morty? There's \\\"\\n else:\\n k = \\\"Are you blind, Morty? There're \\\"\\n\\n d = {}\\n for entity in entities:\\n d[entity] = d.get(entity, 0) + 1\\n\\n blacklist = ''\\n if len(entities) > 0:\\n blacklist = entities[-1]\\n\\n for i in d:\\n if i != blacklist:\\n k += detector.NLG(i, d[i], False, False)\\n\\n if len(d) > 0:\\n k += detector.NLG(blacklist, d[blacklist], True, False if len(d) == 1 else True)\\n k += ' on the picture'\\n else:\\n k = \\\"I can't recognize anything, Morty. Get lost!\\\"\\n except:\\n message = \\\"You broke it again, Morty!\\\"\\n bot.send_message(chat_id=update.message.chat_id, text=message)\\n return -1\\n # return an image with a caption\\n bot.send_photo(chat_id=chat_id, photo=open('recon.jpg', 'rb'), caption=k[:200])\\n return -1\",\n \"def model(msg):\\n url = 'https://southcentralus.api.cognitive.microsoft.com/customvision/v3.0/Prediction/\\\\\\n eff56ac8-0f36-41d9-93a9-da19396b0f30/detect/iterations/Iteration2_ppl_focus/image'\\n headers = {\\n 'Prediction-Key': os.getenv('AZURE_VIS_KEY'),\\n 'Content-Type': 'application/octet-stream'\\n }\\n r = requests.post(url=url, headers=headers, data=msg['img'])\\n predictions = r.json()\\n print('Number of object predictions: {}'.format(\\n len(predictions['predictions'])))\\n print('Frame Number:', msg['frame_num'],\\n 'Image Dimensions:', np.array(Image.open(BytesIO(msg['img']))).shape)\\n Coord_matrix = Bbox(predictions)\\n return Coord_matrix, len(predictions['predictions'])\",\n \"def decode(img):\\r\\n ints=[];#A list of ints that will contain all of our alpha values.\\r\\n width,height=img.size #Get the width and the height of my image.\\r\\n pixelData=ImageUtilities.getPixelList(img); #Get all of the pixels in the image and put them into a list.\\r\\n for y in range(height): #Iterate across the pixels from top to bottom.\\r\\n for x in range(width):#Iterate across out image from left to right.\\r\\n alpha=ImageUtilities.getAlphaFromList(img,pixelData,x,y); #Referenced the dumped contents\\r\\n if(alpha==255): #If the alpha of our pixel is 255....\\r\\n continue; #I don't want 255 values because that means that is not part of my message.\\r\\n ints.append(alpha); #Get the alpha value and append it to my list of ints.\\r\\n\\r\\n msg=\\\"\\\"; #Make an empty string to store our decoded message.\\r\\n for value in ints: #Iterate across my list of ints. (For each int in my list...)\\r\\n msg+=chr(value); #Convert my int to it's character value and add it back to my message.\\r\\n return msg; #Return my message string.\\r\",\n \"def who_is_it(image_path, database, model):\\n \\n ### START CODE HERE ### \\n \\n ## Step 1: Compute the target \\\"encoding\\\" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)\\n encoding = img_to_encoding(image_path,model)\\n \\n ## Step 2: Find the closest encoding ##\\n \\n # Initialize \\\"min_dist\\\" to a large value, say 100 (≈1 line)\\n min_dist = 100\\n \\n # Loop over the database dictionary's names and encodings.\\n for (name, db_enc) in database.items():\\n \\n # Compute L2 distance between the target \\\"encoding\\\" and the current \\\"emb\\\" from the database. (≈ 1 line)\\n dist = np.linalg.norm(encoding - db_enc)\\n\\n # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)\\n if dist<min_dist:\\n min_dist = dist\\n identity = name\\n\\n ### END CODE HERE ###\\n \\n if min_dist > 0.5:\\n print(\\\"Not in the database.\\\")\\n else:\\n print (\\\"it's \\\" + str(identity) + \\\", the distance is \\\" + str(min_dist))\\n \\n return min_dist, identity\",\n \"def _raw_face_locations(img, number_of_times_to_upsample=1, model=\\\"hog\\\"):\\n\\tif model == \\\"cnn\\\":\\n\\t\\treturn cnn_face_detector(img, number_of_times_to_upsample)\\n\\telse:\\n\\t\\treturn face_detector(img, number_of_times_to_upsample)\",\n \"def mesgBetweenRounds(self, message):\\n font = UIC.Medium_Text\\n y_offset = (UIC.Disp_Height * (1 - (UIC.Hand_Row_Fraction * 0.8)))\\n for message_string in message:\\n text_surface = font.render(message_string, True, UIC.Black)\\n text_rect = text_surface.get_rect()\\n text_rect.center = ((UIC.Disp_Width * 0.5), y_offset)\\n y_offset = y_offset + UIC.Medium_Text_Feed\\n self.display.blit(text_surface, text_rect)\",\n \"def detect_fields(img):\\n lines = np.array_split(img, 5, axis=1)\\n fields = [np.array_split(line, 5, axis=0) for line in lines]\\n return fields\",\n \"def find(image):\\n keypoint, description = describe(image)\\n # load keypoints, descriptions from mongodb\\n\\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\n\\n best_match_size = float(\\\"inf\\\")\\n best_match_index = -1\\n match_index = 0\\n best_matches = 0\\n\\n for desc in descriptions:\\n matches = bf.match(desc,description)\\n matches = sorted(matches, key = lambda x:x.distance)\\n if len(matches) > 0:\\n match_size = sum(x.distance for x in matches[:10])\\n\\n print \\\"match size is \\\", match_size\\n if match_size < best_match_size:\\n best_match_size = match_size\\n best_match_index = match_index\\n best_matches = matches\\n\\n match_index += 1\\n\\n needle_color = cv2.imread('needle-stripped.png')[:,:,::-1] # needle\\n best_match_image = cv2.imread(\\\"haystack/\\\"+files[best_match_index])\\n print \\\"best match is \\\", files[best_match_index]\\n\\n # Draw first 10 matches.\\n outImg = cv2.imread(\\\"output/outImg.png\\\")\\n match = cv2.drawMatches(needle_color,keypoint,best_match_image[:,:,::-1],keypoints[best_match_index],best_matches[-20:],outImg, flags=6)\\n\\n plt.imshow(match),plt.show()\\n return\",\n \"def _raw_face_locations(img, number_of_times_to_upsample=1):\\n return face_detector(img, number_of_times_to_upsample)\",\n \"def PoliqarpSubposDict(image, i, n):\\n record = struct.unpack('I' * (n >> 2), image[i: (i+n)])\\n\\n return [(n & 0xfffff, n >> 20) for n in record]\",\n \"def other_object(img):\\n return img[500:570, 1000:1070]\",\n \"def Hexagon(image):\\n return x, y\",\n \"def detect_faces(image):\\n\\n face_locations = face_recognition.face_locations(image)\\n return face_locations\",\n \"def find_shapes(im_bw, kernel, th):\\n im_convolved = scipy.ndimage.filters.convolve(im_bw, kernel[::-1, ::-1])\\n im_hotspots = im_convolved >= th\\n bin_dy, bin_dx = kernel.shape\\n radius_x = bin_dx // 2\\n radius_y = bin_dy // 2\\n positions = []\\n open_for_interpretation = np.ones_like(im_convolved, dtype=bool)\\n xmax = im_hotspots.shape[1] - 1\\n ymax = im_hotspots.shape[0] - 1\\n for x in range(xmax + 1):\\n for y in range(ymax + 1):\\n if open_for_interpretation[y, x] and im_hotspots[y, x]:\\n x0 = max(0, x - radius_x)\\n x1 = min(xmax, x + radius_x)\\n y0 = max(0, y - radius_y)\\n y1 = min(ymax, y + radius_y)\\n window = im_hotspots[y0:y1, x0:x1]\\n cx, cy = center_of_mass(window, x_offset=x - radius_x, y_offset=y - radius_y)\\n positions.append([cx, cy])\\n open_for_interpretation[y0:y1, x0:x1] = False\\n return positions\",\n \"def get_start_positions(img_in):\\n\\n def initialize_coordinates(kernel_h, kernel_w):\\n \\\"\\\"\\\" locates positions of interest by traversing eroded image and\\n saves 9 points on each area of interest to global matrix\\n :param kernel_h height of kernel used for harsh erosion\\n :param kernel_w width of kernel used for harsh erosion\\\"\\\"\\\"\\n global init_coords\\n\\n count = 0\\n y = 0\\n while y < frame_height - kernel_h:\\n x = 0\\n while x < frame_width - kernel_w:\\n locator = img[y:y+kernel_h, x:x+kernel_w, 2] > 0 + numpy.zeros((kernel_h, kernel_w))\\n if numpy.any(locator):\\n if count == 0:\\n init_coords[count][0][0] = y - 2\\n init_coords[count][0][1] = x + 2\\n elif count == 1:\\n init_coords[count][0][0] = y + 2\\n init_coords[count][0][1] = x + 2\\n elif count == 2:\\n init_coords[count][0][0] = y + 2\\n init_coords[count][0][1] = x + 2\\n elif count == 3:\\n init_coords[count][0][0] = y - 3\\n init_coords[count][0][1] = x + 2\\n elif count == 4:\\n init_coords[count][0][0] = y + 3\\n init_coords[count][0][1] = x - 5\\n count += 1\\n break\\n x += kernel_w\\n y += kernel_h\\n\\n # store 8 more points for each body part\\n f = 1.5\\n for count in range(5):\\n init_coords[count][1][1] = init_coords[count][0][1] + 3*f\\n init_coords[count][1][0] = init_coords[count][0][0] + 0\\n init_coords[count][2][1] = init_coords[count][0][1] + 6*f\\n init_coords[count][2][0] = init_coords[count][0][0] + 0\\n init_coords[count][3][1] = init_coords[count][0][1] + 0\\n init_coords[count][3][0] = init_coords[count][0][0] + 3*f\\n init_coords[count][4][1] = init_coords[count][0][1] + 3*f\\n init_coords[count][4][0] = init_coords[count][0][0] + 3*f\\n init_coords[count][5][1] = init_coords[count][0][1] + 6*f\\n init_coords[count][5][0] = init_coords[count][0][0] + 3*f\\n init_coords[count][6][1] = init_coords[count][0][1] + 0\\n init_coords[count][6][0] = init_coords[count][0][0] + 6*f\\n init_coords[count][7][1] = init_coords[count][0][1] + 3*f\\n init_coords[count][7][0] = init_coords[count][0][0] + 6*f\\n init_coords[count][8][1] = init_coords[count][0][1] + 6*f\\n init_coords[count][8][0] = init_coords[count][0][0] + 6*f\\n\\n limb_coords[0][0][0] = init_coords[0][5][0]\\n limb_coords[0][0][1] = init_coords[0][5][1]\\n limb_coords[1][0][0] = init_coords[1][5][0]\\n limb_coords[1][0][1] = init_coords[1][5][1]\\n limb_coords[2][0][0] = init_coords[2][5][0]\\n limb_coords[2][0][1] = init_coords[2][5][1]\\n limb_coords[3][0][0] = init_coords[3][5][0]\\n limb_coords[3][0][1] = init_coords[3][5][1]\\n limb_coords[4][0][0] = init_coords[4][5][0]\\n limb_coords[4][0][1] = init_coords[4][5][1]\\n\\n img = img_in.copy()\\n img = segment_red(img, 205, 135)\\n erode(img, 14, 12)\\n initialize_coordinates(14, 12)\",\n \"def test_find_details(self):\\n input_image = np.array([\\n 0.01, 0.1, 0.2, 0.5, 0.75, 0.99\\n ])\\n blur_input_image = np.array([\\n 0.28641213, 0.32315277, 0.3871898, 0.46174035, 0.52684723, 0.56466555\\n ])\\n expected_image_lower = np.array([\\n -0.277, -0.224, -0.188, 0.038, 0.223, 0.425\\n ])\\n expected_image_upper = np.array([\\n -0.276, -0.223, -0.187, 0.039, 0.224, 0.426\\n ])\\n output = localHDR.find_details(input_image, blur_input_image)\\n self.assertTrue(np.allclose(output, expected_image_lower, atol=6e-03))\\n self.assertTrue(np.allclose(output, expected_image_upper, atol=6e-03))\",\n \"def _raw_face_locations(img, number_of_times_to_upsample=1, model=\\\"hog\\\"):\\n if model == \\\"cnn\\\":\\n cnn_face_detector = dlib.cnn_face_detection_model_v1('mmod_human_face_detector.dat')\\n return cnn_face_detector(img, number_of_times_to_upsample)\\n else:\\n # face_detector = dlib.get_frontal_face_detector()\\n return face_detector(img, number_of_times_to_upsample)\",\n \"def botStack_x(self):\\r\\n self.x_stack=self.img.shape[2]-1\\r\\n \\r\\n self.pixmap5=self.drawPixmap(\\\"yz\\\")\\r\\n self.lbl5.setPixmap(self.pixmap5)\\r\\n self.pixmap6= self.writeEdge(\\\"yz\\\")\\r\\n self.lbl6.setPixmap(self.pixmap6)\\r\\n self.x_stack_lbl.setText(str(self.x_stack+1) + '/' + str(self.img.shape[2]))\",\n \"def imageparts(msg):\\n # Don't want a set here because we want to be able to process them in\\n # order.\\n return filter(lambda part:\\n part.get_content_type().startswith('image/'),\\n msg.walk())\",\n \"def data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend):\\n\\tinfile = glob.glob(grbdir + \\\"/*quad_clean.evt\\\")[0] \\n\\tpredph = evt2image(infile,pre_tstart,pre_tend)\\n\\tgrbdph = evt2image(infile,grb_tstart,grb_tend)\\n\\tpostdph = evt2image(infile,post_tstart,post_tend)\\n\\n\\tbkgddph = predph+postdph\\n\\n\\toneD_grbdph = grbdph.flatten()\\n\\toneD_bkgddph = bkgddph.flatten()\\n\\tt_src = grb_tend - grb_tstart\\n\\tt_total = (pre_tend-pre_tstart)+(post_tend-post_tstart)\\n\\n\\treturn oneD_grbdph,oneD_bkgddph,grbdph,bkgddph,t_src,t_total\",\n \"def find_pixels(self):\\n ref_image=Image.open('sample0000.png')\\n imarray=np.array(ref_image)\\n ref_image.close()\\n self.number_of_pix=imarray.shape\\n print self.number_of_pix\\n ref_image=None\\n imarray=None\",\n \"def _get_image_dimensions(self):\\n\\t\\timageWidth = int(self.labels['IMAGE']['LINE_SAMPLES'])\\n\\t\\timageHeight = int(self.labels['IMAGE']['LINES'])\\n\\t\\treturn imageWidth, imageHeight\",\n \"def draw_on_image(D):\\n \\n #Set up rectangle's position within window\\n lower_left_x = 20 \\n lower_left_y = 42\\n dx = 5\\n dy = 5\\n\\n #Display border for rectangle\\n #Border is a black rectangle under white text rectangle\\n bord_upper_left = (lower_left_x-dx-3, lower_left_y-dy-20-3)\\n bord_lower_right = (lower_left_x+dx+160+3, lower_left_y+dy+50+3)\\n cv.Rectangle(D.image, bord_upper_left, bord_lower_right, D.black, cv.CV_FILLED)\\n \\n #Display white rectangle under text\\n rect_upper_left = (lower_left_x-dx, lower_left_y-dy-20)\\n rect_lower_right = (lower_left_x+dx+160, lower_left_y+dy+50)\\n cv.Rectangle(D.image, rect_upper_left, rect_lower_right, D.white, cv.CV_FILLED)\\n \\n ####\\n hive = \\\"hi!\\\"\\n hiveStat = \\\"hive\\\"\\n \\n #Build Strings\\n robotAssignment = (\\\"Assignment #: %.lf\\\"%R.assignment)\\n robotConverged = (\\\"Converged: %s\\\"%R.converged)\\n robotStatus = (\\\"Robot Status: \\\" + R.status)\\n hiveCommand = (\\\"Hive Command: \\\" + hive)\\n hiveStatus = (\\\"Hive Status: \\\" + hiveStat)\\n \\n # Position strings in a box so they won't overlap\\n firstLineString = (lower_left_x,lower_left_y) \\n secondLineString = (lower_left_x, lower_left_y + 20) \\n thirdLineString = (lower_left_x, lower_left_y + 40)\\n fourthLineString = (lower_left_x, lower_left_y + 60)\\n fifthLineString = (lower_left_x, lower_left_y + 80)\\n\\n #Display strings in window\\n cv.PutText(D.image, robotAssignment, firstLineString, D.font, cv.RGB(0,0,255)) \\n cv.PutText(D.image, robotConverged, secondLineString, D.font, cv.RGB(0,0,255))\\n cv.PutText(D.image, robotStatus, thirdLineString, D.font, cv.RGB(0,0,255))\\n cv.PutText(D.image, hiveCommand, fourthLineString, D.font, cv.RGB(0,0,255))\\n cv.PutText(D.image, hiveStatus, fifthLineString, D.font, cv.RGB(0,0,255))\",\n \"def compare_images(first_img_path, second_img_path):\\n img1 = Image.open(first_img_path)\\n img2 = Image.open(second_img_path)\\n\\n diff = ImageChops.difference(img1, img2)\\n print(diff.getbbox())\",\n \"def computePosition(self, state):\\n d = 0\\n if state[5] == \\\"East\\\":\\n d = 0\\n elif state[5] == \\\"West\\\":\\n d = 1\\n elif state[5] == \\\"North\\\":\\n d = 2\\n else:\\n d = 3\\n return state[0]*64+state[1]*32+state[2]*16+state[3]*8+state[4]*4+d\",\n \"def parse_3ccd_images(hdu_lst):\\n if len(hdu_lst) != 4:\\n raise ValueError\\n\\n # get CCD Binning\\n tmp = hdu_lst[0].header['CCDSUM'].split()\\n binx, biny = int(tmp[0]), int(tmp[1])\\n # get data sect rectanle\\n dataset_lst = {(2, 1): ('[7:1030,1:4096]', (6, 1030), (0, 4096)),\\n (2, 2): ('[7:1030,1:2048]', (6, 1030), (0, 2048)),\\n }\\n datasec, (x1, x2), (y1, y2) = dataset_lst[(binx, biny)]\\n # get data section\\n data_lst = [hdu_lst[i+1].data[y1:y2, x1:x2] for i in range(3)\\n if hdu_lst[i+1].header['DATASEC']==datasec]\\n\\n # get saturated masks\\n mask_sat1 = data_lst[0]==65535 # for UV CCD, saturated pixels are 65535.\\n mask_sat2 = data_lst[1]==0 # for green & red CCDs, saturated pixels\\n mask_sat3 = data_lst[2]==0 # are 0.\\n # get bad pixel masks\\n #mask_bad1 = np.zeros_like(mask_sat1, dtype=np.bool)\\n #mask_bad2 = np.zeros_like(mask_sat1, dtype=np.bool)\\n #mask_bad3 = np.zeros_like(mask_sat1, dtype=np.bool)\\n mask_bad1 = get_badpixel_mask((binx, biny), ccd=1)\\n mask_bad2 = get_badpixel_mask((binx, biny), ccd=2)\\n mask_bad3 = get_badpixel_mask((binx, biny), ccd=3)\\n # pack masks\\n mask1 = np.int16(mask_sat1)*4 + np.int16(mask_bad1)*2\\n mask2 = np.int16(mask_sat2)*4 + np.int16(mask_bad2)*2\\n mask3 = np.int16(mask_sat3)*4 + np.int16(mask_bad3)*2\\n\\n mask_lst = (mask1, mask2, mask3)\\n\\n # fix saturated pixels in the green and red CCDs\\n data_lst[1][mask_sat2] = 65535\\n data_lst[2][mask_sat3] = 65535\\n\\n return (data_lst, mask_lst)\",\n \"def calculate_embeddings(img, _network, dets):\\n \\n face_descriptors = []\\n \\n (_, sp, facerec) = _network\\n for (k, d) in enumerate(dets):\\n \\n print(\\\"Detection {}: Left: {} Top: {} Right: {} Bottom: {}\\\".format(\\n k, d.left(), d.top(), d.right(), d.bottom()))\\n # Get the landmarks/parts for the face in box d.\\n shape = sp(img, d)\\n \\n face_descriptor = facerec.compute_face_descriptor(img, shape)\\n face_descriptors.append(face_descriptor)\\n \\n return face_descriptors\",\n \"def find_body_part(self, body_points, segmentation_image):\\n print(\\\"start the measuring....\\\")\\n body_parts = {}\\n shoulders = self.find_shoulders_point(body_points, segmentation_image)\\n abdomen = self.find_abdomen_point(body_points)\\n chest = self.find_chest_point(body_points)\\n knees = self.find_knee_point(body_points)\\n arms = self.find_arms_point(body_points)\\n elbows = self.find_elbow_point(body_points)\\n ankles = self.find_ankle_point(body_points)\\n head = self.find_head_point(body_points)\\n\\n if shoulders is not None:\\n body_parts['shoulders'] = shoulders\\n\\n if abdomen is not None:\\n body_parts['abdomen'] = abdomen\\n\\n if chest is not None:\\n body_parts['chest'] = chest\\n\\n if knees is not None:\\n body_parts['knees'] = knees\\n\\n if arms is not None:\\n body_parts['arms'] = arms\\n\\n if elbows is not None:\\n body_parts['elbows'] = elbows\\n\\n if ankles is not None:\\n body_parts['ankles'] = ankles\\n\\n if head is not None:\\n body_parts['head'] = head\\n\\n return body_parts\",\n \"def detectBlocksInDepthImage(self):\\n depth_range_dict = {'1':[173,178],'2':[169,172],'3':[165,169],'4':[159,163],'5':[156,158],'6':[147,155],'7':[139,146],'8':[132,138]}\\n depth_frame = self.DepthFrameRaw\\n rgb_frame = self.VideoFrame\\n rgb_frame = cv2.resize(rgb_frame, (640,480))\\n depth_frame = cv2.resize(depth_frame, (640, 480))\\n np.clip(depth_frame,0,2**10 - 1,depth_frame)\\n depth_frame >>= 2\\n depth_frame = depth_frame.astype(np.uint8)\\n filt_block = []\\n for k,v in depth_range_dict.items():\\n thresh = cv2.inRange(depth_frame,v[0],v[1])\\n cv2.imwrite(\\\"/home/student/armlab-w20/log/img.jpeg\\\", thresh)\\n _ , contours, _ = cv2.findContours(thresh, 1, 2)\\n for cnt in contours:\\n area = cv2.contourArea(cnt)\\n if area > 400 and area < 700:\\n block = []\\n rect = cv2.minAreaRect(cnt)\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n color = self.determine_color(rgb_frame, box)\\n org = (box[0][0], box[0][1])\\n rgb_frame = cv2.putText(rgb_frame, color, org,cv2.FONT_HERSHEY_SIMPLEX , 0.5 ,(0,0,0),2, cv2.LINE_AA)\\n rgb_frame = cv2.drawContours(rgb_frame,[box],0,(0,0,0),0)\\n self.VideoFrame = rgb_frame\\n block.append(box)\\n block.append(int(k))\\n block.append(color)\\n filt_block.append(block)\\n return filt_block\",\n \"def debug_image(self, state_index: int = -1):\\n image = self.make_image(state_index, channel_type=\\\"n\\\")\\n return np.array([np.sum(arr) for arr in image])[3:].reshape(8, 12)\",\n \"def process_payload(payload):\\n\\n # Convertion of payload string to image array for opencv\\n ret, img = make_image(payload)#ret is 0 when conversion is successful or 1 when not\\n result='Unable to detect'\\n if ret == 0:\\n cv2.imwrite('received.png', img)\\n try:\\n roi = extract_roi_2(img)\\n \\n result = detect(roi) \\n \\n #write_characters(roi)\\n\\n except:\\n result = \\\"----------------\\\"\\n # # When roi is extracted its a 2d array \\n \\n return result\",\n \"def extract_signature(source_image):\\n # read the input image\\n img = source_image\\n img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1] # ensure binary\\n\\n # connected component analysis by scikit-learn framework\\n blobs = img > img.mean()\\n blobs_labels = measure.label(blobs, background=1)\\n # image_label_overlay = label2rgb(blobs_labels, image=img)\\n\\n fig, ax = plt.subplots(figsize=(10, 6))\\n\\n '''\\n # plot the connected components (for debugging)\\n ax.imshow(image_label_overlay)\\n ax.set_axis_off()\\n plt.tight_layout()\\n plt.show()\\n '''\\n\\n the_biggest_component = 0\\n total_area = 0\\n counter = 0\\n average = 0.0\\n for region in regionprops(blobs_labels):\\n if (region.area > 10):\\n total_area = total_area + region.area\\n counter = counter + 1\\n # print region.area # (for debugging)\\n # take regions with large enough areas\\n if (region.area >= 250):\\n if (region.area > the_biggest_component):\\n the_biggest_component = region.area\\n\\n average = (total_area/counter)\\n print(\\\"the_biggest_component: \\\" + str(the_biggest_component))\\n print(\\\"average: \\\" + str(average))\\n\\n # experimental-based ratio calculation, modify it for your cases\\n # a4_constant is used as a threshold value to remove connected pixels\\n # are smaller than a4_constant for A4 size scanned documents\\n a4_constant = (((average/84.0)*250.0)+100)*1.5\\n print(\\\"a4_constant: \\\" + str(a4_constant))\\n\\n # remove the connected pixels are smaller than a4_constant\\n b = morphology.remove_small_objects(blobs_labels, a4_constant)\\n # save the the pre-version which is the image is labelled with colors\\n # as considering connected components\\n plt.imsave('pre_version.png', b)\\n\\n # read the pre-version\\n img = cv2.imread('pre_version.png', 0)\\n # ensure binary\\n img = cv2.threshold(img, 0, 255,\\n cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\\n # save the the result\\n # cv2.imwrite(\\\"output.png\\\", img)\\n return img\",\n \"def handleImageRunner(self) :\\n tag = self.readByte()\\n if tag == ord(self.imagerunnermarker1[-1]) :\\n oldpos = self.pos-2\\n codop = self.minfile[self.pos:self.pos+2]\\n length = unpack(\\\">H\\\", self.minfile[self.pos+6:self.pos+8])[0]\\n self.pos += 18\\n if codop != self.imagerunnermarker2 :\\n self.pos += length\\n self.logdebug(\\\"IMAGERUNNERTAG SKIP %i AT %08x\\\" % (self.pos-oldpos, self.pos))\\n else :\\n self.pos -= 1 # Adjust position\",\n \"def getTasseledCap(img):\",\n \"def find_dots(img):\\n # will hold all points\\n coordinates = []\\n # will hold only relevant points\\n points = []\\n # losing the side\\n img[:, 475:] = 0\\n # using for finding the best corners in edged image 65\\n corners = cv2.goodFeaturesToTrack(img, 75, 0.085, 61)\\n corners = np.int0(corners)\\n for corner in corners:\\n x, y = corner.ravel()\\n if y > 350 or y < 10: # avoid from top and bottom\\n continue\\n coordinates.append((x, y))\\n # sort in order to start from right to left\\n sort_coordinates = sorted(coordinates)\\n num_of_dot = 1\\n for i in reversed(sort_coordinates):\\n # when its 9, break\\n if num_of_dot > 9:\\n break\\n points.append((i[0], i[1]))\\n num_of_dot += 1\\n return points\",\n \"def testDetect(name = \\\"smokey.gif\\\", amount = 20):\\n image = Image(name)\\n print(\\\"Close the image window to see the transformation\\\")\\n image.draw()\\n image2 = detectEdges(image, amount)\\n image2.draw()\",\n \"def getLines(image):\\r\\n\\treturn pytesseract.image_to_string(Image.open(image), lang='eng').split(\\\"\\\\n\\\")\",\n \"def get_image_position(\\n self, iop: np.ndarray\\n ) -> Tuple[float, float, float]:\\n try:\\n x = self.header[\\\"Rows\\\"]\\n y = self.header[\\\"Columns\\\"]\\n raw_ipp = self.header[\\\"ImagePositionPatient\\\"]\\n pixel_spacing = self.header[\\\"PixelSpacing\\\"]\\n except KeyError:\\n return\\n raw_shape = np.array([x, y])\\n\\n slice_shape = raw_shape / self.size\\n translation_fix = (raw_shape - slice_shape) / 2\\n Q = np.fliplr(iop) * pixel_spacing\\n return raw_ipp + np.dot(Q, translation_fix[:, None]).ravel()\",\n \"def _raw_face_locations_batched(images, number_of_times_to_upsample=1, batch_size=128):\\n\\treturn cnn_face_detector(images, number_of_times_to_upsample, batch_size=batch_size)\",\n \"def botStack(self):\\r\\n\\r\\n self.z_stack=self.img.shape[0]-1\\r\\n #self.pixmap=QtGui.QPixmap.fromImage(ImageQt.ImageQt(misc.toimage(self.img[self.z_stack]))).scaleds(500,500)\\r\\n self.pixmap= self.drawPixmap(\\\"xy\\\")\\r\\n self.lbl.setPixmap(self.pixmap)\\r\\n\\r\\n self.pixmap2=self.writeEdge(\\\"xy\\\")\\r\\n self.lbl2.setPixmap(self.pixmap2)\\r\\n self.z_stack_lbl.setText(str(self.z_stack+1) + '/' + str(self.img.shape[0]))\",\n \"def get_img_coord_tuple(img):\\n\\n lat = convert_to_degress(get_gps_details(img)['GPSLatitude'])\\n if get_gps_details(img)['GPSLatitudeRef'] == 'S':\\n lat = -lat\\n\\n longitude = convert_to_degress(get_gps_details(img)['GPSLongitude'])\\n if get_gps_details(img)['GPSLongitudeRef'] == 'W':\\n longitude = -longitude\\n\\n return lat, longitude\",\n \"def extract_dl(driving_log_path):\\r\\n entries = []\\r\\n with open(driving_log_path) as csv_file:\\r\\n reader = csv.reader(csv_file)\\r\\n for entry in reader:\\r\\n entries.append(entry)\\r\\n empty_lists = [[] for i in range(7)]\\r\\n center_images, left_images, right_images, steerings, throttles, brakes, speeds = empty_lists\\r\\n for entry in entries:\\r\\n center_image_path, left_image_path, right_image_path = (entry[0], entry[1], entry[2])\\r\\n steering = float(entry[3])\\r\\n throttle = float(entry[4])\\r\\n brake = float(entry[5])\\r\\n speed = float(entry[6])\\r\\n center_image = cv2.imread(center_image_path)\\r\\n left_image = cv2.imread(left_image_path)\\r\\n right_image = cv2.imread(right_image_path)\\r\\n center_images.append(center_image)\\r\\n left_images.append(left_image)\\r\\n right_images.append(right_image)\\r\\n steerings.append(steering)\\r\\n throttles.append(throttle)\\r\\n brakes.append(brake)\\r\\n speeds.append(speed)\\r\\n return center_images, left_images, right_images, steerings, throttles, brakes, speeds\",\n \"def devMsg(self, text):\\n # Preprocess text\\n lines = text.splitlines()\\n\\n image = self.devMsgImage.copy()\\n draw = ImageDraw.Draw(image)\\n # Text\\n x0 = 0\\n y0 = -2\\n for i in range(0,len(lines)):\\n draw.text((x0, y0+i*7), lines[i], font=self.font, fill=255)\\n self.disp.image(image.rotate(180))\\n self.disp.display()\\n return\",\n \"def ccsn_pixel_locations(size=100):\\n\\n L_UV_corr = UVimage_corrected()\\n [x,y,width,height] = GordonSurveyArea()\\n L_UV_corr_gord = L_UV_corr[y:y+height+1, x:x+width+1]\\n xpix = np.arange(0,L_UV_corr_gord.shape[1]+1,1)\\n ypix = np.arange(0,L_UV_corr_gord.shape[0]+1,1)\\n\\n#Calculate CDF per pixel\\n p_UV = L_UV_corr_gord/np.sum(L_UV_corr_gord)\\n cdf_UV = np.cumsum(p_UV).reshape(p_UV.shape)\\n \\n#Randomly select pixels\\n snrs = np.random.random(size)\\n xx,yy = np.meshgrid(xpix, ypix)\\n loc_x = np.zeros_like(snrs)\\n loc_y = np.zeros_like(snrs)\\n for i,randcdf in enumerate(snrs):\\n index = zip(*np.where(cdf_UV<=randcdf))\\n loc_x[i] = xx[index[-1]]\\n loc_y[i] = yy[index[-1]]\\n \\n return (loc_x, loc_y)\",\n \"def extract_rows(img):\\n img = img[:, 0:img.shape[1]//2]\\n mid = img.shape[0]//2\\n top, bot = img[:int(1.15*mid)], img[int(0.85*mid):]\\n\\n return top, bot\",\n \"def process_image(file_path):\\n img_array = io.imread(file_path)\\n detections, shapes, descriptors = detect_faces(person_database,img_array)\\n\\n names = []\\n\\n for desc in descriptors:\\n name = find_match(person_database, desc)\\n names.append(name)\\n\\n return pic_array, names, detections, shapes, descriptors\",\n \"def locateImageOnScreen(ImageName):\\n location = pyautogui.locateOnScreen(ImageName) \\n try: \\n for x in location:\\n return location\\n except:\\n sys.exit('The image could not be found in the active screen. \\\\n'+'Stopping program.')\",\n \"def extract_data(filename: str, directory: str) -> Dict:\\n with open(filename) as f:\\n lines = f.readlines()\\n\\n # Split data by :\\n annotations = [line.replace(\\\" \\\", \\\"\\\").split(\\\":\\\") for line in lines]\\n\\n # Split data by ;\\n for annotation in annotations:\\n annotation[1] = annotation[1].split(\\\";\\\")\\n\\n # Loop for saving metadata into dictionary\\n annot_dict = dict()\\n for annotation in annotations:\\n img = annotation[0]\\n bbox_metadata = annotation[1]\\n bbox = list()\\n \\n # Path to images\\n img_path = os.path.join(directory, img)\\n im = Image.open(img_path)\\n width, height = im.size\\n\\n # Iterate over each bounding box\\n for annot in bbox_metadata:\\n \\n if \\\"MISC_SIGNS\\\" == annot:\\n signStatus = 'N/A'\\n signTypes = \\\"MISC_SIGNS\\\"\\n signPurpose = 'N/A'\\n\\n signBB = (-1, -1, -1, -1)\\n signC = (-1, -1)\\n signSize = 0\\n aspectRatio = 0\\n\\n bbox.append({\\\"signStatus\\\": signStatus, \\n \\\"signTypes\\\": signTypes, \\n \\\"signPurpose\\\": signPurpose, \\n \\\"signBB\\\": signBB, \\n \\\"signC\\\": signC, \\n \\\"signSize\\\": signSize, \\n \\\"aspectRatio\\\": aspectRatio})\\n elif \\\"\\\\n\\\" in annot:\\n pass\\n else:\\n data = annot.split(\\\",\\\")\\n \\n signStatus = data[0] # signStatus\\n signTypes = data[6] # signTypes\\n signPurpose = data[5] # PROHIBITORY, WARNING, OTHER, INFORMATION\\n tl_x, tl_y, br_x, br_y = data[3], data[4], data[1], data[2]\\n \\n if is_valid_decimal(tl_x):\\n tl_x = float(tl_x)\\n else:\\n tl_x = float(cutoff_letter(tl_x))\\n\\n if is_valid_decimal(tl_y):\\n tl_y = float(tl_y)\\n else:\\n tl_y = float(cutoff_letter(tl_y))\\n\\n if is_valid_decimal(br_x):\\n br_x = float(br_x)\\n else:\\n br_x = float(cutoff_letter(br_x))\\n\\n if is_valid_decimal(br_y):\\n br_y = float(br_y)\\n else:\\n br_y = float(cutoff_letter(br_y))\\n\\n if tl_x < 0:\\n tl_x = 0\\n elif tl_x > width:\\n tl_x = width\\n \\n if tl_y < 0:\\n tl_y = 0\\n elif tl_y > height:\\n tl_y = height\\n \\n if br_x < 0:\\n br_x = 0\\n elif br_x > width:\\n br_x = width\\n \\n if br_y < 0:\\n br_y = 0\\n elif br_y > height:\\n br_y = height\\n\\n signBB = (tl_x, tl_y, br_x, br_y)\\n signC = (br_x + tl_x)/2, (br_y + tl_y)/2\\n signSize = (br_x - tl_x) * (br_y - tl_y)\\n aspectRatio = (br_x - tl_x) / (br_y - tl_y)\\n\\n bbox.append({\\\"signStatus\\\": signStatus, \\n \\\"signTypes\\\": signTypes, \\n \\\"signPurpose\\\": signPurpose, \\n \\\"signBB\\\": signBB, \\n \\\"signC\\\": signC, \\n \\\"signSize\\\": signSize, \\n \\\"aspectRatio\\\": aspectRatio})\\n \\n \\n annot_dict[img_path] = bbox\\n return annot_dict\",\n \"def face_locations(img, number_of_times_to_upsample=0, model=\\\"hog\\\"):\\n\\tif model == \\\"cnn\\\":\\n\\t\\treturn [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, \\\"cnn\\\")]\\n\\telse:\\n\\t\\treturn [_trim_css_to_bounds(_rect_to_css(face), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, model)]\",\n \"def draw_bboxes_withindex(img,boxes, uids):\\n source = Image.fromarray(img)\\n draw = ImageDraw.Draw(source)\\n w2,h2 = (img.shape[0],img.shape[1])\\n \\n font = ImageFont.truetype('/usr/share/fonts/truetype/freefont/FreeSerif.ttf', 40)\\n #font = ImageFont.truetype('arial.ttf', 24)\\n\\n\\n idx = 0\\n\\n for b in boxes:\\n xmin,ymin,xmax,ymax = b\\n \\n for j in range(3):\\n draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline=\\\"red\\\")\\n draw.text((xmin+20, ymin+70), str(uids[idx]), font = font)\\n idx +=1\\n return source\",\n \"def _get_image_index_position(self) :\\n \\n return self._image_index_position\",\n \"def __get_format_info_locations(self,size):\\n\\t\\tloc = {\\n\\t\\t\\t\\t0: [[8,0],[size-1,8]],\\n\\t\\t\\t\\t1: [[8,1],[size-2,8]],\\n\\t\\t\\t\\t2: [[8,2],[size-3,8]],\\n\\t\\t\\t\\t3: [[8,3],[size-4,8]],\\n\\t\\t\\t\\t4: [[8,4],[size-5,8]],\\n\\t\\t\\t\\t5: [[8,5],[size-6,8]],\\n\\t\\t\\t\\t6: [[8,7],[size-7,8]],\\n\\t\\t\\t\\t7: [[8,8],[8,size-8]],\\n\\t\\t\\t\\t8: [[7,8],[8,size-7]],\\n\\t\\t\\t\\t9: [[5,8],[8,size-6]],\\n\\t\\t\\t\\t10: [[4,8],[8,size-5]],\\n\\t\\t\\t\\t11: [[3,8],[8,size-4]],\\n\\t\\t\\t\\t12: [[2,8],[8,size-3]],\\n\\t\\t\\t\\t13: [[1,8],[8,size-2]],\\n\\t\\t\\t\\t14: [[0,8],[8,size-1]]\\n\\t\\t}\\n\\t\\treturn loc\",\n \"def findPosition(self, img, handNo=0, draw=True):\\n\\n xList = []\\n yList = []\\n bbox = []\\n bboxInfo =[]\\n self.lmList = []\\n if self.results.multi_hand_landmarks:\\n myHand = self.results.multi_hand_landmarks[handNo]\\n for id, lm in enumerate(myHand.landmark):\\n h, w, c = img.shape\\n px, py = int(lm.x * w), int(lm.y * h)\\n xList.append(px)\\n yList.append(py)\\n self.lmList.append([px, py])\\n if draw:\\n cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)\\n xmin, xmax = min(xList), max(xList)\\n ymin, ymax = min(yList), max(yList)\\n boxW, boxH = xmax - xmin, ymax - ymin\\n bbox = xmin, ymin, boxW, boxH\\n cx, cy = bbox[0] + (bbox[2] // 2), \\\\\\n bbox[1] + (bbox[3] // 2)\\n bboxInfo = {\\\"id\\\": id, \\\"bbox\\\": bbox,\\\"center\\\": (cx, cy)}\\n\\n if draw:\\n cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),\\n (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),\\n (0, 255, 0), 2)\\n\\n return self.lmList, bboxInfo\",\n \"def count_blood_cells(image_path):\\n\\n # TODO - Prebrojati crvena i bela krvna zrnca i vratiti njihov broj kao povratnu vrednost ove procedure\\n \\\"\\\"\\\"\\n White cells\\n \\\"\\\"\\\"\\n # Getting image\\n white_cells_img = cv2.imread(image_path)\\n gray_img = cv2.cvtColor(white_cells_img, cv2.COLOR_BGR2GRAY)\\n\\n # Apply median filter for smoothing\\n smooth_img_white = cv2.medianBlur(gray_img, 5)\\n\\n # Morphological operations\\n kernel = np.ones((5, 5), np.uint8)\\n closing_img = cv2.morphologyEx(smooth_img_white, cv2.MORPH_CLOSE, kernel)\\n\\n # Adaptive threshold gaussian filter\\n threshold_img = cv2.adaptiveThreshold(closing_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\\n cv2.THRESH_BINARY, 9, 2)\\n\\n # Segmentation of white cells\\n circles_a = cv2.HoughCircles(threshold_img, cv2.HOUGH_GRADIENT, 1.2, 105,\\n param1=50, param2=28, minRadius=2, maxRadius=28)\\n\\n # Getting count of white cells\\n cell_count_a = []\\n if circles_a is not None:\\n circles_a = np.round(circles_a[0, :]).astype(\\\"int\\\")\\n for (r) in circles_a:\\n cell_count_a.append(r)\\n # print(len(cell_count_a))\\n white_blood_cell_count = len(cell_count_a)\\n\\n \\\"\\\"\\\"\\n Red cells\\n \\\"\\\"\\\"\\n # Getting image\\n red_cells_img = cv2.imread(image_path)\\n\\n # Getting red color\\n red = [(150, 137, 168), (218, 209, 208)] # (lower), (upper)\\n colors = [red]\\n\\n # Apply median filter for smoothing\\n smooth_img_red = cv2.medianBlur(red_cells_img, 3)\\n\\n cell_count_b = 0\\n output = red_cells_img.copy()\\n for lower, upper in colors:\\n mask = cv2.inRange(smooth_img_red, lower, upper)\\n\\n # Segmentation of red cells\\n circles_b = cv2.HoughCircles(mask, cv2.HOUGH_GRADIENT, 1, 20, param1=15, param2=17,\\n minRadius=2, maxRadius=60)\\n\\n # Getting count of red cells\\n if circles_b is not None:\\n circles_b = np.round(circles_b[0, :]).astype(\\\"int\\\")\\n\\n for (x, y, r) in circles_b:\\n cv2.circle(output, (x, y), r, (255, 0, 255), 2)\\n cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (255, 0, 255), -1)\\n cell_count_b += 1\\n\\n # cv2.imwrite('output.png', output)\\n # print(cell_count_b)\\n red_blood_cell_count = cell_count_b\\n\\n # TODO - Odrediti da li na osnovu broja krvnih zrnaca pacijent ima leukemiju i vratiti True/False kao povratnu\\n # vrednost ove procedure\\n\\n if (white_blood_cell_count > 2\\n or\\n white_blood_cell_count >= (red_blood_cell_count / 3)):\\n has_leukemia = True\\n else:\\n has_leukemia = False\\n\\n return red_blood_cell_count, white_blood_cell_count, has_leukemia\",\n \"def get_offsets(i):\\n x = (i % 3) * (100 + PICTURE_BORDER) + PICTURE_BORDER\\n if (i < 3):\\n y = PICTURE_BORDER\\n elif (i >= 3 and i <= 5):\\n y = (100 * 1) + (2 * PICTURE_BORDER)\\n else:\\n y = (100 * 2) + (3 * PICTURE_BORDER)\\n return (x, y)\",\n \"def getDimension(data):\\r\\n # open image for reading in binary mode\\r\\n\\r\\n # read the 2 bytes\\r\\n a = data[163:165]\\r\\n\\r\\n # calculate height\\r\\n height = (a[0] << 8) + a[1]\\r\\n\\r\\n # next 2 bytes is width\\r\\n a = data[165:167]\\r\\n\\r\\n # calculate width\\r\\n width = (a[0] << 8) + a[1]\\r\\n\\r\\n return (width, height)\",\n \"def find_letters(line_image):\\r\\n\\r\\n\\tif line_image.shape[0] < 40:\\r\\n\\t\\tline_image = cv2.resize(line_image, (line_image.shape[1] * 2, line_image.shape[0] * 2))\\r\\n\\r\\n\\t#binary\\r\\n\\tret,thresh = cv2.threshold(line_image, 109, 255, cv2.THRESH_BINARY_INV)\\r\\n\\r\\n\\tif cv2.__version__.startswith('3.'):\\r\\n\\t\\tim2, ctrs, hier = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\\r\\n\\telse:\\r\\n\\t\\t(ctrs, __) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\\r\\n\\r\\n\\t#sort contours\\r\\n\\tsorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0], reverse=True)\\r\\n\\r\\n\\t#creating objects - so we coult hold a few arguments that connected together in the same variable\\r\\n\\r\\n\\tclass contur:\\r\\n\\t\\tdef __init__(self, x, y, w, h):\\r\\n\\t\\t\\tself.x_start = x\\r\\n\\t\\t\\tself.y_start = y\\r\\n\\t\\t\\tself.x_end = x + w\\r\\n\\t\\t\\tself.y_end = y + h\\r\\n\\r\\n\\tletters_images = list()\\r\\n\\tnew_ctr = list()\\r\\n\\r\\n\\tfor j, ctr in enumerate(sorted_ctrs):\\r\\n\\t\\tx, y, w, h = cv2.boundingRect(ctr)\\r\\n\\t\\tc = contur(x, y, w, h)\\r\\n\\t\\tnew_ctr.append(c)\\r\\n\\r\\n\\tlength = len(new_ctr)\\r\\n\\r\\n\\ti = 0\\r\\n\\twhile i < length:\\r\\n\\t\\tx, y, w, h = cv2.boundingRect(sorted_ctrs[i])\\r\\n\\r\\n\\t\\tif h > 3:\\r\\n\\t\\t\\tcanvas = np.ones_like(line_image)\\r\\n\\t\\t\\tcanvas.fill(255)\\r\\n\\t\\t\\tcv2.drawContours(canvas, sorted_ctrs, i, (0, 0, 0), 3)\\r\\n\\r\\n\\t\\t\\tif i < length - 1 and new_ctr[i].x_start >= new_ctr[i + 1].x_start and new_ctr[i].x_end <= new_ctr[i + 1].x_end:\\r\\n\\t\\t\\t\\tY_end_bigger = max(new_ctr[i].y_end, new_ctr[i + 1].y_end)\\r\\n\\t\\t\\t\\tcv2.drawContours(canvas, sorted_ctrs, i + 1, (0, 0, 0), 3)\\r\\n\\r\\n\\t\\t\\t\\tif union_left_ctr(new_ctr[i], new_ctr[i+1], canvas) == 0:\\r\\n\\t\\t\\t\\t\\troi = canvas[y:y + h, x:x + w]\\r\\n\\t\\t\\t\\t\\troiriginal = line_image[y:y + h, x:x + w]\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\troi = canvas[new_ctr[i + 1].y_start:Y_end_bigger, new_ctr[i + 1].x_start:new_ctr[i + 1].x_end]\\r\\n\\t\\t\\t\\t\\troiriginal = line_image[new_ctr[i + 1].y_start:Y_end_bigger, new_ctr[i + 1].x_start:new_ctr[i + 1].x_end]\\r\\n\\t\\t\\t\\t\\ti += 1\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\troi = canvas[y:y + h, x:x + w]\\r\\n\\t\\t\\t\\troiriginal = line_image[y:y + h, x:x + w]\\r\\n\\r\\n\\t\\t\\tletter = np.pad(roiriginal, pad_width=10, mode='constant', constant_values=255)\\r\\n\\r\\n\\t\\t\\tletters_images.append(letter)\\r\\n\\t\\ti += 1\\r\\n\\treturn letters_images\",\n \"def extract_faces(self, img, list_face_coord):\\n #from the img array extract the facees\\n list_faces = []\\n #Go through each face coordinates and store the array\\n #or clip face region in the list\\n for i, coord in enumerate(list_face_coord):\\n left, top, right, bottom = coord\\n list_faces.append(img[top:bottom, left:right])\\n return list_faces\",\n \"def identify(data, image, rgb):\\r\\n\\tdetection_method = \\\"hog\\\"\\r\\n\\r\\n\\t# Locate the faces in the image, embed them\\r\\n\\tprint(\\\"[INFO] recognizing faces...\\\")\\r\\n\\tboxes = face_recognition.face_locations(rgb, model=detection_method)\\r\\n\\tencodings = face_recognition.face_encodings(rgb, boxes)\\r\\n\\r\\n\\t# Initialize the list of names for each face detected\\r\\n\\tnames = []\\r\\n\\t\\r\\n\\t# Loop over the facial embeddings\\r\\n\\tfor encoding in encodings:\\r\\n\\t\\tname = match_face(data,encoding)\\r\\n\\t\\t# Update the list of names\\r\\n\\t\\tnames.append(name)\\r\\n\\t\\r\\n\\trois = []\\r\\n\\t# Loop over the recognized faces\\r\\n\\tfor ((top, right, bottom, left), name) in zip(boxes, names):\\r\\n\\t\\trois.append(((top,right,bottom,left), name))\\r\\n\\r\\n\\treturn rois\",\n \"def parse_image_meta(meta):\\n image_id = meta[:, 0]\\n image_shape = meta[:, 1:4]\\n window = meta[:, 4:8] # (x1, y1, x2, y2) window of image in in pixels\\n active_class_ids = meta[:, 8:]\\n return image_id, image_shape, window, active_class_ids\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5904444","0.58341223","0.5803662","0.5694714","0.564857","0.5638822","0.5634845","0.5602222","0.55758303","0.5494642","0.5491051","0.548308","0.5441922","0.5413018","0.54116","0.5403581","0.5379999","0.53695244","0.53540814","0.5338005","0.53375745","0.53375083","0.53011036","0.5300451","0.52988744","0.5293107","0.5291946","0.5271617","0.5260808","0.5254867","0.524645","0.52329576","0.5210216","0.5207357","0.51976043","0.5197588","0.5194843","0.51946634","0.5178017","0.51702696","0.51582956","0.5145241","0.5137546","0.5131995","0.51311165","0.51234645","0.51201516","0.5115858","0.5112532","0.5108226","0.5107858","0.5105203","0.50858593","0.5084239","0.5079075","0.50775504","0.50682384","0.50610465","0.5060032","0.50541455","0.50531226","0.5051292","0.50496894","0.50481546","0.5046822","0.5045361","0.5044787","0.50406677","0.5038298","0.5036339","0.5032785","0.5023687","0.5023307","0.50217164","0.50199455","0.50152045","0.50124556","0.50106716","0.50085366","0.50073963","0.5002412","0.49928117","0.4986304","0.498359","0.4981665","0.49754483","0.49730137","0.49726903","0.49690425","0.49667495","0.4966234","0.49630645","0.49585155","0.49541998","0.4953928","0.49518585","0.49506953","0.49501342","0.49499768","0.4948985"],"string":"[\n \"0.5904444\",\n \"0.58341223\",\n \"0.5803662\",\n \"0.5694714\",\n \"0.564857\",\n \"0.5638822\",\n \"0.5634845\",\n \"0.5602222\",\n \"0.55758303\",\n \"0.5494642\",\n \"0.5491051\",\n \"0.548308\",\n \"0.5441922\",\n \"0.5413018\",\n \"0.54116\",\n \"0.5403581\",\n \"0.5379999\",\n \"0.53695244\",\n \"0.53540814\",\n \"0.5338005\",\n \"0.53375745\",\n \"0.53375083\",\n \"0.53011036\",\n \"0.5300451\",\n \"0.52988744\",\n \"0.5293107\",\n \"0.5291946\",\n \"0.5271617\",\n \"0.5260808\",\n \"0.5254867\",\n \"0.524645\",\n \"0.52329576\",\n \"0.5210216\",\n \"0.5207357\",\n \"0.51976043\",\n \"0.5197588\",\n \"0.5194843\",\n \"0.51946634\",\n \"0.5178017\",\n \"0.51702696\",\n \"0.51582956\",\n \"0.5145241\",\n \"0.5137546\",\n \"0.5131995\",\n \"0.51311165\",\n \"0.51234645\",\n \"0.51201516\",\n \"0.5115858\",\n \"0.5112532\",\n \"0.5108226\",\n \"0.5107858\",\n \"0.5105203\",\n \"0.50858593\",\n \"0.5084239\",\n \"0.5079075\",\n \"0.50775504\",\n \"0.50682384\",\n \"0.50610465\",\n \"0.5060032\",\n \"0.50541455\",\n \"0.50531226\",\n \"0.5051292\",\n \"0.50496894\",\n \"0.50481546\",\n \"0.5046822\",\n \"0.5045361\",\n \"0.5044787\",\n \"0.50406677\",\n \"0.5038298\",\n \"0.5036339\",\n \"0.5032785\",\n \"0.5023687\",\n \"0.5023307\",\n \"0.50217164\",\n \"0.50199455\",\n \"0.50152045\",\n \"0.50124556\",\n \"0.50106716\",\n \"0.50085366\",\n \"0.50073963\",\n \"0.5002412\",\n \"0.49928117\",\n \"0.4986304\",\n \"0.498359\",\n \"0.4981665\",\n \"0.49754483\",\n \"0.49730137\",\n \"0.49726903\",\n \"0.49690425\",\n \"0.49667495\",\n \"0.4966234\",\n \"0.49630645\",\n \"0.49585155\",\n \"0.49541998\",\n \"0.4953928\",\n \"0.49518585\",\n \"0.49506953\",\n \"0.49501342\",\n \"0.49499768\",\n \"0.4948985\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6506251"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14987,"cells":{"query":{"kind":"string","value":"Takes in a list of Image messages and identifies the block number in each image"},"document":{"kind":"string","value":"def identify_blocks(images):\n locations = {1: Point(), 2: Point(), 3: Point()}\n blocks = {\"left\": 0, \"middle\": 0, \"right\": 0}\n pipeline = keras_ocr.pipeline.Pipeline()\n cv2_images = []\n\n for image in images:\n bridge = cv_bridge.CvBridge()\n cv2_images.append(bridge.imgmsg_to_cv2(image, \"bgr8\"))\n\n predictions = pipeline.recognize(cv2_images)\n rospy.loginfo(predictions)\n\n blocks[\"left\"] = int(predictions[0][0][0])\n blocks[\"middle\"] = int(predictions[1][0][0])\n blocks[\"right\"] = int(predictions[2][0][0])\n\n for position, block in blocks.items():\n locations[block] = block_locations[position]\n \n return locations"},"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 getBlocks(self):\n blocks = self.getBlocksMsg(b'\\x00')\n last_locator = self.largeMessageControl(blocks, 'inv', 0)\n\n while last_locator[1] < TARGET_BLOCK:\n blocks = self.getBlocksMsg(bytearray.fromhex(convertLittleBig(last_locator[0])))\n last_locator = self.largeMessageControl(blocks, 'inv', last_locator[1])\n\n print('\\nSuccessfully found the Block #{}: {}'.format(TARGET_BLOCK, last_locator[0]))\n return last_locator[0]","def detectBlocksInDepthImage(self):\n depth_range_dict = {'1':[173,178],'2':[169,172],'3':[165,169],'4':[159,163],'5':[156,158],'6':[147,155],'7':[139,146],'8':[132,138]}\n depth_frame = self.DepthFrameRaw\n rgb_frame = self.VideoFrame\n rgb_frame = cv2.resize(rgb_frame, (640,480))\n depth_frame = cv2.resize(depth_frame, (640, 480))\n np.clip(depth_frame,0,2**10 - 1,depth_frame)\n depth_frame >>= 2\n depth_frame = depth_frame.astype(np.uint8)\n filt_block = []\n for k,v in depth_range_dict.items():\n thresh = cv2.inRange(depth_frame,v[0],v[1])\n cv2.imwrite(\"/home/student/armlab-w20/log/img.jpeg\", thresh)\n _ , contours, _ = cv2.findContours(thresh, 1, 2)\n for cnt in contours:\n area = cv2.contourArea(cnt)\n if area > 400 and area < 700:\n block = []\n rect = cv2.minAreaRect(cnt)\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n color = self.determine_color(rgb_frame, box)\n org = (box[0][0], box[0][1])\n rgb_frame = cv2.putText(rgb_frame, color, org,cv2.FONT_HERSHEY_SIMPLEX , 0.5 ,(0,0,0),2, cv2.LINE_AA)\n rgb_frame = cv2.drawContours(rgb_frame,[box],0,(0,0,0),0)\n self.VideoFrame = rgb_frame\n block.append(box)\n block.append(int(k))\n block.append(color)\n filt_block.append(block)\n return filt_block","def getparts(image, block_len):\n img = image.convert('L') if image.mode != 'L' else image\n w, h = img.size \n parts = []\n # Bluring image for abandoning image details and noise.\n global opt\n for n in range(int(opt.imblev)):\n img = img.filter(ImageFilter.SMOOTH_MORE)\n # Converting image to custom palette\n imagetopalette(img, [x for x in range(256) if x%int(opt.impalred) == 0])\n pix = img.load()\n \n for x in range(w-block_len):\n for y in range(h-block_len):\n data = list(blockpoints(pix, (x,y), block_len)) + [(x,y)]\n parts.append(data)\n parts = sorted(parts)\n return parts","def num_blocks(self): # -> int:\n ...","def detectBlocksInDepthImage(self):\n self.depth_detect_frame = self.currentDepthFrame\n\n # 1 block\n self.bin_detect_frame_1 = cv2.inRange(self.depth_detect_frame,700,710)\n kernel = np.ones((6,6),np.uint8)\n self.bin_detect_frame_1 = cv2.erode(self.bin_detect_frame_1,kernel,iterations = 1)\n _, self.block_contours, _ = cv2.findContours(self.bin_detect_frame_1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n \n # 2 blocks\n self.bin_detect_frame_2 = cv2.inRange(self.depth_detect_frame,680,699)\n kernel = np.ones((6,6),np.uint8)\n self.bin_detect_frame_2 = cv2.erode(self.bin_detect_frame_2,kernel,iterations = 1)\n _, self.block_contours_2, _ = cv2.findContours(self.bin_detect_frame_2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n \n # 3 blocks\n self.bin_detect_frame_3 = cv2.inRange(self.depth_detect_frame,660,679)\n kernel = np.ones((6,6),np.uint8)\n self.bin_detect_frame_3 = cv2.erode(self.bin_detect_frame_3,kernel,iterations = 1)\n _, self.block_contours_3, _ = cv2.findContours(self.bin_detect_frame_3,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n \n pass","def get_initial_blocks(self):\n block = []\n index = 0\n for number in self.numbers_from_file(self.input_file_name):\n block.append(number)\n if len(block) == self.block_size:\n block.sort()\n self.write_block(index, block)\n block = []\n index += 1\n else:\n if block:\n block.sort()\n self.write_block(index, block)\n index += 1\n return 0, index","def imageparts(msg):\n # Don't want a set here because we want to be able to process them in\n # order.\n return filter(lambda part:\n part.get_content_type().startswith('image/'),\n msg.walk())","def image_generator(self, some_messages):\n offset = 0\n outer = 0\n inner = 0\n\n for a_message in some_messages:\n msg_id = a_message.gmail_id\n for att in a_message.attachments():\n if att.type in ATTACHMENT_MIMES:\n att_type = att.type.split(\"/\")[1]\n an_image = Image(a_message, att)\n\n # map each image id with a corresponding message id for later parsing\n if an_image.id in self.mapping:\n self.mapping[msg_id].append(a_message)\n else:\n self.mapping[msg_id] = [a_message]\n\n self.num_attachments = self.count_attachments(self.num_attachments)\n\n yield an_image","def blockDetector(self):\n # img = cv2.cvtColor(self.VideoFrame, cv2.COLOR_RGB2HSV)\n # img = cv2.resize(img, (640, 480))\n # cv2.imwrite('hsv.jpg', img)\n # mask = None\n # for color in color_ranges:\n # mask = cv2.inRange(img, color_ranges[color][0], color_ranges[color][1])\n # cv2.imwrite('blockdetect.jpg', cv2.bitwise_and(img, img, mask=mask))\n blocks = self.detectBlocksInDepthImage()\n pick_up_locs = []\n for block in blocks:\n coords = block[0]\n u = (coords[0][0] + coords[2][0]) // 2\n v = (coords[0][1] + coords[2][1]) // 2\n self.VideoFrame = cv2.circle(self.VideoFrame,(u,v), 1, (0,0,0))\n d = self.DepthFrameRaw[u,v]\n d = self.convertDepthToSI(d)\n world_coords = self.calculatePixelToWorld(u, v, d)\n world_coords[2] = self.max_depth - d\n pick_up_locs.append(world_coords)\n return pick_up_locs","def getblocknumber(self):\n return self.getblockcount()","def get_block_size(self, img_start):\n pattern_size = 7\n\n left, top = img_start\n block_height, block_width = 0, 0\n for i in range(1, self.image.width - left):\n if self._get_pixel((left + i * pattern_size, top)) == WHITE:\n block_width = i\n break\n for i in range(1, self.image.height - top):\n if self._get_pixel((left, top + i * pattern_size)) == WHITE:\n block_height = i\n break\n return block_width, block_height","def find_block(int_svip, comp_block):\n print(\"-\" * 20 + \" find_block started\")\n bsz = comp_block\n outsz = 0\n bsdict = {}\n bsdict [0] = bsz\n # Build the dictionary of the host networks\n while outsz < 255:\n outsz = outsz + bsz\n bsdict[outsz] = (outsz + bsz) -1\n #print(outsz)\n \n # Determine the upper and lower bounds of the host network\n for key in bsdict.keys():\n if int_svip >= key and int_svip <= bsdict[key]:\n block_start = key\n block_end = bsdict[key]\n\n #print(\"Block start is {}\\nBlock end is {}\".format(block_start, block_end))\n return block_start, block_end","def n_blocks(n_frames, block_length):\n return n_frames - block_length + 1","def get_num_of_images(self):","def image_id_at(self, i):\n return i","def ip_get_blocks():\n # start Requests session\n sc = requests.Session()\n\n # import cookies from Firefox\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\n\n # send request\n vpx = sc.post('https://imhsc.imhadmin.net/index.php?v=IPManager')\n\n # check if login failed\n check_sc_login(vpx.text)\n\n # parse with BS4\n bs = BeautifulSoup(vpx.text, \"xml\")\n\n # get list of provisioning blocks\n blocklist = []\n for tblk in bs.find_all('table')[3].tr.div.table.find_all('tr'):\n tbx = {\n 'id': re.match(r'.+block_id=([0-9]+).*', tblk.find_all('td')[0].a['href']).group(1),\n 'prefix': tblk.find_all('td')[0].a.string,\n 'block': tblk.find_all('td')[1].string,\n 'usage': tblk.find_all('td')[2].string\n }\n blocklist.append(tbx)\n\n return bs, blocklist","def message_received(self, message):\n\n body_lxml = message.body_lxml()\n for image in body_lxml.iterfind(\".//img[@src]\"):\n if image.attrib['src'] == self.no_devil_banana_url:\n logger.debug(\":nodb: found in {0}'s message {1}\".format(\n message.user_name, message.id\n ))\n with self.last_lock:\n self.last_nodb_message = message.id\n elif image.attrib['src'] in self.devil_banana_urls:\n if message.user_name == self.connector.username:\n # ignore my own devil banana messages\n return\n logger.debug(\"devil banana {2} found in {0}'s message {1}\".format(\n message.user_name, message.id, image.attrib['src']\n ))\n with self.last_lock:\n self.last_banana_message_due_to_edit = False\n self.last_banana_message = message.id","def process_next_image(self):\n if self.queue:\n next_queue_item = self.queue.popleft()\n if type(next_queue_item) == str:\n if next_queue_item == 'clear':\n self.signal_status_message.emit('Clearing ROI data (from request in image queue)')\n self.clear()\n return\n [image,file_id,image_num] = next_queue_item\n # print('image_num',image_num)\n # print('next image',self.next_image)\n self.signal_status_message.emit('Started processing ID {} Im {}'.format(file_id,image_num))\n image = image - self.emccd_bias # don't edit in place because this seemed to cause an issue with images not showing in GUI. Maybe not thread safe?\n # print('image min',np.min(image))\n # print('image max',np.max(image))\n image_num_too_big = False\n for group in self.roi_groups:\n for roi in group.rois:\n try:\n roi.counts[image_num][file_id] = image[roi.x:roi.x+roi.w,roi.y:roi.y+roi.h].sum()\n except IndexError: # image_num was not valid for the number of images that MAIA is expecting\n image_num_too_big = True\n if image_num_too_big:\n self.signal_status_message.emit('Image number {} is greater than max expected images, so this image has been ignored (most likely cause is rearrangement toggle).')\n self.signal_status_message.emit('Finished processing ID {} Im {}'.format(file_id,image_num))\n self.calculate_thresholds()","def mine_blocks(self, count):\n\n # Clear out block announcements from each p2p listener\n [x.clear_block_announcements() for x in self.nodes[0].p2ps]\n self.generatetoaddress(self.nodes[0], count, self.nodes[0].get_deterministic_priv_key().address)\n return int(self.nodes[0].getbestblockhash(), 16)","def draw_bboxes_withindex(img,boxes, uids):\n source = Image.fromarray(img)\n draw = ImageDraw.Draw(source)\n w2,h2 = (img.shape[0],img.shape[1])\n \n font = ImageFont.truetype('/usr/share/fonts/truetype/freefont/FreeSerif.ttf', 40)\n #font = ImageFont.truetype('arial.ttf', 24)\n\n\n idx = 0\n\n for b in boxes:\n xmin,ymin,xmax,ymax = b\n \n for j in range(3):\n draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline=\"red\")\n draw.text((xmin+20, ymin+70), str(uids[idx]), font = font)\n idx +=1\n return source","def cover(img: Image, message: bitarray) -> Image:\n width, height = img.size\n check_image_width(width, RGB_PIXEL, message)\n pixels = img.load()\n\n row = random.randint(0, height) # Randomly chooses row.\n i = 0 # Tracks hidden bits\n\n # If Image consist of 8-bit pixels\n if img.mode == \"P\":\n offset = generate_offset(width, P_PIXEL, message)\n for x in range(offset, width):\n p = pixels[x,row]\n if i < len(message):\n p = modify_byte(p, message[i])\n i += 1\n pixels[x,row] = p\n generate_key(row, message, offset * P_PIXEL)\n # If Image consists of 3x8-bit pixels\n elif img.mode == \"RGB\":\n offset = generate_offset(width, RGB_PIXEL, message)\n for x in range(offset, width):\n r, g, b = pixels[x,row]\n if i < len(message):\n r = modify_byte(r, message[i])\n i += 1\n if i < len(message):\n g = modify_byte(g, message[i])\n i += 1\n if i < len(message):\n b = modify_byte(b, message[i])\n i += 1\n pixels[x,row] = (r, g, b)\n generate_key(row, message, offset * RGB_PIXEL)\n # If Image consists of 4x8-bits pixels\n elif img.mode == \"RGBA\":\n offset = generate_offset(width, RGBA_PIXEL, message)\n for x in range(offset, width):\n r, g, b, a = pixels[x,row]\n if i < len(message):\n r = modify_byte(r, message[i])\n i += 1\n if i < len(message):\n g = modify_byte(g, message[i])\n i += 1\n if i < len(message):\n b = modify_byte(b, message[i])\n i += 1\n if i < len(message):\n a = modify_byte(a, message[i])\n i += 1\n pixels[x,row] = (r, g, b, a)\n generate_key(row, message, offset * RGBA_PIXEL)\n\n return img","def get_blockNumber(self, data):\n blockNumber = data['blockNumber']\n return blockNumber","def block_offsets(self):\n ...","def getSnapshotsOf(image):\n snapshotIds = []\n deviceMapping = image.block_device_mapping # dict of devices\n devices = deviceMapping.keys()\n for d in devices:\n snapshotId = deviceMapping[d].snapshot_id\n if snapshotId is not None:\n snapshotIds.append(snapshotId.encode())\n return snapshotIds","def blocks_vision(self):\n return self.blocks_vision","def blockDetector(self, frame):\n self.detectBlocksInDepthImage()\n self.block_pos = np.zeros((50,3))\n self.block_num = 0\n pixal_arm = np.zeros(2)\n if self.kinectCalibrated == True:\n # get current arm position\n real_arm_x, real_arm_y,_,_ = self.rexarm.get_wrist_pose()\n real_arm_x *= -1000\n real_arm_y *= 1000\n real_arm = np.array(([real_arm_x],[real_arm_y],[1]))\n \n # normalize arm_line vector\n arm_length = np.sqrt(real_arm_x**2 + real_arm_y**2)\n l = np.array([real_arm_x,real_arm_y])\n\n # find center of block_1_height and put them into block_position\n for cnt in self.block_contours:\n M = cv2.moments(cnt)\n if M['m00']>0:\n cx = int(M['m10']/M['m00'])\n cy = int(M['m01']/M['m00'])\n c = np.array([[cx],[cy]])\n # Eliminate Arm itself\n real_x = self.real_coord[cx][cy][0]\n real_y = self.real_coord[cx][cy][1]\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\n \n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\n\n \n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\n # Check if its in our ROI\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\n # points\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\n self.block_pos[self.block_num][2] = 1\n # orientation\n rect = cv2.minAreaRect(cnt)\n self.block_ori[self.block_num][0] = -rect[2]\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # detect color\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\n # draw contours\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,145,86),3)\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,145,86),-1)\n self.block_num += 1\n\n # find centers of 2 blocks\n for cnt in self.block_contours_2:\n M = cv2.moments(cnt)\n if M['m00']>0:\n cx = int(M['m10']/M['m00'])\n cy = int(M['m01']/M['m00'])\n c = np.array([[cx],[cy]])\n # Eliminate Arm itself\n real_x = self.real_coord[cx][cy][0]\n real_y = self.real_coord[cx][cy][1]\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\n \n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\n \n \n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\n # Check if its in our ROI\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\n # points\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\n self.block_pos[self.block_num][2] = 2\n # orientation\n rect = cv2.minAreaRect(cnt)\n self.block_ori[self.block_num][0] = -rect[2]\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # detect color\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\n # draw contours\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,87,137),3)\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,87,137),-1)\n self.block_num += 1 \n\n # find centers of 3 blocks\n for cnt in self.block_contours_3:\n M = cv2.moments(cnt)\n if M['m00']>0:\n cx = int(M['m10']/M['m00'])\n cy = int(M['m01']/M['m00'])\n c = np.array([[cx],[cy]])\n # Eliminate Arm itself\n real_x = self.real_coord[cx][cy][0]\n real_y = self.real_coord[cx][cy][1]\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\n \n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\n \n \n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\n # Check if its in our ROI\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\n # points\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\n self.block_pos[self.block_num][2] = 3\n # orientation\n rect = cv2.minAreaRect(cnt)\n self.block_ori[self.block_num][0] = -rect[2]\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # detect color\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\n # draw contours\n cv2.drawContours(self.currentDetectFrame,[box],0,(204,6,6),3)\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(204,6,6),-1)\n self.block_num += 1 \n self.block_pos[self.block_num:50] = 0\n self.block_ori[self.block_num:50] = 0\n\n return frame","def process(self, image):","def retrieve_new_blocks_since(number_of_last_sent_block, web3):\n new_blocks = []\n number_of_last_block = web3.eth.getBlock('latest').number\n if number_of_last_block > number_of_last_sent_block:\n number_of_blocks_to_send = number_of_last_block - number_of_last_sent_block\n for i in range(1, number_of_blocks_to_send + 1):\n new_blocks.append(web3.eth.getBlock(number_of_last_sent_block + i))\n return number_of_last_block, new_blocks\n else:\n return number_of_last_sent_block, new_blocks","def getNum(self):\r\n return self.blockNum","def compute_histogram_blocks(image, text_box, n_bins, color_space=\"RGB\", block_size=16):\n\n image = cv.cvtColor(image, OPENCV_COLOR_SPACES[color_space])\n\n # image_id = int(ntpath.basename(image_path.replace('.jpg', '')))\n # boxes = pickle.load(open(os.path.join(boxes_path), 'rb'))[image_id][0]\n\n if text_box:\n tlx_init = text_box[0]\n tly_init = text_box[1]\n brx_init = text_box[2]\n bry_init = text_box[3]\n\n sizeX = image.shape[1]\n sizeY = image.shape[0]\n\n hist_concat = None\n \n for i in range(0,block_size):\n for j in range(0, block_size):\n # Image block\n img_cell = image[int(i*sizeY/block_size):int(i*sizeY/block_size) + int(sizeY/block_size) ,int(j*sizeX/block_size):int(j*sizeX/block_size) + int(sizeX/block_size)]\n\n if not text_box:\n hist = compute_histogram(img_cell, n_bins, color_space)\n\n # If there's a text bounding box ignore the pixels inside it\n else:\n tlx = tlx_init-int(j*sizeX/block_size)\n tly = tly_init-int(i*sizeY/block_size)\n brx = brx_init-int(j*sizeX/block_size)\n bry = bry_init-int(i*sizeY/block_size)\n\n img_cell_vector = []\n\n for x in range(img_cell.shape[1]-1):\n for y in range(img_cell.shape[0]-1):\n if not (tlx < x < brx and tly < y < bry):\n img_cell_vector.append(img_cell[y,x,:])\n\n img_cell_vector = np.asarray(img_cell_vector)\n\n n_channels = 1 if color_space == \"GRAY\" else image.shape[2]\n # Using 3D histograms --> total_bins = n_bins_per_channel ^ n_channels\n hist=np.zeros(n_bins**n_channels,dtype=np.float32)\n\n if img_cell_vector.size!=0:\n img_cell_matrix = np.reshape(img_cell_vector,(img_cell_vector.shape[0],1,-1))\n hist = compute_histogram(img_cell_matrix, n_bins, color_space)\n\n if hist_concat is None:\n hist_concat = hist\n else:\n hist_concat = cv.hconcat([hist_concat, hist])\n\n return hist_concat","def _get_next_minibatch_inds(self):\n img_next = self._cursor[0].next()\n msk_next = self._cursor[1].next()\n if img_next and msk_next:\n pass\n else:\n print 'BlobFetcher to begin because of cursor point to end.'\n self._cursor = [self._txn[0].cursor(), self._txn[1].cursor()]\n self._cursor[0].next()\n self._cursor[1].next()","def between_blocks(self, frame):\n return []","def identify_image(im):\n score_cures = np.mean(im[1025:1065, 1130:1180, 0])\n score_ingredients = np.mean(im[1025:1065, 675:720, 0])\n if score_cures < 177.5:\n return 'cures'\n if score_ingredients < 177.5:\n return 'ingredients'\n else:\n return 'other'","def getCamera1():\n for msg in camera1:\n yield (b'--frame\\r\\n'\n b'Content-Type: image/jpg\\r\\n\\r\\n' + base64.b64decode(msg.value['image_bytes']) + b'\\r\\n\\r\\n')","def __get_multi_images_ids(self, num_images=0): \n availability_images = imageInstance()\n images = availability_images.get_images()\n images_ids = []\n for image in images:\n if image.type == 'machine':\n images_ids.append( image.id.encode(\"latin-1\") )\n if num_images>1:\n random.shuffle(images_ids)\n return images_ids[:num_images]\n return images_ids","def get_bounding_boxes(frame):\n\n blob = cv2.dnn.blobFromImage(frame,1/255,(320,320),(0,0,0),1,crop=False)\n net.setInput(blob)\n\n output_layer_names = net.getUnconnectedOutLayersNames()\n layer_outputs = net.forward(output_layer_names)\n\n all_boxes, confidences = get_all_boxes(layer_outputs)\n\n indexes=cv2.dnn.NMSBoxes(all_boxes,confidences,0.5,0.3)\n\n return indexes, confidences, all_boxes","def colorimage2block(img, size):\n\timage_height = int(img.shape[0])\n\timage_width = int(img.shape[1])\n\tblock_height = int(size[0])\n\tblock_width = int(size[1])\n\tcolor_num = int(img.shape[2])\n\n\t#print(image_height, image_width, block_height, block_width)\n\n\tif image_height%block_height != 0 or image_width%block_width != 0:\n\t\tprint('Please give an appropriate bloxk size.')\n\t\tprint('You can use only numbers that are divisors of image height and width as block size.')\n\t\tsys.exit()\n\n\trow = int(image_height / block_height)\n\tcol = int(image_width / block_width)\n\tblocks = np.empty([row, col, block_height, block_width, color_num])\n\t\n\t#print('blocks shape =', blocks.shape)\n\n\tfor i in np.arange(row):\n\t\tfor j in np.arange(col):\n\t\t\tfor k in np.arange(block_height):\n\t\t\t\tfor l in np.arange(block_width):\n\t\t\t\t\t\tfor rgb in np.arange(color_num):\n\t\t\t\t\t\t\tblocks[i][j][k][l][rgb] = img[i*block_height+k][j*block_width+l][rgb]\n\n\treturn blocks","def __get_image_id(self):\n return self.__get_multi_images_ids(1)","def toBlocks(im, blocksize):\n blocks = im.reshape(\n im.shape[0]//blocksize, \n blocksize, \n im.shape[1]//blocksize, \n blocksize, 3).swapaxes(1, 2)\n\n\n blocks = blocks.reshape(\n im.shape[0]*im.shape[1]//(blocksize**2), \n blocksize, \n 1,\n blocksize, 3).swapaxes(1, 2)\n\n return blocks","def block_size(self, block_id): # -> int:\n ...","def message_to_blocks(message, check_for_invalid=True):\n block_size = aes.R * aes.NB\n\n blocks = []\n block = []\n for symbol in message:\n if check_for_invalid:\n if symbol not in aes.VALID_SYMBOLS:\n raise Exception('Message includes unsupported symbol \"{}\".'.format(symbol))\n block.append(ord(symbol))\n if len(block) == block_size:\n blocks.append(block)\n block = []\n\n if len(block) < block_size:\n block.extend([aes.EMPTY_SYMBOL_CODE for i in range(len(block), block_size)])\n blocks.append(block)\n return blocks","def GetBlocks(state):\n result = []\n last_pos = 0\n for entry in state:\n pos = entry['pos']\n # Calculate block start points from the beginning of individual lines.\n blocks = [(s[0]-last_pos, s[1]-s[0]) for s in entry['blocks']]\n # Add one end marker block.\n blocks.append((pos-last_pos, 0))\n result.append(blocks)\n last_pos = pos\n return result","def find_blockages(self):\n debug.info(1,\"Finding blockages.\") \n for layer in [self.vert_layer_number,self.horiz_layer_number]:\n self.retrieve_blockages(layer)","def particle_images (sim,frame_id) :\n # get positions of all particles: define first the atom selection, then jump to\n # the user-requested trajectory frame, get the box dimensions (currently works\n # only for orthorhombic boxes, then calculate the image indices\n atoms = sim.u.select_atoms ('all')\n ts = sim.u.trajectory[frame_id]\n L = ts.dimensions[:3]\n pos = atoms.positions + L/2.\n return pos//L","def get_blocks(lines):\n # nalezneme v obsahu cisla stranek\n page_marks = [idx for idx, l in enumerate(lines) if PAGING_RE.match(l)]\n\n # rozdelime obsah na bloky podle stranek\n blocks, from_pm = [], 0\n for pm in page_marks:\n blocks.append(lines[from_pm:pm])\n from_pm = pm + 1\n blocks.append(lines[from_pm:])\n blocks = [i for i in blocks if len(i)]\n\n return blocks","def color_indexes_from_lbm_chunk(bytes_lbm_block, cursor=0, verbose=True, must_check=False):\n block_data = []\n cursor = 0\n block_size = list(map(int, bytes_lbm_block[cursor:cursor+4]))\n cursor += 4\n\n block_size = sum([\n byte*(256**offset)\n for byte, offset\n in zip(block_size, range(4))\n ])\n\n if verbose:\n print(\"Taille du bloc : %s.\" % block_size)\n\n block_data += list(map(int, bytes_lbm_block[cursor:cursor+block_size]))\n cursor += block_size\n\n if must_check:\n if cursor != len(bytes_lbm_block):\n msg = \"Taille de bloc indiqué (%s) différente de la taille de bloc réelle (%s).\"\n raise Exception(msg % (cursor, len(bytes_lbm_block)))\n\n color_indexes = []\n while block_data:\n part_header = block_data.pop(0)\n if part_header > 128:\n part_body = block_data.pop(0)\n color_indexes += [ part_body ] * (257-part_header)\n else:\n body_size = part_header + 1\n part_body = block_data[:body_size]\n block_data = block_data[body_size:]\n color_indexes += part_body\n\n if verbose:\n print(\"Nombres de pixels : %s\" % len(color_indexes))\n if len(color_indexes) != 64000:\n print(\"Warning. La taille de l'image devrait être de 64000 pixels (320x200).\")\n\n return color_indexes","def handleImageRunner(self) :\n tag = self.readByte()\n if tag == ord(self.imagerunnermarker1[-1]) :\n oldpos = self.pos-2\n codop = self.minfile[self.pos:self.pos+2]\n length = unpack(\">H\", self.minfile[self.pos+6:self.pos+8])[0]\n self.pos += 18\n if codop != self.imagerunnermarker2 :\n self.pos += length\n self.logdebug(\"IMAGERUNNERTAG SKIP %i AT %08x\" % (self.pos-oldpos, self.pos))\n else :\n self.pos -= 1 # Adjust position","def extract_blocks(pool, listnos, num_blocks):\n assert len(listnos) % num_blocks == 0, \"The number of lists to append must be divisable by the number of blocks\"\n\n wordlists = {}\n for word in pool:\n wordlists[word['listno']] = wordlists.get(word['listno'], []) + [word]\n\n blocks = []\n for i in range(len(listnos)):\n listno = listnos[i]\n wordlist = [word.copy() for word in wordlists[listno]]\n for word in wordlist:\n word['blockno'] = i//num_blocks\n word['block_listno'] = i\n blocks += wordlist\n\n return blocks","def _block_info(data):\n # check that the data is an array of bytes\n if len(data) != 6:\n raise ValueError(\"'data' should be 6 bytes. Got {} instead.\".format(\n len(data)))\n return struct.unpack('<Hi', data)","def _get_containing_blocks(size, point):\n i, j = point\n block_inds = []\n if i > 0:\n if j > 0:\n block_inds.append((i - 1, j - 1))\n if j < size - 1:\n block_inds.append((i - 1, j))\n if i < size - 1:\n if j > 0:\n block_inds.append((i, j - 1))\n if j < size - 1:\n block_inds.append((i, j))\n \n return block_inds","def _prepare_blocks():\n\n counter = blocks[0]['freeStart']\n maxBlocks = blocks[0]['maxBlocks']\n while(counter < maxBlocks) :\n try:\n # print (mount['parent'] + '/linddata.' + str(counter))\n f = open(mount['parent'] + '/linddata.' + str(counter), 'r') \n except IOError, e:\n return STATUS['M_BD']\n else :\n fdatastring = f.next()\n fdata = deserializedata(fdatastring)\n blocks[counter] = fdata\n counter += 1\n \n return STATUS['OK']","def get_img_index(pred_result: List) -> List:\n img_index = []\n\n for i, line in enumerate(pred_result):\n match = re.search(\"\\./test_pics/\", line)\n if match:\n img_index.append(i)\n if i == len(pred_result) - 1:\n img_index.append(i)\n return img_index","def convert_im(im, tree, lbls, block_imgs):\n h, w, _ = im.shape\n step = 16\n for r in range(0, h, step):\n for c in range(0, w, step):\n rnext = min(r+step, h)\n cnext = min(c+step, w)\n patch = im[r:rnext, c:cnext]\n color = np.average(patch, axis=(0, 1))\n\n # Get closest block\n _, ind = tree.query([color], k=1)\n lbl = lbls[ind[0][0]]\n block = block_imgs[lbl]\n\n # Copy values\n rmax = rnext-r\n cmax = cnext-c\n im[r:rnext, c:cnext] = block[:rmax, :cmax]","def _rgb_frames(self):\n image = self.parent.image.image\n R = image[self.rSpin.value(), :, :]\n G = image[self.gSpin.value(), :, :]\n B = image[self.bSpin.value(), :, :]\n return R, G, B","def find_pins_and_blockages(self, pin_list):\n # This finds the pin shapes and sorts them into \"groups\" that are connected\n # This must come before the blockages, so we can not count the pins themselves\n # as blockages.\n start_time = datetime.now()\n for pin_name in pin_list:\n self.retrieve_pins(pin_name)\n print_time(\"Retrieving pins\",datetime.now(), start_time, 4)\n \n start_time = datetime.now()\n for pin_name in pin_list:\n self.analyze_pins(pin_name)\n print_time(\"Analyzing pins\",datetime.now(), start_time, 4)\n\n # This will get all shapes as blockages and convert to grid units\n # This ignores shapes that were pins\n start_time = datetime.now()\n self.find_blockages()\n print_time(\"Finding blockages\",datetime.now(), start_time, 4)\n\n # Convert the blockages to grid units\n start_time = datetime.now()\n self.convert_blockages()\n print_time(\"Converting blockages\",datetime.now(), start_time, 4)\n \n # This will convert the pins to grid units\n # It must be done after blockages to ensure no DRCs between expanded pins and blocked grids\n start_time = datetime.now()\n for pin in pin_list:\n self.convert_pins(pin)\n print_time(\"Converting pins\",datetime.now(), start_time, 4)\n\n # Combine adjacent pins into pin groups to reduce run-time\n # by reducing the number of maze routes.\n # This algorithm is > O(n^2) so remove it for now\n # start_time = datetime.now()\n # for pin in pin_list:\n # self.combine_adjacent_pins(pin)\n # print_time(\"Combining adjacent pins\",datetime.now(), start_time, 4)\n\n\n # Separate any adjacent grids of differing net names that overlap\n # Must be done before enclosing pins\n start_time = datetime.now()\n self.separate_adjacent_pins(0)\n print_time(\"Separating adjacent pins\",datetime.now(), start_time, 4)\n \n # Enclose the continguous grid units in a metal rectangle to fix some DRCs\n start_time = datetime.now()\n self.enclose_pins()\n print_time(\"Enclosing pins\",datetime.now(), start_time, 4)","def block_num_from_id(block_hash: str) -> int:\n return int(str(block_hash)[:8], base=16)","def image_handler(self, bot, update):\n text = update.message.text\n if text.startswith('/recon'):\n bot.sendMessage(chat_id=update.message.chat_id, text='*Object recognition*\\nSend me an image',\n parse_mode=ParseMode.MARKDOWN)\n return 10\n elif text.startswith('/faces'):\n bot.sendMessage(chat_id=update.message.chat_id, text='*Face recognition*\\nSend me an image',\n parse_mode=ParseMode.MARKDOWN)\n return 11","def total_height_blocks(validator):\n res = 0\n for bhash, b in validator.processed.items():\n if isinstance(b, Block):\n res += 1\n return res","def getCamera3():\n for msg in camera3:\n yield (b'--frame\\r\\n'\n b'Content-Type: image/jpg\\r\\n\\r\\n' + base64.b64decode(msg.value['image_bytes']) + b'\\r\\n\\r\\n')","def get_images_bytesize_match(self, images):\r\n cnt = 0\r\n MAX_BYTES_SIZE = 15728640\r\n good_images = []\r\n for image in images:\r\n if cnt > 30:\r\n return good_images\r\n src = self.parser.getAttribute(image, attr='src')\r\n src = self.build_image_path(src)\r\n local_image = self.get_local_image(src)\r\n if local_image:\r\n bytes = local_image.bytes\r\n if (bytes == 0 or bytes > self.images_min_bytes) \\\r\n and bytes < MAX_BYTES_SIZE:\r\n good_images.append(image)\r\n else:\r\n images.remove(image)\r\n cnt += 1\r\n return good_images if len(good_images) > 0 else None","def parent_block_count(self):\n if not self._tensors_defined():\n return None\n blocks = [len(self.tensor_u), len(self.tensor_v), len(self.tensor_w)]\n return blocks","def block_bodies(self, block_bodies: dict):\n txsCount = 0\n for block_hash, block_txs in block_bodies.items():\n txsCount += len(block_txs)\n message_size = (\n txsCount * self._message_size['tx']) + self._message_size['block_bodies']\n print(\n f'block bodies with {txsCount} txs have a message size: {message_size} kB')\n return {\n 'id': 'block_bodies',\n 'block_bodies': block_bodies,\n 'size': kB_to_MB(message_size)\n }","def process(image):\n pass","def block_eid(self, block_id):\n ...","def read_freecells(image):\n\n\tunlocked = cv2.imread(\"card_back/freecells/cell_unlocked.png\")\n\tlocked = cv2.imread(\"card_back/freecells/cell_locked.png\")\n\n\tlock_results = []\n\tfor i in range(4):\n\t\t# Offsets for approximately here the free cells are given 1600x900 game window size\n\t\tx_offset = 314 + (128 * i)\n\t\ty_offset = 24\n\t\t\n\t\tcrop_image = image[y_offset:y_offset + 4, x_offset:x_offset + 4]\n\t\t# cv2.rectangle(image, (x_offset, y_offset), (x_offset + 4, y_offset + 2), (255, 0, 0), 1)\n\t\tunlocked_result = cv2.matchTemplate(crop_image, unlocked, cv2.TM_SQDIFF)\n\t\tlocked_result = cv2.matchTemplate(crop_image, locked, cv2.TM_SQDIFF)\n\n\t\tlock_results.append(0 if unlocked_result * 10 < locked_result else 1)\n\n\treturn lock_results","def process_frame(img, frame, boxes):\n faces = boxes\n # Loop through all the faces detected and determine whether or not they are in the database\n identities = []\n\n for (y, x, h, w) in faces: #add padding\n x1 = int(x-PADDING)\n y1 = int(y-PADDING)\n x2 = int(w+PADDING)\n y2 = int(h+PADDING)\n\n identity = find_identity(frame, x1, y1, x2, y2)\n\n if identity is not None:\n identities.append(identity)\n\n if identities != []:\n welcome_users(identities)\n\n #set flag to not recognize until face detected again\n ready_to_recognize_file = open(\"varThread\\\\ready_recognize.txt\", \"r+\")\n ready_to_recognize_file.seek(0,0)\n ready_to_recognize_file.write('0')\n ready_to_recognize_file.close()","def get_blocks():\n query = iroha.blocks_query()\n IrohaCrypto.sign_query(query, ADMIN_PRIVATE_KEY)\n for block in net.send_blocks_stream_query(query):\n print('\\nThe next block arrived:', block)","def new_images_index(self):\n first = ct.c_long()\n last = ct.c_long()\n self.lib.GetNumberNewImages(ct.pointer(first), ct.pointer(last))\n\n return (first.value, last.value)","def blocksForImg(img,samplsiz,resc):\n #scale image into square shape\n imgr = resize(img,(resc,resc,3))\n\n #create blocks\n view = []\n view.append(view_as_blocks(imgr[:,:,0], (samplsiz,samplsiz)))\n view.append(view_as_blocks(imgr[:,:,1], (samplsiz,samplsiz)))\n view.append(view_as_blocks(imgr[:,:,2], (samplsiz,samplsiz)))\n\n #calculate mean values and standard deviation for each block\n flv = [v.reshape(v.shape[0],v.shape[1],-1) for v in view]\n meanb = np.array([np.mean(f,axis=2) for f in flv])\n stdb = np.array([np.std(f,axis=2) for f in flv])\n\n #return vector cancatenated from means, standard deviations, sorted means and sorted deviations\n meanb = meanb.flatten()\n stdb = stdb.flatten()\n meanbsort = np.sort(meanb)\n stdbsort = np.sort(stdb)\n return np.append(meanb,[stdb,meanbsort,stdbsort])","def im_list_detections(model, im_list):\n _t = Timer()\n num_images = len(im_list)\n im_list_boxes = [[] for _ in range(num_images)]\n im_list_scores = [[] for _ in range(num_images)]\n im_list_ids = [[] for _ in range(num_images)]\n im_list_classes = [[] for _ in range(num_images)]\n # create anchors for each level\n anchors = create_cell_anchors()\n for i in range(num_images):\n im_list_ids[i] = im_list[i]['id']\n im = cv2.imread(im_list[i]['image'])\n with c2_utils.NamedCudaScope(0):\n _t.tic()\n im_list_boxes[i], im_list_scores[i], im_list_classes[i] = \\\n im_detections(model, im, anchors)\n _t.toc()\n logger.info(\n 'im_detections: {:d}/{:d} {:.3f}s'.format(\n i + 1, num_images, _t.average_time))\n return im_list_boxes, im_list_scores, im_list_classes, im_list_ids","def list_blocks(self, _):\n print(self.data.name)","def get_expected_chip_ids():\n paths = INPUT_IMAGES_DIR.glob(\"*.tif\")\n # images are named something like abc12.tif, we only want the abc12 part\n ids = list(sorted(set(path.stem.split(\"_\")[0] for path in paths)))\n return ids","def image_status_msg(status):\n messages = {\n 'match': 'Baseline image matches',\n 'diff': 'Baseline image differs',\n 'missing': 'Baseline image not found',\n 'generated': 'Baseline image was generated',\n }\n return messages[status]","def get_next_cid(self) -> str:\n self.position += 1\n return \"img{}\".format(self.position)","def get_bb(node_name):\n return int(re.search(r'\\d+', re.search(r'block_\\d+', node_name).group()).group())","def getCamera2():\n for msg in camera2:\n yield (b'--frame\\r\\n'\n b'Content-Type: image/jpg\\r\\n\\r\\n' + base64.b64decode(msg.value['image_bytes']) + b'\\r\\n\\r\\n')","def checkImages(self):\r\n\r\n self.leftImage, self.rightImage, res = self.receiver.getImageData()\r\n\r\n return res","def get_motion(frame1k, frame2k, frame_count):\n frame1 = frame1k.copy()\n frame2 = frame2k.copy()\n\n global limb_coords, init_coords, num_blocks\n cv2.imwrite(\"thisImageAnalyse.png\", frame2)\n block_size = 3\n block_rad = int(block_size/2)\n\n def get_SSD():\n \"\"\" applies SSD formula to search area\n :return SSD value\"\"\"\n dist = 0\n # traversal of pixels in potential Bi+1 block\n # compare corresponding pixel positions with source block in f1 and neighbour block in f2\n y1 = center_y1 - block_rad # start pos.\n for y2 in range(center_y2 - block_rad, (center_y2 - block_rad + block_size)):\n x1 = center_x1 - block_rad # start pos\n for x2 in range(center_x2 - block_rad, (center_x2 - block_rad + block_size)):\n try:\n # displacement formula for RGB channels of each pixel in block\n dist = dist + (frame1[y1][x1][0] - frame2[y2][x2][0])**2 + (frame1[y1][x1][1] - frame2[y2][x2][1])**2 + (frame1[y1][x1][2] - frame2[y2][x2][2])**2\n except RuntimeWarning:\n pass\n x1 += 1\n y1 += 1\n return math.sqrt(dist)\n\n # for each body part\n b = 0\n while b < 5:\n avg_x = 0.0\n avg_y = 0.0\n new_x = 0.0\n new_y = 0.0\n a = 0\n # for each block on body part (9 total)\n while a < num_blocks:\n found = False\n search_rad = 5\n while found is False:\n center_y1 = int(init_coords[b][a][0])\n center_x1 = int(init_coords[b][a][1])\n min_SSD = 999999\n # for pythagoras to ensure closest block gets picked when equality occurs of SSD value\n min_d = 999999\n # this finds the center of the block to compare\n for factor_y in range(-search_rad, search_rad + 1):\n center_y2 = center_y1 + block_size*factor_y\n y_dist = center_y1 - abs(center_y2)\n for factor_x in range(-search_rad, search_rad + 1):\n center_x2 = center_x1 + block_size*factor_x\n x_dist = center_x1 - abs(center_x2)\n # pythagoras\n d = math.sqrt((y_dist**2 + x_dist**2))\n if d < min_d:\n min_d = d\n\n SSD = get_SSD()\n if frame2[center_y2][center_x2][1] != 0 and frame2[center_y2][center_x2][2] != 0:\n found = True\n if SSD < min_SSD:\n min_SSD = SSD\n new_y = center_y2\n new_x = center_x2\n elif SSD == min_SSD and d < min_d:\n new_y = center_y2\n new_x = center_x2\n if found is False:\n # if no block is found repeat the search, increasing the search size by 1\n search_rad += 1\n # draw extracted vectors\n cv2.arrowedLine(frame1k, (int(center_x1), int(center_y1)), (int(new_x), int(new_y)), (150, 200, 30), 1, 4, 0, 0.3)\n avg_x += new_x\n avg_y += new_y\n init_coords[b][a][0] = new_y\n init_coords[b][a][1] = new_x\n a += 1\n cv2.imwrite('monkeyFrames/contrast_enhanced%d.png' % frame_count, frame1k)\n limb_coords[b][frame_count][0] = int(avg_y/num_blocks)\n limb_coords[b][frame_count][1] = int(avg_x/num_blocks)\n b += 1","def _calculate_distribution(self, images):\n positions = {}\n target_width = target_height = 0\n for block, url in self.image_declarations:\n file_name = normalize_filename(url)\n if file_name in positions:\n positions[file_name]['blocks'].append(block)\n else:\n block_images = images[file_name]\n offset_x = target_width\n offset_y = 0\n\n width, height = block_images[1].size\n positions[file_name] = {\n 'x': offset_x, 'y': offset_y,\n 'width': width, 'height': height,\n 'file_name': file_name, 'images': block_images,\n 'blocks': [block]\n }\n\n target_width += width + self.space_x\n target_height = max(target_height, height)\n\n target_width -= self.space_x\n return {\n 'width': target_width,\n 'height': target_height,\n 'positions': positions.values(),\n }","def get_blocks(index):\r\n #call with -1 to get full blocklist\r\n #the reason this is a function instead of just a list is that originally\r\n #i had plans to support dynamic tilesets, for example if only a certain\r\n #number of each tile were available. in the end this didnt happen though\r\n all_blocks = [\r\n [[0,0,0],[1,1,1],[0,0,0]], #0 - (horizontal passage)\r\n [[0,1,0],[0,1,0],[0,1,0]], #1 | (vertical passage)\r\n \r\n [[0,0,0],[1,1,0],[0,1,0]], #2 >v various L-junctions\r\n [[0,1,0],[1,1,0],[0,0,0]], #3 >^\r\n [[0,0,0],[0,1,1],[0,1,0]], #4 ^>\r\n [[0,1,0],[0,1,1],[0,0,0]], #5 v>\r\n \r\n [[0,0,0],[0,0,0],[0,0,0]], #6 0 empty\r\n [[0,1,0],[1,1,1],[0,1,0]], #7 + cross\r\n \r\n [[0,1,0],[1,1,1],[0,0,0]], #8 _|_ various T-junctions\r\n [[0,0,0],[1,1,1],[0,1,0]], #9 T\r\n [[0,1,0],[1,1,0],[0,1,0]], #10 -|\r\n [[0,0,0],[1,1,1],[0,0,0]]] #11 |-\r\n \r\n# [[0,1,0],[0,1,0],[0,0,0]], #12 #unsued \"dead end\" pieces\r\n# [[0,0,0],[0,1,0],[0,1,0]], #13\r\n# [[0,0,0],[0,1,1],[0,0,0]], #14\r\n# [[0,0,0],[1,1,0],[0,0,0]] ]#15\r\n if index == -1:\r\n return all_blocks\r\n else:\r\n return all_blocks[index]","def process():\n config = read_config()\n \n\n img_dir = config['DEFAULT']['images_directory']\n results_dict = {}\n images = list(get_image_files(img_dir))\n for image in tqdm.tqdm(images):\n info = hash_file(image)\n if info == 0:\n continue\n\n hash_value = info['hash']\n\n if hash_value not in results_dict:\n file_name = os.path.basename(info['_id'])\n results_dict[hash_value] = [file_name, 1]\n else:\n results_dict[hash_value][1] += 1\n\n count = list(results_dict.values())\n sorted_count = sorted(count, key=lambda x: x[1], reverse=True)\n \n with ImagesDB(IMG_INFO_DB_FILENAME) as imgDb: \n imgDb.insert_batch(sorted_count)","def read_in():\n list_images_out = []\n index = 0\n current_image_points = []\n with open(\"./darknet/output.txt\") as results:\n lines = results.readlines()\n for line in lines:\n try:\n i = int(line[-(4+6+1):][:6]) # ex of file 000000.jpg 6 + 4 characters\n if i != index:\n print(\"PROBLEM\")\n print(i, index)\n exit()\n \n list_images_out.append(parse_current_images(current_image_points, index))\n # Reset the current image\n current_image_points = []\n index += 1\n except ValueError:\n # Real line\n left, top, right, bottom = line.split(\" \")\n left, right, top, bottom = int(left), int(right), int(top), int(bottom)\n current_image_points.append([0, left, right, bottom, top])\n\n list_images_out.append(parse_current_images(current_image_points, index))\n\n # We remove the first image due to line 36 test\n list_images_out.pop(0)\n print(len(list_images_out))\n return list_images_out","def process_images():\n for message in get_messages_from_sqs():\n try:\n message_content = json.loads(message.body)\n image = unquote_plus(message_content\n ['Records'][0]['s3']['object']\n ['key'])\n s3.download_file(input_bucket_name, image, image)\n process_json(image)\n upload_image(image)\n cleanup_files(image)\n except:\n message.change_visibility(VisibilityTimeout=0)\n continue\n else:\n message.delete()","def grayimage2block(img, size):\n\timage_height = int(img.shape[0])\n\timage_width = int(img.shape[1])\n\tblock_height = int(size[0])\n\tblock_width = int(size[1])\n\n\t#print(image_height, image_width, block_height, block_width)\n\n\tif image_height%block_height != 0 or image_width%block_width != 0:\n\t\tprint('Please give an appropriate bloxk size.')\n\t\tprint('You can use only numbers that are divisors of image height and width as block size.')\n\t\tsys.exit()\n\n\trow = int(image_height / block_height)\n\tcol = int(image_width / block_width)\n\tblocks = np.empty([row, col, block_height, block_width])\n\t#print('blocks shape =', blocks.shape)\n\n\tfor i in np.arange(row):\n\t\tfor j in np.arange(col):\n\t\t\tfor k in np.arange(block_height):\n\t\t\t\tfor l in np.arange(block_width):\n\t\t\t\t\tblocks[i][j][k][l] = img[i*block_height+k][j*block_width+l]\n\n\treturn blocks","def available_images_index(self):\n first = ct.c_long()\n last = ct.c_long()\n self.lib.GetNumberAvailableImages(ct.pointer(first), ct.pointer(last))\n\n return (first.value, last.value)","def test_segment_image(self):\n with Image.open(self.subject) as im:\n image = im.convert(\"RGB\")\n\n segment_generator = image_helper.segment_image(image, 200, 200)\n\n sizes = [\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 200), next(segment_generator)),\n ((200, 50), next(segment_generator)),\n ((200, 50), next(segment_generator)),\n ((200, 50), next(segment_generator)),\n ((200, 50), next(segment_generator))\n ]\n\n for size, segment in sizes:\n self.assertEqual(size, segment.size)","def process_block(self, block):\r\n ret = []\r\n output = None\r\n input_lines = None\r\n lineno = self.IP.execution_count\r\n\r\n input_prompt = self.promptin%lineno\r\n output_prompt = self.promptout%lineno\r\n image_file = None\r\n image_directive = None\r\n\r\n for token, data in block:\r\n if token==COMMENT:\r\n out_data = self.process_comment(data)\r\n elif token==INPUT:\r\n (out_data, input_lines, output, is_doctest, image_file,\r\n image_directive) = \\\r\n self.process_input(data, input_prompt, lineno)\r\n elif token==OUTPUT:\r\n out_data = \\\r\n self.process_output(data, output_prompt,\r\n input_lines, output, is_doctest,\r\n image_file)\r\n if out_data:\r\n ret.extend(out_data)\r\n\r\n # save the image files\r\n if image_file is not None:\r\n self.save_image(image_file)\r\n\r\n return ret, image_directive","def blocks_under_highest_justified(validator):\n res = 0\n for bhash, b in validator.processed.items():\n if isinstance(b, Block):\n if b.height <= validator.highest_justified_checkpoint.height:\n res += 1\n return res","def getblockcount(self):\n return len(self.blocks) - 1","def get_snapshots_of(image):\n snapshot_ids = []\n device_mapping = image.block_device_mapping # dict of devices\n devices = device_mapping.keys()\n for device in devices:\n if device_mapping[device].snapshot_id is not None:\n snapshot_ids.append(device_mapping[device].snapshot_id.encode()) # do I need to have 'encode' here?\n return snapshot_ids","def image(images):\n return images[0]","def get_blocks_before(self, hash_bytes: bytes, num_blocks: int = 100) -> list[Block]:\n raise NotImplementedError","def test_getImages(self): # GIVEN the group chat has at least one image\n testBot = bot.Bot(os.environ['bot_id'], os.environ['token'], os.environ['group_ID'])\n imageList = testBot.run() #AND THEN post_images calls the private get_images method which returns an array\n self.assertTrue(len(imageList) > 0) #THEN there should be at least one element in the array","def updateImageGroups(self):\n self.img_grps = self.splitImages()\n grps = self.img_grps\n self.detail.clear()\n detail = \"Available Groups : \\n\"\n if len(grps) >= 1:\n for i in range(len(grps)):\n detail += \"Group \"+ str(i+1)+ \" : \" + str(grps[i][0]) + \" ... \" + str(grps[i][-1]) + '\\n'\n\n self.detail.insertPlainText(detail)\n self.detail.moveCursor(QTextCursor.End)","def _get_block_indices(y):\n block_labels, block_inv, block_sizes = np.unique(\n y, return_inverse=True, return_counts=True\n )\n\n n_blocks = len(block_labels)\n block_inds = range(n_blocks)\n\n block_vert_inds = []\n for i in block_inds:\n # get the inds from the original graph\n inds = np.where(block_inv == i)[0]\n block_vert_inds.append(inds)\n return block_vert_inds, block_inds, block_inv","def getBlobs(aDiffImg):\n # Get the blobs and number of blobs present in the diff image\n # Returned as (blobs, num_blobs)\n return ndimage.label(aDiffImg)","def get_block_idx(frame_idx, slices):\n idx_slices = { k:{} for k in slices[0].keys() }\n for i in frame_idx:\n for k in slices[i].keys():\n for b in slices[i][k].keys():\n if not b in idx_slices[k]:\n idx_slices[k][b] = []\n idx_slices[k][b] += list(range( slices[i][k][b].start, slices[i][k][b].stop))\n return idx_slices","def _handle_image_descriptors(self):\n while self.file_content[self.data_idx] == 0x2c:\n img_left = self.file_content[self.data_idx + 1] + \\\n (self.file_content[self.data_idx + 2] << 8)\n img_top = self.file_content[self.data_idx + 3] + \\\n (self.file_content[self.data_idx + 4] << 8)\n img_width = self.file_content[self.data_idx+5] + \\\n (self.file_content[self.data_idx + 6] << 8)\n #img_height = self.file_content[self.data_idx+7] + \\\n # (self.file_content[self.data_idx + 8] << 8)\n flags = self.file_content[self.data_idx + 9]\n local_col_table_flag = (flags & 0b10000000) != 0\n #interlace_flag = (flags & 0b01000000) != 0\n self.data_idx = self.data_idx + 10\n if local_col_table_flag:\n # read local color table\n print('read local color table. Not implemented yet')\n\n self.lzw_min_code_sz = self.file_content[self.data_idx]\n self.data_idx = self.data_idx + 1\n\n pix_xix = img_left\n pix_yix = img_top\n subblock_data = []\n while self.file_content[self.data_idx] != 0:\n subblock_sz = self.file_content[self.data_idx]\n self.data_idx = self.data_idx + 1\n subblock_data += self.file_content[self.data_idx:self.data_idx + subblock_sz]\n self.data_idx = self.data_idx + subblock_sz\n self.data_idx = self.data_idx + 1\n dec_data = self.decode_subblock(subblock_data)\n for dat in dec_data:\n self.output_image[pix_xix][pix_yix][0] = self.color_table[dat][0]\n self.output_image[pix_xix][pix_yix][1] = self.color_table[dat][1]\n self.output_image[pix_xix][pix_yix][2] = self.color_table[dat][2]\n pix_xix = pix_xix + 1\n if pix_xix == img_left + img_width:\n pix_xix = img_left\n pix_yix = pix_yix + 1","def image_cb(self, msg):\n\n # Save the camera image\n self.camera_image = msg\n\n # I sufficient information is available...\n if not None in (self.camera_image, self.waypoint_tree, self.lights):\n\n # Find index and color state of next light\n light_wp, state = self.process_traffic_lights()\n\n # If the light is green...\n if state == TrafficLight.GREEN:\n\n # Publish sentinel indicatig no red light\n self.upcoming_red_light_pub.publish(Int32(-1))\n\n else:\n\n # Publish the traffic light index\n self.upcoming_red_light_pub.publish(Int32(light_wp))","def _frame_idx_to_batch_idx(self, boxes):\n frame_idxs = torch.unique(boxes[:, 0])\n for batch_idx, frame_idx in enumerate(frame_idxs):\n idx = torch.where(boxes == frame_idx)[0]\n boxes[idx, 0] = batch_idx\n return boxes"],"string":"[\n \"def getBlocks(self):\\n blocks = self.getBlocksMsg(b'\\\\x00')\\n last_locator = self.largeMessageControl(blocks, 'inv', 0)\\n\\n while last_locator[1] < TARGET_BLOCK:\\n blocks = self.getBlocksMsg(bytearray.fromhex(convertLittleBig(last_locator[0])))\\n last_locator = self.largeMessageControl(blocks, 'inv', last_locator[1])\\n\\n print('\\\\nSuccessfully found the Block #{}: {}'.format(TARGET_BLOCK, last_locator[0]))\\n return last_locator[0]\",\n \"def detectBlocksInDepthImage(self):\\n depth_range_dict = {'1':[173,178],'2':[169,172],'3':[165,169],'4':[159,163],'5':[156,158],'6':[147,155],'7':[139,146],'8':[132,138]}\\n depth_frame = self.DepthFrameRaw\\n rgb_frame = self.VideoFrame\\n rgb_frame = cv2.resize(rgb_frame, (640,480))\\n depth_frame = cv2.resize(depth_frame, (640, 480))\\n np.clip(depth_frame,0,2**10 - 1,depth_frame)\\n depth_frame >>= 2\\n depth_frame = depth_frame.astype(np.uint8)\\n filt_block = []\\n for k,v in depth_range_dict.items():\\n thresh = cv2.inRange(depth_frame,v[0],v[1])\\n cv2.imwrite(\\\"/home/student/armlab-w20/log/img.jpeg\\\", thresh)\\n _ , contours, _ = cv2.findContours(thresh, 1, 2)\\n for cnt in contours:\\n area = cv2.contourArea(cnt)\\n if area > 400 and area < 700:\\n block = []\\n rect = cv2.minAreaRect(cnt)\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n color = self.determine_color(rgb_frame, box)\\n org = (box[0][0], box[0][1])\\n rgb_frame = cv2.putText(rgb_frame, color, org,cv2.FONT_HERSHEY_SIMPLEX , 0.5 ,(0,0,0),2, cv2.LINE_AA)\\n rgb_frame = cv2.drawContours(rgb_frame,[box],0,(0,0,0),0)\\n self.VideoFrame = rgb_frame\\n block.append(box)\\n block.append(int(k))\\n block.append(color)\\n filt_block.append(block)\\n return filt_block\",\n \"def getparts(image, block_len):\\n img = image.convert('L') if image.mode != 'L' else image\\n w, h = img.size \\n parts = []\\n # Bluring image for abandoning image details and noise.\\n global opt\\n for n in range(int(opt.imblev)):\\n img = img.filter(ImageFilter.SMOOTH_MORE)\\n # Converting image to custom palette\\n imagetopalette(img, [x for x in range(256) if x%int(opt.impalred) == 0])\\n pix = img.load()\\n \\n for x in range(w-block_len):\\n for y in range(h-block_len):\\n data = list(blockpoints(pix, (x,y), block_len)) + [(x,y)]\\n parts.append(data)\\n parts = sorted(parts)\\n return parts\",\n \"def num_blocks(self): # -> int:\\n ...\",\n \"def detectBlocksInDepthImage(self):\\n self.depth_detect_frame = self.currentDepthFrame\\n\\n # 1 block\\n self.bin_detect_frame_1 = cv2.inRange(self.depth_detect_frame,700,710)\\n kernel = np.ones((6,6),np.uint8)\\n self.bin_detect_frame_1 = cv2.erode(self.bin_detect_frame_1,kernel,iterations = 1)\\n _, self.block_contours, _ = cv2.findContours(self.bin_detect_frame_1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\\n \\n # 2 blocks\\n self.bin_detect_frame_2 = cv2.inRange(self.depth_detect_frame,680,699)\\n kernel = np.ones((6,6),np.uint8)\\n self.bin_detect_frame_2 = cv2.erode(self.bin_detect_frame_2,kernel,iterations = 1)\\n _, self.block_contours_2, _ = cv2.findContours(self.bin_detect_frame_2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\\n \\n # 3 blocks\\n self.bin_detect_frame_3 = cv2.inRange(self.depth_detect_frame,660,679)\\n kernel = np.ones((6,6),np.uint8)\\n self.bin_detect_frame_3 = cv2.erode(self.bin_detect_frame_3,kernel,iterations = 1)\\n _, self.block_contours_3, _ = cv2.findContours(self.bin_detect_frame_3,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\\n \\n pass\",\n \"def get_initial_blocks(self):\\n block = []\\n index = 0\\n for number in self.numbers_from_file(self.input_file_name):\\n block.append(number)\\n if len(block) == self.block_size:\\n block.sort()\\n self.write_block(index, block)\\n block = []\\n index += 1\\n else:\\n if block:\\n block.sort()\\n self.write_block(index, block)\\n index += 1\\n return 0, index\",\n \"def imageparts(msg):\\n # Don't want a set here because we want to be able to process them in\\n # order.\\n return filter(lambda part:\\n part.get_content_type().startswith('image/'),\\n msg.walk())\",\n \"def image_generator(self, some_messages):\\n offset = 0\\n outer = 0\\n inner = 0\\n\\n for a_message in some_messages:\\n msg_id = a_message.gmail_id\\n for att in a_message.attachments():\\n if att.type in ATTACHMENT_MIMES:\\n att_type = att.type.split(\\\"/\\\")[1]\\n an_image = Image(a_message, att)\\n\\n # map each image id with a corresponding message id for later parsing\\n if an_image.id in self.mapping:\\n self.mapping[msg_id].append(a_message)\\n else:\\n self.mapping[msg_id] = [a_message]\\n\\n self.num_attachments = self.count_attachments(self.num_attachments)\\n\\n yield an_image\",\n \"def blockDetector(self):\\n # img = cv2.cvtColor(self.VideoFrame, cv2.COLOR_RGB2HSV)\\n # img = cv2.resize(img, (640, 480))\\n # cv2.imwrite('hsv.jpg', img)\\n # mask = None\\n # for color in color_ranges:\\n # mask = cv2.inRange(img, color_ranges[color][0], color_ranges[color][1])\\n # cv2.imwrite('blockdetect.jpg', cv2.bitwise_and(img, img, mask=mask))\\n blocks = self.detectBlocksInDepthImage()\\n pick_up_locs = []\\n for block in blocks:\\n coords = block[0]\\n u = (coords[0][0] + coords[2][0]) // 2\\n v = (coords[0][1] + coords[2][1]) // 2\\n self.VideoFrame = cv2.circle(self.VideoFrame,(u,v), 1, (0,0,0))\\n d = self.DepthFrameRaw[u,v]\\n d = self.convertDepthToSI(d)\\n world_coords = self.calculatePixelToWorld(u, v, d)\\n world_coords[2] = self.max_depth - d\\n pick_up_locs.append(world_coords)\\n return pick_up_locs\",\n \"def getblocknumber(self):\\n return self.getblockcount()\",\n \"def get_block_size(self, img_start):\\n pattern_size = 7\\n\\n left, top = img_start\\n block_height, block_width = 0, 0\\n for i in range(1, self.image.width - left):\\n if self._get_pixel((left + i * pattern_size, top)) == WHITE:\\n block_width = i\\n break\\n for i in range(1, self.image.height - top):\\n if self._get_pixel((left, top + i * pattern_size)) == WHITE:\\n block_height = i\\n break\\n return block_width, block_height\",\n \"def find_block(int_svip, comp_block):\\n print(\\\"-\\\" * 20 + \\\" find_block started\\\")\\n bsz = comp_block\\n outsz = 0\\n bsdict = {}\\n bsdict [0] = bsz\\n # Build the dictionary of the host networks\\n while outsz < 255:\\n outsz = outsz + bsz\\n bsdict[outsz] = (outsz + bsz) -1\\n #print(outsz)\\n \\n # Determine the upper and lower bounds of the host network\\n for key in bsdict.keys():\\n if int_svip >= key and int_svip <= bsdict[key]:\\n block_start = key\\n block_end = bsdict[key]\\n\\n #print(\\\"Block start is {}\\\\nBlock end is {}\\\".format(block_start, block_end))\\n return block_start, block_end\",\n \"def n_blocks(n_frames, block_length):\\n return n_frames - block_length + 1\",\n \"def get_num_of_images(self):\",\n \"def image_id_at(self, i):\\n return i\",\n \"def ip_get_blocks():\\n # start Requests session\\n sc = requests.Session()\\n\\n # import cookies from Firefox\\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\\n\\n # send request\\n vpx = sc.post('https://imhsc.imhadmin.net/index.php?v=IPManager')\\n\\n # check if login failed\\n check_sc_login(vpx.text)\\n\\n # parse with BS4\\n bs = BeautifulSoup(vpx.text, \\\"xml\\\")\\n\\n # get list of provisioning blocks\\n blocklist = []\\n for tblk in bs.find_all('table')[3].tr.div.table.find_all('tr'):\\n tbx = {\\n 'id': re.match(r'.+block_id=([0-9]+).*', tblk.find_all('td')[0].a['href']).group(1),\\n 'prefix': tblk.find_all('td')[0].a.string,\\n 'block': tblk.find_all('td')[1].string,\\n 'usage': tblk.find_all('td')[2].string\\n }\\n blocklist.append(tbx)\\n\\n return bs, blocklist\",\n \"def message_received(self, message):\\n\\n body_lxml = message.body_lxml()\\n for image in body_lxml.iterfind(\\\".//img[@src]\\\"):\\n if image.attrib['src'] == self.no_devil_banana_url:\\n logger.debug(\\\":nodb: found in {0}'s message {1}\\\".format(\\n message.user_name, message.id\\n ))\\n with self.last_lock:\\n self.last_nodb_message = message.id\\n elif image.attrib['src'] in self.devil_banana_urls:\\n if message.user_name == self.connector.username:\\n # ignore my own devil banana messages\\n return\\n logger.debug(\\\"devil banana {2} found in {0}'s message {1}\\\".format(\\n message.user_name, message.id, image.attrib['src']\\n ))\\n with self.last_lock:\\n self.last_banana_message_due_to_edit = False\\n self.last_banana_message = message.id\",\n \"def process_next_image(self):\\n if self.queue:\\n next_queue_item = self.queue.popleft()\\n if type(next_queue_item) == str:\\n if next_queue_item == 'clear':\\n self.signal_status_message.emit('Clearing ROI data (from request in image queue)')\\n self.clear()\\n return\\n [image,file_id,image_num] = next_queue_item\\n # print('image_num',image_num)\\n # print('next image',self.next_image)\\n self.signal_status_message.emit('Started processing ID {} Im {}'.format(file_id,image_num))\\n image = image - self.emccd_bias # don't edit in place because this seemed to cause an issue with images not showing in GUI. Maybe not thread safe?\\n # print('image min',np.min(image))\\n # print('image max',np.max(image))\\n image_num_too_big = False\\n for group in self.roi_groups:\\n for roi in group.rois:\\n try:\\n roi.counts[image_num][file_id] = image[roi.x:roi.x+roi.w,roi.y:roi.y+roi.h].sum()\\n except IndexError: # image_num was not valid for the number of images that MAIA is expecting\\n image_num_too_big = True\\n if image_num_too_big:\\n self.signal_status_message.emit('Image number {} is greater than max expected images, so this image has been ignored (most likely cause is rearrangement toggle).')\\n self.signal_status_message.emit('Finished processing ID {} Im {}'.format(file_id,image_num))\\n self.calculate_thresholds()\",\n \"def mine_blocks(self, count):\\n\\n # Clear out block announcements from each p2p listener\\n [x.clear_block_announcements() for x in self.nodes[0].p2ps]\\n self.generatetoaddress(self.nodes[0], count, self.nodes[0].get_deterministic_priv_key().address)\\n return int(self.nodes[0].getbestblockhash(), 16)\",\n \"def draw_bboxes_withindex(img,boxes, uids):\\n source = Image.fromarray(img)\\n draw = ImageDraw.Draw(source)\\n w2,h2 = (img.shape[0],img.shape[1])\\n \\n font = ImageFont.truetype('/usr/share/fonts/truetype/freefont/FreeSerif.ttf', 40)\\n #font = ImageFont.truetype('arial.ttf', 24)\\n\\n\\n idx = 0\\n\\n for b in boxes:\\n xmin,ymin,xmax,ymax = b\\n \\n for j in range(3):\\n draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline=\\\"red\\\")\\n draw.text((xmin+20, ymin+70), str(uids[idx]), font = font)\\n idx +=1\\n return source\",\n \"def cover(img: Image, message: bitarray) -> Image:\\n width, height = img.size\\n check_image_width(width, RGB_PIXEL, message)\\n pixels = img.load()\\n\\n row = random.randint(0, height) # Randomly chooses row.\\n i = 0 # Tracks hidden bits\\n\\n # If Image consist of 8-bit pixels\\n if img.mode == \\\"P\\\":\\n offset = generate_offset(width, P_PIXEL, message)\\n for x in range(offset, width):\\n p = pixels[x,row]\\n if i < len(message):\\n p = modify_byte(p, message[i])\\n i += 1\\n pixels[x,row] = p\\n generate_key(row, message, offset * P_PIXEL)\\n # If Image consists of 3x8-bit pixels\\n elif img.mode == \\\"RGB\\\":\\n offset = generate_offset(width, RGB_PIXEL, message)\\n for x in range(offset, width):\\n r, g, b = pixels[x,row]\\n if i < len(message):\\n r = modify_byte(r, message[i])\\n i += 1\\n if i < len(message):\\n g = modify_byte(g, message[i])\\n i += 1\\n if i < len(message):\\n b = modify_byte(b, message[i])\\n i += 1\\n pixels[x,row] = (r, g, b)\\n generate_key(row, message, offset * RGB_PIXEL)\\n # If Image consists of 4x8-bits pixels\\n elif img.mode == \\\"RGBA\\\":\\n offset = generate_offset(width, RGBA_PIXEL, message)\\n for x in range(offset, width):\\n r, g, b, a = pixels[x,row]\\n if i < len(message):\\n r = modify_byte(r, message[i])\\n i += 1\\n if i < len(message):\\n g = modify_byte(g, message[i])\\n i += 1\\n if i < len(message):\\n b = modify_byte(b, message[i])\\n i += 1\\n if i < len(message):\\n a = modify_byte(a, message[i])\\n i += 1\\n pixels[x,row] = (r, g, b, a)\\n generate_key(row, message, offset * RGBA_PIXEL)\\n\\n return img\",\n \"def get_blockNumber(self, data):\\n blockNumber = data['blockNumber']\\n return blockNumber\",\n \"def block_offsets(self):\\n ...\",\n \"def getSnapshotsOf(image):\\n snapshotIds = []\\n deviceMapping = image.block_device_mapping # dict of devices\\n devices = deviceMapping.keys()\\n for d in devices:\\n snapshotId = deviceMapping[d].snapshot_id\\n if snapshotId is not None:\\n snapshotIds.append(snapshotId.encode())\\n return snapshotIds\",\n \"def blocks_vision(self):\\n return self.blocks_vision\",\n \"def blockDetector(self, frame):\\n self.detectBlocksInDepthImage()\\n self.block_pos = np.zeros((50,3))\\n self.block_num = 0\\n pixal_arm = np.zeros(2)\\n if self.kinectCalibrated == True:\\n # get current arm position\\n real_arm_x, real_arm_y,_,_ = self.rexarm.get_wrist_pose()\\n real_arm_x *= -1000\\n real_arm_y *= 1000\\n real_arm = np.array(([real_arm_x],[real_arm_y],[1]))\\n \\n # normalize arm_line vector\\n arm_length = np.sqrt(real_arm_x**2 + real_arm_y**2)\\n l = np.array([real_arm_x,real_arm_y])\\n\\n # find center of block_1_height and put them into block_position\\n for cnt in self.block_contours:\\n M = cv2.moments(cnt)\\n if M['m00']>0:\\n cx = int(M['m10']/M['m00'])\\n cy = int(M['m01']/M['m00'])\\n c = np.array([[cx],[cy]])\\n # Eliminate Arm itself\\n real_x = self.real_coord[cx][cy][0]\\n real_y = self.real_coord[cx][cy][1]\\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\\n \\n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\\n\\n \\n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\\n # Check if its in our ROI\\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\\n # points\\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\\n self.block_pos[self.block_num][2] = 1\\n # orientation\\n rect = cv2.minAreaRect(cnt)\\n self.block_ori[self.block_num][0] = -rect[2]\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n # detect color\\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\\n # draw contours\\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,145,86),3)\\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,145,86),-1)\\n self.block_num += 1\\n\\n # find centers of 2 blocks\\n for cnt in self.block_contours_2:\\n M = cv2.moments(cnt)\\n if M['m00']>0:\\n cx = int(M['m10']/M['m00'])\\n cy = int(M['m01']/M['m00'])\\n c = np.array([[cx],[cy]])\\n # Eliminate Arm itself\\n real_x = self.real_coord[cx][cy][0]\\n real_y = self.real_coord[cx][cy][1]\\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\\n \\n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\\n \\n \\n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\\n # Check if its in our ROI\\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\\n # points\\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\\n self.block_pos[self.block_num][2] = 2\\n # orientation\\n rect = cv2.minAreaRect(cnt)\\n self.block_ori[self.block_num][0] = -rect[2]\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n # detect color\\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\\n # draw contours\\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,87,137),3)\\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,87,137),-1)\\n self.block_num += 1 \\n\\n # find centers of 3 blocks\\n for cnt in self.block_contours_3:\\n M = cv2.moments(cnt)\\n if M['m00']>0:\\n cx = int(M['m10']/M['m00'])\\n cy = int(M['m01']/M['m00'])\\n c = np.array([[cx],[cy]])\\n # Eliminate Arm itself\\n real_x = self.real_coord[cx][cy][0]\\n real_y = self.real_coord[cx][cy][1]\\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\\n \\n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\\n \\n \\n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\\n # Check if its in our ROI\\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\\n # points\\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\\n self.block_pos[self.block_num][2] = 3\\n # orientation\\n rect = cv2.minAreaRect(cnt)\\n self.block_ori[self.block_num][0] = -rect[2]\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n # detect color\\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\\n # draw contours\\n cv2.drawContours(self.currentDetectFrame,[box],0,(204,6,6),3)\\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(204,6,6),-1)\\n self.block_num += 1 \\n self.block_pos[self.block_num:50] = 0\\n self.block_ori[self.block_num:50] = 0\\n\\n return frame\",\n \"def process(self, image):\",\n \"def retrieve_new_blocks_since(number_of_last_sent_block, web3):\\n new_blocks = []\\n number_of_last_block = web3.eth.getBlock('latest').number\\n if number_of_last_block > number_of_last_sent_block:\\n number_of_blocks_to_send = number_of_last_block - number_of_last_sent_block\\n for i in range(1, number_of_blocks_to_send + 1):\\n new_blocks.append(web3.eth.getBlock(number_of_last_sent_block + i))\\n return number_of_last_block, new_blocks\\n else:\\n return number_of_last_sent_block, new_blocks\",\n \"def getNum(self):\\r\\n return self.blockNum\",\n \"def compute_histogram_blocks(image, text_box, n_bins, color_space=\\\"RGB\\\", block_size=16):\\n\\n image = cv.cvtColor(image, OPENCV_COLOR_SPACES[color_space])\\n\\n # image_id = int(ntpath.basename(image_path.replace('.jpg', '')))\\n # boxes = pickle.load(open(os.path.join(boxes_path), 'rb'))[image_id][0]\\n\\n if text_box:\\n tlx_init = text_box[0]\\n tly_init = text_box[1]\\n brx_init = text_box[2]\\n bry_init = text_box[3]\\n\\n sizeX = image.shape[1]\\n sizeY = image.shape[0]\\n\\n hist_concat = None\\n \\n for i in range(0,block_size):\\n for j in range(0, block_size):\\n # Image block\\n img_cell = image[int(i*sizeY/block_size):int(i*sizeY/block_size) + int(sizeY/block_size) ,int(j*sizeX/block_size):int(j*sizeX/block_size) + int(sizeX/block_size)]\\n\\n if not text_box:\\n hist = compute_histogram(img_cell, n_bins, color_space)\\n\\n # If there's a text bounding box ignore the pixels inside it\\n else:\\n tlx = tlx_init-int(j*sizeX/block_size)\\n tly = tly_init-int(i*sizeY/block_size)\\n brx = brx_init-int(j*sizeX/block_size)\\n bry = bry_init-int(i*sizeY/block_size)\\n\\n img_cell_vector = []\\n\\n for x in range(img_cell.shape[1]-1):\\n for y in range(img_cell.shape[0]-1):\\n if not (tlx < x < brx and tly < y < bry):\\n img_cell_vector.append(img_cell[y,x,:])\\n\\n img_cell_vector = np.asarray(img_cell_vector)\\n\\n n_channels = 1 if color_space == \\\"GRAY\\\" else image.shape[2]\\n # Using 3D histograms --> total_bins = n_bins_per_channel ^ n_channels\\n hist=np.zeros(n_bins**n_channels,dtype=np.float32)\\n\\n if img_cell_vector.size!=0:\\n img_cell_matrix = np.reshape(img_cell_vector,(img_cell_vector.shape[0],1,-1))\\n hist = compute_histogram(img_cell_matrix, n_bins, color_space)\\n\\n if hist_concat is None:\\n hist_concat = hist\\n else:\\n hist_concat = cv.hconcat([hist_concat, hist])\\n\\n return hist_concat\",\n \"def _get_next_minibatch_inds(self):\\n img_next = self._cursor[0].next()\\n msk_next = self._cursor[1].next()\\n if img_next and msk_next:\\n pass\\n else:\\n print 'BlobFetcher to begin because of cursor point to end.'\\n self._cursor = [self._txn[0].cursor(), self._txn[1].cursor()]\\n self._cursor[0].next()\\n self._cursor[1].next()\",\n \"def between_blocks(self, frame):\\n return []\",\n \"def identify_image(im):\\n score_cures = np.mean(im[1025:1065, 1130:1180, 0])\\n score_ingredients = np.mean(im[1025:1065, 675:720, 0])\\n if score_cures < 177.5:\\n return 'cures'\\n if score_ingredients < 177.5:\\n return 'ingredients'\\n else:\\n return 'other'\",\n \"def getCamera1():\\n for msg in camera1:\\n yield (b'--frame\\\\r\\\\n'\\n b'Content-Type: image/jpg\\\\r\\\\n\\\\r\\\\n' + base64.b64decode(msg.value['image_bytes']) + b'\\\\r\\\\n\\\\r\\\\n')\",\n \"def __get_multi_images_ids(self, num_images=0): \\n availability_images = imageInstance()\\n images = availability_images.get_images()\\n images_ids = []\\n for image in images:\\n if image.type == 'machine':\\n images_ids.append( image.id.encode(\\\"latin-1\\\") )\\n if num_images>1:\\n random.shuffle(images_ids)\\n return images_ids[:num_images]\\n return images_ids\",\n \"def get_bounding_boxes(frame):\\n\\n blob = cv2.dnn.blobFromImage(frame,1/255,(320,320),(0,0,0),1,crop=False)\\n net.setInput(blob)\\n\\n output_layer_names = net.getUnconnectedOutLayersNames()\\n layer_outputs = net.forward(output_layer_names)\\n\\n all_boxes, confidences = get_all_boxes(layer_outputs)\\n\\n indexes=cv2.dnn.NMSBoxes(all_boxes,confidences,0.5,0.3)\\n\\n return indexes, confidences, all_boxes\",\n \"def colorimage2block(img, size):\\n\\timage_height = int(img.shape[0])\\n\\timage_width = int(img.shape[1])\\n\\tblock_height = int(size[0])\\n\\tblock_width = int(size[1])\\n\\tcolor_num = int(img.shape[2])\\n\\n\\t#print(image_height, image_width, block_height, block_width)\\n\\n\\tif image_height%block_height != 0 or image_width%block_width != 0:\\n\\t\\tprint('Please give an appropriate bloxk size.')\\n\\t\\tprint('You can use only numbers that are divisors of image height and width as block size.')\\n\\t\\tsys.exit()\\n\\n\\trow = int(image_height / block_height)\\n\\tcol = int(image_width / block_width)\\n\\tblocks = np.empty([row, col, block_height, block_width, color_num])\\n\\t\\n\\t#print('blocks shape =', blocks.shape)\\n\\n\\tfor i in np.arange(row):\\n\\t\\tfor j in np.arange(col):\\n\\t\\t\\tfor k in np.arange(block_height):\\n\\t\\t\\t\\tfor l in np.arange(block_width):\\n\\t\\t\\t\\t\\t\\tfor rgb in np.arange(color_num):\\n\\t\\t\\t\\t\\t\\t\\tblocks[i][j][k][l][rgb] = img[i*block_height+k][j*block_width+l][rgb]\\n\\n\\treturn blocks\",\n \"def __get_image_id(self):\\n return self.__get_multi_images_ids(1)\",\n \"def toBlocks(im, blocksize):\\n blocks = im.reshape(\\n im.shape[0]//blocksize, \\n blocksize, \\n im.shape[1]//blocksize, \\n blocksize, 3).swapaxes(1, 2)\\n\\n\\n blocks = blocks.reshape(\\n im.shape[0]*im.shape[1]//(blocksize**2), \\n blocksize, \\n 1,\\n blocksize, 3).swapaxes(1, 2)\\n\\n return blocks\",\n \"def block_size(self, block_id): # -> int:\\n ...\",\n \"def message_to_blocks(message, check_for_invalid=True):\\n block_size = aes.R * aes.NB\\n\\n blocks = []\\n block = []\\n for symbol in message:\\n if check_for_invalid:\\n if symbol not in aes.VALID_SYMBOLS:\\n raise Exception('Message includes unsupported symbol \\\"{}\\\".'.format(symbol))\\n block.append(ord(symbol))\\n if len(block) == block_size:\\n blocks.append(block)\\n block = []\\n\\n if len(block) < block_size:\\n block.extend([aes.EMPTY_SYMBOL_CODE for i in range(len(block), block_size)])\\n blocks.append(block)\\n return blocks\",\n \"def GetBlocks(state):\\n result = []\\n last_pos = 0\\n for entry in state:\\n pos = entry['pos']\\n # Calculate block start points from the beginning of individual lines.\\n blocks = [(s[0]-last_pos, s[1]-s[0]) for s in entry['blocks']]\\n # Add one end marker block.\\n blocks.append((pos-last_pos, 0))\\n result.append(blocks)\\n last_pos = pos\\n return result\",\n \"def find_blockages(self):\\n debug.info(1,\\\"Finding blockages.\\\") \\n for layer in [self.vert_layer_number,self.horiz_layer_number]:\\n self.retrieve_blockages(layer)\",\n \"def particle_images (sim,frame_id) :\\n # get positions of all particles: define first the atom selection, then jump to\\n # the user-requested trajectory frame, get the box dimensions (currently works\\n # only for orthorhombic boxes, then calculate the image indices\\n atoms = sim.u.select_atoms ('all')\\n ts = sim.u.trajectory[frame_id]\\n L = ts.dimensions[:3]\\n pos = atoms.positions + L/2.\\n return pos//L\",\n \"def get_blocks(lines):\\n # nalezneme v obsahu cisla stranek\\n page_marks = [idx for idx, l in enumerate(lines) if PAGING_RE.match(l)]\\n\\n # rozdelime obsah na bloky podle stranek\\n blocks, from_pm = [], 0\\n for pm in page_marks:\\n blocks.append(lines[from_pm:pm])\\n from_pm = pm + 1\\n blocks.append(lines[from_pm:])\\n blocks = [i for i in blocks if len(i)]\\n\\n return blocks\",\n \"def color_indexes_from_lbm_chunk(bytes_lbm_block, cursor=0, verbose=True, must_check=False):\\n block_data = []\\n cursor = 0\\n block_size = list(map(int, bytes_lbm_block[cursor:cursor+4]))\\n cursor += 4\\n\\n block_size = sum([\\n byte*(256**offset)\\n for byte, offset\\n in zip(block_size, range(4))\\n ])\\n\\n if verbose:\\n print(\\\"Taille du bloc : %s.\\\" % block_size)\\n\\n block_data += list(map(int, bytes_lbm_block[cursor:cursor+block_size]))\\n cursor += block_size\\n\\n if must_check:\\n if cursor != len(bytes_lbm_block):\\n msg = \\\"Taille de bloc indiqué (%s) différente de la taille de bloc réelle (%s).\\\"\\n raise Exception(msg % (cursor, len(bytes_lbm_block)))\\n\\n color_indexes = []\\n while block_data:\\n part_header = block_data.pop(0)\\n if part_header > 128:\\n part_body = block_data.pop(0)\\n color_indexes += [ part_body ] * (257-part_header)\\n else:\\n body_size = part_header + 1\\n part_body = block_data[:body_size]\\n block_data = block_data[body_size:]\\n color_indexes += part_body\\n\\n if verbose:\\n print(\\\"Nombres de pixels : %s\\\" % len(color_indexes))\\n if len(color_indexes) != 64000:\\n print(\\\"Warning. La taille de l'image devrait être de 64000 pixels (320x200).\\\")\\n\\n return color_indexes\",\n \"def handleImageRunner(self) :\\n tag = self.readByte()\\n if tag == ord(self.imagerunnermarker1[-1]) :\\n oldpos = self.pos-2\\n codop = self.minfile[self.pos:self.pos+2]\\n length = unpack(\\\">H\\\", self.minfile[self.pos+6:self.pos+8])[0]\\n self.pos += 18\\n if codop != self.imagerunnermarker2 :\\n self.pos += length\\n self.logdebug(\\\"IMAGERUNNERTAG SKIP %i AT %08x\\\" % (self.pos-oldpos, self.pos))\\n else :\\n self.pos -= 1 # Adjust position\",\n \"def extract_blocks(pool, listnos, num_blocks):\\n assert len(listnos) % num_blocks == 0, \\\"The number of lists to append must be divisable by the number of blocks\\\"\\n\\n wordlists = {}\\n for word in pool:\\n wordlists[word['listno']] = wordlists.get(word['listno'], []) + [word]\\n\\n blocks = []\\n for i in range(len(listnos)):\\n listno = listnos[i]\\n wordlist = [word.copy() for word in wordlists[listno]]\\n for word in wordlist:\\n word['blockno'] = i//num_blocks\\n word['block_listno'] = i\\n blocks += wordlist\\n\\n return blocks\",\n \"def _block_info(data):\\n # check that the data is an array of bytes\\n if len(data) != 6:\\n raise ValueError(\\\"'data' should be 6 bytes. Got {} instead.\\\".format(\\n len(data)))\\n return struct.unpack('<Hi', data)\",\n \"def _get_containing_blocks(size, point):\\n i, j = point\\n block_inds = []\\n if i > 0:\\n if j > 0:\\n block_inds.append((i - 1, j - 1))\\n if j < size - 1:\\n block_inds.append((i - 1, j))\\n if i < size - 1:\\n if j > 0:\\n block_inds.append((i, j - 1))\\n if j < size - 1:\\n block_inds.append((i, j))\\n \\n return block_inds\",\n \"def _prepare_blocks():\\n\\n counter = blocks[0]['freeStart']\\n maxBlocks = blocks[0]['maxBlocks']\\n while(counter < maxBlocks) :\\n try:\\n # print (mount['parent'] + '/linddata.' + str(counter))\\n f = open(mount['parent'] + '/linddata.' + str(counter), 'r') \\n except IOError, e:\\n return STATUS['M_BD']\\n else :\\n fdatastring = f.next()\\n fdata = deserializedata(fdatastring)\\n blocks[counter] = fdata\\n counter += 1\\n \\n return STATUS['OK']\",\n \"def get_img_index(pred_result: List) -> List:\\n img_index = []\\n\\n for i, line in enumerate(pred_result):\\n match = re.search(\\\"\\\\./test_pics/\\\", line)\\n if match:\\n img_index.append(i)\\n if i == len(pred_result) - 1:\\n img_index.append(i)\\n return img_index\",\n \"def convert_im(im, tree, lbls, block_imgs):\\n h, w, _ = im.shape\\n step = 16\\n for r in range(0, h, step):\\n for c in range(0, w, step):\\n rnext = min(r+step, h)\\n cnext = min(c+step, w)\\n patch = im[r:rnext, c:cnext]\\n color = np.average(patch, axis=(0, 1))\\n\\n # Get closest block\\n _, ind = tree.query([color], k=1)\\n lbl = lbls[ind[0][0]]\\n block = block_imgs[lbl]\\n\\n # Copy values\\n rmax = rnext-r\\n cmax = cnext-c\\n im[r:rnext, c:cnext] = block[:rmax, :cmax]\",\n \"def _rgb_frames(self):\\n image = self.parent.image.image\\n R = image[self.rSpin.value(), :, :]\\n G = image[self.gSpin.value(), :, :]\\n B = image[self.bSpin.value(), :, :]\\n return R, G, B\",\n \"def find_pins_and_blockages(self, pin_list):\\n # This finds the pin shapes and sorts them into \\\"groups\\\" that are connected\\n # This must come before the blockages, so we can not count the pins themselves\\n # as blockages.\\n start_time = datetime.now()\\n for pin_name in pin_list:\\n self.retrieve_pins(pin_name)\\n print_time(\\\"Retrieving pins\\\",datetime.now(), start_time, 4)\\n \\n start_time = datetime.now()\\n for pin_name in pin_list:\\n self.analyze_pins(pin_name)\\n print_time(\\\"Analyzing pins\\\",datetime.now(), start_time, 4)\\n\\n # This will get all shapes as blockages and convert to grid units\\n # This ignores shapes that were pins\\n start_time = datetime.now()\\n self.find_blockages()\\n print_time(\\\"Finding blockages\\\",datetime.now(), start_time, 4)\\n\\n # Convert the blockages to grid units\\n start_time = datetime.now()\\n self.convert_blockages()\\n print_time(\\\"Converting blockages\\\",datetime.now(), start_time, 4)\\n \\n # This will convert the pins to grid units\\n # It must be done after blockages to ensure no DRCs between expanded pins and blocked grids\\n start_time = datetime.now()\\n for pin in pin_list:\\n self.convert_pins(pin)\\n print_time(\\\"Converting pins\\\",datetime.now(), start_time, 4)\\n\\n # Combine adjacent pins into pin groups to reduce run-time\\n # by reducing the number of maze routes.\\n # This algorithm is > O(n^2) so remove it for now\\n # start_time = datetime.now()\\n # for pin in pin_list:\\n # self.combine_adjacent_pins(pin)\\n # print_time(\\\"Combining adjacent pins\\\",datetime.now(), start_time, 4)\\n\\n\\n # Separate any adjacent grids of differing net names that overlap\\n # Must be done before enclosing pins\\n start_time = datetime.now()\\n self.separate_adjacent_pins(0)\\n print_time(\\\"Separating adjacent pins\\\",datetime.now(), start_time, 4)\\n \\n # Enclose the continguous grid units in a metal rectangle to fix some DRCs\\n start_time = datetime.now()\\n self.enclose_pins()\\n print_time(\\\"Enclosing pins\\\",datetime.now(), start_time, 4)\",\n \"def block_num_from_id(block_hash: str) -> int:\\n return int(str(block_hash)[:8], base=16)\",\n \"def image_handler(self, bot, update):\\n text = update.message.text\\n if text.startswith('/recon'):\\n bot.sendMessage(chat_id=update.message.chat_id, text='*Object recognition*\\\\nSend me an image',\\n parse_mode=ParseMode.MARKDOWN)\\n return 10\\n elif text.startswith('/faces'):\\n bot.sendMessage(chat_id=update.message.chat_id, text='*Face recognition*\\\\nSend me an image',\\n parse_mode=ParseMode.MARKDOWN)\\n return 11\",\n \"def total_height_blocks(validator):\\n res = 0\\n for bhash, b in validator.processed.items():\\n if isinstance(b, Block):\\n res += 1\\n return res\",\n \"def getCamera3():\\n for msg in camera3:\\n yield (b'--frame\\\\r\\\\n'\\n b'Content-Type: image/jpg\\\\r\\\\n\\\\r\\\\n' + base64.b64decode(msg.value['image_bytes']) + b'\\\\r\\\\n\\\\r\\\\n')\",\n \"def get_images_bytesize_match(self, images):\\r\\n cnt = 0\\r\\n MAX_BYTES_SIZE = 15728640\\r\\n good_images = []\\r\\n for image in images:\\r\\n if cnt > 30:\\r\\n return good_images\\r\\n src = self.parser.getAttribute(image, attr='src')\\r\\n src = self.build_image_path(src)\\r\\n local_image = self.get_local_image(src)\\r\\n if local_image:\\r\\n bytes = local_image.bytes\\r\\n if (bytes == 0 or bytes > self.images_min_bytes) \\\\\\r\\n and bytes < MAX_BYTES_SIZE:\\r\\n good_images.append(image)\\r\\n else:\\r\\n images.remove(image)\\r\\n cnt += 1\\r\\n return good_images if len(good_images) > 0 else None\",\n \"def parent_block_count(self):\\n if not self._tensors_defined():\\n return None\\n blocks = [len(self.tensor_u), len(self.tensor_v), len(self.tensor_w)]\\n return blocks\",\n \"def block_bodies(self, block_bodies: dict):\\n txsCount = 0\\n for block_hash, block_txs in block_bodies.items():\\n txsCount += len(block_txs)\\n message_size = (\\n txsCount * self._message_size['tx']) + self._message_size['block_bodies']\\n print(\\n f'block bodies with {txsCount} txs have a message size: {message_size} kB')\\n return {\\n 'id': 'block_bodies',\\n 'block_bodies': block_bodies,\\n 'size': kB_to_MB(message_size)\\n }\",\n \"def process(image):\\n pass\",\n \"def block_eid(self, block_id):\\n ...\",\n \"def read_freecells(image):\\n\\n\\tunlocked = cv2.imread(\\\"card_back/freecells/cell_unlocked.png\\\")\\n\\tlocked = cv2.imread(\\\"card_back/freecells/cell_locked.png\\\")\\n\\n\\tlock_results = []\\n\\tfor i in range(4):\\n\\t\\t# Offsets for approximately here the free cells are given 1600x900 game window size\\n\\t\\tx_offset = 314 + (128 * i)\\n\\t\\ty_offset = 24\\n\\t\\t\\n\\t\\tcrop_image = image[y_offset:y_offset + 4, x_offset:x_offset + 4]\\n\\t\\t# cv2.rectangle(image, (x_offset, y_offset), (x_offset + 4, y_offset + 2), (255, 0, 0), 1)\\n\\t\\tunlocked_result = cv2.matchTemplate(crop_image, unlocked, cv2.TM_SQDIFF)\\n\\t\\tlocked_result = cv2.matchTemplate(crop_image, locked, cv2.TM_SQDIFF)\\n\\n\\t\\tlock_results.append(0 if unlocked_result * 10 < locked_result else 1)\\n\\n\\treturn lock_results\",\n \"def process_frame(img, frame, boxes):\\n faces = boxes\\n # Loop through all the faces detected and determine whether or not they are in the database\\n identities = []\\n\\n for (y, x, h, w) in faces: #add padding\\n x1 = int(x-PADDING)\\n y1 = int(y-PADDING)\\n x2 = int(w+PADDING)\\n y2 = int(h+PADDING)\\n\\n identity = find_identity(frame, x1, y1, x2, y2)\\n\\n if identity is not None:\\n identities.append(identity)\\n\\n if identities != []:\\n welcome_users(identities)\\n\\n #set flag to not recognize until face detected again\\n ready_to_recognize_file = open(\\\"varThread\\\\\\\\ready_recognize.txt\\\", \\\"r+\\\")\\n ready_to_recognize_file.seek(0,0)\\n ready_to_recognize_file.write('0')\\n ready_to_recognize_file.close()\",\n \"def get_blocks():\\n query = iroha.blocks_query()\\n IrohaCrypto.sign_query(query, ADMIN_PRIVATE_KEY)\\n for block in net.send_blocks_stream_query(query):\\n print('\\\\nThe next block arrived:', block)\",\n \"def new_images_index(self):\\n first = ct.c_long()\\n last = ct.c_long()\\n self.lib.GetNumberNewImages(ct.pointer(first), ct.pointer(last))\\n\\n return (first.value, last.value)\",\n \"def blocksForImg(img,samplsiz,resc):\\n #scale image into square shape\\n imgr = resize(img,(resc,resc,3))\\n\\n #create blocks\\n view = []\\n view.append(view_as_blocks(imgr[:,:,0], (samplsiz,samplsiz)))\\n view.append(view_as_blocks(imgr[:,:,1], (samplsiz,samplsiz)))\\n view.append(view_as_blocks(imgr[:,:,2], (samplsiz,samplsiz)))\\n\\n #calculate mean values and standard deviation for each block\\n flv = [v.reshape(v.shape[0],v.shape[1],-1) for v in view]\\n meanb = np.array([np.mean(f,axis=2) for f in flv])\\n stdb = np.array([np.std(f,axis=2) for f in flv])\\n\\n #return vector cancatenated from means, standard deviations, sorted means and sorted deviations\\n meanb = meanb.flatten()\\n stdb = stdb.flatten()\\n meanbsort = np.sort(meanb)\\n stdbsort = np.sort(stdb)\\n return np.append(meanb,[stdb,meanbsort,stdbsort])\",\n \"def im_list_detections(model, im_list):\\n _t = Timer()\\n num_images = len(im_list)\\n im_list_boxes = [[] for _ in range(num_images)]\\n im_list_scores = [[] for _ in range(num_images)]\\n im_list_ids = [[] for _ in range(num_images)]\\n im_list_classes = [[] for _ in range(num_images)]\\n # create anchors for each level\\n anchors = create_cell_anchors()\\n for i in range(num_images):\\n im_list_ids[i] = im_list[i]['id']\\n im = cv2.imread(im_list[i]['image'])\\n with c2_utils.NamedCudaScope(0):\\n _t.tic()\\n im_list_boxes[i], im_list_scores[i], im_list_classes[i] = \\\\\\n im_detections(model, im, anchors)\\n _t.toc()\\n logger.info(\\n 'im_detections: {:d}/{:d} {:.3f}s'.format(\\n i + 1, num_images, _t.average_time))\\n return im_list_boxes, im_list_scores, im_list_classes, im_list_ids\",\n \"def list_blocks(self, _):\\n print(self.data.name)\",\n \"def get_expected_chip_ids():\\n paths = INPUT_IMAGES_DIR.glob(\\\"*.tif\\\")\\n # images are named something like abc12.tif, we only want the abc12 part\\n ids = list(sorted(set(path.stem.split(\\\"_\\\")[0] for path in paths)))\\n return ids\",\n \"def image_status_msg(status):\\n messages = {\\n 'match': 'Baseline image matches',\\n 'diff': 'Baseline image differs',\\n 'missing': 'Baseline image not found',\\n 'generated': 'Baseline image was generated',\\n }\\n return messages[status]\",\n \"def get_next_cid(self) -> str:\\n self.position += 1\\n return \\\"img{}\\\".format(self.position)\",\n \"def get_bb(node_name):\\n return int(re.search(r'\\\\d+', re.search(r'block_\\\\d+', node_name).group()).group())\",\n \"def getCamera2():\\n for msg in camera2:\\n yield (b'--frame\\\\r\\\\n'\\n b'Content-Type: image/jpg\\\\r\\\\n\\\\r\\\\n' + base64.b64decode(msg.value['image_bytes']) + b'\\\\r\\\\n\\\\r\\\\n')\",\n \"def checkImages(self):\\r\\n\\r\\n self.leftImage, self.rightImage, res = self.receiver.getImageData()\\r\\n\\r\\n return res\",\n \"def get_motion(frame1k, frame2k, frame_count):\\n frame1 = frame1k.copy()\\n frame2 = frame2k.copy()\\n\\n global limb_coords, init_coords, num_blocks\\n cv2.imwrite(\\\"thisImageAnalyse.png\\\", frame2)\\n block_size = 3\\n block_rad = int(block_size/2)\\n\\n def get_SSD():\\n \\\"\\\"\\\" applies SSD formula to search area\\n :return SSD value\\\"\\\"\\\"\\n dist = 0\\n # traversal of pixels in potential Bi+1 block\\n # compare corresponding pixel positions with source block in f1 and neighbour block in f2\\n y1 = center_y1 - block_rad # start pos.\\n for y2 in range(center_y2 - block_rad, (center_y2 - block_rad + block_size)):\\n x1 = center_x1 - block_rad # start pos\\n for x2 in range(center_x2 - block_rad, (center_x2 - block_rad + block_size)):\\n try:\\n # displacement formula for RGB channels of each pixel in block\\n dist = dist + (frame1[y1][x1][0] - frame2[y2][x2][0])**2 + (frame1[y1][x1][1] - frame2[y2][x2][1])**2 + (frame1[y1][x1][2] - frame2[y2][x2][2])**2\\n except RuntimeWarning:\\n pass\\n x1 += 1\\n y1 += 1\\n return math.sqrt(dist)\\n\\n # for each body part\\n b = 0\\n while b < 5:\\n avg_x = 0.0\\n avg_y = 0.0\\n new_x = 0.0\\n new_y = 0.0\\n a = 0\\n # for each block on body part (9 total)\\n while a < num_blocks:\\n found = False\\n search_rad = 5\\n while found is False:\\n center_y1 = int(init_coords[b][a][0])\\n center_x1 = int(init_coords[b][a][1])\\n min_SSD = 999999\\n # for pythagoras to ensure closest block gets picked when equality occurs of SSD value\\n min_d = 999999\\n # this finds the center of the block to compare\\n for factor_y in range(-search_rad, search_rad + 1):\\n center_y2 = center_y1 + block_size*factor_y\\n y_dist = center_y1 - abs(center_y2)\\n for factor_x in range(-search_rad, search_rad + 1):\\n center_x2 = center_x1 + block_size*factor_x\\n x_dist = center_x1 - abs(center_x2)\\n # pythagoras\\n d = math.sqrt((y_dist**2 + x_dist**2))\\n if d < min_d:\\n min_d = d\\n\\n SSD = get_SSD()\\n if frame2[center_y2][center_x2][1] != 0 and frame2[center_y2][center_x2][2] != 0:\\n found = True\\n if SSD < min_SSD:\\n min_SSD = SSD\\n new_y = center_y2\\n new_x = center_x2\\n elif SSD == min_SSD and d < min_d:\\n new_y = center_y2\\n new_x = center_x2\\n if found is False:\\n # if no block is found repeat the search, increasing the search size by 1\\n search_rad += 1\\n # draw extracted vectors\\n cv2.arrowedLine(frame1k, (int(center_x1), int(center_y1)), (int(new_x), int(new_y)), (150, 200, 30), 1, 4, 0, 0.3)\\n avg_x += new_x\\n avg_y += new_y\\n init_coords[b][a][0] = new_y\\n init_coords[b][a][1] = new_x\\n a += 1\\n cv2.imwrite('monkeyFrames/contrast_enhanced%d.png' % frame_count, frame1k)\\n limb_coords[b][frame_count][0] = int(avg_y/num_blocks)\\n limb_coords[b][frame_count][1] = int(avg_x/num_blocks)\\n b += 1\",\n \"def _calculate_distribution(self, images):\\n positions = {}\\n target_width = target_height = 0\\n for block, url in self.image_declarations:\\n file_name = normalize_filename(url)\\n if file_name in positions:\\n positions[file_name]['blocks'].append(block)\\n else:\\n block_images = images[file_name]\\n offset_x = target_width\\n offset_y = 0\\n\\n width, height = block_images[1].size\\n positions[file_name] = {\\n 'x': offset_x, 'y': offset_y,\\n 'width': width, 'height': height,\\n 'file_name': file_name, 'images': block_images,\\n 'blocks': [block]\\n }\\n\\n target_width += width + self.space_x\\n target_height = max(target_height, height)\\n\\n target_width -= self.space_x\\n return {\\n 'width': target_width,\\n 'height': target_height,\\n 'positions': positions.values(),\\n }\",\n \"def get_blocks(index):\\r\\n #call with -1 to get full blocklist\\r\\n #the reason this is a function instead of just a list is that originally\\r\\n #i had plans to support dynamic tilesets, for example if only a certain\\r\\n #number of each tile were available. in the end this didnt happen though\\r\\n all_blocks = [\\r\\n [[0,0,0],[1,1,1],[0,0,0]], #0 - (horizontal passage)\\r\\n [[0,1,0],[0,1,0],[0,1,0]], #1 | (vertical passage)\\r\\n \\r\\n [[0,0,0],[1,1,0],[0,1,0]], #2 >v various L-junctions\\r\\n [[0,1,0],[1,1,0],[0,0,0]], #3 >^\\r\\n [[0,0,0],[0,1,1],[0,1,0]], #4 ^>\\r\\n [[0,1,0],[0,1,1],[0,0,0]], #5 v>\\r\\n \\r\\n [[0,0,0],[0,0,0],[0,0,0]], #6 0 empty\\r\\n [[0,1,0],[1,1,1],[0,1,0]], #7 + cross\\r\\n \\r\\n [[0,1,0],[1,1,1],[0,0,0]], #8 _|_ various T-junctions\\r\\n [[0,0,0],[1,1,1],[0,1,0]], #9 T\\r\\n [[0,1,0],[1,1,0],[0,1,0]], #10 -|\\r\\n [[0,0,0],[1,1,1],[0,0,0]]] #11 |-\\r\\n \\r\\n# [[0,1,0],[0,1,0],[0,0,0]], #12 #unsued \\\"dead end\\\" pieces\\r\\n# [[0,0,0],[0,1,0],[0,1,0]], #13\\r\\n# [[0,0,0],[0,1,1],[0,0,0]], #14\\r\\n# [[0,0,0],[1,1,0],[0,0,0]] ]#15\\r\\n if index == -1:\\r\\n return all_blocks\\r\\n else:\\r\\n return all_blocks[index]\",\n \"def process():\\n config = read_config()\\n \\n\\n img_dir = config['DEFAULT']['images_directory']\\n results_dict = {}\\n images = list(get_image_files(img_dir))\\n for image in tqdm.tqdm(images):\\n info = hash_file(image)\\n if info == 0:\\n continue\\n\\n hash_value = info['hash']\\n\\n if hash_value not in results_dict:\\n file_name = os.path.basename(info['_id'])\\n results_dict[hash_value] = [file_name, 1]\\n else:\\n results_dict[hash_value][1] += 1\\n\\n count = list(results_dict.values())\\n sorted_count = sorted(count, key=lambda x: x[1], reverse=True)\\n \\n with ImagesDB(IMG_INFO_DB_FILENAME) as imgDb: \\n imgDb.insert_batch(sorted_count)\",\n \"def read_in():\\n list_images_out = []\\n index = 0\\n current_image_points = []\\n with open(\\\"./darknet/output.txt\\\") as results:\\n lines = results.readlines()\\n for line in lines:\\n try:\\n i = int(line[-(4+6+1):][:6]) # ex of file 000000.jpg 6 + 4 characters\\n if i != index:\\n print(\\\"PROBLEM\\\")\\n print(i, index)\\n exit()\\n \\n list_images_out.append(parse_current_images(current_image_points, index))\\n # Reset the current image\\n current_image_points = []\\n index += 1\\n except ValueError:\\n # Real line\\n left, top, right, bottom = line.split(\\\" \\\")\\n left, right, top, bottom = int(left), int(right), int(top), int(bottom)\\n current_image_points.append([0, left, right, bottom, top])\\n\\n list_images_out.append(parse_current_images(current_image_points, index))\\n\\n # We remove the first image due to line 36 test\\n list_images_out.pop(0)\\n print(len(list_images_out))\\n return list_images_out\",\n \"def process_images():\\n for message in get_messages_from_sqs():\\n try:\\n message_content = json.loads(message.body)\\n image = unquote_plus(message_content\\n ['Records'][0]['s3']['object']\\n ['key'])\\n s3.download_file(input_bucket_name, image, image)\\n process_json(image)\\n upload_image(image)\\n cleanup_files(image)\\n except:\\n message.change_visibility(VisibilityTimeout=0)\\n continue\\n else:\\n message.delete()\",\n \"def grayimage2block(img, size):\\n\\timage_height = int(img.shape[0])\\n\\timage_width = int(img.shape[1])\\n\\tblock_height = int(size[0])\\n\\tblock_width = int(size[1])\\n\\n\\t#print(image_height, image_width, block_height, block_width)\\n\\n\\tif image_height%block_height != 0 or image_width%block_width != 0:\\n\\t\\tprint('Please give an appropriate bloxk size.')\\n\\t\\tprint('You can use only numbers that are divisors of image height and width as block size.')\\n\\t\\tsys.exit()\\n\\n\\trow = int(image_height / block_height)\\n\\tcol = int(image_width / block_width)\\n\\tblocks = np.empty([row, col, block_height, block_width])\\n\\t#print('blocks shape =', blocks.shape)\\n\\n\\tfor i in np.arange(row):\\n\\t\\tfor j in np.arange(col):\\n\\t\\t\\tfor k in np.arange(block_height):\\n\\t\\t\\t\\tfor l in np.arange(block_width):\\n\\t\\t\\t\\t\\tblocks[i][j][k][l] = img[i*block_height+k][j*block_width+l]\\n\\n\\treturn blocks\",\n \"def available_images_index(self):\\n first = ct.c_long()\\n last = ct.c_long()\\n self.lib.GetNumberAvailableImages(ct.pointer(first), ct.pointer(last))\\n\\n return (first.value, last.value)\",\n \"def test_segment_image(self):\\n with Image.open(self.subject) as im:\\n image = im.convert(\\\"RGB\\\")\\n\\n segment_generator = image_helper.segment_image(image, 200, 200)\\n\\n sizes = [\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 200), next(segment_generator)),\\n ((200, 50), next(segment_generator)),\\n ((200, 50), next(segment_generator)),\\n ((200, 50), next(segment_generator)),\\n ((200, 50), next(segment_generator))\\n ]\\n\\n for size, segment in sizes:\\n self.assertEqual(size, segment.size)\",\n \"def process_block(self, block):\\r\\n ret = []\\r\\n output = None\\r\\n input_lines = None\\r\\n lineno = self.IP.execution_count\\r\\n\\r\\n input_prompt = self.promptin%lineno\\r\\n output_prompt = self.promptout%lineno\\r\\n image_file = None\\r\\n image_directive = None\\r\\n\\r\\n for token, data in block:\\r\\n if token==COMMENT:\\r\\n out_data = self.process_comment(data)\\r\\n elif token==INPUT:\\r\\n (out_data, input_lines, output, is_doctest, image_file,\\r\\n image_directive) = \\\\\\r\\n self.process_input(data, input_prompt, lineno)\\r\\n elif token==OUTPUT:\\r\\n out_data = \\\\\\r\\n self.process_output(data, output_prompt,\\r\\n input_lines, output, is_doctest,\\r\\n image_file)\\r\\n if out_data:\\r\\n ret.extend(out_data)\\r\\n\\r\\n # save the image files\\r\\n if image_file is not None:\\r\\n self.save_image(image_file)\\r\\n\\r\\n return ret, image_directive\",\n \"def blocks_under_highest_justified(validator):\\n res = 0\\n for bhash, b in validator.processed.items():\\n if isinstance(b, Block):\\n if b.height <= validator.highest_justified_checkpoint.height:\\n res += 1\\n return res\",\n \"def getblockcount(self):\\n return len(self.blocks) - 1\",\n \"def get_snapshots_of(image):\\n snapshot_ids = []\\n device_mapping = image.block_device_mapping # dict of devices\\n devices = device_mapping.keys()\\n for device in devices:\\n if device_mapping[device].snapshot_id is not None:\\n snapshot_ids.append(device_mapping[device].snapshot_id.encode()) # do I need to have 'encode' here?\\n return snapshot_ids\",\n \"def image(images):\\n return images[0]\",\n \"def get_blocks_before(self, hash_bytes: bytes, num_blocks: int = 100) -> list[Block]:\\n raise NotImplementedError\",\n \"def test_getImages(self): # GIVEN the group chat has at least one image\\n testBot = bot.Bot(os.environ['bot_id'], os.environ['token'], os.environ['group_ID'])\\n imageList = testBot.run() #AND THEN post_images calls the private get_images method which returns an array\\n self.assertTrue(len(imageList) > 0) #THEN there should be at least one element in the array\",\n \"def updateImageGroups(self):\\n self.img_grps = self.splitImages()\\n grps = self.img_grps\\n self.detail.clear()\\n detail = \\\"Available Groups : \\\\n\\\"\\n if len(grps) >= 1:\\n for i in range(len(grps)):\\n detail += \\\"Group \\\"+ str(i+1)+ \\\" : \\\" + str(grps[i][0]) + \\\" ... \\\" + str(grps[i][-1]) + '\\\\n'\\n\\n self.detail.insertPlainText(detail)\\n self.detail.moveCursor(QTextCursor.End)\",\n \"def _get_block_indices(y):\\n block_labels, block_inv, block_sizes = np.unique(\\n y, return_inverse=True, return_counts=True\\n )\\n\\n n_blocks = len(block_labels)\\n block_inds = range(n_blocks)\\n\\n block_vert_inds = []\\n for i in block_inds:\\n # get the inds from the original graph\\n inds = np.where(block_inv == i)[0]\\n block_vert_inds.append(inds)\\n return block_vert_inds, block_inds, block_inv\",\n \"def getBlobs(aDiffImg):\\n # Get the blobs and number of blobs present in the diff image\\n # Returned as (blobs, num_blobs)\\n return ndimage.label(aDiffImg)\",\n \"def get_block_idx(frame_idx, slices):\\n idx_slices = { k:{} for k in slices[0].keys() }\\n for i in frame_idx:\\n for k in slices[i].keys():\\n for b in slices[i][k].keys():\\n if not b in idx_slices[k]:\\n idx_slices[k][b] = []\\n idx_slices[k][b] += list(range( slices[i][k][b].start, slices[i][k][b].stop))\\n return idx_slices\",\n \"def _handle_image_descriptors(self):\\n while self.file_content[self.data_idx] == 0x2c:\\n img_left = self.file_content[self.data_idx + 1] + \\\\\\n (self.file_content[self.data_idx + 2] << 8)\\n img_top = self.file_content[self.data_idx + 3] + \\\\\\n (self.file_content[self.data_idx + 4] << 8)\\n img_width = self.file_content[self.data_idx+5] + \\\\\\n (self.file_content[self.data_idx + 6] << 8)\\n #img_height = self.file_content[self.data_idx+7] + \\\\\\n # (self.file_content[self.data_idx + 8] << 8)\\n flags = self.file_content[self.data_idx + 9]\\n local_col_table_flag = (flags & 0b10000000) != 0\\n #interlace_flag = (flags & 0b01000000) != 0\\n self.data_idx = self.data_idx + 10\\n if local_col_table_flag:\\n # read local color table\\n print('read local color table. Not implemented yet')\\n\\n self.lzw_min_code_sz = self.file_content[self.data_idx]\\n self.data_idx = self.data_idx + 1\\n\\n pix_xix = img_left\\n pix_yix = img_top\\n subblock_data = []\\n while self.file_content[self.data_idx] != 0:\\n subblock_sz = self.file_content[self.data_idx]\\n self.data_idx = self.data_idx + 1\\n subblock_data += self.file_content[self.data_idx:self.data_idx + subblock_sz]\\n self.data_idx = self.data_idx + subblock_sz\\n self.data_idx = self.data_idx + 1\\n dec_data = self.decode_subblock(subblock_data)\\n for dat in dec_data:\\n self.output_image[pix_xix][pix_yix][0] = self.color_table[dat][0]\\n self.output_image[pix_xix][pix_yix][1] = self.color_table[dat][1]\\n self.output_image[pix_xix][pix_yix][2] = self.color_table[dat][2]\\n pix_xix = pix_xix + 1\\n if pix_xix == img_left + img_width:\\n pix_xix = img_left\\n pix_yix = pix_yix + 1\",\n \"def image_cb(self, msg):\\n\\n # Save the camera image\\n self.camera_image = msg\\n\\n # I sufficient information is available...\\n if not None in (self.camera_image, self.waypoint_tree, self.lights):\\n\\n # Find index and color state of next light\\n light_wp, state = self.process_traffic_lights()\\n\\n # If the light is green...\\n if state == TrafficLight.GREEN:\\n\\n # Publish sentinel indicatig no red light\\n self.upcoming_red_light_pub.publish(Int32(-1))\\n\\n else:\\n\\n # Publish the traffic light index\\n self.upcoming_red_light_pub.publish(Int32(light_wp))\",\n \"def _frame_idx_to_batch_idx(self, boxes):\\n frame_idxs = torch.unique(boxes[:, 0])\\n for batch_idx, frame_idx in enumerate(frame_idxs):\\n idx = torch.where(boxes == frame_idx)[0]\\n boxes[idx, 0] = batch_idx\\n return boxes\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5884302","0.5805182","0.5722689","0.5625986","0.5602621","0.55785567","0.5520678","0.5473761","0.5423533","0.53958166","0.5387366","0.53733194","0.537314","0.5340807","0.5339228","0.5338712","0.533123","0.53037155","0.5302177","0.52923036","0.5285601","0.5277623","0.5265335","0.52329016","0.522606","0.5209315","0.5189084","0.51883864","0.5183319","0.5172357","0.51606643","0.51579857","0.51331204","0.5127984","0.5113182","0.5100601","0.5096972","0.5085964","0.5074466","0.5062691","0.50617665","0.5059184","0.50583315","0.5057408","0.50558656","0.5047711","0.50451565","0.50359064","0.5028824","0.5022311","0.50147396","0.50015795","0.49984443","0.49946663","0.49836597","0.49835834","0.4980269","0.49635807","0.49586147","0.4938147","0.4933892","0.49335787","0.49323955","0.4929201","0.49291235","0.49239105","0.49177504","0.49157387","0.49155387","0.49079973","0.49004912","0.48971954","0.48953608","0.48922652","0.48898232","0.48811775","0.4880962","0.48785892","0.48777327","0.4872482","0.4866956","0.4858918","0.48582947","0.48569536","0.48547783","0.4843585","0.4842192","0.48369205","0.4831494","0.48308808","0.48286724","0.4828463","0.4826973","0.48208275","0.48144236","0.4806815","0.48055628","0.47934705","0.47922724","0.4791851"],"string":"[\n \"0.5884302\",\n \"0.5805182\",\n \"0.5722689\",\n \"0.5625986\",\n \"0.5602621\",\n \"0.55785567\",\n \"0.5520678\",\n \"0.5473761\",\n \"0.5423533\",\n \"0.53958166\",\n \"0.5387366\",\n \"0.53733194\",\n \"0.537314\",\n \"0.5340807\",\n \"0.5339228\",\n \"0.5338712\",\n \"0.533123\",\n \"0.53037155\",\n \"0.5302177\",\n \"0.52923036\",\n \"0.5285601\",\n \"0.5277623\",\n \"0.5265335\",\n \"0.52329016\",\n \"0.522606\",\n \"0.5209315\",\n \"0.5189084\",\n \"0.51883864\",\n \"0.5183319\",\n \"0.5172357\",\n \"0.51606643\",\n \"0.51579857\",\n \"0.51331204\",\n \"0.5127984\",\n \"0.5113182\",\n \"0.5100601\",\n \"0.5096972\",\n \"0.5085964\",\n \"0.5074466\",\n \"0.5062691\",\n \"0.50617665\",\n \"0.5059184\",\n \"0.50583315\",\n \"0.5057408\",\n \"0.50558656\",\n \"0.5047711\",\n \"0.50451565\",\n \"0.50359064\",\n \"0.5028824\",\n \"0.5022311\",\n \"0.50147396\",\n \"0.50015795\",\n \"0.49984443\",\n \"0.49946663\",\n \"0.49836597\",\n \"0.49835834\",\n \"0.4980269\",\n \"0.49635807\",\n \"0.49586147\",\n \"0.4938147\",\n \"0.4933892\",\n \"0.49335787\",\n \"0.49323955\",\n \"0.4929201\",\n \"0.49291235\",\n \"0.49239105\",\n \"0.49177504\",\n \"0.49157387\",\n \"0.49155387\",\n \"0.49079973\",\n \"0.49004912\",\n \"0.48971954\",\n \"0.48953608\",\n \"0.48922652\",\n \"0.48898232\",\n \"0.48811775\",\n \"0.4880962\",\n \"0.48785892\",\n \"0.48777327\",\n \"0.4872482\",\n \"0.4866956\",\n \"0.4858918\",\n \"0.48582947\",\n \"0.48569536\",\n \"0.48547783\",\n \"0.4843585\",\n \"0.4842192\",\n \"0.48369205\",\n \"0.4831494\",\n \"0.48308808\",\n \"0.48286724\",\n \"0.4828463\",\n \"0.4826973\",\n \"0.48208275\",\n \"0.48144236\",\n \"0.4806815\",\n \"0.48055628\",\n \"0.47934705\",\n \"0.47922724\",\n \"0.4791851\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6633512"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14988,"cells":{"query":{"kind":"string","value":"Initialize attributes for simulated use."},"document":{"kind":"string","value":"def __init__(self,gameboard,events,mouse_pos):\n super().__init__(gameboard)\n self.events = events\n self.mouse_pos = mouse_pos"},"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 init_attrs(self):\n raise NotImplementedError","def _init_attributes(self):\n self.attr = {\n 'name': None,\n 'tags': [],\n 'openHours': None,\n 'type': None,\n 'parent': None,\n 'locationId': None,\n 'bannerAbbreviation': None,\n 'arcGisAbbreviation': None,\n 'geoLocation': None,\n 'geometry': None,\n 'summary': None,\n 'description': None,\n 'descriptionHtml': None,\n 'address': None,\n 'city': None,\n 'state': None,\n 'zip': None,\n 'county': None,\n 'telephone': None,\n 'fax': None,\n 'thumbnails': [],\n 'images': [],\n 'departments': [],\n 'website': None,\n 'sqft': None,\n 'calendar': None,\n 'campus': None,\n 'girCount': None,\n 'girLimit': False,\n 'girLocations': None,\n 'synonyms': [],\n 'bldgId': None,\n 'parkingZoneGroup': None,\n 'propId': None,\n 'adaParkingSpaceCount': None,\n 'motorcycleParkingSpaceCount': None,\n 'evParkingSpaceCount': None,\n 'weeklyMenu': None,\n 'notes': None,\n 'labels': {},\n 'steward': None,\n 'shape': {}\n }","def __init__(self, **initial_attributes):\n\n for attribute_name, attribute_value in initial_attributes.items():\n setattr(self, attribute_name, attribute_value)","def initAttributes(self):\n Packet.initAttributes(self)\n self.packetType = TM_PACKET_TYPE","def __init__(self, **attributes):\n for key, value in attributes.items():\n setattr(self, key, value)","def _init_attributes(self):\n if os.name == \"nt\":\n if \"64\" in platform.architecture()[0]:\n platform_arch = \"x86_64\"\n elif \"32\" in platform.architecture()[0]:\n platform_arch = \"i386\"\n else:\n platform_arch = platform.architecture()\n os_ver = f\"Windows-{platform.win32_ver()[1]}\"\n else:\n platform_arch = platform.machine()\n if platform.system() == \"Darwin\":\n os_ver = f\"macOS-{platform.mac_ver()[0]}\"\n else:\n os_ver = \"-\".join(linux_distribution()[0:2])\n\n license_chunks = LICENSE.split(\" \")\n if license_chunks[0] == \"GPLv2\":\n client_license = \"GPL-2.0\"\n else:\n client_license = \"Commercial\"\n\n default_attributes = {\n # Process id\n \"_pid\": str(os.getpid()),\n # Platform architecture\n \"_platform\": platform_arch,\n # OS version\n \"_os\": os_ver,\n # Hostname of the local machine\n \"_source_host\": socket.gethostname(),\n # Client's name\n \"_client_name\": \"mysql-connector-python\",\n # Client's version\n \"_client_version\": \".\".join([str(x) for x in VERSION[0:3]]),\n # Client's License identifier\n \"_client_license\": client_license,\n }\n self._settings[\"attributes\"].update(default_attributes)\n\n if \"connection-attributes\" in self._settings:\n for attr_name in self._settings[\"connection-attributes\"]:\n attr_value = self._settings[\"connection-attributes\"][attr_name]\n # Validate name type\n if not isinstance(attr_name, str):\n raise InterfaceError(\n f\"Attribute name '{attr_name}' must be a string type\"\n )\n # Validate attribute name limit 32 characters\n if len(attr_name) > 32:\n raise InterfaceError(\n f\"Attribute name '{attr_name}' exceeds 32 characters \"\n \"limit size\"\n )\n # Validate names in connection-attributes cannot start with \"_\"\n if attr_name.startswith(\"_\"):\n raise InterfaceError(\n \"Key names in 'session-connect-attributes' cannot \"\n f\"start with '_', found: {attr_name}\"\n )\n # Validate value type\n if not isinstance(attr_value, str):\n raise InterfaceError(\n f\"Attribute name '{attr_name}' value '{attr_value}' \"\n \" must be a string type\"\n )\n\n # Validate attribute value limit 1024 characters\n if len(attr_value) > 1024:\n raise InterfaceError(\n f\"Attribute name '{attr_name}' value: '{attr_value}' \"\n \"exceeds 1024 characters limit size\"\n )\n\n self._settings[\"attributes\"][attr_name] = attr_value","def _init_node_attributes(self):\n assert False","def test_attributes(sim):\n config = {\n \"mass\": 1,\n \"spring_constant\": 1,\n \"pusher\": \"ForwardEuler\",\n \"x0\": [[0, 1, 0]],\n }\n block = BlockOnSpring(sim, config)\n # These attributes get set in __init__()\n assert block.mass == 1\n assert block.spring_constant == 1\n np.testing.assert_allclose(block.position, [[0, 0, 0]])\n block.initialize()\n # These attributes get set in initialize()\n np.testing.assert_allclose(block.position, config[\"x0\"])","def initAttributes(self):\n Packet.initAttributes(self)\n self.packetType = TC_PACKET_TYPE","def initAttributes(self):\n CCSDS.DU.DataUnit.initAttributes(self)\n self.dataFieldHeaderFlag = 0\n self.setPacketLength()","def __init__(self, simulation_attributes):\n for attr in ['locations','dprime_fnc','next_fixation',\n 'threshold', 'num_of_searches']:\n if getattr(simulation_attributes,attr) is None:\n assert False, (\n \"Precondition violation: none attribute in simulation_attributes \"\n + attr\n )\n if not isinstance(simulation_attributes, SimulationAttributes):\n raise TypeError(\n \"The argument isn't an instance of SimulationAttributes class\"\n )\n self.senzory_map = self._locations_to_senzory_map(\n simulation_attributes.locations\n )\n self.number_of_locs = self.senzory_map.shape[0]\n self.dprime_fnc = simulation_attributes.dprime_fnc\n self.dprime_map = generate_dprime_map(self.dprime_fnc,self.senzory_map)\n self.next_fixation = simulation_attributes.next_fixation\n self.threshold = simulation_attributes.threshold\n self.num_of_searches = simulation_attributes.num_of_searches","def _set_attr(self):\n self.as_skeletal = self._import_as_skeleton()\n self.materials = self._import_materials()\n self.textures = self._import_textures()","def __init__(self):\n\n try:\n # read attributes from attributes file\n with open(const.Storage.ATTRIBUTES) as attributes_file:\n # read the file and parse it to JSON data\n json_data = attributes_file.read()\n attributes = json.loads(json_data)\n\n # set attributes\n self.id = str(attributes[\"id\"])\n self.length = float(attributes[\"length\"])\n self.width = float(attributes[\"width\"])\n except OSError:\n raise OSError(\"The attributes file could not be opened.\")","def init_attributes(self):\n # Set default values\n for key, value in self.defaults.items():\n setattr(self, key, value)\n\n # Parse all arguments in kwargs\n for key, value in self.kwargs.items():\n parsed_value = eval_arg(value, key)\n logging.info('Setting ' + str(type(parsed_value)) + ' self.' + str(key) + ' = ' + str(parsed_value))\n setattr(self, key, parsed_value)\n\n # self.today = date_utils.get_datetime_from_timezone(self.date_offset, self.timezone)\n self.today = datetime.datetime.today()","def __init__(self, **attrs):\n \n # set given attributes\n for name, value in attrs.items():\n if hasattr(self, name):\n setattr(self, name, value)\n else:\n raise AttributeError(\"Attribute not found! --> %s\" % name)","def __init__(self, attributes_names: list):\r\n self.attributes_names = attributes_names","def __init__(self, attributes_names: list):\r\n self.attributes_names = attributes_names","def test_default_attributes_set_in_init(self):\n\n x = BaseTransformer()\n\n expected_attributes = {\n \"version_\": tubular._version.__version__,\n \"verbose\": False,\n \"columns\": None,\n \"copy\": True,\n }\n\n h.test_object_attributes(\n obj=x,\n expected_attributes=expected_attributes,\n msg=\"Default attributes set in init\",\n )","def __init__(self, attrs = None):\n\n if attrs != None:\n self.__dict__.update(attrs)","def __init__(self):\n self.time_limit = None\n self.time_step = None\n self.robot = None\n self.humans = None\n self.global_time = None\n self.human_times = None\n # reward function\n self.discomfort_dist = None\n # simulation configuration\n self.config = None\n self.randomize_attributes = None\n self.square_width = None\n self.circle_radius = None\n self.human_num = None\n # for visualization\n self.states = None\n self.states_traj = None","def _set_attributes(self):","def test_init(self, r, attribute_tup):\n num_wires, num_gates, gate_types, gate_sizes, depth, shots = attribute_tup\n\n assert r.num_wires == num_wires\n assert r.num_gates == num_gates\n assert r.depth == depth\n assert r.shots == shots\n assert r.gate_types == gate_types\n assert r.gate_sizes == gate_sizes","def __init__(self, **attributes):\n self.set(**attributes)","def __init__(self):\n self.relation = ''\n self.attributes = []\n self.attribute_types = dict()\n self.attribute_data = dict()\n self.comment = []\n self.data = []\n pass","def __init__(self, attribs):\n self.__instanced = False\n self.__initAccessor(attribs)\n self.__setValues(attribs)\n self.__instanced = True","def _initialize_attributes(self):\n height, width = self.image.shape[:2]\n\n self.confidence = (1 - self.mask).astype(float)\n self.data = np.zeros([height, width])\n\n self.working_image = np.copy(self.image)\n self.working_mask = np.copy(self.mask)","def init_attributes(self):\n\n # Creats two lists: one for x-values, one for y-valuse\n self.xValues = []\n self.yValues = []\n self.colors = []\n\n # Colors of the graph\n self.saveColor = \"limegreen\"\n self.currentColor = \"r\"\n self.unsavedColor = \"royalblue\"\n\n # Initialization of some other attributes\n self.filename = None\n self.mouseY = 0\n\n self.startIndex = 0\n self.current_position = 0\n self.position_index = 0\n\n self.savedRecently = False\n self.videoOpened = False\n\n self.animation = None\n self.curve = None\n self.k = 25\n self.dt = self.k","def __init__(self, events={}, attributes={}):\n self.events = events.copy()\n self.attributes = attributes.copy()\n if not AT.VALUE_STRATEGY in self.attributes:\n self.attributes[AT.VALUE_STRATEGY] = ValueStrategy.PRESET","def initialize_object_attributes(self, ai_game):\r\n self.ship_bullets = ai_game.ship_bullets\r\n self.alien_bullets = ai_game.alien_bullets\r\n self.aliens = ai_game.aliens\r\n self.explosions = ai_game.explosions\r\n self.stats = ai_game.stats\r\n self.sb = ai_game.sb\r\n self.ship = ai_game.ship","def __init__(self):\n self.X = None\n self.Y = None\n self.features = None\n self.max = self.min = None\n self._look_up = None\n self.attr_weight = None","def initialize_attributes(self):\n self.host = self.netloc\n self.url = self.geturl()\n\n self.set_scheme_if_non('https')\n \n # The file extensions we are watching for. Either load the extensions\n # from a text file, or create a seperate python file contain a list\n # supported file extensions\n self.listed_file_extensions = [ \n '.jpg', '.bmp', '.png',\n '.mp3', '.mp4', '.flv', '.avi',\n '.zip', '.7z', '.tar', '.tar.gz', '.tar.bz', '.rar',\n '.exe', '.git', '.torrent',\n ] \n # Type Boolean: True or False\n # Urls contain some useful information. Depending on the framework the \n # website is built on, a url can contain information about paths and files.\n # This is a glimpse of the sites computer system. Pretty Useful!\n self.is_file_extension = None # Does this path end as a file?\n #self.file_extension = self.check_for_file_extension()","def __init__(self, attributes=None):\n super().__init__(attributes)\n \n # processing parameters\n self.set = _Settings()\n\n # results storage\n self.measure_time = None # store here in case we average FIDs, filled by chain!\n self.frequency_shift = None\n self.phase_0 = None\n self.data = None\n \n if attributes is not None:\n self.inflate(attributes)\n\n self.chain = None","def initialize(self):\n\t\tpass","def initialise_sim(self):\n pass","def _init_generate_physical_attributes(self):\n # Prepare these now, for speedier access\n config = self.person.cosmos.config\n year = self.person.cosmos.year\n male = self.person.male\n # Determine age of physical peak, i.e., baseball prime\n self.age_of_physical_peak = config.determine_age_of_physical_peak()\n # Determine handedness\n self.lefty = True if random.random() < config.chance_of_being_left_handed else False\n self.righty = not self.lefty\n self.left_handed = 1.0 if self.lefty else 0.0\n self.right_handed = 1.0 if self.righty else 0.0\n # Determine hustle\n self.hustle = config.determine_hustle()\n # Determine adult height this person will attain, in inches\n if male:\n self.adult_height = normal(\n config.adult_male_height_mean(year=year), config.adult_male_height_sd(year=year)\n )\n else:\n self.adult_height = normal(\n config.adult_female_height_mean(year=year), config.adult_female_height_sd(year=year)\n )\n # Determine this person's BMI TODO BMI INCREASES AS ADULTHOOD PROGRESSES\n if male:\n self.bmi = normal(\n config.young_adult_male_bmi_mean(year=year), config.young_adult_male_bmi_sd(year=year)\n )\n else:\n self.bmi = normal(\n config.young_adult_female_bmi_mean(year=year), config.young_adult_female_bmi_sd(year=year)\n )\n # Determine propensities for coordination, reflexes, agility, jumping...\n self.coordination_propensity = config.determine_coordination_propensity()\n self.reflexes_propensity = config.determine_reflexes_propensity(\n coordination_propensity=self.coordination_propensity\n )\n self.agility_propensity = config.determine_agility_propensity()\n self.jumping_propensity = config.determine_jumping_propensity() # Number of inches added/subtracted to base\n # ...and finally footspeed propensity, which is a bit more convoluted to compute\n primitive_coordination = config.determine_primitive_coordination(bmi=self.bmi) if self.bmi > 24 else 1.0\n adult_coordination = primitive_coordination * self.coordination_propensity\n primitive_footspeed = config.determine_primitive_footspeed(\n coordination=adult_coordination, height=self.adult_height\n )\n self.footspeed_propensity = config.determine_footspeed_propensity(primitive_footspeed=primitive_footspeed)\n # Finally, fit these potentials to the person's current age\n self.develop()","def __init__(self, attributes=None):\n self.coil_combine_method = 'Siemens'\n self.fids_to_average = 1\n self.fid_left_shift = 0\n self.gaussian_apodization = 2.0\n self.apply_peak_shift = True\n self.reference_peak_center = 2.01\n self.peak_search_width = 0.2\n self.apply_phase0 = True\n self.phase0_range_start = 3.5\n self.phase0_range_end = 0.5\n self.global_phase0 = 0.0\n self.global_phase1 = 0.0\n \n if attributes is not None:\n self.inflate(attributes)","def __init__(self, attrs: Dict[str, Any]) -> None:\n self.attrs = attrs","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def __attrs_post_init__(self):","def _set_conf_attrs():\n\tdebug_msg = \"initializing the configuration\"\n\tlogger.debug(debug_msg)","def __init__(self):\n self.swagger_types = {\n 'maximum_over_capacity': 'float',\n 'minimum_health_capacity': 'float'\n }\n\n self.attribute_map = {\n 'maximum_over_capacity': 'maximumOverCapacity',\n 'minimum_health_capacity': 'minimumHealthCapacity'\n }\n\n self._maximum_over_capacity = None\n self._minimum_health_capacity = None","def initialize(self):\r\n pass","def initialize(self):\r\n pass","def _initialize(self):\n pass","def _initialize(self):\n pass","def _initialize(self):\n pass","def __init__(self,\n *,\n attributes: List['Attribute'] = None) -> None:\n self.attributes = attributes","def initialize(self, **kwargs):","def __init__(self, *args):\n _snap.TAttr_swiginit(self, _snap.new_TAttr(*args))","def initialize(self):\n self.initialize_edges()\n self.initialize_prob()\n self.initialize_total_input_dict()\n\n self.initialize_fpmusigv_dict()","def test_define_attributes(self):\n\n class Test(pyperry.Base): pass\n\n self.assertEqual(len(Test.defined_attributes), 0)\n\n Test.define_attributes(['id', 'name', 'name'])\n self.assertEqual(len(Test.defined_attributes), 2)\n\n Test.define_attributes(['foo_id', 'foo_id', 'id'])\n self.assertEqual(len(Test.defined_attributes), 3)","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0 #Setting a default value for an Attribute","def __init__(self) -> None:\n self.simulation = None\n self.update_time = 0.1\n self.time = None\n self.config = None","def __init__(self):\r\n\r\n super(Metallized, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.spec_sheet = 0\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def __init__(self):\n self.py = random.getstate()\n self.np = np.random.get_state()\n self.torch = torch.get_rng_state()","def _real_initialize(self):\n pass","def initialise(self):","def init(self) -> None:","def __init__(self, attr=None):\r\n self.attr = attr","def initialize(self) -> None:\n pass","def __init__(self, *args, **kwargs):\n super(AttrDict, self).__init__(*args, **kwargs)\n self.__dict__ = self","def _initialize_node_attributes(dag):\n # Whether each node has been visited by the search yet.\n nx.set_node_attributes(dag, 'visited', False)\n # The title of the row in the mutations dataset corresponding to this node.\n nx.set_node_attributes(dag, 'dataset', None)\n # The function chosen at this (internal) node.\n nx.set_node_attributes(dag, 'function', None)\n # The mutual information of this node's dataset with the phenotype.\n nx.set_node_attributes(dag, 'value', None)\n # The number of genes in the subtree rooted at this node.\n nx.set_node_attributes(dag, 'genes', None)","def initialize(self):\n raise NotImplementedError","def initialize(self):\n raise NotImplementedError","def initialize(self):\n raise NotImplementedError","def initialize(self):\n pass # pragma: no cover","def _manually_initialize(self) -> None:\n # XXX: maybe refactor, this is actually part of the public interface\n pass","def initialize(self):\n\n # --------- BEGIN YOUR CODE ----------\n\n # This is exactly the same as Human.initialize, just copy the code over\n\n # --------- END YOUR CODE ----------\n pass","def initialize_simulation(self) -> Simulation:\n pass","def __init__(self, **kwargs):\n\n args = {\n 'nobs': None, # Number of observations\n 'npred': None, # Number of predictors\n 'nrelpred': None, # Number of relevant predictors\n 'relpos': None, # Position of relevant predictor components\n 'gamma': None, # Decay factor of eigenvalue of predictor\n 'rsq': None, # Coefficient of determination\n 'sim_type': None, # Type of simulation: univariate, bivariate, multivariate\n }\n for key, value in args.items():\n setattr(self, key, value)\n\n for key, value in kwargs.items():\n setattr(self, key, value)","def __init__(self):\r\n\r\n super(Bypass, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.specification = 0 # MIL-C-25 or MIL-C-12889.\r\n self.spec_sheet = 0 #\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0\r\n\r\n# def set_attributes(self, values):\r\n \"\"\"\r\n Method to set the Capacitor data model attributes.\r\n\r\n :param tuple values: tuple of values to assign to the instance\r\n attributes.\r\n :return: (_code, _msg); the error code and error message.\r\n :rtype: tuple\r\n \"\"\"","def __init__(self):\n\n self.start_datetime_wallclock = None;\n self.end_datetime_wallclock = None;\n self.action_count = 0;\n self.reward_cumulative = 0;\n self.mission_xml = None;\n self.mission_type = None;\n self.mission_seed = None;\n self.student_guid = None;\n self.mission_xml_as_expected = None;\n self.is_goal = None;\n self.is_timeout = None;","def __init__(self):\n # initialize a bird to default values.\n self.set_instance_vars()\n\n # randomize some parameters, such as starting height\n self.pos_y = self.random_height()\n\n # tag each bird\n\n self.identifier = Bird.num_birds\n\n # create ai net for each bird\n self.initialize_ai()\n\n # increment Bird counter\n Bird.num_birds += 1\n\n # remember time of birth\n self.birth_time = 0","def initialize(self):","def initialize(self):","def initialize(self):","def initialize(self):","def initialize(self):\n return","def setup(self): \n self.suburbs_dict = dict()\n self.raw_proIds_dict = dict()\n self.propertyIds_dict = dict()\n self.valuations = dict()","def do_init(self):\n\n pass","def _initialize_data(self):\n self.reset_count = 0\n self._idn_no_firmware = \"KEPCO,BOP 50-20,E1234,\"\n self._firmware = 2.6\n self._init_data()","def initialize(self): \r\n pass","def attributes(self):\n ...","def __init__(self):\n # The current information for the environment\n self.state_space = ()\n self.action_space = ()\n self.state = None\n self.reward = 0\n self.terminal = False\n self.truncated = False\n self.info = None","def __init__(self, weight, attributes, first_incident_node, second_incident_node):\n self.weight = weight # initialize all necessary fields\n self.attributes = attributes\n self.first_incident_node = first_incident_node\n self.second_incident_node = second_incident_node","def _init(self):\n pass","def __init__(self, simulator):\r\n self.initialize(simulator)","def initialize(cls):","def _instantiate_attributes_before_function(self, context=None):\n\n super()._instantiate_attributes_before_function(context=context)"],"string":"[\n \"def init_attrs(self):\\n raise NotImplementedError\",\n \"def _init_attributes(self):\\n self.attr = {\\n 'name': None,\\n 'tags': [],\\n 'openHours': None,\\n 'type': None,\\n 'parent': None,\\n 'locationId': None,\\n 'bannerAbbreviation': None,\\n 'arcGisAbbreviation': None,\\n 'geoLocation': None,\\n 'geometry': None,\\n 'summary': None,\\n 'description': None,\\n 'descriptionHtml': None,\\n 'address': None,\\n 'city': None,\\n 'state': None,\\n 'zip': None,\\n 'county': None,\\n 'telephone': None,\\n 'fax': None,\\n 'thumbnails': [],\\n 'images': [],\\n 'departments': [],\\n 'website': None,\\n 'sqft': None,\\n 'calendar': None,\\n 'campus': None,\\n 'girCount': None,\\n 'girLimit': False,\\n 'girLocations': None,\\n 'synonyms': [],\\n 'bldgId': None,\\n 'parkingZoneGroup': None,\\n 'propId': None,\\n 'adaParkingSpaceCount': None,\\n 'motorcycleParkingSpaceCount': None,\\n 'evParkingSpaceCount': None,\\n 'weeklyMenu': None,\\n 'notes': None,\\n 'labels': {},\\n 'steward': None,\\n 'shape': {}\\n }\",\n \"def __init__(self, **initial_attributes):\\n\\n for attribute_name, attribute_value in initial_attributes.items():\\n setattr(self, attribute_name, attribute_value)\",\n \"def initAttributes(self):\\n Packet.initAttributes(self)\\n self.packetType = TM_PACKET_TYPE\",\n \"def __init__(self, **attributes):\\n for key, value in attributes.items():\\n setattr(self, key, value)\",\n \"def _init_attributes(self):\\n if os.name == \\\"nt\\\":\\n if \\\"64\\\" in platform.architecture()[0]:\\n platform_arch = \\\"x86_64\\\"\\n elif \\\"32\\\" in platform.architecture()[0]:\\n platform_arch = \\\"i386\\\"\\n else:\\n platform_arch = platform.architecture()\\n os_ver = f\\\"Windows-{platform.win32_ver()[1]}\\\"\\n else:\\n platform_arch = platform.machine()\\n if platform.system() == \\\"Darwin\\\":\\n os_ver = f\\\"macOS-{platform.mac_ver()[0]}\\\"\\n else:\\n os_ver = \\\"-\\\".join(linux_distribution()[0:2])\\n\\n license_chunks = LICENSE.split(\\\" \\\")\\n if license_chunks[0] == \\\"GPLv2\\\":\\n client_license = \\\"GPL-2.0\\\"\\n else:\\n client_license = \\\"Commercial\\\"\\n\\n default_attributes = {\\n # Process id\\n \\\"_pid\\\": str(os.getpid()),\\n # Platform architecture\\n \\\"_platform\\\": platform_arch,\\n # OS version\\n \\\"_os\\\": os_ver,\\n # Hostname of the local machine\\n \\\"_source_host\\\": socket.gethostname(),\\n # Client's name\\n \\\"_client_name\\\": \\\"mysql-connector-python\\\",\\n # Client's version\\n \\\"_client_version\\\": \\\".\\\".join([str(x) for x in VERSION[0:3]]),\\n # Client's License identifier\\n \\\"_client_license\\\": client_license,\\n }\\n self._settings[\\\"attributes\\\"].update(default_attributes)\\n\\n if \\\"connection-attributes\\\" in self._settings:\\n for attr_name in self._settings[\\\"connection-attributes\\\"]:\\n attr_value = self._settings[\\\"connection-attributes\\\"][attr_name]\\n # Validate name type\\n if not isinstance(attr_name, str):\\n raise InterfaceError(\\n f\\\"Attribute name '{attr_name}' must be a string type\\\"\\n )\\n # Validate attribute name limit 32 characters\\n if len(attr_name) > 32:\\n raise InterfaceError(\\n f\\\"Attribute name '{attr_name}' exceeds 32 characters \\\"\\n \\\"limit size\\\"\\n )\\n # Validate names in connection-attributes cannot start with \\\"_\\\"\\n if attr_name.startswith(\\\"_\\\"):\\n raise InterfaceError(\\n \\\"Key names in 'session-connect-attributes' cannot \\\"\\n f\\\"start with '_', found: {attr_name}\\\"\\n )\\n # Validate value type\\n if not isinstance(attr_value, str):\\n raise InterfaceError(\\n f\\\"Attribute name '{attr_name}' value '{attr_value}' \\\"\\n \\\" must be a string type\\\"\\n )\\n\\n # Validate attribute value limit 1024 characters\\n if len(attr_value) > 1024:\\n raise InterfaceError(\\n f\\\"Attribute name '{attr_name}' value: '{attr_value}' \\\"\\n \\\"exceeds 1024 characters limit size\\\"\\n )\\n\\n self._settings[\\\"attributes\\\"][attr_name] = attr_value\",\n \"def _init_node_attributes(self):\\n assert False\",\n \"def test_attributes(sim):\\n config = {\\n \\\"mass\\\": 1,\\n \\\"spring_constant\\\": 1,\\n \\\"pusher\\\": \\\"ForwardEuler\\\",\\n \\\"x0\\\": [[0, 1, 0]],\\n }\\n block = BlockOnSpring(sim, config)\\n # These attributes get set in __init__()\\n assert block.mass == 1\\n assert block.spring_constant == 1\\n np.testing.assert_allclose(block.position, [[0, 0, 0]])\\n block.initialize()\\n # These attributes get set in initialize()\\n np.testing.assert_allclose(block.position, config[\\\"x0\\\"])\",\n \"def initAttributes(self):\\n Packet.initAttributes(self)\\n self.packetType = TC_PACKET_TYPE\",\n \"def initAttributes(self):\\n CCSDS.DU.DataUnit.initAttributes(self)\\n self.dataFieldHeaderFlag = 0\\n self.setPacketLength()\",\n \"def __init__(self, simulation_attributes):\\n for attr in ['locations','dprime_fnc','next_fixation',\\n 'threshold', 'num_of_searches']:\\n if getattr(simulation_attributes,attr) is None:\\n assert False, (\\n \\\"Precondition violation: none attribute in simulation_attributes \\\"\\n + attr\\n )\\n if not isinstance(simulation_attributes, SimulationAttributes):\\n raise TypeError(\\n \\\"The argument isn't an instance of SimulationAttributes class\\\"\\n )\\n self.senzory_map = self._locations_to_senzory_map(\\n simulation_attributes.locations\\n )\\n self.number_of_locs = self.senzory_map.shape[0]\\n self.dprime_fnc = simulation_attributes.dprime_fnc\\n self.dprime_map = generate_dprime_map(self.dprime_fnc,self.senzory_map)\\n self.next_fixation = simulation_attributes.next_fixation\\n self.threshold = simulation_attributes.threshold\\n self.num_of_searches = simulation_attributes.num_of_searches\",\n \"def _set_attr(self):\\n self.as_skeletal = self._import_as_skeleton()\\n self.materials = self._import_materials()\\n self.textures = self._import_textures()\",\n \"def __init__(self):\\n\\n try:\\n # read attributes from attributes file\\n with open(const.Storage.ATTRIBUTES) as attributes_file:\\n # read the file and parse it to JSON data\\n json_data = attributes_file.read()\\n attributes = json.loads(json_data)\\n\\n # set attributes\\n self.id = str(attributes[\\\"id\\\"])\\n self.length = float(attributes[\\\"length\\\"])\\n self.width = float(attributes[\\\"width\\\"])\\n except OSError:\\n raise OSError(\\\"The attributes file could not be opened.\\\")\",\n \"def init_attributes(self):\\n # Set default values\\n for key, value in self.defaults.items():\\n setattr(self, key, value)\\n\\n # Parse all arguments in kwargs\\n for key, value in self.kwargs.items():\\n parsed_value = eval_arg(value, key)\\n logging.info('Setting ' + str(type(parsed_value)) + ' self.' + str(key) + ' = ' + str(parsed_value))\\n setattr(self, key, parsed_value)\\n\\n # self.today = date_utils.get_datetime_from_timezone(self.date_offset, self.timezone)\\n self.today = datetime.datetime.today()\",\n \"def __init__(self, **attrs):\\n \\n # set given attributes\\n for name, value in attrs.items():\\n if hasattr(self, name):\\n setattr(self, name, value)\\n else:\\n raise AttributeError(\\\"Attribute not found! --> %s\\\" % name)\",\n \"def __init__(self, attributes_names: list):\\r\\n self.attributes_names = attributes_names\",\n \"def __init__(self, attributes_names: list):\\r\\n self.attributes_names = attributes_names\",\n \"def test_default_attributes_set_in_init(self):\\n\\n x = BaseTransformer()\\n\\n expected_attributes = {\\n \\\"version_\\\": tubular._version.__version__,\\n \\\"verbose\\\": False,\\n \\\"columns\\\": None,\\n \\\"copy\\\": True,\\n }\\n\\n h.test_object_attributes(\\n obj=x,\\n expected_attributes=expected_attributes,\\n msg=\\\"Default attributes set in init\\\",\\n )\",\n \"def __init__(self, attrs = None):\\n\\n if attrs != None:\\n self.__dict__.update(attrs)\",\n \"def __init__(self):\\n self.time_limit = None\\n self.time_step = None\\n self.robot = None\\n self.humans = None\\n self.global_time = None\\n self.human_times = None\\n # reward function\\n self.discomfort_dist = None\\n # simulation configuration\\n self.config = None\\n self.randomize_attributes = None\\n self.square_width = None\\n self.circle_radius = None\\n self.human_num = None\\n # for visualization\\n self.states = None\\n self.states_traj = None\",\n \"def _set_attributes(self):\",\n \"def test_init(self, r, attribute_tup):\\n num_wires, num_gates, gate_types, gate_sizes, depth, shots = attribute_tup\\n\\n assert r.num_wires == num_wires\\n assert r.num_gates == num_gates\\n assert r.depth == depth\\n assert r.shots == shots\\n assert r.gate_types == gate_types\\n assert r.gate_sizes == gate_sizes\",\n \"def __init__(self, **attributes):\\n self.set(**attributes)\",\n \"def __init__(self):\\n self.relation = ''\\n self.attributes = []\\n self.attribute_types = dict()\\n self.attribute_data = dict()\\n self.comment = []\\n self.data = []\\n pass\",\n \"def __init__(self, attribs):\\n self.__instanced = False\\n self.__initAccessor(attribs)\\n self.__setValues(attribs)\\n self.__instanced = True\",\n \"def _initialize_attributes(self):\\n height, width = self.image.shape[:2]\\n\\n self.confidence = (1 - self.mask).astype(float)\\n self.data = np.zeros([height, width])\\n\\n self.working_image = np.copy(self.image)\\n self.working_mask = np.copy(self.mask)\",\n \"def init_attributes(self):\\n\\n # Creats two lists: one for x-values, one for y-valuse\\n self.xValues = []\\n self.yValues = []\\n self.colors = []\\n\\n # Colors of the graph\\n self.saveColor = \\\"limegreen\\\"\\n self.currentColor = \\\"r\\\"\\n self.unsavedColor = \\\"royalblue\\\"\\n\\n # Initialization of some other attributes\\n self.filename = None\\n self.mouseY = 0\\n\\n self.startIndex = 0\\n self.current_position = 0\\n self.position_index = 0\\n\\n self.savedRecently = False\\n self.videoOpened = False\\n\\n self.animation = None\\n self.curve = None\\n self.k = 25\\n self.dt = self.k\",\n \"def __init__(self, events={}, attributes={}):\\n self.events = events.copy()\\n self.attributes = attributes.copy()\\n if not AT.VALUE_STRATEGY in self.attributes:\\n self.attributes[AT.VALUE_STRATEGY] = ValueStrategy.PRESET\",\n \"def initialize_object_attributes(self, ai_game):\\r\\n self.ship_bullets = ai_game.ship_bullets\\r\\n self.alien_bullets = ai_game.alien_bullets\\r\\n self.aliens = ai_game.aliens\\r\\n self.explosions = ai_game.explosions\\r\\n self.stats = ai_game.stats\\r\\n self.sb = ai_game.sb\\r\\n self.ship = ai_game.ship\",\n \"def __init__(self):\\n self.X = None\\n self.Y = None\\n self.features = None\\n self.max = self.min = None\\n self._look_up = None\\n self.attr_weight = None\",\n \"def initialize_attributes(self):\\n self.host = self.netloc\\n self.url = self.geturl()\\n\\n self.set_scheme_if_non('https')\\n \\n # The file extensions we are watching for. Either load the extensions\\n # from a text file, or create a seperate python file contain a list\\n # supported file extensions\\n self.listed_file_extensions = [ \\n '.jpg', '.bmp', '.png',\\n '.mp3', '.mp4', '.flv', '.avi',\\n '.zip', '.7z', '.tar', '.tar.gz', '.tar.bz', '.rar',\\n '.exe', '.git', '.torrent',\\n ] \\n # Type Boolean: True or False\\n # Urls contain some useful information. Depending on the framework the \\n # website is built on, a url can contain information about paths and files.\\n # This is a glimpse of the sites computer system. Pretty Useful!\\n self.is_file_extension = None # Does this path end as a file?\\n #self.file_extension = self.check_for_file_extension()\",\n \"def __init__(self, attributes=None):\\n super().__init__(attributes)\\n \\n # processing parameters\\n self.set = _Settings()\\n\\n # results storage\\n self.measure_time = None # store here in case we average FIDs, filled by chain!\\n self.frequency_shift = None\\n self.phase_0 = None\\n self.data = None\\n \\n if attributes is not None:\\n self.inflate(attributes)\\n\\n self.chain = None\",\n \"def initialize(self):\\n\\t\\tpass\",\n \"def initialise_sim(self):\\n pass\",\n \"def _init_generate_physical_attributes(self):\\n # Prepare these now, for speedier access\\n config = self.person.cosmos.config\\n year = self.person.cosmos.year\\n male = self.person.male\\n # Determine age of physical peak, i.e., baseball prime\\n self.age_of_physical_peak = config.determine_age_of_physical_peak()\\n # Determine handedness\\n self.lefty = True if random.random() < config.chance_of_being_left_handed else False\\n self.righty = not self.lefty\\n self.left_handed = 1.0 if self.lefty else 0.0\\n self.right_handed = 1.0 if self.righty else 0.0\\n # Determine hustle\\n self.hustle = config.determine_hustle()\\n # Determine adult height this person will attain, in inches\\n if male:\\n self.adult_height = normal(\\n config.adult_male_height_mean(year=year), config.adult_male_height_sd(year=year)\\n )\\n else:\\n self.adult_height = normal(\\n config.adult_female_height_mean(year=year), config.adult_female_height_sd(year=year)\\n )\\n # Determine this person's BMI TODO BMI INCREASES AS ADULTHOOD PROGRESSES\\n if male:\\n self.bmi = normal(\\n config.young_adult_male_bmi_mean(year=year), config.young_adult_male_bmi_sd(year=year)\\n )\\n else:\\n self.bmi = normal(\\n config.young_adult_female_bmi_mean(year=year), config.young_adult_female_bmi_sd(year=year)\\n )\\n # Determine propensities for coordination, reflexes, agility, jumping...\\n self.coordination_propensity = config.determine_coordination_propensity()\\n self.reflexes_propensity = config.determine_reflexes_propensity(\\n coordination_propensity=self.coordination_propensity\\n )\\n self.agility_propensity = config.determine_agility_propensity()\\n self.jumping_propensity = config.determine_jumping_propensity() # Number of inches added/subtracted to base\\n # ...and finally footspeed propensity, which is a bit more convoluted to compute\\n primitive_coordination = config.determine_primitive_coordination(bmi=self.bmi) if self.bmi > 24 else 1.0\\n adult_coordination = primitive_coordination * self.coordination_propensity\\n primitive_footspeed = config.determine_primitive_footspeed(\\n coordination=adult_coordination, height=self.adult_height\\n )\\n self.footspeed_propensity = config.determine_footspeed_propensity(primitive_footspeed=primitive_footspeed)\\n # Finally, fit these potentials to the person's current age\\n self.develop()\",\n \"def __init__(self, attributes=None):\\n self.coil_combine_method = 'Siemens'\\n self.fids_to_average = 1\\n self.fid_left_shift = 0\\n self.gaussian_apodization = 2.0\\n self.apply_peak_shift = True\\n self.reference_peak_center = 2.01\\n self.peak_search_width = 0.2\\n self.apply_phase0 = True\\n self.phase0_range_start = 3.5\\n self.phase0_range_end = 0.5\\n self.global_phase0 = 0.0\\n self.global_phase1 = 0.0\\n \\n if attributes is not None:\\n self.inflate(attributes)\",\n \"def __init__(self, attrs: Dict[str, Any]) -> None:\\n self.attrs = attrs\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def __attrs_post_init__(self):\",\n \"def _set_conf_attrs():\\n\\tdebug_msg = \\\"initializing the configuration\\\"\\n\\tlogger.debug(debug_msg)\",\n \"def __init__(self):\\n self.swagger_types = {\\n 'maximum_over_capacity': 'float',\\n 'minimum_health_capacity': 'float'\\n }\\n\\n self.attribute_map = {\\n 'maximum_over_capacity': 'maximumOverCapacity',\\n 'minimum_health_capacity': 'minimumHealthCapacity'\\n }\\n\\n self._maximum_over_capacity = None\\n self._minimum_health_capacity = None\",\n \"def initialize(self):\\r\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def __init__(self,\\n *,\\n attributes: List['Attribute'] = None) -> None:\\n self.attributes = attributes\",\n \"def initialize(self, **kwargs):\",\n \"def __init__(self, *args):\\n _snap.TAttr_swiginit(self, _snap.new_TAttr(*args))\",\n \"def initialize(self):\\n self.initialize_edges()\\n self.initialize_prob()\\n self.initialize_total_input_dict()\\n\\n self.initialize_fpmusigv_dict()\",\n \"def test_define_attributes(self):\\n\\n class Test(pyperry.Base): pass\\n\\n self.assertEqual(len(Test.defined_attributes), 0)\\n\\n Test.define_attributes(['id', 'name', 'name'])\\n self.assertEqual(len(Test.defined_attributes), 2)\\n\\n Test.define_attributes(['foo_id', 'foo_id', 'id'])\\n self.assertEqual(len(Test.defined_attributes), 3)\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0 #Setting a default value for an Attribute\",\n \"def __init__(self) -> None:\\n self.simulation = None\\n self.update_time = 0.1\\n self.time = None\\n self.config = None\",\n \"def __init__(self):\\r\\n\\r\\n super(Metallized, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.spec_sheet = 0\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def __init__(self):\\n self.py = random.getstate()\\n self.np = np.random.get_state()\\n self.torch = torch.get_rng_state()\",\n \"def _real_initialize(self):\\n pass\",\n \"def initialise(self):\",\n \"def init(self) -> None:\",\n \"def __init__(self, attr=None):\\r\\n self.attr = attr\",\n \"def initialize(self) -> None:\\n pass\",\n \"def __init__(self, *args, **kwargs):\\n super(AttrDict, self).__init__(*args, **kwargs)\\n self.__dict__ = self\",\n \"def _initialize_node_attributes(dag):\\n # Whether each node has been visited by the search yet.\\n nx.set_node_attributes(dag, 'visited', False)\\n # The title of the row in the mutations dataset corresponding to this node.\\n nx.set_node_attributes(dag, 'dataset', None)\\n # The function chosen at this (internal) node.\\n nx.set_node_attributes(dag, 'function', None)\\n # The mutual information of this node's dataset with the phenotype.\\n nx.set_node_attributes(dag, 'value', None)\\n # The number of genes in the subtree rooted at this node.\\n nx.set_node_attributes(dag, 'genes', None)\",\n \"def initialize(self):\\n raise NotImplementedError\",\n \"def initialize(self):\\n raise NotImplementedError\",\n \"def initialize(self):\\n raise NotImplementedError\",\n \"def initialize(self):\\n pass # pragma: no cover\",\n \"def _manually_initialize(self) -> None:\\n # XXX: maybe refactor, this is actually part of the public interface\\n pass\",\n \"def initialize(self):\\n\\n # --------- BEGIN YOUR CODE ----------\\n\\n # This is exactly the same as Human.initialize, just copy the code over\\n\\n # --------- END YOUR CODE ----------\\n pass\",\n \"def initialize_simulation(self) -> Simulation:\\n pass\",\n \"def __init__(self, **kwargs):\\n\\n args = {\\n 'nobs': None, # Number of observations\\n 'npred': None, # Number of predictors\\n 'nrelpred': None, # Number of relevant predictors\\n 'relpos': None, # Position of relevant predictor components\\n 'gamma': None, # Decay factor of eigenvalue of predictor\\n 'rsq': None, # Coefficient of determination\\n 'sim_type': None, # Type of simulation: univariate, bivariate, multivariate\\n }\\n for key, value in args.items():\\n setattr(self, key, value)\\n\\n for key, value in kwargs.items():\\n setattr(self, key, value)\",\n \"def __init__(self):\\r\\n\\r\\n super(Bypass, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.specification = 0 # MIL-C-25 or MIL-C-12889.\\r\\n self.spec_sheet = 0 #\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\\r\\n\\r\\n# def set_attributes(self, values):\\r\\n \\\"\\\"\\\"\\r\\n Method to set the Capacitor data model attributes.\\r\\n\\r\\n :param tuple values: tuple of values to assign to the instance\\r\\n attributes.\\r\\n :return: (_code, _msg); the error code and error message.\\r\\n :rtype: tuple\\r\\n \\\"\\\"\\\"\",\n \"def __init__(self):\\n\\n self.start_datetime_wallclock = None;\\n self.end_datetime_wallclock = None;\\n self.action_count = 0;\\n self.reward_cumulative = 0;\\n self.mission_xml = None;\\n self.mission_type = None;\\n self.mission_seed = None;\\n self.student_guid = None;\\n self.mission_xml_as_expected = None;\\n self.is_goal = None;\\n self.is_timeout = None;\",\n \"def __init__(self):\\n # initialize a bird to default values.\\n self.set_instance_vars()\\n\\n # randomize some parameters, such as starting height\\n self.pos_y = self.random_height()\\n\\n # tag each bird\\n\\n self.identifier = Bird.num_birds\\n\\n # create ai net for each bird\\n self.initialize_ai()\\n\\n # increment Bird counter\\n Bird.num_birds += 1\\n\\n # remember time of birth\\n self.birth_time = 0\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\\n return\",\n \"def setup(self): \\n self.suburbs_dict = dict()\\n self.raw_proIds_dict = dict()\\n self.propertyIds_dict = dict()\\n self.valuations = dict()\",\n \"def do_init(self):\\n\\n pass\",\n \"def _initialize_data(self):\\n self.reset_count = 0\\n self._idn_no_firmware = \\\"KEPCO,BOP 50-20,E1234,\\\"\\n self._firmware = 2.6\\n self._init_data()\",\n \"def initialize(self): \\r\\n pass\",\n \"def attributes(self):\\n ...\",\n \"def __init__(self):\\n # The current information for the environment\\n self.state_space = ()\\n self.action_space = ()\\n self.state = None\\n self.reward = 0\\n self.terminal = False\\n self.truncated = False\\n self.info = None\",\n \"def __init__(self, weight, attributes, first_incident_node, second_incident_node):\\n self.weight = weight # initialize all necessary fields\\n self.attributes = attributes\\n self.first_incident_node = first_incident_node\\n self.second_incident_node = second_incident_node\",\n \"def _init(self):\\n pass\",\n \"def __init__(self, simulator):\\r\\n self.initialize(simulator)\",\n \"def initialize(cls):\",\n \"def _instantiate_attributes_before_function(self, context=None):\\n\\n super()._instantiate_attributes_before_function(context=context)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7777734","0.76090693","0.74320334","0.72761893","0.7228054","0.7202334","0.71504873","0.7127317","0.7108561","0.69425267","0.6913489","0.6910913","0.6900658","0.68599164","0.6859132","0.6831615","0.6831615","0.67674065","0.67426705","0.6740823","0.6733855","0.66997516","0.66840065","0.66798604","0.6652513","0.66459066","0.66416025","0.6626057","0.6624812","0.66014326","0.6594169","0.65858406","0.6548944","0.6506917","0.6505685","0.64967185","0.6478724","0.6474211","0.6474211","0.6474211","0.6474211","0.6474211","0.6463688","0.6451572","0.6441506","0.6432246","0.6432246","0.6415201","0.6415201","0.6415201","0.6407418","0.63983357","0.6391286","0.6388205","0.6377866","0.63774097","0.6359868","0.6338618","0.6321825","0.6321825","0.6321825","0.6321825","0.6321825","0.6321825","0.6321825","0.6321825","0.63081336","0.6305243","0.6303574","0.6300456","0.6296746","0.6291966","0.6289484","0.6267232","0.6263998","0.6263998","0.6263998","0.625899","0.62576413","0.62528396","0.62480605","0.62467504","0.6246213","0.6243685","0.62432396","0.62368953","0.62368953","0.62368953","0.62368953","0.6235012","0.62290037","0.6209659","0.62024313","0.61906606","0.6184706","0.6184682","0.61789227","0.6178424","0.6173529","0.61726075","0.6171651"],"string":"[\n \"0.7777734\",\n \"0.76090693\",\n \"0.74320334\",\n \"0.72761893\",\n \"0.7228054\",\n \"0.7202334\",\n \"0.71504873\",\n \"0.7127317\",\n \"0.7108561\",\n \"0.69425267\",\n \"0.6913489\",\n \"0.6910913\",\n \"0.6900658\",\n \"0.68599164\",\n \"0.6859132\",\n \"0.6831615\",\n \"0.6831615\",\n \"0.67674065\",\n \"0.67426705\",\n \"0.6740823\",\n \"0.6733855\",\n \"0.66997516\",\n \"0.66840065\",\n \"0.66798604\",\n \"0.6652513\",\n \"0.66459066\",\n \"0.66416025\",\n \"0.6626057\",\n \"0.6624812\",\n \"0.66014326\",\n \"0.6594169\",\n \"0.65858406\",\n \"0.6548944\",\n \"0.6506917\",\n \"0.6505685\",\n \"0.64967185\",\n \"0.6478724\",\n \"0.6474211\",\n \"0.6474211\",\n \"0.6474211\",\n \"0.6474211\",\n \"0.6474211\",\n \"0.6463688\",\n \"0.6451572\",\n \"0.6441506\",\n \"0.6432246\",\n \"0.6432246\",\n \"0.6415201\",\n \"0.6415201\",\n \"0.6415201\",\n \"0.6407418\",\n \"0.63983357\",\n \"0.6391286\",\n \"0.6388205\",\n \"0.6377866\",\n \"0.63774097\",\n \"0.6359868\",\n \"0.6338618\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.6321825\",\n \"0.63081336\",\n \"0.6305243\",\n \"0.6303574\",\n \"0.6300456\",\n \"0.6296746\",\n \"0.6291966\",\n \"0.6289484\",\n \"0.6267232\",\n \"0.6263998\",\n \"0.6263998\",\n \"0.6263998\",\n \"0.625899\",\n \"0.62576413\",\n \"0.62528396\",\n \"0.62480605\",\n \"0.62467504\",\n \"0.6246213\",\n \"0.6243685\",\n \"0.62432396\",\n \"0.62368953\",\n \"0.62368953\",\n \"0.62368953\",\n \"0.62368953\",\n \"0.6235012\",\n \"0.62290037\",\n \"0.6209659\",\n \"0.62024313\",\n \"0.61906606\",\n \"0.6184706\",\n \"0.6184682\",\n \"0.61789227\",\n \"0.6178424\",\n \"0.6173529\",\n \"0.61726075\",\n \"0.6171651\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14989,"cells":{"query":{"kind":"string","value":"Return inputted mouse position."},"document":{"kind":"string","value":"def get_mouse_pos(self):\n return self.mouse_pos"},"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 get_mouse_position(self):\n raise NotImplementedError","def mousePos():\n data = display.Display().screen().root.query_pointer()._data\n return data[\"root_x\"], data[\"root_y\"]","def read_current_mouse_position():\n import pyautogui\n pyautogui.FAILSAFE = False\n return pyautogui.position()","def mouse_position(self):\r\n # TODO: add: Now deprecated in favor of pi3d.events\r\n if self.mouse:\r\n return self.mouse.position()\r\n elif self.tkwin:\r\n return self.tkwin.winfo_pointerxy()\r\n else:\r\n return -1, -1","def getMousePosition(self):\n return (self.mouseData.x, self.mouseData.y)","def mousePosition(self):","def getMouse():\n return pygame.mouse.get_pos()","def mousePositionRaw(self):","def mousePositionRaw(self):","def mouse_motion_current_mouse_position() -> EventType:\n x, y = pygame.mouse.get_pos()\n return pygame.event.Event(pygame.MOUSEMOTION, {'pos': (int(x), int(y))})","def get_mouse_pos(new_x_coord, new_y_coord):\n\n x_change = 0\n y_change = 0\n \n # if the joystick returned to its default position (0,0), stop mouse movement\n if not (new_x_coord == 0 and new_y_coord == 0):\n if new_x_coord == 0:\n x_change = 0\n else:\n x_change = new_x_coord\n\n if new_y_coord == 0:\n y_change = 0\n else:\n y_change = -new_y_coord\n \n return (int(x_change), int(y_change))","def mouse_position_event(self, x: int, y: int):\n pass","def get_mouse():\n if CONST.render == 'sfml':\n mouse_pos = Vector2(sfml.Mouse.get_position())/engine.screen_diff_ratio+engine.get_origin_pos()\n return mouse_pos,\\\n [sfml.Mouse.is_button_pressed(sfml.Mouse.LEFT),\n sfml.Mouse.is_button_pressed(sfml.Mouse.RIGHT),\n sfml.Mouse.is_button_pressed(sfml.Mouse.MIDDLE)]\n elif CONST.render == 'pookoo':\n return Vector2(pookoo.input.mouse.position()), [\n False,False,False\n ]\n elif CONST.render == 'kivy':\n return Vector2(), [False,False,False]","def pos(self):\n x = (self.ec._win._mouse_x -\n self.ec._win.width / 2.) / (self.ec._win.width / 2.)\n y = (self.ec._win._mouse_y -\n self.ec._win.height / 2.) / (self.ec._win.height / 2.)\n return np.array([x, y])","def getMouse(self):\n self.mouseX = None\n self.mouseY = None\n while self.mouseX == None or self.mouseY == None:\n #self.update()\n _tkCall(self.update)\n if self.isClosed(): raise GraphicsError, \"getMouse in closed window\"\n time.sleep(.1) # give up thread\n x,y = self.toWorld(self.mouseX, self.mouseY)\n self.mouseX = None\n self.mouseY = None\n return Point(x,y)","def get_mouse_coordinate(self):\n pos = pygame.mouse.get_pos()\n mov = pygame.mouse.get_rel()\n row = pos[0] // (self.CELL_WIDTH + self.MARGIN)\n col = (pos[1] - self.PANEL_HEIGHT) // (self.CELL_WIDTH + self.MARGIN)\n if mov != (0, 0) and not self.env.not_in_grid(row, col):\n return (row, col)\n return self.markerPos","def mouse_coords(desktop=False):\n x, y = c_int(0), c_int(0)\n if desktop:\n mouse.SDL_GetGlobalMouseState(byref(x), byref(y))\n else:\n mouse.SDL_GetMouseState(byref(x), byref(y))\n return (int(x.value), int(y.value))","def getMouse(self):\n self.update() # flush any prior clicks\n self.mouseX = None\n self.mouseY = None\n while self.mouseX == None or self.mouseY == None:\n self.update()\n if self.isClosed(): raise GraphicsError(\"getMouse in closed window\")\n time.sleep(.1) # give up thread\n x,y = self.mouseX, self.mouseY\n self.mouseX = None\n self.mouseY = None\n return Point(x,y)","def mouse_delta():\n x, y = c_int(0), c_int(0)\n mouse.SDL_GetRelativeMouseState(byref(x), byref(y))\n return (int(x.value), int(y.value))","def show_mouse_position_with_px(self):\n self.main_menu_greets_fonts = pygame.font.Font(os.path.join(PATH_TO_RESOURCE, 'font_forever.ttf'), 10)\n self.positiontext(f'Mouse position {pygame.mouse.get_pos()}', (770, 20))\n self.mouse = pygame.mouse.get_pos()\n return self.mouse","def mouse_position(pos):\n x, y = pos\n m = x // SQUARE_SIZE\n n = y // SQUARE_SIZE\n return n, m","def get_position():\n return win32api.GetCursorPos()","def getInputPoint(self):\n return self[0].getPoint()","def convert_mousepos(self, pos):\n tokenx, tokeny = pos\n row = int((tokenx - self.x_margin) / SPACESIZE)\n column = int((tokeny - self.y_margin) / SPACESIZE)\n return column, row","def checkMouse(self):\n if self.isClosed():\n raise GraphicsError, \"checkMouse in closed window\"\n _tkCall(self.update)\n if self.mouseX != None and self.mouseY != None:\n x,y = self.toWorld(self.mouseX, self.mouseY)\n self.mouseX = None\n self.mouseY = None\n return Point(x,y)\n else:\n return None","def mouse_move(camera, mouse, width, height):\n # distance moved from screen center\n # Using the '//' operator (floor division) to produce an integer result\n x = width//2 - mouse.position[0]\n y = height//2 - mouse.position[1]\n \n # intialize mouse so it doesn't jerk first time\n try:\n camera['mouseInit']\n except KeyError:\n x = 0\n y = 0\n # bug in Add Property\n # can't use True. Have to use 1\n camera['mouseInit'] = 1\n\n logger.debug(\"Read displacement: %s, %s\" % (x, y))\n \n # return mouse movement\n return (x, y)","def cursorPosGL(self):\n globalPos = QtGui.QCursor.pos()\n pos = self.mapFromGlobal(globalPos)\n y = self.size().height() - pos.y()\n return pos.x(), y","def cursorPosGL(self):\n globalPos = QtGui.QCursor.pos()\n pos = self.mapFromGlobal(globalPos)\n y = self.size().height() - pos.y()\n return pos.x(), y","def handle_mouse(self, x, y):\n pass","def checkMouse(self):\n if self.isClosed():\n raise GraphicsError(\"checkMouse in closed window\")\n self.update()\n if self.mouseX != None and self.mouseY != None:\n x,y = self.mouseX, self.mouseY\n self.mouseX = None\n self.mouseY = None\n return Point(x,y)\n else:\n return None","def mouse_move(human, mouse, width, height):\n # distance moved from screen center\n # Using the '//' operator (floor division) to produce an integer result\n x = width//2 - mouse.position[0]\n y = height//2 - mouse.position[1]\n\n # intialize mouse so it doesn't jerk first time\n try:\n human['mouseInit']\n except KeyError:\n x = 0\n y = 0\n # bug in Add Property\n # can't use True. Have to use 1\n human['mouseInit'] = 1\n\n # return mouse movement\n return (x, y)","def print_mouse_positions() -> None:\n mouse_pos = ctrl.mouse_pos()\n print(f\"Absolute mouse pos: {mouse_pos}\")\n screen = ui.main_screen().rect\n print(f\"Main screen: {screen}\")\n for corner in [\n Corner.TOP_LEFT,\n Corner.TOP_RIGHT,\n Corner.BOTTOM_LEFT,\n Corner.BOTTOM_RIGHT,\n ]:\n corner_pos = Corner.absolute_position(corner)\n relative = (mouse_pos[0] - corner_pos[0], mouse_pos[1] - corner_pos[1])\n print(f\"Position relative to {corner}: {relative}\")","def report_mouse_position(x_pos=0, y_pos=0):\n print('x-axis:', x_pos, ' Y-axis: ', y_pos, flush=True)","def get_last_click_x():\r\n return _cue.getMouseLocation().getX()","def cursorPosQt(self):\n globalPos = QtGui.QCursor.pos()\n pos = self.mapFromGlobal(globalPos)\n return pos.x(), pos.y()","def cursorPosQt(self):\n globalPos = QtGui.QCursor.pos()\n pos = self.mapFromGlobal(globalPos)\n return pos.x(), pos.y()","def getPosition(self):\n return self.x","def _mouse_event(pos=QPointF(), type_=QEvent.MouseMove):\n return QMouseEvent(type_, QPointF(pos), Qt.LeftButton, Qt.LeftButton, Qt.NoModifier)","def get_mouse_movement(self, index=0):\r\n return self.handler.get_rel_movement(index)","def get_pick_position(self):\n x0 = int(self.GetPickX1())\n x1 = int(self.GetPickX2())\n y0 = int(self.GetPickY1())\n y1 = int(self.GetPickY2())\n return x0, y0, x1, y1","def OnMouseDown(self, evt):\n self.CaptureMouse()\n self.x, self.y = self.lastx, self.lasty = evt.GetPosition()","def pos(self, x, y):\n\n if isinstance(x, float):\n x = int(x)\n\n self.screen.write(colorama.Cursor.POS(x, y), ansi=True)\n self.x = x\n self.y = y\n\n return x, y","def grid_to_mouse( pos ):\n ix,iy=pos\n px= ix*CELLSIZE + H_CELLSIZE + ix*CELLGAP\n py= iy*CELLSIZE + H_CELLSIZE + iy*CELLGAP\n return (px,py)","def cursor_position(self) -> Point:\n cpos = self.ui_content.cursor_position\n try:\n y, x = self._rowcol_to_yx[cpos.y, cpos.x]\n except KeyError:\n # For `DummyControl` for instance, the content can be empty, and so\n # will `_rowcol_to_yx` be. Return 0/0 by default.\n return Point(x=0, y=0)\n else:\n return Point(x=x - self._x_offset, y=y - self._y_offset)","def update(self):\n # Get the current mouse position. This returns the position\n # as a list of two numbers.\n pos = pygame.mouse.get_pos()\n \n # Set the player x position to the mouse x position\n self.rect.x = pos[0]","def on_mouse_move(self, event: PointEvent):\n self.x = event.x\n self.y = event.y\n self.handle_mouse(self.x, self.y)","def getPos(self):\n return self.Xpos,self.Ypos","def get_cursor_pos(self):\n return (self.text_maker.pos[0] + 9, self.text_maker.pos[1] + 120 + 8)","def normal_mouse_move(self, event):\n plot = self.component\n if plot is not None:\n if isinstance(plot, BaseXYPlot):\n ndx = plot.map_index((event.x, event.y), index_only = True)\n x = plot.index.get_data()[ndx]\n y = plot.value.get_data()[ndx]\n print self.format % (x,y)\n else:\n print \"dataprinter: don't know how to handle plots of type\",\n print plot.__class__.__name__\n return","def get_mouse_state(self):\n try:\n mouse_state = curses.getmouse()\n return mouse_state\n except:\n self.app.log(get_error_info())\n return False","def x(self):\r\n return self.position.x","def update(self):\n # Get the current mouse position. This returns the position\n # as a list of two numbers.\n pos = pygame.mouse.get_pos()\n\n # Set the player x position to the mouse x position\n self.rect.x = pos[0]","def getPosition(self):\n return self.target, min(self.points), max(self.points)","def get_pos(self):\n return (self.x, self.y)","def _get_x(self):\n return self.position.x","def get_x(self):\n return self.posX","def _get_cursor_physical_position(self) :\n return self._cursor_physical_position","def follow(self):\n\t\tpos = pygame.mouse.get_pos()\n\t\tself.x = pos[0]\n\t\tself.y = pos[1]\n\t\tself.draw()","def grab(self, event):\n self.ypos = event.y\n self.xpos = event.x\n self.config(cursor='fleur')","def handle_input(self, ncode, wparam, lparam):\n x_pos = lparam.contents.x_pos\n y_pos = lparam.contents.y_pos\n data = lparam.contents.mousedata\n\n # This is how we can distinguish mouse 1 from mouse 2\n # extrainfo = lparam.contents.extrainfo\n # The way windows seems to do it is there is primary mouse\n # and all other mouses report as mouse 2\n\n # Also useful later will be to support the flags field\n # flags = lparam.contents.flags\n # This shows if the event was from a real device or whether it\n # was injected somehow via software\n\n self.emulate_mouse(wparam, x_pos, y_pos, data)\n\n # Give back control to Windows to wait for and process the\n # next event\n return ctypes.windll.user32.CallNextHookEx(\n self.hooked, ncode, wparam, lparam)","def cursor_coordinates(self):\n text = self.getText()\n lines = text.split(\"\\n\")\n pos = self.getCursorPos()\n if pos == 0:\n return (0, 0)\n i = 0\n cursor_row = -1\n cursor_col = -1\n for row, line in enumerate(lines):\n i += len(line) + 1 # we need to include \"\\n\"\n if pos < i:\n cursor_row = row\n cursor_col = pos - i + len(line) + 1\n break\n return (cursor_col, cursor_row)","def position(self):\n return self.x, self.y","def clickCell(self, event):\n position = self.input.checkMouseInput(event)\n if not position:\n return None\n x = math.floor(position[0] / self.imageWidth)\n y = math.floor(position[1] / self.imageHeight)\n return (int(x), int(y))","def follow_mouse(self, mouse):\n half_width = self.width() / 2\n self.left = mouse.get_x() - half_width\n self.right = mouse.get_x() + half_width","def get_pos(self):\r\n return self.pos","def get_pos_x(self):\n return self.__pos_x","def position(self):\n index = self._ordered_input_names.index('position')\n return self._inputs[index]","def getX(self):\n return self.position[0]","def getMouseClick(plotcoords = 0):\n coords = dislin.csrpt1()\n if plotcoords:\n return coords\n else:\n return dislin.xinvrs(coords[0]), dislin.yinvrs(coords[1])","def getPosition(self):\n\tleft = self.getLeft()\n\ttop = self.getTop()\n\treturn (left,top)","def handle_mouse(obj, event):\n if event:\n x = event.globalX()\n y = event.globalY()\n x_w = obj.offset.x()\n y_w = obj.offset.y()\n obj.move(x - x_w, y - y_w)","def getPos(self):\n return self.__current_pos","def getX(self):\n return self.position.getX()","def get_position(self):\n return (self.x_pos, self.y_pos)","def mousePressed(index):\n return (pygame.mouse.get_pressed()[index])","def get_coords(self):\n while True:\n try:\n coords = tuple(map(int, input('y, x = ').split(',')))\n while not self.correct_player_turn(coords):\n coords = tuple(map(int, input('y, x = ').split(','))) # coordinates as a tuple\n return coords\n except ValueError:\n print(\"Oppsy daisy! That's not a corect input! 'x,y'\")","def update(self):\n self.x = games.mouse.x\n #self.y = games.mouse.y\n self.check_collide()","def mouseResponse(event, x, y, flags, param):\n global uppLeft, lowLeft, uppRight\n if event == cv2.EVENT_LBUTTONDOWN:\n cv2.circle(workImg, (x, y), 5, (0, 255, 255), -1)\n if uppLeft is None:\n uppLeft = [x, y]\n print(\"x = \", x, \"y = \", y)\n elif lowLeft is None:\n lowLeft = [x, y]\n print(\"x = \", x, \"y = \", y)\n elif uppRight is None:\n uppRight = [x, y]\n print(\"x = \", x, \"y = \", y)","def get_cursor() -> CursorInputType:\n try:\n return pygame.mouse.get_cursor()\n except TypeError as e:\n warn(str(e))\n return None","def get_point(self):\n return self._x, self._y","def __on_click(self,event, x, y, p1, p2): \r\n \r\n # global variables of the class with mouse click position\r\n global mouse_click_pos, mouse_click_list \r\n \r\n mouse_click_list = []\r\n if event == cv2.EVENT_LBUTTONDOWN:\r\n \r\n mouse_click_pos = (x,y)\r\n print(mouse_click_pos)\r\n mouse_click_list.append((x, y))","def get_pos(self):\n return self.pos","def mouse_move_callback(self, event):\n # TODO drag and drop figuriek\n print(\"moving at \", event.x + self.offset_x, event.y + self.offset_y)","def position(self):\n return self._x, self._y","def get_mouse_state() -> MouseState:\n xy = ffi.new(\"int[2]\")\n buttons = lib.SDL_GetMouseState(xy, xy + 1)\n x, y = _pixel_to_tile(*xy)\n return MouseState((xy[0], xy[1]), (int(x), int(y)), buttons)","def get_mouse_state() -> MouseState:\n xy = ffi.new(\"int[2]\")\n buttons = lib.SDL_GetMouseState(xy, xy + 1)\n tile = _pixel_to_tile(*xy)\n if tile is None:\n return MouseState((xy[0], xy[1]), None, buttons)\n return MouseState((xy[0], xy[1]), (int(tile[0]), int(tile[1])), buttons)","def _read_mouse(self, task):\n if base.mouseWatcherNode.hasMouse():\n x = base.win.getPointer(0).getX()\n y = base.win.getPointer(0).getY()\n deltax=(x-self._pre_mox)\n deltay=(y-self._pre_moy)\n if deltax or deltay:\n self.readmouse_binds[\"mouse-x\"](deltax/self.mouse_speed_factor)\n self.readmouse_binds[\"mouse-y\"](deltay/self.mouse_speed_factor)\n self._predelta=True\n elif self._predelta:\n self._predelta=False\n self.readmouse_binds[\"mouse-x\"](0)\n self.readmouse_binds[\"mouse-y\"](0)\n self._pre_mox,self._pre_moy=(x, y)\n else:\n self.readmouse_binds[\"mouse-x\"](0)\n self.readmouse_binds[\"mouse-y\"](0)\n\n return Task.cont","def on_mouse_motion(self, x, y, delta_x, delta_y):\r\n pass","def get_pos(term):\n # pylint: disable=invalid-name\n # Invalid variable name \"Position\"\n Position = collections.namedtuple('Position', ('row', 'column'))\n\n pos = Position(*term.get_location(timeout=5.0))\n\n if -1 in pos:\n print('stdin: not a human', file=sys.stderr)\n exit(2)\n\n return pos","def input_position(self) -> int:\n return self._input_position","def mouse_move(self, pos):\n if (self.setup_type == \"position\"):\n x, y = pos\n self.canvas.move(x, y)","def ev_MOUSEMOTION(self, event):","def last_pos(self) -> tuple[int, int]:\n if not self.actions:\n return (self.start_x, self.start_y)\n else:\n box = self.get_hitbox_at(self.time_consumed)\n return box.pos_x, box.pos_y","def _get_pos(self):\n return self._pos","def get_mouse():\n try:\n mouse = devices.mice[0]\n except IndexError:\n raise UnpluggedError(\"No mice found.\")\n return mouse.read()","def getMouseLeft(self):\n return self.mouseData.leftActive","def xy(event):\n return map(int, event.get_coords())","def mouseResponse(event, x, y, flags, param):\n\n\n global uppLeft, lowLeft, uppRight\n\n if event == cv2.EVENT_LBUTTONDOWN:\n cv2.circle(workImg, (x, y), 5, (255, 0, 255), -1)\n if uppLeft is None:\n uppLeft = [x, y]\n print(\"uppLeft is \" + str(uppLeft))\n\n elif lowLeft is None:\n lowLeft = [x, y]\n print(\"lowLeft is \" + str(lowLeft))\n\n elif uppRight is None:\n uppRight = [x, y]\n print(\"uppRight is \" + str(uppRight))","def get_x_position(self):\n return self.rect.x","def get_x_position(self):\n return self.actual_coordinates[0]"],"string":"[\n \"def get_mouse_position(self):\\n raise NotImplementedError\",\n \"def mousePos():\\n data = display.Display().screen().root.query_pointer()._data\\n return data[\\\"root_x\\\"], data[\\\"root_y\\\"]\",\n \"def read_current_mouse_position():\\n import pyautogui\\n pyautogui.FAILSAFE = False\\n return pyautogui.position()\",\n \"def mouse_position(self):\\r\\n # TODO: add: Now deprecated in favor of pi3d.events\\r\\n if self.mouse:\\r\\n return self.mouse.position()\\r\\n elif self.tkwin:\\r\\n return self.tkwin.winfo_pointerxy()\\r\\n else:\\r\\n return -1, -1\",\n \"def getMousePosition(self):\\n return (self.mouseData.x, self.mouseData.y)\",\n \"def mousePosition(self):\",\n \"def getMouse():\\n return pygame.mouse.get_pos()\",\n \"def mousePositionRaw(self):\",\n \"def mousePositionRaw(self):\",\n \"def mouse_motion_current_mouse_position() -> EventType:\\n x, y = pygame.mouse.get_pos()\\n return pygame.event.Event(pygame.MOUSEMOTION, {'pos': (int(x), int(y))})\",\n \"def get_mouse_pos(new_x_coord, new_y_coord):\\n\\n x_change = 0\\n y_change = 0\\n \\n # if the joystick returned to its default position (0,0), stop mouse movement\\n if not (new_x_coord == 0 and new_y_coord == 0):\\n if new_x_coord == 0:\\n x_change = 0\\n else:\\n x_change = new_x_coord\\n\\n if new_y_coord == 0:\\n y_change = 0\\n else:\\n y_change = -new_y_coord\\n \\n return (int(x_change), int(y_change))\",\n \"def mouse_position_event(self, x: int, y: int):\\n pass\",\n \"def get_mouse():\\n if CONST.render == 'sfml':\\n mouse_pos = Vector2(sfml.Mouse.get_position())/engine.screen_diff_ratio+engine.get_origin_pos()\\n return mouse_pos,\\\\\\n [sfml.Mouse.is_button_pressed(sfml.Mouse.LEFT),\\n sfml.Mouse.is_button_pressed(sfml.Mouse.RIGHT),\\n sfml.Mouse.is_button_pressed(sfml.Mouse.MIDDLE)]\\n elif CONST.render == 'pookoo':\\n return Vector2(pookoo.input.mouse.position()), [\\n False,False,False\\n ]\\n elif CONST.render == 'kivy':\\n return Vector2(), [False,False,False]\",\n \"def pos(self):\\n x = (self.ec._win._mouse_x -\\n self.ec._win.width / 2.) / (self.ec._win.width / 2.)\\n y = (self.ec._win._mouse_y -\\n self.ec._win.height / 2.) / (self.ec._win.height / 2.)\\n return np.array([x, y])\",\n \"def getMouse(self):\\n self.mouseX = None\\n self.mouseY = None\\n while self.mouseX == None or self.mouseY == None:\\n #self.update()\\n _tkCall(self.update)\\n if self.isClosed(): raise GraphicsError, \\\"getMouse in closed window\\\"\\n time.sleep(.1) # give up thread\\n x,y = self.toWorld(self.mouseX, self.mouseY)\\n self.mouseX = None\\n self.mouseY = None\\n return Point(x,y)\",\n \"def get_mouse_coordinate(self):\\n pos = pygame.mouse.get_pos()\\n mov = pygame.mouse.get_rel()\\n row = pos[0] // (self.CELL_WIDTH + self.MARGIN)\\n col = (pos[1] - self.PANEL_HEIGHT) // (self.CELL_WIDTH + self.MARGIN)\\n if mov != (0, 0) and not self.env.not_in_grid(row, col):\\n return (row, col)\\n return self.markerPos\",\n \"def mouse_coords(desktop=False):\\n x, y = c_int(0), c_int(0)\\n if desktop:\\n mouse.SDL_GetGlobalMouseState(byref(x), byref(y))\\n else:\\n mouse.SDL_GetMouseState(byref(x), byref(y))\\n return (int(x.value), int(y.value))\",\n \"def getMouse(self):\\n self.update() # flush any prior clicks\\n self.mouseX = None\\n self.mouseY = None\\n while self.mouseX == None or self.mouseY == None:\\n self.update()\\n if self.isClosed(): raise GraphicsError(\\\"getMouse in closed window\\\")\\n time.sleep(.1) # give up thread\\n x,y = self.mouseX, self.mouseY\\n self.mouseX = None\\n self.mouseY = None\\n return Point(x,y)\",\n \"def mouse_delta():\\n x, y = c_int(0), c_int(0)\\n mouse.SDL_GetRelativeMouseState(byref(x), byref(y))\\n return (int(x.value), int(y.value))\",\n \"def show_mouse_position_with_px(self):\\n self.main_menu_greets_fonts = pygame.font.Font(os.path.join(PATH_TO_RESOURCE, 'font_forever.ttf'), 10)\\n self.positiontext(f'Mouse position {pygame.mouse.get_pos()}', (770, 20))\\n self.mouse = pygame.mouse.get_pos()\\n return self.mouse\",\n \"def mouse_position(pos):\\n x, y = pos\\n m = x // SQUARE_SIZE\\n n = y // SQUARE_SIZE\\n return n, m\",\n \"def get_position():\\n return win32api.GetCursorPos()\",\n \"def getInputPoint(self):\\n return self[0].getPoint()\",\n \"def convert_mousepos(self, pos):\\n tokenx, tokeny = pos\\n row = int((tokenx - self.x_margin) / SPACESIZE)\\n column = int((tokeny - self.y_margin) / SPACESIZE)\\n return column, row\",\n \"def checkMouse(self):\\n if self.isClosed():\\n raise GraphicsError, \\\"checkMouse in closed window\\\"\\n _tkCall(self.update)\\n if self.mouseX != None and self.mouseY != None:\\n x,y = self.toWorld(self.mouseX, self.mouseY)\\n self.mouseX = None\\n self.mouseY = None\\n return Point(x,y)\\n else:\\n return None\",\n \"def mouse_move(camera, mouse, width, height):\\n # distance moved from screen center\\n # Using the '//' operator (floor division) to produce an integer result\\n x = width//2 - mouse.position[0]\\n y = height//2 - mouse.position[1]\\n \\n # intialize mouse so it doesn't jerk first time\\n try:\\n camera['mouseInit']\\n except KeyError:\\n x = 0\\n y = 0\\n # bug in Add Property\\n # can't use True. Have to use 1\\n camera['mouseInit'] = 1\\n\\n logger.debug(\\\"Read displacement: %s, %s\\\" % (x, y))\\n \\n # return mouse movement\\n return (x, y)\",\n \"def cursorPosGL(self):\\n globalPos = QtGui.QCursor.pos()\\n pos = self.mapFromGlobal(globalPos)\\n y = self.size().height() - pos.y()\\n return pos.x(), y\",\n \"def cursorPosGL(self):\\n globalPos = QtGui.QCursor.pos()\\n pos = self.mapFromGlobal(globalPos)\\n y = self.size().height() - pos.y()\\n return pos.x(), y\",\n \"def handle_mouse(self, x, y):\\n pass\",\n \"def checkMouse(self):\\n if self.isClosed():\\n raise GraphicsError(\\\"checkMouse in closed window\\\")\\n self.update()\\n if self.mouseX != None and self.mouseY != None:\\n x,y = self.mouseX, self.mouseY\\n self.mouseX = None\\n self.mouseY = None\\n return Point(x,y)\\n else:\\n return None\",\n \"def mouse_move(human, mouse, width, height):\\n # distance moved from screen center\\n # Using the '//' operator (floor division) to produce an integer result\\n x = width//2 - mouse.position[0]\\n y = height//2 - mouse.position[1]\\n\\n # intialize mouse so it doesn't jerk first time\\n try:\\n human['mouseInit']\\n except KeyError:\\n x = 0\\n y = 0\\n # bug in Add Property\\n # can't use True. Have to use 1\\n human['mouseInit'] = 1\\n\\n # return mouse movement\\n return (x, y)\",\n \"def print_mouse_positions() -> None:\\n mouse_pos = ctrl.mouse_pos()\\n print(f\\\"Absolute mouse pos: {mouse_pos}\\\")\\n screen = ui.main_screen().rect\\n print(f\\\"Main screen: {screen}\\\")\\n for corner in [\\n Corner.TOP_LEFT,\\n Corner.TOP_RIGHT,\\n Corner.BOTTOM_LEFT,\\n Corner.BOTTOM_RIGHT,\\n ]:\\n corner_pos = Corner.absolute_position(corner)\\n relative = (mouse_pos[0] - corner_pos[0], mouse_pos[1] - corner_pos[1])\\n print(f\\\"Position relative to {corner}: {relative}\\\")\",\n \"def report_mouse_position(x_pos=0, y_pos=0):\\n print('x-axis:', x_pos, ' Y-axis: ', y_pos, flush=True)\",\n \"def get_last_click_x():\\r\\n return _cue.getMouseLocation().getX()\",\n \"def cursorPosQt(self):\\n globalPos = QtGui.QCursor.pos()\\n pos = self.mapFromGlobal(globalPos)\\n return pos.x(), pos.y()\",\n \"def cursorPosQt(self):\\n globalPos = QtGui.QCursor.pos()\\n pos = self.mapFromGlobal(globalPos)\\n return pos.x(), pos.y()\",\n \"def getPosition(self):\\n return self.x\",\n \"def _mouse_event(pos=QPointF(), type_=QEvent.MouseMove):\\n return QMouseEvent(type_, QPointF(pos), Qt.LeftButton, Qt.LeftButton, Qt.NoModifier)\",\n \"def get_mouse_movement(self, index=0):\\r\\n return self.handler.get_rel_movement(index)\",\n \"def get_pick_position(self):\\n x0 = int(self.GetPickX1())\\n x1 = int(self.GetPickX2())\\n y0 = int(self.GetPickY1())\\n y1 = int(self.GetPickY2())\\n return x0, y0, x1, y1\",\n \"def OnMouseDown(self, evt):\\n self.CaptureMouse()\\n self.x, self.y = self.lastx, self.lasty = evt.GetPosition()\",\n \"def pos(self, x, y):\\n\\n if isinstance(x, float):\\n x = int(x)\\n\\n self.screen.write(colorama.Cursor.POS(x, y), ansi=True)\\n self.x = x\\n self.y = y\\n\\n return x, y\",\n \"def grid_to_mouse( pos ):\\n ix,iy=pos\\n px= ix*CELLSIZE + H_CELLSIZE + ix*CELLGAP\\n py= iy*CELLSIZE + H_CELLSIZE + iy*CELLGAP\\n return (px,py)\",\n \"def cursor_position(self) -> Point:\\n cpos = self.ui_content.cursor_position\\n try:\\n y, x = self._rowcol_to_yx[cpos.y, cpos.x]\\n except KeyError:\\n # For `DummyControl` for instance, the content can be empty, and so\\n # will `_rowcol_to_yx` be. Return 0/0 by default.\\n return Point(x=0, y=0)\\n else:\\n return Point(x=x - self._x_offset, y=y - self._y_offset)\",\n \"def update(self):\\n # Get the current mouse position. This returns the position\\n # as a list of two numbers.\\n pos = pygame.mouse.get_pos()\\n \\n # Set the player x position to the mouse x position\\n self.rect.x = pos[0]\",\n \"def on_mouse_move(self, event: PointEvent):\\n self.x = event.x\\n self.y = event.y\\n self.handle_mouse(self.x, self.y)\",\n \"def getPos(self):\\n return self.Xpos,self.Ypos\",\n \"def get_cursor_pos(self):\\n return (self.text_maker.pos[0] + 9, self.text_maker.pos[1] + 120 + 8)\",\n \"def normal_mouse_move(self, event):\\n plot = self.component\\n if plot is not None:\\n if isinstance(plot, BaseXYPlot):\\n ndx = plot.map_index((event.x, event.y), index_only = True)\\n x = plot.index.get_data()[ndx]\\n y = plot.value.get_data()[ndx]\\n print self.format % (x,y)\\n else:\\n print \\\"dataprinter: don't know how to handle plots of type\\\",\\n print plot.__class__.__name__\\n return\",\n \"def get_mouse_state(self):\\n try:\\n mouse_state = curses.getmouse()\\n return mouse_state\\n except:\\n self.app.log(get_error_info())\\n return False\",\n \"def x(self):\\r\\n return self.position.x\",\n \"def update(self):\\n # Get the current mouse position. This returns the position\\n # as a list of two numbers.\\n pos = pygame.mouse.get_pos()\\n\\n # Set the player x position to the mouse x position\\n self.rect.x = pos[0]\",\n \"def getPosition(self):\\n return self.target, min(self.points), max(self.points)\",\n \"def get_pos(self):\\n return (self.x, self.y)\",\n \"def _get_x(self):\\n return self.position.x\",\n \"def get_x(self):\\n return self.posX\",\n \"def _get_cursor_physical_position(self) :\\n return self._cursor_physical_position\",\n \"def follow(self):\\n\\t\\tpos = pygame.mouse.get_pos()\\n\\t\\tself.x = pos[0]\\n\\t\\tself.y = pos[1]\\n\\t\\tself.draw()\",\n \"def grab(self, event):\\n self.ypos = event.y\\n self.xpos = event.x\\n self.config(cursor='fleur')\",\n \"def handle_input(self, ncode, wparam, lparam):\\n x_pos = lparam.contents.x_pos\\n y_pos = lparam.contents.y_pos\\n data = lparam.contents.mousedata\\n\\n # This is how we can distinguish mouse 1 from mouse 2\\n # extrainfo = lparam.contents.extrainfo\\n # The way windows seems to do it is there is primary mouse\\n # and all other mouses report as mouse 2\\n\\n # Also useful later will be to support the flags field\\n # flags = lparam.contents.flags\\n # This shows if the event was from a real device or whether it\\n # was injected somehow via software\\n\\n self.emulate_mouse(wparam, x_pos, y_pos, data)\\n\\n # Give back control to Windows to wait for and process the\\n # next event\\n return ctypes.windll.user32.CallNextHookEx(\\n self.hooked, ncode, wparam, lparam)\",\n \"def cursor_coordinates(self):\\n text = self.getText()\\n lines = text.split(\\\"\\\\n\\\")\\n pos = self.getCursorPos()\\n if pos == 0:\\n return (0, 0)\\n i = 0\\n cursor_row = -1\\n cursor_col = -1\\n for row, line in enumerate(lines):\\n i += len(line) + 1 # we need to include \\\"\\\\n\\\"\\n if pos < i:\\n cursor_row = row\\n cursor_col = pos - i + len(line) + 1\\n break\\n return (cursor_col, cursor_row)\",\n \"def position(self):\\n return self.x, self.y\",\n \"def clickCell(self, event):\\n position = self.input.checkMouseInput(event)\\n if not position:\\n return None\\n x = math.floor(position[0] / self.imageWidth)\\n y = math.floor(position[1] / self.imageHeight)\\n return (int(x), int(y))\",\n \"def follow_mouse(self, mouse):\\n half_width = self.width() / 2\\n self.left = mouse.get_x() - half_width\\n self.right = mouse.get_x() + half_width\",\n \"def get_pos(self):\\r\\n return self.pos\",\n \"def get_pos_x(self):\\n return self.__pos_x\",\n \"def position(self):\\n index = self._ordered_input_names.index('position')\\n return self._inputs[index]\",\n \"def getX(self):\\n return self.position[0]\",\n \"def getMouseClick(plotcoords = 0):\\n coords = dislin.csrpt1()\\n if plotcoords:\\n return coords\\n else:\\n return dislin.xinvrs(coords[0]), dislin.yinvrs(coords[1])\",\n \"def getPosition(self):\\n\\tleft = self.getLeft()\\n\\ttop = self.getTop()\\n\\treturn (left,top)\",\n \"def handle_mouse(obj, event):\\n if event:\\n x = event.globalX()\\n y = event.globalY()\\n x_w = obj.offset.x()\\n y_w = obj.offset.y()\\n obj.move(x - x_w, y - y_w)\",\n \"def getPos(self):\\n return self.__current_pos\",\n \"def getX(self):\\n return self.position.getX()\",\n \"def get_position(self):\\n return (self.x_pos, self.y_pos)\",\n \"def mousePressed(index):\\n return (pygame.mouse.get_pressed()[index])\",\n \"def get_coords(self):\\n while True:\\n try:\\n coords = tuple(map(int, input('y, x = ').split(',')))\\n while not self.correct_player_turn(coords):\\n coords = tuple(map(int, input('y, x = ').split(','))) # coordinates as a tuple\\n return coords\\n except ValueError:\\n print(\\\"Oppsy daisy! That's not a corect input! 'x,y'\\\")\",\n \"def update(self):\\n self.x = games.mouse.x\\n #self.y = games.mouse.y\\n self.check_collide()\",\n \"def mouseResponse(event, x, y, flags, param):\\n global uppLeft, lowLeft, uppRight\\n if event == cv2.EVENT_LBUTTONDOWN:\\n cv2.circle(workImg, (x, y), 5, (0, 255, 255), -1)\\n if uppLeft is None:\\n uppLeft = [x, y]\\n print(\\\"x = \\\", x, \\\"y = \\\", y)\\n elif lowLeft is None:\\n lowLeft = [x, y]\\n print(\\\"x = \\\", x, \\\"y = \\\", y)\\n elif uppRight is None:\\n uppRight = [x, y]\\n print(\\\"x = \\\", x, \\\"y = \\\", y)\",\n \"def get_cursor() -> CursorInputType:\\n try:\\n return pygame.mouse.get_cursor()\\n except TypeError as e:\\n warn(str(e))\\n return None\",\n \"def get_point(self):\\n return self._x, self._y\",\n \"def __on_click(self,event, x, y, p1, p2): \\r\\n \\r\\n # global variables of the class with mouse click position\\r\\n global mouse_click_pos, mouse_click_list \\r\\n \\r\\n mouse_click_list = []\\r\\n if event == cv2.EVENT_LBUTTONDOWN:\\r\\n \\r\\n mouse_click_pos = (x,y)\\r\\n print(mouse_click_pos)\\r\\n mouse_click_list.append((x, y))\",\n \"def get_pos(self):\\n return self.pos\",\n \"def mouse_move_callback(self, event):\\n # TODO drag and drop figuriek\\n print(\\\"moving at \\\", event.x + self.offset_x, event.y + self.offset_y)\",\n \"def position(self):\\n return self._x, self._y\",\n \"def get_mouse_state() -> MouseState:\\n xy = ffi.new(\\\"int[2]\\\")\\n buttons = lib.SDL_GetMouseState(xy, xy + 1)\\n x, y = _pixel_to_tile(*xy)\\n return MouseState((xy[0], xy[1]), (int(x), int(y)), buttons)\",\n \"def get_mouse_state() -> MouseState:\\n xy = ffi.new(\\\"int[2]\\\")\\n buttons = lib.SDL_GetMouseState(xy, xy + 1)\\n tile = _pixel_to_tile(*xy)\\n if tile is None:\\n return MouseState((xy[0], xy[1]), None, buttons)\\n return MouseState((xy[0], xy[1]), (int(tile[0]), int(tile[1])), buttons)\",\n \"def _read_mouse(self, task):\\n if base.mouseWatcherNode.hasMouse():\\n x = base.win.getPointer(0).getX()\\n y = base.win.getPointer(0).getY()\\n deltax=(x-self._pre_mox)\\n deltay=(y-self._pre_moy)\\n if deltax or deltay:\\n self.readmouse_binds[\\\"mouse-x\\\"](deltax/self.mouse_speed_factor)\\n self.readmouse_binds[\\\"mouse-y\\\"](deltay/self.mouse_speed_factor)\\n self._predelta=True\\n elif self._predelta:\\n self._predelta=False\\n self.readmouse_binds[\\\"mouse-x\\\"](0)\\n self.readmouse_binds[\\\"mouse-y\\\"](0)\\n self._pre_mox,self._pre_moy=(x, y)\\n else:\\n self.readmouse_binds[\\\"mouse-x\\\"](0)\\n self.readmouse_binds[\\\"mouse-y\\\"](0)\\n\\n return Task.cont\",\n \"def on_mouse_motion(self, x, y, delta_x, delta_y):\\r\\n pass\",\n \"def get_pos(term):\\n # pylint: disable=invalid-name\\n # Invalid variable name \\\"Position\\\"\\n Position = collections.namedtuple('Position', ('row', 'column'))\\n\\n pos = Position(*term.get_location(timeout=5.0))\\n\\n if -1 in pos:\\n print('stdin: not a human', file=sys.stderr)\\n exit(2)\\n\\n return pos\",\n \"def input_position(self) -> int:\\n return self._input_position\",\n \"def mouse_move(self, pos):\\n if (self.setup_type == \\\"position\\\"):\\n x, y = pos\\n self.canvas.move(x, y)\",\n \"def ev_MOUSEMOTION(self, event):\",\n \"def last_pos(self) -> tuple[int, int]:\\n if not self.actions:\\n return (self.start_x, self.start_y)\\n else:\\n box = self.get_hitbox_at(self.time_consumed)\\n return box.pos_x, box.pos_y\",\n \"def _get_pos(self):\\n return self._pos\",\n \"def get_mouse():\\n try:\\n mouse = devices.mice[0]\\n except IndexError:\\n raise UnpluggedError(\\\"No mice found.\\\")\\n return mouse.read()\",\n \"def getMouseLeft(self):\\n return self.mouseData.leftActive\",\n \"def xy(event):\\n return map(int, event.get_coords())\",\n \"def mouseResponse(event, x, y, flags, param):\\n\\n\\n global uppLeft, lowLeft, uppRight\\n\\n if event == cv2.EVENT_LBUTTONDOWN:\\n cv2.circle(workImg, (x, y), 5, (255, 0, 255), -1)\\n if uppLeft is None:\\n uppLeft = [x, y]\\n print(\\\"uppLeft is \\\" + str(uppLeft))\\n\\n elif lowLeft is None:\\n lowLeft = [x, y]\\n print(\\\"lowLeft is \\\" + str(lowLeft))\\n\\n elif uppRight is None:\\n uppRight = [x, y]\\n print(\\\"uppRight is \\\" + str(uppRight))\",\n \"def get_x_position(self):\\n return self.rect.x\",\n \"def get_x_position(self):\\n return self.actual_coordinates[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80970645","0.79738057","0.7952894","0.7875623","0.7716459","0.7669065","0.7658517","0.7630226","0.7630226","0.74635327","0.7459193","0.73358524","0.7272576","0.7244995","0.71246344","0.70662653","0.70661056","0.7031796","0.69420946","0.6930538","0.6928015","0.6922833","0.6825276","0.66713905","0.6654901","0.6612146","0.659677","0.659677","0.6590026","0.6584995","0.6555021","0.64876115","0.64181757","0.64169335","0.63656265","0.63656265","0.63644236","0.6356343","0.63259584","0.6301498","0.62567365","0.6245463","0.62380946","0.6197744","0.61905533","0.6186721","0.61772776","0.6157431","0.61005586","0.6100368","0.6081775","0.6078553","0.6073194","0.6065657","0.6045637","0.6037251","0.6026181","0.6012928","0.6004227","0.60026443","0.5999963","0.59960437","0.5995739","0.59937394","0.5984769","0.5982345","0.5972628","0.5969651","0.59601426","0.59580564","0.5939788","0.5939245","0.59355056","0.592278","0.59092027","0.5900939","0.5892331","0.5889627","0.58823663","0.5878612","0.5867508","0.5867468","0.58610165","0.5856274","0.5849253","0.584447","0.58420825","0.5824311","0.5820441","0.58190256","0.5808989","0.58041877","0.58027923","0.5785671","0.57827276","0.5781847","0.5777311","0.5756715","0.5756253","0.5754581"],"string":"[\n \"0.80970645\",\n \"0.79738057\",\n \"0.7952894\",\n \"0.7875623\",\n \"0.7716459\",\n \"0.7669065\",\n \"0.7658517\",\n \"0.7630226\",\n \"0.7630226\",\n \"0.74635327\",\n \"0.7459193\",\n \"0.73358524\",\n \"0.7272576\",\n \"0.7244995\",\n \"0.71246344\",\n \"0.70662653\",\n \"0.70661056\",\n \"0.7031796\",\n \"0.69420946\",\n \"0.6930538\",\n \"0.6928015\",\n \"0.6922833\",\n \"0.6825276\",\n \"0.66713905\",\n \"0.6654901\",\n \"0.6612146\",\n \"0.659677\",\n \"0.659677\",\n \"0.6590026\",\n \"0.6584995\",\n \"0.6555021\",\n \"0.64876115\",\n \"0.64181757\",\n \"0.64169335\",\n \"0.63656265\",\n \"0.63656265\",\n \"0.63644236\",\n \"0.6356343\",\n \"0.63259584\",\n \"0.6301498\",\n \"0.62567365\",\n \"0.6245463\",\n \"0.62380946\",\n \"0.6197744\",\n \"0.61905533\",\n \"0.6186721\",\n \"0.61772776\",\n \"0.6157431\",\n \"0.61005586\",\n \"0.6100368\",\n \"0.6081775\",\n \"0.6078553\",\n \"0.6073194\",\n \"0.6065657\",\n \"0.6045637\",\n \"0.6037251\",\n \"0.6026181\",\n \"0.6012928\",\n \"0.6004227\",\n \"0.60026443\",\n \"0.5999963\",\n \"0.59960437\",\n \"0.5995739\",\n \"0.59937394\",\n \"0.5984769\",\n \"0.5982345\",\n \"0.5972628\",\n \"0.5969651\",\n \"0.59601426\",\n \"0.59580564\",\n \"0.5939788\",\n \"0.5939245\",\n \"0.59355056\",\n \"0.592278\",\n \"0.59092027\",\n \"0.5900939\",\n \"0.5892331\",\n \"0.5889627\",\n \"0.58823663\",\n \"0.5878612\",\n \"0.5867508\",\n \"0.5867468\",\n \"0.58610165\",\n \"0.5856274\",\n \"0.5849253\",\n \"0.584447\",\n \"0.58420825\",\n \"0.5824311\",\n \"0.5820441\",\n \"0.58190256\",\n \"0.5808989\",\n \"0.58041877\",\n \"0.58027923\",\n \"0.5785671\",\n \"0.57827276\",\n \"0.5781847\",\n \"0.5777311\",\n \"0.5756715\",\n \"0.5756253\",\n \"0.5754581\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8032351"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14990,"cells":{"query":{"kind":"string","value":"Initialize type and button."},"document":{"kind":"string","value":"def __init__(self, event_type, button):\n self.type = event_type\n self.button = button"},"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 __init__(self):\n self.handlers = {}\n self.start_state = None\n self.end_states = []\n self.btn = Button()","def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._go)\r\n\t\tbutton_ok.pack()\r\n\t\t\r\n\t\tself.result_frame = Result(self)\r\n\t\tself.result_frame.pack()","def init_widget(self):","def __init__(self):\r\n\r\n self.id = -1 # button's id\r\n self.cur_state = AUI_BUTTON_STATE_NORMAL # current state (normal, hover, pressed, etc.)\r\n self.location = wx.LEFT # buttons location (wxLEFT, wxRIGHT, or wxCENTER)\r\n self.bitmap = wx.NullBitmap # button's hover bitmap\r\n self.dis_bitmap = wx.NullBitmap # button's disabled bitmap\r\n self.rect = wx.Rect() # button's hit rectangle\r","def _initializeUi(self):\r\n if self._mode == 'imperial':\r\n self.imperial_button.setChecked(True)\r\n self.imperial_button.clicked.emit()\r\n else:\r\n self.metric_button.setChecked(True)\r\n self.metric_button.clicked.emit()","def init(self, type, cfg = \"\"):\n\t\tself.setType(type)\n\t\tself.setConfig(cfg)","def __init__(self, type_):\n\n self.type = type_","def initializePage(self):\n topLayout = self.layout()\n # remove old buttons from a previously set subtype\n for button in self.subtypeButtons.buttons():\n self.subtypeButtons.removeButton(button)\n topLayout.removeWidget(button)\n button.deleteLater()\n\n for id, subtype in enumerate(ExportDialog.\n exportSubtypes[ExportDialog.currentType]):\n button = QtGui.QRadioButton(ExportDialog.subtypeDescript[subtype])\n self.subtypeButtons.addButton(button, id)\n topLayout.addWidget(button)\n if subtype == ExportDialog.currentSubtype:\n button.setChecked(True)","def _initialize(self):\n\n cancel_button = ttk.Button(\n master=self._frame,\n text=\"Cancel\",\n command=self._hide_confirmation_window\n )\n\n delete_button = ttk.Button(\n master=self._frame,\n text=\"Delete\",\n command=self._handle_delete\n )\n\n self._initilize_message()\n\n cancel_button.grid(row=1, column=0, padx=5, pady=5)\n delete_button.grid(row=1, column=1, padx=5, pady=5)","def __init__(self, parent=None):\n super().__init__(parent)\n\n topLayout = QtGui.QVBoxLayout(self)\n self.setLayout(topLayout)\n self.setTitle(_('Choose export format type'))\n\n typeButtons = QtGui.QButtonGroup(self)\n for id, exportType in enumerate(ExportDialog.exportTypes):\n button = QtGui.QRadioButton(ExportDialog.\n exportTypeDescript[exportType])\n typeButtons.addButton(button, id)\n topLayout.addWidget(button)\n if exportType == ExportDialog.currentType:\n button.setChecked(True)\n typeButtons.buttonClicked[int].connect(self.setCurrentType)","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def __init__(self, button_id):\r\n\r\n self.button_id = button_id","def initialize(self):\n self._ui.img_name.setText('No files selected')\n self._ui.bt_next.setEnabled(False)\n self._ui.bt_prev.setEnabled(False)\n self._ui.bt_right.setEnabled(False)\n self._ui.bt_left.setEnabled(False)\n self._ui.gps_button.setEnabled(False)\n\n self._open_btn = QPushButton('Open File', self._ui.img_label)\n self.adjustSize()","def initUI(self):\n startbtn = QPushButton(\"Start Recroding\", self)\n startbtn.move(30, 50)\n\n stopbtn = QPushButton(\"Stop Recording\", self)\n stopbtn.move(150, 50)\n\n initbtn = QPushButton(\"Initilize\", self)\n initbtn.move(30, 100)\n\n plotbtn = QPushButton(\"Plot\", self)\n plotbtn.move(150, 100)\n\n startbtn.clicked.connect(self.start_recording)\n stopbtn.clicked.connect(self.stop_recording)\n initbtn.clicked.connect(self.init_recording)\n plotbtn.clicked.connect(self.plot_signals)\n\n self.statusBar()\n self.statusBar().showMessage('Click Init')\n\n self.setGeometry(300, 300, 290, 150)\n self.setWindowTitle('Recorder 1.0')\n self.setWindowIcon(QIcon(\"./Static/Images/icon.jpg\"))\n self.show()","def setup_options(self):\n self.analysis_type_label = Label(self, text='Select what you wish to do:')\n self.analysis_type_label.pack(fill=BOTH, expand=True)\n\n # Create Select option\n self._analysis_type_variable = StringVar(self)\n options1 = [\n 'Regression',\n 'Classification'\n ]\n self._analysis_type_variable.set(options1[0])\n self.type_option_menu = OptionMenu(self, self._analysis_type_variable, *options1)\n self.type_option_menu.pack()\n\n self.analyse_button = Button(self, text='NEXT', background='White', command=self.perform_analysis)\n self.analyse_button.pack(padx=5, pady=10)","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def controls_setup(self):\n pass","def initialize(self):\n self.actions = []\n \"*** YOUR CODE HERE\"\n #raise NotImplementedError()","def configure_widgets(self):\r\n\r\n # 'command' - callback function executed when button is pressed\r\n # since we can't pass it a function with arguments, we use the partial \r\n # function from the functools module\r\n self.btn_tl['command'] = partial(self.play, \"x\", (0,0))\r\n self.btn_tm['command'] = partial(self.play, \"x\", (0,1))\r\n self.btn_tr['command'] = partial(self.play, \"x\", (0,2))\r\n self.btn_ml['command'] = partial(self.play, \"x\", (1,0))\r\n self.btn_mm['command'] = partial(self.play, \"x\", (1,1))\r\n self.btn_mr['command'] = partial(self.play, \"x\", (1,2))\r\n self.btn_bl['command'] = partial(self.play, \"x\", (2,0))\r\n self.btn_bm['command'] = partial(self.play, \"x\", (2,1))\r\n self.btn_br['command'] = partial(self.play, \"x\", (2,2))\r\n\r\n self.btn_reset['text'] = \"Reset\"\r\n self.btn_reset['command'] = self.reset","def __init__(self):\n\n GPIO.setup(PIN_BTN, GPIO.IN, GPIO.PUD_UP)\n GPIO.setup(PIN_RED_LED_0, GPIO.OUT, GPIO.LOW)\n GPIO.setup(PIN_BLUE_LED, GPIO.OUT, GPIO.LOW)","def initialize_buttons(self):\r\n self.start_button = tk.Button(self.master, text='Start', command = self.startRunning)\r\n self.start_button.grid(row=0, column=0)\r\n\r\n self.pause_button = tk.Button(self.master, text='Pause', command = self.pauseRunning)\r\n self.pause_button.grid(row=0, column=1)\r\n\r\n self.graph_button = tk.Button(self.master, text='Graph', command = self.showGraph)\r\n self.graph_button.grid(row=0, column=2)\r\n \r\n self.plot_button = tk.Button(self.master, text='Plot', command = self.showPlot)\r\n self.plot_button.grid(row=0, column=3)\r\n \r\n self.draw_button = tk.Button(self.master, text='Draw', command = self.drawCells)\r\n self.draw_button.grid(row=0, column=4)\r\n \r\n # Initialize Button States and Actions\r\n self.pause_button['state'] = 'disabled'\r\n # Boolean switch to control flow of placement process\r\n self.running = False\r\n # Boolean switch to plot placement connections and tags, turn off for faster processing\r\n self.plot = False\r\n self.drawing = False\r\n self.graph = False\r\n # Boolean switch to specify first run and allow stop/continue behavior that doesn't initialize program\r\n self.firstRun = True","def __init__(self, the_type, enabled, generate_code):\n self.__type = the_type\n self.setEnabled(enabled)\n self.setGenerateCode(generate_code)","def gui_init(self):\n GUI_element.gui_init(self)\n\n self.hover_sound = False\n \n if not self.image is None:\n self.generic_button = False\n self.width = self.image.width if self.width == 0 else self.width\n self.height = self.image.height if self.height == 0 else self.height\n else:\n # Set up a generic button\n self.generic_button = True\n self.image = self.game.core.media.gfx['gui_button_generic_background']\n self.draw_strategy = \"gui_button\"\n\n # fixed height\n self.height = 30\n \n # Create the text\n self.generic_button_text_object = Text(self.game.core.media.fonts[\"generic_buttons\"], self.x, self.y + (self.height / 2), TEXT_ALIGN_CENTER, self.generic_button_text)\n self.generic_button_text_object.z = self.z - 1\n self.generic_button_text_object.colour = (1.0,1.0,1.0)\n \n # Set up the width, if we have a larger than normal width then we want to centre the text.\n if self.width < self.generic_button_text_object.text_width + 20:\n self.width = self.generic_button_text_object.text_width + 20\n self.generic_button_text_object.x += (self.width / 2)\n \n self.sequence_count = self.image.num_of_frames\n self.draw_strategy_call_parent = False","def init_ui(self):\n self.master.title(\"Backbone\")\n self.master.geometry(\"300x150\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=90, y=10)\n\n self.btn_create_training_file = Button(self, text=\"Create & upload training file\",\n command=self.create_training_file)\n self.btn_create_training_file.place(x=30, y=40)\n\n self.btn_run_algorithm = Button(self, text=\"Run algorithm\", command=self.run_algorithm)\n self.btn_run_algorithm.place(x=80, y=70)\n\n self.btn_view_results = Button(self, text=\"View Results\", command=self.view_results)\n self.btn_view_results.place(x=85, y=100)","def __init__(self, parent=None):\n super().__init__(parent)\n\n topLayout = QtGui.QVBoxLayout(self)\n self.setLayout(topLayout)\n self.setTitle(_('Choose export format subtype'))\n self.subtypeButtons = QtGui.QButtonGroup(self)\n self.subtypeButtons.buttonClicked[int].connect(self.setCurrentSubtype)","def initialize(self,init_info):\n self.action_info = init_info.actions\n return True","def __init__buttons(language, color_writing, color_end_b_writing, field_size, button_bg_color):\r\n _create_buttons(field_size, button_bg_color) # creates buttons\r\n __init__button_writings(language, color_writing, color_end_b_writing, field_size) # creates writings\r","def __init__(self):\n self.window = Tk() # The main window\n self.__initialize_variables__() # Initialize the variables\n self.__initialize_menu__() # Initialize the Menu\n self.__initialize_status_bar__()\n self.__initialize_gui__() # Initialize the GUI widgets","def create_widgets(self):\n #create first button\n self.button1 = Button(self, text = \"Start\")\n self.button1.bind\n self.button1.grid()","def _initialize(self):\n self._entry = tk.Entry(master = self._window)\n self._entry.focus_set()\n \n self._confirm_button = tk.Button(master = self._window,\n text = \"Ok\",\n font = DEFAULT_FONT,\n command = self._confirm_color)\n\n self._entry.grid(row = 0, column = 0,\n padx = 10, pady = 10)\n self._confirm_button.grid(row = 1, column = 0,\n padx = 10, pady = 10)\n\n self._window.bind(\"<Return>\", self._confirm_color)","def __init__(self):\r\n super().__init__()\r\n self.init_ui()","def init_objects(self):\n self.start_button_local = box(pos=vector(0, 1.2, 0), size=vector(8, 2, 1))\n # if no IP address was defined, the button \"remote game\" will not appear\n self.start_button_remote = None if not self.remote_enabled else box(pos=vector(0, -1.2, 0), size=vector(8, 2, 1))\n # a label is a text associated to a box\n self.start_label_local = label(pos=self.start_button_local.pos, xoffset=0, yoffset=0, text='Local Game', color=color.black, opacity=0, line=False, height=20, box=False)\n self.start_label_remote = None if not self.remote_enabled else label(pos=self.start_button_remote.pos, xoffset=0, yoffset=0, text='Remote Game', color=color.black, opacity=0, line=False, height=20, box=False)\n self.title = text(pos=vector(0, 5, 0), text='Morpion 3D', align='center', color=color.green, billboard=True, depth=0.5)\n # return the list of elements that actually appear\n return filter(lambda x: x is not None, [self.start_button_local, self.start_button_remote, self.start_label_local, self.start_label_remote, self.title])","def create_widgets(self):\n # self.var_spherical = IntVar()\n # self.var_3d = IntVar()\n # self.var_spatial_audio = IntVar()\n # self.button_open[\"command\"] = self.action_open\n # self.button_inject[\"command\"] = self.action_inject\n pass","def create_widgets (self):\r\n # create first button\r\n self.bttn1 = Button (self, text = \"I do nothing!\")\r\n self.bttn1.grid ()\r\n\r\n # create a second button\r\n self.bttn2 = Button (self)\r\n self.bttn2.grid ()\r\n self.bttn2.configure (text = \"Me too\")\r\n\r\n # create a third button\r\n self.bttn3 = Button (self)\r\n self.bttn3.grid ()\r\n self.bttn3 [\"text\"] = \"Same here!\"","def _initialize(self):\n \n self.view.lineEdit_3.setText(\"C,H,N,O,P,S\")\n self.view.spin_hit.setValue(20)\n self.view.lineEdit_2.setValue(10.)\n self.view.checkBox_8.setChecked(True)","def __init__(self, type_=None, title=None, content=None, text=None,\n image=None):\n default_attr = dict(type=str(),\n title=str(),\n content=str(),\n text=str(),\n image=Image())\n self.type = type_\n self.title = title\n self.content = content\n self.text = text\n self.image = image\n self._set_default_attr(default_attr)","def prepare_UI(self):","def __init__(self, controller):\n super().__init__(controller)\n\n # The hovered input when entering this View.\n self.first_inp = \"s\"\n\n # Initialize selected variable.\n self.selected = None\n\n # Make background graphics.\n self.make_background_graphics()\n\n # Make Buttons.\n self.make_buttons()\n\n # Make the information box. This explains each Button.\n self.make_info_box()\n\n # Initializes popup.\n self.make_popup()\n\n # Map of input to functions.\n enter = self.graphics.ENTER_KEY\n self.controls = {\n # Pressing \"q\" will go back to the main menu.\n \"q\": lambda: Action(\"goto main menu view\", []),\n\n # Movement keys.\n \"w\": lambda: self.move_cursor(Direction.U),\n \"a\": lambda: self.move_cursor(Direction.L),\n \"s\": lambda: self.move_cursor(Direction.D),\n \"d\": lambda: self.move_cursor(Direction.R),\n\n # Repeat the last valid input.\n enter: self.repeat_last_valid_input,\n\n # Click the selected UIElement.\n \"m\": self.click\n }","def _initFields(self):\n self.windowType = \"volume\"\n\n self.masterVol = 0\n self.masterMute = 0","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def init_ui(self):\n self.parent.title(\"Roku Player Controller\")\n self.style.theme_use(\"default\")","def __init__(self, type, onReset = None, idt = None):\n self.onReset = onReset\n self.btype = type\n self.idt = idt\n super(Reg, self).__init__(idt)","def __init__(self, tag_type):\n self.action_type = 'push_vlan'\n self.tag_type = tag_type","def __init_widgets(self):\n self.add_events(Gdk.EventMask.ENTER_NOTIFY_MASK)\n\n headerbar = self.get_header_bar()\n scanner_image = Gtk.Image.new_from_icon_name(\"qrscanner-symbolic\", Gtk.IconSize.BUTTON)\n self.scan_button.add(scanner_image)\n\n self.add_button.get_style_context().add_class(\"suggested-action\")\n self.add_button.set_sensitive(False)\n self.add_button.set_can_default(True)\n\n headerbar.pack_end(self.add_button)\n headerbar.pack_end(self.scan_button)\n\n self.account_config.connect(\"changed\", self._on_account_config_changed)\n self.add_button.connect(\"clicked\", self._on_add)\n self.scan_button.connect(\"clicked\", self.account_config.scan_qr)\n self.get_content_area().pack_start(self.account_config, True, True, 0)","def init_ui(self):\n\n self.master.title(\"Upload file\")\n self.master.geometry(\"300x200\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_select_file = Button(self, text=\"Select file\", command=self.on_open)\n self.btn_select_file.place(x=80, y=50)\n\n self.selected_file_name = Label(self, text=\"<Selected file name>\")\n self.selected_file_name.place(x=60, y=90)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=80, y=130)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.place(x=10, y=10)","def __init__(self, x, y, width, height, action = RETURN_TRUE, inactivecolour = red, activecolour = orange,\n text = None, textcolour = black, size = 25, border = None):\n super(Button, self).__init__(x, y, width, height)\n def tempTrue(): return True\n def tempFalse(): return False\n def tempNone(): return None\n self.string = False\n if action == Button.RETURN_TRUE:\n action = tempTrue\n elif action == Button.RETURN_FALSE:\n action = tempFalse\n elif action == Button.RETURN_NONE:\n action = tempNone\n elif isinstance(action, str):\n self.string = True\n self.action = action\n self.inactive = inactivecolour\n self.active = activecolour\n self.border = border \n if text == None:\n text = action.__name__.upper()\n if self.string:\n text = self.action\n self.text = text\n self.textcolour = textcolour\n self.size = size","def setup_button_block(self):\n self.btn_contrast_invert.setText(\"Contrast Invert\")\n self.btn_display.setText(\"Display\")\n self.btn_display_hist.setText(\"Display HIST\")\n self.btn_display_color_hist.setText(\"Display Color HIST\")\n self.btn_compare.setText(\"Compare\")\n self.btn_equalize_hist.setText(\"Equalize Histogram\")\n self.btn_contrast_stretch.setText(\"Contrast Stretch\")\n self.btn_log_compress.setText(\"Log Compress\")\n self.btn_dload_jpeg.setText(\"Download JPEG\")\n self.btn_dload_tiff.setText(\"Download TIFF\")\n self.btn_dload_png.setText(\"Download PNG\")\n self.btn_upload.setText(\"Upload\")\n\n self.btn_upload.clicked.connect(self.btn_upload_callback)\n\n self.btn_display.clicked.connect(self.btn_display_callback)\n self.btn_contrast_invert.clicked.connect(\n self.btn_contrast_invert_callback)\n self.btn_display_hist.clicked.connect(self.btn_display_hist_callback)\n self.btn_display_color_hist.clicked.connect(\n self.btn_display_color_hist_callback)\n self.btn_compare.clicked.connect(self.btn_compare_callback)\n self.btn_equalize_hist.clicked.connect(self.btn_equalize_hist_callback)\n self.btn_contrast_stretch.clicked.connect(\n self.btn_contrast_stretch_callback)\n self.btn_log_compress.clicked.connect(self.btn_log_compress_callback)\n self.btn_dload_jpeg.clicked.connect(self.download_images_jpg)\n self.btn_dload_png.clicked.connect(self.download_images_png)\n self.btn_dload_tiff.clicked.connect(self.download_images_tiff)","def __init__(self, genotype):\n\n\t\t# instantiate the root\n\t\ttk.Tk.__init__(self)\n\t\tself.title(\"CPPN playground\")\n\n\t\t# create container to hold all grames in the GUI\n\t\tcontainer = tk.Frame(self)\n\t\tcontainer.pack(side=\"top\", fill=\"both\", expand=True)\n\t\tcontainer.grid_rowconfigure(0, weight=1)\n\t\tcontainer.grid_columnconfigure(0, weight=1)\n\n\t\t# initiate GUI\n\n\t\t# add menu bar for the separate Tk frame\n\t\tmenubar = tk.Menu(self)\n\t\tfilemenu = tk.Menu(menubar)\n\t\tfilemenu.add_command(label=\"Main Page\", command=lambda: self.raise_frame(\"MainPage\", container))\n\t\tfilemenu.add_command(label=\"Slider Page\", command=lambda: self.raise_frame(\"SliderPage\", container))\n\t\tfilemenu.add_command(label=\"Save\", command=lambda: save_gen_GUI(genotype))\n\t\tmenubar.add_cascade(label=\"Options\", menu=filemenu)\n\t\tself.config(menu=menubar)\n\n\t\t# add frames to the main GUI\n\t\tself.frames = {}\n\n\t\t# create main frame\n\t\tframe1 = MainPage(container=container, master=self, genotype=genotype)\n\t\tself.frames[\"MainPage\"] = frame1\n\t\tframe1.grid(row=0, column=0, stick=\"nsew\")\n\n\t\t# raise main page to the front initially\n\t\tself.raise_frame(\"MainPage\", container)","def setup(self):\n self.ui_manager.purge_ui_elements()\n\n button = buttons.MenuButton(\n 'Menu',\n center_x=self.window.width // 2,\n center_y=self.window.height // 6,\n width=200,\n user=self.user.text\n )\n button.set_style_attrs(\n bg_color=(255, 153, 204),\n bg_color_hover=(255, 102, 178),\n bg_color_press=(204, 0, 102),\n )\n self.ui_manager.add_ui_element(button)","def setup(self):\n self.ui_manager.purge_ui_elements()\n\n button = buttons.MenuButton(\n 'Menu',\n center_x=self.window.width // 2,\n center_y=self.window.height // 9,\n width=200,\n user=self.user.text\n )\n button.set_style_attrs(\n bg_color_hover=(159, 255, 233),\n bg_color_press=(51, 201, 166),\n )\n self.ui_manager.add_ui_element(button)","def __init__(self, type, idt = None):\n self.btype = type\n self.idt = idt\n super(Wire, self).__init__(idt)","def __init__(self, parent, rokucontrol=None, is_error=False, error_message=''):\n tk.Frame.__init__(self, parent, background=\"black\")\n self.style = ttk.Style()\n self.parent = parent\n self.rokucontrol = rokucontrol\n\n self.init_ui()\n\n if is_error:\n self.place_error_message(error_message)\n else:\n self.place_buttons()\n self.place_app_buttons()","def __init__(self, type_=\"text\", name=\"\"):\n super().__init__(\"input\")\n self.type = type_\n self.name = name","def __init__(self, parent):\n tk.Frame.__init__(self, parent)\n _ConfigUIComponent.__init__(self)\n gen_label = tk.Label(self, text=\"Generations:\")\n gen_label.pack(side=tk.LEFT)\n is_valid_int = (self.register(is_valid_integer), '%P')\n self.gen_entry = tk.Entry(self, validate='key',\n validatecommand=is_valid_int, width=4)\n self.set_default()\n self.gen_entry.pack(side=tk.LEFT)","def __init__(self, parent=None):\n\t\tScriptedLoadableModuleWidget.__init__(self, parent)\n\t\tVTKObservationMixin.__init__(self) # needed for parameter node observation\n\t\tself.logic = None\n\t\tself._parameterNode = None\n\t\tself._updatingGUIFromParameterNode = False\n\t\t\n\t\tself.elecModel = None\n\t\tself.elecModelLastButton = None\n\t\tself.elecModelButton = 0\n\t\tself.elecChanLastButton = None\n\t\tself.elecChanButton = 0\n\t\tself.lastPolButton=0\n\t\tself.active = False","def __init__(\n self,\n type,\n text):\n self.type = type\n self.text = text","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def initializePage(self):\n subtype = ExportDialog.currentSubtype\n treeButton, branchButton, nodeButton = self.whatButtons.buttons()\n treeButton.setEnabled(subtype not in ExportDialog.disableEntireTree)\n branchButton.setEnabled(subtype not in ExportDialog.disableSelBranches\n and self.selectionAvail)\n nodeButton.setEnabled(subtype not in ExportDialog.disableSelNodes and\n self.selectionAvail)\n num = 0\n while not self.whatButtons.checkedButton().isEnabled():\n self.whatButtons.button(num).setChecked(True)\n num += 1\n\n if (subtype in ExportDialog.enableRootNode and\n ExportDialog.exportWhat != ExportDialog.selectNode):\n self.rootButton.setEnabled(True)\n self.rootButton.setChecked(ExportDialog.includeRoot)\n else:\n self.rootButton.setEnabled(False)\n self.rootButton.setChecked(subtype not in\n ExportDialog.forceRootNodeOff)\n\n if (subtype in ExportDialog.enableOpenOnly and\n ExportDialog.exportWhat != ExportDialog.selectNode):\n self.openOnlyButton.setEnabled(True)\n else:\n self.openOnlyButton.setEnabled(False)\n self.openOnlyButton.setChecked(False)\n\n self.headerButton.setEnabled(subtype in ExportDialog.enableHeader)\n if subtype not in ExportDialog.enableHeader:\n self.headerButton.setChecked(False)\n\n columnsEnabled = subtype in ExportDialog.enableColumns\n self.numColSpin.setVisible(columnsEnabled)\n self.colLabel.setVisible(columnsEnabled)\n if not columnsEnabled:\n self.numColSpin.setValue(1)\n\n navLevelsEnabled = subtype in ExportDialog.enableNavLevels\n self.navLevelsSpin.setVisible(navLevelsEnabled)\n self.navLevelsLabel.setVisible(navLevelsEnabled)","def initGui(self):\n\n icon_path = ':/plugins/new_raptor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Add New Raptor nest'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def __init__(__self__, *,\n label_name: str,\n label_type: str):\n pulumi.set(__self__, \"label_name\", label_name)\n pulumi.set(__self__, \"label_type\", label_type)","def controls_setup(self):\n\n self.email = element.TextBox(self, dom_id='mailing-list-email', alias='E-mail Textbox')\n self.close = element.Button(self, button_type='button', css_selector='.mailing-list-confirm .btn-close',\n alias='Close Button')\n self.signup = element.Button(self, css_selector='form.slide-left button[type=submit]', alias='Subscribe Button')","def test_mpushbutton_init(qtbot, dayu_type, dayu_size, icon, text):\n widget = MPushButton(icon=MIcon(icon) if icon else icon, text=text)\n widget.set_dayu_size(dayu_size)\n widget.set_dayu_type(dayu_type)\n qtbot.addWidget(widget)\n\n assert widget.property('dayu_type') == dayu_type\n assert widget.property('dayu_size') == dayu_size","def create_button(self) -> MyButton:\n pass","def controls_setup(self):\n\n self.drain_types = container.AdminDashboardSubmodule(\n self, alias=\"Drain Types Submodule\",\n css_selector='tr.CodeDrainType')\n self.depot_types = container.AdminDashboardSubmodule(\n self, alias=\"Depot Types Submodule\",\n css_selector='tr.CodeJobType')\n self.file_types = container.AdminDashboardSubmodule(\n self, alias=\"File Types Submodule\",\n css_selector='tr.CodeFileType')\n self.job_types = container.AdminDashboardSubmodule(\n self, alias=\"Job Types Submodule\",\n css_selector='tr.CodeJobType')\n self.paving_colours = container.AdminDashboardSubmodule(\n self, alias=\"Paving Colours Submodule\",\n css_selector='tr.CodePavingColour')\n self.paving_types = container.AdminDashboardSubmodule(\n self, alias=\"Paving Types Submodule\",\n css_selector='tr.CodePavingType')\n self.repair_types = container.AdminDashboardSubmodule(\n self, alias=\"Repair Types Submodule\",\n css_selector='tr.CodeRepairType')\n self.subbie_types = container.AdminDashboardSubmodule(\n self, alias=\"Subbie Types Submodule\",\n css_selector='tr.CodeSubbieType')\n self.task_types = container.AdminDashboardSubmodule(\n self, alias=\"Task Types Submodule\",\n css_selector='tr.CodeTaskType')","def __init__(self: object) -> None:\n super().__init__()\n self.title(\"dnazip\")\n self.configure(bg='#ebebeb')\n self.create_main()\n self.create_menu()\n self.create_buttons()\n self.file = None","def create_widgets(self):\r\n\r\n # we pass self since 'self' is a ttk frame\r\n # the tic tac toe buttons\r\n self.btn_tl = ttk.Button(self)\r\n self.btn_tm = ttk.Button(self)\r\n self.btn_tr = ttk.Button(self)\r\n self.btn_ml = ttk.Button(self)\r\n self.btn_mm = ttk.Button(self)\r\n self.btn_mr = ttk.Button(self)\r\n self.btn_bl = ttk.Button(self)\r\n self.btn_bm = ttk.Button(self)\r\n self.btn_br = ttk.Button(self)\r\n\r\n # the reset button\r\n self.btn_reset = ttk.Button(self)","def init_widget(self):\n super(QtToolButton, self).init_widget()\n d = self.declaration\n self.set_button_style(d.button_style)\n self.set_auto_raise(d.auto_raise)\n self.set_popup_mode(d.popup_mode)","def init ( self, parent ):\n # Create a panel to hold all of the buttons:\n self.control = panel = wx.Panel( parent, -1 )\n sizer = wx.BoxSizer( wx.VERTICAL )\n \n # Add the standard font control:\n font = self._font = wx.TextCtrl( panel, -1, self.str_value )\n wx.EVT_KILL_FOCUS( font, self.update_object )\n wx.EVT_TEXT_ENTER( panel, font.GetId(), self.update_object )\n sizer.Add( font, 0, wx.EXPAND | wx.BOTTOM, 3 )\n \n # Add all of the font choice controls:\n sizer2 = wx.BoxSizer( wx.HORIZONTAL )\n facenames = all_facenames()\n control = self._facename = wx.Choice( panel, -1, wx.Point( 0, 0 ), \n wx.Size( choice_width( facenames ), 20 ), \n facenames )\n \n sizer2.Add( control, 2, wx.EXPAND )\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts )\n \n control = self._point_size = wx.Choice( panel, -1, \n wx.Point( 0, 0 ), wx.Size( 30, 20 ), \n PointSizes )\n sizer2.Add( control, 1, wx.EXPAND | wx.RIGHT, 3 )\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts ) \n \n sizer.Add( sizer2, 0, wx.EXPAND )\n \n # Set-up the layout:\n panel.SetAutoLayout( True )\n panel.SetSizer( sizer )\n sizer.Fit( panel )","def __init__(self,\n type: str) -> None:\n # pylint: disable=super-init-not-called\n self.type = type","def __init__(self,\n type: str) -> None:\n # pylint: disable=super-init-not-called\n self.type = type","def initialization(self):\n super().initialization()\n self.register_uniq_ids([self.id_modal, self.id_modal_close, self.id_wip_button])\n\n # Register modules\n self.modules = [self.mod_table, self.mod_cache, self.mod_upload]","def __init__(self, type, name):\n self.id = len(OFFICES)\n self.type = type\n self.name = name","def create_widgets(self):","def __init__(self, parent=None):\n super(Representative, self).__init__(parent)\n self.setupUi(self)","def __init__(self, content, start, space, size, selected=None):\n self.command = None\n self.selected = selected\n self.buttons = self.create_buttons(content, start, space, size)","def set_type(self, type: int):\r\n self.type = type\r\n self.canvas.itemconfig(self.item, image=self._get_image())","def __init__(self, type, value):\n self.type = type\n self.value = value","def __init__(self, type, value):\n self.type = type\n self.value = value","def init_main(self):\n self.start_game = tk.Button(self.view.frame_2, text=\"Start Game\",\n command=lambda: self.draw_game_table())\n\n self.start_game.pack(side=tk.LEFT)\n self.see_log = tk.Button(self.view.frame_2, text=\"See Log\",\n command=lambda: self.look_log())\n self.see_log.pack(side=tk.LEFT)\n\n self.clean_log = tk.Button(self.view.frame_2, text=\"Clean Log\",\n command=lambda: self.model.clean_log_file())\n self.clean_log.pack(side=tk.LEFT)\n self.close_game = tk.Button(self.view.frame_2, text=\"Close Game\",\n command=lambda: self.view.root.destroy())\n self.close_game.pack(side=tk.LEFT)","def initUI(self):\n\n self.wid = RosGenWidget()\n self.setCentralWidget(self.wid)\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&Файл')\n editMenu = menubar.addMenu('&Редактирование')\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\n self.statusbar = self.statusBar()\n self.statusbar.showMessage('Ожидание данных')\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\n self.setGeometry(600, 200, 700, 400)\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\n self.show()","def controls_setup(self):\n\n self.date_received = element.Link(self, alias=\"Date Received\",\n css_selector='td:nth-child(1) > a', angular=True)\n self.job_type = element.Caption(self, alias=\"Job Type\", css_selector='td:nth-child(2)', angular=True)\n self.description = element.Caption(self, alias=\"Client Name\", css_selector='td:nth-child(3)', angular=True)\n self.address = element.Caption(self, alias=\"Address\", css_selector='td:nth-child(4)', angular=True)\n self.suburb = element.Caption(self, alias=\"Suburb\", css_selector='td:nth-child(5)', angular=True)\n self.client = element.Caption(self, alias=\"Client\", css_selector='td:nth-child(6)', angular=True)","def _init(self):\n pass","def setup(self):\n self.ui_manager.purge_ui_elements()\n y_slot = self.window.height // 12\n\n ui_input_box = arcade.gui.UIInputBox(\n center_x=self.window.width // 2,\n center_y=y_slot * 7,\n width=250\n )\n ui_input_box.set_style_attrs(\n bg_color=(66, 179, 208),\n bg_color_hover=(112, 212, 238),\n bg_color_focus=(255, 228, 14)\n )\n ui_input_box.text = self.name\n ui_input_box.cursor_index = len(ui_input_box.text)\n self.ui_manager.add_ui_element(ui_input_box)\n\n button = buttons.ExitButton(\n 'Exit',\n center_x=self.window.width // 2,\n center_y=y_slot * 1,\n width=250\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(135, 21, 25),\n bg_color_press=(122, 21, 24),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.AuthorButton(\"Author\",\n center_x=self.window.width // 2,\n center_y=y_slot * 2,\n width=250,\n user=ui_input_box\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.ResultButton(\"Results\",\n center_x=self.window.width // 2,\n center_y=y_slot * 3,\n width=250,\n user=ui_input_box,\n level = \"level1\"\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.RulesButton(\"Rules\",\n center_x=self.window.width // 2,\n center_y=y_slot * 4,\n width=250,\n user=ui_input_box\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.LevelButton(\"Play level 1\",\n center_x=self.window.width // 2,\n center_y=y_slot * 6,\n width=250,\n user=ui_input_box,\n level=\"level1\"\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.LevelButton(\"Play level 2\",\n center_x=self.window.width // 2,\n center_y=y_slot * 5,\n width=250,\n user=ui_input_box,\n level = \"level2\"\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)","def init_all_buttons(self) -> bool:\n raise NotImplementedError","def setup(self):\n self.ui_manager.purge_ui_elements()\n\n button = FlatButton(\n 'Lockdown',\n center_x=1000,\n center_y=20,\n width=250\n )\n button.balls(self.ball_list)\n self.ui_manager.add_ui_element(button)","def init(self) -> None:\n ...","def __init__(self): \n self.types = {}","def onInit(self):\n pass","def initUI(self):\n self.logger.debug('Setting up the Measurement GUI')\n self.setWindowTitle(self.title)\n\n self.show()\n\n self.make_combobox_scanner()\n self.make_combobox_movements()\n self.make_combobox_configurate()\n self.make_combobox_basic()","def init_load_params_button(self):\n def load_params():\n \"\"\"\n load parameters from the Curve class to the fastfractal\n \"\"\"\n file_name = filedialog.askopenfilename(\n filetypes=[(\"JSON\", \"*.json\")])\n if file_name:\n self.parent_class.classes[\"fractal\"].curve.load_from_file(\n file_name)\n self.parent_class.classes[\"fractal\"].curve.set_parent_parameters(\n )\n self.rules_frame_class.fill_entries_from_rules(\n self.parent_class.classes[\"fractal\"].rules)\n # fill the entries in rules input on load\n self.set_recursion_depth_entry(\n self.parent_class.classes[\"fractal\"].recursion_depth)\n self.set_base_length_entry(\n self.parent_class.classes[\"fractal\"].base_length)\n self.rules_frame_class.render_preview()\n\n self.buttons[\"btn_load_params\"] = Button(\n self.frame, text=\"Load Parameters\", command=load_params)\n self.buttons[\"btn_load_params\"].grid(row=4, column=0)","def _init(self):\n self.wx_menu = wx.Menu()","def init_buttons(self):\r\n self.btn_encrypt = QtWidgets.QPushButton('Encrypt')\r\n self.btn_encrypt.clicked.connect(self.encrypt)\r\n self.btn_encrypt.setEnabled(False)\r\n\r\n self.btn_decrypt = QtWidgets.QPushButton('Decrypt')\r\n self.btn_decrypt.clicked.connect(self.decrypt)\r\n self.btn_decrypt.setEnabled(False) \r\n\r\n self.layout_buttons = QtWidgets.QGridLayout()\r\n\r\n self.layout_buttons.addWidget(self.btn_encrypt,0,0)\r\n self.layout_buttons.addWidget(self.btn_decrypt,0,1)"],"string":"[\n \"def __init__(self):\\n self.handlers = {}\\n self.start_state = None\\n self.end_states = []\\n self.btn = Button()\",\n \"def _init_ui(self):\\r\\n\\t\\t\\r\\n\\t\\tself.input_frame = Input(self)\\r\\n\\t\\tself.input_frame.pack()\\r\\n\\t\\t\\r\\n\\t\\tbutton_ok = Button(self, text = \\\"Ping\\\", command = self._go)\\r\\n\\t\\tbutton_ok.pack()\\r\\n\\t\\t\\r\\n\\t\\tself.result_frame = Result(self)\\r\\n\\t\\tself.result_frame.pack()\",\n \"def init_widget(self):\",\n \"def __init__(self):\\r\\n\\r\\n self.id = -1 # button's id\\r\\n self.cur_state = AUI_BUTTON_STATE_NORMAL # current state (normal, hover, pressed, etc.)\\r\\n self.location = wx.LEFT # buttons location (wxLEFT, wxRIGHT, or wxCENTER)\\r\\n self.bitmap = wx.NullBitmap # button's hover bitmap\\r\\n self.dis_bitmap = wx.NullBitmap # button's disabled bitmap\\r\\n self.rect = wx.Rect() # button's hit rectangle\\r\",\n \"def _initializeUi(self):\\r\\n if self._mode == 'imperial':\\r\\n self.imperial_button.setChecked(True)\\r\\n self.imperial_button.clicked.emit()\\r\\n else:\\r\\n self.metric_button.setChecked(True)\\r\\n self.metric_button.clicked.emit()\",\n \"def init(self, type, cfg = \\\"\\\"):\\n\\t\\tself.setType(type)\\n\\t\\tself.setConfig(cfg)\",\n \"def __init__(self, type_):\\n\\n self.type = type_\",\n \"def initializePage(self):\\n topLayout = self.layout()\\n # remove old buttons from a previously set subtype\\n for button in self.subtypeButtons.buttons():\\n self.subtypeButtons.removeButton(button)\\n topLayout.removeWidget(button)\\n button.deleteLater()\\n\\n for id, subtype in enumerate(ExportDialog.\\n exportSubtypes[ExportDialog.currentType]):\\n button = QtGui.QRadioButton(ExportDialog.subtypeDescript[subtype])\\n self.subtypeButtons.addButton(button, id)\\n topLayout.addWidget(button)\\n if subtype == ExportDialog.currentSubtype:\\n button.setChecked(True)\",\n \"def _initialize(self):\\n\\n cancel_button = ttk.Button(\\n master=self._frame,\\n text=\\\"Cancel\\\",\\n command=self._hide_confirmation_window\\n )\\n\\n delete_button = ttk.Button(\\n master=self._frame,\\n text=\\\"Delete\\\",\\n command=self._handle_delete\\n )\\n\\n self._initilize_message()\\n\\n cancel_button.grid(row=1, column=0, padx=5, pady=5)\\n delete_button.grid(row=1, column=1, padx=5, pady=5)\",\n \"def __init__(self, parent=None):\\n super().__init__(parent)\\n\\n topLayout = QtGui.QVBoxLayout(self)\\n self.setLayout(topLayout)\\n self.setTitle(_('Choose export format type'))\\n\\n typeButtons = QtGui.QButtonGroup(self)\\n for id, exportType in enumerate(ExportDialog.exportTypes):\\n button = QtGui.QRadioButton(ExportDialog.\\n exportTypeDescript[exportType])\\n typeButtons.addButton(button, id)\\n topLayout.addWidget(button)\\n if exportType == ExportDialog.currentType:\\n button.setChecked(True)\\n typeButtons.buttonClicked[int].connect(self.setCurrentType)\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def __init__(self, button_id):\\r\\n\\r\\n self.button_id = button_id\",\n \"def initialize(self):\\n self._ui.img_name.setText('No files selected')\\n self._ui.bt_next.setEnabled(False)\\n self._ui.bt_prev.setEnabled(False)\\n self._ui.bt_right.setEnabled(False)\\n self._ui.bt_left.setEnabled(False)\\n self._ui.gps_button.setEnabled(False)\\n\\n self._open_btn = QPushButton('Open File', self._ui.img_label)\\n self.adjustSize()\",\n \"def initUI(self):\\n startbtn = QPushButton(\\\"Start Recroding\\\", self)\\n startbtn.move(30, 50)\\n\\n stopbtn = QPushButton(\\\"Stop Recording\\\", self)\\n stopbtn.move(150, 50)\\n\\n initbtn = QPushButton(\\\"Initilize\\\", self)\\n initbtn.move(30, 100)\\n\\n plotbtn = QPushButton(\\\"Plot\\\", self)\\n plotbtn.move(150, 100)\\n\\n startbtn.clicked.connect(self.start_recording)\\n stopbtn.clicked.connect(self.stop_recording)\\n initbtn.clicked.connect(self.init_recording)\\n plotbtn.clicked.connect(self.plot_signals)\\n\\n self.statusBar()\\n self.statusBar().showMessage('Click Init')\\n\\n self.setGeometry(300, 300, 290, 150)\\n self.setWindowTitle('Recorder 1.0')\\n self.setWindowIcon(QIcon(\\\"./Static/Images/icon.jpg\\\"))\\n self.show()\",\n \"def setup_options(self):\\n self.analysis_type_label = Label(self, text='Select what you wish to do:')\\n self.analysis_type_label.pack(fill=BOTH, expand=True)\\n\\n # Create Select option\\n self._analysis_type_variable = StringVar(self)\\n options1 = [\\n 'Regression',\\n 'Classification'\\n ]\\n self._analysis_type_variable.set(options1[0])\\n self.type_option_menu = OptionMenu(self, self._analysis_type_variable, *options1)\\n self.type_option_menu.pack()\\n\\n self.analyse_button = Button(self, text='NEXT', background='White', command=self.perform_analysis)\\n self.analyse_button.pack(padx=5, pady=10)\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def controls_setup(self):\\n pass\",\n \"def initialize(self):\\n self.actions = []\\n \\\"*** YOUR CODE HERE\\\"\\n #raise NotImplementedError()\",\n \"def configure_widgets(self):\\r\\n\\r\\n # 'command' - callback function executed when button is pressed\\r\\n # since we can't pass it a function with arguments, we use the partial \\r\\n # function from the functools module\\r\\n self.btn_tl['command'] = partial(self.play, \\\"x\\\", (0,0))\\r\\n self.btn_tm['command'] = partial(self.play, \\\"x\\\", (0,1))\\r\\n self.btn_tr['command'] = partial(self.play, \\\"x\\\", (0,2))\\r\\n self.btn_ml['command'] = partial(self.play, \\\"x\\\", (1,0))\\r\\n self.btn_mm['command'] = partial(self.play, \\\"x\\\", (1,1))\\r\\n self.btn_mr['command'] = partial(self.play, \\\"x\\\", (1,2))\\r\\n self.btn_bl['command'] = partial(self.play, \\\"x\\\", (2,0))\\r\\n self.btn_bm['command'] = partial(self.play, \\\"x\\\", (2,1))\\r\\n self.btn_br['command'] = partial(self.play, \\\"x\\\", (2,2))\\r\\n\\r\\n self.btn_reset['text'] = \\\"Reset\\\"\\r\\n self.btn_reset['command'] = self.reset\",\n \"def __init__(self):\\n\\n GPIO.setup(PIN_BTN, GPIO.IN, GPIO.PUD_UP)\\n GPIO.setup(PIN_RED_LED_0, GPIO.OUT, GPIO.LOW)\\n GPIO.setup(PIN_BLUE_LED, GPIO.OUT, GPIO.LOW)\",\n \"def initialize_buttons(self):\\r\\n self.start_button = tk.Button(self.master, text='Start', command = self.startRunning)\\r\\n self.start_button.grid(row=0, column=0)\\r\\n\\r\\n self.pause_button = tk.Button(self.master, text='Pause', command = self.pauseRunning)\\r\\n self.pause_button.grid(row=0, column=1)\\r\\n\\r\\n self.graph_button = tk.Button(self.master, text='Graph', command = self.showGraph)\\r\\n self.graph_button.grid(row=0, column=2)\\r\\n \\r\\n self.plot_button = tk.Button(self.master, text='Plot', command = self.showPlot)\\r\\n self.plot_button.grid(row=0, column=3)\\r\\n \\r\\n self.draw_button = tk.Button(self.master, text='Draw', command = self.drawCells)\\r\\n self.draw_button.grid(row=0, column=4)\\r\\n \\r\\n # Initialize Button States and Actions\\r\\n self.pause_button['state'] = 'disabled'\\r\\n # Boolean switch to control flow of placement process\\r\\n self.running = False\\r\\n # Boolean switch to plot placement connections and tags, turn off for faster processing\\r\\n self.plot = False\\r\\n self.drawing = False\\r\\n self.graph = False\\r\\n # Boolean switch to specify first run and allow stop/continue behavior that doesn't initialize program\\r\\n self.firstRun = True\",\n \"def __init__(self, the_type, enabled, generate_code):\\n self.__type = the_type\\n self.setEnabled(enabled)\\n self.setGenerateCode(generate_code)\",\n \"def gui_init(self):\\n GUI_element.gui_init(self)\\n\\n self.hover_sound = False\\n \\n if not self.image is None:\\n self.generic_button = False\\n self.width = self.image.width if self.width == 0 else self.width\\n self.height = self.image.height if self.height == 0 else self.height\\n else:\\n # Set up a generic button\\n self.generic_button = True\\n self.image = self.game.core.media.gfx['gui_button_generic_background']\\n self.draw_strategy = \\\"gui_button\\\"\\n\\n # fixed height\\n self.height = 30\\n \\n # Create the text\\n self.generic_button_text_object = Text(self.game.core.media.fonts[\\\"generic_buttons\\\"], self.x, self.y + (self.height / 2), TEXT_ALIGN_CENTER, self.generic_button_text)\\n self.generic_button_text_object.z = self.z - 1\\n self.generic_button_text_object.colour = (1.0,1.0,1.0)\\n \\n # Set up the width, if we have a larger than normal width then we want to centre the text.\\n if self.width < self.generic_button_text_object.text_width + 20:\\n self.width = self.generic_button_text_object.text_width + 20\\n self.generic_button_text_object.x += (self.width / 2)\\n \\n self.sequence_count = self.image.num_of_frames\\n self.draw_strategy_call_parent = False\",\n \"def init_ui(self):\\n self.master.title(\\\"Backbone\\\")\\n self.master.geometry(\\\"300x150\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=90, y=10)\\n\\n self.btn_create_training_file = Button(self, text=\\\"Create & upload training file\\\",\\n command=self.create_training_file)\\n self.btn_create_training_file.place(x=30, y=40)\\n\\n self.btn_run_algorithm = Button(self, text=\\\"Run algorithm\\\", command=self.run_algorithm)\\n self.btn_run_algorithm.place(x=80, y=70)\\n\\n self.btn_view_results = Button(self, text=\\\"View Results\\\", command=self.view_results)\\n self.btn_view_results.place(x=85, y=100)\",\n \"def __init__(self, parent=None):\\n super().__init__(parent)\\n\\n topLayout = QtGui.QVBoxLayout(self)\\n self.setLayout(topLayout)\\n self.setTitle(_('Choose export format subtype'))\\n self.subtypeButtons = QtGui.QButtonGroup(self)\\n self.subtypeButtons.buttonClicked[int].connect(self.setCurrentSubtype)\",\n \"def initialize(self,init_info):\\n self.action_info = init_info.actions\\n return True\",\n \"def __init__buttons(language, color_writing, color_end_b_writing, field_size, button_bg_color):\\r\\n _create_buttons(field_size, button_bg_color) # creates buttons\\r\\n __init__button_writings(language, color_writing, color_end_b_writing, field_size) # creates writings\\r\",\n \"def __init__(self):\\n self.window = Tk() # The main window\\n self.__initialize_variables__() # Initialize the variables\\n self.__initialize_menu__() # Initialize the Menu\\n self.__initialize_status_bar__()\\n self.__initialize_gui__() # Initialize the GUI widgets\",\n \"def create_widgets(self):\\n #create first button\\n self.button1 = Button(self, text = \\\"Start\\\")\\n self.button1.bind\\n self.button1.grid()\",\n \"def _initialize(self):\\n self._entry = tk.Entry(master = self._window)\\n self._entry.focus_set()\\n \\n self._confirm_button = tk.Button(master = self._window,\\n text = \\\"Ok\\\",\\n font = DEFAULT_FONT,\\n command = self._confirm_color)\\n\\n self._entry.grid(row = 0, column = 0,\\n padx = 10, pady = 10)\\n self._confirm_button.grid(row = 1, column = 0,\\n padx = 10, pady = 10)\\n\\n self._window.bind(\\\"<Return>\\\", self._confirm_color)\",\n \"def __init__(self):\\r\\n super().__init__()\\r\\n self.init_ui()\",\n \"def init_objects(self):\\n self.start_button_local = box(pos=vector(0, 1.2, 0), size=vector(8, 2, 1))\\n # if no IP address was defined, the button \\\"remote game\\\" will not appear\\n self.start_button_remote = None if not self.remote_enabled else box(pos=vector(0, -1.2, 0), size=vector(8, 2, 1))\\n # a label is a text associated to a box\\n self.start_label_local = label(pos=self.start_button_local.pos, xoffset=0, yoffset=0, text='Local Game', color=color.black, opacity=0, line=False, height=20, box=False)\\n self.start_label_remote = None if not self.remote_enabled else label(pos=self.start_button_remote.pos, xoffset=0, yoffset=0, text='Remote Game', color=color.black, opacity=0, line=False, height=20, box=False)\\n self.title = text(pos=vector(0, 5, 0), text='Morpion 3D', align='center', color=color.green, billboard=True, depth=0.5)\\n # return the list of elements that actually appear\\n return filter(lambda x: x is not None, [self.start_button_local, self.start_button_remote, self.start_label_local, self.start_label_remote, self.title])\",\n \"def create_widgets(self):\\n # self.var_spherical = IntVar()\\n # self.var_3d = IntVar()\\n # self.var_spatial_audio = IntVar()\\n # self.button_open[\\\"command\\\"] = self.action_open\\n # self.button_inject[\\\"command\\\"] = self.action_inject\\n pass\",\n \"def create_widgets (self):\\r\\n # create first button\\r\\n self.bttn1 = Button (self, text = \\\"I do nothing!\\\")\\r\\n self.bttn1.grid ()\\r\\n\\r\\n # create a second button\\r\\n self.bttn2 = Button (self)\\r\\n self.bttn2.grid ()\\r\\n self.bttn2.configure (text = \\\"Me too\\\")\\r\\n\\r\\n # create a third button\\r\\n self.bttn3 = Button (self)\\r\\n self.bttn3.grid ()\\r\\n self.bttn3 [\\\"text\\\"] = \\\"Same here!\\\"\",\n \"def _initialize(self):\\n \\n self.view.lineEdit_3.setText(\\\"C,H,N,O,P,S\\\")\\n self.view.spin_hit.setValue(20)\\n self.view.lineEdit_2.setValue(10.)\\n self.view.checkBox_8.setChecked(True)\",\n \"def __init__(self, type_=None, title=None, content=None, text=None,\\n image=None):\\n default_attr = dict(type=str(),\\n title=str(),\\n content=str(),\\n text=str(),\\n image=Image())\\n self.type = type_\\n self.title = title\\n self.content = content\\n self.text = text\\n self.image = image\\n self._set_default_attr(default_attr)\",\n \"def prepare_UI(self):\",\n \"def __init__(self, controller):\\n super().__init__(controller)\\n\\n # The hovered input when entering this View.\\n self.first_inp = \\\"s\\\"\\n\\n # Initialize selected variable.\\n self.selected = None\\n\\n # Make background graphics.\\n self.make_background_graphics()\\n\\n # Make Buttons.\\n self.make_buttons()\\n\\n # Make the information box. This explains each Button.\\n self.make_info_box()\\n\\n # Initializes popup.\\n self.make_popup()\\n\\n # Map of input to functions.\\n enter = self.graphics.ENTER_KEY\\n self.controls = {\\n # Pressing \\\"q\\\" will go back to the main menu.\\n \\\"q\\\": lambda: Action(\\\"goto main menu view\\\", []),\\n\\n # Movement keys.\\n \\\"w\\\": lambda: self.move_cursor(Direction.U),\\n \\\"a\\\": lambda: self.move_cursor(Direction.L),\\n \\\"s\\\": lambda: self.move_cursor(Direction.D),\\n \\\"d\\\": lambda: self.move_cursor(Direction.R),\\n\\n # Repeat the last valid input.\\n enter: self.repeat_last_valid_input,\\n\\n # Click the selected UIElement.\\n \\\"m\\\": self.click\\n }\",\n \"def _initFields(self):\\n self.windowType = \\\"volume\\\"\\n\\n self.masterVol = 0\\n self.masterMute = 0\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def init_ui(self):\\n self.parent.title(\\\"Roku Player Controller\\\")\\n self.style.theme_use(\\\"default\\\")\",\n \"def __init__(self, type, onReset = None, idt = None):\\n self.onReset = onReset\\n self.btype = type\\n self.idt = idt\\n super(Reg, self).__init__(idt)\",\n \"def __init__(self, tag_type):\\n self.action_type = 'push_vlan'\\n self.tag_type = tag_type\",\n \"def __init_widgets(self):\\n self.add_events(Gdk.EventMask.ENTER_NOTIFY_MASK)\\n\\n headerbar = self.get_header_bar()\\n scanner_image = Gtk.Image.new_from_icon_name(\\\"qrscanner-symbolic\\\", Gtk.IconSize.BUTTON)\\n self.scan_button.add(scanner_image)\\n\\n self.add_button.get_style_context().add_class(\\\"suggested-action\\\")\\n self.add_button.set_sensitive(False)\\n self.add_button.set_can_default(True)\\n\\n headerbar.pack_end(self.add_button)\\n headerbar.pack_end(self.scan_button)\\n\\n self.account_config.connect(\\\"changed\\\", self._on_account_config_changed)\\n self.add_button.connect(\\\"clicked\\\", self._on_add)\\n self.scan_button.connect(\\\"clicked\\\", self.account_config.scan_qr)\\n self.get_content_area().pack_start(self.account_config, True, True, 0)\",\n \"def init_ui(self):\\n\\n self.master.title(\\\"Upload file\\\")\\n self.master.geometry(\\\"300x200\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_select_file = Button(self, text=\\\"Select file\\\", command=self.on_open)\\n self.btn_select_file.place(x=80, y=50)\\n\\n self.selected_file_name = Label(self, text=\\\"<Selected file name>\\\")\\n self.selected_file_name.place(x=60, y=90)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=80, y=130)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.place(x=10, y=10)\",\n \"def __init__(self, x, y, width, height, action = RETURN_TRUE, inactivecolour = red, activecolour = orange,\\n text = None, textcolour = black, size = 25, border = None):\\n super(Button, self).__init__(x, y, width, height)\\n def tempTrue(): return True\\n def tempFalse(): return False\\n def tempNone(): return None\\n self.string = False\\n if action == Button.RETURN_TRUE:\\n action = tempTrue\\n elif action == Button.RETURN_FALSE:\\n action = tempFalse\\n elif action == Button.RETURN_NONE:\\n action = tempNone\\n elif isinstance(action, str):\\n self.string = True\\n self.action = action\\n self.inactive = inactivecolour\\n self.active = activecolour\\n self.border = border \\n if text == None:\\n text = action.__name__.upper()\\n if self.string:\\n text = self.action\\n self.text = text\\n self.textcolour = textcolour\\n self.size = size\",\n \"def setup_button_block(self):\\n self.btn_contrast_invert.setText(\\\"Contrast Invert\\\")\\n self.btn_display.setText(\\\"Display\\\")\\n self.btn_display_hist.setText(\\\"Display HIST\\\")\\n self.btn_display_color_hist.setText(\\\"Display Color HIST\\\")\\n self.btn_compare.setText(\\\"Compare\\\")\\n self.btn_equalize_hist.setText(\\\"Equalize Histogram\\\")\\n self.btn_contrast_stretch.setText(\\\"Contrast Stretch\\\")\\n self.btn_log_compress.setText(\\\"Log Compress\\\")\\n self.btn_dload_jpeg.setText(\\\"Download JPEG\\\")\\n self.btn_dload_tiff.setText(\\\"Download TIFF\\\")\\n self.btn_dload_png.setText(\\\"Download PNG\\\")\\n self.btn_upload.setText(\\\"Upload\\\")\\n\\n self.btn_upload.clicked.connect(self.btn_upload_callback)\\n\\n self.btn_display.clicked.connect(self.btn_display_callback)\\n self.btn_contrast_invert.clicked.connect(\\n self.btn_contrast_invert_callback)\\n self.btn_display_hist.clicked.connect(self.btn_display_hist_callback)\\n self.btn_display_color_hist.clicked.connect(\\n self.btn_display_color_hist_callback)\\n self.btn_compare.clicked.connect(self.btn_compare_callback)\\n self.btn_equalize_hist.clicked.connect(self.btn_equalize_hist_callback)\\n self.btn_contrast_stretch.clicked.connect(\\n self.btn_contrast_stretch_callback)\\n self.btn_log_compress.clicked.connect(self.btn_log_compress_callback)\\n self.btn_dload_jpeg.clicked.connect(self.download_images_jpg)\\n self.btn_dload_png.clicked.connect(self.download_images_png)\\n self.btn_dload_tiff.clicked.connect(self.download_images_tiff)\",\n \"def __init__(self, genotype):\\n\\n\\t\\t# instantiate the root\\n\\t\\ttk.Tk.__init__(self)\\n\\t\\tself.title(\\\"CPPN playground\\\")\\n\\n\\t\\t# create container to hold all grames in the GUI\\n\\t\\tcontainer = tk.Frame(self)\\n\\t\\tcontainer.pack(side=\\\"top\\\", fill=\\\"both\\\", expand=True)\\n\\t\\tcontainer.grid_rowconfigure(0, weight=1)\\n\\t\\tcontainer.grid_columnconfigure(0, weight=1)\\n\\n\\t\\t# initiate GUI\\n\\n\\t\\t# add menu bar for the separate Tk frame\\n\\t\\tmenubar = tk.Menu(self)\\n\\t\\tfilemenu = tk.Menu(menubar)\\n\\t\\tfilemenu.add_command(label=\\\"Main Page\\\", command=lambda: self.raise_frame(\\\"MainPage\\\", container))\\n\\t\\tfilemenu.add_command(label=\\\"Slider Page\\\", command=lambda: self.raise_frame(\\\"SliderPage\\\", container))\\n\\t\\tfilemenu.add_command(label=\\\"Save\\\", command=lambda: save_gen_GUI(genotype))\\n\\t\\tmenubar.add_cascade(label=\\\"Options\\\", menu=filemenu)\\n\\t\\tself.config(menu=menubar)\\n\\n\\t\\t# add frames to the main GUI\\n\\t\\tself.frames = {}\\n\\n\\t\\t# create main frame\\n\\t\\tframe1 = MainPage(container=container, master=self, genotype=genotype)\\n\\t\\tself.frames[\\\"MainPage\\\"] = frame1\\n\\t\\tframe1.grid(row=0, column=0, stick=\\\"nsew\\\")\\n\\n\\t\\t# raise main page to the front initially\\n\\t\\tself.raise_frame(\\\"MainPage\\\", container)\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n button = buttons.MenuButton(\\n 'Menu',\\n center_x=self.window.width // 2,\\n center_y=self.window.height // 6,\\n width=200,\\n user=self.user.text\\n )\\n button.set_style_attrs(\\n bg_color=(255, 153, 204),\\n bg_color_hover=(255, 102, 178),\\n bg_color_press=(204, 0, 102),\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n button = buttons.MenuButton(\\n 'Menu',\\n center_x=self.window.width // 2,\\n center_y=self.window.height // 9,\\n width=200,\\n user=self.user.text\\n )\\n button.set_style_attrs(\\n bg_color_hover=(159, 255, 233),\\n bg_color_press=(51, 201, 166),\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"def __init__(self, type, idt = None):\\n self.btype = type\\n self.idt = idt\\n super(Wire, self).__init__(idt)\",\n \"def __init__(self, parent, rokucontrol=None, is_error=False, error_message=''):\\n tk.Frame.__init__(self, parent, background=\\\"black\\\")\\n self.style = ttk.Style()\\n self.parent = parent\\n self.rokucontrol = rokucontrol\\n\\n self.init_ui()\\n\\n if is_error:\\n self.place_error_message(error_message)\\n else:\\n self.place_buttons()\\n self.place_app_buttons()\",\n \"def __init__(self, type_=\\\"text\\\", name=\\\"\\\"):\\n super().__init__(\\\"input\\\")\\n self.type = type_\\n self.name = name\",\n \"def __init__(self, parent):\\n tk.Frame.__init__(self, parent)\\n _ConfigUIComponent.__init__(self)\\n gen_label = tk.Label(self, text=\\\"Generations:\\\")\\n gen_label.pack(side=tk.LEFT)\\n is_valid_int = (self.register(is_valid_integer), '%P')\\n self.gen_entry = tk.Entry(self, validate='key',\\n validatecommand=is_valid_int, width=4)\\n self.set_default()\\n self.gen_entry.pack(side=tk.LEFT)\",\n \"def __init__(self, parent=None):\\n\\t\\tScriptedLoadableModuleWidget.__init__(self, parent)\\n\\t\\tVTKObservationMixin.__init__(self) # needed for parameter node observation\\n\\t\\tself.logic = None\\n\\t\\tself._parameterNode = None\\n\\t\\tself._updatingGUIFromParameterNode = False\\n\\t\\t\\n\\t\\tself.elecModel = None\\n\\t\\tself.elecModelLastButton = None\\n\\t\\tself.elecModelButton = 0\\n\\t\\tself.elecChanLastButton = None\\n\\t\\tself.elecChanButton = 0\\n\\t\\tself.lastPolButton=0\\n\\t\\tself.active = False\",\n \"def __init__(\\n self,\\n type,\\n text):\\n self.type = type\\n self.text = text\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def initializePage(self):\\n subtype = ExportDialog.currentSubtype\\n treeButton, branchButton, nodeButton = self.whatButtons.buttons()\\n treeButton.setEnabled(subtype not in ExportDialog.disableEntireTree)\\n branchButton.setEnabled(subtype not in ExportDialog.disableSelBranches\\n and self.selectionAvail)\\n nodeButton.setEnabled(subtype not in ExportDialog.disableSelNodes and\\n self.selectionAvail)\\n num = 0\\n while not self.whatButtons.checkedButton().isEnabled():\\n self.whatButtons.button(num).setChecked(True)\\n num += 1\\n\\n if (subtype in ExportDialog.enableRootNode and\\n ExportDialog.exportWhat != ExportDialog.selectNode):\\n self.rootButton.setEnabled(True)\\n self.rootButton.setChecked(ExportDialog.includeRoot)\\n else:\\n self.rootButton.setEnabled(False)\\n self.rootButton.setChecked(subtype not in\\n ExportDialog.forceRootNodeOff)\\n\\n if (subtype in ExportDialog.enableOpenOnly and\\n ExportDialog.exportWhat != ExportDialog.selectNode):\\n self.openOnlyButton.setEnabled(True)\\n else:\\n self.openOnlyButton.setEnabled(False)\\n self.openOnlyButton.setChecked(False)\\n\\n self.headerButton.setEnabled(subtype in ExportDialog.enableHeader)\\n if subtype not in ExportDialog.enableHeader:\\n self.headerButton.setChecked(False)\\n\\n columnsEnabled = subtype in ExportDialog.enableColumns\\n self.numColSpin.setVisible(columnsEnabled)\\n self.colLabel.setVisible(columnsEnabled)\\n if not columnsEnabled:\\n self.numColSpin.setValue(1)\\n\\n navLevelsEnabled = subtype in ExportDialog.enableNavLevels\\n self.navLevelsSpin.setVisible(navLevelsEnabled)\\n self.navLevelsLabel.setVisible(navLevelsEnabled)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/new_raptor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Add New Raptor nest'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def __init__(__self__, *,\\n label_name: str,\\n label_type: str):\\n pulumi.set(__self__, \\\"label_name\\\", label_name)\\n pulumi.set(__self__, \\\"label_type\\\", label_type)\",\n \"def controls_setup(self):\\n\\n self.email = element.TextBox(self, dom_id='mailing-list-email', alias='E-mail Textbox')\\n self.close = element.Button(self, button_type='button', css_selector='.mailing-list-confirm .btn-close',\\n alias='Close Button')\\n self.signup = element.Button(self, css_selector='form.slide-left button[type=submit]', alias='Subscribe Button')\",\n \"def test_mpushbutton_init(qtbot, dayu_type, dayu_size, icon, text):\\n widget = MPushButton(icon=MIcon(icon) if icon else icon, text=text)\\n widget.set_dayu_size(dayu_size)\\n widget.set_dayu_type(dayu_type)\\n qtbot.addWidget(widget)\\n\\n assert widget.property('dayu_type') == dayu_type\\n assert widget.property('dayu_size') == dayu_size\",\n \"def create_button(self) -> MyButton:\\n pass\",\n \"def controls_setup(self):\\n\\n self.drain_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Drain Types Submodule\\\",\\n css_selector='tr.CodeDrainType')\\n self.depot_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Depot Types Submodule\\\",\\n css_selector='tr.CodeJobType')\\n self.file_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"File Types Submodule\\\",\\n css_selector='tr.CodeFileType')\\n self.job_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Job Types Submodule\\\",\\n css_selector='tr.CodeJobType')\\n self.paving_colours = container.AdminDashboardSubmodule(\\n self, alias=\\\"Paving Colours Submodule\\\",\\n css_selector='tr.CodePavingColour')\\n self.paving_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Paving Types Submodule\\\",\\n css_selector='tr.CodePavingType')\\n self.repair_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Repair Types Submodule\\\",\\n css_selector='tr.CodeRepairType')\\n self.subbie_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Subbie Types Submodule\\\",\\n css_selector='tr.CodeSubbieType')\\n self.task_types = container.AdminDashboardSubmodule(\\n self, alias=\\\"Task Types Submodule\\\",\\n css_selector='tr.CodeTaskType')\",\n \"def __init__(self: object) -> None:\\n super().__init__()\\n self.title(\\\"dnazip\\\")\\n self.configure(bg='#ebebeb')\\n self.create_main()\\n self.create_menu()\\n self.create_buttons()\\n self.file = None\",\n \"def create_widgets(self):\\r\\n\\r\\n # we pass self since 'self' is a ttk frame\\r\\n # the tic tac toe buttons\\r\\n self.btn_tl = ttk.Button(self)\\r\\n self.btn_tm = ttk.Button(self)\\r\\n self.btn_tr = ttk.Button(self)\\r\\n self.btn_ml = ttk.Button(self)\\r\\n self.btn_mm = ttk.Button(self)\\r\\n self.btn_mr = ttk.Button(self)\\r\\n self.btn_bl = ttk.Button(self)\\r\\n self.btn_bm = ttk.Button(self)\\r\\n self.btn_br = ttk.Button(self)\\r\\n\\r\\n # the reset button\\r\\n self.btn_reset = ttk.Button(self)\",\n \"def init_widget(self):\\n super(QtToolButton, self).init_widget()\\n d = self.declaration\\n self.set_button_style(d.button_style)\\n self.set_auto_raise(d.auto_raise)\\n self.set_popup_mode(d.popup_mode)\",\n \"def init ( self, parent ):\\n # Create a panel to hold all of the buttons:\\n self.control = panel = wx.Panel( parent, -1 )\\n sizer = wx.BoxSizer( wx.VERTICAL )\\n \\n # Add the standard font control:\\n font = self._font = wx.TextCtrl( panel, -1, self.str_value )\\n wx.EVT_KILL_FOCUS( font, self.update_object )\\n wx.EVT_TEXT_ENTER( panel, font.GetId(), self.update_object )\\n sizer.Add( font, 0, wx.EXPAND | wx.BOTTOM, 3 )\\n \\n # Add all of the font choice controls:\\n sizer2 = wx.BoxSizer( wx.HORIZONTAL )\\n facenames = all_facenames()\\n control = self._facename = wx.Choice( panel, -1, wx.Point( 0, 0 ), \\n wx.Size( choice_width( facenames ), 20 ), \\n facenames )\\n \\n sizer2.Add( control, 2, wx.EXPAND )\\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts )\\n \\n control = self._point_size = wx.Choice( panel, -1, \\n wx.Point( 0, 0 ), wx.Size( 30, 20 ), \\n PointSizes )\\n sizer2.Add( control, 1, wx.EXPAND | wx.RIGHT, 3 )\\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts ) \\n \\n sizer.Add( sizer2, 0, wx.EXPAND )\\n \\n # Set-up the layout:\\n panel.SetAutoLayout( True )\\n panel.SetSizer( sizer )\\n sizer.Fit( panel )\",\n \"def __init__(self,\\n type: str) -> None:\\n # pylint: disable=super-init-not-called\\n self.type = type\",\n \"def __init__(self,\\n type: str) -> None:\\n # pylint: disable=super-init-not-called\\n self.type = type\",\n \"def initialization(self):\\n super().initialization()\\n self.register_uniq_ids([self.id_modal, self.id_modal_close, self.id_wip_button])\\n\\n # Register modules\\n self.modules = [self.mod_table, self.mod_cache, self.mod_upload]\",\n \"def __init__(self, type, name):\\n self.id = len(OFFICES)\\n self.type = type\\n self.name = name\",\n \"def create_widgets(self):\",\n \"def __init__(self, parent=None):\\n super(Representative, self).__init__(parent)\\n self.setupUi(self)\",\n \"def __init__(self, content, start, space, size, selected=None):\\n self.command = None\\n self.selected = selected\\n self.buttons = self.create_buttons(content, start, space, size)\",\n \"def set_type(self, type: int):\\r\\n self.type = type\\r\\n self.canvas.itemconfig(self.item, image=self._get_image())\",\n \"def __init__(self, type, value):\\n self.type = type\\n self.value = value\",\n \"def __init__(self, type, value):\\n self.type = type\\n self.value = value\",\n \"def init_main(self):\\n self.start_game = tk.Button(self.view.frame_2, text=\\\"Start Game\\\",\\n command=lambda: self.draw_game_table())\\n\\n self.start_game.pack(side=tk.LEFT)\\n self.see_log = tk.Button(self.view.frame_2, text=\\\"See Log\\\",\\n command=lambda: self.look_log())\\n self.see_log.pack(side=tk.LEFT)\\n\\n self.clean_log = tk.Button(self.view.frame_2, text=\\\"Clean Log\\\",\\n command=lambda: self.model.clean_log_file())\\n self.clean_log.pack(side=tk.LEFT)\\n self.close_game = tk.Button(self.view.frame_2, text=\\\"Close Game\\\",\\n command=lambda: self.view.root.destroy())\\n self.close_game.pack(side=tk.LEFT)\",\n \"def initUI(self):\\n\\n self.wid = RosGenWidget()\\n self.setCentralWidget(self.wid)\\n menubar = self.menuBar()\\n fileMenu = menubar.addMenu('&Файл')\\n editMenu = menubar.addMenu('&Редактирование')\\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\\n self.statusbar = self.statusBar()\\n self.statusbar.showMessage('Ожидание данных')\\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\\n self.setGeometry(600, 200, 700, 400)\\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\\n self.show()\",\n \"def controls_setup(self):\\n\\n self.date_received = element.Link(self, alias=\\\"Date Received\\\",\\n css_selector='td:nth-child(1) > a', angular=True)\\n self.job_type = element.Caption(self, alias=\\\"Job Type\\\", css_selector='td:nth-child(2)', angular=True)\\n self.description = element.Caption(self, alias=\\\"Client Name\\\", css_selector='td:nth-child(3)', angular=True)\\n self.address = element.Caption(self, alias=\\\"Address\\\", css_selector='td:nth-child(4)', angular=True)\\n self.suburb = element.Caption(self, alias=\\\"Suburb\\\", css_selector='td:nth-child(5)', angular=True)\\n self.client = element.Caption(self, alias=\\\"Client\\\", css_selector='td:nth-child(6)', angular=True)\",\n \"def _init(self):\\n pass\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n y_slot = self.window.height // 12\\n\\n ui_input_box = arcade.gui.UIInputBox(\\n center_x=self.window.width // 2,\\n center_y=y_slot * 7,\\n width=250\\n )\\n ui_input_box.set_style_attrs(\\n bg_color=(66, 179, 208),\\n bg_color_hover=(112, 212, 238),\\n bg_color_focus=(255, 228, 14)\\n )\\n ui_input_box.text = self.name\\n ui_input_box.cursor_index = len(ui_input_box.text)\\n self.ui_manager.add_ui_element(ui_input_box)\\n\\n button = buttons.ExitButton(\\n 'Exit',\\n center_x=self.window.width // 2,\\n center_y=y_slot * 1,\\n width=250\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(135, 21, 25),\\n bg_color_press=(122, 21, 24),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.AuthorButton(\\\"Author\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 2,\\n width=250,\\n user=ui_input_box\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.ResultButton(\\\"Results\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 3,\\n width=250,\\n user=ui_input_box,\\n level = \\\"level1\\\"\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.RulesButton(\\\"Rules\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 4,\\n width=250,\\n user=ui_input_box\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.LevelButton(\\\"Play level 1\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 6,\\n width=250,\\n user=ui_input_box,\\n level=\\\"level1\\\"\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.LevelButton(\\\"Play level 2\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 5,\\n width=250,\\n user=ui_input_box,\\n level = \\\"level2\\\"\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"def init_all_buttons(self) -> bool:\\n raise NotImplementedError\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n button = FlatButton(\\n 'Lockdown',\\n center_x=1000,\\n center_y=20,\\n width=250\\n )\\n button.balls(self.ball_list)\\n self.ui_manager.add_ui_element(button)\",\n \"def init(self) -> None:\\n ...\",\n \"def __init__(self): \\n self.types = {}\",\n \"def onInit(self):\\n pass\",\n \"def initUI(self):\\n self.logger.debug('Setting up the Measurement GUI')\\n self.setWindowTitle(self.title)\\n\\n self.show()\\n\\n self.make_combobox_scanner()\\n self.make_combobox_movements()\\n self.make_combobox_configurate()\\n self.make_combobox_basic()\",\n \"def init_load_params_button(self):\\n def load_params():\\n \\\"\\\"\\\"\\n load parameters from the Curve class to the fastfractal\\n \\\"\\\"\\\"\\n file_name = filedialog.askopenfilename(\\n filetypes=[(\\\"JSON\\\", \\\"*.json\\\")])\\n if file_name:\\n self.parent_class.classes[\\\"fractal\\\"].curve.load_from_file(\\n file_name)\\n self.parent_class.classes[\\\"fractal\\\"].curve.set_parent_parameters(\\n )\\n self.rules_frame_class.fill_entries_from_rules(\\n self.parent_class.classes[\\\"fractal\\\"].rules)\\n # fill the entries in rules input on load\\n self.set_recursion_depth_entry(\\n self.parent_class.classes[\\\"fractal\\\"].recursion_depth)\\n self.set_base_length_entry(\\n self.parent_class.classes[\\\"fractal\\\"].base_length)\\n self.rules_frame_class.render_preview()\\n\\n self.buttons[\\\"btn_load_params\\\"] = Button(\\n self.frame, text=\\\"Load Parameters\\\", command=load_params)\\n self.buttons[\\\"btn_load_params\\\"].grid(row=4, column=0)\",\n \"def _init(self):\\n self.wx_menu = wx.Menu()\",\n \"def init_buttons(self):\\r\\n self.btn_encrypt = QtWidgets.QPushButton('Encrypt')\\r\\n self.btn_encrypt.clicked.connect(self.encrypt)\\r\\n self.btn_encrypt.setEnabled(False)\\r\\n\\r\\n self.btn_decrypt = QtWidgets.QPushButton('Decrypt')\\r\\n self.btn_decrypt.clicked.connect(self.decrypt)\\r\\n self.btn_decrypt.setEnabled(False) \\r\\n\\r\\n self.layout_buttons = QtWidgets.QGridLayout()\\r\\n\\r\\n self.layout_buttons.addWidget(self.btn_encrypt,0,0)\\r\\n self.layout_buttons.addWidget(self.btn_decrypt,0,1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6788451","0.6683954","0.65892327","0.6497996","0.64862543","0.6442674","0.6437974","0.64198774","0.64093477","0.63769287","0.63739365","0.63739365","0.6325634","0.6292619","0.6286793","0.62425756","0.62415385","0.6241002","0.6228872","0.619486","0.61938703","0.6183539","0.6172546","0.61667246","0.6157678","0.61474746","0.613011","0.61263514","0.61243594","0.61141825","0.60968673","0.6082453","0.6081151","0.6079407","0.6056812","0.6045466","0.6041611","0.6025136","0.60114425","0.60007066","0.5982638","0.5960154","0.59468335","0.5930284","0.5929649","0.59219223","0.59214944","0.5916922","0.5907646","0.5907479","0.5895506","0.5890128","0.58827096","0.5880276","0.587764","0.58712983","0.58686036","0.5865468","0.5865468","0.5865468","0.5865468","0.5865468","0.5865468","0.5865468","0.5865468","0.5854208","0.5854047","0.5852821","0.5839837","0.5838247","0.58373225","0.5820357","0.5808737","0.5807957","0.5802732","0.5800416","0.579891","0.579891","0.57933414","0.5790746","0.57889897","0.57817155","0.57681614","0.57645315","0.5749364","0.5749364","0.5745667","0.57322574","0.5731752","0.57301295","0.5730065","0.5724592","0.5724177","0.57179093","0.5710447","0.5709684","0.5708445","0.57035506","0.5700849","0.56991833"],"string":"[\n \"0.6788451\",\n \"0.6683954\",\n \"0.65892327\",\n \"0.6497996\",\n \"0.64862543\",\n \"0.6442674\",\n \"0.6437974\",\n \"0.64198774\",\n \"0.64093477\",\n \"0.63769287\",\n \"0.63739365\",\n \"0.63739365\",\n \"0.6325634\",\n \"0.6292619\",\n \"0.6286793\",\n \"0.62425756\",\n \"0.62415385\",\n \"0.6241002\",\n \"0.6228872\",\n \"0.619486\",\n \"0.61938703\",\n \"0.6183539\",\n \"0.6172546\",\n \"0.61667246\",\n \"0.6157678\",\n \"0.61474746\",\n \"0.613011\",\n \"0.61263514\",\n \"0.61243594\",\n \"0.61141825\",\n \"0.60968673\",\n \"0.6082453\",\n \"0.6081151\",\n \"0.6079407\",\n \"0.6056812\",\n \"0.6045466\",\n \"0.6041611\",\n \"0.6025136\",\n \"0.60114425\",\n \"0.60007066\",\n \"0.5982638\",\n \"0.5960154\",\n \"0.59468335\",\n \"0.5930284\",\n \"0.5929649\",\n \"0.59219223\",\n \"0.59214944\",\n \"0.5916922\",\n \"0.5907646\",\n \"0.5907479\",\n \"0.5895506\",\n \"0.5890128\",\n \"0.58827096\",\n \"0.5880276\",\n \"0.587764\",\n \"0.58712983\",\n \"0.58686036\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5865468\",\n \"0.5854208\",\n \"0.5854047\",\n \"0.5852821\",\n \"0.5839837\",\n \"0.5838247\",\n \"0.58373225\",\n \"0.5820357\",\n \"0.5808737\",\n \"0.5807957\",\n \"0.5802732\",\n \"0.5800416\",\n \"0.579891\",\n \"0.579891\",\n \"0.57933414\",\n \"0.5790746\",\n \"0.57889897\",\n \"0.57817155\",\n \"0.57681614\",\n \"0.57645315\",\n \"0.5749364\",\n \"0.5749364\",\n \"0.5745667\",\n \"0.57322574\",\n \"0.5731752\",\n \"0.57301295\",\n \"0.5730065\",\n \"0.5724592\",\n \"0.5724177\",\n \"0.57179093\",\n \"0.5710447\",\n \"0.5709684\",\n \"0.5708445\",\n \"0.57035506\",\n \"0.5700849\",\n \"0.56991833\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7603971"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14991,"cells":{"query":{"kind":"string","value":"calls the appropriate funciton to resample an image based on the interpolation method"},"document":{"kind":"string","value":"def resize(self, image, fx, fy, interpolation):\n\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, fx, fy)\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, fx, fy)"},"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 resampled_to_img(self, target_image, interpolation=None):\n # IMPORTANT: Polymorphism can be implemented by walking the \n # MRO and finding a method that does not raise\n # NotImplementedError. \n raise NotImplementedError","def Resampler(name):\n\n def resample_average(path, dsquery, dstile, image_format):\n for i in range(1, dstile.RasterCount+1):\n res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i), \"average\")\n if res != 0:\n raise ImageOutputException(\"RegenerateOverview() failed with error %d\" % res)\n\n gdal_write(path, dstile, image_format)\n\n def resample_antialias(path, dsquery, dstile, image_format):\n querysize = dsquery.RasterXSize\n tilesize = dstile.RasterXSize\n\n array = numpy.zeros((querysize, querysize, 4), numpy.uint8)\n for i in range(dstile.RasterCount):\n array[:,:,i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1), 0, 0, querysize, querysize)\n im = Image.fromarray(array, 'RGBA') # Always four bands\n im1 = im.resize((tilesize,tilesize), Image.ANTIALIAS)\n\n if os.path.exists(path):\n im0 = Image.open(path)\n im1 = Image.composite(im1, im0, im1)\n\n ensure_dir_exists(path)\n\n if image_format == \"JPEG\":\n im1.save(path, image_format, quality=jpeg_quality)\n else:\n im1.save(path, image_format)\n\n\n if name == \"average\":\n return resample_average\n elif name == \"antialias\":\n return resample_antialias\n\n resampling_methods = {\n \"near\" : gdal.GRA_NearestNeighbour,\n \"bilinear\" : gdal.GRA_Bilinear,\n \"cubic\" : gdal.GRA_Cubic,\n \"cubicspline\" : gdal.GRA_CubicSpline,\n \"lanczos\" : gdal.GRA_Lanczos\n }\n\n resampling_method = resampling_methods[name]\n\n def resample_gdal(path, dsquery, dstile, image_format):\n querysize = dsquery.RasterXSize\n tilesize = dstile.RasterXSize\n\n dsquery.SetGeoTransform( (0.0, tilesize / float(querysize), 0.0, 0.0, 0.0, tilesize / float(querysize)) )\n dstile.SetGeoTransform( (0.0, 1.0, 0.0, 0.0, 0.0, 1.0) )\n\n res = gdal.ReprojectImage(dsquery, dstile, None, None, resampling_method)\n if res != 0:\n raise ImageOutputException(\"ReprojectImage() failed with error %d\" % res)\n\n gdal_write(path, dstile, image_format)\n\n return resample_gdal","def resample(self):\n pass","def image_resample(f, oversamp=1.0):\n img = imageio.imread(f)\n x = np.linspace(0,img.shape[0],img.shape[0])\n y = np.linspace(0,img.shape[1],img.shape[1])\n #\n # 2x oversample the image since we'll dither it.\n #\n xnew = np.linspace(0, img.shape[0], img.shape[0]*oversamp)\n ynew = np.linspace(0, img.shape[1], img.shape[1]*oversamp)\n from scipy import interpolate\n rc = interpolate.interp2d(x, y, img[:,:,0].flatten(), kind='linear')\n gc = interpolate.interp2d(x, y, img[:,:,1].flatten(), kind='linear')\n bc = interpolate.interp2d(x, y, img[:,:,2].flatten(), kind='linear')\n rgb_new = np.stack([rc(xnew.flatten(), ynew.flatten()),\n gc(xnew.flatten(), ynew.flatten()),\n bc(xnew.flatten(), ynew.flatten())],-1).transpose(1,0,2).astype(np.uint8)\n plt.imshow(rgb_new)\n return rgb_new","def resample_img(img, img_type, size, spacing):\n resampler = sitk.ResampleImageFilter()\n resampler.SetOutputDirection(img.GetDirection())\n resampler.SetOutputOrigin(img.GetOrigin())\n resampler.SetOutputSpacing(spacing)\n resampler.SetSize(size)\n if img_type is \"Label\":\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\n elif img_type is \"Image\":\n resampler.SetInterpolator(sitk.sitkLinear)\n imgResampled = resampler.Execute(img)\n\n #axis have to be switched since np.array and keras use them in different order...\n x = np.transpose(sitk.GetArrayFromImage(imgResampled).astype(dtype=np.float), [2, 1, 0])\n return x","def interpolate(self, image):\n return","def resample_img(image, target_shape, mode='nearest'):\n print(target_shape)\n resize_factor = np.array(target_shape)/image.shape\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def resample(self):\n self.variational_strategy.resample()","def imgResample(img, spacing, size=[], useNearest=False, origin=[], outsideValue=0):\n if len(spacing) != img.GetDimension(): raise Exception(\"len(spacing) != \" + str(img.GetDimension()))\n\n # Set Size\n if size == []:\n inSpacing = img.GetSpacing()\n inSize = img.GetSize()\n size = [int(math.ceil(inSize[i]*(inSpacing[i]/spacing[i]))) for i in range(img.GetDimension())]\n else:\n if len(size) != img.GetDimension(): raise Exception(\"len(size) != \" + str(img.GetDimension()))\n \n if origin == []:\n origin = [0]*img.GetDimension()\n else:\n if len(origin) != img.GetDimension(): raise Exception(\"len(origin) != \" + str(img.GetDimension()))\n \n # Resample input image\n interpolator = [sitk.sitkLinear, sitk.sitkNearestNeighbor][useNearest]\n identityTransform = sitk.Transform()\n identityDirection = list(sitk.AffineTransform(img.GetDimension()).GetMatrix())\n\n return sitk.Resample(img, size, identityTransform, interpolator, origin, spacing, identityDirection, outsideValue)","def resample(img, newSize):\n # use float as datatype to preserve float precision\n # will convert to np.uint8 when display result\n # get the new dimensions\n nH, nW = newSize\n if np.ndim(img) == 2:\n H, W = img.shape\n res = np.zeros((nH, nW), dtype=np.float32)\n elif np.ndim(img) == 3:\n H, W, _ = img.shape\n res = np.zeros((nH, nW, _), dtype=np.float32)\n else:\n raise ValueError(\"input image has invalid dimension %s\" % (img.shape))\n\n # interpolate the value for the result\n for idx in range(nH * nW):\n i = idx // nW\n j = idx % nW\n orig_i = int((i * H) // nH)\n orig_j = int((j * W) // nW)\n res[i, j] = img[orig_i, orig_j]\n return res","def resample(self, image, target_shape, mode='nearest'):\n resize_factor = image.shape / np.array(target_shape)\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\n if isinstance(target_spacing, numbers.Number):\n target_spacing = [target_spacing] * img.ndim\n \n # compute zoom values\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\n\n print \"Zoom Factors\"\n print zoom_factors\n\n oldImageShape = img.shape\n \n # zoom image\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\n\n newImageShape = img.shape\n old_pixel_spacing = header.get_pixel_spacing(hdr)\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\n print \"Target Pixel Spacing\"\n print target_spacing\n\n print \"Actual Pixel Spacing\"\n print new_pixel_spacing\n # set new voxel spacing\n header.set_pixel_spacing(hdr, new_pixel_spacing)\n \n return img, hdr","def resample(image_array, target_pixel_dims_list, is_seg=False):\n # if dealing with segmentation images, do not interpolate\n # this is done by choosing interpolation order == 0\n order = 0 if is_seg == True else 3\n\n original_dims_in_pixels = [image_array.shape[d]\n for d in range(len(image_array.shape))]\n compression_list = [target_pixel_dims_list[d] / original_dims_in_pixels[d]\n for d in range(len(image_array.shape))]\n\n if (is_seg):\n resized_image = ndimage.interpolation.zoom(\n image_array, zoom=compression_list, order=order, cval=0)\n return resized_image\n else:\n resized_image = ndimage.interpolation.zoom(\n image_array, zoom=compression_list, order=order, cval=0)\n return resized_image","def resample_image(source_path, dest_fmt, indices=[], wipe=False):\n\n from numpy import array_equal\n from os import remove\n\n \n # unpack indices\n z_start = indices[0][0]\n z_stop = indices[0][1]\n y_start = indices[1][0]\n y_stop = indices[1][1]\n x_start = indices[2][0]\n x_stop = indices[2][1]\n\n # the name of the file before format extension\n source_name = source_path.split('.')[0]\n dest_name = source_name.replace('/im/','/forebrain/')\n \n dest_path = dest_name + '.' + dest_fmt\n source_image = read_image(source_path)\n\n resampled_image = source_image[z_start:z_stop, \\\n y_start:y_stop, \\\n x_start:x_stop]\n write_image(dest_path, resampled_image)\n print(dest_path + ' written')\n\n if wipe:\n check_image = read_image(dest_path)\n if array_equal(check_image, source_image):\n remove(source_path)\n else:\n print('{0} and {1} differ... something went wrong!'.format(source_path, dest_path))","def upsample(self, method):\n from scipy.signal import resample\n from scipy.ndimage.interpolation import zoom\n #print \"mm: 100 x 100 x 131\"\n #print \"Dims:\", self.D.shape\n fact = np.array(self.info.shape).astype(\"float32\") / np.array(self.info.read_shape).astype(\"float32\")+0.00001 # hrmpf!!\n if method == \"zoom\":\n print \"Resampling...\"\n self.D = zoom(self.D, fact).astype(\"float32\")\n elif method == \"resample\":\n print \"Resampling...\"\n a = self.info.resample_ax\n s = self.info.shape[a]\n self.D = resample(self.D, s, axis=a, window=10).astype(\"float32\")\n elif method == None:\n pass\n else:\n raise NotImplementedError(\"Unknown upsampling method: %s\" % method)\n #print \"Dims:\", self.D.shape\n print \"done.\"","def _resample(ct_scan, origin, spacing, spacing_x, spacing_y, spacing_z,\n interpolator=sitk.sitkLinear):\n img = LungsLoader._get_itk_from_scan(ct_scan, origin, spacing)\n # Compute new dimensions\n spacing = img.GetSpacing()\n size = img.GetSize()\n fact_x = spacing[0] / spacing_x\n fact_y = spacing[1] / spacing_y\n fact_z = spacing[2] / spacing_z\n size_x = int(round(size[0] * fact_x))\n size_y = int(round(size[1] * fact_y))\n size_z = int(round(size[2] * fact_z))\n # to do resampling\n f = sitk.ResampleImageFilter()\n f.SetReferenceImage(img)\n f.SetOutputOrigin(img.GetOrigin())\n f.SetOutputSpacing((spacing_x, spacing_y, spacing_z))\n f.SetSize((size_x, size_y, size_z))\n f.SetInterpolator(interpolator)\n result = f.Execute(img)\n return np.around(sitk.GetArrayFromImage(result))","def bilinear_interpolation(self, image, fx, fy):\n\n # Write your code for bilinear interpolation here\n (width, height) = image.shape\n (scaledWidth, scaledHeight) = (int(fy * width), int(fy * height))\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\n\n for x in range(0, scaledWidth):\n for y in range(0, scaledHeight):\n nx = math.floor(x / fx)\n ny = math.floor(y / fy)\n if (nx + 1) > width - 1:\n nx = width - 2\n if (ny + 1) > height - 1:\n ny = height - 2\n\n n1 = (nx, ny)\n n2 = (nx, ny+1)\n n3 = (nx+1, ny)\n n4 = (nx+1, ny+1)\n\n resampledImage[x, y] = self.inter.bilinear_interpolation([n1, image[n1]], [n2, image[n2]], [n3, image[n3]],[n4, image[n4]], [x/fx, y/fy])\n\n return resampledImage","def test_resample_methods():\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\n try:\n cvs.raster(src, upsample_method='santaclaus', downsample_method='toothfairy')\n except ValueError:\n pass\n else:\n assert False\n\n try:\n cvs.raster(src, upsample_method='honestlawyer')\n except ValueError:\n pass\n else:\n assert False\n\n try:\n cvs.raster(src, downsample_method='tenantfriendlylease')\n except ValueError:\n pass\n else:\n assert False","def resample(medical_image, image, new_spacing=[1, 1, 1]):\n image_shape = np.array(list(medical_image.header.get_data_shape()))\n spacing = np.array(list(medical_image.header.get_zooms()))\n resize_factor = spacing / new_spacing\n\n new_shape = image_shape * resize_factor\n new_shape = np.round(new_shape)\n\n real_resize_factor = new_shape / image_shape\n\n new_spacing = spacing / real_resize_factor\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\n\n return resampled_image, new_spacing","def get_resample(name: str) -> str:\n\n methods = {\n \"first\":\n \"\"\"\nimport numpy as np\n\ndef first(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\n y = np.ones(in_ar[0].shape)\n for i in reversed(range(len(in_ar))):\n mask = in_ar[i] == 0\n y *= mask\n y += in_ar[i]\n\n np.clip(y,0,255, out=out_ar)\n\"\"\",\n \"last\":\n \"\"\"\nimport numpy as np\n\ndef last(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\n y = np.ones(in_ar[0].shape)\n for i in range(len(in_ar)):\n mask = in_ar[i] == 0\n y *= mask\n y += in_ar[i]\n\n np.clip(y,0,255, out=out_ar)\n\"\"\",\n \"max\":\n \"\"\"\nimport numpy as np\n\ndef max(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\n y = np.max(in_ar, axis=0)\n np.clip(y,0,255, out=out_ar)\n\"\"\",\n \"average\":\n \"\"\"\nimport numpy as np\n\ndef average(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\n div = np.zeros(in_ar[0].shape)\n for i in range(len(in_ar)):\n div += (in_ar[i] != 0)\n div[div == 0] = 1\n \n y = np.sum(in_ar, axis = 0, dtype = 'uint16')\n y = y / div\n \n np.clip(y,0,255, out = out_ar)\n\"\"\"}\n\n if name not in methods:\n raise ValueError(\n \"ERROR: Unrecognized resampling method (see documentation): '{}'.\".\n format(name))\n\n return methods[name]","def resample_image(image, out_spacing=(1.0, 1.0, 1.0), out_size=None, is_label=False, pad_value=0):\n\n original_spacing = np.array(image.GetSpacing())\n original_size = np.array(image.GetSize())\n\n if out_size is None:\n out_size = np.round(np.array(original_size * original_spacing / np.array(out_spacing))).astype(int)\n else:\n out_size = np.array(out_size)\n\n original_direction = np.array(image.GetDirection()).reshape(len(original_spacing), -1)\n original_center = (np.array(original_size, dtype=float) - 1.0) / 2.0 * original_spacing\n out_center = (np.array(out_size, dtype=float) - 1.0) / 2.0 * np.array(out_spacing)\n\n original_center = np.matmul(original_direction, original_center)\n out_center = np.matmul(original_direction, out_center)\n out_origin = np.array(image.GetOrigin()) + (original_center - out_center)\n\n resample = sitk.ResampleImageFilter()\n resample.SetOutputSpacing(out_spacing)\n resample.SetSize(out_size.tolist())\n resample.SetOutputDirection(image.GetDirection())\n resample.SetOutputOrigin(out_origin.tolist())\n resample.SetTransform(sitk.Transform())\n resample.SetDefaultPixelValue(pad_value)\n\n if is_label:\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\n else:\n resample.SetInterpolator(sitk.sitkBSpline)\n\n return resample.Execute(image)","def resample_by(image_array, compression_factor_list, is_seg=False):\n original_dims_in_pixels = [image_array.shape[d]\n for d in range(len(image_array.shape))]\n target_pixel_dims_list = [int(math.floor(compression_factor_list[d] * original_dims_in_pixels[d]))\n for d in range(len(image_array.shape))]\n\n resized_image = resample(image_array, target_pixel_dims_list, is_seg)\n\n return(resized_image)","def resample(image, pixsize):\n assert pixsize in [1, 2, 4, 8, 16] # return error and exit otherwise\n imsize = 128/pixsize\n newimage = np.zeros((imsize, imsize))\n for xn, x in enumerate(np.arange(0, 128, pixsize)):\n for yn, y in enumerate(np.arange(0, 128, pixsize)):\n newimage[xn, yn] = np.nansum(image[x:x+pixsize, y:y+pixsize]) # Nansum is important as sum of masked array can be nan\n return newimage","def rasterio_resample(dataset: Dataset, target_resolution: float, new_nodata: object = 'auto',\n resampling_method: Optional[rio_warp.Resampling] = rio_warp.Resampling.nearest,\n output_datatype: Optional[np.dtype] = np.float32) -> Dataset:\n\n # Detect if resolution is not square\n if dataset.meta[\"resolution\"][0] != dataset.meta[\"resolution\"][1]:\n raise RuntimeError(\"Tried to resample a dataset that doesn't have a square resolution!\")\n\n # Calculate scale factor\n scaling = int(dataset.meta[\"resolution\"][0]) / float(target_resolution)\n\n # Copy data from source Dataset\n source_crs = deepcopy(dataset.profile[\"crs\"])\n source_transform = deepcopy(dataset.profile[\"transform\"])\n source_nodata = deepcopy(dataset.nodata)\n\n # Calculate profile/transform and transfer elements\n new_transform = Affine(source_transform.a / scaling, source_transform.b, source_transform.c, source_transform.d,\n source_transform.e / scaling, source_transform.f)\n new_height = int(dataset.profile[\"height\"] * scaling)\n new_width = int(dataset.profile[\"width\"] * scaling)\n new_resolution = (target_resolution, target_resolution)\n new_nodata = source_nodata if new_nodata == \"auto\" else new_nodata\n new_crs = source_crs # [TODO] Add ability to change CRS in rasterio resample function\n\n # Update profile to match output\n dataset.nodata = new_nodata\n dataset.profile.update(\n res=(float(target_resolution), float(target_resolution)),\n transform=new_transform,\n height=new_height,\n width=new_width,\n nodata=new_nodata\n )\n dataset.profile.data[\"pixel_dimensions\"] = (float(target_resolution), float(target_resolution))\n dataset.meta[\"resolution\"] = new_resolution\n\n # Change datatype of all bands\n # [TODO] There may be a way to save memory by freeing old type ndarrays\n for band_id, band_value in dataset.bands.items():\n dataset.bands[band_id] = band_value.astype(output_datatype)\n\n # Run resampling on each band\n for band_key, band_data in dataset.bands.items():\n # Create an array in the correct resampled dimensions\n resampled_array = np.ma.asanyarray(np.empty(shape=(new_width, new_height), dtype=output_datatype))\n\n # Resample/warp\n rio_warp.reproject(band_data, resampled_array, src_transform=source_transform, dst_transform=new_transform,\n width=new_width, height=new_height, src_nodata=source_nodata, dst_nodata=new_nodata,\n src_crs=source_crs, dst_crs=new_crs, resample=resampling_method)\n\n # Set value in dictionary\n dataset.bands[band_key] = resampled_array\n\n return dataset","def resample_transform(self, resample_kwargs, arg, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.transform)(\n self, resample_kwargs, arg, *args, **kwargs\n )","def resize(self, image, fx=None, fy=None, interpolation=None):\r\n if interpolation == 'bilinear':\r\n return self.bilinear_interpolation(image, float(fx), float(fy))\r\n\r\n elif interpolation == 'nearest_neighbor':\r\n return self.nearest_neighbor(image, float(fx), float(fy))","def resample(x, xp, yp, xedges=False, xpedges=False):\n\n assert 1 <= x.ndim\n assert x.ndim <= xp.ndim\n assert xp.ndim <= yp.ndim\n assert yp.ndim <= 2\n\n input_edges = xp if xpedges else pixspline.cen2bound(xp)\n ys = pixspline.PixelSpline(input_edges, yp)\n \n edges = x if xedges else pixspline.cen2bound(x)\n return ys.resample(edges)","def resample(scan, image, new_spacing=[1,1,1]):\n spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)\n\n resize_factor = spacing / new_spacing\n new_real_shape = image.shape * resize_factor\n new_shape = np.round(new_real_shape)\n real_resize_factor = new_shape / image.shape\n new_spacing = spacing / real_resize_factor\n\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\n\n batch = []\n for i in range(0, image.shape[0] - 3, 3):\n tmp = []\n for j in range(3):\n img = image[i + j]\n img= cv2.resize(img, (224, 224))\n tmp.append(img)\n\n tmp = np.array(tmp)\n batch.append(tmp)\n\n batch = np.array(batch)\n\n return batch","def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def resize(self, image, fx=None, fy=None, interpolation=None):\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, float(fx), float(fy))\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, float(fx), float(fy))","def resample(image, flow):\r\n assert flow.shape[1] == 2\r\n b, c, h, w = image.shape\r\n grid = get_grid(b, (h, w))\r\n flow = L.concat([flow[:, 0:1, :, :] / ((w - 1.0) / 2.0),\r\n flow[:, 1:2, :, :] / ((h - 1.0) / 2.0)], 1)\r\n final_grid = L.transpose((grid + flow), (0, 2, 3, 1))\r\n image.stop_gradient = False\r\n try:\r\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border', align_corners=True)\r\n except Exception:\r\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border')\r\n \r\n return output\r\n # return image\r\n # return L.zeros_like(image)\r","def resample(s, p, q, h=None):\n gcd = fractions.gcd(p,q)\n if gcd>1:\n p=p/gcd\n q=q/gcd\n \n if h is None: #design filter\n #properties of the antialiasing filter\n log10_rejection = -3.0\n stopband_cutoff_f = 1.0/(2.0 * max(p,q))\n roll_off_width = stopband_cutoff_f / 10.0\n \n #determine filter length\n #use empirical formula from [2] Chap 7, Eq. (7.63) p 476\n rejection_db = -20.0*log10_rejection;\n l = ceil((rejection_db-8.0) / (28.714 * roll_off_width))\n \n #ideal sinc filter\n t = arange(-l, l + 1)\n ideal_filter=2*p*stopband_cutoff_f*sinc(2*stopband_cutoff_f*t) \n \n #determine parameter of Kaiser window\n #use empirical formula from [2] Chap 7, Eq. (7.62) p 474\n beta = kaiser_beta(rejection_db)\n \n #apodize ideal filter response\n h = kaiser(2*l+1, beta)*ideal_filter\n\n ls = len(s)\n lh = len(h)\n\n l = (lh - 1)/2.0\n ly = ceil(ls*p/float(q))\n\n #pre and postpad filter response\n nz_pre = floor(q - mod(l,q))\n nz_pre = int(nz_pre)\n hpad = h[-lh+nz_pre:]\n\n offset = floor((l+nz_pre)/q)\n nz_post = 0;\n while ceil(((ls-1)*p + nz_pre + lh + nz_post )/q ) - offset < ly:\n nz_post += 1\n hpad = hpad[:lh + nz_pre + nz_post]\n\n #filtering\n xfilt = upfirdn(s, hpad, p, q)\n\n return xfilt[int(offset)-1:int(offset)-1+int(ly)]","def resample_sliprate(self, dt, nsamp):\n for ps in self.pointsources:\n ps.resample_sliprate(dt, nsamp)","def img_zoom(img, fx, fy, interp=cv2.INTER_AREA):\n res = cv2.resize(img, None, fx=fx, fy=fy,\n interpolation=interp)\n return res","def rescale_scan(ct_scan, origin, spacing, new_width, new_height, new_depth,\n normalize=True, interpolator=sitk.sitkLinear):\n if not np.all(origin == 0):\n raise ValueError(\"Please feed only resampled images to this function.\")\n if ct_scan.shape[2] != ct_scan.shape[1]:\n raise ValueError(\"Scans should have the same width and height.\")\n img = LungsLoader._get_itk_from_scan(ct_scan, origin, spacing)\n f = sitk.ResampleImageFilter()\n f.SetReferenceImage(img)\n f.SetOutputOrigin(img.GetOrigin())\n spacing_x, spacing_y, spacing_z = img.GetSpacing()\n size_x, size_y, size_z = img.GetSize()\n spacing_x /= (new_width / size_x)\n spacing_y /= (new_height / size_y)\n spacing_z /= (new_depth / size_z)\n size_x = new_width\n size_y = new_height\n size_z = new_depth\n f.SetOutputSpacing((spacing_x, spacing_y, spacing_z))\n f.SetSize((size_x, size_y, size_z))\n f.SetInterpolator(interpolator)\n result = f.Execute(img)\n new_scan = sitk.GetArrayFromImage(result)\n if normalize:\n new_scan = (new_scan - np.mean(new_scan)) / np.std(new_scan)\n return new_scan, result.GetOrigin(), result.GetSpacing()","def resample_image(I,spacing,desiredSize, spline_order=1,zero_boundary=False,identity_map=None):\n if len(I.shape) != len(desiredSize)+2:\n desiredSize = desiredSize[2:]\n sz = np.array(list(I.size()))\n # check that the batch size and the number of channels is the same\n nrOfI = sz[0]\n nrOfC = sz[1]\n\n desiredSizeNC = np.array([nrOfI,nrOfC]+list(desiredSize))\n\n newspacing = spacing*((sz[2::].astype('float')-1.)/(desiredSizeNC[2::].astype('float')-1.)) ###########################################\n if identity_map is not None:\n idDes= identity_map\n else:\n idDes = torch.from_numpy(py_utils.identity_map_multiN(desiredSizeNC,newspacing)).to(I.device)\n # now use this map for resampling\n ID = py_utils.compute_warped_image_multiNC(I, idDes, newspacing, spline_order,zero_boundary)\n\n return ID, newspacing","def nn_resample_image(image_path, pxl_box_width, pxl_box_height):\n image = Image.open(image_path)\n if image.format == \"JPEG\" or image.format == \"PNG\":\n __nn_resample_png_jpeg_image(image, pxl_box_width, pxl_box_height)\n elif image.format == \"GIF\":\n __nn_resample_gif_image(image, pxl_box_width, pxl_box_height)\n\n return image","def downsample(self, hr_img):\r\n lr_img = hr_img.resize((int(hr_img.width / self.scaling_factor), int(hr_img.height / self.scaling_factor)),\r\n Image.BICUBIC)\r\n # Convert the LR and HR image to the required type\r\n lr_img = convert_image(lr_img, source='pil', target=self.lr_img_type)\r\n hr_img = convert_image(hr_img, source='pil', target=self.hr_img_type)\r\n return lr_img, hr_img","def resample(image, target_affine, target_shape, interpolation='trilinear', pad=False, dtype=None, align_corners=True,\n margin=1e-6):\n if dtype:\n image = image.to(dtype)\n if (torch.all(torch.abs(image.affine - target_affine) < margin)\n and torch.all(torch.tensor(image.shape[-3:]) == torch.tensor(target_shape))):\n return image\n mode = monai_interpolation_mode(interpolation)\n resampler = SpatialResample(mode=mode, align_corners=align_corners)\n\n return resampler(img=image, dst_affine=target_affine, spatial_size=target_shape)","def _resample(x: np.ndarray, y: np.ndarray, n_samples: Optional[int]) -> Tuple[np.ndarray, np.ndarray]:\n if n_samples is None:\n n_samples = y.size\n new_x = np.linspace(x[0], x[1], n_samples)\n new_y = interp1d(x, y)(new_x)\n return new_x, new_y","def resample(wrksppath, region, model):\n logging.info('\\nResampling the rasters for ' + region)\n # Define app workspace and sub-paths\n tiffs = os.path.join(wrksppath, region, model + '_GeoTIFFs')\n resampleds = os.path.join(wrksppath, region, model + '_GeoTIFFs_resampled')\n\n # Create directory for the resampled GeoTIFFs\n if not os.path.exists(tiffs):\n logging.info('There is no tiffs folder. You must have already resampled them. Skipping resampling')\n return\n\n # List all Resampled GeoTIFFs\n files = os.listdir(tiffs)\n files = [tif for tif in files if tif.endswith('.tif')]\n files.sort()\n\n # Read raster dimensions\n path = os.path.join(tiffs, files[0])\n raster_dim = rasterio.open(path)\n width = raster_dim.width\n height = raster_dim.height\n lon_min = raster_dim.bounds.left\n lon_max = raster_dim.bounds.right\n lat_min = raster_dim.bounds.bottom\n lat_max = raster_dim.bounds.top\n\n # Geotransform for each resampled raster (east, south, west, north, width, height)\n geotransform_res = rasterio.transform.from_bounds(lon_min, lat_min, lon_max, lat_max, width * 100, height * 100)\n\n # Resample each GeoTIFF\n for file in files:\n path = os.path.join(tiffs, file)\n logging.info(path)\n with rasterio.open(path) as dataset:\n data = dataset.read(\n out_shape=(int(dataset.height * 100), int(dataset.width * 100)),\n # Reduce 100 to 10 if using the whole globe\n resampling=Resampling.nearest\n )\n\n # Convert new resampled array from 3D to 2D\n data = numpy.squeeze(data, axis=0)\n\n # Specify the filepath of the resampled raster\n resample_filename = file.replace('.tif', '_resampled.tif')\n resample_filepath = os.path.join(resampleds, resample_filename)\n\n # Save the GeoTIFF\n with rasterio.open(\n resample_filepath,\n 'w',\n driver='GTiff',\n height=data.shape[0],\n width=data.shape[1],\n count=1,\n dtype=data.dtype,\n nodata=numpy.nan,\n crs='+proj=latlong',\n transform=geotransform_res,\n ) as dst:\n dst.write(data, 1)\n\n # delete the non-resampled tiffs now that we dont need them\n shutil.rmtree(tiffs)\n\n return","def upsample(x):\n return F.interpolate(x, scale_factor=2, mode=\"nearest\")","def resample_2d(array, sample_pts, query_pts, kind='linear'):\n interpf = interpolate.interp2d(*sample_pts, array, kind=kind)\n return interpf(*query_pts)","def interpolate(x, ratio):\n (batch_size, time_steps, classes_num) = x.shape\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\n return upsampled","def interpolate(x, ratio):\n (batch_size, time_steps, classes_num) = x.shape\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\n return upsampled","def bilinear_interpolation(self, image, fx, fy):\r\n\r\n # Write your code for bilinear interpolation here\r\n width, height = image.shape[:2]\r\n nw = int(width * fx)\r\n nh = int(height * fy)\r\n new_img = np.zeros((nw, nh), np.uint8)\r\n inter = interpolation.interpolation()\r\n for i in range(nw):\r\n for j in range(nh):\r\n ii = int(np.floor((i * (image.shape[0]-1))/(nw - 1)))\r\n jj = int(np.floor((j * (image.shape[1]-1))/(nh - 1)))\r\n\r\n # All four corner pixels copied as it is\r\n if (ii == 0 and jj == 0) or \\\r\n (ii == 0 and jj == image.shape[1] - 1) or \\\r\n (ii == image.shape[0] - 1 and jj == 0) or \\\r\n (ii == image.shape[0] - 1 and jj == image.shape[1] - 1):\r\n new_img[i][j] = image[ii][jj]\r\n\r\n # Linear interpolation along left and right edges\r\n elif ii == 0 or ii == image.shape[0] - 1:\r\n new_img[i][j] = inter.linear_interpolation([jj - 1, image[ii][jj - 1]], [jj + 1, image[ii][jj + 1]], jj)\r\n\r\n # Linear interpolation along top and bottom edges\r\n elif jj == 0 or jj == image.shape[1] - 1:\r\n new_img[i][j] = inter.linear_interpolation([ii - 1, image[ii - 1][jj]], [ii + 1, image[ii + 1][jj]], ii)\r\n\r\n else:\r\n\r\n q11i, q11j = ii - 1, jj - 1\r\n q12i, q12j = ii - 1, jj + 1\r\n q21i, q21j = ii + 1, jj - 1\r\n q22i, q22j = ii + 1, jj + 1\r\n\r\n point_a = [q11i, q11j, image[q11i][q11j]]\r\n point_b = [q12i, q12j, image[q12i][q12j]]\r\n point_c = [q21i, q21j, image[q21i][q21j]]\r\n point_d = [q22i, q22j, image[q22i][q22j]]\r\n unknown = [ii, jj]\r\n\r\n new_img[i][j] = inter.bilinear_interpolation(point_a, point_b, point_c, point_d, unknown)\r\n\r\n return new_img","def data_resampler (self, data):\r\n data = librosa.resample(data, orig_sr = original_sampling, target_sr = self.target_sampling)\r\n \r\n return data","def scale_image(image, scale=1, interpolation='linear'):\n if interpolation == 'linear':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n if interpolation == 'nearest':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)","def resample(img, factors=[2.0, 2.0]):\n if np.iscomplexobj(img):\n myft = ft(img)\n newsize = resampledSize(img.shape, factors)\n res = ift(image.extractFt(myft, newsize, ModifyInput=True)) # the FT can be modified since it is anyway temporarily existing only\n else:\n rf = rft(img, shift_before=True) # why is the shift necessary??\n oldsize = rf.shape\n # print(oldsize)\n newrftsize = resampledSize(oldsize, factors, RFT=True)\n newsize = resampledSize(img.shape, factors)\n # print(newsize)\n # the function below already takes care of the pixelsize\n rfre = resampleRFT(rf, newrftsize, img.shape, newsize, ModifyInput=True)\n # print(rfre)\n res = irft(rfre, newsize, shift_after=True) # why is the shift necessary??\n # no modification is needed to warrant that the integral does not change!\n return res","def resample_img(niimg, target_affine=None, target_shape=None, interpolation=\"continuous\", copy=True, order=\"F\"):\n # Do as many checks as possible before loading data, to avoid potentially\n # costly calls before raising an exception.\n if target_shape is not None and target_affine is None:\n raise ValueError(\"If target_shape is specified, target_affine should\"\n \" be specified too.\")\n\n if target_shape is not None and not len(target_shape) == 3:\n raise ValueError(\"The shape specified should be the shape of\"\n \"the 3D grid, and thus of length 3. %s was specified\"\n % str(target_shape))\n\n if interpolation == \"continuous\":\n interpolation_order = 3\n elif interpolation == \"nearest\":\n interpolation_order = 0\n else:\n raise ValueError(\"interpolation must be either \"continuous\" \"\n \"or \"nearest\"\")\n\n # noop cases\n if isinstance(niimg, basestring):\n niimg = nibabel.load(niimg)\n\n if target_affine is not None:\n target_affine = np.asarray(target_affine)\n\n affine = niimg.get_affine()\n\n # We now know that some resampling must be done.\n # The value of \"copy\" is of no importance: output is always a separate\n # array.\n data = niimg.get_data()\n\n if target_shape is None:\n target_shape = data.shape[:3]\n target_shape = list(target_shape)\n\n if target_affine.shape[0] == 3:\n # We have a 3D affine, we need to find out the offset and\n # shape to keep the same bounding box in the new space\n affine4d = np.eye(4)\n affine4d[:3, :3] = target_affine\n transform_affine = np.dot(linalg.inv(affine4d), affine)\n # The bounding box in the new world, if no offset is given\n (xmin, xmax), (ymin, ymax), (zmin, zmax) = \\\n get_bounds(data.shape[:3], transform_affine)\n\n offset = np.array((xmin, ymin, zmin))\n offset = np.dot(target_affine, offset)\n target_affine = from_matrix_vector(target_affine, offset[:3])\n target_shape = (int(np.ceil(xmax - xmin)) + 1,\n int(np.ceil(ymax - ymin)) + 1,\n int(np.ceil(zmax - zmin)) + 1, )\n\n if np.all(target_affine == affine):\n # Small trick to be more numerically stable\n transform_affine = np.eye(4)\n else:\n transform_affine = np.dot(linalg.inv(affine), target_affine)\n A, b = to_matrix_vector(transform_affine)\n A_inv = linalg.inv(A)\n # If A is diagonal, ndimage.affine_transform is clever enough to use a\n # better algorithm.\n if np.all(np.diag(np.diag(A)) == A):\n A = np.diag(A)\n else:\n b = np.dot(A, b)\n\n data_shape = list(data.shape)\n # For images with dimensions larger than 3D:\n if len(data_shape) > 3:\n # Iter in a set of 3D volumes, as the interpolation problem is\n # separable in the extra dimensions. This reduces the\n # computational cost\n other_shape = data_shape[3:]\n resampled_data = np.ndarray(list(target_shape) + other_shape,\n order=order)\n\n all_img = (slice(None), ) * 3\n\n for ind in np.ndindex(*other_shape):\n img = data[all_img + ind]\n resampled_data[all_img + ind] = \\\n ndimage.affine_transform(img, A,\n offset=np.dot(A_inv, b),\n output_shape=target_shape,\n order=interpolation_order)\n else:\n resampled_data = ndimage.affine_transform(data, A,\n offset=np.dot(A_inv, b),\n output_shape=target_shape,\n order=interpolation_order)\n return Nifti1Image(resampled_data, target_affine)","def resample_data(self, data, freq, resampler='mean'):\n\n if resampler == 'mean':\n data = data.resample(freq).mean()\n elif resampler == 'max':\n data = data.resample(freq).max()\n else:\n raise ValueError('Resampler can be \\'mean\\' or \\'max\\' only.')\n \n return data","def resample_photons(self, srcs, verbose=False):\n # first, clear out old sample images\n for src in srcs:\n src.clear_sample_images()\n\n # generate per-source sample image patch for each fits image in\n # this field. keep track of photons due to noise\n noise_sums = {}\n for band, img in self.img_dict.iteritems():\n if verbose:\n print \" ... resampling band %s \" % band\n samp_imgs, noise_sum = \\\n cel_mcmc.sample_source_photons_single_image_cython(\n img, [s.params for s in srcs]\n )\n\n # tell each source to keep track of it's source-specific sampled\n # images (and the image it was stripped out of)\n for src, samp_img in zip(srcs, samp_imgs):\n if samp_img is not None:\n\n # cache pixel grid for each sample image\n y_grid = np.arange(samp_img.y0, samp_img.y1, dtype=np.float)\n x_grid = np.arange(samp_img.x0, samp_img.x1, dtype=np.float)\n xx, yy = np.meshgrid(x_grid, y_grid, indexing='xy')\n pixel_grid = np.column_stack((xx.ravel(order='C'), yy.ravel(order='C')))\n src.sample_image_list.append((samp_img, img, pixel_grid))\n\n # keep track of noise sums\n noise_sums[band] = noise_sum\n\n # resample noise parameter in each fits image\n for band, img in self.img_dict.iteritems():\n a_n = self.a_0 + noise_sums[band]\n b_n = self.b_0 + img.nelec.size\n #eps_tmp = img.epsilon\n img.epsilon = np.random.gamma(a_n, 1./b_n)","def downsampling(inp_img):\n\n\n img = np.array(inp_img)\n f = max(1, np.rint(np.amin(img)/256))\n\n if f > 1:\n lpf = np.ones((f, f))\n f = (1/(f*f))*lpf\n img = cv2.filter2D(img, -1, kernel=f)\n out = np.hstack((img[:, :, 0], img[:, :, 1], img[:, :, 2]))\n\n return out","def bilinear_interpolation(self, image, fx, fy):\n\n # Write your code for bilinear interpolation here\n w = image.shape[1]\n h = image.shape[0]\n\n newW = int(w*float(fx))\n newH = int(h*float(fy))\n\n ratioW = w / newW\n ratioH = h / newH\n\n import numpy as np\n import math as ma\n from . import interpolation as bilinear\n bi = bilinear.interpolation()\n newImg = np.zeros((newH, newW), np.uint8)\n for i in range(newImg.shape[0]):\n for j in range(newImg.shape[1]):\n\n x = ratioH * i\n y = ratioW * j\n\n x1 = ma.floor(x)\n x2 = ma.ceil(x)\n y1 = ma.floor(y)\n y2 = ma.ceil(y)\n\n x1 = x1-1 if x1 == h else x1\n x2 = x2-1 if x2 == h else x2\n y1 = y1-1 if y1 == w else y1\n y2 = y2-1 if y2 == w else y2\n\n pt1 = (x1, y1, image[x1, y1])\n pt2 = (x1, y2, image[x1, y2])\n pt3 = (x2, y1, image[x2, y1])\n pt4 = (x2, y2, image[x2, y2])\n unknown = (x, y, 0)\n\n if pt1[0] == pt3[0] and pt1[2] == pt2[2]:\n newImg[i, j] = image[int(x), int(y)]\n else:\n newImg[i, j] = bi.bilinear_interpolation(pt1, pt2, pt3, pt4, unknown)\n\n image = newImg\n return image","def transform(self, size, method, data=None, resample=NEAREST,\r\n fill=1, fillcolor=None):\r\n if method == EXTENT:\r\n x0, y0, x1, y1 = data\r\n part = self._instance[y0:y1, x0:x1]\r\n _im = cv2.resize(part, size)\r\n elif method == AFFINE:\r\n x0, y0, x1, y1, x2, y2, x3, y3, x4, y4, x5, y5 = data\r\n pts1 = np.float32([[x0, y0], [x1, y1], [x2, y2]])\r\n pts2 = np.float32([[x3, y3], [x4, y4], [x5, y5]])\r\n M = cv2.getAffineTransform(pts1,pts2)\r\n _im = cv2.warpAffine(self._instance, M, size)\r\n elif method == PERSPECTIVE or method == QUAD:\r\n x0, y0, x1, y1, x2, y2, x3, y3 = data\r\n pts1 = np.float32([[x0, y0], [x1, y1], [x2, y2], [x3, y3]])\r\n pts2 = np.float32([[0,0],[size[0], 0], [0, size[1]], [size[0], size[1]]])\r\n M = cv2.getPerspectiveTransform(pts1, pts2)\r\n _im = cv2.warpPerspective(self._instance, M, size)\r\n elif method == MESH:\r\n _im = self._instance.copy()\r\n for elem in data:\r\n box, quad = elem\r\n x0, y0, x1, y1, x2, y2, x3, y3 = quad\r\n pts1 = np.float32([[x0, y0], [x1, y1], [x2, y2], [x3, y3]])\r\n pts2 = np.float32([[box[0], box[1]],[box[2], box[1]], [box[0], box[3]], [box[2], box[3]]])\r\n M = cv2.getPerspectiveTransform(pts1, pts2)\r\n _im = cv2.warpPerspective(_im, M, size)\r\n return Image(_im)","def reScaleLandsat(self,img):\n \n\t\tthermalBand = ee.List(['thermal'])\n\t\tthermal = ee.Image(img).select(thermalBand).multiply(10)\n \n\t\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\n\t\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\n \n\t\timage = ee.Image(scaled.addBands(thermal)).int16()\n \n\t\treturn image.copyProperties(img)","def resample(self, data_arr):\n data_arr = np.asarray(data_arr, dtype=np.float32, order='C')\n check_audio(data_arr, is_mono=False)\n\n fn = self._lib['resampleObj_resample']\n fn.argtypes = [POINTER(OpaqueResample),\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'),\n c_int,\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS')]\n data_len = data_arr.shape[-1]\n\n if data_arr.ndim == 1:\n ret_arr = np.zeros(data_len * 5, dtype=np.float32)\n new_arr_len = fn(self._obj, data_arr, c_int(data_len), ret_arr)\n else:\n data_arr, o_channel_shape = format_channel(data_arr, 1)\n channel_num = data_arr.shape[0]\n\n ret_arr = np.zeros((channel_num, data_len * 5), dtype=np.float32)\n new_arr_len = 0\n for i in range(channel_num):\n _new_arr_len = fn(self._obj, data_arr[i], c_int(data_len), ret_arr[i])\n new_arr_len = max(new_arr_len, _new_arr_len)\n ret_arr = revoke_channel(ret_arr, o_channel_shape, 1)\n\n return ret_arr[..., :new_arr_len]","def resample(source, target_size, transform):\n dtype = source.dtype\n dev = source.device\n\n height_, width_ = target_size\n ur_ = torch.arange(width_, dtype=dtype, device=dev) + 0.5\n vr_ = torch.arange(height_, dtype=dtype, device=dev) + 0.5\n\n height, weight = source.shape[2:]\n ur = 2 * ((ur_ + transform[0, 1]) / transform[0, 0]) / weight - 1\n vr = 2 * ((vr_ + transform[1, 1]) / transform[1, 0]) / height - 1\n\n v, u = torch.meshgrid(vr, ur)\n v = v.unsqueeze(2)\n u = u.unsqueeze(2)\n\n grid = torch.cat((u, v), dim=2)\n grid = grid.unsqueeze(0).expand(len(source), -1, -1, -1)\n\n return torch.nn.functional.grid_sample(source, grid)","def resample(self, num, use_mp=False, dps=None):\n if self.get_dof() == num:\n return\n if self.is_orthogonal():\n self.a_n = resize_coeffs(self.a_n, (self.N, 1), (num, 1))\n else:\n with self.context(use_mp, dps):\n self.approximate(self.evaluator(use_mp), num=num,\n use_mp=use_mp)","def keras_transform(img, trf, interp_method='linear', rescale=None):\n img_input = tf.keras.Input(shape=img.shape[1:])\n trf_input = tf.keras.Input(shape=trf.shape[1:])\n trf_scaled = trf_input if rescale is None else layers.RescaleTransform(rescale)(trf_input)\n y_img = layers.SpatialTransformer(interp_method=interp_method)([img_input, trf_scaled])\n return tf.keras.Model([img_input, trf_input], y_img).predict([img, trf])","def resample(s, up, down, axis=0, fc='nn', **kwargs):\n\n # BUG FIX: if up==down, sig.fir_filter_design.firwin will throw\n # an exception. In compute_filt that is OK (the user is assumed\n # to know what they are doing if they use that function), but in\n # the \"user-friendly\" resample function, we should just silently\n # return a copy of the input. (Principle of least surprise)\n # Thanks to johndoe46 on github for pointing this out.\n if up==down:\n return np.array(s)\n\n # from design parameters, find the generative parameters\n N, beta, As = disambiguate_params(**kwargs)\n\n # check if a resampling filter with the chosen parameters already exists\n params = (up, down, fc, beta, N)\n if params in _precomputed_filters.keys():\n # if so, use it.\n filt = _precomputed_filters[params]\n else:\n # if not, generate filter, store it, use it\n filt = compute_filt(up, down, fc, beta=beta, N=N)\n _precomputed_filters[params] = filt\n\n return sig.resample_poly(s, up, down, window=np.array(filt), axis=axis)","def transform(self, *args, **kwargs):\n if hasattr(self.object, 'fit_resample'):\n if self.verbose:\n print('[WORC WARNING] Sampler does have fit_resample construction: fit and resampling.')\n try:\n return self.object.fit_resample(*args, **kwargs)\n except ValueError as message:\n message = str(message)\n message = 'The ObjectSampler could not ' +\\\n 'resample the objects with ' +\\\n 'the given parameters. ' +\\\n 'Probably your number of samples ' +\\\n 'is too small for the parameters ' +\\\n 'used. Original error: ' + message\n raise ae.WORCValueError(message)\n\n else:\n return self.object.transform(*args, **kwargs)","def rescale_tif(arr, clamp_low=262, clamp_high=1775):\n img_array = exposure.rescale_intensity(arr, in_range=(clamp_low, clamp_high)) # Landsat 5 ARD .25 and 97.75 percentile range used in training\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n img_array = img_as_ubyte(img_array)\n return img_array","def SetInterpolation(self, *args, **kwargs):\n pass","def apply_interpolation(self, transect, interpolation_method=None):\n\n # Reset processed data\n if self.u_mps is not None:\n self.u_processed_mps = np.copy(self.u_mps)\n self.v_processed_mps = np.copy(self.v_mps)\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan\n\n # Determine interpolation methods to apply\n if interpolation_method is None:\n interpolation_method = self.interpolate\n else:\n self.interpolate = interpolation_method\n\n # Apply specified interpolation method\n\n if interpolation_method == 'None':\n # Sets invalid data to nan with no interpolation\n self.interpolate_none()\n\n elif interpolation_method == 'ExpandedT':\n # Set interpolate to none as the interpolation done is in the QComp\n self.interpolate_next()\n\n elif interpolation_method == 'Hold9':\n # Interpolates using SonTek method of holding last valid for up to 9 samples\n self.interpolate_hold_9()\n\n elif interpolation_method == 'HoldLast':\n # Interpolates by holding last valid indefinitely\n self.interpolate_hold_last()\n\n elif interpolation_method == 'Linear':\n # Interpolates using linear interpolation\n self.interpolate_linear(transect)\n\n elif interpolation_method == 'Smooth':\n # Interpolates using smooth interpolation\n self.interpolate_smooth(transect)\n\n elif interpolation_method == 'TRDI':\n # TRDI interpolation is done in discharge.\n # For TRDI the interpolation is done on discharge not on velocities\n self.interpolate_none()","def resample_and_also_resample_operating_regime(self,\n resample_args: tuple = ('10T',),\n resample_kwargs: dict = None, *,\n operating_regime_file_path: Path = None,\n additional_outlier_mask: ndarray):\n\n @load_exist_pkl_file_otherwise_run_and_save(\n self.default_results_saving_path['resample_and_also_resample_operating_regime'])\n def func():\n nonlocal operating_regime_file_path\n nonlocal resample_kwargs\n self_copy = copy.deepcopy(self)\n\n # For single sensor WF, the operating regime must be detected using as high resolution data as possible.\n # Therefore, object function 'operating_regime_detector' must be called (so there will be results in\n # self.default_results_saving_path[\"operating regime single senor\"], or as specified)\n if operating_regime_file_path is None:\n operating_regime_file_path = self.default_results_saving_path[\"operating regime single senor\"]\n operating_regime = load_pkl_file(operating_regime_file_path) # type: DataCategoryData\n assert operating_regime is not None\n\n resample_kwargs = resample_kwargs or {\n 'resampler_obj_func_source_code': \"agg(lambda x: np.mean(x.values))\"\n }\n # Check outlier\n existing_outlier = load_pkl_file(self_copy.default_results_saving_path[\"outlier\"])['DataCategoryData obj']\n self_copy.loc[np.bitwise_or(~existing_outlier('others'),\n additional_outlier_mask), :] = np.nan\n # Resample self\n resampled_self = self_copy.resample(*resample_args, **resample_kwargs)\n resampled_self.obj_name = self_copy.obj_name + f\" resampled\"\n\n # Resample operating regime\n def rolling_func(x):\n (values, counts) = np.unique(x, return_counts=True)\n index = np.argmax(counts)\n return int(values[index])\n\n rolling_obj = operating_regime.pd_view.applymap(lambda x: int(x[1:])).rolling(*resample_args)\n resampled_operating_regime = rolling_obj.apply(rolling_func, raw=True)\n resampled_operating_regime = resampled_operating_regime.reindex(resampled_self.index, method='nearest')\n resampled_operating_regime = resampled_operating_regime.astype(int)\n resampled_operating_regime = DataCategoryData(\n abbreviation=resampled_operating_regime.applymap(lambda x: f\"S{int(x)}\").values.flatten(),\n index=resampled_operating_regime.index\n )\n\n return resampled_self, resampled_operating_regime\n\n return func()","def __call__(self, input_image):\n output_image = input_image\n if self.shift is not None or self.scale is not None:\n output_image = rescale(output_image, shift=self.shift, scale=self.scale)\n if self.random_shift is not None or self.random_scale is not None:\n current_random_shift = random.float_uniform(-self.random_shift, self.random_shift)\n current_random_scale = 1 + random.float_uniform(-self.random_scale, self.random_scale)\n output_image = rescale(output_image, shift=current_random_shift, scale=current_random_scale)\n if self.clamp_min is not None or self.clamp_max is not None:\n output_image = clamp(output_image, clamp_min=self.clamp_min, clamp_max=self.clamp_max)\n\n return output_image","def resample(self, data):\n # Resample signal\n if self._fs_out != self._fs_in:\n\n # Check that new sample rate is compatable with batch size\n if self._next_frame_idx != 0:\n raise ValueError('New sample rate incompatable with batch size.')\n\n # Input dimensions\n frames_in = data.shape[0] # samples\n len_data = float(frames_in) / self._fs_in # seconds\n\n # Output dimensions\n frames_out = int(np.round(len_data*self._fs_out)) # samples\n delta_out = 1.0 / self._fs_out # seconds\n\n # Predict next frame\n self._next_frame_idx = int(np.round(frames_out*delta_out*self._fs_in)) - \\\n frames_in\n\n # Compute indices to output\n idx_out = np.round(np.arange(frames_out)*delta_out*self._fs_in)\n\n # Sample data using output indices\n data_out = np.zeros((frames_out, data.shape[1]))\n for i in range(frames_out):\n idx = min(frames_in - 1, idx_out[i])\n data_out[i, :] = data[int(idx), :]\n\n else:\n\n # No resampling\n data_out = data\n\n return data_out","def resample_pipe(self, resample_kwargs, func, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.pipe)(\n self, resample_kwargs, func, *args, **kwargs\n )","def nearest_neighbor(self, image, fx, fy):\n\n # Write your code for nearest neighbor interpolation here\n\n (width, height) = image.shape\n (scaledWidth, scaledHeight) = (int(fx * width), int(fy * height))\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\n\n for x in range(0, scaledWidth):\n for y in range(0, scaledHeight):\n nx = int(x / fx)\n ny = int(y / fy)\n if (nx + 1) > width - 1:\n nx = width - 2\n if (ny + 1) > height - 1:\n ny = height - 2\n resampledImage[x, y] = self.inter.linear_interpolation([ny, image[nx, ny]], [ny+1, image[nx, ny+1]], y/fy)\n\n return resampledImage","def resample_sliprate(self, dt, nsamp):\n t_new = np.linspace(0, nsamp * dt, nsamp, endpoint=False)\n t_old = np.linspace(0, self.dt * len(self.sliprate),\n len(self.sliprate), endpoint=False)\n\n self.sliprate = interp(t_new, t_old, self.sliprate)\n self.dt = dt","def rescale_intensity(image, thres=(1.0, 99.0)):\n val_l, val_h = np.percentile(image, thres)\n image2 = image\n image2[image < val_l] = val_l\n image2[image > val_h] = val_h\n image2 = (image2.astype(np.float32) - val_l) / (val_h - val_l)\n return image2","def resample(self):\n # propagate networks\n self.z = self.prior_latent_distribution.sample()\n # reconstruct image\n self.y_hat_raw = self.fcomb(self.unet_features, self.z)\n\n return self.y_hat_raw","def upsample_bilinear(input, size=None, scale_factor=None):\n return interpolate(input, size, scale_factor, 'linear', align_corners=True)","def downsample_image(img, output_path, ratio=2):\n assert ratio>=1, ratio\n if ratio == 1:\n return True\n old_im = Image.open(img)\n old_size = old_im.size\n new_size = (int(old_size[0]/ratio), int(old_size[1]/ratio))\n\n new_im = old_im.resize(new_size, PIL.Image.LANCZOS)\n new_im.save(output_path)\n return True","def resample_filters(self):\n a = len(self.filtered_ids)\n b = len(self.orig_image_ids)\n imbalance_ratio = a / b\n min_ratio = 0.5\n if imbalance_ratio > min_ratio:\n return\n minr = min_ratio\n num_req = int((minr * b - a) / (1 - minr))\n new_ids = ((num_req) // a) * self.filtered_ids\n if num_req % a != 0:\n some_more = random.sample(self.filtered_ids, k=(num_req % a))\n new_ids += some_more\n self.image_ids = self.orig_image_ids + new_ids\n print(\"Resampled total:\", len(self.image_ids))","def interpolate(self, interp):\n x = np.linspace(0, 29, len(self.ya))\n f_ya = interpolate.interp1d(x, self.ya)\n f_yv = interpolate.interp1d(x, self.yv)\n f_pa = interpolate.interp1d(x, np.reshape(self.pa, [-1]))\n f_pv = interpolate.interp1d(x, np.reshape(self.pv, [-1]))\n\n x_interp = np.linspace(0, 29, len(self.ya)*interp)\n self.ya = list(f_ya(x_interp))\n self.yv = list(f_yv(x_interp))\n self.pa = list(f_pa(x_interp))\n self.pv = list(f_pv(x_interp))","def interpolate(x: torch.Tensor, ratio: int):\n (batch_size, time_steps, classes_num) = x.shape\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\n return upsampled","def interpolate(x: torch.Tensor, ratio: int):\n (batch_size, time_steps, classes_num) = x.shape\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\n return upsampled","def regrid(self, *args, **kwargs):\n return _image.image_regrid(self, *args, **kwargs)","def test_nib_resample_image_4d(fake_4dimage_nib):\n img_r = resampling.resample_nib(fake_4dimage_nib, new_size=[2, 2, 1, 1], new_size_type='factor', interpolation='nn')\n assert img_r.get_data().shape == (18, 18, 9, 3)\n assert img_r.get_data()[8, 8, 4, 0] == 1.0 # make sure there is no displacement in world coordinate system\n assert img_r.get_data()[8, 8, 4, 1] == 0.0\n assert img_r.header.get_zooms() == (0.5, 0.5, 1.0, 1.0)","def run_resample(self):\n\n self.in_data.open()\n self.out_data.open()\n\n try:\n # Get the fields from the input file and set them/write headers in output:\n self.all_fields = self.in_data.fields\n\n self.out_data.set_fields(self.all_fields)\n self.out_data.write_headers()\n\n # Set up the sensor fields by removing non-sensor fields:\n self.set_sensor_only_fields()\n\n # Read the first event from the input file:\n self.get_next_input_event()\n\n # Warn and exit if we have no input data to read:\n if self.next_input_event is None:\n msg = f\"The input file {self.in_file} did not have any data rows\"\n warn(msg)\n\n return\n\n self.first_event_stamp = self.next_input_event[self.stamp_field]\n\n # Set up the sample tracking (here mostly to set the start of the first interval):\n self.reset_sample_tracking()\n\n # Now iterate through the output intervals:\n while True:\n self.process_next_interval()\n except EOFError: # catch when we are at the end of the file\n pass\n finally:\n self.in_data.close()\n self.out_data.close()\n\n print() # make sure we go to a new output line","def resample_reflectance(self):\n\n if hasattr(self, \"rwl\"):\n p = interp1d(self.rwl, self.rfl, fill_value=\"extrapolate\")\n self.rfl = p(self.wl)","def raster_copy(\n output_fp,\n input_fp,\n mode=\"warp\",\n resample=1,\n out_crs=None,\n bands=None,\n bounds=None,\n bounds_crs=None,\n multi_core=False,\n pbar=None,\n):\n\n # Common options\n base_options = dict(\n creationOptions=[\n \"TILED=YES\",\n \"BLOCKXSIZE=256\",\n \"BLOCKYSIZE=256\",\n \"BIGTIFF=IF_SAFER\",\n \"NUM_THREADS=ALL_CPUS\",\n ],\n callback=gdal_progress_callback,\n callback_data=pbar,\n )\n if resample != 1:\n # Get input pixel sizes\n raster = gdal.Open(input_fp)\n gt = raster.GetGeoTransform()\n x_res, y_res = gt[1], -gt[5]\n base_options[\"xRes\"] = (x_res / resample,)\n base_options[\"yRes\"] = (y_res / resample,)\n base_options[\"resampleAlg\"] = gdalconst.GRA_Bilinear\n\n # Use GDAL Warp\n if mode.lower() == \"warp\":\n warp_options = dict(\n dstSRS=out_crs,\n outputBounds=bounds,\n outputBoundsSRS=bounds_crs,\n multithread=multi_core,\n warpOptions=[\"NUM_THREADS=ALL_CPUS\"] if multi_core else [],\n warpMemoryLimit=1000000000, # processing chunk size. higher is not always better, around 1-4GB seems good\n )\n return gdal.Warp(output_fp, input_fp, **base_options, **warp_options)\n\n # Use GDAL Translate\n elif mode.lower() == \"translate\":\n translate_options = dict(\n bandList=bands,\n outputSRS=out_crs,\n projWin=[\n bounds[0],\n bounds[3],\n bounds[2],\n bounds[1],\n ], # what the hell gdal.Translate....\n projWinSRS=bounds_crs,\n )\n return gdal.Translate(output_fp, input_fp, **base_options, **translate_options)\n\n else:\n raise Exception(\n \"Invalid mode argument, supported modes are 'warp' or 'translate'.\"\n )","def warp_to_raster(\r\n base_raster_path, canonical_raster_path, resample_method,\r\n target_raster_path):\r\n canonical_raster_info = pygeoprocessing.get_raster_info(\r\n canonical_raster_path)\r\n pygeoprocessing.warp_raster(\r\n base_raster_path, canonical_raster_info['pixel_size'],\r\n target_raster_path,\r\n resample_method, target_bb=canonical_raster_info['bounding_box'],\r\n n_threads=2)","def problem2():\n\n data = loaddata(\"data/bayerdata.npy\")\n r, g, b = separatechannels(data)\n\n img = assembleimage(r, g, b)\n display_image(img)\n\n img_interpolated = interpolate(r, g, b)\n display_image(img_interpolated)","def resample(self, nsamples, dataset_bounds=None, derivative=0):\n if dataset_bounds is None:\n dataset_bounds = self.dataset_bounds\n resampled_dataset = numpy.linspace(dataset_bounds[0],\n dataset_bounds[1], nsamples)\n resampled_data = super(ReSampler, self).__call__(\n x=resampled_dataset, nu=derivative)\n return resampled_dataset, resampled_data","def preprocess(path, path2 , scale):\n image = imread(path)\n label_ = imread(path2)\n\n #label_ = modcrop(label, scale)\n\n # Must be normalized\n input_ = image / 255.\n label_ = label_ / 255.\n\n #input_ = scipy.ndimage.interpolation.zoom(label_, (1./scale), prefilter=False)\n #input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)\n\n return input_, label_","def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\n assert img_r.get_data().shape == (29, 39, 19)\n assert img_r.get_data()[4, 4, 4] == 1.0","def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)","def grid_resample(x, y, z, x_int, y_int, digits=3, method='multiquadric'):\n\n interp1 = Rbf(x, y, z, function=method)\n z_int = interp1(x_int, y_int).round(digits)\n z_int[z_int < 0] = 0\n\n z_int2 = z_int.flatten()\n return(z_int2)","def _batch_resize(self, ims, res, strategy='nearest'):\n order = {'nearest': 0, 'bilinear': 1, 'bicubic': 3}[strategy]\n bs = ims.shape[0]\n out = []\n log.info('Input ims shape: %s' % repr(ims.shape))\n has_extra_dim = len(ims.shape) == 4\n if not has_extra_dim:\n ims = ims[..., np.newaxis]\n h, w = ims.shape[1:3]\n for i in range(bs):\n o = interpolation.zoom(\n ims[i, ...], [res[0] / h, res[1] / w, 1.0], np.float32, order=order)\n out.append(o)\n out = np.stack(out)\n if not has_extra_dim:\n out = np.reshape(out, out.shape[:-1])\n return out","def apply_transform(self, data, params, interp_mode='bilinear'):\n tensor_size = params['jitter size'].tolist()\n crop_pos = params['crop pos']\n crop_size = params['crop size']\n data = F.interpolate(data.unsqueeze(0), size=tensor_size, mode=interp_mode).squeeze(0)\n data = data[:, crop_pos[0]:crop_pos[0] + crop_size[0], crop_pos[1]:crop_pos[1] + crop_size[1]]\n return data","def _image_transform(self, img, source, title):\n conf = source.conf[title]\n \n xmin = conf.get('xmin', 0)\n ymin = conf.get('ymin', 0)\n\n xmax = img.shape[-1] + xmin\n ymax = img.shape[-2] + ymin\n if \"xmax\" in conf:\n if(conf['xmax'] <= xmin):\n logging.warning(\"xmax <= xmin for title %s on %s. Ignoring xmax\", title, source.name())\n else:\n xmax = conf['xmax']\n if \"ymax\" in conf:\n if(conf['ymax'] <= ymin):\n logging.warning(\"ymax <= ymin for title %s on %s. Ignoring xmax\", title, source.name())\n else:\n ymax = conf['ymax']\n\n \n translate_transform = QtGui.QTransform().translate(ymin, xmin)\n\n # The order of dimensions in the scale call is (y,x) as in the numpy\n # array the last dimension corresponds to the x.\n scale_transform = QtGui.QTransform().scale((ymax-ymin)/img.shape[-2],\n (xmax-xmin)/img.shape[-1])\n \n #rotate_transform = QtGui.QTransform()\n #if source.data_type[title] == 'image':\n # if \"angle\" in conf:\n # rotate_transform = QtGui.QTransform(numpy.cos(conf[\"angle\"]), numpy.sin(conf[\"angle\"]), -numpy.sin(conf[\"angle\"]), numpy.cos(conf[\"angle\"]), 0, 0)\n\n transpose_transform = QtGui.QTransform()\n if source.data_type[title] == 'image':\n transpose_transform *= QtGui.QTransform(0, 1, 0,\n 1, 0, 0,\n 0, 0, 1)\n if(self.settingsWidget.ui.transpose.currentText() == 'Yes' or\n (self.settingsWidget.ui.transpose.currentText() == 'Auto' \n and \"transpose\" in conf)):\n transpose_transform *= QtGui.QTransform(0, 1, 0,\n 1, 0, 0,\n 0, 0, 1)\n \n transform = scale_transform * translate_transform * transpose_transform\n #transform = scale_transform * translate_transform * rotate_transform * transpose_transform\n \n # print('|%f %f %f|' % (transform.m11(), transform.m12(), transform.m13()))\n # print('|%f %f %f|' % (transform.m21(), transform.m22(), transform.m23()))\n # print('|%f %f %f|' % (transform.m31(), transform.m32(), transform.m33()))\n return transform","def _scale(self, image):\n\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\n image.Rescale(self._width, self._height)\n \n return image","def rescale(self, img):\n\n if self.scale != 1:\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\n else:\n return img","def apply_model(self, original, t1, t2, resolution_scaling_factor=1):\n img = Image()\n img.time_stamp = t2\n\n if t1 == t2:\n img.initialize_with_image(original)\n return img\n\n calc_shift_fnc = self.calculate_shift\n orig_get_fnc = original.get\n interp_fnc = my_math.linear_interpolation\n\n def generate(y, x):\n \"\"\"Function describing the transformed image\"\"\"\n realy = y / resolution_scaling_factor\n realx = x / resolution_scaling_factor\n\n # move to time t2\n posy = y + calc_shift_fnc(realy, realx, t2, 0) - \\\n calc_shift_fnc(realy, realx, t1, 0)\n posx = x + calc_shift_fnc(realy, realx, t2, 1) - \\\n calc_shift_fnc(realy, realx, t1, 1)\n\n x_left = int(posx) # math.floor(pos[0])\n x_right = x_left + 1 # math.ceil(pos[0])\n y_down = int(posy) # math.floor(pos[1])\n y_up = y_down + 1 # math.ceil(pos[1])\n\n v11 = orig_get_fnc(y_down, x_left, resolution_scaling_factor)\n v12 = orig_get_fnc(y_down, x_right, resolution_scaling_factor)\n v21 = orig_get_fnc(y_up, x_left, resolution_scaling_factor)\n v22 = orig_get_fnc(y_up, x_right, resolution_scaling_factor)\n\n return interp_fnc(y_down, x_left, y_up, x_right, v11, v12, v21, v22,\n posy, posx)\n\n img.image_data = np.fromfunction(np.vectorize(generate),\n (original.shape()[0]*resolution_scaling_factor,\n original.shape()[1]*resolution_scaling_factor))\n\n if resolution_scaling_factor != 1:\n img.image_data = skimage.transform.resize(img.image_data,\n original.shape(),\n preserve_range=True)\n\n return img","def upsampleImage( arr, kernelSize ):\n return scipy.ndimage.zoom( arr, kernelSize )","def downsample(self, factor):\n self.img = self.img[::factor, ::factor, :] if self.fast else self.img\n self.comb_structure_mask = self.comb_structure_mask[::factor, ::factor]\n self.unknown_mask = self.unknown_mask[::factor, ::factor]","def resample(input_samples, time_resample, desired_samples):\n if time_resample == 1.0:\n return input_samples\n\n shape = tf.shape(input_samples)\n samples = tf.expand_dims(input_samples, 0)\n samples = tf.expand_dims(samples, 2)\n samples_resized = tf.image.resize(\n images=samples,\n size=(tf.cast((tf.cast(shape[0], tf.float32) * time_resample),\n tf.int32), 1),\n preserve_aspect_ratio=False)\n samples_resized_cropped = tf.image.resize_with_crop_or_pad(\n samples_resized,\n target_height=desired_samples,\n target_width=1,\n )\n samples_resized_cropped = tf.squeeze(samples_resized_cropped, axis=[0, 3])\n return samples_resized_cropped"],"string":"[\n \"def resampled_to_img(self, target_image, interpolation=None):\\n # IMPORTANT: Polymorphism can be implemented by walking the \\n # MRO and finding a method that does not raise\\n # NotImplementedError. \\n raise NotImplementedError\",\n \"def Resampler(name):\\n\\n def resample_average(path, dsquery, dstile, image_format):\\n for i in range(1, dstile.RasterCount+1):\\n res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i), \\\"average\\\")\\n if res != 0:\\n raise ImageOutputException(\\\"RegenerateOverview() failed with error %d\\\" % res)\\n\\n gdal_write(path, dstile, image_format)\\n\\n def resample_antialias(path, dsquery, dstile, image_format):\\n querysize = dsquery.RasterXSize\\n tilesize = dstile.RasterXSize\\n\\n array = numpy.zeros((querysize, querysize, 4), numpy.uint8)\\n for i in range(dstile.RasterCount):\\n array[:,:,i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1), 0, 0, querysize, querysize)\\n im = Image.fromarray(array, 'RGBA') # Always four bands\\n im1 = im.resize((tilesize,tilesize), Image.ANTIALIAS)\\n\\n if os.path.exists(path):\\n im0 = Image.open(path)\\n im1 = Image.composite(im1, im0, im1)\\n\\n ensure_dir_exists(path)\\n\\n if image_format == \\\"JPEG\\\":\\n im1.save(path, image_format, quality=jpeg_quality)\\n else:\\n im1.save(path, image_format)\\n\\n\\n if name == \\\"average\\\":\\n return resample_average\\n elif name == \\\"antialias\\\":\\n return resample_antialias\\n\\n resampling_methods = {\\n \\\"near\\\" : gdal.GRA_NearestNeighbour,\\n \\\"bilinear\\\" : gdal.GRA_Bilinear,\\n \\\"cubic\\\" : gdal.GRA_Cubic,\\n \\\"cubicspline\\\" : gdal.GRA_CubicSpline,\\n \\\"lanczos\\\" : gdal.GRA_Lanczos\\n }\\n\\n resampling_method = resampling_methods[name]\\n\\n def resample_gdal(path, dsquery, dstile, image_format):\\n querysize = dsquery.RasterXSize\\n tilesize = dstile.RasterXSize\\n\\n dsquery.SetGeoTransform( (0.0, tilesize / float(querysize), 0.0, 0.0, 0.0, tilesize / float(querysize)) )\\n dstile.SetGeoTransform( (0.0, 1.0, 0.0, 0.0, 0.0, 1.0) )\\n\\n res = gdal.ReprojectImage(dsquery, dstile, None, None, resampling_method)\\n if res != 0:\\n raise ImageOutputException(\\\"ReprojectImage() failed with error %d\\\" % res)\\n\\n gdal_write(path, dstile, image_format)\\n\\n return resample_gdal\",\n \"def resample(self):\\n pass\",\n \"def image_resample(f, oversamp=1.0):\\n img = imageio.imread(f)\\n x = np.linspace(0,img.shape[0],img.shape[0])\\n y = np.linspace(0,img.shape[1],img.shape[1])\\n #\\n # 2x oversample the image since we'll dither it.\\n #\\n xnew = np.linspace(0, img.shape[0], img.shape[0]*oversamp)\\n ynew = np.linspace(0, img.shape[1], img.shape[1]*oversamp)\\n from scipy import interpolate\\n rc = interpolate.interp2d(x, y, img[:,:,0].flatten(), kind='linear')\\n gc = interpolate.interp2d(x, y, img[:,:,1].flatten(), kind='linear')\\n bc = interpolate.interp2d(x, y, img[:,:,2].flatten(), kind='linear')\\n rgb_new = np.stack([rc(xnew.flatten(), ynew.flatten()),\\n gc(xnew.flatten(), ynew.flatten()),\\n bc(xnew.flatten(), ynew.flatten())],-1).transpose(1,0,2).astype(np.uint8)\\n plt.imshow(rgb_new)\\n return rgb_new\",\n \"def resample_img(img, img_type, size, spacing):\\n resampler = sitk.ResampleImageFilter()\\n resampler.SetOutputDirection(img.GetDirection())\\n resampler.SetOutputOrigin(img.GetOrigin())\\n resampler.SetOutputSpacing(spacing)\\n resampler.SetSize(size)\\n if img_type is \\\"Label\\\":\\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\\n elif img_type is \\\"Image\\\":\\n resampler.SetInterpolator(sitk.sitkLinear)\\n imgResampled = resampler.Execute(img)\\n\\n #axis have to be switched since np.array and keras use them in different order...\\n x = np.transpose(sitk.GetArrayFromImage(imgResampled).astype(dtype=np.float), [2, 1, 0])\\n return x\",\n \"def interpolate(self, image):\\n return\",\n \"def resample_img(image, target_shape, mode='nearest'):\\n print(target_shape)\\n resize_factor = np.array(target_shape)/image.shape\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def resample(self):\\n self.variational_strategy.resample()\",\n \"def imgResample(img, spacing, size=[], useNearest=False, origin=[], outsideValue=0):\\n if len(spacing) != img.GetDimension(): raise Exception(\\\"len(spacing) != \\\" + str(img.GetDimension()))\\n\\n # Set Size\\n if size == []:\\n inSpacing = img.GetSpacing()\\n inSize = img.GetSize()\\n size = [int(math.ceil(inSize[i]*(inSpacing[i]/spacing[i]))) for i in range(img.GetDimension())]\\n else:\\n if len(size) != img.GetDimension(): raise Exception(\\\"len(size) != \\\" + str(img.GetDimension()))\\n \\n if origin == []:\\n origin = [0]*img.GetDimension()\\n else:\\n if len(origin) != img.GetDimension(): raise Exception(\\\"len(origin) != \\\" + str(img.GetDimension()))\\n \\n # Resample input image\\n interpolator = [sitk.sitkLinear, sitk.sitkNearestNeighbor][useNearest]\\n identityTransform = sitk.Transform()\\n identityDirection = list(sitk.AffineTransform(img.GetDimension()).GetMatrix())\\n\\n return sitk.Resample(img, size, identityTransform, interpolator, origin, spacing, identityDirection, outsideValue)\",\n \"def resample(img, newSize):\\n # use float as datatype to preserve float precision\\n # will convert to np.uint8 when display result\\n # get the new dimensions\\n nH, nW = newSize\\n if np.ndim(img) == 2:\\n H, W = img.shape\\n res = np.zeros((nH, nW), dtype=np.float32)\\n elif np.ndim(img) == 3:\\n H, W, _ = img.shape\\n res = np.zeros((nH, nW, _), dtype=np.float32)\\n else:\\n raise ValueError(\\\"input image has invalid dimension %s\\\" % (img.shape))\\n\\n # interpolate the value for the result\\n for idx in range(nH * nW):\\n i = idx // nW\\n j = idx % nW\\n orig_i = int((i * H) // nH)\\n orig_j = int((j * W) // nW)\\n res[i, j] = img[orig_i, orig_j]\\n return res\",\n \"def resample(self, image, target_shape, mode='nearest'):\\n resize_factor = image.shape / np.array(target_shape)\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\\n if isinstance(target_spacing, numbers.Number):\\n target_spacing = [target_spacing] * img.ndim\\n \\n # compute zoom values\\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\\n\\n print \\\"Zoom Factors\\\"\\n print zoom_factors\\n\\n oldImageShape = img.shape\\n \\n # zoom image\\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\\n\\n newImageShape = img.shape\\n old_pixel_spacing = header.get_pixel_spacing(hdr)\\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\\n print \\\"Target Pixel Spacing\\\"\\n print target_spacing\\n\\n print \\\"Actual Pixel Spacing\\\"\\n print new_pixel_spacing\\n # set new voxel spacing\\n header.set_pixel_spacing(hdr, new_pixel_spacing)\\n \\n return img, hdr\",\n \"def resample(image_array, target_pixel_dims_list, is_seg=False):\\n # if dealing with segmentation images, do not interpolate\\n # this is done by choosing interpolation order == 0\\n order = 0 if is_seg == True else 3\\n\\n original_dims_in_pixels = [image_array.shape[d]\\n for d in range(len(image_array.shape))]\\n compression_list = [target_pixel_dims_list[d] / original_dims_in_pixels[d]\\n for d in range(len(image_array.shape))]\\n\\n if (is_seg):\\n resized_image = ndimage.interpolation.zoom(\\n image_array, zoom=compression_list, order=order, cval=0)\\n return resized_image\\n else:\\n resized_image = ndimage.interpolation.zoom(\\n image_array, zoom=compression_list, order=order, cval=0)\\n return resized_image\",\n \"def resample_image(source_path, dest_fmt, indices=[], wipe=False):\\n\\n from numpy import array_equal\\n from os import remove\\n\\n \\n # unpack indices\\n z_start = indices[0][0]\\n z_stop = indices[0][1]\\n y_start = indices[1][0]\\n y_stop = indices[1][1]\\n x_start = indices[2][0]\\n x_stop = indices[2][1]\\n\\n # the name of the file before format extension\\n source_name = source_path.split('.')[0]\\n dest_name = source_name.replace('/im/','/forebrain/')\\n \\n dest_path = dest_name + '.' + dest_fmt\\n source_image = read_image(source_path)\\n\\n resampled_image = source_image[z_start:z_stop, \\\\\\n y_start:y_stop, \\\\\\n x_start:x_stop]\\n write_image(dest_path, resampled_image)\\n print(dest_path + ' written')\\n\\n if wipe:\\n check_image = read_image(dest_path)\\n if array_equal(check_image, source_image):\\n remove(source_path)\\n else:\\n print('{0} and {1} differ... something went wrong!'.format(source_path, dest_path))\",\n \"def upsample(self, method):\\n from scipy.signal import resample\\n from scipy.ndimage.interpolation import zoom\\n #print \\\"mm: 100 x 100 x 131\\\"\\n #print \\\"Dims:\\\", self.D.shape\\n fact = np.array(self.info.shape).astype(\\\"float32\\\") / np.array(self.info.read_shape).astype(\\\"float32\\\")+0.00001 # hrmpf!!\\n if method == \\\"zoom\\\":\\n print \\\"Resampling...\\\"\\n self.D = zoom(self.D, fact).astype(\\\"float32\\\")\\n elif method == \\\"resample\\\":\\n print \\\"Resampling...\\\"\\n a = self.info.resample_ax\\n s = self.info.shape[a]\\n self.D = resample(self.D, s, axis=a, window=10).astype(\\\"float32\\\")\\n elif method == None:\\n pass\\n else:\\n raise NotImplementedError(\\\"Unknown upsampling method: %s\\\" % method)\\n #print \\\"Dims:\\\", self.D.shape\\n print \\\"done.\\\"\",\n \"def _resample(ct_scan, origin, spacing, spacing_x, spacing_y, spacing_z,\\n interpolator=sitk.sitkLinear):\\n img = LungsLoader._get_itk_from_scan(ct_scan, origin, spacing)\\n # Compute new dimensions\\n spacing = img.GetSpacing()\\n size = img.GetSize()\\n fact_x = spacing[0] / spacing_x\\n fact_y = spacing[1] / spacing_y\\n fact_z = spacing[2] / spacing_z\\n size_x = int(round(size[0] * fact_x))\\n size_y = int(round(size[1] * fact_y))\\n size_z = int(round(size[2] * fact_z))\\n # to do resampling\\n f = sitk.ResampleImageFilter()\\n f.SetReferenceImage(img)\\n f.SetOutputOrigin(img.GetOrigin())\\n f.SetOutputSpacing((spacing_x, spacing_y, spacing_z))\\n f.SetSize((size_x, size_y, size_z))\\n f.SetInterpolator(interpolator)\\n result = f.Execute(img)\\n return np.around(sitk.GetArrayFromImage(result))\",\n \"def bilinear_interpolation(self, image, fx, fy):\\n\\n # Write your code for bilinear interpolation here\\n (width, height) = image.shape\\n (scaledWidth, scaledHeight) = (int(fy * width), int(fy * height))\\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\\n\\n for x in range(0, scaledWidth):\\n for y in range(0, scaledHeight):\\n nx = math.floor(x / fx)\\n ny = math.floor(y / fy)\\n if (nx + 1) > width - 1:\\n nx = width - 2\\n if (ny + 1) > height - 1:\\n ny = height - 2\\n\\n n1 = (nx, ny)\\n n2 = (nx, ny+1)\\n n3 = (nx+1, ny)\\n n4 = (nx+1, ny+1)\\n\\n resampledImage[x, y] = self.inter.bilinear_interpolation([n1, image[n1]], [n2, image[n2]], [n3, image[n3]],[n4, image[n4]], [x/fx, y/fy])\\n\\n return resampledImage\",\n \"def test_resample_methods():\\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\\n try:\\n cvs.raster(src, upsample_method='santaclaus', downsample_method='toothfairy')\\n except ValueError:\\n pass\\n else:\\n assert False\\n\\n try:\\n cvs.raster(src, upsample_method='honestlawyer')\\n except ValueError:\\n pass\\n else:\\n assert False\\n\\n try:\\n cvs.raster(src, downsample_method='tenantfriendlylease')\\n except ValueError:\\n pass\\n else:\\n assert False\",\n \"def resample(medical_image, image, new_spacing=[1, 1, 1]):\\n image_shape = np.array(list(medical_image.header.get_data_shape()))\\n spacing = np.array(list(medical_image.header.get_zooms()))\\n resize_factor = spacing / new_spacing\\n\\n new_shape = image_shape * resize_factor\\n new_shape = np.round(new_shape)\\n\\n real_resize_factor = new_shape / image_shape\\n\\n new_spacing = spacing / real_resize_factor\\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\\n\\n return resampled_image, new_spacing\",\n \"def get_resample(name: str) -> str:\\n\\n methods = {\\n \\\"first\\\":\\n \\\"\\\"\\\"\\nimport numpy as np\\n\\ndef first(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\\n y = np.ones(in_ar[0].shape)\\n for i in reversed(range(len(in_ar))):\\n mask = in_ar[i] == 0\\n y *= mask\\n y += in_ar[i]\\n\\n np.clip(y,0,255, out=out_ar)\\n\\\"\\\"\\\",\\n \\\"last\\\":\\n \\\"\\\"\\\"\\nimport numpy as np\\n\\ndef last(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\\n y = np.ones(in_ar[0].shape)\\n for i in range(len(in_ar)):\\n mask = in_ar[i] == 0\\n y *= mask\\n y += in_ar[i]\\n\\n np.clip(y,0,255, out=out_ar)\\n\\\"\\\"\\\",\\n \\\"max\\\":\\n \\\"\\\"\\\"\\nimport numpy as np\\n\\ndef max(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\\n y = np.max(in_ar, axis=0)\\n np.clip(y,0,255, out=out_ar)\\n\\\"\\\"\\\",\\n \\\"average\\\":\\n \\\"\\\"\\\"\\nimport numpy as np\\n\\ndef average(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):\\n div = np.zeros(in_ar[0].shape)\\n for i in range(len(in_ar)):\\n div += (in_ar[i] != 0)\\n div[div == 0] = 1\\n \\n y = np.sum(in_ar, axis = 0, dtype = 'uint16')\\n y = y / div\\n \\n np.clip(y,0,255, out = out_ar)\\n\\\"\\\"\\\"}\\n\\n if name not in methods:\\n raise ValueError(\\n \\\"ERROR: Unrecognized resampling method (see documentation): '{}'.\\\".\\n format(name))\\n\\n return methods[name]\",\n \"def resample_image(image, out_spacing=(1.0, 1.0, 1.0), out_size=None, is_label=False, pad_value=0):\\n\\n original_spacing = np.array(image.GetSpacing())\\n original_size = np.array(image.GetSize())\\n\\n if out_size is None:\\n out_size = np.round(np.array(original_size * original_spacing / np.array(out_spacing))).astype(int)\\n else:\\n out_size = np.array(out_size)\\n\\n original_direction = np.array(image.GetDirection()).reshape(len(original_spacing), -1)\\n original_center = (np.array(original_size, dtype=float) - 1.0) / 2.0 * original_spacing\\n out_center = (np.array(out_size, dtype=float) - 1.0) / 2.0 * np.array(out_spacing)\\n\\n original_center = np.matmul(original_direction, original_center)\\n out_center = np.matmul(original_direction, out_center)\\n out_origin = np.array(image.GetOrigin()) + (original_center - out_center)\\n\\n resample = sitk.ResampleImageFilter()\\n resample.SetOutputSpacing(out_spacing)\\n resample.SetSize(out_size.tolist())\\n resample.SetOutputDirection(image.GetDirection())\\n resample.SetOutputOrigin(out_origin.tolist())\\n resample.SetTransform(sitk.Transform())\\n resample.SetDefaultPixelValue(pad_value)\\n\\n if is_label:\\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\\n else:\\n resample.SetInterpolator(sitk.sitkBSpline)\\n\\n return resample.Execute(image)\",\n \"def resample_by(image_array, compression_factor_list, is_seg=False):\\n original_dims_in_pixels = [image_array.shape[d]\\n for d in range(len(image_array.shape))]\\n target_pixel_dims_list = [int(math.floor(compression_factor_list[d] * original_dims_in_pixels[d]))\\n for d in range(len(image_array.shape))]\\n\\n resized_image = resample(image_array, target_pixel_dims_list, is_seg)\\n\\n return(resized_image)\",\n \"def resample(image, pixsize):\\n assert pixsize in [1, 2, 4, 8, 16] # return error and exit otherwise\\n imsize = 128/pixsize\\n newimage = np.zeros((imsize, imsize))\\n for xn, x in enumerate(np.arange(0, 128, pixsize)):\\n for yn, y in enumerate(np.arange(0, 128, pixsize)):\\n newimage[xn, yn] = np.nansum(image[x:x+pixsize, y:y+pixsize]) # Nansum is important as sum of masked array can be nan\\n return newimage\",\n \"def rasterio_resample(dataset: Dataset, target_resolution: float, new_nodata: object = 'auto',\\n resampling_method: Optional[rio_warp.Resampling] = rio_warp.Resampling.nearest,\\n output_datatype: Optional[np.dtype] = np.float32) -> Dataset:\\n\\n # Detect if resolution is not square\\n if dataset.meta[\\\"resolution\\\"][0] != dataset.meta[\\\"resolution\\\"][1]:\\n raise RuntimeError(\\\"Tried to resample a dataset that doesn't have a square resolution!\\\")\\n\\n # Calculate scale factor\\n scaling = int(dataset.meta[\\\"resolution\\\"][0]) / float(target_resolution)\\n\\n # Copy data from source Dataset\\n source_crs = deepcopy(dataset.profile[\\\"crs\\\"])\\n source_transform = deepcopy(dataset.profile[\\\"transform\\\"])\\n source_nodata = deepcopy(dataset.nodata)\\n\\n # Calculate profile/transform and transfer elements\\n new_transform = Affine(source_transform.a / scaling, source_transform.b, source_transform.c, source_transform.d,\\n source_transform.e / scaling, source_transform.f)\\n new_height = int(dataset.profile[\\\"height\\\"] * scaling)\\n new_width = int(dataset.profile[\\\"width\\\"] * scaling)\\n new_resolution = (target_resolution, target_resolution)\\n new_nodata = source_nodata if new_nodata == \\\"auto\\\" else new_nodata\\n new_crs = source_crs # [TODO] Add ability to change CRS in rasterio resample function\\n\\n # Update profile to match output\\n dataset.nodata = new_nodata\\n dataset.profile.update(\\n res=(float(target_resolution), float(target_resolution)),\\n transform=new_transform,\\n height=new_height,\\n width=new_width,\\n nodata=new_nodata\\n )\\n dataset.profile.data[\\\"pixel_dimensions\\\"] = (float(target_resolution), float(target_resolution))\\n dataset.meta[\\\"resolution\\\"] = new_resolution\\n\\n # Change datatype of all bands\\n # [TODO] There may be a way to save memory by freeing old type ndarrays\\n for band_id, band_value in dataset.bands.items():\\n dataset.bands[band_id] = band_value.astype(output_datatype)\\n\\n # Run resampling on each band\\n for band_key, band_data in dataset.bands.items():\\n # Create an array in the correct resampled dimensions\\n resampled_array = np.ma.asanyarray(np.empty(shape=(new_width, new_height), dtype=output_datatype))\\n\\n # Resample/warp\\n rio_warp.reproject(band_data, resampled_array, src_transform=source_transform, dst_transform=new_transform,\\n width=new_width, height=new_height, src_nodata=source_nodata, dst_nodata=new_nodata,\\n src_crs=source_crs, dst_crs=new_crs, resample=resampling_method)\\n\\n # Set value in dictionary\\n dataset.bands[band_key] = resampled_array\\n\\n return dataset\",\n \"def resample_transform(self, resample_kwargs, arg, *args, **kwargs):\\n return ResampleDefault.register(pandas.core.resample.Resampler.transform)(\\n self, resample_kwargs, arg, *args, **kwargs\\n )\",\n \"def resize(self, image, fx=None, fy=None, interpolation=None):\\r\\n if interpolation == 'bilinear':\\r\\n return self.bilinear_interpolation(image, float(fx), float(fy))\\r\\n\\r\\n elif interpolation == 'nearest_neighbor':\\r\\n return self.nearest_neighbor(image, float(fx), float(fy))\",\n \"def resample(x, xp, yp, xedges=False, xpedges=False):\\n\\n assert 1 <= x.ndim\\n assert x.ndim <= xp.ndim\\n assert xp.ndim <= yp.ndim\\n assert yp.ndim <= 2\\n\\n input_edges = xp if xpedges else pixspline.cen2bound(xp)\\n ys = pixspline.PixelSpline(input_edges, yp)\\n \\n edges = x if xedges else pixspline.cen2bound(x)\\n return ys.resample(edges)\",\n \"def resample(scan, image, new_spacing=[1,1,1]):\\n spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)\\n\\n resize_factor = spacing / new_spacing\\n new_real_shape = image.shape * resize_factor\\n new_shape = np.round(new_real_shape)\\n real_resize_factor = new_shape / image.shape\\n new_spacing = spacing / real_resize_factor\\n\\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\\n\\n batch = []\\n for i in range(0, image.shape[0] - 3, 3):\\n tmp = []\\n for j in range(3):\\n img = image[i + j]\\n img= cv2.resize(img, (224, 224))\\n tmp.append(img)\\n\\n tmp = np.array(tmp)\\n batch.append(tmp)\\n\\n batch = np.array(batch)\\n\\n return batch\",\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def resize(self, image, fx=None, fy=None, interpolation=None):\\n if interpolation == 'bilinear':\\n return self.bilinear_interpolation(image, float(fx), float(fy))\\n\\n elif interpolation == 'nearest_neighbor':\\n return self.nearest_neighbor(image, float(fx), float(fy))\",\n \"def resample(image, flow):\\r\\n assert flow.shape[1] == 2\\r\\n b, c, h, w = image.shape\\r\\n grid = get_grid(b, (h, w))\\r\\n flow = L.concat([flow[:, 0:1, :, :] / ((w - 1.0) / 2.0),\\r\\n flow[:, 1:2, :, :] / ((h - 1.0) / 2.0)], 1)\\r\\n final_grid = L.transpose((grid + flow), (0, 2, 3, 1))\\r\\n image.stop_gradient = False\\r\\n try:\\r\\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border', align_corners=True)\\r\\n except Exception:\\r\\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border')\\r\\n \\r\\n return output\\r\\n # return image\\r\\n # return L.zeros_like(image)\\r\",\n \"def resample(s, p, q, h=None):\\n gcd = fractions.gcd(p,q)\\n if gcd>1:\\n p=p/gcd\\n q=q/gcd\\n \\n if h is None: #design filter\\n #properties of the antialiasing filter\\n log10_rejection = -3.0\\n stopband_cutoff_f = 1.0/(2.0 * max(p,q))\\n roll_off_width = stopband_cutoff_f / 10.0\\n \\n #determine filter length\\n #use empirical formula from [2] Chap 7, Eq. (7.63) p 476\\n rejection_db = -20.0*log10_rejection;\\n l = ceil((rejection_db-8.0) / (28.714 * roll_off_width))\\n \\n #ideal sinc filter\\n t = arange(-l, l + 1)\\n ideal_filter=2*p*stopband_cutoff_f*sinc(2*stopband_cutoff_f*t) \\n \\n #determine parameter of Kaiser window\\n #use empirical formula from [2] Chap 7, Eq. (7.62) p 474\\n beta = kaiser_beta(rejection_db)\\n \\n #apodize ideal filter response\\n h = kaiser(2*l+1, beta)*ideal_filter\\n\\n ls = len(s)\\n lh = len(h)\\n\\n l = (lh - 1)/2.0\\n ly = ceil(ls*p/float(q))\\n\\n #pre and postpad filter response\\n nz_pre = floor(q - mod(l,q))\\n nz_pre = int(nz_pre)\\n hpad = h[-lh+nz_pre:]\\n\\n offset = floor((l+nz_pre)/q)\\n nz_post = 0;\\n while ceil(((ls-1)*p + nz_pre + lh + nz_post )/q ) - offset < ly:\\n nz_post += 1\\n hpad = hpad[:lh + nz_pre + nz_post]\\n\\n #filtering\\n xfilt = upfirdn(s, hpad, p, q)\\n\\n return xfilt[int(offset)-1:int(offset)-1+int(ly)]\",\n \"def resample_sliprate(self, dt, nsamp):\\n for ps in self.pointsources:\\n ps.resample_sliprate(dt, nsamp)\",\n \"def img_zoom(img, fx, fy, interp=cv2.INTER_AREA):\\n res = cv2.resize(img, None, fx=fx, fy=fy,\\n interpolation=interp)\\n return res\",\n \"def rescale_scan(ct_scan, origin, spacing, new_width, new_height, new_depth,\\n normalize=True, interpolator=sitk.sitkLinear):\\n if not np.all(origin == 0):\\n raise ValueError(\\\"Please feed only resampled images to this function.\\\")\\n if ct_scan.shape[2] != ct_scan.shape[1]:\\n raise ValueError(\\\"Scans should have the same width and height.\\\")\\n img = LungsLoader._get_itk_from_scan(ct_scan, origin, spacing)\\n f = sitk.ResampleImageFilter()\\n f.SetReferenceImage(img)\\n f.SetOutputOrigin(img.GetOrigin())\\n spacing_x, spacing_y, spacing_z = img.GetSpacing()\\n size_x, size_y, size_z = img.GetSize()\\n spacing_x /= (new_width / size_x)\\n spacing_y /= (new_height / size_y)\\n spacing_z /= (new_depth / size_z)\\n size_x = new_width\\n size_y = new_height\\n size_z = new_depth\\n f.SetOutputSpacing((spacing_x, spacing_y, spacing_z))\\n f.SetSize((size_x, size_y, size_z))\\n f.SetInterpolator(interpolator)\\n result = f.Execute(img)\\n new_scan = sitk.GetArrayFromImage(result)\\n if normalize:\\n new_scan = (new_scan - np.mean(new_scan)) / np.std(new_scan)\\n return new_scan, result.GetOrigin(), result.GetSpacing()\",\n \"def resample_image(I,spacing,desiredSize, spline_order=1,zero_boundary=False,identity_map=None):\\n if len(I.shape) != len(desiredSize)+2:\\n desiredSize = desiredSize[2:]\\n sz = np.array(list(I.size()))\\n # check that the batch size and the number of channels is the same\\n nrOfI = sz[0]\\n nrOfC = sz[1]\\n\\n desiredSizeNC = np.array([nrOfI,nrOfC]+list(desiredSize))\\n\\n newspacing = spacing*((sz[2::].astype('float')-1.)/(desiredSizeNC[2::].astype('float')-1.)) ###########################################\\n if identity_map is not None:\\n idDes= identity_map\\n else:\\n idDes = torch.from_numpy(py_utils.identity_map_multiN(desiredSizeNC,newspacing)).to(I.device)\\n # now use this map for resampling\\n ID = py_utils.compute_warped_image_multiNC(I, idDes, newspacing, spline_order,zero_boundary)\\n\\n return ID, newspacing\",\n \"def nn_resample_image(image_path, pxl_box_width, pxl_box_height):\\n image = Image.open(image_path)\\n if image.format == \\\"JPEG\\\" or image.format == \\\"PNG\\\":\\n __nn_resample_png_jpeg_image(image, pxl_box_width, pxl_box_height)\\n elif image.format == \\\"GIF\\\":\\n __nn_resample_gif_image(image, pxl_box_width, pxl_box_height)\\n\\n return image\",\n \"def downsample(self, hr_img):\\r\\n lr_img = hr_img.resize((int(hr_img.width / self.scaling_factor), int(hr_img.height / self.scaling_factor)),\\r\\n Image.BICUBIC)\\r\\n # Convert the LR and HR image to the required type\\r\\n lr_img = convert_image(lr_img, source='pil', target=self.lr_img_type)\\r\\n hr_img = convert_image(hr_img, source='pil', target=self.hr_img_type)\\r\\n return lr_img, hr_img\",\n \"def resample(image, target_affine, target_shape, interpolation='trilinear', pad=False, dtype=None, align_corners=True,\\n margin=1e-6):\\n if dtype:\\n image = image.to(dtype)\\n if (torch.all(torch.abs(image.affine - target_affine) < margin)\\n and torch.all(torch.tensor(image.shape[-3:]) == torch.tensor(target_shape))):\\n return image\\n mode = monai_interpolation_mode(interpolation)\\n resampler = SpatialResample(mode=mode, align_corners=align_corners)\\n\\n return resampler(img=image, dst_affine=target_affine, spatial_size=target_shape)\",\n \"def _resample(x: np.ndarray, y: np.ndarray, n_samples: Optional[int]) -> Tuple[np.ndarray, np.ndarray]:\\n if n_samples is None:\\n n_samples = y.size\\n new_x = np.linspace(x[0], x[1], n_samples)\\n new_y = interp1d(x, y)(new_x)\\n return new_x, new_y\",\n \"def resample(wrksppath, region, model):\\n logging.info('\\\\nResampling the rasters for ' + region)\\n # Define app workspace and sub-paths\\n tiffs = os.path.join(wrksppath, region, model + '_GeoTIFFs')\\n resampleds = os.path.join(wrksppath, region, model + '_GeoTIFFs_resampled')\\n\\n # Create directory for the resampled GeoTIFFs\\n if not os.path.exists(tiffs):\\n logging.info('There is no tiffs folder. You must have already resampled them. Skipping resampling')\\n return\\n\\n # List all Resampled GeoTIFFs\\n files = os.listdir(tiffs)\\n files = [tif for tif in files if tif.endswith('.tif')]\\n files.sort()\\n\\n # Read raster dimensions\\n path = os.path.join(tiffs, files[0])\\n raster_dim = rasterio.open(path)\\n width = raster_dim.width\\n height = raster_dim.height\\n lon_min = raster_dim.bounds.left\\n lon_max = raster_dim.bounds.right\\n lat_min = raster_dim.bounds.bottom\\n lat_max = raster_dim.bounds.top\\n\\n # Geotransform for each resampled raster (east, south, west, north, width, height)\\n geotransform_res = rasterio.transform.from_bounds(lon_min, lat_min, lon_max, lat_max, width * 100, height * 100)\\n\\n # Resample each GeoTIFF\\n for file in files:\\n path = os.path.join(tiffs, file)\\n logging.info(path)\\n with rasterio.open(path) as dataset:\\n data = dataset.read(\\n out_shape=(int(dataset.height * 100), int(dataset.width * 100)),\\n # Reduce 100 to 10 if using the whole globe\\n resampling=Resampling.nearest\\n )\\n\\n # Convert new resampled array from 3D to 2D\\n data = numpy.squeeze(data, axis=0)\\n\\n # Specify the filepath of the resampled raster\\n resample_filename = file.replace('.tif', '_resampled.tif')\\n resample_filepath = os.path.join(resampleds, resample_filename)\\n\\n # Save the GeoTIFF\\n with rasterio.open(\\n resample_filepath,\\n 'w',\\n driver='GTiff',\\n height=data.shape[0],\\n width=data.shape[1],\\n count=1,\\n dtype=data.dtype,\\n nodata=numpy.nan,\\n crs='+proj=latlong',\\n transform=geotransform_res,\\n ) as dst:\\n dst.write(data, 1)\\n\\n # delete the non-resampled tiffs now that we dont need them\\n shutil.rmtree(tiffs)\\n\\n return\",\n \"def upsample(x):\\n return F.interpolate(x, scale_factor=2, mode=\\\"nearest\\\")\",\n \"def resample_2d(array, sample_pts, query_pts, kind='linear'):\\n interpf = interpolate.interp2d(*sample_pts, array, kind=kind)\\n return interpf(*query_pts)\",\n \"def interpolate(x, ratio):\\n (batch_size, time_steps, classes_num) = x.shape\\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\\n return upsampled\",\n \"def interpolate(x, ratio):\\n (batch_size, time_steps, classes_num) = x.shape\\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\\n return upsampled\",\n \"def bilinear_interpolation(self, image, fx, fy):\\r\\n\\r\\n # Write your code for bilinear interpolation here\\r\\n width, height = image.shape[:2]\\r\\n nw = int(width * fx)\\r\\n nh = int(height * fy)\\r\\n new_img = np.zeros((nw, nh), np.uint8)\\r\\n inter = interpolation.interpolation()\\r\\n for i in range(nw):\\r\\n for j in range(nh):\\r\\n ii = int(np.floor((i * (image.shape[0]-1))/(nw - 1)))\\r\\n jj = int(np.floor((j * (image.shape[1]-1))/(nh - 1)))\\r\\n\\r\\n # All four corner pixels copied as it is\\r\\n if (ii == 0 and jj == 0) or \\\\\\r\\n (ii == 0 and jj == image.shape[1] - 1) or \\\\\\r\\n (ii == image.shape[0] - 1 and jj == 0) or \\\\\\r\\n (ii == image.shape[0] - 1 and jj == image.shape[1] - 1):\\r\\n new_img[i][j] = image[ii][jj]\\r\\n\\r\\n # Linear interpolation along left and right edges\\r\\n elif ii == 0 or ii == image.shape[0] - 1:\\r\\n new_img[i][j] = inter.linear_interpolation([jj - 1, image[ii][jj - 1]], [jj + 1, image[ii][jj + 1]], jj)\\r\\n\\r\\n # Linear interpolation along top and bottom edges\\r\\n elif jj == 0 or jj == image.shape[1] - 1:\\r\\n new_img[i][j] = inter.linear_interpolation([ii - 1, image[ii - 1][jj]], [ii + 1, image[ii + 1][jj]], ii)\\r\\n\\r\\n else:\\r\\n\\r\\n q11i, q11j = ii - 1, jj - 1\\r\\n q12i, q12j = ii - 1, jj + 1\\r\\n q21i, q21j = ii + 1, jj - 1\\r\\n q22i, q22j = ii + 1, jj + 1\\r\\n\\r\\n point_a = [q11i, q11j, image[q11i][q11j]]\\r\\n point_b = [q12i, q12j, image[q12i][q12j]]\\r\\n point_c = [q21i, q21j, image[q21i][q21j]]\\r\\n point_d = [q22i, q22j, image[q22i][q22j]]\\r\\n unknown = [ii, jj]\\r\\n\\r\\n new_img[i][j] = inter.bilinear_interpolation(point_a, point_b, point_c, point_d, unknown)\\r\\n\\r\\n return new_img\",\n \"def data_resampler (self, data):\\r\\n data = librosa.resample(data, orig_sr = original_sampling, target_sr = self.target_sampling)\\r\\n \\r\\n return data\",\n \"def scale_image(image, scale=1, interpolation='linear'):\\n if interpolation == 'linear':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\\n if interpolation == 'nearest':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)\",\n \"def resample(img, factors=[2.0, 2.0]):\\n if np.iscomplexobj(img):\\n myft = ft(img)\\n newsize = resampledSize(img.shape, factors)\\n res = ift(image.extractFt(myft, newsize, ModifyInput=True)) # the FT can be modified since it is anyway temporarily existing only\\n else:\\n rf = rft(img, shift_before=True) # why is the shift necessary??\\n oldsize = rf.shape\\n # print(oldsize)\\n newrftsize = resampledSize(oldsize, factors, RFT=True)\\n newsize = resampledSize(img.shape, factors)\\n # print(newsize)\\n # the function below already takes care of the pixelsize\\n rfre = resampleRFT(rf, newrftsize, img.shape, newsize, ModifyInput=True)\\n # print(rfre)\\n res = irft(rfre, newsize, shift_after=True) # why is the shift necessary??\\n # no modification is needed to warrant that the integral does not change!\\n return res\",\n \"def resample_img(niimg, target_affine=None, target_shape=None, interpolation=\\\"continuous\\\", copy=True, order=\\\"F\\\"):\\n # Do as many checks as possible before loading data, to avoid potentially\\n # costly calls before raising an exception.\\n if target_shape is not None and target_affine is None:\\n raise ValueError(\\\"If target_shape is specified, target_affine should\\\"\\n \\\" be specified too.\\\")\\n\\n if target_shape is not None and not len(target_shape) == 3:\\n raise ValueError(\\\"The shape specified should be the shape of\\\"\\n \\\"the 3D grid, and thus of length 3. %s was specified\\\"\\n % str(target_shape))\\n\\n if interpolation == \\\"continuous\\\":\\n interpolation_order = 3\\n elif interpolation == \\\"nearest\\\":\\n interpolation_order = 0\\n else:\\n raise ValueError(\\\"interpolation must be either \\\"continuous\\\" \\\"\\n \\\"or \\\"nearest\\\"\\\")\\n\\n # noop cases\\n if isinstance(niimg, basestring):\\n niimg = nibabel.load(niimg)\\n\\n if target_affine is not None:\\n target_affine = np.asarray(target_affine)\\n\\n affine = niimg.get_affine()\\n\\n # We now know that some resampling must be done.\\n # The value of \\\"copy\\\" is of no importance: output is always a separate\\n # array.\\n data = niimg.get_data()\\n\\n if target_shape is None:\\n target_shape = data.shape[:3]\\n target_shape = list(target_shape)\\n\\n if target_affine.shape[0] == 3:\\n # We have a 3D affine, we need to find out the offset and\\n # shape to keep the same bounding box in the new space\\n affine4d = np.eye(4)\\n affine4d[:3, :3] = target_affine\\n transform_affine = np.dot(linalg.inv(affine4d), affine)\\n # The bounding box in the new world, if no offset is given\\n (xmin, xmax), (ymin, ymax), (zmin, zmax) = \\\\\\n get_bounds(data.shape[:3], transform_affine)\\n\\n offset = np.array((xmin, ymin, zmin))\\n offset = np.dot(target_affine, offset)\\n target_affine = from_matrix_vector(target_affine, offset[:3])\\n target_shape = (int(np.ceil(xmax - xmin)) + 1,\\n int(np.ceil(ymax - ymin)) + 1,\\n int(np.ceil(zmax - zmin)) + 1, )\\n\\n if np.all(target_affine == affine):\\n # Small trick to be more numerically stable\\n transform_affine = np.eye(4)\\n else:\\n transform_affine = np.dot(linalg.inv(affine), target_affine)\\n A, b = to_matrix_vector(transform_affine)\\n A_inv = linalg.inv(A)\\n # If A is diagonal, ndimage.affine_transform is clever enough to use a\\n # better algorithm.\\n if np.all(np.diag(np.diag(A)) == A):\\n A = np.diag(A)\\n else:\\n b = np.dot(A, b)\\n\\n data_shape = list(data.shape)\\n # For images with dimensions larger than 3D:\\n if len(data_shape) > 3:\\n # Iter in a set of 3D volumes, as the interpolation problem is\\n # separable in the extra dimensions. This reduces the\\n # computational cost\\n other_shape = data_shape[3:]\\n resampled_data = np.ndarray(list(target_shape) + other_shape,\\n order=order)\\n\\n all_img = (slice(None), ) * 3\\n\\n for ind in np.ndindex(*other_shape):\\n img = data[all_img + ind]\\n resampled_data[all_img + ind] = \\\\\\n ndimage.affine_transform(img, A,\\n offset=np.dot(A_inv, b),\\n output_shape=target_shape,\\n order=interpolation_order)\\n else:\\n resampled_data = ndimage.affine_transform(data, A,\\n offset=np.dot(A_inv, b),\\n output_shape=target_shape,\\n order=interpolation_order)\\n return Nifti1Image(resampled_data, target_affine)\",\n \"def resample_data(self, data, freq, resampler='mean'):\\n\\n if resampler == 'mean':\\n data = data.resample(freq).mean()\\n elif resampler == 'max':\\n data = data.resample(freq).max()\\n else:\\n raise ValueError('Resampler can be \\\\'mean\\\\' or \\\\'max\\\\' only.')\\n \\n return data\",\n \"def resample_photons(self, srcs, verbose=False):\\n # first, clear out old sample images\\n for src in srcs:\\n src.clear_sample_images()\\n\\n # generate per-source sample image patch for each fits image in\\n # this field. keep track of photons due to noise\\n noise_sums = {}\\n for band, img in self.img_dict.iteritems():\\n if verbose:\\n print \\\" ... resampling band %s \\\" % band\\n samp_imgs, noise_sum = \\\\\\n cel_mcmc.sample_source_photons_single_image_cython(\\n img, [s.params for s in srcs]\\n )\\n\\n # tell each source to keep track of it's source-specific sampled\\n # images (and the image it was stripped out of)\\n for src, samp_img in zip(srcs, samp_imgs):\\n if samp_img is not None:\\n\\n # cache pixel grid for each sample image\\n y_grid = np.arange(samp_img.y0, samp_img.y1, dtype=np.float)\\n x_grid = np.arange(samp_img.x0, samp_img.x1, dtype=np.float)\\n xx, yy = np.meshgrid(x_grid, y_grid, indexing='xy')\\n pixel_grid = np.column_stack((xx.ravel(order='C'), yy.ravel(order='C')))\\n src.sample_image_list.append((samp_img, img, pixel_grid))\\n\\n # keep track of noise sums\\n noise_sums[band] = noise_sum\\n\\n # resample noise parameter in each fits image\\n for band, img in self.img_dict.iteritems():\\n a_n = self.a_0 + noise_sums[band]\\n b_n = self.b_0 + img.nelec.size\\n #eps_tmp = img.epsilon\\n img.epsilon = np.random.gamma(a_n, 1./b_n)\",\n \"def downsampling(inp_img):\\n\\n\\n img = np.array(inp_img)\\n f = max(1, np.rint(np.amin(img)/256))\\n\\n if f > 1:\\n lpf = np.ones((f, f))\\n f = (1/(f*f))*lpf\\n img = cv2.filter2D(img, -1, kernel=f)\\n out = np.hstack((img[:, :, 0], img[:, :, 1], img[:, :, 2]))\\n\\n return out\",\n \"def bilinear_interpolation(self, image, fx, fy):\\n\\n # Write your code for bilinear interpolation here\\n w = image.shape[1]\\n h = image.shape[0]\\n\\n newW = int(w*float(fx))\\n newH = int(h*float(fy))\\n\\n ratioW = w / newW\\n ratioH = h / newH\\n\\n import numpy as np\\n import math as ma\\n from . import interpolation as bilinear\\n bi = bilinear.interpolation()\\n newImg = np.zeros((newH, newW), np.uint8)\\n for i in range(newImg.shape[0]):\\n for j in range(newImg.shape[1]):\\n\\n x = ratioH * i\\n y = ratioW * j\\n\\n x1 = ma.floor(x)\\n x2 = ma.ceil(x)\\n y1 = ma.floor(y)\\n y2 = ma.ceil(y)\\n\\n x1 = x1-1 if x1 == h else x1\\n x2 = x2-1 if x2 == h else x2\\n y1 = y1-1 if y1 == w else y1\\n y2 = y2-1 if y2 == w else y2\\n\\n pt1 = (x1, y1, image[x1, y1])\\n pt2 = (x1, y2, image[x1, y2])\\n pt3 = (x2, y1, image[x2, y1])\\n pt4 = (x2, y2, image[x2, y2])\\n unknown = (x, y, 0)\\n\\n if pt1[0] == pt3[0] and pt1[2] == pt2[2]:\\n newImg[i, j] = image[int(x), int(y)]\\n else:\\n newImg[i, j] = bi.bilinear_interpolation(pt1, pt2, pt3, pt4, unknown)\\n\\n image = newImg\\n return image\",\n \"def transform(self, size, method, data=None, resample=NEAREST,\\r\\n fill=1, fillcolor=None):\\r\\n if method == EXTENT:\\r\\n x0, y0, x1, y1 = data\\r\\n part = self._instance[y0:y1, x0:x1]\\r\\n _im = cv2.resize(part, size)\\r\\n elif method == AFFINE:\\r\\n x0, y0, x1, y1, x2, y2, x3, y3, x4, y4, x5, y5 = data\\r\\n pts1 = np.float32([[x0, y0], [x1, y1], [x2, y2]])\\r\\n pts2 = np.float32([[x3, y3], [x4, y4], [x5, y5]])\\r\\n M = cv2.getAffineTransform(pts1,pts2)\\r\\n _im = cv2.warpAffine(self._instance, M, size)\\r\\n elif method == PERSPECTIVE or method == QUAD:\\r\\n x0, y0, x1, y1, x2, y2, x3, y3 = data\\r\\n pts1 = np.float32([[x0, y0], [x1, y1], [x2, y2], [x3, y3]])\\r\\n pts2 = np.float32([[0,0],[size[0], 0], [0, size[1]], [size[0], size[1]]])\\r\\n M = cv2.getPerspectiveTransform(pts1, pts2)\\r\\n _im = cv2.warpPerspective(self._instance, M, size)\\r\\n elif method == MESH:\\r\\n _im = self._instance.copy()\\r\\n for elem in data:\\r\\n box, quad = elem\\r\\n x0, y0, x1, y1, x2, y2, x3, y3 = quad\\r\\n pts1 = np.float32([[x0, y0], [x1, y1], [x2, y2], [x3, y3]])\\r\\n pts2 = np.float32([[box[0], box[1]],[box[2], box[1]], [box[0], box[3]], [box[2], box[3]]])\\r\\n M = cv2.getPerspectiveTransform(pts1, pts2)\\r\\n _im = cv2.warpPerspective(_im, M, size)\\r\\n return Image(_im)\",\n \"def reScaleLandsat(self,img):\\n \\n\\t\\tthermalBand = ee.List(['thermal'])\\n\\t\\tthermal = ee.Image(img).select(thermalBand).multiply(10)\\n \\n\\t\\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\\n\\t\\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\\n \\n\\t\\timage = ee.Image(scaled.addBands(thermal)).int16()\\n \\n\\t\\treturn image.copyProperties(img)\",\n \"def resample(self, data_arr):\\n data_arr = np.asarray(data_arr, dtype=np.float32, order='C')\\n check_audio(data_arr, is_mono=False)\\n\\n fn = self._lib['resampleObj_resample']\\n fn.argtypes = [POINTER(OpaqueResample),\\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'),\\n c_int,\\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS')]\\n data_len = data_arr.shape[-1]\\n\\n if data_arr.ndim == 1:\\n ret_arr = np.zeros(data_len * 5, dtype=np.float32)\\n new_arr_len = fn(self._obj, data_arr, c_int(data_len), ret_arr)\\n else:\\n data_arr, o_channel_shape = format_channel(data_arr, 1)\\n channel_num = data_arr.shape[0]\\n\\n ret_arr = np.zeros((channel_num, data_len * 5), dtype=np.float32)\\n new_arr_len = 0\\n for i in range(channel_num):\\n _new_arr_len = fn(self._obj, data_arr[i], c_int(data_len), ret_arr[i])\\n new_arr_len = max(new_arr_len, _new_arr_len)\\n ret_arr = revoke_channel(ret_arr, o_channel_shape, 1)\\n\\n return ret_arr[..., :new_arr_len]\",\n \"def resample(source, target_size, transform):\\n dtype = source.dtype\\n dev = source.device\\n\\n height_, width_ = target_size\\n ur_ = torch.arange(width_, dtype=dtype, device=dev) + 0.5\\n vr_ = torch.arange(height_, dtype=dtype, device=dev) + 0.5\\n\\n height, weight = source.shape[2:]\\n ur = 2 * ((ur_ + transform[0, 1]) / transform[0, 0]) / weight - 1\\n vr = 2 * ((vr_ + transform[1, 1]) / transform[1, 0]) / height - 1\\n\\n v, u = torch.meshgrid(vr, ur)\\n v = v.unsqueeze(2)\\n u = u.unsqueeze(2)\\n\\n grid = torch.cat((u, v), dim=2)\\n grid = grid.unsqueeze(0).expand(len(source), -1, -1, -1)\\n\\n return torch.nn.functional.grid_sample(source, grid)\",\n \"def resample(self, num, use_mp=False, dps=None):\\n if self.get_dof() == num:\\n return\\n if self.is_orthogonal():\\n self.a_n = resize_coeffs(self.a_n, (self.N, 1), (num, 1))\\n else:\\n with self.context(use_mp, dps):\\n self.approximate(self.evaluator(use_mp), num=num,\\n use_mp=use_mp)\",\n \"def keras_transform(img, trf, interp_method='linear', rescale=None):\\n img_input = tf.keras.Input(shape=img.shape[1:])\\n trf_input = tf.keras.Input(shape=trf.shape[1:])\\n trf_scaled = trf_input if rescale is None else layers.RescaleTransform(rescale)(trf_input)\\n y_img = layers.SpatialTransformer(interp_method=interp_method)([img_input, trf_scaled])\\n return tf.keras.Model([img_input, trf_input], y_img).predict([img, trf])\",\n \"def resample(s, up, down, axis=0, fc='nn', **kwargs):\\n\\n # BUG FIX: if up==down, sig.fir_filter_design.firwin will throw\\n # an exception. In compute_filt that is OK (the user is assumed\\n # to know what they are doing if they use that function), but in\\n # the \\\"user-friendly\\\" resample function, we should just silently\\n # return a copy of the input. (Principle of least surprise)\\n # Thanks to johndoe46 on github for pointing this out.\\n if up==down:\\n return np.array(s)\\n\\n # from design parameters, find the generative parameters\\n N, beta, As = disambiguate_params(**kwargs)\\n\\n # check if a resampling filter with the chosen parameters already exists\\n params = (up, down, fc, beta, N)\\n if params in _precomputed_filters.keys():\\n # if so, use it.\\n filt = _precomputed_filters[params]\\n else:\\n # if not, generate filter, store it, use it\\n filt = compute_filt(up, down, fc, beta=beta, N=N)\\n _precomputed_filters[params] = filt\\n\\n return sig.resample_poly(s, up, down, window=np.array(filt), axis=axis)\",\n \"def transform(self, *args, **kwargs):\\n if hasattr(self.object, 'fit_resample'):\\n if self.verbose:\\n print('[WORC WARNING] Sampler does have fit_resample construction: fit and resampling.')\\n try:\\n return self.object.fit_resample(*args, **kwargs)\\n except ValueError as message:\\n message = str(message)\\n message = 'The ObjectSampler could not ' +\\\\\\n 'resample the objects with ' +\\\\\\n 'the given parameters. ' +\\\\\\n 'Probably your number of samples ' +\\\\\\n 'is too small for the parameters ' +\\\\\\n 'used. Original error: ' + message\\n raise ae.WORCValueError(message)\\n\\n else:\\n return self.object.transform(*args, **kwargs)\",\n \"def rescale_tif(arr, clamp_low=262, clamp_high=1775):\\n img_array = exposure.rescale_intensity(arr, in_range=(clamp_low, clamp_high)) # Landsat 5 ARD .25 and 97.75 percentile range used in training\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n img_array = img_as_ubyte(img_array)\\n return img_array\",\n \"def SetInterpolation(self, *args, **kwargs):\\n pass\",\n \"def apply_interpolation(self, transect, interpolation_method=None):\\n\\n # Reset processed data\\n if self.u_mps is not None:\\n self.u_processed_mps = np.copy(self.u_mps)\\n self.v_processed_mps = np.copy(self.v_mps)\\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan\\n\\n # Determine interpolation methods to apply\\n if interpolation_method is None:\\n interpolation_method = self.interpolate\\n else:\\n self.interpolate = interpolation_method\\n\\n # Apply specified interpolation method\\n\\n if interpolation_method == 'None':\\n # Sets invalid data to nan with no interpolation\\n self.interpolate_none()\\n\\n elif interpolation_method == 'ExpandedT':\\n # Set interpolate to none as the interpolation done is in the QComp\\n self.interpolate_next()\\n\\n elif interpolation_method == 'Hold9':\\n # Interpolates using SonTek method of holding last valid for up to 9 samples\\n self.interpolate_hold_9()\\n\\n elif interpolation_method == 'HoldLast':\\n # Interpolates by holding last valid indefinitely\\n self.interpolate_hold_last()\\n\\n elif interpolation_method == 'Linear':\\n # Interpolates using linear interpolation\\n self.interpolate_linear(transect)\\n\\n elif interpolation_method == 'Smooth':\\n # Interpolates using smooth interpolation\\n self.interpolate_smooth(transect)\\n\\n elif interpolation_method == 'TRDI':\\n # TRDI interpolation is done in discharge.\\n # For TRDI the interpolation is done on discharge not on velocities\\n self.interpolate_none()\",\n \"def resample_and_also_resample_operating_regime(self,\\n resample_args: tuple = ('10T',),\\n resample_kwargs: dict = None, *,\\n operating_regime_file_path: Path = None,\\n additional_outlier_mask: ndarray):\\n\\n @load_exist_pkl_file_otherwise_run_and_save(\\n self.default_results_saving_path['resample_and_also_resample_operating_regime'])\\n def func():\\n nonlocal operating_regime_file_path\\n nonlocal resample_kwargs\\n self_copy = copy.deepcopy(self)\\n\\n # For single sensor WF, the operating regime must be detected using as high resolution data as possible.\\n # Therefore, object function 'operating_regime_detector' must be called (so there will be results in\\n # self.default_results_saving_path[\\\"operating regime single senor\\\"], or as specified)\\n if operating_regime_file_path is None:\\n operating_regime_file_path = self.default_results_saving_path[\\\"operating regime single senor\\\"]\\n operating_regime = load_pkl_file(operating_regime_file_path) # type: DataCategoryData\\n assert operating_regime is not None\\n\\n resample_kwargs = resample_kwargs or {\\n 'resampler_obj_func_source_code': \\\"agg(lambda x: np.mean(x.values))\\\"\\n }\\n # Check outlier\\n existing_outlier = load_pkl_file(self_copy.default_results_saving_path[\\\"outlier\\\"])['DataCategoryData obj']\\n self_copy.loc[np.bitwise_or(~existing_outlier('others'),\\n additional_outlier_mask), :] = np.nan\\n # Resample self\\n resampled_self = self_copy.resample(*resample_args, **resample_kwargs)\\n resampled_self.obj_name = self_copy.obj_name + f\\\" resampled\\\"\\n\\n # Resample operating regime\\n def rolling_func(x):\\n (values, counts) = np.unique(x, return_counts=True)\\n index = np.argmax(counts)\\n return int(values[index])\\n\\n rolling_obj = operating_regime.pd_view.applymap(lambda x: int(x[1:])).rolling(*resample_args)\\n resampled_operating_regime = rolling_obj.apply(rolling_func, raw=True)\\n resampled_operating_regime = resampled_operating_regime.reindex(resampled_self.index, method='nearest')\\n resampled_operating_regime = resampled_operating_regime.astype(int)\\n resampled_operating_regime = DataCategoryData(\\n abbreviation=resampled_operating_regime.applymap(lambda x: f\\\"S{int(x)}\\\").values.flatten(),\\n index=resampled_operating_regime.index\\n )\\n\\n return resampled_self, resampled_operating_regime\\n\\n return func()\",\n \"def __call__(self, input_image):\\n output_image = input_image\\n if self.shift is not None or self.scale is not None:\\n output_image = rescale(output_image, shift=self.shift, scale=self.scale)\\n if self.random_shift is not None or self.random_scale is not None:\\n current_random_shift = random.float_uniform(-self.random_shift, self.random_shift)\\n current_random_scale = 1 + random.float_uniform(-self.random_scale, self.random_scale)\\n output_image = rescale(output_image, shift=current_random_shift, scale=current_random_scale)\\n if self.clamp_min is not None or self.clamp_max is not None:\\n output_image = clamp(output_image, clamp_min=self.clamp_min, clamp_max=self.clamp_max)\\n\\n return output_image\",\n \"def resample(self, data):\\n # Resample signal\\n if self._fs_out != self._fs_in:\\n\\n # Check that new sample rate is compatable with batch size\\n if self._next_frame_idx != 0:\\n raise ValueError('New sample rate incompatable with batch size.')\\n\\n # Input dimensions\\n frames_in = data.shape[0] # samples\\n len_data = float(frames_in) / self._fs_in # seconds\\n\\n # Output dimensions\\n frames_out = int(np.round(len_data*self._fs_out)) # samples\\n delta_out = 1.0 / self._fs_out # seconds\\n\\n # Predict next frame\\n self._next_frame_idx = int(np.round(frames_out*delta_out*self._fs_in)) - \\\\\\n frames_in\\n\\n # Compute indices to output\\n idx_out = np.round(np.arange(frames_out)*delta_out*self._fs_in)\\n\\n # Sample data using output indices\\n data_out = np.zeros((frames_out, data.shape[1]))\\n for i in range(frames_out):\\n idx = min(frames_in - 1, idx_out[i])\\n data_out[i, :] = data[int(idx), :]\\n\\n else:\\n\\n # No resampling\\n data_out = data\\n\\n return data_out\",\n \"def resample_pipe(self, resample_kwargs, func, *args, **kwargs):\\n return ResampleDefault.register(pandas.core.resample.Resampler.pipe)(\\n self, resample_kwargs, func, *args, **kwargs\\n )\",\n \"def nearest_neighbor(self, image, fx, fy):\\n\\n # Write your code for nearest neighbor interpolation here\\n\\n (width, height) = image.shape\\n (scaledWidth, scaledHeight) = (int(fx * width), int(fy * height))\\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\\n\\n for x in range(0, scaledWidth):\\n for y in range(0, scaledHeight):\\n nx = int(x / fx)\\n ny = int(y / fy)\\n if (nx + 1) > width - 1:\\n nx = width - 2\\n if (ny + 1) > height - 1:\\n ny = height - 2\\n resampledImage[x, y] = self.inter.linear_interpolation([ny, image[nx, ny]], [ny+1, image[nx, ny+1]], y/fy)\\n\\n return resampledImage\",\n \"def resample_sliprate(self, dt, nsamp):\\n t_new = np.linspace(0, nsamp * dt, nsamp, endpoint=False)\\n t_old = np.linspace(0, self.dt * len(self.sliprate),\\n len(self.sliprate), endpoint=False)\\n\\n self.sliprate = interp(t_new, t_old, self.sliprate)\\n self.dt = dt\",\n \"def rescale_intensity(image, thres=(1.0, 99.0)):\\n val_l, val_h = np.percentile(image, thres)\\n image2 = image\\n image2[image < val_l] = val_l\\n image2[image > val_h] = val_h\\n image2 = (image2.astype(np.float32) - val_l) / (val_h - val_l)\\n return image2\",\n \"def resample(self):\\n # propagate networks\\n self.z = self.prior_latent_distribution.sample()\\n # reconstruct image\\n self.y_hat_raw = self.fcomb(self.unet_features, self.z)\\n\\n return self.y_hat_raw\",\n \"def upsample_bilinear(input, size=None, scale_factor=None):\\n return interpolate(input, size, scale_factor, 'linear', align_corners=True)\",\n \"def downsample_image(img, output_path, ratio=2):\\n assert ratio>=1, ratio\\n if ratio == 1:\\n return True\\n old_im = Image.open(img)\\n old_size = old_im.size\\n new_size = (int(old_size[0]/ratio), int(old_size[1]/ratio))\\n\\n new_im = old_im.resize(new_size, PIL.Image.LANCZOS)\\n new_im.save(output_path)\\n return True\",\n \"def resample_filters(self):\\n a = len(self.filtered_ids)\\n b = len(self.orig_image_ids)\\n imbalance_ratio = a / b\\n min_ratio = 0.5\\n if imbalance_ratio > min_ratio:\\n return\\n minr = min_ratio\\n num_req = int((minr * b - a) / (1 - minr))\\n new_ids = ((num_req) // a) * self.filtered_ids\\n if num_req % a != 0:\\n some_more = random.sample(self.filtered_ids, k=(num_req % a))\\n new_ids += some_more\\n self.image_ids = self.orig_image_ids + new_ids\\n print(\\\"Resampled total:\\\", len(self.image_ids))\",\n \"def interpolate(self, interp):\\n x = np.linspace(0, 29, len(self.ya))\\n f_ya = interpolate.interp1d(x, self.ya)\\n f_yv = interpolate.interp1d(x, self.yv)\\n f_pa = interpolate.interp1d(x, np.reshape(self.pa, [-1]))\\n f_pv = interpolate.interp1d(x, np.reshape(self.pv, [-1]))\\n\\n x_interp = np.linspace(0, 29, len(self.ya)*interp)\\n self.ya = list(f_ya(x_interp))\\n self.yv = list(f_yv(x_interp))\\n self.pa = list(f_pa(x_interp))\\n self.pv = list(f_pv(x_interp))\",\n \"def interpolate(x: torch.Tensor, ratio: int):\\n (batch_size, time_steps, classes_num) = x.shape\\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\\n return upsampled\",\n \"def interpolate(x: torch.Tensor, ratio: int):\\n (batch_size, time_steps, classes_num) = x.shape\\n upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\\n upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\\n return upsampled\",\n \"def regrid(self, *args, **kwargs):\\n return _image.image_regrid(self, *args, **kwargs)\",\n \"def test_nib_resample_image_4d(fake_4dimage_nib):\\n img_r = resampling.resample_nib(fake_4dimage_nib, new_size=[2, 2, 1, 1], new_size_type='factor', interpolation='nn')\\n assert img_r.get_data().shape == (18, 18, 9, 3)\\n assert img_r.get_data()[8, 8, 4, 0] == 1.0 # make sure there is no displacement in world coordinate system\\n assert img_r.get_data()[8, 8, 4, 1] == 0.0\\n assert img_r.header.get_zooms() == (0.5, 0.5, 1.0, 1.0)\",\n \"def run_resample(self):\\n\\n self.in_data.open()\\n self.out_data.open()\\n\\n try:\\n # Get the fields from the input file and set them/write headers in output:\\n self.all_fields = self.in_data.fields\\n\\n self.out_data.set_fields(self.all_fields)\\n self.out_data.write_headers()\\n\\n # Set up the sensor fields by removing non-sensor fields:\\n self.set_sensor_only_fields()\\n\\n # Read the first event from the input file:\\n self.get_next_input_event()\\n\\n # Warn and exit if we have no input data to read:\\n if self.next_input_event is None:\\n msg = f\\\"The input file {self.in_file} did not have any data rows\\\"\\n warn(msg)\\n\\n return\\n\\n self.first_event_stamp = self.next_input_event[self.stamp_field]\\n\\n # Set up the sample tracking (here mostly to set the start of the first interval):\\n self.reset_sample_tracking()\\n\\n # Now iterate through the output intervals:\\n while True:\\n self.process_next_interval()\\n except EOFError: # catch when we are at the end of the file\\n pass\\n finally:\\n self.in_data.close()\\n self.out_data.close()\\n\\n print() # make sure we go to a new output line\",\n \"def resample_reflectance(self):\\n\\n if hasattr(self, \\\"rwl\\\"):\\n p = interp1d(self.rwl, self.rfl, fill_value=\\\"extrapolate\\\")\\n self.rfl = p(self.wl)\",\n \"def raster_copy(\\n output_fp,\\n input_fp,\\n mode=\\\"warp\\\",\\n resample=1,\\n out_crs=None,\\n bands=None,\\n bounds=None,\\n bounds_crs=None,\\n multi_core=False,\\n pbar=None,\\n):\\n\\n # Common options\\n base_options = dict(\\n creationOptions=[\\n \\\"TILED=YES\\\",\\n \\\"BLOCKXSIZE=256\\\",\\n \\\"BLOCKYSIZE=256\\\",\\n \\\"BIGTIFF=IF_SAFER\\\",\\n \\\"NUM_THREADS=ALL_CPUS\\\",\\n ],\\n callback=gdal_progress_callback,\\n callback_data=pbar,\\n )\\n if resample != 1:\\n # Get input pixel sizes\\n raster = gdal.Open(input_fp)\\n gt = raster.GetGeoTransform()\\n x_res, y_res = gt[1], -gt[5]\\n base_options[\\\"xRes\\\"] = (x_res / resample,)\\n base_options[\\\"yRes\\\"] = (y_res / resample,)\\n base_options[\\\"resampleAlg\\\"] = gdalconst.GRA_Bilinear\\n\\n # Use GDAL Warp\\n if mode.lower() == \\\"warp\\\":\\n warp_options = dict(\\n dstSRS=out_crs,\\n outputBounds=bounds,\\n outputBoundsSRS=bounds_crs,\\n multithread=multi_core,\\n warpOptions=[\\\"NUM_THREADS=ALL_CPUS\\\"] if multi_core else [],\\n warpMemoryLimit=1000000000, # processing chunk size. higher is not always better, around 1-4GB seems good\\n )\\n return gdal.Warp(output_fp, input_fp, **base_options, **warp_options)\\n\\n # Use GDAL Translate\\n elif mode.lower() == \\\"translate\\\":\\n translate_options = dict(\\n bandList=bands,\\n outputSRS=out_crs,\\n projWin=[\\n bounds[0],\\n bounds[3],\\n bounds[2],\\n bounds[1],\\n ], # what the hell gdal.Translate....\\n projWinSRS=bounds_crs,\\n )\\n return gdal.Translate(output_fp, input_fp, **base_options, **translate_options)\\n\\n else:\\n raise Exception(\\n \\\"Invalid mode argument, supported modes are 'warp' or 'translate'.\\\"\\n )\",\n \"def warp_to_raster(\\r\\n base_raster_path, canonical_raster_path, resample_method,\\r\\n target_raster_path):\\r\\n canonical_raster_info = pygeoprocessing.get_raster_info(\\r\\n canonical_raster_path)\\r\\n pygeoprocessing.warp_raster(\\r\\n base_raster_path, canonical_raster_info['pixel_size'],\\r\\n target_raster_path,\\r\\n resample_method, target_bb=canonical_raster_info['bounding_box'],\\r\\n n_threads=2)\",\n \"def problem2():\\n\\n data = loaddata(\\\"data/bayerdata.npy\\\")\\n r, g, b = separatechannels(data)\\n\\n img = assembleimage(r, g, b)\\n display_image(img)\\n\\n img_interpolated = interpolate(r, g, b)\\n display_image(img_interpolated)\",\n \"def resample(self, nsamples, dataset_bounds=None, derivative=0):\\n if dataset_bounds is None:\\n dataset_bounds = self.dataset_bounds\\n resampled_dataset = numpy.linspace(dataset_bounds[0],\\n dataset_bounds[1], nsamples)\\n resampled_data = super(ReSampler, self).__call__(\\n x=resampled_dataset, nu=derivative)\\n return resampled_dataset, resampled_data\",\n \"def preprocess(path, path2 , scale):\\n image = imread(path)\\n label_ = imread(path2)\\n\\n #label_ = modcrop(label, scale)\\n\\n # Must be normalized\\n input_ = image / 255.\\n label_ = label_ / 255.\\n\\n #input_ = scipy.ndimage.interpolation.zoom(label_, (1./scale), prefilter=False)\\n #input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)\\n\\n return input_, label_\",\n \"def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\\n assert img_r.get_data().shape == (29, 39, 19)\\n assert img_r.get_data()[4, 4, 4] == 1.0\",\n \"def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)\",\n \"def grid_resample(x, y, z, x_int, y_int, digits=3, method='multiquadric'):\\n\\n interp1 = Rbf(x, y, z, function=method)\\n z_int = interp1(x_int, y_int).round(digits)\\n z_int[z_int < 0] = 0\\n\\n z_int2 = z_int.flatten()\\n return(z_int2)\",\n \"def _batch_resize(self, ims, res, strategy='nearest'):\\n order = {'nearest': 0, 'bilinear': 1, 'bicubic': 3}[strategy]\\n bs = ims.shape[0]\\n out = []\\n log.info('Input ims shape: %s' % repr(ims.shape))\\n has_extra_dim = len(ims.shape) == 4\\n if not has_extra_dim:\\n ims = ims[..., np.newaxis]\\n h, w = ims.shape[1:3]\\n for i in range(bs):\\n o = interpolation.zoom(\\n ims[i, ...], [res[0] / h, res[1] / w, 1.0], np.float32, order=order)\\n out.append(o)\\n out = np.stack(out)\\n if not has_extra_dim:\\n out = np.reshape(out, out.shape[:-1])\\n return out\",\n \"def apply_transform(self, data, params, interp_mode='bilinear'):\\n tensor_size = params['jitter size'].tolist()\\n crop_pos = params['crop pos']\\n crop_size = params['crop size']\\n data = F.interpolate(data.unsqueeze(0), size=tensor_size, mode=interp_mode).squeeze(0)\\n data = data[:, crop_pos[0]:crop_pos[0] + crop_size[0], crop_pos[1]:crop_pos[1] + crop_size[1]]\\n return data\",\n \"def _image_transform(self, img, source, title):\\n conf = source.conf[title]\\n \\n xmin = conf.get('xmin', 0)\\n ymin = conf.get('ymin', 0)\\n\\n xmax = img.shape[-1] + xmin\\n ymax = img.shape[-2] + ymin\\n if \\\"xmax\\\" in conf:\\n if(conf['xmax'] <= xmin):\\n logging.warning(\\\"xmax <= xmin for title %s on %s. Ignoring xmax\\\", title, source.name())\\n else:\\n xmax = conf['xmax']\\n if \\\"ymax\\\" in conf:\\n if(conf['ymax'] <= ymin):\\n logging.warning(\\\"ymax <= ymin for title %s on %s. Ignoring xmax\\\", title, source.name())\\n else:\\n ymax = conf['ymax']\\n\\n \\n translate_transform = QtGui.QTransform().translate(ymin, xmin)\\n\\n # The order of dimensions in the scale call is (y,x) as in the numpy\\n # array the last dimension corresponds to the x.\\n scale_transform = QtGui.QTransform().scale((ymax-ymin)/img.shape[-2],\\n (xmax-xmin)/img.shape[-1])\\n \\n #rotate_transform = QtGui.QTransform()\\n #if source.data_type[title] == 'image':\\n # if \\\"angle\\\" in conf:\\n # rotate_transform = QtGui.QTransform(numpy.cos(conf[\\\"angle\\\"]), numpy.sin(conf[\\\"angle\\\"]), -numpy.sin(conf[\\\"angle\\\"]), numpy.cos(conf[\\\"angle\\\"]), 0, 0)\\n\\n transpose_transform = QtGui.QTransform()\\n if source.data_type[title] == 'image':\\n transpose_transform *= QtGui.QTransform(0, 1, 0,\\n 1, 0, 0,\\n 0, 0, 1)\\n if(self.settingsWidget.ui.transpose.currentText() == 'Yes' or\\n (self.settingsWidget.ui.transpose.currentText() == 'Auto' \\n and \\\"transpose\\\" in conf)):\\n transpose_transform *= QtGui.QTransform(0, 1, 0,\\n 1, 0, 0,\\n 0, 0, 1)\\n \\n transform = scale_transform * translate_transform * transpose_transform\\n #transform = scale_transform * translate_transform * rotate_transform * transpose_transform\\n \\n # print('|%f %f %f|' % (transform.m11(), transform.m12(), transform.m13()))\\n # print('|%f %f %f|' % (transform.m21(), transform.m22(), transform.m23()))\\n # print('|%f %f %f|' % (transform.m31(), transform.m32(), transform.m33()))\\n return transform\",\n \"def _scale(self, image):\\n\\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\\n image.Rescale(self._width, self._height)\\n \\n return image\",\n \"def rescale(self, img):\\n\\n if self.scale != 1:\\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\\n else:\\n return img\",\n \"def apply_model(self, original, t1, t2, resolution_scaling_factor=1):\\n img = Image()\\n img.time_stamp = t2\\n\\n if t1 == t2:\\n img.initialize_with_image(original)\\n return img\\n\\n calc_shift_fnc = self.calculate_shift\\n orig_get_fnc = original.get\\n interp_fnc = my_math.linear_interpolation\\n\\n def generate(y, x):\\n \\\"\\\"\\\"Function describing the transformed image\\\"\\\"\\\"\\n realy = y / resolution_scaling_factor\\n realx = x / resolution_scaling_factor\\n\\n # move to time t2\\n posy = y + calc_shift_fnc(realy, realx, t2, 0) - \\\\\\n calc_shift_fnc(realy, realx, t1, 0)\\n posx = x + calc_shift_fnc(realy, realx, t2, 1) - \\\\\\n calc_shift_fnc(realy, realx, t1, 1)\\n\\n x_left = int(posx) # math.floor(pos[0])\\n x_right = x_left + 1 # math.ceil(pos[0])\\n y_down = int(posy) # math.floor(pos[1])\\n y_up = y_down + 1 # math.ceil(pos[1])\\n\\n v11 = orig_get_fnc(y_down, x_left, resolution_scaling_factor)\\n v12 = orig_get_fnc(y_down, x_right, resolution_scaling_factor)\\n v21 = orig_get_fnc(y_up, x_left, resolution_scaling_factor)\\n v22 = orig_get_fnc(y_up, x_right, resolution_scaling_factor)\\n\\n return interp_fnc(y_down, x_left, y_up, x_right, v11, v12, v21, v22,\\n posy, posx)\\n\\n img.image_data = np.fromfunction(np.vectorize(generate),\\n (original.shape()[0]*resolution_scaling_factor,\\n original.shape()[1]*resolution_scaling_factor))\\n\\n if resolution_scaling_factor != 1:\\n img.image_data = skimage.transform.resize(img.image_data,\\n original.shape(),\\n preserve_range=True)\\n\\n return img\",\n \"def upsampleImage( arr, kernelSize ):\\n return scipy.ndimage.zoom( arr, kernelSize )\",\n \"def downsample(self, factor):\\n self.img = self.img[::factor, ::factor, :] if self.fast else self.img\\n self.comb_structure_mask = self.comb_structure_mask[::factor, ::factor]\\n self.unknown_mask = self.unknown_mask[::factor, ::factor]\",\n \"def resample(input_samples, time_resample, desired_samples):\\n if time_resample == 1.0:\\n return input_samples\\n\\n shape = tf.shape(input_samples)\\n samples = tf.expand_dims(input_samples, 0)\\n samples = tf.expand_dims(samples, 2)\\n samples_resized = tf.image.resize(\\n images=samples,\\n size=(tf.cast((tf.cast(shape[0], tf.float32) * time_resample),\\n tf.int32), 1),\\n preserve_aspect_ratio=False)\\n samples_resized_cropped = tf.image.resize_with_crop_or_pad(\\n samples_resized,\\n target_height=desired_samples,\\n target_width=1,\\n )\\n samples_resized_cropped = tf.squeeze(samples_resized_cropped, axis=[0, 3])\\n return samples_resized_cropped\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.777782","0.72679806","0.69222504","0.6829409","0.67728364","0.6588891","0.6578838","0.65778905","0.65753907","0.6556866","0.6504161","0.64685297","0.6434631","0.6428031","0.64210665","0.6419218","0.6414669","0.6380477","0.6313253","0.6260271","0.6226804","0.62131196","0.62055176","0.6141554","0.6067512","0.60285","0.60145265","0.60053694","0.6004778","0.59817404","0.5952546","0.5934526","0.59144956","0.5909072","0.5898609","0.5880784","0.58761406","0.58627194","0.583493","0.5831071","0.5819839","0.58198196","0.5810447","0.57776344","0.57776344","0.57491386","0.57481414","0.5706154","0.57046604","0.5701504","0.5701356","0.5691339","0.56790066","0.5670834","0.56540823","0.5648921","0.56452686","0.56397593","0.5620831","0.558534","0.5583915","0.55812097","0.55792993","0.5572688","0.55638576","0.55558014","0.55522853","0.5501113","0.54868853","0.5485604","0.54781467","0.54770815","0.54716146","0.54681295","0.546594","0.545323","0.54291","0.5428594","0.5428594","0.5422967","0.5418157","0.54131","0.5399298","0.53963906","0.53919125","0.5388783","0.5380026","0.53712165","0.53704816","0.5360046","0.53477865","0.5339123","0.5332478","0.53267527","0.53090066","0.53077084","0.5299809","0.5299665","0.5299106","0.529821"],"string":"[\n \"0.777782\",\n \"0.72679806\",\n \"0.69222504\",\n \"0.6829409\",\n \"0.67728364\",\n \"0.6588891\",\n \"0.6578838\",\n \"0.65778905\",\n \"0.65753907\",\n \"0.6556866\",\n \"0.6504161\",\n \"0.64685297\",\n \"0.6434631\",\n \"0.6428031\",\n \"0.64210665\",\n \"0.6419218\",\n \"0.6414669\",\n \"0.6380477\",\n \"0.6313253\",\n \"0.6260271\",\n \"0.6226804\",\n \"0.62131196\",\n \"0.62055176\",\n \"0.6141554\",\n \"0.6067512\",\n \"0.60285\",\n \"0.60145265\",\n \"0.60053694\",\n \"0.6004778\",\n \"0.59817404\",\n \"0.5952546\",\n \"0.5934526\",\n \"0.59144956\",\n \"0.5909072\",\n \"0.5898609\",\n \"0.5880784\",\n \"0.58761406\",\n \"0.58627194\",\n \"0.583493\",\n \"0.5831071\",\n \"0.5819839\",\n \"0.58198196\",\n \"0.5810447\",\n \"0.57776344\",\n \"0.57776344\",\n \"0.57491386\",\n \"0.57481414\",\n \"0.5706154\",\n \"0.57046604\",\n \"0.5701504\",\n \"0.5701356\",\n \"0.5691339\",\n \"0.56790066\",\n \"0.5670834\",\n \"0.56540823\",\n \"0.5648921\",\n \"0.56452686\",\n \"0.56397593\",\n \"0.5620831\",\n \"0.558534\",\n \"0.5583915\",\n \"0.55812097\",\n \"0.55792993\",\n \"0.5572688\",\n \"0.55638576\",\n \"0.55558014\",\n \"0.55522853\",\n \"0.5501113\",\n \"0.54868853\",\n \"0.5485604\",\n \"0.54781467\",\n \"0.54770815\",\n \"0.54716146\",\n \"0.54681295\",\n \"0.546594\",\n \"0.545323\",\n \"0.54291\",\n \"0.5428594\",\n \"0.5428594\",\n \"0.5422967\",\n \"0.5418157\",\n \"0.54131\",\n \"0.5399298\",\n \"0.53963906\",\n \"0.53919125\",\n \"0.5388783\",\n \"0.5380026\",\n \"0.53712165\",\n \"0.53704816\",\n \"0.5360046\",\n \"0.53477865\",\n \"0.5339123\",\n \"0.5332478\",\n \"0.53267527\",\n \"0.53090066\",\n \"0.53077084\",\n \"0.5299809\",\n \"0.5299665\",\n \"0.5299106\",\n \"0.529821\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63449955"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":14992,"cells":{"query":{"kind":"string","value":"resizes an image using bilinear interpolation approximation for resampling"},"document":{"kind":"string","value":"def nearest_neighbor(self, image, fx, fy):\n #Write your code for nearest neighbor interpolation here\n w = image.shape[1]\n h = image.shape[0]\n\n newW = int(w*float(fx))\n newH = int(h*float(fy))\n\n ratioW = w/newW\n ratioH = h/newH\n\n import numpy as np\n\n newImg = np.zeros((newH, newW), np.uint8)\n for i in range(newImg.shape[0]):\n for j in range(newImg.shape[1]):\n\n x = round(ratioH*i)\n y = round(ratioW*j)\n\n x = x-1 if x == h else x\n y = y-1 if y == w else y\n\n temp = image[x, y]\n newImg[i,j] = temp\n\n image = newImg\n return image"},"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 scale_image(image, scale=1, interpolation='linear'):\n if interpolation == 'linear':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n if interpolation == 'nearest':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)","def rescale_image(image, scale=0.50):\r\n \r\n wi = int(image.shape[1]*scale)\r\n hei = int(image.shape[0]*scale)\r\n dimension = (wi, hei)\r\n return cv.resize(image, dimension, interpolation = cv.INTER_AREA)","def _batch_resize(self, ims, res, strategy='nearest'):\n order = {'nearest': 0, 'bilinear': 1, 'bicubic': 3}[strategy]\n bs = ims.shape[0]\n out = []\n log.info('Input ims shape: %s' % repr(ims.shape))\n has_extra_dim = len(ims.shape) == 4\n if not has_extra_dim:\n ims = ims[..., np.newaxis]\n h, w = ims.shape[1:3]\n for i in range(bs):\n o = interpolation.zoom(\n ims[i, ...], [res[0] / h, res[1] / w, 1.0], np.float32, order=order)\n out.append(o)\n out = np.stack(out)\n if not has_extra_dim:\n out = np.reshape(out, out.shape[:-1])\n return out","def resize(self, image, fx, fy, interpolation):\n\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, fx, fy)\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, fx, fy)","def resize(img, size, method=\"bilinear\"):\n if method == Image.BILINEAR or method == \"bilinear\":\n method = cv2.INTER_AREA\n elif method == Image.CUBIC or method == \"cubic\":\n method = cv2.INTER_CUBIC\n elif method == Image.NEAREST or method == \"nearest\":\n method = cv2.INTER_NEAREST\n else:\n raise RuntimeError(\"wrong method {}\".format(method))\n if not isinstance(size, (tuple, list)):\n size = (size, size)\n if isinstance(size ,list):\n size = tuple(size)\n im = np.array(img)\n im = cv2.resize(im, size, interpolation=method)\n return Image.fromarray(im)","def resize(img, size, interpolation=Image.BILINEAR):\n\n if isinstance(size, int):\n w, h = img.size\n if (w <= h and w == size) or (h <= w and h == size):\n return img\n if w < h:\n ow = size\n oh = int(size * h / w)\n return img.resize((ow, oh), interpolation)\n else:\n oh = size\n ow = int(size * w / h)\n return img.resize((ow, oh), interpolation)\n else:\n return img.resize(size[::-1], interpolation)","def scale(input_img, size):\n width, height = size\n old_height, old_width = input_img.shape\n x_scale = float(height) / old_height\n y_scale = float(width) / old_width\n\n output_img = np.zeros((height, width), dtype=np.uint8)\n for xidx in xrange(height):\n old_x = float(xidx) / x_scale\n for yidx in xrange(width):\n old_y = float(yidx) / y_scale\n if old_x.is_integer() or old_y.is_integer():\n output_img[xidx, yidx] = input_img[int(old_x), int(old_y)]\n else: # use bilinear interpolation\n x1 = int(np.floor(old_x))\n x2 = int(np.ceil(old_x)) if int(np.ceil(old_x)) < old_height else old_height - 1\n y1 = int(np.floor(old_y))\n y2 = int(np.ceil(old_y)) if int(np.ceil(old_y)) < old_width else old_width - 1\n\n q11 = input_img[x1, y1]\n q12 = input_img[x1, y2]\n q21 = input_img[x2, y1]\n q22 = input_img[x2, y2]\n\n output_img[xidx, yidx] = (q11 * (x2 - old_x) * (y2 - old_y)\n + q21 * (old_x - x1) * (y2 - old_y)\n + q12 * (x2 - old_x) * (old_y - y1)\n + q22 * (old_x - x1) * (old_y - y1)) \\\n / ((x2 - x1) * (y2 - y1) + 1e-10)\n\n return output_img","def upsample_bilinear(input, size=None, scale_factor=None):\n return interpolate(input, size, scale_factor, 'linear', align_corners=True)","def resample_img(image, target_shape, mode='nearest'):\n print(target_shape)\n resize_factor = np.array(target_shape)/image.shape\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def resample(img, newSize):\n # use float as datatype to preserve float precision\n # will convert to np.uint8 when display result\n # get the new dimensions\n nH, nW = newSize\n if np.ndim(img) == 2:\n H, W = img.shape\n res = np.zeros((nH, nW), dtype=np.float32)\n elif np.ndim(img) == 3:\n H, W, _ = img.shape\n res = np.zeros((nH, nW, _), dtype=np.float32)\n else:\n raise ValueError(\"input image has invalid dimension %s\" % (img.shape))\n\n # interpolate the value for the result\n for idx in range(nH * nW):\n i = idx // nW\n j = idx % nW\n orig_i = int((i * H) // nH)\n orig_j = int((j * W) // nW)\n res[i, j] = img[orig_i, orig_j]\n return res","def img_zoom(img, fx, fy, interp=cv2.INTER_AREA):\n res = cv2.resize(img, None, fx=fx, fy=fy,\n interpolation=interp)\n return res","def resample(image, pixsize):\n assert pixsize in [1, 2, 4, 8, 16] # return error and exit otherwise\n imsize = 128/pixsize\n newimage = np.zeros((imsize, imsize))\n for xn, x in enumerate(np.arange(0, 128, pixsize)):\n for yn, y in enumerate(np.arange(0, 128, pixsize)):\n newimage[xn, yn] = np.nansum(image[x:x+pixsize, y:y+pixsize]) # Nansum is important as sum of masked array can be nan\n return newimage","def resize(img, size, interpolation='bilinear', data_format='CHW'):\n _assert_image_tensor(img, data_format)\n\n if not (\n isinstance(size, int)\n or (isinstance(size, (tuple, list)) and len(size) == 2)\n ):\n raise TypeError(f'Got inappropriate size arg: {size}')\n\n if isinstance(size, int):\n w, h = _get_image_size(img, data_format)\n # TODO(Aurelius84): In static graph mode, w and h will be -1 for dynamic shape.\n # We should consider to support this case in future.\n if w <= 0 or h <= 0:\n raise NotImplementedError(\n \"Not support while w<=0 or h<=0, but received w={}, h={}\".format(\n w, h\n )\n )\n if (w <= h and w == size) or (h <= w and h == size):\n return img\n if w < h:\n ow = size\n oh = int(size * h / w)\n else:\n oh = size\n ow = int(size * w / h)\n else:\n oh, ow = size\n\n img = img.unsqueeze(0)\n img = F.interpolate(\n img,\n size=(oh, ow),\n mode=interpolation.lower(),\n data_format='N' + data_format.upper(),\n )\n\n return img.squeeze(0)","def image_resample(f, oversamp=1.0):\n img = imageio.imread(f)\n x = np.linspace(0,img.shape[0],img.shape[0])\n y = np.linspace(0,img.shape[1],img.shape[1])\n #\n # 2x oversample the image since we'll dither it.\n #\n xnew = np.linspace(0, img.shape[0], img.shape[0]*oversamp)\n ynew = np.linspace(0, img.shape[1], img.shape[1]*oversamp)\n from scipy import interpolate\n rc = interpolate.interp2d(x, y, img[:,:,0].flatten(), kind='linear')\n gc = interpolate.interp2d(x, y, img[:,:,1].flatten(), kind='linear')\n bc = interpolate.interp2d(x, y, img[:,:,2].flatten(), kind='linear')\n rgb_new = np.stack([rc(xnew.flatten(), ynew.flatten()),\n gc(xnew.flatten(), ynew.flatten()),\n bc(xnew.flatten(), ynew.flatten())],-1).transpose(1,0,2).astype(np.uint8)\n plt.imshow(rgb_new)\n return rgb_new","def imgResample(img, spacing, size=[], useNearest=False, origin=[], outsideValue=0):\n if len(spacing) != img.GetDimension(): raise Exception(\"len(spacing) != \" + str(img.GetDimension()))\n\n # Set Size\n if size == []:\n inSpacing = img.GetSpacing()\n inSize = img.GetSize()\n size = [int(math.ceil(inSize[i]*(inSpacing[i]/spacing[i]))) for i in range(img.GetDimension())]\n else:\n if len(size) != img.GetDimension(): raise Exception(\"len(size) != \" + str(img.GetDimension()))\n \n if origin == []:\n origin = [0]*img.GetDimension()\n else:\n if len(origin) != img.GetDimension(): raise Exception(\"len(origin) != \" + str(img.GetDimension()))\n \n # Resample input image\n interpolator = [sitk.sitkLinear, sitk.sitkNearestNeighbor][useNearest]\n identityTransform = sitk.Transform()\n identityDirection = list(sitk.AffineTransform(img.GetDimension()).GetMatrix())\n\n return sitk.Resample(img, size, identityTransform, interpolator, origin, spacing, identityDirection, outsideValue)","def scale(img, scale):\n return resize(img, x_scale=scale, y_scale=scale)","def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def pixelate(image, n_w, n_h):\n image = image.resize((n_w, n_h), Image.ANTIALIAS)\n return image","def resample(medical_image, image, new_spacing=[1, 1, 1]):\n image_shape = np.array(list(medical_image.header.get_data_shape()))\n spacing = np.array(list(medical_image.header.get_zooms()))\n resize_factor = spacing / new_spacing\n\n new_shape = image_shape * resize_factor\n new_shape = np.round(new_shape)\n\n real_resize_factor = new_shape / image_shape\n\n new_spacing = spacing / real_resize_factor\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\n\n return resampled_image, new_spacing","def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\n (height, width) = image.shape[:2]\n new_dims = (int(width * scale), int(height * scale))\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)","def resample_img(img, img_type, size, spacing):\n resampler = sitk.ResampleImageFilter()\n resampler.SetOutputDirection(img.GetDirection())\n resampler.SetOutputOrigin(img.GetOrigin())\n resampler.SetOutputSpacing(spacing)\n resampler.SetSize(size)\n if img_type is \"Label\":\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\n elif img_type is \"Image\":\n resampler.SetInterpolator(sitk.sitkLinear)\n imgResampled = resampler.Execute(img)\n\n #axis have to be switched since np.array and keras use them in different order...\n x = np.transpose(sitk.GetArrayFromImage(imgResampled).astype(dtype=np.float), [2, 1, 0])\n return x","def _scale(self, image):\n\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\n image.Rescale(self._width, self._height)\n \n return image","def resampled_to_img(self, target_image, interpolation=None):\n # IMPORTANT: Polymorphism can be implemented by walking the \n # MRO and finding a method that does not raise\n # NotImplementedError. \n raise NotImplementedError","def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)","def resize(self, image, fx=None, fy=None, interpolation=None):\r\n if interpolation == 'bilinear':\r\n return self.bilinear_interpolation(image, float(fx), float(fy))\r\n\r\n elif interpolation == 'nearest_neighbor':\r\n return self.nearest_neighbor(image, float(fx), float(fy))","def resize(img, size, interpolation=Image.BILINEAR):\n\n if not _is_numpy_image(img):\n raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\n raise TypeError('Got inappropriate size arg: {}'.format(size))\n\n if isinstance(size, int):\n size = (size, size)\n \n if img.dtype == np.int32:\n resized = img.astype(np.uint16)\n resized = cv2.resize(resized, size , interpolation)\n else:\n resized = cv2.resize(img, size , interpolation)\n resized = resized.astype(img.dtype)\n return resized","def img_resize(img, img_height):\n\n\timg_width = int(img_height*img.shape[1]/img.shape[0])\n\n\timg_pil = Image.fromarray(img) # convert array back to image\n\n\timg_pil_resize = img_pil.resize((img_width, img_height), Image.LANCZOS) # resize\n\n\treturn np.array(img_pil_resize)","def resize(self, image, fx=None, fy=None, interpolation=None):\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, float(fx), float(fy))\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, float(fx), float(fy))","def resample(image_array, target_pixel_dims_list, is_seg=False):\n # if dealing with segmentation images, do not interpolate\n # this is done by choosing interpolation order == 0\n order = 0 if is_seg == True else 3\n\n original_dims_in_pixels = [image_array.shape[d]\n for d in range(len(image_array.shape))]\n compression_list = [target_pixel_dims_list[d] / original_dims_in_pixels[d]\n for d in range(len(image_array.shape))]\n\n if (is_seg):\n resized_image = ndimage.interpolation.zoom(\n image_array, zoom=compression_list, order=order, cval=0)\n return resized_image\n else:\n resized_image = ndimage.interpolation.zoom(\n image_array, zoom=compression_list, order=order, cval=0)\n return resized_image","def process_image(image):\n image = resize(image)\n return image","def scale_down(image:np.array)->np.array:\n src = image\n scale_percent = 25\n width = int(src.shape[1] * scale_percent / 100)\n height = int(src.shape[0] * scale_percent / 100)\n dsize = (width, height)\n output = cv2.resize(src, dsize)\n return output","def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\n\n if scaling_method == 'scale':\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\n elif scaling_method == 'ratio':\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\n im = scipy.misc.imresize(im, scale)\n return (im, scale)","def resize_3d_image_nearest_interpolation(im, res_s):\n new_shape = [res_s, im.shape[1], im.shape[2]]\n ret0 = np.zeros(new_shape, dtype=im.dtype)\n for i in range(im.shape[2]):\n im_slice = np.array(\n Image.fromarray(im[..., i]).resize(\n (im.shape[1], res_s), resample=Image.Resampling.NEAREST\n )\n )\n ret0[..., i] = im_slice\n\n new_shape = [res_s, res_s, res_s]\n ret = np.zeros(new_shape, dtype=im.dtype)\n for i in range(res_s):\n im_slice = np.array(\n Image.fromarray(ret0[i, ...]).resize(\n (res_s, res_s), resample=Image.Resampling.NEAREST\n )\n )\n ret[i, ...] = im_slice\n return ret","def resize(img, x_scale, y_scale, optimize=True):\n interpolation = cv.INTER_LINEAR\n\n # pick an optimized scaler if asked to\n if optimize:\n if x_scale > 1 and y_scale > 1:\n interpolation = cv.INTER_CUBIC\n else:\n interpolation = cv.INTER_AREA\n\n return cv.resize(\n img,\n None,\n fx=x_scale,\n fy=y_scale,\n interpolation=interpolation,\n )","def bilinear_interpolation(self, image, fx, fy):\n\n # Write your code for bilinear interpolation here\n (width, height) = image.shape\n (scaledWidth, scaledHeight) = (int(fy * width), int(fy * height))\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\n\n for x in range(0, scaledWidth):\n for y in range(0, scaledHeight):\n nx = math.floor(x / fx)\n ny = math.floor(y / fy)\n if (nx + 1) > width - 1:\n nx = width - 2\n if (ny + 1) > height - 1:\n ny = height - 2\n\n n1 = (nx, ny)\n n2 = (nx, ny+1)\n n3 = (nx+1, ny)\n n4 = (nx+1, ny+1)\n\n resampledImage[x, y] = self.inter.bilinear_interpolation([n1, image[n1]], [n2, image[n2]], [n3, image[n3]],[n4, image[n4]], [x/fx, y/fy])\n\n return resampledImage","def downsample_image(img, output_path, ratio=2):\n assert ratio>=1, ratio\n if ratio == 1:\n return True\n old_im = Image.open(img)\n old_size = old_im.size\n new_size = (int(old_size[0]/ratio), int(old_size[1]/ratio))\n\n new_im = old_im.resize(new_size, PIL.Image.LANCZOS)\n new_im.save(output_path)\n return True","def _resize_short_within(self, img, short, max_size, mult_base=1, interp=Image.BILINEAR):\n w, h = img.size\n im_size_min, im_size_max = (h, w) if w > h else (w, h)\n scale = float(short) / float(im_size_min)\n if np.round(scale * im_size_max / mult_base) * mult_base > max_size:\n # fit in max_size\n scale = float(np.floor(max_size / mult_base) * mult_base) / float(im_size_max)\n new_w, new_h = (int(np.round(w * scale / mult_base) * mult_base),\n int(np.round(h * scale / mult_base) * mult_base))\n img = img.resize((new_w, new_h), interp)\n return img","def _resize(img, max_dim=128):\n if max(img.shape[:3]) <= max_dim:\n return img\n else:\n new_size = [max_dim / s if s >= max_dim else 1.0 for s in img.shape[:3]]\n new_size.append(1.0) # for channel\n return scipy.ndimage.zoom(img, new_size, order=2)","def resizeImage(IMG,IMAGE_SIZE):\n\n RESCALED_IMAGE = skimage.transform.resize(IMG,[IMG.shape[0],IMAGE_SIZE,IMAGE_SIZE])\n return RESCALED_IMAGE","def rescale_and_resize_image(img_name, img_size, save_img):\n \n try:\n \n # Open image\n img = Image.open(img_name)\n \n # Resize image\n img = img.resize((int(img_size), int(img_size)), Image.ANTIALIAS) \n \n # Get data from image\n img_list = list(img.getdata())\n \n # Find minimum and maximum value pixels in the image\n img_max = max(img_list)\n img_min = min(img_list)\n \n # Determine factor to scale to a 8-bit image\n scale_factor = 255.0/(img_max - img_min)\n \n img_list_new = [0] * img_size * img_size\n \n # Rescale all pixels to the range 0 to 255 (in line with unit8 values)\n for i in range(0,img_size):\n for j in range(0,img_size):\n img_list_new[i*img_size + j] = int((img_list[i*img_size + j]-img_min)*scale_factor)\n if (img_list_new[i*img_size + j] > 255) or (img_list_new[i*img_size + j] < 0) or (img_list_new[i*img_size + j]-int(img_list_new[i*img_size + j]) != 0):\n print(\"img_list_new[%d][%d] = %r\" % (i,j,img_list_new[i*img_size + j]))\n \n img.putdata(img_list_new)\n \n # Convert to uint8 greyscale image\n img = img.convert('L')\n \n # Save image\n if save_img:\n img.save(img_name)\n else:\n ph = ImageTk.PhotoImage(img)\n return ph\n \n finally:\n \n # Close image\n img.close()","def resample(self, image, target_shape, mode='nearest'):\n resize_factor = image.shape / np.array(target_shape)\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def bilinear_interpolation(self, image, fx, fy):\r\n\r\n # Write your code for bilinear interpolation here\r\n width, height = image.shape[:2]\r\n nw = int(width * fx)\r\n nh = int(height * fy)\r\n new_img = np.zeros((nw, nh), np.uint8)\r\n inter = interpolation.interpolation()\r\n for i in range(nw):\r\n for j in range(nh):\r\n ii = int(np.floor((i * (image.shape[0]-1))/(nw - 1)))\r\n jj = int(np.floor((j * (image.shape[1]-1))/(nh - 1)))\r\n\r\n # All four corner pixels copied as it is\r\n if (ii == 0 and jj == 0) or \\\r\n (ii == 0 and jj == image.shape[1] - 1) or \\\r\n (ii == image.shape[0] - 1 and jj == 0) or \\\r\n (ii == image.shape[0] - 1 and jj == image.shape[1] - 1):\r\n new_img[i][j] = image[ii][jj]\r\n\r\n # Linear interpolation along left and right edges\r\n elif ii == 0 or ii == image.shape[0] - 1:\r\n new_img[i][j] = inter.linear_interpolation([jj - 1, image[ii][jj - 1]], [jj + 1, image[ii][jj + 1]], jj)\r\n\r\n # Linear interpolation along top and bottom edges\r\n elif jj == 0 or jj == image.shape[1] - 1:\r\n new_img[i][j] = inter.linear_interpolation([ii - 1, image[ii - 1][jj]], [ii + 1, image[ii + 1][jj]], ii)\r\n\r\n else:\r\n\r\n q11i, q11j = ii - 1, jj - 1\r\n q12i, q12j = ii - 1, jj + 1\r\n q21i, q21j = ii + 1, jj - 1\r\n q22i, q22j = ii + 1, jj + 1\r\n\r\n point_a = [q11i, q11j, image[q11i][q11j]]\r\n point_b = [q12i, q12j, image[q12i][q12j]]\r\n point_c = [q21i, q21j, image[q21i][q21j]]\r\n point_d = [q22i, q22j, image[q22i][q22j]]\r\n unknown = [ii, jj]\r\n\r\n new_img[i][j] = inter.bilinear_interpolation(point_a, point_b, point_c, point_d, unknown)\r\n\r\n return new_img","def resize(img, size):\n img = tf.expand_dims(img, 0)\n return tf.image.resize_bilinear(img, size)[0,:,:,:]","def rescale_image(image, rescale_width):\r\n\r\n # image size\r\n image_height = pdb.gimp_image_height(image)\r\n image_width = pdb.gimp_image_width(image)\r\n\r\n # new image height\r\n rescale_height = round(image_height * (rescale_width * 1.0 / image_width))\r\n\r\n pdb.gimp_image_scale(image, rescale_width, rescale_height)\r\n gimp.message('Rescaled image')","def scale_image(image=np.zeros((100,100)), \n new_width=100,\n ascii_block_size=(2,3)):\n \n original_width, original_height = image.shape\n aspect_ratio = original_height / float(original_width)\n w,h = ascii_block_size\n new_height = int(h/w * aspect_ratio * new_width)\n\n return skimage.transform.resize(image, (new_width, new_height))","def resize_img(self,scale=1):\n reduced = self.image.reduce((scale,scale))\n reduced.save(\"../edited/{}\".format(self.image.filename))\n\n reduced = Image.open(\"../edited/{}\".format(self.image.filename))\n return reduced","def get_scaled_image(self, min_width=1024):\n self.dispersion = self.image.getCalibration().pixelWidth\n self.offset = self.image.getCalibration().xOrigin\n binning = 1\n while binning * min_width < self.image.getWidth():\n binning *= 2\n if binning > 1:\n binning /= 2\n IJ.run(self.image, 'Select None', '')\n new_image = self.image.crop()\n IJ.run(new_image, 'Bin...', 'x=%d y=%d binning=Average' %\n (binning, binning))\n self.dispersion *= binning\n self.offset /= binning\n self.image = new_image\n return new_image","def imrescale(img,scale,return_scale=False,interpolation='bilinear'):\n h,w = img.shape[:2]\n if isinstance(scale,(float,int)):\n if scale <= 0:\n raise ValueError('Invalid scale:{},must be positive.'.format(scale))\n scale_factor = scale\n \n elif isinstance(scale,tuple):\n max_long_edge = max(scale)\n max_short_edge = min(scale)\n scale_factor = min(max_long_edge/max(h,w),\n max_short_edge/min(h,w))\n else:\n raise ValueError('Scale must be a number or tuple of int,but got {}'.format(type(scale)))\n \n new_size = _scale_size((w,h),scale_factor)\n rescaled_img = imresize(img,new_size,interpolation = interpolation)\n if return_scale:\n return rescaled_img,scale_factor\n else:\n return rescaled_img","def test_nib_resample_image_4d(fake_4dimage_nib):\n img_r = resampling.resample_nib(fake_4dimage_nib, new_size=[2, 2, 1, 1], new_size_type='factor', interpolation='nn')\n assert img_r.get_data().shape == (18, 18, 9, 3)\n assert img_r.get_data()[8, 8, 4, 0] == 1.0 # make sure there is no displacement in world coordinate system\n assert img_r.get_data()[8, 8, 4, 1] == 0.0\n assert img_r.header.get_zooms() == (0.5, 0.5, 1.0, 1.0)","def rescale(self, img):\n\n if self.scale != 1:\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\n else:\n return img","def _resize_image_tuple(self, image_tup):\n if self.api_info is None:\n self.get_info() # sets the image size and other such info from server.\n try:\n MIN_SIZE = self.api_info['min_image_size']\n MAX_SIZE = self.api_info['max_image_size']\n img = Image.open(image_tup[0])\n min_dimension = min(img.size)\n max_dimension = max(img.size)\n min_ratio = float(MIN_SIZE) / min_dimension\n max_ratio = float(MAX_SIZE) / max_dimension\n def get_newsize(img, ratio, SIZE):\n if img.size[0] == min_dimension:\n newsize = (SIZE, int(round(ratio * img.size[1])))\n else:\n newsize = (int(round(ratio * img.size[0])), SIZE)\n return newsize\n im_changed = False\n # Only resample if min size is > 512 or < 256\n if max_ratio < 1.0: # downsample to MAX_SIZE\n newsize = get_newsize(img, max_ratio, MAX_SIZE)\n img = img.resize(newsize, Image.BILINEAR)\n im_changed = True\n elif min_ratio > 1.0: # upsample to MIN_SIZE\n newsize = get_newsize(img, min_ratio, MIN_SIZE)\n img = img.resize(newsize, Image.BICUBIC)\n im_changed = True\n else: # no changes needed so rewind file-object.\n image_tup[0].seek(0)\n # Finally make sure we have RGB images.\n if img.mode != \"RGB\":\n img = img.convert(\"RGB\")\n im_changed = True\n if im_changed:\n io = StringIO()\n img.save(io, 'jpeg', quality=IM_QUALITY)\n io.seek(0) # rewind file-object to read() below is good to go.\n image_tup = (io, image_tup[1])\n except IOError, e:\n logger.warning('Could not open image file: %s, still sending to server.', image_tup[1])\n return image_tup","def resize(img):\n size = (500, 500)\n img.thumbnail(size)\n return img","def scale_img(img, scale=1.0):\n dim = (int(img.shape[1]*scale), int(img.shape[0]*scale))\n return cv2.resize(img.copy(), dim, interpolation=cv2.INTER_AREA)","def resample(scan, image, new_spacing=[1,1,1]):\n spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)\n\n resize_factor = spacing / new_spacing\n new_real_shape = image.shape * resize_factor\n new_shape = np.round(new_real_shape)\n real_resize_factor = new_shape / image.shape\n new_spacing = spacing / real_resize_factor\n\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\n\n batch = []\n for i in range(0, image.shape[0] - 3, 3):\n tmp = []\n for j in range(3):\n img = image[i + j]\n img= cv2.resize(img, (224, 224))\n tmp.append(img)\n\n tmp = np.array(tmp)\n batch.append(tmp)\n\n batch = np.array(batch)\n\n return batch","def __resize_bilinear_with_factor(self, x, factor_size=None, data_format=None):\n height_factor = factor_size[0]\n width_factor = factor_size[1]\n if data_format == 'channels_first':\n height = x.shape[2] * height_factor\n width = x.shape[3] * width_factor\n new_shape = tf.constant(np.array([height, width]).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"invilid data format\", data_format)","def retinanet_resize_image(img, min_side=800, max_side=1333):\n # compute scale to resize the image\n scale = retinanet_compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)\n # scale = round(scale, 4)\n # print(\"scale: \", scale)\n\n # resize the image with the computed scale\n img = cv2.resize(img, None, fx=scale, fy=scale)\n\n return img, scale","def resize(img, size, interpolation=cv2.INTER_LINEAR):\n if not _is_numpy_image(img):\n raise TypeError('img should be nparrary Image. Got {}'.format(type(img)))\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\n raise TypeError('Got inappropriate size arg: {}'.format(size))\n\n if isinstance(size, int):\n h, w = img.shape[:2]\n if (w <= h and w == size) or (h <= w and h == size):\n return img\n if w < h:\n ow = size\n oh = int(size * h / w)\n return cv2.resize(img, (ow, oh), interpolation)\n else:\n oh = size\n ow = int(size * w / h)\n return cv2.resize(img, (ow, oh), interpolation)\n else:\n return cv2.resize(img, tuple(size[::-1]), interpolation)","def scale_image(self, image):\n err_m1 = \"image must be a numpy.ndarray with shape (h, w, 3)\"\n if not isinstance(image, np.ndarray):\n raise TypeError(err_m1)\n if len(image.shape) != 3 or image.shape[2] != 3:\n raise TypeError(err_m1)\n shape0 = image.shape[0]\n shape1 = image.shape[1]\n\n if shape0 > shape1:\n new_w = int(image.shape[1] * 512 / image.shape[0])\n new_h = 512\n elif shape0 < shape1:\n new_h = int(image.shape[0] * 512 / image.shape[1])\n new_w = 512\n else:\n new_h = 512\n new_w = 512\n tf.image.ResizeMethod.BICUBIC\n image = tf.expand_dims(image, 0)\n image = tf.image.resize_bicubic(image,\n (new_h, new_w),\n align_corners=False)\n image = image / 255\n image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\n\n return image","def bilinear_interpolation(x, y, img):\n Warning(\"This method should not be use, use instead pytorch grid_sample method\")\n _, height, width = img.shape\n x_min = x.int()\n y_min = y.int()\n\n w_x = x - x_min\n w_y = y - y_min\n \n w1 = torch.cat((w_x, w_y), 1).norm(dim=1)\n w2 = torch.cat((1-w_x, w_y), 1).norm(dim=1)\n w3 = torch.cat((1-w_x, 1-w_y), 1).norm(dim=1)\n w4 = torch.cat((w_x, 1-w_y), 1).norm(dim=1)\n\n sum_w = w1 + w2 + w3 + w4\n\n w1 = w1 / sum_w\n w2 = w2 / sum_w\n w3 = w3 / sum_w\n w4 = w4 / sum_w\n\n # TODO how to deal with out of the sample sampling when the pixel is from outside\n x_min = x_min.clamp(0, width - 2).view(-1)\n y_min = y_min.clamp(0, height - 2).view(-1)\n\n pix = \\\n w1 * img[:, y_min.long(), x_min.long()] + \\\n w2 * img[:, y_min.long(), x_min.long() + 1] + \\\n w3 * img[:, y_min.long() + 1, x_min.long() + 1] + \\\n w4 * img[:, y_min.long() + 1, x_min.long()]\n\n pix = pix.view(img.shape)\n return pix","def resize_image(image: Image) -> Image:\n expected_x: int = 1024\n expected_y: int = 768\n x, y = image.size\n if x > expected_x or y > expected_y:\n scale = min(expected_x / x, expected_y / y)\n return image.resize((int(x * scale), int(y * scale)))\n else:\n return image.resize((int(0.9*x), int(0.9*y)))","def Resampler(name):\n\n def resample_average(path, dsquery, dstile, image_format):\n for i in range(1, dstile.RasterCount+1):\n res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i), \"average\")\n if res != 0:\n raise ImageOutputException(\"RegenerateOverview() failed with error %d\" % res)\n\n gdal_write(path, dstile, image_format)\n\n def resample_antialias(path, dsquery, dstile, image_format):\n querysize = dsquery.RasterXSize\n tilesize = dstile.RasterXSize\n\n array = numpy.zeros((querysize, querysize, 4), numpy.uint8)\n for i in range(dstile.RasterCount):\n array[:,:,i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1), 0, 0, querysize, querysize)\n im = Image.fromarray(array, 'RGBA') # Always four bands\n im1 = im.resize((tilesize,tilesize), Image.ANTIALIAS)\n\n if os.path.exists(path):\n im0 = Image.open(path)\n im1 = Image.composite(im1, im0, im1)\n\n ensure_dir_exists(path)\n\n if image_format == \"JPEG\":\n im1.save(path, image_format, quality=jpeg_quality)\n else:\n im1.save(path, image_format)\n\n\n if name == \"average\":\n return resample_average\n elif name == \"antialias\":\n return resample_antialias\n\n resampling_methods = {\n \"near\" : gdal.GRA_NearestNeighbour,\n \"bilinear\" : gdal.GRA_Bilinear,\n \"cubic\" : gdal.GRA_Cubic,\n \"cubicspline\" : gdal.GRA_CubicSpline,\n \"lanczos\" : gdal.GRA_Lanczos\n }\n\n resampling_method = resampling_methods[name]\n\n def resample_gdal(path, dsquery, dstile, image_format):\n querysize = dsquery.RasterXSize\n tilesize = dstile.RasterXSize\n\n dsquery.SetGeoTransform( (0.0, tilesize / float(querysize), 0.0, 0.0, 0.0, tilesize / float(querysize)) )\n dstile.SetGeoTransform( (0.0, 1.0, 0.0, 0.0, 0.0, 1.0) )\n\n res = gdal.ReprojectImage(dsquery, dstile, None, None, resampling_method)\n if res != 0:\n raise ImageOutputException(\"ReprojectImage() failed with error %d\" % res)\n\n gdal_write(path, dstile, image_format)\n\n return resample_gdal","def scale_image(img, factor=1):\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))","def Rescale(self):\r\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\r\n bitmap = self.scaled = self.bitmap\r\n if not bitmap: return\r\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\r\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\r\n image = bitmap.ConvertToImage()\r\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\r\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\r\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\r\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\r","def resample_image(I,spacing,desiredSize, spline_order=1,zero_boundary=False,identity_map=None):\n if len(I.shape) != len(desiredSize)+2:\n desiredSize = desiredSize[2:]\n sz = np.array(list(I.size()))\n # check that the batch size and the number of channels is the same\n nrOfI = sz[0]\n nrOfC = sz[1]\n\n desiredSizeNC = np.array([nrOfI,nrOfC]+list(desiredSize))\n\n newspacing = spacing*((sz[2::].astype('float')-1.)/(desiredSizeNC[2::].astype('float')-1.)) ###########################################\n if identity_map is not None:\n idDes= identity_map\n else:\n idDes = torch.from_numpy(py_utils.identity_map_multiN(desiredSizeNC,newspacing)).to(I.device)\n # now use this map for resampling\n ID = py_utils.compute_warped_image_multiNC(I, idDes, newspacing, spline_order,zero_boundary)\n\n return ID, newspacing","def resize_img(input_img, scale=1.1):\n if scale == 1.0:\n return input_img\n rows, cols, ch = input_img.shape\n d = rows * (scale - 1) # overall image size change from rows, cols, to rows - 2d, cols - 2d\n pts1 = np.float32([[d, d], [rows - 1 - d, d], [d, cols - 1 - d], [rows - 1 - d, cols - 1 - d]])\n pts2 = np.float32([[0, 0], [rows - 1, 0], [0, cols - 1], [rows - 1, cols - 1]])\n M = cv2.getPerspectiveTransform(pts1, pts2)\n dst = cv2.warpPerspective(input_img, M, (cols, rows), borderMode=1)\n return dst","def shrink_image(image, output_size, interp_type=InterpolationType.INTEGRAL, output=None):\n\n if isinstance(output_size, (int, int)):\n scale = float(output_size) / max(image.getWidth(), image.getHeight())\n output_shape = (int(image.getHeight()*scale), int(image.getWidth()*scale))\n else:\n output_shape = output_size\n\n if image.getWidth() < output_shape[1] or image.getHeight() < output_shape[0]:\n raise RuntimeError(\"Either width or height is larger in output than input\")\n\n if output is None:\n output = image.createNew(output_shape[1], output_shape[0])\n else:\n output.reshape(output_shape[1], output_shape[0])\n\n if interp_type == InterpolationType.INTEGRAL:\n pbg.gateway.jvm.boofcv.alg.filter.misc.AverageDownSampleOps.down(image, output)\n else:\n scale_x = output_shape[1] / float(image.getWidth())\n scale_y = output_shape[0] / float(image.getHeight())\n\n fdist = pbg.gateway.jvm.boofcv.abst.distort.FDistort(image, output)\n fdist.interp(interpolation_type_to_java(interp_type))\n fdist.affine(scale_x, 0.0, 0.0, scale_y, 0.0, 0.0)\n fdist.apply()\n\n return output","def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):\n assert _is_numpy_image(img), 'img should be nparray Image'\n img = crop(img, i, j, h, w)\n img = resize(img, size, interpolation)\n return img","def __resize_image(self, img):\n return cv2.resize(img, self.g.img_size, \n interpolation = cv2.INTER_CUBIC)","def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):\n factors = [new_size[i] / im.shape[i] for i in range(2)]\n\n #assert factors[0] == factors[1], \"Must have same factor for now\"\n f = factors[0] \n \n if f < 1:\n im2 = pyramid_reduce(im, downscale=1/f)\n elif f > 1:\n im2 = pyramid_expand(im, upscale=f)\n else:\n im2 = im\n\n assert im2.shape[:2] == tuple(new_size), \"{0} != {1} (original size: {2})\".format(im2.shape, new_size, im.shape)\n \n return im2","def resize(img,scale):\n sdims=img.shape\n datatype=c_double\n if img.dtype!=datatype:\n print \"Error the image must be of doubles!\"\n raise RuntimeError\n\n if scale>1.0:\n print \"Invalid scaling factor!\"\n raise RuntimeError \n \n img = asfortranarray(img,c_double) # make array continguous\n \n try:\n mresize = ctypeslib.load_library(\"libresize.so\",\".\") \n except:\n print \"Unable to load resize library\"\n raise RuntimeError\n \n #use two times the 1d resize to get a 2d resize\n fresize = mresize.resize1dtran\n fresize.restype = None\n fresize.argtypes = [ ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int,ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int, c_int , c_int ]\n ddims = [int(round(sdims[0]*scale)),int(round(sdims[1]*scale)),sdims[2]];\n mxdst = zeros((ddims), dtype=datatype)\n tmp = zeros((ddims[0],sdims[1],sdims[2]), dtype=datatype)\n img1=img\n t1=time()\n fresize(img1, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);\n fresize(tmp, sdims[1], mxdst, ddims[1], ddims[0], sdims[2]);\n t2=time()\n return mxdst.reshape(ddims[2],ddims[1],ddims[0]).T","def resize(image, size):\n return np.array(Image.fromarray(image).resize(size))","def bilinear_interpolation(self, image, fx, fy):\n\n # Write your code for bilinear interpolation here\n w = image.shape[1]\n h = image.shape[0]\n\n newW = int(w*float(fx))\n newH = int(h*float(fy))\n\n ratioW = w / newW\n ratioH = h / newH\n\n import numpy as np\n import math as ma\n from . import interpolation as bilinear\n bi = bilinear.interpolation()\n newImg = np.zeros((newH, newW), np.uint8)\n for i in range(newImg.shape[0]):\n for j in range(newImg.shape[1]):\n\n x = ratioH * i\n y = ratioW * j\n\n x1 = ma.floor(x)\n x2 = ma.ceil(x)\n y1 = ma.floor(y)\n y2 = ma.ceil(y)\n\n x1 = x1-1 if x1 == h else x1\n x2 = x2-1 if x2 == h else x2\n y1 = y1-1 if y1 == w else y1\n y2 = y2-1 if y2 == w else y2\n\n pt1 = (x1, y1, image[x1, y1])\n pt2 = (x1, y2, image[x1, y2])\n pt3 = (x2, y1, image[x2, y1])\n pt4 = (x2, y2, image[x2, y2])\n unknown = (x, y, 0)\n\n if pt1[0] == pt3[0] and pt1[2] == pt2[2]:\n newImg[i, j] = image[int(x), int(y)]\n else:\n newImg[i, j] = bi.bilinear_interpolation(pt1, pt2, pt3, pt4, unknown)\n\n image = newImg\n return image","def prepare_image(im):\n width, height = im.size\n if width > 256 or height > 256:\n factor = 256.0 / max(width, height)\n im = im.resize((int(factor * width), int(factor * height)),\n Image.BILINEAR)\n return im","def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\n target_height = target_size[0]\n target_width = target_size[1]\n if data_format == 'channels_first':\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"Invilid data format\", data_format)","def nn_resample_image(image_path, pxl_box_width, pxl_box_height):\n image = Image.open(image_path)\n if image.format == \"JPEG\" or image.format == \"PNG\":\n __nn_resample_png_jpeg_image(image, pxl_box_width, pxl_box_height)\n elif image.format == \"GIF\":\n __nn_resample_gif_image(image, pxl_box_width, pxl_box_height)\n\n return image","def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\n if isinstance(target_spacing, numbers.Number):\n target_spacing = [target_spacing] * img.ndim\n \n # compute zoom values\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\n\n print \"Zoom Factors\"\n print zoom_factors\n\n oldImageShape = img.shape\n \n # zoom image\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\n\n newImageShape = img.shape\n old_pixel_spacing = header.get_pixel_spacing(hdr)\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\n print \"Target Pixel Spacing\"\n print target_spacing\n\n print \"Actual Pixel Spacing\"\n print new_pixel_spacing\n # set new voxel spacing\n header.set_pixel_spacing(hdr, new_pixel_spacing)\n \n return img, hdr","def scale_image(self, pixels, size):\n x_min, x_max = np.amin(pixels[:,0]), np.amax(pixels[:,0])\n y_min, y_max = np.amin(pixels[:,1]), np.amax(pixels[:,1])\n z_min, z_max = np.amin(pixels[:,2]), np.amax(pixels[:,2])\n \n pixels[:,0] -= x_min \n pixels[:,1] -= y_min\n pixels[:,2] -= z_min\n \n x_max -= x_min\n y_max -= y_min\n z_max -= z_min\n \n scale_factor = size / max(x_max, y_max, z_max) \n # All points are now between [0..max]\n\n pixels *= scale_factor\n return pixels","def image_resize(img, min_size=600, max_size=1000):\n C, H, W = img.shape\n scale1 = min_size / min(H, W)\n scale2 = max_size / max(H, W)\n scale = min(scale1, scale2)\n img = img / 255\n img = transform.resize(img, (C, H * scale, W * scale),\n mode='reflect', anti_aliasing=False)\n # img = pytorch_normalize(img)\n # img = caffe_normalize(img)\n return img","def resample_by(image_array, compression_factor_list, is_seg=False):\n original_dims_in_pixels = [image_array.shape[d]\n for d in range(len(image_array.shape))]\n target_pixel_dims_list = [int(math.floor(compression_factor_list[d] * original_dims_in_pixels[d]))\n for d in range(len(image_array.shape))]\n\n resized_image = resample(image_array, target_pixel_dims_list, is_seg)\n\n return(resized_image)","def imresize(img, size):\n if hasattr(size, \"__len__\"):\n num_rows, num_cols = size\n assert (num_rows > 0) or (num_cols > 0)\n if num_rows < 0:\n num_rows = num_cols * img.shape[0] / img.shape[1]\n if num_cols < 0:\n num_cols = num_rows * img.shape[1] / img.shape[0]\n else:\n num_rows = int(round(img.shape[0] * size))\n num_cols = int(round(img.shape[1] * size))\n return skimage.transform.resize(img, (num_rows, num_cols))","def resize_image(img):\n ratio = float(img.size[1]) / float(img.size[0])\n\n if ratio > 1.73:\n h, w = 320, 160 # 512, 256\n elif ratio < 1.0 / 1.73:\n h, w = 160, 320 # 256, 512\n elif ratio > 1.41:\n h, w = 384, 256\n elif ratio < 1. / 1.41:\n h, w = 256, 384\n elif ratio > 1.15:\n h, w = 320, 240 # 512, 384\n elif ratio < 1. / 1.15:\n h, w = 240, 320 # 384, 512\n else:\n h, w = 320, 320 # 384, 384\n\n h = int(h)\n w = int(w)\n img = TF.resize(img, size=[h, w], interpolation=Image.BILINEAR)\n return img","def resizeImage(self):\n ratio = float(self.qIma.width()) / float(self.qIma.height())\n if self.qIma.width() > self.qIma.height():\n maxWidth = 300\n maxHeight = int(300 / ratio)\n else:\n maxWidth = int(300 / ratio)\n maxHeight = 300\n img = self.qIma.toImage().scaled(maxWidth, maxHeight, QtCore.Qt.KeepAspectRatio)\n return img","def scale_image(img, factor=1):\n\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))","def resample_image(image, out_spacing=(1.0, 1.0, 1.0), out_size=None, is_label=False, pad_value=0):\n\n original_spacing = np.array(image.GetSpacing())\n original_size = np.array(image.GetSize())\n\n if out_size is None:\n out_size = np.round(np.array(original_size * original_spacing / np.array(out_spacing))).astype(int)\n else:\n out_size = np.array(out_size)\n\n original_direction = np.array(image.GetDirection()).reshape(len(original_spacing), -1)\n original_center = (np.array(original_size, dtype=float) - 1.0) / 2.0 * original_spacing\n out_center = (np.array(out_size, dtype=float) - 1.0) / 2.0 * np.array(out_spacing)\n\n original_center = np.matmul(original_direction, original_center)\n out_center = np.matmul(original_direction, out_center)\n out_origin = np.array(image.GetOrigin()) + (original_center - out_center)\n\n resample = sitk.ResampleImageFilter()\n resample.SetOutputSpacing(out_spacing)\n resample.SetSize(out_size.tolist())\n resample.SetOutputDirection(image.GetDirection())\n resample.SetOutputOrigin(out_origin.tolist())\n resample.SetTransform(sitk.Transform())\n resample.SetDefaultPixelValue(pad_value)\n\n if is_label:\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\n else:\n resample.SetInterpolator(sitk.sitkBSpline)\n\n return resample.Execute(image)","def __resize_512p(input_data):\n rate = 1\n test_size=512\n if input_data['img'].shape[0] > input_data['img'].shape[1]:\n if True: # input_data['img'].shape[1] < 512:\n rate = test_size / input_data['img'].shape[1]\n seq = iaa.Sequential([\n iaa.Scale({'height': \"keep-aspect-ratio\", 'width': test_size}, 'cubic')\n ])\n input_data['img'] = seq.augment_image(input_data['img'])\n else:\n if True: # input_data['img'].shape[0] < 512:\n rate = test_size / input_data['img'].shape[0]\n seq = iaa.Sequential([\n iaa.Scale({'height': test_size, 'width': \"keep-aspect-ratio\"}, 'cubic')\n ])\n input_data['img'] = seq.augment_image(input_data['img'])\n\n if DataAugmentor._is_synthtext(input_data):\n input_data['contour'] = [[np.cast['int32'](contour * rate) for contour in contours] for contours in\n input_data['contour']]\n else:\n input_data['contour'] = [np.cast['int32'](contour * rate) for contour in input_data['contour']]\n input_data['center_point'] = [(np.cast['int32'](point[0] * rate),\n np.cast['int32'](point[1] * rate)) for point in input_data['center_point']]\n return input_data","def resize(image):\r\n return cv2.resize(image, (200, 66), interpolation=cv2.INTER_AREA)","def upsampleImage( arr, kernelSize ):\n return scipy.ndimage.zoom( arr, kernelSize )","def reScaleLandsat(self,img):\n \n\t\tthermalBand = ee.List(['thermal'])\n\t\tthermal = ee.Image(img).select(thermalBand).multiply(10)\n \n\t\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\n\t\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\n \n\t\timage = ee.Image(scaled.addBands(thermal)).int16()\n \n\t\treturn image.copyProperties(img)","def resize(im: Image) -> Image:\n size_factor = random.uniform(*ImageOperations.config.get('resize_interval'))\n return im.resize((\n int(round(im.height * size_factor, 0)),\n int(round(im.width * size_factor, 0))\n ))","def autoscale(self, img: Image, max_height: int, max_width: int):\n height = img.bgr.shape[0]\n width = img.bgr.shape[1]\n\n diff_height = max_height / height\n diff_width = max_width / width\n\n diff = min(diff_height, diff_width)\n\n height = int(height * diff)\n width = int(width * diff)\n\n return img.rescale_image(height, width)","def _resize_img(self, results):\n for key in results.get('img_fields', ['img']):\n if self.keep_ratio:\n img, scale_factor = general_ocr.imrescale(\n results[key],\n results['scale'],\n return_scale=True,\n backend=self.backend)\n # the w_scale and h_scale has minor difference\n # a real fix should be done in the general_ocr.imrescale in the future\n new_h, new_w = img.shape[:2]\n h, w = results[key].shape[:2]\n w_scale = new_w / w\n h_scale = new_h / h\n else:\n img, w_scale, h_scale = general_ocr.imresize(\n results[key],\n results['scale'],\n return_scale=True,\n backend=self.backend)\n results[key] = img\n\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\n dtype=np.float32)\n results['img_shape'] = img.shape\n # in case that there is no padding\n results['pad_shape'] = img.shape\n results['scale_factor'] = scale_factor\n results['keep_ratio'] = self.keep_ratio","def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):\r\n w = int(round(target_height * image.shape[1] / image.shape[0]))\r\n return cv2.resize(image, (w, target_height), interpolation=method)","def ulab_bilinear_interpolation():\n GRID_DATA[1::2, ::2] = SENSOR_DATA[:-1, :]\n GRID_DATA[1::2, ::2] += SENSOR_DATA[1:, :]\n GRID_DATA[1::2, ::2] /= 2\n GRID_DATA[::, 1::2] = GRID_DATA[::, :-1:2]\n GRID_DATA[::, 1::2] += GRID_DATA[::, 2::2]\n GRID_DATA[::, 1::2] /= 2","def __scale_image(image, scale: float):\r\n height, width, _ = image.shape\r\n\r\n width_scaled = int(np.ceil(width * scale))\r\n height_scaled = int(np.ceil(height * scale))\r\n\r\n im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)\r\n\r\n # Normalize the image's pixels\r\n im_data_normalized = (im_data - 127.5) * 0.0078125\r\n\r\n return im_data_normalized","def downscale_image(img, bottom, x, y):\n width, height = tuple(img.shape[1::-1])\n img = img[int(round((1 - bottom) * (height - 1))):(height - 1), 1:(width - 1)]\n img = cv2.resize(img, (x, y))\n #img = cv2.Canny(img, 100, 200)\n ret, img = cv2.threshold(img, img.mean(), 255, cv2.THRESH_BINARY)\n return img","def _resize_img(self, results):\n for key in results.get('img_fields', ['img']):\n if self.keep_ratio:\n img, scale_factor = mmcv.imrescale(\n results[key],\n results['scale'],\n return_scale=True,\n interpolation=self.interpolation,\n backend=self.backend)\n # the w_scale and h_scale has minor difference\n # a real fix should be done in the mmcv.imrescale in the future\n new_h, new_w = img.shape[:2]\n h, w = results[key].shape[:2]\n w_scale = new_w / w\n h_scale = new_h / h\n else:\n img, w_scale, h_scale = mmcv.imresize(\n results[key],\n results['scale'],\n return_scale=True,\n interpolation=self.interpolation,\n backend=self.backend)\n results[key] = img\n\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\n dtype=np.float32)\n results['img_shape'] = img.shape\n # in case that there is no padding\n results['pad_shape'] = img.shape\n results['scale_factor'] = scale_factor\n results['keep_ratio'] = self.keep_ratio","def upsample(x, name, size):\n with tf.name_scope(name):\n outputs = tf.image.resize_bilinear(x, size)\n # Return layer's output\n return outputs","def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\n assert img_r.get_data().shape == (29, 39, 19)\n assert img_r.get_data()[4, 4, 4] == 1.0","def resize_img_norescale(img, size):\n w,h = size\n shape = (h,w)\n out = np.zeros(shape, dtype=img.dtype)\n i = min(img.shape[0], out.shape[0])\n j = min(img.shape[1], out.shape[1])\n out[0:i,0:j] = img[0:i, 0:j]\n return out","def preprocess(img: 'np.ndarray') -> 'np.ndarray':\n return cv2.resize(img, (45, 45), interpolation=cv2.INTER_AREA)[:,:,:3]","def obscure_image(image):\n size = image.size\n pixel_size = 9\n if size[0] < 9 or size[1] < 9:\n return image\n image_f = image.filter(ImageFilter.MaxFilter)\n image_s = image_f.resize((size[0] / pixel_size, size[1] / pixel_size), Image.NEAREST)\n image_l = image_s.resize((size[0], size[1]), Image.NEAREST)\n\n return image_l"],"string":"[\n \"def scale_image(image, scale=1, interpolation='linear'):\\n if interpolation == 'linear':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\\n if interpolation == 'nearest':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)\",\n \"def rescale_image(image, scale=0.50):\\r\\n \\r\\n wi = int(image.shape[1]*scale)\\r\\n hei = int(image.shape[0]*scale)\\r\\n dimension = (wi, hei)\\r\\n return cv.resize(image, dimension, interpolation = cv.INTER_AREA)\",\n \"def _batch_resize(self, ims, res, strategy='nearest'):\\n order = {'nearest': 0, 'bilinear': 1, 'bicubic': 3}[strategy]\\n bs = ims.shape[0]\\n out = []\\n log.info('Input ims shape: %s' % repr(ims.shape))\\n has_extra_dim = len(ims.shape) == 4\\n if not has_extra_dim:\\n ims = ims[..., np.newaxis]\\n h, w = ims.shape[1:3]\\n for i in range(bs):\\n o = interpolation.zoom(\\n ims[i, ...], [res[0] / h, res[1] / w, 1.0], np.float32, order=order)\\n out.append(o)\\n out = np.stack(out)\\n if not has_extra_dim:\\n out = np.reshape(out, out.shape[:-1])\\n return out\",\n \"def resize(self, image, fx, fy, interpolation):\\n\\n if interpolation == 'bilinear':\\n return self.bilinear_interpolation(image, fx, fy)\\n\\n elif interpolation == 'nearest_neighbor':\\n return self.nearest_neighbor(image, fx, fy)\",\n \"def resize(img, size, method=\\\"bilinear\\\"):\\n if method == Image.BILINEAR or method == \\\"bilinear\\\":\\n method = cv2.INTER_AREA\\n elif method == Image.CUBIC or method == \\\"cubic\\\":\\n method = cv2.INTER_CUBIC\\n elif method == Image.NEAREST or method == \\\"nearest\\\":\\n method = cv2.INTER_NEAREST\\n else:\\n raise RuntimeError(\\\"wrong method {}\\\".format(method))\\n if not isinstance(size, (tuple, list)):\\n size = (size, size)\\n if isinstance(size ,list):\\n size = tuple(size)\\n im = np.array(img)\\n im = cv2.resize(im, size, interpolation=method)\\n return Image.fromarray(im)\",\n \"def resize(img, size, interpolation=Image.BILINEAR):\\n\\n if isinstance(size, int):\\n w, h = img.size\\n if (w <= h and w == size) or (h <= w and h == size):\\n return img\\n if w < h:\\n ow = size\\n oh = int(size * h / w)\\n return img.resize((ow, oh), interpolation)\\n else:\\n oh = size\\n ow = int(size * w / h)\\n return img.resize((ow, oh), interpolation)\\n else:\\n return img.resize(size[::-1], interpolation)\",\n \"def scale(input_img, size):\\n width, height = size\\n old_height, old_width = input_img.shape\\n x_scale = float(height) / old_height\\n y_scale = float(width) / old_width\\n\\n output_img = np.zeros((height, width), dtype=np.uint8)\\n for xidx in xrange(height):\\n old_x = float(xidx) / x_scale\\n for yidx in xrange(width):\\n old_y = float(yidx) / y_scale\\n if old_x.is_integer() or old_y.is_integer():\\n output_img[xidx, yidx] = input_img[int(old_x), int(old_y)]\\n else: # use bilinear interpolation\\n x1 = int(np.floor(old_x))\\n x2 = int(np.ceil(old_x)) if int(np.ceil(old_x)) < old_height else old_height - 1\\n y1 = int(np.floor(old_y))\\n y2 = int(np.ceil(old_y)) if int(np.ceil(old_y)) < old_width else old_width - 1\\n\\n q11 = input_img[x1, y1]\\n q12 = input_img[x1, y2]\\n q21 = input_img[x2, y1]\\n q22 = input_img[x2, y2]\\n\\n output_img[xidx, yidx] = (q11 * (x2 - old_x) * (y2 - old_y)\\n + q21 * (old_x - x1) * (y2 - old_y)\\n + q12 * (x2 - old_x) * (old_y - y1)\\n + q22 * (old_x - x1) * (old_y - y1)) \\\\\\n / ((x2 - x1) * (y2 - y1) + 1e-10)\\n\\n return output_img\",\n \"def upsample_bilinear(input, size=None, scale_factor=None):\\n return interpolate(input, size, scale_factor, 'linear', align_corners=True)\",\n \"def resample_img(image, target_shape, mode='nearest'):\\n print(target_shape)\\n resize_factor = np.array(target_shape)/image.shape\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def resample(img, newSize):\\n # use float as datatype to preserve float precision\\n # will convert to np.uint8 when display result\\n # get the new dimensions\\n nH, nW = newSize\\n if np.ndim(img) == 2:\\n H, W = img.shape\\n res = np.zeros((nH, nW), dtype=np.float32)\\n elif np.ndim(img) == 3:\\n H, W, _ = img.shape\\n res = np.zeros((nH, nW, _), dtype=np.float32)\\n else:\\n raise ValueError(\\\"input image has invalid dimension %s\\\" % (img.shape))\\n\\n # interpolate the value for the result\\n for idx in range(nH * nW):\\n i = idx // nW\\n j = idx % nW\\n orig_i = int((i * H) // nH)\\n orig_j = int((j * W) // nW)\\n res[i, j] = img[orig_i, orig_j]\\n return res\",\n \"def img_zoom(img, fx, fy, interp=cv2.INTER_AREA):\\n res = cv2.resize(img, None, fx=fx, fy=fy,\\n interpolation=interp)\\n return res\",\n \"def resample(image, pixsize):\\n assert pixsize in [1, 2, 4, 8, 16] # return error and exit otherwise\\n imsize = 128/pixsize\\n newimage = np.zeros((imsize, imsize))\\n for xn, x in enumerate(np.arange(0, 128, pixsize)):\\n for yn, y in enumerate(np.arange(0, 128, pixsize)):\\n newimage[xn, yn] = np.nansum(image[x:x+pixsize, y:y+pixsize]) # Nansum is important as sum of masked array can be nan\\n return newimage\",\n \"def resize(img, size, interpolation='bilinear', data_format='CHW'):\\n _assert_image_tensor(img, data_format)\\n\\n if not (\\n isinstance(size, int)\\n or (isinstance(size, (tuple, list)) and len(size) == 2)\\n ):\\n raise TypeError(f'Got inappropriate size arg: {size}')\\n\\n if isinstance(size, int):\\n w, h = _get_image_size(img, data_format)\\n # TODO(Aurelius84): In static graph mode, w and h will be -1 for dynamic shape.\\n # We should consider to support this case in future.\\n if w <= 0 or h <= 0:\\n raise NotImplementedError(\\n \\\"Not support while w<=0 or h<=0, but received w={}, h={}\\\".format(\\n w, h\\n )\\n )\\n if (w <= h and w == size) or (h <= w and h == size):\\n return img\\n if w < h:\\n ow = size\\n oh = int(size * h / w)\\n else:\\n oh = size\\n ow = int(size * w / h)\\n else:\\n oh, ow = size\\n\\n img = img.unsqueeze(0)\\n img = F.interpolate(\\n img,\\n size=(oh, ow),\\n mode=interpolation.lower(),\\n data_format='N' + data_format.upper(),\\n )\\n\\n return img.squeeze(0)\",\n \"def image_resample(f, oversamp=1.0):\\n img = imageio.imread(f)\\n x = np.linspace(0,img.shape[0],img.shape[0])\\n y = np.linspace(0,img.shape[1],img.shape[1])\\n #\\n # 2x oversample the image since we'll dither it.\\n #\\n xnew = np.linspace(0, img.shape[0], img.shape[0]*oversamp)\\n ynew = np.linspace(0, img.shape[1], img.shape[1]*oversamp)\\n from scipy import interpolate\\n rc = interpolate.interp2d(x, y, img[:,:,0].flatten(), kind='linear')\\n gc = interpolate.interp2d(x, y, img[:,:,1].flatten(), kind='linear')\\n bc = interpolate.interp2d(x, y, img[:,:,2].flatten(), kind='linear')\\n rgb_new = np.stack([rc(xnew.flatten(), ynew.flatten()),\\n gc(xnew.flatten(), ynew.flatten()),\\n bc(xnew.flatten(), ynew.flatten())],-1).transpose(1,0,2).astype(np.uint8)\\n plt.imshow(rgb_new)\\n return rgb_new\",\n \"def imgResample(img, spacing, size=[], useNearest=False, origin=[], outsideValue=0):\\n if len(spacing) != img.GetDimension(): raise Exception(\\\"len(spacing) != \\\" + str(img.GetDimension()))\\n\\n # Set Size\\n if size == []:\\n inSpacing = img.GetSpacing()\\n inSize = img.GetSize()\\n size = [int(math.ceil(inSize[i]*(inSpacing[i]/spacing[i]))) for i in range(img.GetDimension())]\\n else:\\n if len(size) != img.GetDimension(): raise Exception(\\\"len(size) != \\\" + str(img.GetDimension()))\\n \\n if origin == []:\\n origin = [0]*img.GetDimension()\\n else:\\n if len(origin) != img.GetDimension(): raise Exception(\\\"len(origin) != \\\" + str(img.GetDimension()))\\n \\n # Resample input image\\n interpolator = [sitk.sitkLinear, sitk.sitkNearestNeighbor][useNearest]\\n identityTransform = sitk.Transform()\\n identityDirection = list(sitk.AffineTransform(img.GetDimension()).GetMatrix())\\n\\n return sitk.Resample(img, size, identityTransform, interpolator, origin, spacing, identityDirection, outsideValue)\",\n \"def scale(img, scale):\\n return resize(img, x_scale=scale, y_scale=scale)\",\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def pixelate(image, n_w, n_h):\\n image = image.resize((n_w, n_h), Image.ANTIALIAS)\\n return image\",\n \"def resample(medical_image, image, new_spacing=[1, 1, 1]):\\n image_shape = np.array(list(medical_image.header.get_data_shape()))\\n spacing = np.array(list(medical_image.header.get_zooms()))\\n resize_factor = spacing / new_spacing\\n\\n new_shape = image_shape * resize_factor\\n new_shape = np.round(new_shape)\\n\\n real_resize_factor = new_shape / image_shape\\n\\n new_spacing = spacing / real_resize_factor\\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\\n\\n return resampled_image, new_spacing\",\n \"def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\\n (height, width) = image.shape[:2]\\n new_dims = (int(width * scale), int(height * scale))\\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)\",\n \"def resample_img(img, img_type, size, spacing):\\n resampler = sitk.ResampleImageFilter()\\n resampler.SetOutputDirection(img.GetDirection())\\n resampler.SetOutputOrigin(img.GetOrigin())\\n resampler.SetOutputSpacing(spacing)\\n resampler.SetSize(size)\\n if img_type is \\\"Label\\\":\\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\\n elif img_type is \\\"Image\\\":\\n resampler.SetInterpolator(sitk.sitkLinear)\\n imgResampled = resampler.Execute(img)\\n\\n #axis have to be switched since np.array and keras use them in different order...\\n x = np.transpose(sitk.GetArrayFromImage(imgResampled).astype(dtype=np.float), [2, 1, 0])\\n return x\",\n \"def _scale(self, image):\\n\\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\\n image.Rescale(self._width, self._height)\\n \\n return image\",\n \"def resampled_to_img(self, target_image, interpolation=None):\\n # IMPORTANT: Polymorphism can be implemented by walking the \\n # MRO and finding a method that does not raise\\n # NotImplementedError. \\n raise NotImplementedError\",\n \"def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)\",\n \"def resize(self, image, fx=None, fy=None, interpolation=None):\\r\\n if interpolation == 'bilinear':\\r\\n return self.bilinear_interpolation(image, float(fx), float(fy))\\r\\n\\r\\n elif interpolation == 'nearest_neighbor':\\r\\n return self.nearest_neighbor(image, float(fx), float(fy))\",\n \"def resize(img, size, interpolation=Image.BILINEAR):\\n\\n if not _is_numpy_image(img):\\n raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\\n raise TypeError('Got inappropriate size arg: {}'.format(size))\\n\\n if isinstance(size, int):\\n size = (size, size)\\n \\n if img.dtype == np.int32:\\n resized = img.astype(np.uint16)\\n resized = cv2.resize(resized, size , interpolation)\\n else:\\n resized = cv2.resize(img, size , interpolation)\\n resized = resized.astype(img.dtype)\\n return resized\",\n \"def img_resize(img, img_height):\\n\\n\\timg_width = int(img_height*img.shape[1]/img.shape[0])\\n\\n\\timg_pil = Image.fromarray(img) # convert array back to image\\n\\n\\timg_pil_resize = img_pil.resize((img_width, img_height), Image.LANCZOS) # resize\\n\\n\\treturn np.array(img_pil_resize)\",\n \"def resize(self, image, fx=None, fy=None, interpolation=None):\\n if interpolation == 'bilinear':\\n return self.bilinear_interpolation(image, float(fx), float(fy))\\n\\n elif interpolation == 'nearest_neighbor':\\n return self.nearest_neighbor(image, float(fx), float(fy))\",\n \"def resample(image_array, target_pixel_dims_list, is_seg=False):\\n # if dealing with segmentation images, do not interpolate\\n # this is done by choosing interpolation order == 0\\n order = 0 if is_seg == True else 3\\n\\n original_dims_in_pixels = [image_array.shape[d]\\n for d in range(len(image_array.shape))]\\n compression_list = [target_pixel_dims_list[d] / original_dims_in_pixels[d]\\n for d in range(len(image_array.shape))]\\n\\n if (is_seg):\\n resized_image = ndimage.interpolation.zoom(\\n image_array, zoom=compression_list, order=order, cval=0)\\n return resized_image\\n else:\\n resized_image = ndimage.interpolation.zoom(\\n image_array, zoom=compression_list, order=order, cval=0)\\n return resized_image\",\n \"def process_image(image):\\n image = resize(image)\\n return image\",\n \"def scale_down(image:np.array)->np.array:\\n src = image\\n scale_percent = 25\\n width = int(src.shape[1] * scale_percent / 100)\\n height = int(src.shape[0] * scale_percent / 100)\\n dsize = (width, height)\\n output = cv2.resize(src, dsize)\\n return output\",\n \"def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\\n\\n if scaling_method == 'scale':\\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\\n elif scaling_method == 'ratio':\\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\\n im = scipy.misc.imresize(im, scale)\\n return (im, scale)\",\n \"def resize_3d_image_nearest_interpolation(im, res_s):\\n new_shape = [res_s, im.shape[1], im.shape[2]]\\n ret0 = np.zeros(new_shape, dtype=im.dtype)\\n for i in range(im.shape[2]):\\n im_slice = np.array(\\n Image.fromarray(im[..., i]).resize(\\n (im.shape[1], res_s), resample=Image.Resampling.NEAREST\\n )\\n )\\n ret0[..., i] = im_slice\\n\\n new_shape = [res_s, res_s, res_s]\\n ret = np.zeros(new_shape, dtype=im.dtype)\\n for i in range(res_s):\\n im_slice = np.array(\\n Image.fromarray(ret0[i, ...]).resize(\\n (res_s, res_s), resample=Image.Resampling.NEAREST\\n )\\n )\\n ret[i, ...] = im_slice\\n return ret\",\n \"def resize(img, x_scale, y_scale, optimize=True):\\n interpolation = cv.INTER_LINEAR\\n\\n # pick an optimized scaler if asked to\\n if optimize:\\n if x_scale > 1 and y_scale > 1:\\n interpolation = cv.INTER_CUBIC\\n else:\\n interpolation = cv.INTER_AREA\\n\\n return cv.resize(\\n img,\\n None,\\n fx=x_scale,\\n fy=y_scale,\\n interpolation=interpolation,\\n )\",\n \"def bilinear_interpolation(self, image, fx, fy):\\n\\n # Write your code for bilinear interpolation here\\n (width, height) = image.shape\\n (scaledWidth, scaledHeight) = (int(fy * width), int(fy * height))\\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\\n\\n for x in range(0, scaledWidth):\\n for y in range(0, scaledHeight):\\n nx = math.floor(x / fx)\\n ny = math.floor(y / fy)\\n if (nx + 1) > width - 1:\\n nx = width - 2\\n if (ny + 1) > height - 1:\\n ny = height - 2\\n\\n n1 = (nx, ny)\\n n2 = (nx, ny+1)\\n n3 = (nx+1, ny)\\n n4 = (nx+1, ny+1)\\n\\n resampledImage[x, y] = self.inter.bilinear_interpolation([n1, image[n1]], [n2, image[n2]], [n3, image[n3]],[n4, image[n4]], [x/fx, y/fy])\\n\\n return resampledImage\",\n \"def downsample_image(img, output_path, ratio=2):\\n assert ratio>=1, ratio\\n if ratio == 1:\\n return True\\n old_im = Image.open(img)\\n old_size = old_im.size\\n new_size = (int(old_size[0]/ratio), int(old_size[1]/ratio))\\n\\n new_im = old_im.resize(new_size, PIL.Image.LANCZOS)\\n new_im.save(output_path)\\n return True\",\n \"def _resize_short_within(self, img, short, max_size, mult_base=1, interp=Image.BILINEAR):\\n w, h = img.size\\n im_size_min, im_size_max = (h, w) if w > h else (w, h)\\n scale = float(short) / float(im_size_min)\\n if np.round(scale * im_size_max / mult_base) * mult_base > max_size:\\n # fit in max_size\\n scale = float(np.floor(max_size / mult_base) * mult_base) / float(im_size_max)\\n new_w, new_h = (int(np.round(w * scale / mult_base) * mult_base),\\n int(np.round(h * scale / mult_base) * mult_base))\\n img = img.resize((new_w, new_h), interp)\\n return img\",\n \"def _resize(img, max_dim=128):\\n if max(img.shape[:3]) <= max_dim:\\n return img\\n else:\\n new_size = [max_dim / s if s >= max_dim else 1.0 for s in img.shape[:3]]\\n new_size.append(1.0) # for channel\\n return scipy.ndimage.zoom(img, new_size, order=2)\",\n \"def resizeImage(IMG,IMAGE_SIZE):\\n\\n RESCALED_IMAGE = skimage.transform.resize(IMG,[IMG.shape[0],IMAGE_SIZE,IMAGE_SIZE])\\n return RESCALED_IMAGE\",\n \"def rescale_and_resize_image(img_name, img_size, save_img):\\n \\n try:\\n \\n # Open image\\n img = Image.open(img_name)\\n \\n # Resize image\\n img = img.resize((int(img_size), int(img_size)), Image.ANTIALIAS) \\n \\n # Get data from image\\n img_list = list(img.getdata())\\n \\n # Find minimum and maximum value pixels in the image\\n img_max = max(img_list)\\n img_min = min(img_list)\\n \\n # Determine factor to scale to a 8-bit image\\n scale_factor = 255.0/(img_max - img_min)\\n \\n img_list_new = [0] * img_size * img_size\\n \\n # Rescale all pixels to the range 0 to 255 (in line with unit8 values)\\n for i in range(0,img_size):\\n for j in range(0,img_size):\\n img_list_new[i*img_size + j] = int((img_list[i*img_size + j]-img_min)*scale_factor)\\n if (img_list_new[i*img_size + j] > 255) or (img_list_new[i*img_size + j] < 0) or (img_list_new[i*img_size + j]-int(img_list_new[i*img_size + j]) != 0):\\n print(\\\"img_list_new[%d][%d] = %r\\\" % (i,j,img_list_new[i*img_size + j]))\\n \\n img.putdata(img_list_new)\\n \\n # Convert to uint8 greyscale image\\n img = img.convert('L')\\n \\n # Save image\\n if save_img:\\n img.save(img_name)\\n else:\\n ph = ImageTk.PhotoImage(img)\\n return ph\\n \\n finally:\\n \\n # Close image\\n img.close()\",\n \"def resample(self, image, target_shape, mode='nearest'):\\n resize_factor = image.shape / np.array(target_shape)\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def bilinear_interpolation(self, image, fx, fy):\\r\\n\\r\\n # Write your code for bilinear interpolation here\\r\\n width, height = image.shape[:2]\\r\\n nw = int(width * fx)\\r\\n nh = int(height * fy)\\r\\n new_img = np.zeros((nw, nh), np.uint8)\\r\\n inter = interpolation.interpolation()\\r\\n for i in range(nw):\\r\\n for j in range(nh):\\r\\n ii = int(np.floor((i * (image.shape[0]-1))/(nw - 1)))\\r\\n jj = int(np.floor((j * (image.shape[1]-1))/(nh - 1)))\\r\\n\\r\\n # All four corner pixels copied as it is\\r\\n if (ii == 0 and jj == 0) or \\\\\\r\\n (ii == 0 and jj == image.shape[1] - 1) or \\\\\\r\\n (ii == image.shape[0] - 1 and jj == 0) or \\\\\\r\\n (ii == image.shape[0] - 1 and jj == image.shape[1] - 1):\\r\\n new_img[i][j] = image[ii][jj]\\r\\n\\r\\n # Linear interpolation along left and right edges\\r\\n elif ii == 0 or ii == image.shape[0] - 1:\\r\\n new_img[i][j] = inter.linear_interpolation([jj - 1, image[ii][jj - 1]], [jj + 1, image[ii][jj + 1]], jj)\\r\\n\\r\\n # Linear interpolation along top and bottom edges\\r\\n elif jj == 0 or jj == image.shape[1] - 1:\\r\\n new_img[i][j] = inter.linear_interpolation([ii - 1, image[ii - 1][jj]], [ii + 1, image[ii + 1][jj]], ii)\\r\\n\\r\\n else:\\r\\n\\r\\n q11i, q11j = ii - 1, jj - 1\\r\\n q12i, q12j = ii - 1, jj + 1\\r\\n q21i, q21j = ii + 1, jj - 1\\r\\n q22i, q22j = ii + 1, jj + 1\\r\\n\\r\\n point_a = [q11i, q11j, image[q11i][q11j]]\\r\\n point_b = [q12i, q12j, image[q12i][q12j]]\\r\\n point_c = [q21i, q21j, image[q21i][q21j]]\\r\\n point_d = [q22i, q22j, image[q22i][q22j]]\\r\\n unknown = [ii, jj]\\r\\n\\r\\n new_img[i][j] = inter.bilinear_interpolation(point_a, point_b, point_c, point_d, unknown)\\r\\n\\r\\n return new_img\",\n \"def resize(img, size):\\n img = tf.expand_dims(img, 0)\\n return tf.image.resize_bilinear(img, size)[0,:,:,:]\",\n \"def rescale_image(image, rescale_width):\\r\\n\\r\\n # image size\\r\\n image_height = pdb.gimp_image_height(image)\\r\\n image_width = pdb.gimp_image_width(image)\\r\\n\\r\\n # new image height\\r\\n rescale_height = round(image_height * (rescale_width * 1.0 / image_width))\\r\\n\\r\\n pdb.gimp_image_scale(image, rescale_width, rescale_height)\\r\\n gimp.message('Rescaled image')\",\n \"def scale_image(image=np.zeros((100,100)), \\n new_width=100,\\n ascii_block_size=(2,3)):\\n \\n original_width, original_height = image.shape\\n aspect_ratio = original_height / float(original_width)\\n w,h = ascii_block_size\\n new_height = int(h/w * aspect_ratio * new_width)\\n\\n return skimage.transform.resize(image, (new_width, new_height))\",\n \"def resize_img(self,scale=1):\\n reduced = self.image.reduce((scale,scale))\\n reduced.save(\\\"../edited/{}\\\".format(self.image.filename))\\n\\n reduced = Image.open(\\\"../edited/{}\\\".format(self.image.filename))\\n return reduced\",\n \"def get_scaled_image(self, min_width=1024):\\n self.dispersion = self.image.getCalibration().pixelWidth\\n self.offset = self.image.getCalibration().xOrigin\\n binning = 1\\n while binning * min_width < self.image.getWidth():\\n binning *= 2\\n if binning > 1:\\n binning /= 2\\n IJ.run(self.image, 'Select None', '')\\n new_image = self.image.crop()\\n IJ.run(new_image, 'Bin...', 'x=%d y=%d binning=Average' %\\n (binning, binning))\\n self.dispersion *= binning\\n self.offset /= binning\\n self.image = new_image\\n return new_image\",\n \"def imrescale(img,scale,return_scale=False,interpolation='bilinear'):\\n h,w = img.shape[:2]\\n if isinstance(scale,(float,int)):\\n if scale <= 0:\\n raise ValueError('Invalid scale:{},must be positive.'.format(scale))\\n scale_factor = scale\\n \\n elif isinstance(scale,tuple):\\n max_long_edge = max(scale)\\n max_short_edge = min(scale)\\n scale_factor = min(max_long_edge/max(h,w),\\n max_short_edge/min(h,w))\\n else:\\n raise ValueError('Scale must be a number or tuple of int,but got {}'.format(type(scale)))\\n \\n new_size = _scale_size((w,h),scale_factor)\\n rescaled_img = imresize(img,new_size,interpolation = interpolation)\\n if return_scale:\\n return rescaled_img,scale_factor\\n else:\\n return rescaled_img\",\n \"def test_nib_resample_image_4d(fake_4dimage_nib):\\n img_r = resampling.resample_nib(fake_4dimage_nib, new_size=[2, 2, 1, 1], new_size_type='factor', interpolation='nn')\\n assert img_r.get_data().shape == (18, 18, 9, 3)\\n assert img_r.get_data()[8, 8, 4, 0] == 1.0 # make sure there is no displacement in world coordinate system\\n assert img_r.get_data()[8, 8, 4, 1] == 0.0\\n assert img_r.header.get_zooms() == (0.5, 0.5, 1.0, 1.0)\",\n \"def rescale(self, img):\\n\\n if self.scale != 1:\\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\\n else:\\n return img\",\n \"def _resize_image_tuple(self, image_tup):\\n if self.api_info is None:\\n self.get_info() # sets the image size and other such info from server.\\n try:\\n MIN_SIZE = self.api_info['min_image_size']\\n MAX_SIZE = self.api_info['max_image_size']\\n img = Image.open(image_tup[0])\\n min_dimension = min(img.size)\\n max_dimension = max(img.size)\\n min_ratio = float(MIN_SIZE) / min_dimension\\n max_ratio = float(MAX_SIZE) / max_dimension\\n def get_newsize(img, ratio, SIZE):\\n if img.size[0] == min_dimension:\\n newsize = (SIZE, int(round(ratio * img.size[1])))\\n else:\\n newsize = (int(round(ratio * img.size[0])), SIZE)\\n return newsize\\n im_changed = False\\n # Only resample if min size is > 512 or < 256\\n if max_ratio < 1.0: # downsample to MAX_SIZE\\n newsize = get_newsize(img, max_ratio, MAX_SIZE)\\n img = img.resize(newsize, Image.BILINEAR)\\n im_changed = True\\n elif min_ratio > 1.0: # upsample to MIN_SIZE\\n newsize = get_newsize(img, min_ratio, MIN_SIZE)\\n img = img.resize(newsize, Image.BICUBIC)\\n im_changed = True\\n else: # no changes needed so rewind file-object.\\n image_tup[0].seek(0)\\n # Finally make sure we have RGB images.\\n if img.mode != \\\"RGB\\\":\\n img = img.convert(\\\"RGB\\\")\\n im_changed = True\\n if im_changed:\\n io = StringIO()\\n img.save(io, 'jpeg', quality=IM_QUALITY)\\n io.seek(0) # rewind file-object to read() below is good to go.\\n image_tup = (io, image_tup[1])\\n except IOError, e:\\n logger.warning('Could not open image file: %s, still sending to server.', image_tup[1])\\n return image_tup\",\n \"def resize(img):\\n size = (500, 500)\\n img.thumbnail(size)\\n return img\",\n \"def scale_img(img, scale=1.0):\\n dim = (int(img.shape[1]*scale), int(img.shape[0]*scale))\\n return cv2.resize(img.copy(), dim, interpolation=cv2.INTER_AREA)\",\n \"def resample(scan, image, new_spacing=[1,1,1]):\\n spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)\\n\\n resize_factor = spacing / new_spacing\\n new_real_shape = image.shape * resize_factor\\n new_shape = np.round(new_real_shape)\\n real_resize_factor = new_shape / image.shape\\n new_spacing = spacing / real_resize_factor\\n\\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\\n\\n batch = []\\n for i in range(0, image.shape[0] - 3, 3):\\n tmp = []\\n for j in range(3):\\n img = image[i + j]\\n img= cv2.resize(img, (224, 224))\\n tmp.append(img)\\n\\n tmp = np.array(tmp)\\n batch.append(tmp)\\n\\n batch = np.array(batch)\\n\\n return batch\",\n \"def __resize_bilinear_with_factor(self, x, factor_size=None, data_format=None):\\n height_factor = factor_size[0]\\n width_factor = factor_size[1]\\n if data_format == 'channels_first':\\n height = x.shape[2] * height_factor\\n width = x.shape[3] * width_factor\\n new_shape = tf.constant(np.array([height, width]).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"invilid data format\\\", data_format)\",\n \"def retinanet_resize_image(img, min_side=800, max_side=1333):\\n # compute scale to resize the image\\n scale = retinanet_compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)\\n # scale = round(scale, 4)\\n # print(\\\"scale: \\\", scale)\\n\\n # resize the image with the computed scale\\n img = cv2.resize(img, None, fx=scale, fy=scale)\\n\\n return img, scale\",\n \"def resize(img, size, interpolation=cv2.INTER_LINEAR):\\n if not _is_numpy_image(img):\\n raise TypeError('img should be nparrary Image. Got {}'.format(type(img)))\\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\\n raise TypeError('Got inappropriate size arg: {}'.format(size))\\n\\n if isinstance(size, int):\\n h, w = img.shape[:2]\\n if (w <= h and w == size) or (h <= w and h == size):\\n return img\\n if w < h:\\n ow = size\\n oh = int(size * h / w)\\n return cv2.resize(img, (ow, oh), interpolation)\\n else:\\n oh = size\\n ow = int(size * w / h)\\n return cv2.resize(img, (ow, oh), interpolation)\\n else:\\n return cv2.resize(img, tuple(size[::-1]), interpolation)\",\n \"def scale_image(self, image):\\n err_m1 = \\\"image must be a numpy.ndarray with shape (h, w, 3)\\\"\\n if not isinstance(image, np.ndarray):\\n raise TypeError(err_m1)\\n if len(image.shape) != 3 or image.shape[2] != 3:\\n raise TypeError(err_m1)\\n shape0 = image.shape[0]\\n shape1 = image.shape[1]\\n\\n if shape0 > shape1:\\n new_w = int(image.shape[1] * 512 / image.shape[0])\\n new_h = 512\\n elif shape0 < shape1:\\n new_h = int(image.shape[0] * 512 / image.shape[1])\\n new_w = 512\\n else:\\n new_h = 512\\n new_w = 512\\n tf.image.ResizeMethod.BICUBIC\\n image = tf.expand_dims(image, 0)\\n image = tf.image.resize_bicubic(image,\\n (new_h, new_w),\\n align_corners=False)\\n image = image / 255\\n image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\\n\\n return image\",\n \"def bilinear_interpolation(x, y, img):\\n Warning(\\\"This method should not be use, use instead pytorch grid_sample method\\\")\\n _, height, width = img.shape\\n x_min = x.int()\\n y_min = y.int()\\n\\n w_x = x - x_min\\n w_y = y - y_min\\n \\n w1 = torch.cat((w_x, w_y), 1).norm(dim=1)\\n w2 = torch.cat((1-w_x, w_y), 1).norm(dim=1)\\n w3 = torch.cat((1-w_x, 1-w_y), 1).norm(dim=1)\\n w4 = torch.cat((w_x, 1-w_y), 1).norm(dim=1)\\n\\n sum_w = w1 + w2 + w3 + w4\\n\\n w1 = w1 / sum_w\\n w2 = w2 / sum_w\\n w3 = w3 / sum_w\\n w4 = w4 / sum_w\\n\\n # TODO how to deal with out of the sample sampling when the pixel is from outside\\n x_min = x_min.clamp(0, width - 2).view(-1)\\n y_min = y_min.clamp(0, height - 2).view(-1)\\n\\n pix = \\\\\\n w1 * img[:, y_min.long(), x_min.long()] + \\\\\\n w2 * img[:, y_min.long(), x_min.long() + 1] + \\\\\\n w3 * img[:, y_min.long() + 1, x_min.long() + 1] + \\\\\\n w4 * img[:, y_min.long() + 1, x_min.long()]\\n\\n pix = pix.view(img.shape)\\n return pix\",\n \"def resize_image(image: Image) -> Image:\\n expected_x: int = 1024\\n expected_y: int = 768\\n x, y = image.size\\n if x > expected_x or y > expected_y:\\n scale = min(expected_x / x, expected_y / y)\\n return image.resize((int(x * scale), int(y * scale)))\\n else:\\n return image.resize((int(0.9*x), int(0.9*y)))\",\n \"def Resampler(name):\\n\\n def resample_average(path, dsquery, dstile, image_format):\\n for i in range(1, dstile.RasterCount+1):\\n res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i), \\\"average\\\")\\n if res != 0:\\n raise ImageOutputException(\\\"RegenerateOverview() failed with error %d\\\" % res)\\n\\n gdal_write(path, dstile, image_format)\\n\\n def resample_antialias(path, dsquery, dstile, image_format):\\n querysize = dsquery.RasterXSize\\n tilesize = dstile.RasterXSize\\n\\n array = numpy.zeros((querysize, querysize, 4), numpy.uint8)\\n for i in range(dstile.RasterCount):\\n array[:,:,i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1), 0, 0, querysize, querysize)\\n im = Image.fromarray(array, 'RGBA') # Always four bands\\n im1 = im.resize((tilesize,tilesize), Image.ANTIALIAS)\\n\\n if os.path.exists(path):\\n im0 = Image.open(path)\\n im1 = Image.composite(im1, im0, im1)\\n\\n ensure_dir_exists(path)\\n\\n if image_format == \\\"JPEG\\\":\\n im1.save(path, image_format, quality=jpeg_quality)\\n else:\\n im1.save(path, image_format)\\n\\n\\n if name == \\\"average\\\":\\n return resample_average\\n elif name == \\\"antialias\\\":\\n return resample_antialias\\n\\n resampling_methods = {\\n \\\"near\\\" : gdal.GRA_NearestNeighbour,\\n \\\"bilinear\\\" : gdal.GRA_Bilinear,\\n \\\"cubic\\\" : gdal.GRA_Cubic,\\n \\\"cubicspline\\\" : gdal.GRA_CubicSpline,\\n \\\"lanczos\\\" : gdal.GRA_Lanczos\\n }\\n\\n resampling_method = resampling_methods[name]\\n\\n def resample_gdal(path, dsquery, dstile, image_format):\\n querysize = dsquery.RasterXSize\\n tilesize = dstile.RasterXSize\\n\\n dsquery.SetGeoTransform( (0.0, tilesize / float(querysize), 0.0, 0.0, 0.0, tilesize / float(querysize)) )\\n dstile.SetGeoTransform( (0.0, 1.0, 0.0, 0.0, 0.0, 1.0) )\\n\\n res = gdal.ReprojectImage(dsquery, dstile, None, None, resampling_method)\\n if res != 0:\\n raise ImageOutputException(\\\"ReprojectImage() failed with error %d\\\" % res)\\n\\n gdal_write(path, dstile, image_format)\\n\\n return resample_gdal\",\n \"def scale_image(img, factor=1):\\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))\",\n \"def Rescale(self):\\r\\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\\r\\n bitmap = self.scaled = self.bitmap\\r\\n if not bitmap: return\\r\\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\\r\\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\\r\\n image = bitmap.ConvertToImage()\\r\\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\\r\\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\\r\\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\\r\\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\\r\",\n \"def resample_image(I,spacing,desiredSize, spline_order=1,zero_boundary=False,identity_map=None):\\n if len(I.shape) != len(desiredSize)+2:\\n desiredSize = desiredSize[2:]\\n sz = np.array(list(I.size()))\\n # check that the batch size and the number of channels is the same\\n nrOfI = sz[0]\\n nrOfC = sz[1]\\n\\n desiredSizeNC = np.array([nrOfI,nrOfC]+list(desiredSize))\\n\\n newspacing = spacing*((sz[2::].astype('float')-1.)/(desiredSizeNC[2::].astype('float')-1.)) ###########################################\\n if identity_map is not None:\\n idDes= identity_map\\n else:\\n idDes = torch.from_numpy(py_utils.identity_map_multiN(desiredSizeNC,newspacing)).to(I.device)\\n # now use this map for resampling\\n ID = py_utils.compute_warped_image_multiNC(I, idDes, newspacing, spline_order,zero_boundary)\\n\\n return ID, newspacing\",\n \"def resize_img(input_img, scale=1.1):\\n if scale == 1.0:\\n return input_img\\n rows, cols, ch = input_img.shape\\n d = rows * (scale - 1) # overall image size change from rows, cols, to rows - 2d, cols - 2d\\n pts1 = np.float32([[d, d], [rows - 1 - d, d], [d, cols - 1 - d], [rows - 1 - d, cols - 1 - d]])\\n pts2 = np.float32([[0, 0], [rows - 1, 0], [0, cols - 1], [rows - 1, cols - 1]])\\n M = cv2.getPerspectiveTransform(pts1, pts2)\\n dst = cv2.warpPerspective(input_img, M, (cols, rows), borderMode=1)\\n return dst\",\n \"def shrink_image(image, output_size, interp_type=InterpolationType.INTEGRAL, output=None):\\n\\n if isinstance(output_size, (int, int)):\\n scale = float(output_size) / max(image.getWidth(), image.getHeight())\\n output_shape = (int(image.getHeight()*scale), int(image.getWidth()*scale))\\n else:\\n output_shape = output_size\\n\\n if image.getWidth() < output_shape[1] or image.getHeight() < output_shape[0]:\\n raise RuntimeError(\\\"Either width or height is larger in output than input\\\")\\n\\n if output is None:\\n output = image.createNew(output_shape[1], output_shape[0])\\n else:\\n output.reshape(output_shape[1], output_shape[0])\\n\\n if interp_type == InterpolationType.INTEGRAL:\\n pbg.gateway.jvm.boofcv.alg.filter.misc.AverageDownSampleOps.down(image, output)\\n else:\\n scale_x = output_shape[1] / float(image.getWidth())\\n scale_y = output_shape[0] / float(image.getHeight())\\n\\n fdist = pbg.gateway.jvm.boofcv.abst.distort.FDistort(image, output)\\n fdist.interp(interpolation_type_to_java(interp_type))\\n fdist.affine(scale_x, 0.0, 0.0, scale_y, 0.0, 0.0)\\n fdist.apply()\\n\\n return output\",\n \"def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):\\n assert _is_numpy_image(img), 'img should be nparray Image'\\n img = crop(img, i, j, h, w)\\n img = resize(img, size, interpolation)\\n return img\",\n \"def __resize_image(self, img):\\n return cv2.resize(img, self.g.img_size, \\n interpolation = cv2.INTER_CUBIC)\",\n \"def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):\\n factors = [new_size[i] / im.shape[i] for i in range(2)]\\n\\n #assert factors[0] == factors[1], \\\"Must have same factor for now\\\"\\n f = factors[0] \\n \\n if f < 1:\\n im2 = pyramid_reduce(im, downscale=1/f)\\n elif f > 1:\\n im2 = pyramid_expand(im, upscale=f)\\n else:\\n im2 = im\\n\\n assert im2.shape[:2] == tuple(new_size), \\\"{0} != {1} (original size: {2})\\\".format(im2.shape, new_size, im.shape)\\n \\n return im2\",\n \"def resize(img,scale):\\n sdims=img.shape\\n datatype=c_double\\n if img.dtype!=datatype:\\n print \\\"Error the image must be of doubles!\\\"\\n raise RuntimeError\\n\\n if scale>1.0:\\n print \\\"Invalid scaling factor!\\\"\\n raise RuntimeError \\n \\n img = asfortranarray(img,c_double) # make array continguous\\n \\n try:\\n mresize = ctypeslib.load_library(\\\"libresize.so\\\",\\\".\\\") \\n except:\\n print \\\"Unable to load resize library\\\"\\n raise RuntimeError\\n \\n #use two times the 1d resize to get a 2d resize\\n fresize = mresize.resize1dtran\\n fresize.restype = None\\n fresize.argtypes = [ ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int,ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int, c_int , c_int ]\\n ddims = [int(round(sdims[0]*scale)),int(round(sdims[1]*scale)),sdims[2]];\\n mxdst = zeros((ddims), dtype=datatype)\\n tmp = zeros((ddims[0],sdims[1],sdims[2]), dtype=datatype)\\n img1=img\\n t1=time()\\n fresize(img1, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);\\n fresize(tmp, sdims[1], mxdst, ddims[1], ddims[0], sdims[2]);\\n t2=time()\\n return mxdst.reshape(ddims[2],ddims[1],ddims[0]).T\",\n \"def resize(image, size):\\n return np.array(Image.fromarray(image).resize(size))\",\n \"def bilinear_interpolation(self, image, fx, fy):\\n\\n # Write your code for bilinear interpolation here\\n w = image.shape[1]\\n h = image.shape[0]\\n\\n newW = int(w*float(fx))\\n newH = int(h*float(fy))\\n\\n ratioW = w / newW\\n ratioH = h / newH\\n\\n import numpy as np\\n import math as ma\\n from . import interpolation as bilinear\\n bi = bilinear.interpolation()\\n newImg = np.zeros((newH, newW), np.uint8)\\n for i in range(newImg.shape[0]):\\n for j in range(newImg.shape[1]):\\n\\n x = ratioH * i\\n y = ratioW * j\\n\\n x1 = ma.floor(x)\\n x2 = ma.ceil(x)\\n y1 = ma.floor(y)\\n y2 = ma.ceil(y)\\n\\n x1 = x1-1 if x1 == h else x1\\n x2 = x2-1 if x2 == h else x2\\n y1 = y1-1 if y1 == w else y1\\n y2 = y2-1 if y2 == w else y2\\n\\n pt1 = (x1, y1, image[x1, y1])\\n pt2 = (x1, y2, image[x1, y2])\\n pt3 = (x2, y1, image[x2, y1])\\n pt4 = (x2, y2, image[x2, y2])\\n unknown = (x, y, 0)\\n\\n if pt1[0] == pt3[0] and pt1[2] == pt2[2]:\\n newImg[i, j] = image[int(x), int(y)]\\n else:\\n newImg[i, j] = bi.bilinear_interpolation(pt1, pt2, pt3, pt4, unknown)\\n\\n image = newImg\\n return image\",\n \"def prepare_image(im):\\n width, height = im.size\\n if width > 256 or height > 256:\\n factor = 256.0 / max(width, height)\\n im = im.resize((int(factor * width), int(factor * height)),\\n Image.BILINEAR)\\n return im\",\n \"def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\\n target_height = target_size[0]\\n target_width = target_size[1]\\n if data_format == 'channels_first':\\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"Invilid data format\\\", data_format)\",\n \"def nn_resample_image(image_path, pxl_box_width, pxl_box_height):\\n image = Image.open(image_path)\\n if image.format == \\\"JPEG\\\" or image.format == \\\"PNG\\\":\\n __nn_resample_png_jpeg_image(image, pxl_box_width, pxl_box_height)\\n elif image.format == \\\"GIF\\\":\\n __nn_resample_gif_image(image, pxl_box_width, pxl_box_height)\\n\\n return image\",\n \"def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\\n if isinstance(target_spacing, numbers.Number):\\n target_spacing = [target_spacing] * img.ndim\\n \\n # compute zoom values\\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\\n\\n print \\\"Zoom Factors\\\"\\n print zoom_factors\\n\\n oldImageShape = img.shape\\n \\n # zoom image\\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\\n\\n newImageShape = img.shape\\n old_pixel_spacing = header.get_pixel_spacing(hdr)\\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\\n print \\\"Target Pixel Spacing\\\"\\n print target_spacing\\n\\n print \\\"Actual Pixel Spacing\\\"\\n print new_pixel_spacing\\n # set new voxel spacing\\n header.set_pixel_spacing(hdr, new_pixel_spacing)\\n \\n return img, hdr\",\n \"def scale_image(self, pixels, size):\\n x_min, x_max = np.amin(pixels[:,0]), np.amax(pixels[:,0])\\n y_min, y_max = np.amin(pixels[:,1]), np.amax(pixels[:,1])\\n z_min, z_max = np.amin(pixels[:,2]), np.amax(pixels[:,2])\\n \\n pixels[:,0] -= x_min \\n pixels[:,1] -= y_min\\n pixels[:,2] -= z_min\\n \\n x_max -= x_min\\n y_max -= y_min\\n z_max -= z_min\\n \\n scale_factor = size / max(x_max, y_max, z_max) \\n # All points are now between [0..max]\\n\\n pixels *= scale_factor\\n return pixels\",\n \"def image_resize(img, min_size=600, max_size=1000):\\n C, H, W = img.shape\\n scale1 = min_size / min(H, W)\\n scale2 = max_size / max(H, W)\\n scale = min(scale1, scale2)\\n img = img / 255\\n img = transform.resize(img, (C, H * scale, W * scale),\\n mode='reflect', anti_aliasing=False)\\n # img = pytorch_normalize(img)\\n # img = caffe_normalize(img)\\n return img\",\n \"def resample_by(image_array, compression_factor_list, is_seg=False):\\n original_dims_in_pixels = [image_array.shape[d]\\n for d in range(len(image_array.shape))]\\n target_pixel_dims_list = [int(math.floor(compression_factor_list[d] * original_dims_in_pixels[d]))\\n for d in range(len(image_array.shape))]\\n\\n resized_image = resample(image_array, target_pixel_dims_list, is_seg)\\n\\n return(resized_image)\",\n \"def imresize(img, size):\\n if hasattr(size, \\\"__len__\\\"):\\n num_rows, num_cols = size\\n assert (num_rows > 0) or (num_cols > 0)\\n if num_rows < 0:\\n num_rows = num_cols * img.shape[0] / img.shape[1]\\n if num_cols < 0:\\n num_cols = num_rows * img.shape[1] / img.shape[0]\\n else:\\n num_rows = int(round(img.shape[0] * size))\\n num_cols = int(round(img.shape[1] * size))\\n return skimage.transform.resize(img, (num_rows, num_cols))\",\n \"def resize_image(img):\\n ratio = float(img.size[1]) / float(img.size[0])\\n\\n if ratio > 1.73:\\n h, w = 320, 160 # 512, 256\\n elif ratio < 1.0 / 1.73:\\n h, w = 160, 320 # 256, 512\\n elif ratio > 1.41:\\n h, w = 384, 256\\n elif ratio < 1. / 1.41:\\n h, w = 256, 384\\n elif ratio > 1.15:\\n h, w = 320, 240 # 512, 384\\n elif ratio < 1. / 1.15:\\n h, w = 240, 320 # 384, 512\\n else:\\n h, w = 320, 320 # 384, 384\\n\\n h = int(h)\\n w = int(w)\\n img = TF.resize(img, size=[h, w], interpolation=Image.BILINEAR)\\n return img\",\n \"def resizeImage(self):\\n ratio = float(self.qIma.width()) / float(self.qIma.height())\\n if self.qIma.width() > self.qIma.height():\\n maxWidth = 300\\n maxHeight = int(300 / ratio)\\n else:\\n maxWidth = int(300 / ratio)\\n maxHeight = 300\\n img = self.qIma.toImage().scaled(maxWidth, maxHeight, QtCore.Qt.KeepAspectRatio)\\n return img\",\n \"def scale_image(img, factor=1):\\n\\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))\",\n \"def resample_image(image, out_spacing=(1.0, 1.0, 1.0), out_size=None, is_label=False, pad_value=0):\\n\\n original_spacing = np.array(image.GetSpacing())\\n original_size = np.array(image.GetSize())\\n\\n if out_size is None:\\n out_size = np.round(np.array(original_size * original_spacing / np.array(out_spacing))).astype(int)\\n else:\\n out_size = np.array(out_size)\\n\\n original_direction = np.array(image.GetDirection()).reshape(len(original_spacing), -1)\\n original_center = (np.array(original_size, dtype=float) - 1.0) / 2.0 * original_spacing\\n out_center = (np.array(out_size, dtype=float) - 1.0) / 2.0 * np.array(out_spacing)\\n\\n original_center = np.matmul(original_direction, original_center)\\n out_center = np.matmul(original_direction, out_center)\\n out_origin = np.array(image.GetOrigin()) + (original_center - out_center)\\n\\n resample = sitk.ResampleImageFilter()\\n resample.SetOutputSpacing(out_spacing)\\n resample.SetSize(out_size.tolist())\\n resample.SetOutputDirection(image.GetDirection())\\n resample.SetOutputOrigin(out_origin.tolist())\\n resample.SetTransform(sitk.Transform())\\n resample.SetDefaultPixelValue(pad_value)\\n\\n if is_label:\\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\\n else:\\n resample.SetInterpolator(sitk.sitkBSpline)\\n\\n return resample.Execute(image)\",\n \"def __resize_512p(input_data):\\n rate = 1\\n test_size=512\\n if input_data['img'].shape[0] > input_data['img'].shape[1]:\\n if True: # input_data['img'].shape[1] < 512:\\n rate = test_size / input_data['img'].shape[1]\\n seq = iaa.Sequential([\\n iaa.Scale({'height': \\\"keep-aspect-ratio\\\", 'width': test_size}, 'cubic')\\n ])\\n input_data['img'] = seq.augment_image(input_data['img'])\\n else:\\n if True: # input_data['img'].shape[0] < 512:\\n rate = test_size / input_data['img'].shape[0]\\n seq = iaa.Sequential([\\n iaa.Scale({'height': test_size, 'width': \\\"keep-aspect-ratio\\\"}, 'cubic')\\n ])\\n input_data['img'] = seq.augment_image(input_data['img'])\\n\\n if DataAugmentor._is_synthtext(input_data):\\n input_data['contour'] = [[np.cast['int32'](contour * rate) for contour in contours] for contours in\\n input_data['contour']]\\n else:\\n input_data['contour'] = [np.cast['int32'](contour * rate) for contour in input_data['contour']]\\n input_data['center_point'] = [(np.cast['int32'](point[0] * rate),\\n np.cast['int32'](point[1] * rate)) for point in input_data['center_point']]\\n return input_data\",\n \"def resize(image):\\r\\n return cv2.resize(image, (200, 66), interpolation=cv2.INTER_AREA)\",\n \"def upsampleImage( arr, kernelSize ):\\n return scipy.ndimage.zoom( arr, kernelSize )\",\n \"def reScaleLandsat(self,img):\\n \\n\\t\\tthermalBand = ee.List(['thermal'])\\n\\t\\tthermal = ee.Image(img).select(thermalBand).multiply(10)\\n \\n\\t\\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\\n\\t\\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\\n \\n\\t\\timage = ee.Image(scaled.addBands(thermal)).int16()\\n \\n\\t\\treturn image.copyProperties(img)\",\n \"def resize(im: Image) -> Image:\\n size_factor = random.uniform(*ImageOperations.config.get('resize_interval'))\\n return im.resize((\\n int(round(im.height * size_factor, 0)),\\n int(round(im.width * size_factor, 0))\\n ))\",\n \"def autoscale(self, img: Image, max_height: int, max_width: int):\\n height = img.bgr.shape[0]\\n width = img.bgr.shape[1]\\n\\n diff_height = max_height / height\\n diff_width = max_width / width\\n\\n diff = min(diff_height, diff_width)\\n\\n height = int(height * diff)\\n width = int(width * diff)\\n\\n return img.rescale_image(height, width)\",\n \"def _resize_img(self, results):\\n for key in results.get('img_fields', ['img']):\\n if self.keep_ratio:\\n img, scale_factor = general_ocr.imrescale(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n backend=self.backend)\\n # the w_scale and h_scale has minor difference\\n # a real fix should be done in the general_ocr.imrescale in the future\\n new_h, new_w = img.shape[:2]\\n h, w = results[key].shape[:2]\\n w_scale = new_w / w\\n h_scale = new_h / h\\n else:\\n img, w_scale, h_scale = general_ocr.imresize(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n backend=self.backend)\\n results[key] = img\\n\\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\\n dtype=np.float32)\\n results['img_shape'] = img.shape\\n # in case that there is no padding\\n results['pad_shape'] = img.shape\\n results['scale_factor'] = scale_factor\\n results['keep_ratio'] = self.keep_ratio\",\n \"def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):\\r\\n w = int(round(target_height * image.shape[1] / image.shape[0]))\\r\\n return cv2.resize(image, (w, target_height), interpolation=method)\",\n \"def ulab_bilinear_interpolation():\\n GRID_DATA[1::2, ::2] = SENSOR_DATA[:-1, :]\\n GRID_DATA[1::2, ::2] += SENSOR_DATA[1:, :]\\n GRID_DATA[1::2, ::2] /= 2\\n GRID_DATA[::, 1::2] = GRID_DATA[::, :-1:2]\\n GRID_DATA[::, 1::2] += GRID_DATA[::, 2::2]\\n GRID_DATA[::, 1::2] /= 2\",\n \"def __scale_image(image, scale: float):\\r\\n height, width, _ = image.shape\\r\\n\\r\\n width_scaled = int(np.ceil(width * scale))\\r\\n height_scaled = int(np.ceil(height * scale))\\r\\n\\r\\n im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)\\r\\n\\r\\n # Normalize the image's pixels\\r\\n im_data_normalized = (im_data - 127.5) * 0.0078125\\r\\n\\r\\n return im_data_normalized\",\n \"def downscale_image(img, bottom, x, y):\\n width, height = tuple(img.shape[1::-1])\\n img = img[int(round((1 - bottom) * (height - 1))):(height - 1), 1:(width - 1)]\\n img = cv2.resize(img, (x, y))\\n #img = cv2.Canny(img, 100, 200)\\n ret, img = cv2.threshold(img, img.mean(), 255, cv2.THRESH_BINARY)\\n return img\",\n \"def _resize_img(self, results):\\n for key in results.get('img_fields', ['img']):\\n if self.keep_ratio:\\n img, scale_factor = mmcv.imrescale(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n interpolation=self.interpolation,\\n backend=self.backend)\\n # the w_scale and h_scale has minor difference\\n # a real fix should be done in the mmcv.imrescale in the future\\n new_h, new_w = img.shape[:2]\\n h, w = results[key].shape[:2]\\n w_scale = new_w / w\\n h_scale = new_h / h\\n else:\\n img, w_scale, h_scale = mmcv.imresize(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n interpolation=self.interpolation,\\n backend=self.backend)\\n results[key] = img\\n\\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\\n dtype=np.float32)\\n results['img_shape'] = img.shape\\n # in case that there is no padding\\n results['pad_shape'] = img.shape\\n results['scale_factor'] = scale_factor\\n results['keep_ratio'] = self.keep_ratio\",\n \"def upsample(x, name, size):\\n with tf.name_scope(name):\\n outputs = tf.image.resize_bilinear(x, size)\\n # Return layer's output\\n return outputs\",\n \"def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\\n assert img_r.get_data().shape == (29, 39, 19)\\n assert img_r.get_data()[4, 4, 4] == 1.0\",\n \"def resize_img_norescale(img, size):\\n w,h = size\\n shape = (h,w)\\n out = np.zeros(shape, dtype=img.dtype)\\n i = min(img.shape[0], out.shape[0])\\n j = min(img.shape[1], out.shape[1])\\n out[0:i,0:j] = img[0:i, 0:j]\\n return out\",\n \"def preprocess(img: 'np.ndarray') -> 'np.ndarray':\\n return cv2.resize(img, (45, 45), interpolation=cv2.INTER_AREA)[:,:,:3]\",\n \"def obscure_image(image):\\n size = image.size\\n pixel_size = 9\\n if size[0] < 9 or size[1] < 9:\\n return image\\n image_f = image.filter(ImageFilter.MaxFilter)\\n image_s = image_f.resize((size[0] / pixel_size, size[1] / pixel_size), Image.NEAREST)\\n image_l = image_s.resize((size[0], size[1]), Image.NEAREST)\\n\\n return image_l\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6840502","0.68318045","0.68003356","0.6773299","0.6769946","0.6753389","0.67483824","0.670136","0.6658069","0.65596086","0.65593255","0.6544679","0.65280706","0.6521803","0.6505017","0.649654","0.64671725","0.6443924","0.64256537","0.64148885","0.6406771","0.64033127","0.638599","0.63809955","0.6380628","0.635128","0.63378125","0.63342035","0.6332541","0.63315064","0.63308096","0.6312704","0.6310343","0.63100487","0.63084835","0.6292528","0.628559","0.6269192","0.62619925","0.6260315","0.6258949","0.6255552","0.6250877","0.62360126","0.6226542","0.62205994","0.6216868","0.6205259","0.619899","0.6188594","0.61880016","0.6187558","0.61856985","0.617247","0.6169228","0.61617464","0.61557853","0.6152847","0.61512995","0.61467826","0.6127331","0.61202645","0.61183846","0.6113976","0.6107256","0.6107211","0.61071575","0.6101693","0.6101503","0.60929203","0.60842997","0.60836416","0.60807025","0.6076029","0.60714364","0.6070393","0.605997","0.6059361","0.6033927","0.6031578","0.60284644","0.60280436","0.6027436","0.602318","0.60231775","0.6020624","0.60182744","0.6008209","0.60050946","0.60036045","0.6002614","0.6001213","0.59954536","0.59772855","0.5967249","0.5954752","0.59547454","0.5953776","0.59512","0.5945213","0.5943691"],"string":"[\n \"0.6840502\",\n \"0.68318045\",\n \"0.68003356\",\n \"0.6773299\",\n \"0.6769946\",\n \"0.6753389\",\n \"0.67483824\",\n \"0.670136\",\n \"0.6658069\",\n \"0.65596086\",\n \"0.65593255\",\n \"0.6544679\",\n \"0.65280706\",\n \"0.6521803\",\n \"0.6505017\",\n \"0.649654\",\n \"0.64671725\",\n \"0.6443924\",\n \"0.64256537\",\n \"0.64148885\",\n \"0.6406771\",\n \"0.64033127\",\n \"0.638599\",\n \"0.63809955\",\n \"0.6380628\",\n \"0.635128\",\n \"0.63378125\",\n \"0.63342035\",\n \"0.6332541\",\n \"0.63315064\",\n \"0.63308096\",\n \"0.6312704\",\n \"0.6310343\",\n \"0.63100487\",\n \"0.63084835\",\n \"0.6292528\",\n \"0.628559\",\n \"0.6269192\",\n \"0.62619925\",\n \"0.6260315\",\n \"0.6258949\",\n \"0.6255552\",\n \"0.6250877\",\n \"0.62360126\",\n \"0.6226542\",\n \"0.62205994\",\n \"0.6216868\",\n \"0.6205259\",\n \"0.619899\",\n \"0.6188594\",\n \"0.61880016\",\n \"0.6187558\",\n \"0.61856985\",\n \"0.617247\",\n \"0.6169228\",\n \"0.61617464\",\n \"0.61557853\",\n \"0.6152847\",\n \"0.61512995\",\n \"0.61467826\",\n \"0.6127331\",\n \"0.61202645\",\n \"0.61183846\",\n \"0.6113976\",\n \"0.6107256\",\n \"0.6107211\",\n \"0.61071575\",\n \"0.6101693\",\n \"0.6101503\",\n \"0.60929203\",\n \"0.60842997\",\n \"0.60836416\",\n \"0.60807025\",\n \"0.6076029\",\n \"0.60714364\",\n \"0.6070393\",\n \"0.605997\",\n \"0.6059361\",\n \"0.6033927\",\n \"0.6031578\",\n \"0.60284644\",\n \"0.60280436\",\n \"0.6027436\",\n \"0.602318\",\n \"0.60231775\",\n \"0.6020624\",\n \"0.60182744\",\n \"0.6008209\",\n \"0.60050946\",\n \"0.60036045\",\n \"0.6002614\",\n \"0.6001213\",\n \"0.59954536\",\n \"0.59772855\",\n \"0.5967249\",\n \"0.5954752\",\n \"0.59547454\",\n \"0.5953776\",\n \"0.59512\",\n \"0.5945213\",\n \"0.5943691\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14993,"cells":{"query":{"kind":"string","value":"resizes an image using bilinear interpolation approximation for resampling"},"document":{"kind":"string","value":"def bilinear_interpolation(self, image, fx, fy):\n\n # Write your code for bilinear interpolation here\n w = image.shape[1]\n h = image.shape[0]\n\n newW = int(w*float(fx))\n newH = int(h*float(fy))\n\n ratioW = w / newW\n ratioH = h / newH\n\n import numpy as np\n import math as ma\n from . import interpolation as bilinear\n bi = bilinear.interpolation()\n newImg = np.zeros((newH, newW), np.uint8)\n for i in range(newImg.shape[0]):\n for j in range(newImg.shape[1]):\n\n x = ratioH * i\n y = ratioW * j\n\n x1 = ma.floor(x)\n x2 = ma.ceil(x)\n y1 = ma.floor(y)\n y2 = ma.ceil(y)\n\n x1 = x1-1 if x1 == h else x1\n x2 = x2-1 if x2 == h else x2\n y1 = y1-1 if y1 == w else y1\n y2 = y2-1 if y2 == w else y2\n\n pt1 = (x1, y1, image[x1, y1])\n pt2 = (x1, y2, image[x1, y2])\n pt3 = (x2, y1, image[x2, y1])\n pt4 = (x2, y2, image[x2, y2])\n unknown = (x, y, 0)\n\n if pt1[0] == pt3[0] and pt1[2] == pt2[2]:\n newImg[i, j] = image[int(x), int(y)]\n else:\n newImg[i, j] = bi.bilinear_interpolation(pt1, pt2, pt3, pt4, unknown)\n\n image = newImg\n return image"},"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 scale_image(image, scale=1, interpolation='linear'):\n if interpolation == 'linear':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n if interpolation == 'nearest':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)","def rescale_image(image, scale=0.50):\r\n \r\n wi = int(image.shape[1]*scale)\r\n hei = int(image.shape[0]*scale)\r\n dimension = (wi, hei)\r\n return cv.resize(image, dimension, interpolation = cv.INTER_AREA)","def _batch_resize(self, ims, res, strategy='nearest'):\n order = {'nearest': 0, 'bilinear': 1, 'bicubic': 3}[strategy]\n bs = ims.shape[0]\n out = []\n log.info('Input ims shape: %s' % repr(ims.shape))\n has_extra_dim = len(ims.shape) == 4\n if not has_extra_dim:\n ims = ims[..., np.newaxis]\n h, w = ims.shape[1:3]\n for i in range(bs):\n o = interpolation.zoom(\n ims[i, ...], [res[0] / h, res[1] / w, 1.0], np.float32, order=order)\n out.append(o)\n out = np.stack(out)\n if not has_extra_dim:\n out = np.reshape(out, out.shape[:-1])\n return out","def resize(self, image, fx, fy, interpolation):\n\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, fx, fy)\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, fx, fy)","def resize(img, size, method=\"bilinear\"):\n if method == Image.BILINEAR or method == \"bilinear\":\n method = cv2.INTER_AREA\n elif method == Image.CUBIC or method == \"cubic\":\n method = cv2.INTER_CUBIC\n elif method == Image.NEAREST or method == \"nearest\":\n method = cv2.INTER_NEAREST\n else:\n raise RuntimeError(\"wrong method {}\".format(method))\n if not isinstance(size, (tuple, list)):\n size = (size, size)\n if isinstance(size ,list):\n size = tuple(size)\n im = np.array(img)\n im = cv2.resize(im, size, interpolation=method)\n return Image.fromarray(im)","def resize(img, size, interpolation=Image.BILINEAR):\n\n if isinstance(size, int):\n w, h = img.size\n if (w <= h and w == size) or (h <= w and h == size):\n return img\n if w < h:\n ow = size\n oh = int(size * h / w)\n return img.resize((ow, oh), interpolation)\n else:\n oh = size\n ow = int(size * w / h)\n return img.resize((ow, oh), interpolation)\n else:\n return img.resize(size[::-1], interpolation)","def scale(input_img, size):\n width, height = size\n old_height, old_width = input_img.shape\n x_scale = float(height) / old_height\n y_scale = float(width) / old_width\n\n output_img = np.zeros((height, width), dtype=np.uint8)\n for xidx in xrange(height):\n old_x = float(xidx) / x_scale\n for yidx in xrange(width):\n old_y = float(yidx) / y_scale\n if old_x.is_integer() or old_y.is_integer():\n output_img[xidx, yidx] = input_img[int(old_x), int(old_y)]\n else: # use bilinear interpolation\n x1 = int(np.floor(old_x))\n x2 = int(np.ceil(old_x)) if int(np.ceil(old_x)) < old_height else old_height - 1\n y1 = int(np.floor(old_y))\n y2 = int(np.ceil(old_y)) if int(np.ceil(old_y)) < old_width else old_width - 1\n\n q11 = input_img[x1, y1]\n q12 = input_img[x1, y2]\n q21 = input_img[x2, y1]\n q22 = input_img[x2, y2]\n\n output_img[xidx, yidx] = (q11 * (x2 - old_x) * (y2 - old_y)\n + q21 * (old_x - x1) * (y2 - old_y)\n + q12 * (x2 - old_x) * (old_y - y1)\n + q22 * (old_x - x1) * (old_y - y1)) \\\n / ((x2 - x1) * (y2 - y1) + 1e-10)\n\n return output_img","def upsample_bilinear(input, size=None, scale_factor=None):\n return interpolate(input, size, scale_factor, 'linear', align_corners=True)","def resample_img(image, target_shape, mode='nearest'):\n print(target_shape)\n resize_factor = np.array(target_shape)/image.shape\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def resample(img, newSize):\n # use float as datatype to preserve float precision\n # will convert to np.uint8 when display result\n # get the new dimensions\n nH, nW = newSize\n if np.ndim(img) == 2:\n H, W = img.shape\n res = np.zeros((nH, nW), dtype=np.float32)\n elif np.ndim(img) == 3:\n H, W, _ = img.shape\n res = np.zeros((nH, nW, _), dtype=np.float32)\n else:\n raise ValueError(\"input image has invalid dimension %s\" % (img.shape))\n\n # interpolate the value for the result\n for idx in range(nH * nW):\n i = idx // nW\n j = idx % nW\n orig_i = int((i * H) // nH)\n orig_j = int((j * W) // nW)\n res[i, j] = img[orig_i, orig_j]\n return res","def img_zoom(img, fx, fy, interp=cv2.INTER_AREA):\n res = cv2.resize(img, None, fx=fx, fy=fy,\n interpolation=interp)\n return res","def resample(image, pixsize):\n assert pixsize in [1, 2, 4, 8, 16] # return error and exit otherwise\n imsize = 128/pixsize\n newimage = np.zeros((imsize, imsize))\n for xn, x in enumerate(np.arange(0, 128, pixsize)):\n for yn, y in enumerate(np.arange(0, 128, pixsize)):\n newimage[xn, yn] = np.nansum(image[x:x+pixsize, y:y+pixsize]) # Nansum is important as sum of masked array can be nan\n return newimage","def resize(img, size, interpolation='bilinear', data_format='CHW'):\n _assert_image_tensor(img, data_format)\n\n if not (\n isinstance(size, int)\n or (isinstance(size, (tuple, list)) and len(size) == 2)\n ):\n raise TypeError(f'Got inappropriate size arg: {size}')\n\n if isinstance(size, int):\n w, h = _get_image_size(img, data_format)\n # TODO(Aurelius84): In static graph mode, w and h will be -1 for dynamic shape.\n # We should consider to support this case in future.\n if w <= 0 or h <= 0:\n raise NotImplementedError(\n \"Not support while w<=0 or h<=0, but received w={}, h={}\".format(\n w, h\n )\n )\n if (w <= h and w == size) or (h <= w and h == size):\n return img\n if w < h:\n ow = size\n oh = int(size * h / w)\n else:\n oh = size\n ow = int(size * w / h)\n else:\n oh, ow = size\n\n img = img.unsqueeze(0)\n img = F.interpolate(\n img,\n size=(oh, ow),\n mode=interpolation.lower(),\n data_format='N' + data_format.upper(),\n )\n\n return img.squeeze(0)","def image_resample(f, oversamp=1.0):\n img = imageio.imread(f)\n x = np.linspace(0,img.shape[0],img.shape[0])\n y = np.linspace(0,img.shape[1],img.shape[1])\n #\n # 2x oversample the image since we'll dither it.\n #\n xnew = np.linspace(0, img.shape[0], img.shape[0]*oversamp)\n ynew = np.linspace(0, img.shape[1], img.shape[1]*oversamp)\n from scipy import interpolate\n rc = interpolate.interp2d(x, y, img[:,:,0].flatten(), kind='linear')\n gc = interpolate.interp2d(x, y, img[:,:,1].flatten(), kind='linear')\n bc = interpolate.interp2d(x, y, img[:,:,2].flatten(), kind='linear')\n rgb_new = np.stack([rc(xnew.flatten(), ynew.flatten()),\n gc(xnew.flatten(), ynew.flatten()),\n bc(xnew.flatten(), ynew.flatten())],-1).transpose(1,0,2).astype(np.uint8)\n plt.imshow(rgb_new)\n return rgb_new","def imgResample(img, spacing, size=[], useNearest=False, origin=[], outsideValue=0):\n if len(spacing) != img.GetDimension(): raise Exception(\"len(spacing) != \" + str(img.GetDimension()))\n\n # Set Size\n if size == []:\n inSpacing = img.GetSpacing()\n inSize = img.GetSize()\n size = [int(math.ceil(inSize[i]*(inSpacing[i]/spacing[i]))) for i in range(img.GetDimension())]\n else:\n if len(size) != img.GetDimension(): raise Exception(\"len(size) != \" + str(img.GetDimension()))\n \n if origin == []:\n origin = [0]*img.GetDimension()\n else:\n if len(origin) != img.GetDimension(): raise Exception(\"len(origin) != \" + str(img.GetDimension()))\n \n # Resample input image\n interpolator = [sitk.sitkLinear, sitk.sitkNearestNeighbor][useNearest]\n identityTransform = sitk.Transform()\n identityDirection = list(sitk.AffineTransform(img.GetDimension()).GetMatrix())\n\n return sitk.Resample(img, size, identityTransform, interpolator, origin, spacing, identityDirection, outsideValue)","def scale(img, scale):\n return resize(img, x_scale=scale, y_scale=scale)","def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def pixelate(image, n_w, n_h):\n image = image.resize((n_w, n_h), Image.ANTIALIAS)\n return image","def resample(medical_image, image, new_spacing=[1, 1, 1]):\n image_shape = np.array(list(medical_image.header.get_data_shape()))\n spacing = np.array(list(medical_image.header.get_zooms()))\n resize_factor = spacing / new_spacing\n\n new_shape = image_shape * resize_factor\n new_shape = np.round(new_shape)\n\n real_resize_factor = new_shape / image_shape\n\n new_spacing = spacing / real_resize_factor\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\n\n return resampled_image, new_spacing","def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\n (height, width) = image.shape[:2]\n new_dims = (int(width * scale), int(height * scale))\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)","def resample_img(img, img_type, size, spacing):\n resampler = sitk.ResampleImageFilter()\n resampler.SetOutputDirection(img.GetDirection())\n resampler.SetOutputOrigin(img.GetOrigin())\n resampler.SetOutputSpacing(spacing)\n resampler.SetSize(size)\n if img_type is \"Label\":\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\n elif img_type is \"Image\":\n resampler.SetInterpolator(sitk.sitkLinear)\n imgResampled = resampler.Execute(img)\n\n #axis have to be switched since np.array and keras use them in different order...\n x = np.transpose(sitk.GetArrayFromImage(imgResampled).astype(dtype=np.float), [2, 1, 0])\n return x","def _scale(self, image):\n\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\n image.Rescale(self._width, self._height)\n \n return image","def resampled_to_img(self, target_image, interpolation=None):\n # IMPORTANT: Polymorphism can be implemented by walking the \n # MRO and finding a method that does not raise\n # NotImplementedError. \n raise NotImplementedError","def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)","def resize(self, image, fx=None, fy=None, interpolation=None):\r\n if interpolation == 'bilinear':\r\n return self.bilinear_interpolation(image, float(fx), float(fy))\r\n\r\n elif interpolation == 'nearest_neighbor':\r\n return self.nearest_neighbor(image, float(fx), float(fy))","def resize(img, size, interpolation=Image.BILINEAR):\n\n if not _is_numpy_image(img):\n raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\n raise TypeError('Got inappropriate size arg: {}'.format(size))\n\n if isinstance(size, int):\n size = (size, size)\n \n if img.dtype == np.int32:\n resized = img.astype(np.uint16)\n resized = cv2.resize(resized, size , interpolation)\n else:\n resized = cv2.resize(img, size , interpolation)\n resized = resized.astype(img.dtype)\n return resized","def img_resize(img, img_height):\n\n\timg_width = int(img_height*img.shape[1]/img.shape[0])\n\n\timg_pil = Image.fromarray(img) # convert array back to image\n\n\timg_pil_resize = img_pil.resize((img_width, img_height), Image.LANCZOS) # resize\n\n\treturn np.array(img_pil_resize)","def resize(self, image, fx=None, fy=None, interpolation=None):\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, float(fx), float(fy))\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, float(fx), float(fy))","def resample(image_array, target_pixel_dims_list, is_seg=False):\n # if dealing with segmentation images, do not interpolate\n # this is done by choosing interpolation order == 0\n order = 0 if is_seg == True else 3\n\n original_dims_in_pixels = [image_array.shape[d]\n for d in range(len(image_array.shape))]\n compression_list = [target_pixel_dims_list[d] / original_dims_in_pixels[d]\n for d in range(len(image_array.shape))]\n\n if (is_seg):\n resized_image = ndimage.interpolation.zoom(\n image_array, zoom=compression_list, order=order, cval=0)\n return resized_image\n else:\n resized_image = ndimage.interpolation.zoom(\n image_array, zoom=compression_list, order=order, cval=0)\n return resized_image","def process_image(image):\n image = resize(image)\n return image","def scale_down(image:np.array)->np.array:\n src = image\n scale_percent = 25\n width = int(src.shape[1] * scale_percent / 100)\n height = int(src.shape[0] * scale_percent / 100)\n dsize = (width, height)\n output = cv2.resize(src, dsize)\n return output","def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\n\n if scaling_method == 'scale':\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\n elif scaling_method == 'ratio':\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\n im = scipy.misc.imresize(im, scale)\n return (im, scale)","def resize_3d_image_nearest_interpolation(im, res_s):\n new_shape = [res_s, im.shape[1], im.shape[2]]\n ret0 = np.zeros(new_shape, dtype=im.dtype)\n for i in range(im.shape[2]):\n im_slice = np.array(\n Image.fromarray(im[..., i]).resize(\n (im.shape[1], res_s), resample=Image.Resampling.NEAREST\n )\n )\n ret0[..., i] = im_slice\n\n new_shape = [res_s, res_s, res_s]\n ret = np.zeros(new_shape, dtype=im.dtype)\n for i in range(res_s):\n im_slice = np.array(\n Image.fromarray(ret0[i, ...]).resize(\n (res_s, res_s), resample=Image.Resampling.NEAREST\n )\n )\n ret[i, ...] = im_slice\n return ret","def bilinear_interpolation(self, image, fx, fy):\n\n # Write your code for bilinear interpolation here\n (width, height) = image.shape\n (scaledWidth, scaledHeight) = (int(fy * width), int(fy * height))\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\n\n for x in range(0, scaledWidth):\n for y in range(0, scaledHeight):\n nx = math.floor(x / fx)\n ny = math.floor(y / fy)\n if (nx + 1) > width - 1:\n nx = width - 2\n if (ny + 1) > height - 1:\n ny = height - 2\n\n n1 = (nx, ny)\n n2 = (nx, ny+1)\n n3 = (nx+1, ny)\n n4 = (nx+1, ny+1)\n\n resampledImage[x, y] = self.inter.bilinear_interpolation([n1, image[n1]], [n2, image[n2]], [n3, image[n3]],[n4, image[n4]], [x/fx, y/fy])\n\n return resampledImage","def resize(img, x_scale, y_scale, optimize=True):\n interpolation = cv.INTER_LINEAR\n\n # pick an optimized scaler if asked to\n if optimize:\n if x_scale > 1 and y_scale > 1:\n interpolation = cv.INTER_CUBIC\n else:\n interpolation = cv.INTER_AREA\n\n return cv.resize(\n img,\n None,\n fx=x_scale,\n fy=y_scale,\n interpolation=interpolation,\n )","def downsample_image(img, output_path, ratio=2):\n assert ratio>=1, ratio\n if ratio == 1:\n return True\n old_im = Image.open(img)\n old_size = old_im.size\n new_size = (int(old_size[0]/ratio), int(old_size[1]/ratio))\n\n new_im = old_im.resize(new_size, PIL.Image.LANCZOS)\n new_im.save(output_path)\n return True","def _resize_short_within(self, img, short, max_size, mult_base=1, interp=Image.BILINEAR):\n w, h = img.size\n im_size_min, im_size_max = (h, w) if w > h else (w, h)\n scale = float(short) / float(im_size_min)\n if np.round(scale * im_size_max / mult_base) * mult_base > max_size:\n # fit in max_size\n scale = float(np.floor(max_size / mult_base) * mult_base) / float(im_size_max)\n new_w, new_h = (int(np.round(w * scale / mult_base) * mult_base),\n int(np.round(h * scale / mult_base) * mult_base))\n img = img.resize((new_w, new_h), interp)\n return img","def _resize(img, max_dim=128):\n if max(img.shape[:3]) <= max_dim:\n return img\n else:\n new_size = [max_dim / s if s >= max_dim else 1.0 for s in img.shape[:3]]\n new_size.append(1.0) # for channel\n return scipy.ndimage.zoom(img, new_size, order=2)","def resizeImage(IMG,IMAGE_SIZE):\n\n RESCALED_IMAGE = skimage.transform.resize(IMG,[IMG.shape[0],IMAGE_SIZE,IMAGE_SIZE])\n return RESCALED_IMAGE","def resample(self, image, target_shape, mode='nearest'):\n resize_factor = image.shape / np.array(target_shape)\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def rescale_and_resize_image(img_name, img_size, save_img):\n \n try:\n \n # Open image\n img = Image.open(img_name)\n \n # Resize image\n img = img.resize((int(img_size), int(img_size)), Image.ANTIALIAS) \n \n # Get data from image\n img_list = list(img.getdata())\n \n # Find minimum and maximum value pixels in the image\n img_max = max(img_list)\n img_min = min(img_list)\n \n # Determine factor to scale to a 8-bit image\n scale_factor = 255.0/(img_max - img_min)\n \n img_list_new = [0] * img_size * img_size\n \n # Rescale all pixels to the range 0 to 255 (in line with unit8 values)\n for i in range(0,img_size):\n for j in range(0,img_size):\n img_list_new[i*img_size + j] = int((img_list[i*img_size + j]-img_min)*scale_factor)\n if (img_list_new[i*img_size + j] > 255) or (img_list_new[i*img_size + j] < 0) or (img_list_new[i*img_size + j]-int(img_list_new[i*img_size + j]) != 0):\n print(\"img_list_new[%d][%d] = %r\" % (i,j,img_list_new[i*img_size + j]))\n \n img.putdata(img_list_new)\n \n # Convert to uint8 greyscale image\n img = img.convert('L')\n \n # Save image\n if save_img:\n img.save(img_name)\n else:\n ph = ImageTk.PhotoImage(img)\n return ph\n \n finally:\n \n # Close image\n img.close()","def bilinear_interpolation(self, image, fx, fy):\r\n\r\n # Write your code for bilinear interpolation here\r\n width, height = image.shape[:2]\r\n nw = int(width * fx)\r\n nh = int(height * fy)\r\n new_img = np.zeros((nw, nh), np.uint8)\r\n inter = interpolation.interpolation()\r\n for i in range(nw):\r\n for j in range(nh):\r\n ii = int(np.floor((i * (image.shape[0]-1))/(nw - 1)))\r\n jj = int(np.floor((j * (image.shape[1]-1))/(nh - 1)))\r\n\r\n # All four corner pixels copied as it is\r\n if (ii == 0 and jj == 0) or \\\r\n (ii == 0 and jj == image.shape[1] - 1) or \\\r\n (ii == image.shape[0] - 1 and jj == 0) or \\\r\n (ii == image.shape[0] - 1 and jj == image.shape[1] - 1):\r\n new_img[i][j] = image[ii][jj]\r\n\r\n # Linear interpolation along left and right edges\r\n elif ii == 0 or ii == image.shape[0] - 1:\r\n new_img[i][j] = inter.linear_interpolation([jj - 1, image[ii][jj - 1]], [jj + 1, image[ii][jj + 1]], jj)\r\n\r\n # Linear interpolation along top and bottom edges\r\n elif jj == 0 or jj == image.shape[1] - 1:\r\n new_img[i][j] = inter.linear_interpolation([ii - 1, image[ii - 1][jj]], [ii + 1, image[ii + 1][jj]], ii)\r\n\r\n else:\r\n\r\n q11i, q11j = ii - 1, jj - 1\r\n q12i, q12j = ii - 1, jj + 1\r\n q21i, q21j = ii + 1, jj - 1\r\n q22i, q22j = ii + 1, jj + 1\r\n\r\n point_a = [q11i, q11j, image[q11i][q11j]]\r\n point_b = [q12i, q12j, image[q12i][q12j]]\r\n point_c = [q21i, q21j, image[q21i][q21j]]\r\n point_d = [q22i, q22j, image[q22i][q22j]]\r\n unknown = [ii, jj]\r\n\r\n new_img[i][j] = inter.bilinear_interpolation(point_a, point_b, point_c, point_d, unknown)\r\n\r\n return new_img","def resize(img, size):\n img = tf.expand_dims(img, 0)\n return tf.image.resize_bilinear(img, size)[0,:,:,:]","def rescale_image(image, rescale_width):\r\n\r\n # image size\r\n image_height = pdb.gimp_image_height(image)\r\n image_width = pdb.gimp_image_width(image)\r\n\r\n # new image height\r\n rescale_height = round(image_height * (rescale_width * 1.0 / image_width))\r\n\r\n pdb.gimp_image_scale(image, rescale_width, rescale_height)\r\n gimp.message('Rescaled image')","def scale_image(image=np.zeros((100,100)), \n new_width=100,\n ascii_block_size=(2,3)):\n \n original_width, original_height = image.shape\n aspect_ratio = original_height / float(original_width)\n w,h = ascii_block_size\n new_height = int(h/w * aspect_ratio * new_width)\n\n return skimage.transform.resize(image, (new_width, new_height))","def resize_img(self,scale=1):\n reduced = self.image.reduce((scale,scale))\n reduced.save(\"../edited/{}\".format(self.image.filename))\n\n reduced = Image.open(\"../edited/{}\".format(self.image.filename))\n return reduced","def get_scaled_image(self, min_width=1024):\n self.dispersion = self.image.getCalibration().pixelWidth\n self.offset = self.image.getCalibration().xOrigin\n binning = 1\n while binning * min_width < self.image.getWidth():\n binning *= 2\n if binning > 1:\n binning /= 2\n IJ.run(self.image, 'Select None', '')\n new_image = self.image.crop()\n IJ.run(new_image, 'Bin...', 'x=%d y=%d binning=Average' %\n (binning, binning))\n self.dispersion *= binning\n self.offset /= binning\n self.image = new_image\n return new_image","def imrescale(img,scale,return_scale=False,interpolation='bilinear'):\n h,w = img.shape[:2]\n if isinstance(scale,(float,int)):\n if scale <= 0:\n raise ValueError('Invalid scale:{},must be positive.'.format(scale))\n scale_factor = scale\n \n elif isinstance(scale,tuple):\n max_long_edge = max(scale)\n max_short_edge = min(scale)\n scale_factor = min(max_long_edge/max(h,w),\n max_short_edge/min(h,w))\n else:\n raise ValueError('Scale must be a number or tuple of int,but got {}'.format(type(scale)))\n \n new_size = _scale_size((w,h),scale_factor)\n rescaled_img = imresize(img,new_size,interpolation = interpolation)\n if return_scale:\n return rescaled_img,scale_factor\n else:\n return rescaled_img","def test_nib_resample_image_4d(fake_4dimage_nib):\n img_r = resampling.resample_nib(fake_4dimage_nib, new_size=[2, 2, 1, 1], new_size_type='factor', interpolation='nn')\n assert img_r.get_data().shape == (18, 18, 9, 3)\n assert img_r.get_data()[8, 8, 4, 0] == 1.0 # make sure there is no displacement in world coordinate system\n assert img_r.get_data()[8, 8, 4, 1] == 0.0\n assert img_r.header.get_zooms() == (0.5, 0.5, 1.0, 1.0)","def rescale(self, img):\n\n if self.scale != 1:\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\n else:\n return img","def _resize_image_tuple(self, image_tup):\n if self.api_info is None:\n self.get_info() # sets the image size and other such info from server.\n try:\n MIN_SIZE = self.api_info['min_image_size']\n MAX_SIZE = self.api_info['max_image_size']\n img = Image.open(image_tup[0])\n min_dimension = min(img.size)\n max_dimension = max(img.size)\n min_ratio = float(MIN_SIZE) / min_dimension\n max_ratio = float(MAX_SIZE) / max_dimension\n def get_newsize(img, ratio, SIZE):\n if img.size[0] == min_dimension:\n newsize = (SIZE, int(round(ratio * img.size[1])))\n else:\n newsize = (int(round(ratio * img.size[0])), SIZE)\n return newsize\n im_changed = False\n # Only resample if min size is > 512 or < 256\n if max_ratio < 1.0: # downsample to MAX_SIZE\n newsize = get_newsize(img, max_ratio, MAX_SIZE)\n img = img.resize(newsize, Image.BILINEAR)\n im_changed = True\n elif min_ratio > 1.0: # upsample to MIN_SIZE\n newsize = get_newsize(img, min_ratio, MIN_SIZE)\n img = img.resize(newsize, Image.BICUBIC)\n im_changed = True\n else: # no changes needed so rewind file-object.\n image_tup[0].seek(0)\n # Finally make sure we have RGB images.\n if img.mode != \"RGB\":\n img = img.convert(\"RGB\")\n im_changed = True\n if im_changed:\n io = StringIO()\n img.save(io, 'jpeg', quality=IM_QUALITY)\n io.seek(0) # rewind file-object to read() below is good to go.\n image_tup = (io, image_tup[1])\n except IOError, e:\n logger.warning('Could not open image file: %s, still sending to server.', image_tup[1])\n return image_tup","def resize(img):\n size = (500, 500)\n img.thumbnail(size)\n return img","def scale_img(img, scale=1.0):\n dim = (int(img.shape[1]*scale), int(img.shape[0]*scale))\n return cv2.resize(img.copy(), dim, interpolation=cv2.INTER_AREA)","def resample(scan, image, new_spacing=[1,1,1]):\n spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)\n\n resize_factor = spacing / new_spacing\n new_real_shape = image.shape * resize_factor\n new_shape = np.round(new_real_shape)\n real_resize_factor = new_shape / image.shape\n new_spacing = spacing / real_resize_factor\n\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\n\n batch = []\n for i in range(0, image.shape[0] - 3, 3):\n tmp = []\n for j in range(3):\n img = image[i + j]\n img= cv2.resize(img, (224, 224))\n tmp.append(img)\n\n tmp = np.array(tmp)\n batch.append(tmp)\n\n batch = np.array(batch)\n\n return batch","def __resize_bilinear_with_factor(self, x, factor_size=None, data_format=None):\n height_factor = factor_size[0]\n width_factor = factor_size[1]\n if data_format == 'channels_first':\n height = x.shape[2] * height_factor\n width = x.shape[3] * width_factor\n new_shape = tf.constant(np.array([height, width]).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"invilid data format\", data_format)","def retinanet_resize_image(img, min_side=800, max_side=1333):\n # compute scale to resize the image\n scale = retinanet_compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)\n # scale = round(scale, 4)\n # print(\"scale: \", scale)\n\n # resize the image with the computed scale\n img = cv2.resize(img, None, fx=scale, fy=scale)\n\n return img, scale","def resize(img, size, interpolation=cv2.INTER_LINEAR):\n if not _is_numpy_image(img):\n raise TypeError('img should be nparrary Image. Got {}'.format(type(img)))\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\n raise TypeError('Got inappropriate size arg: {}'.format(size))\n\n if isinstance(size, int):\n h, w = img.shape[:2]\n if (w <= h and w == size) or (h <= w and h == size):\n return img\n if w < h:\n ow = size\n oh = int(size * h / w)\n return cv2.resize(img, (ow, oh), interpolation)\n else:\n oh = size\n ow = int(size * w / h)\n return cv2.resize(img, (ow, oh), interpolation)\n else:\n return cv2.resize(img, tuple(size[::-1]), interpolation)","def scale_image(self, image):\n err_m1 = \"image must be a numpy.ndarray with shape (h, w, 3)\"\n if not isinstance(image, np.ndarray):\n raise TypeError(err_m1)\n if len(image.shape) != 3 or image.shape[2] != 3:\n raise TypeError(err_m1)\n shape0 = image.shape[0]\n shape1 = image.shape[1]\n\n if shape0 > shape1:\n new_w = int(image.shape[1] * 512 / image.shape[0])\n new_h = 512\n elif shape0 < shape1:\n new_h = int(image.shape[0] * 512 / image.shape[1])\n new_w = 512\n else:\n new_h = 512\n new_w = 512\n tf.image.ResizeMethod.BICUBIC\n image = tf.expand_dims(image, 0)\n image = tf.image.resize_bicubic(image,\n (new_h, new_w),\n align_corners=False)\n image = image / 255\n image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\n\n return image","def bilinear_interpolation(x, y, img):\n Warning(\"This method should not be use, use instead pytorch grid_sample method\")\n _, height, width = img.shape\n x_min = x.int()\n y_min = y.int()\n\n w_x = x - x_min\n w_y = y - y_min\n \n w1 = torch.cat((w_x, w_y), 1).norm(dim=1)\n w2 = torch.cat((1-w_x, w_y), 1).norm(dim=1)\n w3 = torch.cat((1-w_x, 1-w_y), 1).norm(dim=1)\n w4 = torch.cat((w_x, 1-w_y), 1).norm(dim=1)\n\n sum_w = w1 + w2 + w3 + w4\n\n w1 = w1 / sum_w\n w2 = w2 / sum_w\n w3 = w3 / sum_w\n w4 = w4 / sum_w\n\n # TODO how to deal with out of the sample sampling when the pixel is from outside\n x_min = x_min.clamp(0, width - 2).view(-1)\n y_min = y_min.clamp(0, height - 2).view(-1)\n\n pix = \\\n w1 * img[:, y_min.long(), x_min.long()] + \\\n w2 * img[:, y_min.long(), x_min.long() + 1] + \\\n w3 * img[:, y_min.long() + 1, x_min.long() + 1] + \\\n w4 * img[:, y_min.long() + 1, x_min.long()]\n\n pix = pix.view(img.shape)\n return pix","def resize_image(image: Image) -> Image:\n expected_x: int = 1024\n expected_y: int = 768\n x, y = image.size\n if x > expected_x or y > expected_y:\n scale = min(expected_x / x, expected_y / y)\n return image.resize((int(x * scale), int(y * scale)))\n else:\n return image.resize((int(0.9*x), int(0.9*y)))","def Resampler(name):\n\n def resample_average(path, dsquery, dstile, image_format):\n for i in range(1, dstile.RasterCount+1):\n res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i), \"average\")\n if res != 0:\n raise ImageOutputException(\"RegenerateOverview() failed with error %d\" % res)\n\n gdal_write(path, dstile, image_format)\n\n def resample_antialias(path, dsquery, dstile, image_format):\n querysize = dsquery.RasterXSize\n tilesize = dstile.RasterXSize\n\n array = numpy.zeros((querysize, querysize, 4), numpy.uint8)\n for i in range(dstile.RasterCount):\n array[:,:,i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1), 0, 0, querysize, querysize)\n im = Image.fromarray(array, 'RGBA') # Always four bands\n im1 = im.resize((tilesize,tilesize), Image.ANTIALIAS)\n\n if os.path.exists(path):\n im0 = Image.open(path)\n im1 = Image.composite(im1, im0, im1)\n\n ensure_dir_exists(path)\n\n if image_format == \"JPEG\":\n im1.save(path, image_format, quality=jpeg_quality)\n else:\n im1.save(path, image_format)\n\n\n if name == \"average\":\n return resample_average\n elif name == \"antialias\":\n return resample_antialias\n\n resampling_methods = {\n \"near\" : gdal.GRA_NearestNeighbour,\n \"bilinear\" : gdal.GRA_Bilinear,\n \"cubic\" : gdal.GRA_Cubic,\n \"cubicspline\" : gdal.GRA_CubicSpline,\n \"lanczos\" : gdal.GRA_Lanczos\n }\n\n resampling_method = resampling_methods[name]\n\n def resample_gdal(path, dsquery, dstile, image_format):\n querysize = dsquery.RasterXSize\n tilesize = dstile.RasterXSize\n\n dsquery.SetGeoTransform( (0.0, tilesize / float(querysize), 0.0, 0.0, 0.0, tilesize / float(querysize)) )\n dstile.SetGeoTransform( (0.0, 1.0, 0.0, 0.0, 0.0, 1.0) )\n\n res = gdal.ReprojectImage(dsquery, dstile, None, None, resampling_method)\n if res != 0:\n raise ImageOutputException(\"ReprojectImage() failed with error %d\" % res)\n\n gdal_write(path, dstile, image_format)\n\n return resample_gdal","def scale_image(img, factor=1):\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))","def Rescale(self):\r\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\r\n bitmap = self.scaled = self.bitmap\r\n if not bitmap: return\r\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\r\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\r\n image = bitmap.ConvertToImage()\r\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\r\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\r\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\r\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\r","def resample_image(I,spacing,desiredSize, spline_order=1,zero_boundary=False,identity_map=None):\n if len(I.shape) != len(desiredSize)+2:\n desiredSize = desiredSize[2:]\n sz = np.array(list(I.size()))\n # check that the batch size and the number of channels is the same\n nrOfI = sz[0]\n nrOfC = sz[1]\n\n desiredSizeNC = np.array([nrOfI,nrOfC]+list(desiredSize))\n\n newspacing = spacing*((sz[2::].astype('float')-1.)/(desiredSizeNC[2::].astype('float')-1.)) ###########################################\n if identity_map is not None:\n idDes= identity_map\n else:\n idDes = torch.from_numpy(py_utils.identity_map_multiN(desiredSizeNC,newspacing)).to(I.device)\n # now use this map for resampling\n ID = py_utils.compute_warped_image_multiNC(I, idDes, newspacing, spline_order,zero_boundary)\n\n return ID, newspacing","def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):\n assert _is_numpy_image(img), 'img should be nparray Image'\n img = crop(img, i, j, h, w)\n img = resize(img, size, interpolation)\n return img","def resize_img(input_img, scale=1.1):\n if scale == 1.0:\n return input_img\n rows, cols, ch = input_img.shape\n d = rows * (scale - 1) # overall image size change from rows, cols, to rows - 2d, cols - 2d\n pts1 = np.float32([[d, d], [rows - 1 - d, d], [d, cols - 1 - d], [rows - 1 - d, cols - 1 - d]])\n pts2 = np.float32([[0, 0], [rows - 1, 0], [0, cols - 1], [rows - 1, cols - 1]])\n M = cv2.getPerspectiveTransform(pts1, pts2)\n dst = cv2.warpPerspective(input_img, M, (cols, rows), borderMode=1)\n return dst","def shrink_image(image, output_size, interp_type=InterpolationType.INTEGRAL, output=None):\n\n if isinstance(output_size, (int, int)):\n scale = float(output_size) / max(image.getWidth(), image.getHeight())\n output_shape = (int(image.getHeight()*scale), int(image.getWidth()*scale))\n else:\n output_shape = output_size\n\n if image.getWidth() < output_shape[1] or image.getHeight() < output_shape[0]:\n raise RuntimeError(\"Either width or height is larger in output than input\")\n\n if output is None:\n output = image.createNew(output_shape[1], output_shape[0])\n else:\n output.reshape(output_shape[1], output_shape[0])\n\n if interp_type == InterpolationType.INTEGRAL:\n pbg.gateway.jvm.boofcv.alg.filter.misc.AverageDownSampleOps.down(image, output)\n else:\n scale_x = output_shape[1] / float(image.getWidth())\n scale_y = output_shape[0] / float(image.getHeight())\n\n fdist = pbg.gateway.jvm.boofcv.abst.distort.FDistort(image, output)\n fdist.interp(interpolation_type_to_java(interp_type))\n fdist.affine(scale_x, 0.0, 0.0, scale_y, 0.0, 0.0)\n fdist.apply()\n\n return output","def __resize_image(self, img):\n return cv2.resize(img, self.g.img_size, \n interpolation = cv2.INTER_CUBIC)","def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):\n factors = [new_size[i] / im.shape[i] for i in range(2)]\n\n #assert factors[0] == factors[1], \"Must have same factor for now\"\n f = factors[0] \n \n if f < 1:\n im2 = pyramid_reduce(im, downscale=1/f)\n elif f > 1:\n im2 = pyramid_expand(im, upscale=f)\n else:\n im2 = im\n\n assert im2.shape[:2] == tuple(new_size), \"{0} != {1} (original size: {2})\".format(im2.shape, new_size, im.shape)\n \n return im2","def resize(img,scale):\n sdims=img.shape\n datatype=c_double\n if img.dtype!=datatype:\n print \"Error the image must be of doubles!\"\n raise RuntimeError\n\n if scale>1.0:\n print \"Invalid scaling factor!\"\n raise RuntimeError \n \n img = asfortranarray(img,c_double) # make array continguous\n \n try:\n mresize = ctypeslib.load_library(\"libresize.so\",\".\") \n except:\n print \"Unable to load resize library\"\n raise RuntimeError\n \n #use two times the 1d resize to get a 2d resize\n fresize = mresize.resize1dtran\n fresize.restype = None\n fresize.argtypes = [ ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int,ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int, c_int , c_int ]\n ddims = [int(round(sdims[0]*scale)),int(round(sdims[1]*scale)),sdims[2]];\n mxdst = zeros((ddims), dtype=datatype)\n tmp = zeros((ddims[0],sdims[1],sdims[2]), dtype=datatype)\n img1=img\n t1=time()\n fresize(img1, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);\n fresize(tmp, sdims[1], mxdst, ddims[1], ddims[0], sdims[2]);\n t2=time()\n return mxdst.reshape(ddims[2],ddims[1],ddims[0]).T","def resize(image, size):\n return np.array(Image.fromarray(image).resize(size))","def prepare_image(im):\n width, height = im.size\n if width > 256 or height > 256:\n factor = 256.0 / max(width, height)\n im = im.resize((int(factor * width), int(factor * height)),\n Image.BILINEAR)\n return im","def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\n target_height = target_size[0]\n target_width = target_size[1]\n if data_format == 'channels_first':\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"Invilid data format\", data_format)","def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\n if isinstance(target_spacing, numbers.Number):\n target_spacing = [target_spacing] * img.ndim\n \n # compute zoom values\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\n\n print \"Zoom Factors\"\n print zoom_factors\n\n oldImageShape = img.shape\n \n # zoom image\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\n\n newImageShape = img.shape\n old_pixel_spacing = header.get_pixel_spacing(hdr)\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\n print \"Target Pixel Spacing\"\n print target_spacing\n\n print \"Actual Pixel Spacing\"\n print new_pixel_spacing\n # set new voxel spacing\n header.set_pixel_spacing(hdr, new_pixel_spacing)\n \n return img, hdr","def nn_resample_image(image_path, pxl_box_width, pxl_box_height):\n image = Image.open(image_path)\n if image.format == \"JPEG\" or image.format == \"PNG\":\n __nn_resample_png_jpeg_image(image, pxl_box_width, pxl_box_height)\n elif image.format == \"GIF\":\n __nn_resample_gif_image(image, pxl_box_width, pxl_box_height)\n\n return image","def image_resize(img, min_size=600, max_size=1000):\n C, H, W = img.shape\n scale1 = min_size / min(H, W)\n scale2 = max_size / max(H, W)\n scale = min(scale1, scale2)\n img = img / 255\n img = transform.resize(img, (C, H * scale, W * scale),\n mode='reflect', anti_aliasing=False)\n # img = pytorch_normalize(img)\n # img = caffe_normalize(img)\n return img","def scale_image(self, pixels, size):\n x_min, x_max = np.amin(pixels[:,0]), np.amax(pixels[:,0])\n y_min, y_max = np.amin(pixels[:,1]), np.amax(pixels[:,1])\n z_min, z_max = np.amin(pixels[:,2]), np.amax(pixels[:,2])\n \n pixels[:,0] -= x_min \n pixels[:,1] -= y_min\n pixels[:,2] -= z_min\n \n x_max -= x_min\n y_max -= y_min\n z_max -= z_min\n \n scale_factor = size / max(x_max, y_max, z_max) \n # All points are now between [0..max]\n\n pixels *= scale_factor\n return pixels","def resample_by(image_array, compression_factor_list, is_seg=False):\n original_dims_in_pixels = [image_array.shape[d]\n for d in range(len(image_array.shape))]\n target_pixel_dims_list = [int(math.floor(compression_factor_list[d] * original_dims_in_pixels[d]))\n for d in range(len(image_array.shape))]\n\n resized_image = resample(image_array, target_pixel_dims_list, is_seg)\n\n return(resized_image)","def imresize(img, size):\n if hasattr(size, \"__len__\"):\n num_rows, num_cols = size\n assert (num_rows > 0) or (num_cols > 0)\n if num_rows < 0:\n num_rows = num_cols * img.shape[0] / img.shape[1]\n if num_cols < 0:\n num_cols = num_rows * img.shape[1] / img.shape[0]\n else:\n num_rows = int(round(img.shape[0] * size))\n num_cols = int(round(img.shape[1] * size))\n return skimage.transform.resize(img, (num_rows, num_cols))","def resize_image(img):\n ratio = float(img.size[1]) / float(img.size[0])\n\n if ratio > 1.73:\n h, w = 320, 160 # 512, 256\n elif ratio < 1.0 / 1.73:\n h, w = 160, 320 # 256, 512\n elif ratio > 1.41:\n h, w = 384, 256\n elif ratio < 1. / 1.41:\n h, w = 256, 384\n elif ratio > 1.15:\n h, w = 320, 240 # 512, 384\n elif ratio < 1. / 1.15:\n h, w = 240, 320 # 384, 512\n else:\n h, w = 320, 320 # 384, 384\n\n h = int(h)\n w = int(w)\n img = TF.resize(img, size=[h, w], interpolation=Image.BILINEAR)\n return img","def resizeImage(self):\n ratio = float(self.qIma.width()) / float(self.qIma.height())\n if self.qIma.width() > self.qIma.height():\n maxWidth = 300\n maxHeight = int(300 / ratio)\n else:\n maxWidth = int(300 / ratio)\n maxHeight = 300\n img = self.qIma.toImage().scaled(maxWidth, maxHeight, QtCore.Qt.KeepAspectRatio)\n return img","def scale_image(img, factor=1):\n\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))","def resample_image(image, out_spacing=(1.0, 1.0, 1.0), out_size=None, is_label=False, pad_value=0):\n\n original_spacing = np.array(image.GetSpacing())\n original_size = np.array(image.GetSize())\n\n if out_size is None:\n out_size = np.round(np.array(original_size * original_spacing / np.array(out_spacing))).astype(int)\n else:\n out_size = np.array(out_size)\n\n original_direction = np.array(image.GetDirection()).reshape(len(original_spacing), -1)\n original_center = (np.array(original_size, dtype=float) - 1.0) / 2.0 * original_spacing\n out_center = (np.array(out_size, dtype=float) - 1.0) / 2.0 * np.array(out_spacing)\n\n original_center = np.matmul(original_direction, original_center)\n out_center = np.matmul(original_direction, out_center)\n out_origin = np.array(image.GetOrigin()) + (original_center - out_center)\n\n resample = sitk.ResampleImageFilter()\n resample.SetOutputSpacing(out_spacing)\n resample.SetSize(out_size.tolist())\n resample.SetOutputDirection(image.GetDirection())\n resample.SetOutputOrigin(out_origin.tolist())\n resample.SetTransform(sitk.Transform())\n resample.SetDefaultPixelValue(pad_value)\n\n if is_label:\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\n else:\n resample.SetInterpolator(sitk.sitkBSpline)\n\n return resample.Execute(image)","def __resize_512p(input_data):\n rate = 1\n test_size=512\n if input_data['img'].shape[0] > input_data['img'].shape[1]:\n if True: # input_data['img'].shape[1] < 512:\n rate = test_size / input_data['img'].shape[1]\n seq = iaa.Sequential([\n iaa.Scale({'height': \"keep-aspect-ratio\", 'width': test_size}, 'cubic')\n ])\n input_data['img'] = seq.augment_image(input_data['img'])\n else:\n if True: # input_data['img'].shape[0] < 512:\n rate = test_size / input_data['img'].shape[0]\n seq = iaa.Sequential([\n iaa.Scale({'height': test_size, 'width': \"keep-aspect-ratio\"}, 'cubic')\n ])\n input_data['img'] = seq.augment_image(input_data['img'])\n\n if DataAugmentor._is_synthtext(input_data):\n input_data['contour'] = [[np.cast['int32'](contour * rate) for contour in contours] for contours in\n input_data['contour']]\n else:\n input_data['contour'] = [np.cast['int32'](contour * rate) for contour in input_data['contour']]\n input_data['center_point'] = [(np.cast['int32'](point[0] * rate),\n np.cast['int32'](point[1] * rate)) for point in input_data['center_point']]\n return input_data","def resize(image):\r\n return cv2.resize(image, (200, 66), interpolation=cv2.INTER_AREA)","def upsampleImage( arr, kernelSize ):\n return scipy.ndimage.zoom( arr, kernelSize )","def reScaleLandsat(self,img):\n \n\t\tthermalBand = ee.List(['thermal'])\n\t\tthermal = ee.Image(img).select(thermalBand).multiply(10)\n \n\t\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\n\t\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\n \n\t\timage = ee.Image(scaled.addBands(thermal)).int16()\n \n\t\treturn image.copyProperties(img)","def resize(im: Image) -> Image:\n size_factor = random.uniform(*ImageOperations.config.get('resize_interval'))\n return im.resize((\n int(round(im.height * size_factor, 0)),\n int(round(im.width * size_factor, 0))\n ))","def autoscale(self, img: Image, max_height: int, max_width: int):\n height = img.bgr.shape[0]\n width = img.bgr.shape[1]\n\n diff_height = max_height / height\n diff_width = max_width / width\n\n diff = min(diff_height, diff_width)\n\n height = int(height * diff)\n width = int(width * diff)\n\n return img.rescale_image(height, width)","def _resize_img(self, results):\n for key in results.get('img_fields', ['img']):\n if self.keep_ratio:\n img, scale_factor = general_ocr.imrescale(\n results[key],\n results['scale'],\n return_scale=True,\n backend=self.backend)\n # the w_scale and h_scale has minor difference\n # a real fix should be done in the general_ocr.imrescale in the future\n new_h, new_w = img.shape[:2]\n h, w = results[key].shape[:2]\n w_scale = new_w / w\n h_scale = new_h / h\n else:\n img, w_scale, h_scale = general_ocr.imresize(\n results[key],\n results['scale'],\n return_scale=True,\n backend=self.backend)\n results[key] = img\n\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\n dtype=np.float32)\n results['img_shape'] = img.shape\n # in case that there is no padding\n results['pad_shape'] = img.shape\n results['scale_factor'] = scale_factor\n results['keep_ratio'] = self.keep_ratio","def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):\r\n w = int(round(target_height * image.shape[1] / image.shape[0]))\r\n return cv2.resize(image, (w, target_height), interpolation=method)","def ulab_bilinear_interpolation():\n GRID_DATA[1::2, ::2] = SENSOR_DATA[:-1, :]\n GRID_DATA[1::2, ::2] += SENSOR_DATA[1:, :]\n GRID_DATA[1::2, ::2] /= 2\n GRID_DATA[::, 1::2] = GRID_DATA[::, :-1:2]\n GRID_DATA[::, 1::2] += GRID_DATA[::, 2::2]\n GRID_DATA[::, 1::2] /= 2","def __scale_image(image, scale: float):\r\n height, width, _ = image.shape\r\n\r\n width_scaled = int(np.ceil(width * scale))\r\n height_scaled = int(np.ceil(height * scale))\r\n\r\n im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)\r\n\r\n # Normalize the image's pixels\r\n im_data_normalized = (im_data - 127.5) * 0.0078125\r\n\r\n return im_data_normalized","def downscale_image(img, bottom, x, y):\n width, height = tuple(img.shape[1::-1])\n img = img[int(round((1 - bottom) * (height - 1))):(height - 1), 1:(width - 1)]\n img = cv2.resize(img, (x, y))\n #img = cv2.Canny(img, 100, 200)\n ret, img = cv2.threshold(img, img.mean(), 255, cv2.THRESH_BINARY)\n return img","def upsample(x, name, size):\n with tf.name_scope(name):\n outputs = tf.image.resize_bilinear(x, size)\n # Return layer's output\n return outputs","def _resize_img(self, results):\n for key in results.get('img_fields', ['img']):\n if self.keep_ratio:\n img, scale_factor = mmcv.imrescale(\n results[key],\n results['scale'],\n return_scale=True,\n interpolation=self.interpolation,\n backend=self.backend)\n # the w_scale and h_scale has minor difference\n # a real fix should be done in the mmcv.imrescale in the future\n new_h, new_w = img.shape[:2]\n h, w = results[key].shape[:2]\n w_scale = new_w / w\n h_scale = new_h / h\n else:\n img, w_scale, h_scale = mmcv.imresize(\n results[key],\n results['scale'],\n return_scale=True,\n interpolation=self.interpolation,\n backend=self.backend)\n results[key] = img\n\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\n dtype=np.float32)\n results['img_shape'] = img.shape\n # in case that there is no padding\n results['pad_shape'] = img.shape\n results['scale_factor'] = scale_factor\n results['keep_ratio'] = self.keep_ratio","def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\n assert img_r.get_data().shape == (29, 39, 19)\n assert img_r.get_data()[4, 4, 4] == 1.0","def resize_img_norescale(img, size):\n w,h = size\n shape = (h,w)\n out = np.zeros(shape, dtype=img.dtype)\n i = min(img.shape[0], out.shape[0])\n j = min(img.shape[1], out.shape[1])\n out[0:i,0:j] = img[0:i, 0:j]\n return out","def preprocess(img: 'np.ndarray') -> 'np.ndarray':\n return cv2.resize(img, (45, 45), interpolation=cv2.INTER_AREA)[:,:,:3]","def obscure_image(image):\n size = image.size\n pixel_size = 9\n if size[0] < 9 or size[1] < 9:\n return image\n image_f = image.filter(ImageFilter.MaxFilter)\n image_s = image_f.resize((size[0] / pixel_size, size[1] / pixel_size), Image.NEAREST)\n image_l = image_s.resize((size[0], size[1]), Image.NEAREST)\n\n return image_l"],"string":"[\n \"def scale_image(image, scale=1, interpolation='linear'):\\n if interpolation == 'linear':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\\n if interpolation == 'nearest':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)\",\n \"def rescale_image(image, scale=0.50):\\r\\n \\r\\n wi = int(image.shape[1]*scale)\\r\\n hei = int(image.shape[0]*scale)\\r\\n dimension = (wi, hei)\\r\\n return cv.resize(image, dimension, interpolation = cv.INTER_AREA)\",\n \"def _batch_resize(self, ims, res, strategy='nearest'):\\n order = {'nearest': 0, 'bilinear': 1, 'bicubic': 3}[strategy]\\n bs = ims.shape[0]\\n out = []\\n log.info('Input ims shape: %s' % repr(ims.shape))\\n has_extra_dim = len(ims.shape) == 4\\n if not has_extra_dim:\\n ims = ims[..., np.newaxis]\\n h, w = ims.shape[1:3]\\n for i in range(bs):\\n o = interpolation.zoom(\\n ims[i, ...], [res[0] / h, res[1] / w, 1.0], np.float32, order=order)\\n out.append(o)\\n out = np.stack(out)\\n if not has_extra_dim:\\n out = np.reshape(out, out.shape[:-1])\\n return out\",\n \"def resize(self, image, fx, fy, interpolation):\\n\\n if interpolation == 'bilinear':\\n return self.bilinear_interpolation(image, fx, fy)\\n\\n elif interpolation == 'nearest_neighbor':\\n return self.nearest_neighbor(image, fx, fy)\",\n \"def resize(img, size, method=\\\"bilinear\\\"):\\n if method == Image.BILINEAR or method == \\\"bilinear\\\":\\n method = cv2.INTER_AREA\\n elif method == Image.CUBIC or method == \\\"cubic\\\":\\n method = cv2.INTER_CUBIC\\n elif method == Image.NEAREST or method == \\\"nearest\\\":\\n method = cv2.INTER_NEAREST\\n else:\\n raise RuntimeError(\\\"wrong method {}\\\".format(method))\\n if not isinstance(size, (tuple, list)):\\n size = (size, size)\\n if isinstance(size ,list):\\n size = tuple(size)\\n im = np.array(img)\\n im = cv2.resize(im, size, interpolation=method)\\n return Image.fromarray(im)\",\n \"def resize(img, size, interpolation=Image.BILINEAR):\\n\\n if isinstance(size, int):\\n w, h = img.size\\n if (w <= h and w == size) or (h <= w and h == size):\\n return img\\n if w < h:\\n ow = size\\n oh = int(size * h / w)\\n return img.resize((ow, oh), interpolation)\\n else:\\n oh = size\\n ow = int(size * w / h)\\n return img.resize((ow, oh), interpolation)\\n else:\\n return img.resize(size[::-1], interpolation)\",\n \"def scale(input_img, size):\\n width, height = size\\n old_height, old_width = input_img.shape\\n x_scale = float(height) / old_height\\n y_scale = float(width) / old_width\\n\\n output_img = np.zeros((height, width), dtype=np.uint8)\\n for xidx in xrange(height):\\n old_x = float(xidx) / x_scale\\n for yidx in xrange(width):\\n old_y = float(yidx) / y_scale\\n if old_x.is_integer() or old_y.is_integer():\\n output_img[xidx, yidx] = input_img[int(old_x), int(old_y)]\\n else: # use bilinear interpolation\\n x1 = int(np.floor(old_x))\\n x2 = int(np.ceil(old_x)) if int(np.ceil(old_x)) < old_height else old_height - 1\\n y1 = int(np.floor(old_y))\\n y2 = int(np.ceil(old_y)) if int(np.ceil(old_y)) < old_width else old_width - 1\\n\\n q11 = input_img[x1, y1]\\n q12 = input_img[x1, y2]\\n q21 = input_img[x2, y1]\\n q22 = input_img[x2, y2]\\n\\n output_img[xidx, yidx] = (q11 * (x2 - old_x) * (y2 - old_y)\\n + q21 * (old_x - x1) * (y2 - old_y)\\n + q12 * (x2 - old_x) * (old_y - y1)\\n + q22 * (old_x - x1) * (old_y - y1)) \\\\\\n / ((x2 - x1) * (y2 - y1) + 1e-10)\\n\\n return output_img\",\n \"def upsample_bilinear(input, size=None, scale_factor=None):\\n return interpolate(input, size, scale_factor, 'linear', align_corners=True)\",\n \"def resample_img(image, target_shape, mode='nearest'):\\n print(target_shape)\\n resize_factor = np.array(target_shape)/image.shape\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def resample(img, newSize):\\n # use float as datatype to preserve float precision\\n # will convert to np.uint8 when display result\\n # get the new dimensions\\n nH, nW = newSize\\n if np.ndim(img) == 2:\\n H, W = img.shape\\n res = np.zeros((nH, nW), dtype=np.float32)\\n elif np.ndim(img) == 3:\\n H, W, _ = img.shape\\n res = np.zeros((nH, nW, _), dtype=np.float32)\\n else:\\n raise ValueError(\\\"input image has invalid dimension %s\\\" % (img.shape))\\n\\n # interpolate the value for the result\\n for idx in range(nH * nW):\\n i = idx // nW\\n j = idx % nW\\n orig_i = int((i * H) // nH)\\n orig_j = int((j * W) // nW)\\n res[i, j] = img[orig_i, orig_j]\\n return res\",\n \"def img_zoom(img, fx, fy, interp=cv2.INTER_AREA):\\n res = cv2.resize(img, None, fx=fx, fy=fy,\\n interpolation=interp)\\n return res\",\n \"def resample(image, pixsize):\\n assert pixsize in [1, 2, 4, 8, 16] # return error and exit otherwise\\n imsize = 128/pixsize\\n newimage = np.zeros((imsize, imsize))\\n for xn, x in enumerate(np.arange(0, 128, pixsize)):\\n for yn, y in enumerate(np.arange(0, 128, pixsize)):\\n newimage[xn, yn] = np.nansum(image[x:x+pixsize, y:y+pixsize]) # Nansum is important as sum of masked array can be nan\\n return newimage\",\n \"def resize(img, size, interpolation='bilinear', data_format='CHW'):\\n _assert_image_tensor(img, data_format)\\n\\n if not (\\n isinstance(size, int)\\n or (isinstance(size, (tuple, list)) and len(size) == 2)\\n ):\\n raise TypeError(f'Got inappropriate size arg: {size}')\\n\\n if isinstance(size, int):\\n w, h = _get_image_size(img, data_format)\\n # TODO(Aurelius84): In static graph mode, w and h will be -1 for dynamic shape.\\n # We should consider to support this case in future.\\n if w <= 0 or h <= 0:\\n raise NotImplementedError(\\n \\\"Not support while w<=0 or h<=0, but received w={}, h={}\\\".format(\\n w, h\\n )\\n )\\n if (w <= h and w == size) or (h <= w and h == size):\\n return img\\n if w < h:\\n ow = size\\n oh = int(size * h / w)\\n else:\\n oh = size\\n ow = int(size * w / h)\\n else:\\n oh, ow = size\\n\\n img = img.unsqueeze(0)\\n img = F.interpolate(\\n img,\\n size=(oh, ow),\\n mode=interpolation.lower(),\\n data_format='N' + data_format.upper(),\\n )\\n\\n return img.squeeze(0)\",\n \"def image_resample(f, oversamp=1.0):\\n img = imageio.imread(f)\\n x = np.linspace(0,img.shape[0],img.shape[0])\\n y = np.linspace(0,img.shape[1],img.shape[1])\\n #\\n # 2x oversample the image since we'll dither it.\\n #\\n xnew = np.linspace(0, img.shape[0], img.shape[0]*oversamp)\\n ynew = np.linspace(0, img.shape[1], img.shape[1]*oversamp)\\n from scipy import interpolate\\n rc = interpolate.interp2d(x, y, img[:,:,0].flatten(), kind='linear')\\n gc = interpolate.interp2d(x, y, img[:,:,1].flatten(), kind='linear')\\n bc = interpolate.interp2d(x, y, img[:,:,2].flatten(), kind='linear')\\n rgb_new = np.stack([rc(xnew.flatten(), ynew.flatten()),\\n gc(xnew.flatten(), ynew.flatten()),\\n bc(xnew.flatten(), ynew.flatten())],-1).transpose(1,0,2).astype(np.uint8)\\n plt.imshow(rgb_new)\\n return rgb_new\",\n \"def imgResample(img, spacing, size=[], useNearest=False, origin=[], outsideValue=0):\\n if len(spacing) != img.GetDimension(): raise Exception(\\\"len(spacing) != \\\" + str(img.GetDimension()))\\n\\n # Set Size\\n if size == []:\\n inSpacing = img.GetSpacing()\\n inSize = img.GetSize()\\n size = [int(math.ceil(inSize[i]*(inSpacing[i]/spacing[i]))) for i in range(img.GetDimension())]\\n else:\\n if len(size) != img.GetDimension(): raise Exception(\\\"len(size) != \\\" + str(img.GetDimension()))\\n \\n if origin == []:\\n origin = [0]*img.GetDimension()\\n else:\\n if len(origin) != img.GetDimension(): raise Exception(\\\"len(origin) != \\\" + str(img.GetDimension()))\\n \\n # Resample input image\\n interpolator = [sitk.sitkLinear, sitk.sitkNearestNeighbor][useNearest]\\n identityTransform = sitk.Transform()\\n identityDirection = list(sitk.AffineTransform(img.GetDimension()).GetMatrix())\\n\\n return sitk.Resample(img, size, identityTransform, interpolator, origin, spacing, identityDirection, outsideValue)\",\n \"def scale(img, scale):\\n return resize(img, x_scale=scale, y_scale=scale)\",\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def pixelate(image, n_w, n_h):\\n image = image.resize((n_w, n_h), Image.ANTIALIAS)\\n return image\",\n \"def resample(medical_image, image, new_spacing=[1, 1, 1]):\\n image_shape = np.array(list(medical_image.header.get_data_shape()))\\n spacing = np.array(list(medical_image.header.get_zooms()))\\n resize_factor = spacing / new_spacing\\n\\n new_shape = image_shape * resize_factor\\n new_shape = np.round(new_shape)\\n\\n real_resize_factor = new_shape / image_shape\\n\\n new_spacing = spacing / real_resize_factor\\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\\n\\n return resampled_image, new_spacing\",\n \"def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\\n (height, width) = image.shape[:2]\\n new_dims = (int(width * scale), int(height * scale))\\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)\",\n \"def resample_img(img, img_type, size, spacing):\\n resampler = sitk.ResampleImageFilter()\\n resampler.SetOutputDirection(img.GetDirection())\\n resampler.SetOutputOrigin(img.GetOrigin())\\n resampler.SetOutputSpacing(spacing)\\n resampler.SetSize(size)\\n if img_type is \\\"Label\\\":\\n resampler.SetInterpolator(sitk.sitkNearestNeighbor)\\n elif img_type is \\\"Image\\\":\\n resampler.SetInterpolator(sitk.sitkLinear)\\n imgResampled = resampler.Execute(img)\\n\\n #axis have to be switched since np.array and keras use them in different order...\\n x = np.transpose(sitk.GetArrayFromImage(imgResampled).astype(dtype=np.float), [2, 1, 0])\\n return x\",\n \"def _scale(self, image):\\n\\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\\n image.Rescale(self._width, self._height)\\n \\n return image\",\n \"def resampled_to_img(self, target_image, interpolation=None):\\n # IMPORTANT: Polymorphism can be implemented by walking the \\n # MRO and finding a method that does not raise\\n # NotImplementedError. \\n raise NotImplementedError\",\n \"def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)\",\n \"def resize(self, image, fx=None, fy=None, interpolation=None):\\r\\n if interpolation == 'bilinear':\\r\\n return self.bilinear_interpolation(image, float(fx), float(fy))\\r\\n\\r\\n elif interpolation == 'nearest_neighbor':\\r\\n return self.nearest_neighbor(image, float(fx), float(fy))\",\n \"def resize(img, size, interpolation=Image.BILINEAR):\\n\\n if not _is_numpy_image(img):\\n raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\\n raise TypeError('Got inappropriate size arg: {}'.format(size))\\n\\n if isinstance(size, int):\\n size = (size, size)\\n \\n if img.dtype == np.int32:\\n resized = img.astype(np.uint16)\\n resized = cv2.resize(resized, size , interpolation)\\n else:\\n resized = cv2.resize(img, size , interpolation)\\n resized = resized.astype(img.dtype)\\n return resized\",\n \"def img_resize(img, img_height):\\n\\n\\timg_width = int(img_height*img.shape[1]/img.shape[0])\\n\\n\\timg_pil = Image.fromarray(img) # convert array back to image\\n\\n\\timg_pil_resize = img_pil.resize((img_width, img_height), Image.LANCZOS) # resize\\n\\n\\treturn np.array(img_pil_resize)\",\n \"def resize(self, image, fx=None, fy=None, interpolation=None):\\n if interpolation == 'bilinear':\\n return self.bilinear_interpolation(image, float(fx), float(fy))\\n\\n elif interpolation == 'nearest_neighbor':\\n return self.nearest_neighbor(image, float(fx), float(fy))\",\n \"def resample(image_array, target_pixel_dims_list, is_seg=False):\\n # if dealing with segmentation images, do not interpolate\\n # this is done by choosing interpolation order == 0\\n order = 0 if is_seg == True else 3\\n\\n original_dims_in_pixels = [image_array.shape[d]\\n for d in range(len(image_array.shape))]\\n compression_list = [target_pixel_dims_list[d] / original_dims_in_pixels[d]\\n for d in range(len(image_array.shape))]\\n\\n if (is_seg):\\n resized_image = ndimage.interpolation.zoom(\\n image_array, zoom=compression_list, order=order, cval=0)\\n return resized_image\\n else:\\n resized_image = ndimage.interpolation.zoom(\\n image_array, zoom=compression_list, order=order, cval=0)\\n return resized_image\",\n \"def process_image(image):\\n image = resize(image)\\n return image\",\n \"def scale_down(image:np.array)->np.array:\\n src = image\\n scale_percent = 25\\n width = int(src.shape[1] * scale_percent / 100)\\n height = int(src.shape[0] * scale_percent / 100)\\n dsize = (width, height)\\n output = cv2.resize(src, dsize)\\n return output\",\n \"def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\\n\\n if scaling_method == 'scale':\\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\\n elif scaling_method == 'ratio':\\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\\n im = scipy.misc.imresize(im, scale)\\n return (im, scale)\",\n \"def resize_3d_image_nearest_interpolation(im, res_s):\\n new_shape = [res_s, im.shape[1], im.shape[2]]\\n ret0 = np.zeros(new_shape, dtype=im.dtype)\\n for i in range(im.shape[2]):\\n im_slice = np.array(\\n Image.fromarray(im[..., i]).resize(\\n (im.shape[1], res_s), resample=Image.Resampling.NEAREST\\n )\\n )\\n ret0[..., i] = im_slice\\n\\n new_shape = [res_s, res_s, res_s]\\n ret = np.zeros(new_shape, dtype=im.dtype)\\n for i in range(res_s):\\n im_slice = np.array(\\n Image.fromarray(ret0[i, ...]).resize(\\n (res_s, res_s), resample=Image.Resampling.NEAREST\\n )\\n )\\n ret[i, ...] = im_slice\\n return ret\",\n \"def bilinear_interpolation(self, image, fx, fy):\\n\\n # Write your code for bilinear interpolation here\\n (width, height) = image.shape\\n (scaledWidth, scaledHeight) = (int(fy * width), int(fy * height))\\n resampledImage = np.zeros((scaledWidth, scaledHeight), dtype=int)\\n\\n for x in range(0, scaledWidth):\\n for y in range(0, scaledHeight):\\n nx = math.floor(x / fx)\\n ny = math.floor(y / fy)\\n if (nx + 1) > width - 1:\\n nx = width - 2\\n if (ny + 1) > height - 1:\\n ny = height - 2\\n\\n n1 = (nx, ny)\\n n2 = (nx, ny+1)\\n n3 = (nx+1, ny)\\n n4 = (nx+1, ny+1)\\n\\n resampledImage[x, y] = self.inter.bilinear_interpolation([n1, image[n1]], [n2, image[n2]], [n3, image[n3]],[n4, image[n4]], [x/fx, y/fy])\\n\\n return resampledImage\",\n \"def resize(img, x_scale, y_scale, optimize=True):\\n interpolation = cv.INTER_LINEAR\\n\\n # pick an optimized scaler if asked to\\n if optimize:\\n if x_scale > 1 and y_scale > 1:\\n interpolation = cv.INTER_CUBIC\\n else:\\n interpolation = cv.INTER_AREA\\n\\n return cv.resize(\\n img,\\n None,\\n fx=x_scale,\\n fy=y_scale,\\n interpolation=interpolation,\\n )\",\n \"def downsample_image(img, output_path, ratio=2):\\n assert ratio>=1, ratio\\n if ratio == 1:\\n return True\\n old_im = Image.open(img)\\n old_size = old_im.size\\n new_size = (int(old_size[0]/ratio), int(old_size[1]/ratio))\\n\\n new_im = old_im.resize(new_size, PIL.Image.LANCZOS)\\n new_im.save(output_path)\\n return True\",\n \"def _resize_short_within(self, img, short, max_size, mult_base=1, interp=Image.BILINEAR):\\n w, h = img.size\\n im_size_min, im_size_max = (h, w) if w > h else (w, h)\\n scale = float(short) / float(im_size_min)\\n if np.round(scale * im_size_max / mult_base) * mult_base > max_size:\\n # fit in max_size\\n scale = float(np.floor(max_size / mult_base) * mult_base) / float(im_size_max)\\n new_w, new_h = (int(np.round(w * scale / mult_base) * mult_base),\\n int(np.round(h * scale / mult_base) * mult_base))\\n img = img.resize((new_w, new_h), interp)\\n return img\",\n \"def _resize(img, max_dim=128):\\n if max(img.shape[:3]) <= max_dim:\\n return img\\n else:\\n new_size = [max_dim / s if s >= max_dim else 1.0 for s in img.shape[:3]]\\n new_size.append(1.0) # for channel\\n return scipy.ndimage.zoom(img, new_size, order=2)\",\n \"def resizeImage(IMG,IMAGE_SIZE):\\n\\n RESCALED_IMAGE = skimage.transform.resize(IMG,[IMG.shape[0],IMAGE_SIZE,IMAGE_SIZE])\\n return RESCALED_IMAGE\",\n \"def resample(self, image, target_shape, mode='nearest'):\\n resize_factor = image.shape / np.array(target_shape)\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def rescale_and_resize_image(img_name, img_size, save_img):\\n \\n try:\\n \\n # Open image\\n img = Image.open(img_name)\\n \\n # Resize image\\n img = img.resize((int(img_size), int(img_size)), Image.ANTIALIAS) \\n \\n # Get data from image\\n img_list = list(img.getdata())\\n \\n # Find minimum and maximum value pixels in the image\\n img_max = max(img_list)\\n img_min = min(img_list)\\n \\n # Determine factor to scale to a 8-bit image\\n scale_factor = 255.0/(img_max - img_min)\\n \\n img_list_new = [0] * img_size * img_size\\n \\n # Rescale all pixels to the range 0 to 255 (in line with unit8 values)\\n for i in range(0,img_size):\\n for j in range(0,img_size):\\n img_list_new[i*img_size + j] = int((img_list[i*img_size + j]-img_min)*scale_factor)\\n if (img_list_new[i*img_size + j] > 255) or (img_list_new[i*img_size + j] < 0) or (img_list_new[i*img_size + j]-int(img_list_new[i*img_size + j]) != 0):\\n print(\\\"img_list_new[%d][%d] = %r\\\" % (i,j,img_list_new[i*img_size + j]))\\n \\n img.putdata(img_list_new)\\n \\n # Convert to uint8 greyscale image\\n img = img.convert('L')\\n \\n # Save image\\n if save_img:\\n img.save(img_name)\\n else:\\n ph = ImageTk.PhotoImage(img)\\n return ph\\n \\n finally:\\n \\n # Close image\\n img.close()\",\n \"def bilinear_interpolation(self, image, fx, fy):\\r\\n\\r\\n # Write your code for bilinear interpolation here\\r\\n width, height = image.shape[:2]\\r\\n nw = int(width * fx)\\r\\n nh = int(height * fy)\\r\\n new_img = np.zeros((nw, nh), np.uint8)\\r\\n inter = interpolation.interpolation()\\r\\n for i in range(nw):\\r\\n for j in range(nh):\\r\\n ii = int(np.floor((i * (image.shape[0]-1))/(nw - 1)))\\r\\n jj = int(np.floor((j * (image.shape[1]-1))/(nh - 1)))\\r\\n\\r\\n # All four corner pixels copied as it is\\r\\n if (ii == 0 and jj == 0) or \\\\\\r\\n (ii == 0 and jj == image.shape[1] - 1) or \\\\\\r\\n (ii == image.shape[0] - 1 and jj == 0) or \\\\\\r\\n (ii == image.shape[0] - 1 and jj == image.shape[1] - 1):\\r\\n new_img[i][j] = image[ii][jj]\\r\\n\\r\\n # Linear interpolation along left and right edges\\r\\n elif ii == 0 or ii == image.shape[0] - 1:\\r\\n new_img[i][j] = inter.linear_interpolation([jj - 1, image[ii][jj - 1]], [jj + 1, image[ii][jj + 1]], jj)\\r\\n\\r\\n # Linear interpolation along top and bottom edges\\r\\n elif jj == 0 or jj == image.shape[1] - 1:\\r\\n new_img[i][j] = inter.linear_interpolation([ii - 1, image[ii - 1][jj]], [ii + 1, image[ii + 1][jj]], ii)\\r\\n\\r\\n else:\\r\\n\\r\\n q11i, q11j = ii - 1, jj - 1\\r\\n q12i, q12j = ii - 1, jj + 1\\r\\n q21i, q21j = ii + 1, jj - 1\\r\\n q22i, q22j = ii + 1, jj + 1\\r\\n\\r\\n point_a = [q11i, q11j, image[q11i][q11j]]\\r\\n point_b = [q12i, q12j, image[q12i][q12j]]\\r\\n point_c = [q21i, q21j, image[q21i][q21j]]\\r\\n point_d = [q22i, q22j, image[q22i][q22j]]\\r\\n unknown = [ii, jj]\\r\\n\\r\\n new_img[i][j] = inter.bilinear_interpolation(point_a, point_b, point_c, point_d, unknown)\\r\\n\\r\\n return new_img\",\n \"def resize(img, size):\\n img = tf.expand_dims(img, 0)\\n return tf.image.resize_bilinear(img, size)[0,:,:,:]\",\n \"def rescale_image(image, rescale_width):\\r\\n\\r\\n # image size\\r\\n image_height = pdb.gimp_image_height(image)\\r\\n image_width = pdb.gimp_image_width(image)\\r\\n\\r\\n # new image height\\r\\n rescale_height = round(image_height * (rescale_width * 1.0 / image_width))\\r\\n\\r\\n pdb.gimp_image_scale(image, rescale_width, rescale_height)\\r\\n gimp.message('Rescaled image')\",\n \"def scale_image(image=np.zeros((100,100)), \\n new_width=100,\\n ascii_block_size=(2,3)):\\n \\n original_width, original_height = image.shape\\n aspect_ratio = original_height / float(original_width)\\n w,h = ascii_block_size\\n new_height = int(h/w * aspect_ratio * new_width)\\n\\n return skimage.transform.resize(image, (new_width, new_height))\",\n \"def resize_img(self,scale=1):\\n reduced = self.image.reduce((scale,scale))\\n reduced.save(\\\"../edited/{}\\\".format(self.image.filename))\\n\\n reduced = Image.open(\\\"../edited/{}\\\".format(self.image.filename))\\n return reduced\",\n \"def get_scaled_image(self, min_width=1024):\\n self.dispersion = self.image.getCalibration().pixelWidth\\n self.offset = self.image.getCalibration().xOrigin\\n binning = 1\\n while binning * min_width < self.image.getWidth():\\n binning *= 2\\n if binning > 1:\\n binning /= 2\\n IJ.run(self.image, 'Select None', '')\\n new_image = self.image.crop()\\n IJ.run(new_image, 'Bin...', 'x=%d y=%d binning=Average' %\\n (binning, binning))\\n self.dispersion *= binning\\n self.offset /= binning\\n self.image = new_image\\n return new_image\",\n \"def imrescale(img,scale,return_scale=False,interpolation='bilinear'):\\n h,w = img.shape[:2]\\n if isinstance(scale,(float,int)):\\n if scale <= 0:\\n raise ValueError('Invalid scale:{},must be positive.'.format(scale))\\n scale_factor = scale\\n \\n elif isinstance(scale,tuple):\\n max_long_edge = max(scale)\\n max_short_edge = min(scale)\\n scale_factor = min(max_long_edge/max(h,w),\\n max_short_edge/min(h,w))\\n else:\\n raise ValueError('Scale must be a number or tuple of int,but got {}'.format(type(scale)))\\n \\n new_size = _scale_size((w,h),scale_factor)\\n rescaled_img = imresize(img,new_size,interpolation = interpolation)\\n if return_scale:\\n return rescaled_img,scale_factor\\n else:\\n return rescaled_img\",\n \"def test_nib_resample_image_4d(fake_4dimage_nib):\\n img_r = resampling.resample_nib(fake_4dimage_nib, new_size=[2, 2, 1, 1], new_size_type='factor', interpolation='nn')\\n assert img_r.get_data().shape == (18, 18, 9, 3)\\n assert img_r.get_data()[8, 8, 4, 0] == 1.0 # make sure there is no displacement in world coordinate system\\n assert img_r.get_data()[8, 8, 4, 1] == 0.0\\n assert img_r.header.get_zooms() == (0.5, 0.5, 1.0, 1.0)\",\n \"def rescale(self, img):\\n\\n if self.scale != 1:\\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\\n else:\\n return img\",\n \"def _resize_image_tuple(self, image_tup):\\n if self.api_info is None:\\n self.get_info() # sets the image size and other such info from server.\\n try:\\n MIN_SIZE = self.api_info['min_image_size']\\n MAX_SIZE = self.api_info['max_image_size']\\n img = Image.open(image_tup[0])\\n min_dimension = min(img.size)\\n max_dimension = max(img.size)\\n min_ratio = float(MIN_SIZE) / min_dimension\\n max_ratio = float(MAX_SIZE) / max_dimension\\n def get_newsize(img, ratio, SIZE):\\n if img.size[0] == min_dimension:\\n newsize = (SIZE, int(round(ratio * img.size[1])))\\n else:\\n newsize = (int(round(ratio * img.size[0])), SIZE)\\n return newsize\\n im_changed = False\\n # Only resample if min size is > 512 or < 256\\n if max_ratio < 1.0: # downsample to MAX_SIZE\\n newsize = get_newsize(img, max_ratio, MAX_SIZE)\\n img = img.resize(newsize, Image.BILINEAR)\\n im_changed = True\\n elif min_ratio > 1.0: # upsample to MIN_SIZE\\n newsize = get_newsize(img, min_ratio, MIN_SIZE)\\n img = img.resize(newsize, Image.BICUBIC)\\n im_changed = True\\n else: # no changes needed so rewind file-object.\\n image_tup[0].seek(0)\\n # Finally make sure we have RGB images.\\n if img.mode != \\\"RGB\\\":\\n img = img.convert(\\\"RGB\\\")\\n im_changed = True\\n if im_changed:\\n io = StringIO()\\n img.save(io, 'jpeg', quality=IM_QUALITY)\\n io.seek(0) # rewind file-object to read() below is good to go.\\n image_tup = (io, image_tup[1])\\n except IOError, e:\\n logger.warning('Could not open image file: %s, still sending to server.', image_tup[1])\\n return image_tup\",\n \"def resize(img):\\n size = (500, 500)\\n img.thumbnail(size)\\n return img\",\n \"def scale_img(img, scale=1.0):\\n dim = (int(img.shape[1]*scale), int(img.shape[0]*scale))\\n return cv2.resize(img.copy(), dim, interpolation=cv2.INTER_AREA)\",\n \"def resample(scan, image, new_spacing=[1,1,1]):\\n spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)\\n\\n resize_factor = spacing / new_spacing\\n new_real_shape = image.shape * resize_factor\\n new_shape = np.round(new_real_shape)\\n real_resize_factor = new_shape / image.shape\\n new_spacing = spacing / real_resize_factor\\n\\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\\n\\n batch = []\\n for i in range(0, image.shape[0] - 3, 3):\\n tmp = []\\n for j in range(3):\\n img = image[i + j]\\n img= cv2.resize(img, (224, 224))\\n tmp.append(img)\\n\\n tmp = np.array(tmp)\\n batch.append(tmp)\\n\\n batch = np.array(batch)\\n\\n return batch\",\n \"def __resize_bilinear_with_factor(self, x, factor_size=None, data_format=None):\\n height_factor = factor_size[0]\\n width_factor = factor_size[1]\\n if data_format == 'channels_first':\\n height = x.shape[2] * height_factor\\n width = x.shape[3] * width_factor\\n new_shape = tf.constant(np.array([height, width]).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"invilid data format\\\", data_format)\",\n \"def retinanet_resize_image(img, min_side=800, max_side=1333):\\n # compute scale to resize the image\\n scale = retinanet_compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)\\n # scale = round(scale, 4)\\n # print(\\\"scale: \\\", scale)\\n\\n # resize the image with the computed scale\\n img = cv2.resize(img, None, fx=scale, fy=scale)\\n\\n return img, scale\",\n \"def resize(img, size, interpolation=cv2.INTER_LINEAR):\\n if not _is_numpy_image(img):\\n raise TypeError('img should be nparrary Image. Got {}'.format(type(img)))\\n if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):\\n raise TypeError('Got inappropriate size arg: {}'.format(size))\\n\\n if isinstance(size, int):\\n h, w = img.shape[:2]\\n if (w <= h and w == size) or (h <= w and h == size):\\n return img\\n if w < h:\\n ow = size\\n oh = int(size * h / w)\\n return cv2.resize(img, (ow, oh), interpolation)\\n else:\\n oh = size\\n ow = int(size * w / h)\\n return cv2.resize(img, (ow, oh), interpolation)\\n else:\\n return cv2.resize(img, tuple(size[::-1]), interpolation)\",\n \"def scale_image(self, image):\\n err_m1 = \\\"image must be a numpy.ndarray with shape (h, w, 3)\\\"\\n if not isinstance(image, np.ndarray):\\n raise TypeError(err_m1)\\n if len(image.shape) != 3 or image.shape[2] != 3:\\n raise TypeError(err_m1)\\n shape0 = image.shape[0]\\n shape1 = image.shape[1]\\n\\n if shape0 > shape1:\\n new_w = int(image.shape[1] * 512 / image.shape[0])\\n new_h = 512\\n elif shape0 < shape1:\\n new_h = int(image.shape[0] * 512 / image.shape[1])\\n new_w = 512\\n else:\\n new_h = 512\\n new_w = 512\\n tf.image.ResizeMethod.BICUBIC\\n image = tf.expand_dims(image, 0)\\n image = tf.image.resize_bicubic(image,\\n (new_h, new_w),\\n align_corners=False)\\n image = image / 255\\n image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\\n\\n return image\",\n \"def bilinear_interpolation(x, y, img):\\n Warning(\\\"This method should not be use, use instead pytorch grid_sample method\\\")\\n _, height, width = img.shape\\n x_min = x.int()\\n y_min = y.int()\\n\\n w_x = x - x_min\\n w_y = y - y_min\\n \\n w1 = torch.cat((w_x, w_y), 1).norm(dim=1)\\n w2 = torch.cat((1-w_x, w_y), 1).norm(dim=1)\\n w3 = torch.cat((1-w_x, 1-w_y), 1).norm(dim=1)\\n w4 = torch.cat((w_x, 1-w_y), 1).norm(dim=1)\\n\\n sum_w = w1 + w2 + w3 + w4\\n\\n w1 = w1 / sum_w\\n w2 = w2 / sum_w\\n w3 = w3 / sum_w\\n w4 = w4 / sum_w\\n\\n # TODO how to deal with out of the sample sampling when the pixel is from outside\\n x_min = x_min.clamp(0, width - 2).view(-1)\\n y_min = y_min.clamp(0, height - 2).view(-1)\\n\\n pix = \\\\\\n w1 * img[:, y_min.long(), x_min.long()] + \\\\\\n w2 * img[:, y_min.long(), x_min.long() + 1] + \\\\\\n w3 * img[:, y_min.long() + 1, x_min.long() + 1] + \\\\\\n w4 * img[:, y_min.long() + 1, x_min.long()]\\n\\n pix = pix.view(img.shape)\\n return pix\",\n \"def resize_image(image: Image) -> Image:\\n expected_x: int = 1024\\n expected_y: int = 768\\n x, y = image.size\\n if x > expected_x or y > expected_y:\\n scale = min(expected_x / x, expected_y / y)\\n return image.resize((int(x * scale), int(y * scale)))\\n else:\\n return image.resize((int(0.9*x), int(0.9*y)))\",\n \"def Resampler(name):\\n\\n def resample_average(path, dsquery, dstile, image_format):\\n for i in range(1, dstile.RasterCount+1):\\n res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i), \\\"average\\\")\\n if res != 0:\\n raise ImageOutputException(\\\"RegenerateOverview() failed with error %d\\\" % res)\\n\\n gdal_write(path, dstile, image_format)\\n\\n def resample_antialias(path, dsquery, dstile, image_format):\\n querysize = dsquery.RasterXSize\\n tilesize = dstile.RasterXSize\\n\\n array = numpy.zeros((querysize, querysize, 4), numpy.uint8)\\n for i in range(dstile.RasterCount):\\n array[:,:,i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1), 0, 0, querysize, querysize)\\n im = Image.fromarray(array, 'RGBA') # Always four bands\\n im1 = im.resize((tilesize,tilesize), Image.ANTIALIAS)\\n\\n if os.path.exists(path):\\n im0 = Image.open(path)\\n im1 = Image.composite(im1, im0, im1)\\n\\n ensure_dir_exists(path)\\n\\n if image_format == \\\"JPEG\\\":\\n im1.save(path, image_format, quality=jpeg_quality)\\n else:\\n im1.save(path, image_format)\\n\\n\\n if name == \\\"average\\\":\\n return resample_average\\n elif name == \\\"antialias\\\":\\n return resample_antialias\\n\\n resampling_methods = {\\n \\\"near\\\" : gdal.GRA_NearestNeighbour,\\n \\\"bilinear\\\" : gdal.GRA_Bilinear,\\n \\\"cubic\\\" : gdal.GRA_Cubic,\\n \\\"cubicspline\\\" : gdal.GRA_CubicSpline,\\n \\\"lanczos\\\" : gdal.GRA_Lanczos\\n }\\n\\n resampling_method = resampling_methods[name]\\n\\n def resample_gdal(path, dsquery, dstile, image_format):\\n querysize = dsquery.RasterXSize\\n tilesize = dstile.RasterXSize\\n\\n dsquery.SetGeoTransform( (0.0, tilesize / float(querysize), 0.0, 0.0, 0.0, tilesize / float(querysize)) )\\n dstile.SetGeoTransform( (0.0, 1.0, 0.0, 0.0, 0.0, 1.0) )\\n\\n res = gdal.ReprojectImage(dsquery, dstile, None, None, resampling_method)\\n if res != 0:\\n raise ImageOutputException(\\\"ReprojectImage() failed with error %d\\\" % res)\\n\\n gdal_write(path, dstile, image_format)\\n\\n return resample_gdal\",\n \"def scale_image(img, factor=1):\\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))\",\n \"def Rescale(self):\\r\\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\\r\\n bitmap = self.scaled = self.bitmap\\r\\n if not bitmap: return\\r\\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\\r\\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\\r\\n image = bitmap.ConvertToImage()\\r\\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\\r\\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\\r\\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\\r\\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\\r\",\n \"def resample_image(I,spacing,desiredSize, spline_order=1,zero_boundary=False,identity_map=None):\\n if len(I.shape) != len(desiredSize)+2:\\n desiredSize = desiredSize[2:]\\n sz = np.array(list(I.size()))\\n # check that the batch size and the number of channels is the same\\n nrOfI = sz[0]\\n nrOfC = sz[1]\\n\\n desiredSizeNC = np.array([nrOfI,nrOfC]+list(desiredSize))\\n\\n newspacing = spacing*((sz[2::].astype('float')-1.)/(desiredSizeNC[2::].astype('float')-1.)) ###########################################\\n if identity_map is not None:\\n idDes= identity_map\\n else:\\n idDes = torch.from_numpy(py_utils.identity_map_multiN(desiredSizeNC,newspacing)).to(I.device)\\n # now use this map for resampling\\n ID = py_utils.compute_warped_image_multiNC(I, idDes, newspacing, spline_order,zero_boundary)\\n\\n return ID, newspacing\",\n \"def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):\\n assert _is_numpy_image(img), 'img should be nparray Image'\\n img = crop(img, i, j, h, w)\\n img = resize(img, size, interpolation)\\n return img\",\n \"def resize_img(input_img, scale=1.1):\\n if scale == 1.0:\\n return input_img\\n rows, cols, ch = input_img.shape\\n d = rows * (scale - 1) # overall image size change from rows, cols, to rows - 2d, cols - 2d\\n pts1 = np.float32([[d, d], [rows - 1 - d, d], [d, cols - 1 - d], [rows - 1 - d, cols - 1 - d]])\\n pts2 = np.float32([[0, 0], [rows - 1, 0], [0, cols - 1], [rows - 1, cols - 1]])\\n M = cv2.getPerspectiveTransform(pts1, pts2)\\n dst = cv2.warpPerspective(input_img, M, (cols, rows), borderMode=1)\\n return dst\",\n \"def shrink_image(image, output_size, interp_type=InterpolationType.INTEGRAL, output=None):\\n\\n if isinstance(output_size, (int, int)):\\n scale = float(output_size) / max(image.getWidth(), image.getHeight())\\n output_shape = (int(image.getHeight()*scale), int(image.getWidth()*scale))\\n else:\\n output_shape = output_size\\n\\n if image.getWidth() < output_shape[1] or image.getHeight() < output_shape[0]:\\n raise RuntimeError(\\\"Either width or height is larger in output than input\\\")\\n\\n if output is None:\\n output = image.createNew(output_shape[1], output_shape[0])\\n else:\\n output.reshape(output_shape[1], output_shape[0])\\n\\n if interp_type == InterpolationType.INTEGRAL:\\n pbg.gateway.jvm.boofcv.alg.filter.misc.AverageDownSampleOps.down(image, output)\\n else:\\n scale_x = output_shape[1] / float(image.getWidth())\\n scale_y = output_shape[0] / float(image.getHeight())\\n\\n fdist = pbg.gateway.jvm.boofcv.abst.distort.FDistort(image, output)\\n fdist.interp(interpolation_type_to_java(interp_type))\\n fdist.affine(scale_x, 0.0, 0.0, scale_y, 0.0, 0.0)\\n fdist.apply()\\n\\n return output\",\n \"def __resize_image(self, img):\\n return cv2.resize(img, self.g.img_size, \\n interpolation = cv2.INTER_CUBIC)\",\n \"def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):\\n factors = [new_size[i] / im.shape[i] for i in range(2)]\\n\\n #assert factors[0] == factors[1], \\\"Must have same factor for now\\\"\\n f = factors[0] \\n \\n if f < 1:\\n im2 = pyramid_reduce(im, downscale=1/f)\\n elif f > 1:\\n im2 = pyramid_expand(im, upscale=f)\\n else:\\n im2 = im\\n\\n assert im2.shape[:2] == tuple(new_size), \\\"{0} != {1} (original size: {2})\\\".format(im2.shape, new_size, im.shape)\\n \\n return im2\",\n \"def resize(img,scale):\\n sdims=img.shape\\n datatype=c_double\\n if img.dtype!=datatype:\\n print \\\"Error the image must be of doubles!\\\"\\n raise RuntimeError\\n\\n if scale>1.0:\\n print \\\"Invalid scaling factor!\\\"\\n raise RuntimeError \\n \\n img = asfortranarray(img,c_double) # make array continguous\\n \\n try:\\n mresize = ctypeslib.load_library(\\\"libresize.so\\\",\\\".\\\") \\n except:\\n print \\\"Unable to load resize library\\\"\\n raise RuntimeError\\n \\n #use two times the 1d resize to get a 2d resize\\n fresize = mresize.resize1dtran\\n fresize.restype = None\\n fresize.argtypes = [ ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int,ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int, c_int , c_int ]\\n ddims = [int(round(sdims[0]*scale)),int(round(sdims[1]*scale)),sdims[2]];\\n mxdst = zeros((ddims), dtype=datatype)\\n tmp = zeros((ddims[0],sdims[1],sdims[2]), dtype=datatype)\\n img1=img\\n t1=time()\\n fresize(img1, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);\\n fresize(tmp, sdims[1], mxdst, ddims[1], ddims[0], sdims[2]);\\n t2=time()\\n return mxdst.reshape(ddims[2],ddims[1],ddims[0]).T\",\n \"def resize(image, size):\\n return np.array(Image.fromarray(image).resize(size))\",\n \"def prepare_image(im):\\n width, height = im.size\\n if width > 256 or height > 256:\\n factor = 256.0 / max(width, height)\\n im = im.resize((int(factor * width), int(factor * height)),\\n Image.BILINEAR)\\n return im\",\n \"def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\\n target_height = target_size[0]\\n target_width = target_size[1]\\n if data_format == 'channels_first':\\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"Invilid data format\\\", data_format)\",\n \"def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\\n if isinstance(target_spacing, numbers.Number):\\n target_spacing = [target_spacing] * img.ndim\\n \\n # compute zoom values\\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\\n\\n print \\\"Zoom Factors\\\"\\n print zoom_factors\\n\\n oldImageShape = img.shape\\n \\n # zoom image\\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\\n\\n newImageShape = img.shape\\n old_pixel_spacing = header.get_pixel_spacing(hdr)\\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\\n print \\\"Target Pixel Spacing\\\"\\n print target_spacing\\n\\n print \\\"Actual Pixel Spacing\\\"\\n print new_pixel_spacing\\n # set new voxel spacing\\n header.set_pixel_spacing(hdr, new_pixel_spacing)\\n \\n return img, hdr\",\n \"def nn_resample_image(image_path, pxl_box_width, pxl_box_height):\\n image = Image.open(image_path)\\n if image.format == \\\"JPEG\\\" or image.format == \\\"PNG\\\":\\n __nn_resample_png_jpeg_image(image, pxl_box_width, pxl_box_height)\\n elif image.format == \\\"GIF\\\":\\n __nn_resample_gif_image(image, pxl_box_width, pxl_box_height)\\n\\n return image\",\n \"def image_resize(img, min_size=600, max_size=1000):\\n C, H, W = img.shape\\n scale1 = min_size / min(H, W)\\n scale2 = max_size / max(H, W)\\n scale = min(scale1, scale2)\\n img = img / 255\\n img = transform.resize(img, (C, H * scale, W * scale),\\n mode='reflect', anti_aliasing=False)\\n # img = pytorch_normalize(img)\\n # img = caffe_normalize(img)\\n return img\",\n \"def scale_image(self, pixels, size):\\n x_min, x_max = np.amin(pixels[:,0]), np.amax(pixels[:,0])\\n y_min, y_max = np.amin(pixels[:,1]), np.amax(pixels[:,1])\\n z_min, z_max = np.amin(pixels[:,2]), np.amax(pixels[:,2])\\n \\n pixels[:,0] -= x_min \\n pixels[:,1] -= y_min\\n pixels[:,2] -= z_min\\n \\n x_max -= x_min\\n y_max -= y_min\\n z_max -= z_min\\n \\n scale_factor = size / max(x_max, y_max, z_max) \\n # All points are now between [0..max]\\n\\n pixels *= scale_factor\\n return pixels\",\n \"def resample_by(image_array, compression_factor_list, is_seg=False):\\n original_dims_in_pixels = [image_array.shape[d]\\n for d in range(len(image_array.shape))]\\n target_pixel_dims_list = [int(math.floor(compression_factor_list[d] * original_dims_in_pixels[d]))\\n for d in range(len(image_array.shape))]\\n\\n resized_image = resample(image_array, target_pixel_dims_list, is_seg)\\n\\n return(resized_image)\",\n \"def imresize(img, size):\\n if hasattr(size, \\\"__len__\\\"):\\n num_rows, num_cols = size\\n assert (num_rows > 0) or (num_cols > 0)\\n if num_rows < 0:\\n num_rows = num_cols * img.shape[0] / img.shape[1]\\n if num_cols < 0:\\n num_cols = num_rows * img.shape[1] / img.shape[0]\\n else:\\n num_rows = int(round(img.shape[0] * size))\\n num_cols = int(round(img.shape[1] * size))\\n return skimage.transform.resize(img, (num_rows, num_cols))\",\n \"def resize_image(img):\\n ratio = float(img.size[1]) / float(img.size[0])\\n\\n if ratio > 1.73:\\n h, w = 320, 160 # 512, 256\\n elif ratio < 1.0 / 1.73:\\n h, w = 160, 320 # 256, 512\\n elif ratio > 1.41:\\n h, w = 384, 256\\n elif ratio < 1. / 1.41:\\n h, w = 256, 384\\n elif ratio > 1.15:\\n h, w = 320, 240 # 512, 384\\n elif ratio < 1. / 1.15:\\n h, w = 240, 320 # 384, 512\\n else:\\n h, w = 320, 320 # 384, 384\\n\\n h = int(h)\\n w = int(w)\\n img = TF.resize(img, size=[h, w], interpolation=Image.BILINEAR)\\n return img\",\n \"def resizeImage(self):\\n ratio = float(self.qIma.width()) / float(self.qIma.height())\\n if self.qIma.width() > self.qIma.height():\\n maxWidth = 300\\n maxHeight = int(300 / ratio)\\n else:\\n maxWidth = int(300 / ratio)\\n maxHeight = 300\\n img = self.qIma.toImage().scaled(maxWidth, maxHeight, QtCore.Qt.KeepAspectRatio)\\n return img\",\n \"def scale_image(img, factor=1):\\n\\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))\",\n \"def resample_image(image, out_spacing=(1.0, 1.0, 1.0), out_size=None, is_label=False, pad_value=0):\\n\\n original_spacing = np.array(image.GetSpacing())\\n original_size = np.array(image.GetSize())\\n\\n if out_size is None:\\n out_size = np.round(np.array(original_size * original_spacing / np.array(out_spacing))).astype(int)\\n else:\\n out_size = np.array(out_size)\\n\\n original_direction = np.array(image.GetDirection()).reshape(len(original_spacing), -1)\\n original_center = (np.array(original_size, dtype=float) - 1.0) / 2.0 * original_spacing\\n out_center = (np.array(out_size, dtype=float) - 1.0) / 2.0 * np.array(out_spacing)\\n\\n original_center = np.matmul(original_direction, original_center)\\n out_center = np.matmul(original_direction, out_center)\\n out_origin = np.array(image.GetOrigin()) + (original_center - out_center)\\n\\n resample = sitk.ResampleImageFilter()\\n resample.SetOutputSpacing(out_spacing)\\n resample.SetSize(out_size.tolist())\\n resample.SetOutputDirection(image.GetDirection())\\n resample.SetOutputOrigin(out_origin.tolist())\\n resample.SetTransform(sitk.Transform())\\n resample.SetDefaultPixelValue(pad_value)\\n\\n if is_label:\\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\\n else:\\n resample.SetInterpolator(sitk.sitkBSpline)\\n\\n return resample.Execute(image)\",\n \"def __resize_512p(input_data):\\n rate = 1\\n test_size=512\\n if input_data['img'].shape[0] > input_data['img'].shape[1]:\\n if True: # input_data['img'].shape[1] < 512:\\n rate = test_size / input_data['img'].shape[1]\\n seq = iaa.Sequential([\\n iaa.Scale({'height': \\\"keep-aspect-ratio\\\", 'width': test_size}, 'cubic')\\n ])\\n input_data['img'] = seq.augment_image(input_data['img'])\\n else:\\n if True: # input_data['img'].shape[0] < 512:\\n rate = test_size / input_data['img'].shape[0]\\n seq = iaa.Sequential([\\n iaa.Scale({'height': test_size, 'width': \\\"keep-aspect-ratio\\\"}, 'cubic')\\n ])\\n input_data['img'] = seq.augment_image(input_data['img'])\\n\\n if DataAugmentor._is_synthtext(input_data):\\n input_data['contour'] = [[np.cast['int32'](contour * rate) for contour in contours] for contours in\\n input_data['contour']]\\n else:\\n input_data['contour'] = [np.cast['int32'](contour * rate) for contour in input_data['contour']]\\n input_data['center_point'] = [(np.cast['int32'](point[0] * rate),\\n np.cast['int32'](point[1] * rate)) for point in input_data['center_point']]\\n return input_data\",\n \"def resize(image):\\r\\n return cv2.resize(image, (200, 66), interpolation=cv2.INTER_AREA)\",\n \"def upsampleImage( arr, kernelSize ):\\n return scipy.ndimage.zoom( arr, kernelSize )\",\n \"def reScaleLandsat(self,img):\\n \\n\\t\\tthermalBand = ee.List(['thermal'])\\n\\t\\tthermal = ee.Image(img).select(thermalBand).multiply(10)\\n \\n\\t\\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\\n\\t\\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\\n \\n\\t\\timage = ee.Image(scaled.addBands(thermal)).int16()\\n \\n\\t\\treturn image.copyProperties(img)\",\n \"def resize(im: Image) -> Image:\\n size_factor = random.uniform(*ImageOperations.config.get('resize_interval'))\\n return im.resize((\\n int(round(im.height * size_factor, 0)),\\n int(round(im.width * size_factor, 0))\\n ))\",\n \"def autoscale(self, img: Image, max_height: int, max_width: int):\\n height = img.bgr.shape[0]\\n width = img.bgr.shape[1]\\n\\n diff_height = max_height / height\\n diff_width = max_width / width\\n\\n diff = min(diff_height, diff_width)\\n\\n height = int(height * diff)\\n width = int(width * diff)\\n\\n return img.rescale_image(height, width)\",\n \"def _resize_img(self, results):\\n for key in results.get('img_fields', ['img']):\\n if self.keep_ratio:\\n img, scale_factor = general_ocr.imrescale(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n backend=self.backend)\\n # the w_scale and h_scale has minor difference\\n # a real fix should be done in the general_ocr.imrescale in the future\\n new_h, new_w = img.shape[:2]\\n h, w = results[key].shape[:2]\\n w_scale = new_w / w\\n h_scale = new_h / h\\n else:\\n img, w_scale, h_scale = general_ocr.imresize(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n backend=self.backend)\\n results[key] = img\\n\\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\\n dtype=np.float32)\\n results['img_shape'] = img.shape\\n # in case that there is no padding\\n results['pad_shape'] = img.shape\\n results['scale_factor'] = scale_factor\\n results['keep_ratio'] = self.keep_ratio\",\n \"def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):\\r\\n w = int(round(target_height * image.shape[1] / image.shape[0]))\\r\\n return cv2.resize(image, (w, target_height), interpolation=method)\",\n \"def ulab_bilinear_interpolation():\\n GRID_DATA[1::2, ::2] = SENSOR_DATA[:-1, :]\\n GRID_DATA[1::2, ::2] += SENSOR_DATA[1:, :]\\n GRID_DATA[1::2, ::2] /= 2\\n GRID_DATA[::, 1::2] = GRID_DATA[::, :-1:2]\\n GRID_DATA[::, 1::2] += GRID_DATA[::, 2::2]\\n GRID_DATA[::, 1::2] /= 2\",\n \"def __scale_image(image, scale: float):\\r\\n height, width, _ = image.shape\\r\\n\\r\\n width_scaled = int(np.ceil(width * scale))\\r\\n height_scaled = int(np.ceil(height * scale))\\r\\n\\r\\n im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)\\r\\n\\r\\n # Normalize the image's pixels\\r\\n im_data_normalized = (im_data - 127.5) * 0.0078125\\r\\n\\r\\n return im_data_normalized\",\n \"def downscale_image(img, bottom, x, y):\\n width, height = tuple(img.shape[1::-1])\\n img = img[int(round((1 - bottom) * (height - 1))):(height - 1), 1:(width - 1)]\\n img = cv2.resize(img, (x, y))\\n #img = cv2.Canny(img, 100, 200)\\n ret, img = cv2.threshold(img, img.mean(), 255, cv2.THRESH_BINARY)\\n return img\",\n \"def upsample(x, name, size):\\n with tf.name_scope(name):\\n outputs = tf.image.resize_bilinear(x, size)\\n # Return layer's output\\n return outputs\",\n \"def _resize_img(self, results):\\n for key in results.get('img_fields', ['img']):\\n if self.keep_ratio:\\n img, scale_factor = mmcv.imrescale(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n interpolation=self.interpolation,\\n backend=self.backend)\\n # the w_scale and h_scale has minor difference\\n # a real fix should be done in the mmcv.imrescale in the future\\n new_h, new_w = img.shape[:2]\\n h, w = results[key].shape[:2]\\n w_scale = new_w / w\\n h_scale = new_h / h\\n else:\\n img, w_scale, h_scale = mmcv.imresize(\\n results[key],\\n results['scale'],\\n return_scale=True,\\n interpolation=self.interpolation,\\n backend=self.backend)\\n results[key] = img\\n\\n scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],\\n dtype=np.float32)\\n results['img_shape'] = img.shape\\n # in case that there is no padding\\n results['pad_shape'] = img.shape\\n results['scale_factor'] = scale_factor\\n results['keep_ratio'] = self.keep_ratio\",\n \"def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\\n assert img_r.get_data().shape == (29, 39, 19)\\n assert img_r.get_data()[4, 4, 4] == 1.0\",\n \"def resize_img_norescale(img, size):\\n w,h = size\\n shape = (h,w)\\n out = np.zeros(shape, dtype=img.dtype)\\n i = min(img.shape[0], out.shape[0])\\n j = min(img.shape[1], out.shape[1])\\n out[0:i,0:j] = img[0:i, 0:j]\\n return out\",\n \"def preprocess(img: 'np.ndarray') -> 'np.ndarray':\\n return cv2.resize(img, (45, 45), interpolation=cv2.INTER_AREA)[:,:,:3]\",\n \"def obscure_image(image):\\n size = image.size\\n pixel_size = 9\\n if size[0] < 9 or size[1] < 9:\\n return image\\n image_f = image.filter(ImageFilter.MaxFilter)\\n image_s = image_f.resize((size[0] / pixel_size, size[1] / pixel_size), Image.NEAREST)\\n image_l = image_s.resize((size[0], size[1]), Image.NEAREST)\\n\\n return image_l\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6840322","0.6831431","0.68000495","0.67738384","0.6769506","0.67526364","0.674664","0.6701318","0.6659351","0.65608776","0.6559787","0.65459263","0.6527563","0.6523229","0.65071964","0.6495799","0.6469559","0.64450675","0.64279085","0.64148057","0.6409491","0.6403784","0.6387825","0.6381598","0.63812923","0.6350783","0.6337695","0.6334831","0.6333865","0.63321745","0.63305926","0.63121647","0.63111085","0.6309718","0.63095295","0.62931406","0.62854904","0.62688935","0.6262351","0.6260285","0.6259601","0.6256015","0.62513155","0.6235849","0.6226073","0.6220733","0.62175137","0.6203822","0.6199539","0.6189284","0.61892104","0.61889017","0.61852133","0.61747","0.6168565","0.6161727","0.6154645","0.61535555","0.61527216","0.614748","0.6129197","0.6120388","0.611748","0.6115913","0.61070204","0.61068285","0.6106477","0.61035097","0.6101212","0.6091997","0.60842687","0.60808957","0.60751814","0.60727876","0.6071736","0.6059489","0.6058895","0.6035421","0.603111","0.6029484","0.6028962","0.6027682","0.60253745","0.6023583","0.602168","0.60194427","0.60097164","0.6006475","0.60041106","0.60032105","0.59999365","0.5996304","0.5977162","0.5967232","0.59564084","0.59551513","0.59548795","0.595058","0.59463286","0.5944893"],"string":"[\n \"0.6840322\",\n \"0.6831431\",\n \"0.68000495\",\n \"0.67738384\",\n \"0.6769506\",\n \"0.67526364\",\n \"0.674664\",\n \"0.6701318\",\n \"0.6659351\",\n \"0.65608776\",\n \"0.6559787\",\n \"0.65459263\",\n \"0.6527563\",\n \"0.6523229\",\n \"0.65071964\",\n \"0.6495799\",\n \"0.6469559\",\n \"0.64450675\",\n \"0.64279085\",\n \"0.64148057\",\n \"0.6409491\",\n \"0.6403784\",\n \"0.6387825\",\n \"0.6381598\",\n \"0.63812923\",\n \"0.6350783\",\n \"0.6337695\",\n \"0.6334831\",\n \"0.6333865\",\n \"0.63321745\",\n \"0.63305926\",\n \"0.63121647\",\n \"0.63111085\",\n \"0.6309718\",\n \"0.63095295\",\n \"0.62931406\",\n \"0.62854904\",\n \"0.62688935\",\n \"0.6262351\",\n \"0.6260285\",\n \"0.6259601\",\n \"0.6256015\",\n \"0.62513155\",\n \"0.6235849\",\n \"0.6226073\",\n \"0.6220733\",\n \"0.62175137\",\n \"0.6203822\",\n \"0.6199539\",\n \"0.6189284\",\n \"0.61892104\",\n \"0.61889017\",\n \"0.61852133\",\n \"0.61747\",\n \"0.6168565\",\n \"0.6161727\",\n \"0.6154645\",\n \"0.61535555\",\n \"0.61527216\",\n \"0.614748\",\n \"0.6129197\",\n \"0.6120388\",\n \"0.611748\",\n \"0.6115913\",\n \"0.61070204\",\n \"0.61068285\",\n \"0.6106477\",\n \"0.61035097\",\n \"0.6101212\",\n \"0.6091997\",\n \"0.60842687\",\n \"0.60808957\",\n \"0.60751814\",\n \"0.60727876\",\n \"0.6071736\",\n \"0.6059489\",\n \"0.6058895\",\n \"0.6035421\",\n \"0.603111\",\n \"0.6029484\",\n \"0.6028962\",\n \"0.6027682\",\n \"0.60253745\",\n \"0.6023583\",\n \"0.602168\",\n \"0.60194427\",\n \"0.60097164\",\n \"0.6006475\",\n \"0.60041106\",\n \"0.60032105\",\n \"0.59999365\",\n \"0.5996304\",\n \"0.5977162\",\n \"0.5967232\",\n \"0.59564084\",\n \"0.59551513\",\n \"0.59548795\",\n \"0.595058\",\n \"0.59463286\",\n \"0.5944893\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60840994"},"document_rank":{"kind":"string","value":"71"}}},{"rowIdx":14994,"cells":{"query":{"kind":"string","value":"get final coach for each session coach with more than half the season will be the credited coach for eventual playoff and championship won"},"document":{"kind":"string","value":"def get_final_coach_for_each_season(self):\n self.final_coach_for_season = (\n self.num_days_coach_for_season\n .groupby(['Season','TeamID'])\n .agg({\"CoachName\":\"count\"})\n .reset_index()\n .rename(columns={\"CoachName\":\"coach_counts\"})\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \"ignore\")))\n [['Season','TeamID','final_coach']]\n )"},"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 get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"count\"})\n .reset_index()\n .rename(columns={\"NumOT\":\"is_champion\",\"WTeamID\":\"TeamID\"})\n )","def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )","def get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n # get losing games for coaches\n self.post_games_lose_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df for post season\n self.combine_post_games_won_lose = (\n self.post_games_lose_for_coaches\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"post_games_lost\",\"which_coach_for_win\":\"post_games_won\"})\n .fillna(0)\n )","def combine_playoff_championship_for_each_coach(self):\n self.final_coach_with_postseason_champion_each_year = (\n self.final_coach_with_postseason_each_year\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\n .fillna(0)\n )","def get_tourney_rounds(self, conference, year):\n ts_dict = self.get_tourney_slots()\n seed_dict = self.get_tourney_seeds()\n tr_dict = self.get_tourney_results()\n \n round_1 = list()\n round_2 = list()\n round_3 = list()\n round_4 = list()\n winner = list()\n \n round1_winners = list()\n for seed, team in seed_dict[year].items():\n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n round1_winners.append(seed[1:])\n #removes duplicates because I did this part weirdly... HEHEH\n round1_winners = list(set(round1_winners))\n\n win_counter = defaultdict(int)\n for seed, team in seed_dict[year].items(): \n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n win_counter[winning] += 1\n \n for slot, matchup in ts_dict[year].items():\n \n if conference in slot and \"R1\" in slot: \n round_1.append(\"{}-{}\".format(matchup[1:3], matchup[-2:]))\n round_1 = sorted(round_1)\n #for match in round_1:\n for winner1 in round1_winners:\n if winner1 in round_1[0]:\n for winner2 in round1_winners:\n if winner2 in round_1[-1]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[1]:\n for winner2 in round1_winners:\n if winner2 in round_1[-2]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[2]:\n for winner2 in round1_winners:\n if winner2 in round_1[-3]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[3]:\n for winner2 in round1_winners:\n if winner2 in round_1[-4]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n round_2 = sorted(round_2)\n\n round2_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 1:\n round2_winners.append(seed[1:])\n \n for winner1 in round2_winners:\n if winner1 in round_2[0]:\n for winner2 in round2_winners:\n if winner2 in round_2[-1]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_2[1]:\n for winner2 in round2_winners:\n if winner2 in round_2[-2]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n round_3 = sorted(round_3)\n\n round3_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 2:\n round3_winners.append(seed[1:])\n\n for winner1 in round3_winners:\n if winner1 in round_3[0]:\n for winner2 in round3_winners:\n if winner2 in round_3[-1]:\n round_4.append(\"{}-{}\".format(winner1, winner2))\n round_4 = sorted(round_4)\n\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 3:\n winner.append(seed[1:])\n\n conferences = {\"W\": \"East\", \"X\": \"Midwest\", \"Y\": \"South\", \"Z\": \"West\"}\n\n #print(\"CONFERENCE: {}, YEAR: {}\".format(conferences[conference], year))\n #print(\"ROUND1:\", round_1)\n #print(\"ROUND2:\", round_2)\n #print(\"ROUND3:\", round_3)\n #print(\"ROUND4:\", round_4)\n #print(\"WINNER:\", winner)\n\n #clearing out the tourney results dictionary\n #tr_dict.clear()\n\n return round_1, round_2, round_3, round_4, winner","def find_opponent(standings,odds):\n\n # simulate all games\n for i in range(len(odds)):\n play_game(odds.loc[i],standings)\n\n # update the points and GD tally\n standings['P']=standings['W']*3 + standings['D']\n standings['GD']=standings['F']-standings['A']\n\n # see if teams have equal amount of points, and award h2h_points for\n # h2h results against those teams.\n for group in \"ABCDEF\":\n gelijk = standings.loc[standings['Group']==group][standings.loc[standings['Group']==group].duplicated(subset='P',keep=False)]\n gelijk[\"h2h_points\"]=np.zeros(len(gelijk))\n\n for i in gelijk.index:\n for team1 in gelijk.loc[i][\"h2h\"]:\n for team2 in gelijk[\"TEAMS\"]:\n if team1==team2:\n standings.loc[i,\"h2h_points\"]+=1\n\n # sort the final standings\n standings = standings.sort_values(by=['Group','P',\"h2h_points\",'GD','F','W'],ascending=[True,False,False,False,False,False])\n\n # determine third placed teams\n standings = standings.reset_index()\n third = standings.loc[[2,6,10,14,18,22]]\n\n # determine best number threes\n third = third.sort_values(by=['P','GD','F','W'],ascending=False)\n\n groups_of_best_no_3 = \"\"\n for i in third.head(4).Group:\n groups_of_best_no_3+=i\n groups_of_best_no_3 = ''.join(sorted(groups_of_best_no_3))\n\n # look up the opponent of the dutch team\n a = third.loc[third.Group == opponent_matrix[groups_of_best_no_3]]['TEAMS']\n\n return a.reset_index().TEAMS[0]","def play_game(game,standings_):\n rand_nmr = random.random()\n\n standings_.loc[standings_.TEAMS==game['Home'],'MP'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'MP'] += 1\n\n if rand_nmr < game['Prob Home']:\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\n standings_.loc[standings_.TEAMS==game['Home'],'W'] += 1\n standings_.loc[standings_.TEAMS==game['Home'],'F'] += n_goals\n standings_.loc[standings_.TEAMS==game['Away'],'L'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'A'] += n_goals\n standings_.loc[standings_.TEAMS==game['Home']][\"h2h\"].apply(lambda x:x.append(game['Away']))\n\n return 0\n\n elif rand_nmr < game['Prob Home'] + game['Prob Draw']:\n # all draws end in 0-0 this can be improved\n standings_.loc[standings_.TEAMS==game['Home'],'D'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'D'] += 1\n\n return 1\n\n else:\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\n standings_.loc[standings_.TEAMS==game['Away'],'W'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'F'] += n_goals\n standings_.loc[standings_.TEAMS==game['Home'],'A'] += 1\n standings_.loc[standings_.TEAMS==game['Home'],'L'] += n_goals\n standings_.loc[standings_.TEAMS==game['Away']][\"h2h\"].apply(lambda x:x.append(game['Home']))\n\n return 2","def first_round_history(self):\n self.ts_dict = self.get_tourney_slots()\n self.tsr_dict = self.match_seeds()\n first_seed_win = 0\n second_seed_win = 0\n third_seed_win = 0\n fourth_seed_win = 0\n fifth_seed_win = 0\n sixth_seed_win = 0\n seventh_seed_win = 0\n eighth_seed_win = 0\n total_games = 128\n\n for year1 in self.ts_dict: \n for slot, match_up in self.ts_dict[year1].items():\n if slot[:2] == \"R1\":\n for year2 in self.tsr_dict:\n if year1 == year2:\n for winning, losing in self.tsr_dict[year2].items():\n if winning[5:] == match_up[:3]:\n seed = winning[6:] \n if seed == \"01\":\n first_seed_win += 1\n elif seed == \"02\":\n second_seed_win += 1\n elif seed == \"03\":\n third_seed_win += 1\n elif seed == \"04\":\n fourth_seed_win += 1\n elif seed == \"05\":\n fifth_seed_win += 1\n elif seed == \"06\":\n sixth_seed_win += 1\n elif seed == \"07\":\n seventh_seed_win += 1\n elif seed == \"08\":\n eighth_seed_win += 1 \n \n #print(first_seed_win, second_seed_win, third_seed_win, fourth_seed_win, fifth_seed_win, sixth_seed_win, seventh_seed_win, eighth_seed_win, total_games)\n\n gauge = pygal.SolidGauge(inner_radius=0.70, title=\"NCAA First Round Results\")\n ratio_first_seed = int(first_seed_win / total_games * 100)\n ratio_second_seed = int(second_seed_win / total_games * 100)\n ratio_third_seed = int(third_seed_win / total_games * 100)\n ratio_fourth_seed = int(fourth_seed_win / total_games * 100)\n ratio_fifth_seed = int(fifth_seed_win / total_games * 100)\n ratio_sixth_seed = int(sixth_seed_win / total_games * 100)\n ratio_seventh_seed = int(seventh_seed_win / total_games * 100)\n ratio_eighth_seed = int(eighth_seed_win / total_games * 100) \n\n percent_formatter = lambda x: '{:.10g}%'.format(x)\n gauge.value_formatter = percent_formatter\n gauge.add('1 vs. 16', [{'value': ratio_first_seed, 'max_value': 100}])\n gauge.add('2 vs. 15', [{'value': ratio_second_seed, 'max_value': 100}])\n gauge.add('3 vs. 14', [{'value': ratio_third_seed, 'max_value': 100}])\n gauge.add('4 vs. 13', [{'value': ratio_fourth_seed, 'max_value': 100}])\n gauge.add('5 vs. 12', [{'value': ratio_fifth_seed, 'max_value': 100}])\n gauge.add('6 vs. 11', [{'value': ratio_sixth_seed, 'max_value': 100}])\n gauge.add('7 vs. 10', [{'value': ratio_seventh_seed, 'max_value': 100}])\n gauge.add('8 vs. 9', [{'value': ratio_eighth_seed, 'max_value': 100}])\n \n gauge.render_to_file('chart.svg')","def non_current_championships():\n current_championships = (Alfa_Romeo+Ferrari+Haas+McLaren+Mercedes+Racing_Point+Red_Bull+Renault+Toro_Rosso+Williams).constructors_championships_years\n non_current_championships = []\n year = 1958\n while year < 2020:\n if year not in current_championships:\n non_current_championships.append(year)\n year += 1\n return f\"The F1 Constructors' Championships won by teams no longer on the grid are: \\n{non_current_championships}\"","def winning_games_stats(self):\n self.winning_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(winning_num_counts = 1))\n .query(\"Season <= 2013\")\n .groupby(['Season','WTeamID'])\n .agg({\"WScore\":\"sum\",\"WFGM\":\"sum\",\"WFGA\":\"sum\",\"WFGM3\":\"sum\",\"WFGA3\":\"sum\",\"WFTM\":\"sum\",\"WFTA\":\"sum\",\"LScore\":\"sum\",\"winning_num_counts\":\"sum\",\n \"WOR\":\"sum\",\"WDR\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\n \"WAst\":\"sum\",\"WTO\":\"sum\",\"WStl\":\"sum\",\"WBlk\":\"sum\",\"WPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"LScore\":\"losing_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"LFGA\":\"LFGA_opp\",\"LFGM\":\"LFGM_opp\"})\n )","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def marcels_players(goalie, date, df):\n # 0 = that year, 1 is year b4 ....\n marcel_weights = [.36, .29, .21, .14]\n reg_const = 2000\n reg_avg = 0 # Where to regress to\n\n # Use past 3 season to weight games played -> Just take weighted average\n gp_weights = [8, 4, 2, 0]\n\n season = int(helpers.get_season(date))\n\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\n weighted_gp_sum, weights_gp_sum = 0, 0\n\n # Past 4 Seasons\n for i in range(0, 4):\n if season - i > 2006:\n # Subset from stats df\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\n\n # Sanity Check\n if df_goalie.shape[0] > 1:\n print(\"Too many rows!!!!!!!\")\n exit()\n\n # If he played that year\n if not df_goalie.empty:\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\n\n # -> To divide by at end...normalize everything\n weights_marcel_sum += marcel_weights[i]\n weights_gp_sum += gp_weights[i]\n\n # Normalize weighted sums\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n\n # Get Regressed fsv%\n if weighted_fen_sum != 0:\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\n else:\n weighted_adj_fsv = 0\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\n\n # Get weighted gp\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\n\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}","def perc_greedy(population, percentage=80):\n \n\n #initialization\n res_arr = [2] * 10\n total_knights = 80\n\n medians = get_medians(population, percentage);\n\n while(total_knights > 0):\n \n # find \"easiest\" to acheive\n ind = medians.index(min(medians))\n\n # calculate the number of knights to assign to that castle\n assign = min(total_knights, medians[ind]-res_arr[ind] + 1)\n\n # make assignment\n res_arr[ind] += assign\n total_knights -= assign\n\n # mark that castle as \"done\"\n medians[ind] = 100\n \n # get the score of result inst against input population\n res_inst = CBInstance(res_arr)\n res_score = grade_inst(res_inst, population)\n \n return res_inst","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def save_games_copa(season, logging_level=logging.INFO):\n logging.basicConfig(level=logging_level)\n logger = logging.getLogger(__name__)\n\n logger.info('Starting the download of games...')\n\n if season.season == get_current_season():\n current_game_events_ids = season.get_current_game_events_ids_copa()\n game_ids_list = list(current_game_events_ids.values())\n else:\n game_ids_list=season.get_game_ids_copa()\n\n n_checkpoints = 4\n checkpoints = [round(i * float(len(game_ids_list)) / n_checkpoints) for i in range(n_checkpoints + 1)]\n for i in range(len(game_ids_list)):\n\n game_id=int(game_ids_list[i]) % 1000\n url2 = BASE_URL + \"/fichas/CREY{}.php\".format(game_ids_list[i])\n filename = os.path.join(season.GAMES_COPA_PATH, str(game_id)+\"-\" +str(game_ids_list[i]) + '.html')\n\n open_or_download(file_path=filename, url=url2)\n if i in checkpoints:\n logger.info('{}% already downloaded'.format(round(float(i * 100) / len(game_ids_list))))\n\n logger.info('Download finished! (new {} games in {})\\n'.format(len(game_ids_list), season.GAMES_COPA_PATH))","def test_get_player_upcoming_chests(self):\n pass","def strategy_cheap(cookies, cps, history, time_left, build_info):\n print\n print \"STRATEGY PART BEGIN\"\n print\n items_available = []\n for item in build_info.build_items():\n items_available.append(item)\n while items_available:\n min_cost = float('inf')\n for item in items_available:\n #print \"item:\", item, \", cost:\", build_info.get_cost(item)\n if build_info.get_cost(item) < min_cost:\n min_cost = build_info.get_cost(item)\n cheapest = item\n print \"cheapest:\", cheapest\n # check if time enough\n print \"checking time\"\n print \"time left:\", time_left\n print \"cost:\", min_cost\n print \"cookies can be produced:\", cps * time_left\n if cps * time_left + cookies < min_cost:\n print \"not enough,\"\n return None\n else:\n print cheapest, \"chosen\"\n print \"STRATEGY PART END\"\n print\n return cheapest","def _name_champion(self):\n # TODO BREAK TIES\n return max(self.teams, key=lambda team: len(team.wins))","def possessions_home_away(data_frame, mode):\n total_games_dict, total_dict, percentage_dict = dict(), dict(), dict()\n team_name = 'home_team' if mode == 'home' else 'away_team'\n for index, row in data_frame.iterrows():\n if row[team_name] not in total_games_dict:\n percentage_dict[row[\"id\"]] = 0\n else:\n percentage_dict[row[\"id\"]] = format(float(total_dict[row[team_name]]) / float(total_games_dict[row[team_name]]), '.2f')\n\n if row[team_name] in total_games_dict:\n total_games_dict[row[team_name]] += 1\n else:\n total_games_dict[row[team_name]] = 1\n\n fga, orb, fta, to = 'fg_made_attempted_', 'offensive_rebounds_', 'ft_made_attempted_', 'turnovers_'\n\n possessions = int(row[fga + team_name.split('_')[0]].split('-')[1]) - int(row[orb + team_name.split('_')[0]]) + \\\n (0.475 * int(row[fta + team_name.split('_')[0]].split('-')[1])) + int(row[to + team_name.split('_')[0]])\n\n if row[team_name] in total_dict:\n total_dict[row[team_name]] += possessions\n else:\n total_dict[row[team_name]] = possessions\n\n return percentage_dict","def mk_champs(cls, year, start_date, end_date):\n seasons = [\"Quad Quandary\", \"Face Off\", \"Hot Shot\", \"Get Over It\", \"Bowled Over\", \"Ring It Up\", \"Block Party\",\n \"Cascade Effect\", \"RES-Q\", \"Velocity Vortex\", \"Relic Recovery\", \"Rover Ruckus\"]\n season_name = seasons[year-2007]\n start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d\")\n end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n season = f\"{year % 100:02}{(year + 1) % 100:02}\"\n # fyear = f\"{year}-{(year+1)%1000:02d}\"\n if year == 2009:\n city, state_prov, country = \"Atlanta\", \"Georgia\", \"USA\"\n venue = \"Georgia Dome\"\n address = \"1 Georgia Dome Dr, Atlanta, GA 30313\"\n elif year < 2013:\n city, state_prov, country = \"St. Louis\", \"Missouri\", \"USA\"\n venue = \"Edward Jones Dome\"\n address = \"701 Convention Plaza, St. Louis, MO 63101\"\n else:\n city, state_prov, country = \"St. Louis\", \"Missouri\", \"USA\"\n venue = \"Union Station\"\n address = \"1820 Market Street, St. Louis, MO 63103\"\n shared = {\n \"year\": year,\n \"city\": city,\n \"state_prov\": state_prov,\n \"country\": country,\n \"end_date\": end_date,\n \"event_type\": EventType.WORLD_CHAMPIONSHIP,\n \"venue\": venue,\n \"address\": address,\n \"data_sources\": [\"USFIRST Website Archives\"]\n }\n\n finals = Event(key=f\"{season}cmp0\",\n name=f\"FTC {season_name} World Championship - Finals\",\n playoff_type=PlayoffType.BO3_FINALS, \n division_keys=[f\"{season}cmp1\", f\"{season}cmp2\"],\n start_date=end_date,\n **shared)\n franklin = Event(key=f\"{season}cmp{2}\",\n name=f\"FTC {season_name} World Championship - Franklin Division\",\n playoff_type=PlayoffType.STANDARD, \n parent_event_key=f\"{season}cmp0\", \n start_date=start_date,\n **shared)\n edison = Event(key=f\"{season}cmp{1}\",\n name=f\"FTC {season_name} World Championship - Edison Division\",\n playoff_type=PlayoffType.STANDARD,\n parent_event_key=f\"{season}cmp0\", \n start_date=start_date,\n **shared)\n return (franklin, edison, finals)","def cumulative_stats_for_teams_each_year(self):\n self.cumulative_stats_for_team_each_year = (\n self.combine_both_winning_losing_games_stats\n .sort_values(['WTeamID','Season'])\n .groupby(['WTeamID'])\n .cumsum()\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\n .drop(['LTeamID','win_rate'],1)\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n # rebounds cumsum stats\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # assists, turnovers, steals, blocks and personal fouls\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win or lose by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def comp101_game(points, server):\n \n player0_points = 0 # sets initial 'points' of both players\n player1_points = 0 \n final0_score = 0 # final 'score' of both players in a manner peculiar to\n final1_score = 0 # tennis\n remainder = [] # stores the remaining 'points' if the game has ended\n tennis_score = {0: 0, 1: 15, 2: 30, 3: 40, 4: 40} # use to convert\n # 'points' to tennis\n # 'scores'\n winner = None # initial winner of the game\n \n # tests every 'points' in 'points'\n for number in points:\n \n # finds the 'point' differences between both players and make\n # sure it is a positive value\n points_diff = abs(player0_points - player1_points)\n \n if (player0_points >= 4 or player1_points >= 4):\n \n # the case when a 'winner' is found and stores the \n # remaining 'points'\n if points_diff >= 2:\n if player0_points > player1_points:\n winner = 0\n final0_score = \"W\"\n \n else:\n winner = 1\n final1_score = \"W\"\n remainder.append(number)\n \n # the case when there is no 'winner' yet \n else:\n \n if number == 0:\n player0_points += 1\n\n else:\n player1_points += 1\n \n # updates the latest 'point' difference\n points_diff = abs(player0_points - player1_points)\n \n # ONLY runs if a player 'won' the game after exactly getting \n # his next 'point'\n if points_diff >= 2:\n \n if player0_points > player1_points:\n winner = 0\n final0_score = \"W\"\n \n else:\n winner = 1\n final1_score = \"W\"\n \n # if one of the player gets an \"advantage\"\n elif points_diff == 1:\n \n if player0_points > player1_points:\n final0_score = \"Ad\"\n final1_score = 40\n else:\n final0_score = 40\n final1_score = \"Ad\"\n \n # if no players get an \"advantage\" or 'wins' the game\n else:\n final0_score = 40\n final1_score = 40\n \n else:\n \n # adds a 'point' to a 'player' and converts player 'points' to \n # 'scores' in a manner peculiar to tennis\n if number == 0:\n player0_points += 1\n final0_score = tennis_score[player0_points]\n \n else:\n player1_points += 1\n final1_score = tennis_score[player1_points]\n \n # updates the latest score difference\n points_diff = abs(player0_points - player1_points)\n \n # checks if a player gets an \"advantage\" / 'wins' the game at exactly \n # his 4th 'point'\n if (player0_points == 4 or player1_points == 4):\n \n # when a player 'won' the game\n if points_diff >= 2:\n \n if player0_points > player1_points:\n winner = 0\n final0_score = \"W\"\n else:\n winner = 1\n final1_score = \"W\"\n \n # when a player gets an \"advantage\"\n elif points_diff == 1:\n \n if player0_points > player1_points:\n final0_score = \"Ad\"\n else:\n final1_score = \"Ad\" \n \n # determines which player score is displayed first based on 'server'\n if server == 0:\n score = str(final0_score) + \"-\" + str(final1_score)\n else:\n score = str(final1_score) + \"-\" + str(final0_score)\n \n return (score, winner, remainder)","def qualifiedteams(self):\n for i in range(0, len(self.teams)):\n for j in range(i + 1, len(self.teams)):\n WorldCupMatch(self.teams[i], self.teams[j], True)\n self.teams = sorted(self.teams, key=op.attrgetter('points', 'goaldifference', 'scored'))\n self.first_qualified = self.teams[len(self.teams)-1]\n self.second_qualified = self.teams[len(self.teams)-2]","def review(self):\n # Compile standings\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\n # Name a champion\n self.champion = self._name_champion()\n self.league.history.champions_timeline[self.year] = self.champion\n print \"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\".format(\n self.champion.team.name.upper(), self.year, self.league.name.upper()\n )\n # Compile league leaders\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\n # Have each team review its season, as well\n for team_season in self.teams:\n team_season.review()\n # Send the league into the offseason\n self.league.season = None","def get_game(self, game_id):\n \n session = requests.session()\n response = session.get(self.baseURL + str(game_id), headers=self.headers)\n soup = BeautifulSoup(response.text)\n \n #get teams\n defeated_by = False \n game_header = soup.find_all(text=re.compile('defeats'))\n \n if len(game_header) == 0:\n game_header = soup.find_all(text=re.compile('defeated by'))\n \n if (len(game_header)) == 0:\n game_header = soup.find_all(text=re.compile('defeat'))\n \n if (len(game_header)) == 0:\n game_header = soup.find_all(text=re.compile('drew'))\n defeated_by = True \n else:\n defeated_by = True \n\n if defeated_by: \n teams = self.remove_long_names(game_header[1]).replace('\\n', '')\n home_team = teams.split(' ')[0]\n away_team = teams.split(' ')[3]\n else:\n teams = self.remove_long_names(game_header[1]).replace('\\n', '')\n home_team = teams.split(' ')[0]\n away_team = teams.split(' ')[2]\n \n date_string = game_header[0].split(' ')\n date_string_find = [date.lower() for date in date_string]\n \n venue = date_string[date_string_find.index('at') + 1]\n \n #get round\n round_num = None\n \n try:\n date_string_find.remove('')\n except:\n pass\n \n try:\n round_num = int(date_string[date_string_find.index('round') + 1])\n except:\n try:\n round_num = date_string_find[date_string_find.index('final') - 1] + ' final'\n except:\n round_num = date_string_find[date_string_find.index('semi-final')]\n \n date = date_string[-3:]\n date = ' '.join(date) \n date = parser.parse(date)\n \n #get attendance\n attend = soup.find_all(text=re.compile('Attendance'))\n attendance = 0\n \n if (len(attend) > 3):\n attendance = int(attend[1].split(' ')[-1])\n \n #get stats \n away_stats = {}\n home_stats = {}\n \n for stat in stats:\n stat_row = soup.find_all('td', text=stat)[0].find_parent('tr')\n elements = stat_row.find_all('td')\n \n if elements[0].text == '-':\n home_stats[stat] = None\n else:\n home_stats[stat] = elements[0].text\n \n if elements[0].text == '-':\n away_stats[stat] = None\n else:\n away_stats[stat] = elements[2].text\n \n return Game(game_id, home_team, away_team, venue, round_num, date, attendance, home_stats, away_stats)","def build_home_advantage(matches):\n D = {}\n df_goal = matches[[\"season\", \"home_team_goal\", \"away_team_goal\"]]\n\n for i in range(len(df_goal)):\n key_season = df_goal.iloc[i].season\n if key_season not in D:\n D[key_season] = [\n 1,\n df_goal.iloc[i].home_team_goal,\n df_goal.iloc[i].away_team_goal,\n ]\n else:\n D[key_season][0] += 1\n D[key_season][1] += df_goal.iloc[i].home_team_goal\n D[key_season][2] += df_goal.iloc[i].away_team_goal\n\n for key in D:\n D[key][1] /= D[key][0]\n D[key][2] /= D[key][0]\n\n df_goal_info = pd.DataFrame(D)\n column_headers = list(df_goal_info.columns.values)\n\n fig, ax = plt.subplots(nrows=1, ncols=1)\n fig.set_size_inches(w=7, h=4)\n plt.plot(column_headers, df_goal_info.iloc[1], label=\"avg_home_goal\")\n plt.plot(column_headers, df_goal_info.iloc[2], label=\"avg_away_goal\")\n pl.xticks(rotation=270)\n plt.xlabel(\"Season\")\n plt.ylabel(\"Average Goal\")\n plt.legend()\n plt.show()","def find_new_contests(sport):\n\n # def get_pst_from_timestamp(timestamp_str):\n # timestamp = float(re.findall(\"[^\\d]*(\\d+)[^\\d]*\", timestamp_str)[0])\n # return datetime.datetime.fromtimestamp(\n # timestamp / 1000, timezone(\"America/Los_Angeles\")\n # )\n\n url = f\"https://www.draftkings.com/lobby/getcontests?sport={sport}\"\n\n # response = requests.get(url, headers=HEADERS, cookies=COOKIES).json()\n response_contests = get_contests(url)\n\n # create list of Contest objects\n contests = [Contest(c) for c in response_contests]\n # contests = [\n # get_largest_contest(response[\"Contests\"], 3),\n # get_largest_contest(response[\"Contests\"], 0.25),\n # get_largest_contest(response[\"Contests\"], 27),\n # ] + get_contests_by_entries(response[\"Contests\"], 3, 50000)\n target_contests = []\n entry_fees = []\n if sport == \"NFL\":\n entry_fees = [5, 10, 25, 50]\n else:\n entry_fees = [10, 25]\n\n for entry_fee in entry_fees:\n largest_contest = get_largest_contest(contests, entry_fee=entry_fee)\n # check if largest_contest is None\n if largest_contest is not None:\n logger.debug(\"Appending contest %s\", largest_contest)\n target_contests.append(largest_contest)\n\n for contest in target_contests:\n date_time = contest.start_dt\n # make naive datetime aware based on django settings\n aware_datetime = make_aware(date_time)\n dkcontest, created = DKContest.objects.update_or_create(\n dk_id=contest.id,\n defaults={\n \"date\": aware_datetime.date(),\n \"datetime\": aware_datetime,\n \"sport\": sport,\n \"name\": contest.name,\n \"draft_group_id\": contest.draft_group,\n \"total_prizes\": contest.total_prizes,\n \"entries\": contest.entries,\n \"entry_fee\": contest.entry_fee,\n },\n )\n if created:\n logger.info(\"Creating DKContest %s\", dkcontest)","def get_league_goalie_boxcars(league, seasons):\n\n if len(set(seasons))==1:\n scraped_season_list = str(seasons)\n elif len(set(seasons))>2:\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[:-1]).replace(\"'\", \"\").replace(\"[\", \"\").replace(\"]\", \"\") + \", and \" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[-1])\n else:\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[:-1]).replace(\"'\", \"\").replace(\"[\", \"\").replace(\"]\", \"\") + \" and \" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[-1])\n \n \n global hidden_patrick\n hidden_patrick = 0\n global error\n error = 0\n \n output = pd.DataFrame()\n \n if type(seasons) == str:\n single = getgoalies(league, seasons)\n output = output.append(single)\n print(\"Scraping \" + league + \" data is complete. You scraped goalie data from \" + seasons + \".\")\n return(output)\n \n elif ((type(seasons) == tuple) or (type(seasons) == list)):\n \n for i in range(0, len(seasons)):\n try:\n single = getgoalies(league, seasons[i])\n output = output.append(single)\n except KeyboardInterrupt as e:\n hidden_patrick = 4\n error = e\n return output\n except (ConnectionError,\n HTTPError,\n ReadTimeout,\n ConnectTimeout) as e:\n hidden_patrick = 5\n error = e\n return output\n \n print(\"Scraping \" + league + \" data is complete. You scraped goalie data from \" + scraped_season_list + \".\") \n return(output)","def season_game_logs(team, year):\n\n # Check year value\n if year > 2019 or year < 1950:\n raise ValueError('Year Value Incorrect')\n\n # Rename teams that moved\n team = scrape_utils.rename_team(team, year)\n\n # Get HTML content\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n season_stats = soup.find(id='tgl_basic')\n games = season_stats.find('tbody')\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # To find opponent statistics\n opponent = re.compile('^opp_.*$')\n\n # Loop through every game in a team's season\n for game in games.find_all('tr', {'class': None}):\n\n curr_team = {'team': team}\n opp_team = {}\n\n # Loop through each stat\n for stat in game.find_all('td'):\n\n stat_name = stat['data-stat']\n\n # These are opponent stats\n if re.match(opponent, stat_name):\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\n else:\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\n\n # Remove unnecessary information\n del curr_team['game_season']\n del curr_team['x']\n\n # Rename relocated teams\n curr_team['team'] = scrape_utils.rename_team(team)\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\n\n # Use the same ID as basketball reference\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \"%Y-%m-%d\"),\n 'season': year,\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\n '_id': game.find('a')['href'][-17:-5]}\n\n # Place the teams in the correct spot depending on who is the home team\n if curr_team.pop('game_location') == 0:\n result['home'] = curr_team\n result['away'] = opp_team\n else:\n result['home'] = opp_team\n result['away'] = curr_team\n\n # Insert into database\n m.insert('game_log', result)","def Champs(self, summoner):\n encrypted_summoner_id = summoner.get('encrypted_summoner_id', '')\n region = summoner.get('region', DEFAULT_REGION)\n r = self._rito.ListChampionMasteries(region, encrypted_summoner_id)\n if r:\n logging.info('Got champ mastery data for %s/%s [%s]', region,\n encrypted_summoner_id, summoner['summoner'])\n # Calculate total number of chests received\n total_chests = sum(1 for x in r.champion_masteries if x.chest_granted)\n\n top_champs = []\n for champ in r.champion_masteries[:3]:\n top_champs.append(self._game.champion_id_to_name[str(\n champ.champion_id)])\n top_champ_lvl = r.champion_masteries[0].champion_level\n\n chest_verb = ''\n chest_verb_dict = {\n (0, 2): 'receiving',\n (2, 4): 'collecting',\n (4, 8): 'earning',\n (8, 16): 'amassing',\n (16, 32): 'hoarding'\n }\n for range_spec, verb in chest_verb_dict.items():\n if total_chests in range(*range_spec):\n chest_verb = verb\n break\n\n if chest_verb:\n chest_str = '%s %s' % (chest_verb,\n inflect_lib.Plural(total_chests, 'chest'))\n else:\n chest_str = 'with a boatload of chests (%d)' % total_chests\n\n return (u'{0} is a L{1} {2[0]} main, but sometimes likes to play {2[1]} '\n 'and {2[2]}, {3} this season.').format(summoner['summoner'],\n top_champ_lvl, top_champs,\n chest_str)","def generate_matching_courses(self,goal):\n\n searchstring = self.preprocess(goal.goal)\n wordlist = nltk.word_tokenize(searchstring)\n relevant_words = []\n mystopwords = stopwords.words(\"english\") + stopwords.words(\"german\")\n for word in wordlist:\n if word not in mystopwords:\n relevant_words.append(word)\n # TODO: Activate in production\n # TODO: For testing find workaround to make local courses available for local test systems\n #user_origin = goal.user.origin\n # TODO: The following two lines have to be exchanged to filter courses according to origin\n #courses = models.Course.objects.filter(origin=user_origin)\n courses = models.Course.objects.all()\n\n matches = {}\n for course in courses:\n if course == None:\n print(\"Course is None\")\n if course.TF_IDF_scores == {}:\n continue\n score = 0.0\n for word in relevant_words:\n if word in course.TF_IDF_scores:\n if word in course.TF_IDF_scores:\n score += course.TF_IDF_scores[word]\n if score > 0.0:\n if score in matches.keys():\n matches[score].append(course)\n else:\n matches[score] = []\n matches[score].append(course)\n scores = list(matches.keys())\n scores.sort()\n\n bestcourses = []\n\n i = 0\n for score in scores:\n for course in matches[score]:\n bestcourses.append(course)\n i += 1\n if i >= COURSES_TO_DISPLAY:\n break\n\n if len(bestcourses) == 0:\n a = models.Activity.objects.get_or_create(\n title=\"Keine passenden Lehrveranstaltungen\",\n description=\"Aktuell gibt es zu Ihrem Interesse keine passenden Lehrveranstaltungen. \" \\\n \"Siddata wird regelmäßig nach neuen passenden Kursen suchen und diese ggf. hier anzeigen. \",\n type=\"todo\",\n goal=goal,\n image=\"idea.png\",\n status=\"new\"\n )[0]\n a.save()\n i = 0\n for course in bestcourses:\n\n a = models.Activity.objects.get_or_create(\n title=course.title,\n description=course.description,\n type=\"course\",\n goal=goal,\n image=\"%s.png\" %random.choice([\"world\",\"idea\",\"library\",\"cat\",\"brainbulb\",\"braincloud\",\"friends\"]),\n course=course,\n status=\"new\"\n )[0]\n a.save()\n i += 1\n if i == COURSE_MAX:\n break","def calc_winner(self):\n pass","def nth_iteration(Iterations, Moves_ahead, GA_iterations, n_samples,\n current_gen_spectra, next_gen_conc, x_test,\n conc_array_actual, spectra_array_actual, seed,\n median_fitness_list, max_fitness_list,\n iteration, mutation_rate_list, fitness_multiplier_list):\n set_seed(seed)\n mutation_rate, fitness_multiplier, best_move, best_move_turn, \\\n max_fitness, surrogate_score, desired_1, current_gen_spectra_1, \\\n best_conc_array, \\\n dictionary_of_moves = MCTS(Iterations, Moves_ahead,\n GA_iterations, current_gen_spectra,\n next_gen_conc, x_test, conc_array_actual,\n spectra_array_actual, seed, n_samples)\n print('The best move has a fitness value of', max_fitness)\n print('The best move occurs in', best_move_turn, 'turns.')\n print()\n print('The surrogate model has a score of:', surrogate_score)\n print()\n mutation_rate_list.append(mutation_rate)\n fitness_multiplier_list.append(fitness_multiplier)\n current_gen_spectra = current_gen_spectra.T\n current_gen_spectra = MinMaxScaler().fit(current_gen_spectra). \\\n transform(current_gen_spectra).T\n next_gen_conc, median_fitness, max_fitness = perform_iteration(\n current_gen_spectra, next_gen_conc, x_test, 20,\n n_samples, mutation_rate, fitness_multiplier)\n best_conc_array = \\\n best_conc_array[np.argsort(best_conc_array[:, -1])][-1, :]\n print(next_gen_conc)\n return mutation_rate, fitness_multiplier, mutation_rate_list, \\\n fitness_multiplier_list, best_move, best_move_turn, \\\n max_fitness, surrogate_score, next_gen_conc, \\\n best_conc_array, dictionary_of_moves","def tournament(self):\n pass","def get_player_stats_from_game(team, year, week):","def scrape_all_world_cup_goals():\n def scrape_goals_year(year):\n urls = scrape_world_cup_scoreboard(year)\n goals = []\n for url in urls:\n goals.extend(scrape_fifa_goals(url, 'FIFA World Cup'))\n return goals\n\n l = []\n for year in sorted(world_cup_mapping.keys()):\n l.extend(scrape_goals_year(year))\n return l","def get_smmry_data(soup, game_dict):\n\n # Get date and time data.\n try:\n date_soup = soup.find(\"div\", {\"class\": \"spielbericht_tipp_status\"})\n league = date_soup.div.span.text\n date_string = date_soup.div.text\n date = re.search(r'\\d{2}.\\d{2}.\\d{2}', date_string).group(0)\n time = re.search(r'\\d{2}:\\d{2}', date_string).group(0)\n matchday = re.search(r'[|]\\d+', date_string).group(0)[1:]\n\n game_dict[\"league\"] = league\n game_dict[\"fb_date\"] = date\n game_dict[\"fb_time\"] = time\n game_dict[\"matchday\"] = matchday\n except AttributeError:\n pass\n\n # Get game result.\n try:\n result = soup.find(\"div\", {\"class\": \"stand\"}).text\n game_dict[\"result\"] = result\n except AttributeError:\n pass\n\n # Try to get the referee name.\n try:\n referee = soup.find(\"span\", {\"class\": \"schiri_link\"}).text\n game_dict[\"referee\"] = referee\n except AttributeError:\n pass\n\n # Get team, club name and repective url by team.\n try:\n smmry_soup = soup.find(\n \"div\", {\"class\": \"spielbericht_ergebnis_wrapper\"})\n club_title = smmry_soup.find_all(\"img\")\n team_title = smmry_soup.findAll(\"div\", {\"class\": \"teaminfo\"})\n\n # Loop through teams.\n for j, team in enumerate([\"home_\", \"away_\"]):\n game_dict[team + \"team\"] = team_title[j].a[\"title\"]\n game_dict[team + \"team_url\"] = team_title[j].a[\"href\"]\n game_dict[team + \"club\"] = club_title[j][\"title\"]\n except (AttributeError, TypeError):\n pass\n\n return game_dict","async def get_league_players(season): \n if season == '2021-22':\n get_epl = 2021\n if season == '2020-21':\n get_epl = 2020\n if season == '2019-20':\n get_epl = 2019\n async with aiohttp.ClientSession() as session:\n understat = Understat(session)\n player = await understat.get_league_players(\"epl\", get_epl)\n # print(json.dumps(player))\n return player","def main(simulations, userschoice):\n # The teams data are obtained from FIFA statistics\n # Team Name, Attack, Defence\n quarters = ['quarter1', 'quarter2', 'quarter3', 'quarter4', 'quarter5', 'quarter6', 'quarter7', 'quarter8']\n semifinalists = ['semifinalist1', 'semifinalist2', 'semifinalist3', 'semifinalist4']\n finalists = ['finalist1', 'finalist2']\n\n df = pd.read_csv('FifaRankings.csv', index_col=\"Ranking\")\n a_set = set()\n while True:\n a_set.add(randint(42, 85))\n if len(a_set) == 32:\n break\n lst1 = sorted(list(a_set), reverse=True)\n\n a_set = set()\n while True:\n a_set.add(randint(38, 83))\n if len(a_set) == 32:\n break\n lst2 = sorted(list(a_set), reverse=True)\n print(\"\\n\")\n df['Attack'] = lst1\n df['Defence'] = lst2\n a = list(df[\"Team\"])\n\n avgScored = 0\n avgConceded = 0\n avgScored = df['Attack'].sum()\n avgConceded = df['Defence'].sum()\n\n avgScored = avgScored / len(df)\n avgConceded = avgConceded / len(df)\n print(\"\\n\")\n avgattack = []\n avgdefense = []\n\n for i in range(1, 33):\n if df['Matches Played'][i] != 0:\n win_rate = (df['WorldCup Wins'][i] / df['Matches Played'][i])\n else:\n win_rate = 0\n avgattack.append((df['Attack'][i] / avgScored) + win_rate)\n avgdefense.append((df['Defence'][i] / avgConceded) + win_rate)\n\n df['Avg Attack'] = avgattack\n df['Avg Defense'] = avgdefense\n\n\n teamstats=[]\n for i in range(1,len(df)+1):\n teaminfo=[]\n teaminfo = (df[\"Team\"][i], df['Avg Attack'][i], df['Avg Defense'][i])\n teaminfo=list(teaminfo)\n teamstats.append(teaminfo)\n\n germany = WorldCupTeam(\"GERMANY\", teamstats)\n brazil = WorldCupTeam(\"BRAZIL\", teamstats)\n belgium = WorldCupTeam(\"BELGIUM\", teamstats)\n portugal = WorldCupTeam(\"PORTUGAL\", teamstats)\n argentina = WorldCupTeam(\"ARGENTINA\", teamstats)\n france = WorldCupTeam(\"FRANCE\", teamstats)\n switzerland = WorldCupTeam(\"SWITZERLAND\", teamstats)\n spain = WorldCupTeam(\"SPAIN\", teamstats)\n russia = WorldCupTeam(\"RUSSIA\", teamstats)\n japan = WorldCupTeam(\"JAPAN\", teamstats)\n polland=WorldCupTeam(\"POLLAND\", teamstats)\n korea_republic = WorldCupTeam(\"KOREA REPUBLIC\", teamstats)\n england = WorldCupTeam(\"ENGLAND\", teamstats)\n denmark= WorldCupTeam(\"DENMARK\", teamstats)\n peru= WorldCupTeam(\"PERU\", teamstats)\n tunisia=WorldCupTeam(\"TUNISIA\", teamstats)\n mexico = WorldCupTeam(\"MEXICO\", teamstats)\n colombia = WorldCupTeam(\"COLOMBIA\", teamstats)\n uruguay = WorldCupTeam(\"URUGUAY\", teamstats)\n croatia = WorldCupTeam(\"CROATIA\", teamstats)\n australia = WorldCupTeam(\"AUSTRALIA\", teamstats)\n iceland=WorldCupTeam(\"ICELAND\", teamstats)\n sweden=WorldCupTeam(\"SWEDEN\", teamstats)\n costa_rica = WorldCupTeam(\"COSTA RICA\", teamstats)\n senegal=WorldCupTeam(\"SENEGAL\", teamstats)\n serbia=WorldCupTeam(\"SERBIA\", teamstats)\n morrocco=WorldCupTeam(\"MORROCCO\", teamstats)\n egypt=WorldCupTeam(\"EGYPT\", teamstats)\n nigeria = WorldCupTeam(\"NIGERIA\", teamstats)\n saudi_arabia=WorldCupTeam(\"SAUDI ARABIA\", teamstats)\n panama=WorldCupTeam(\"PANAMA\", teamstats)\n iran = WorldCupTeam(\"IRAN\", teamstats)\n\n\n #INPUT USERS CHOICE FOR FIXED CHOICE\n choices= [\"random\", \"Random\", \"RANDOM\"]\n choicess = [\"fixed\", \"Fixed\", \"FIXED\"]\n if userschoice in choices:\n countries = [germany, brazil, belgium, portugal, argentina, france, switzerland, spain, russia, japan, polland,\n korea_republic, england, denmark, peru, tunisia, mexico, colombia, uruguay, croatia, australia,\n iceland, sweden, costa_rica, senegal, serbia, morrocco, egypt, nigeria, saudi_arabia, panama, iran]\n finalresults = {}\n\n GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH = ([] for i in range(8))\n\n Groups = [GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH]\n for i in Groups:\n for j in range(4):\n teamname = choice(countries)\n i.append(teamname)\n countries.remove(teamname)\n\n print(\"DRAWS for the WorldCup 2018 are:\")\n print(\"\\n\")\n for i in range(simulations):\n # Play first stage\n print(\"Result of\", i + 1, \"simulations\")\n print(\"--------------------------------------------\")\n print(\"This is GROUP STAGE\")\n print(\"\\n\")\n print(\"GROUP A RESULTS\")\n print(\"\\n\")\n groupA = TeamPool(Groups[0])\n print(\"\\n\")\n print(\"GROUP B RESULTS\")\n print(\"\\n\")\n groupB = TeamPool(Groups[1])\n print(\"\\n\")\n print(\"GROUP C RESULTS\")\n print(\"\\n\")\n groupC = TeamPool(Groups[2])\n print(\"\\n\")\n print(\"GROUP D RESULTS\")\n print(\"\\n\")\n groupD = TeamPool(Groups[3])\n print(\"\\n\")\n print(\"GROUP E RESULTS\")\n print(\"\\n\")\n groupE = TeamPool(Groups[4])\n print(\"\\n\")\n print(\"GROUP F RESULTS\")\n print(\"\\n\")\n groupF = TeamPool(Groups[5])\n print(\"\\n\")\n print(\"GROUP G RESULTS\")\n print(\"\\n\")\n groupG = TeamPool(Groups[6])\n print(\"\\n\")\n print(\"GROUP H RESULTS\")\n print(\"\\n\")\n groupH = TeamPool(Groups[7])\n\n # Play second stage\n print(\"\\n\")\n print(\"ROUND OF 16\")\n print(\"\\n\")\n r16 = [groupA.first_qualified, groupA.second_qualified, groupB.first_qualified, groupB.second_qualified,\n groupC.first_qualified, groupC.second_qualified, groupD.first_qualified, groupD.second_qualified,\n groupE.first_qualified, groupE.second_qualified, groupF.first_qualified, groupF.second_qualified,\n groupG.first_qualified, groupG.second_qualified, groupH.first_qualified, groupH.second_qualified]\n\n\n GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW =([] for i in range(8))\n\n round16groups = [GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW]\n\n for k in round16groups:\n for j in range(2):\n teamname = choice(r16)\n k.append(teamname)\n r16.remove(teamname)\n\n for i in range(8):\n quarters[i]=WorldCupMatch(round16groups[i][0], round16groups[i][1], False).winner\n\n # Quarters\n print(\"\\n\")\n print(\"QUARTER - FINALS\")\n print(\"\\n\")\n quarterfinal = [quarters[0], quarters[1], quarters[2], quarters[3], quarters[4], quarters[5], quarters[6],\n quarters[7]]\n GroupA1, GroupB1, GroupC1, GroupD1 = ([] for i in range(4))\n\n quarterfinalgroups = [GroupA1, GroupB1, GroupC1, GroupD1]\n\n i = 0\n for i in quarterfinalgroups:\n for j in range(2):\n teamname = choice(quarterfinal)\n i.append(teamname)\n quarterfinal.remove(teamname)\n\n for i in range(4):\n semifinalists[i] = WorldCupMatch(quarterfinalgroups[i][0], quarterfinalgroups[i][1], False).winner\n\n # Semifinals\n print(\"\\n\")\n print(\"SEMI - FINALS\")\n print(\"\\n\")\n\n semifinal = [semifinalists[0], semifinalists[1], semifinalists[2], semifinalists[3]]\n GroupP1, GroupQ1 = ([] for i in range(2))\n semifinalgroups = [GroupP1, GroupQ1]\n\n i = 0\n for i in semifinalgroups:\n for j in range(2):\n teamname = choice(semifinal)\n i.append(teamname)\n semifinal.remove(teamname)\n\n for i in range(2):\n finalists[i] = WorldCupMatch(semifinalgroups[i][0], semifinalgroups[i][1], False).winner\n # Finals\n print(\"\\n\")\n print(\"WORLD-CUP FINAL\")\n print(\"\\n\")\n winner = WorldCupMatch(finalists[0], finalists[1], False).winner\n print(\"\\n\")\n\n if winner.name in finalresults:\n finalresults[winner.name] += 1\n else:\n finalresults[winner.name] = 1\n\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\n print(key + \": \" + str(finalresults[key] / simulations))\n ro=(finalresults[key] / simulations) * 100\n print(str(ro) + \"% chance of winning the worldcup\")\n print(\"\\n\")\n print(\"\\n\")\n\n\n elif userschoice in choicess:\n\n print(\"\\n\")\n finalresults = {}\n groupA1 = [russia , saudi_arabia,egypt, uruguay]\n groupB1 = [portugal, spain, morrocco, iran]\n groupC1 = [france, australia, peru, denmark]\n groupD1 = [argentina, iceland, croatia, nigeria]\n groupE1 = [brazil, switzerland, costa_rica, serbia]\n groupF1 = [germany, mexico, sweden, korea_republic]\n groupG1 = [belgium, panama, tunisia, england]\n groupH1 = [polland, senegal, colombia, japan]\n print(\"\\n\")\n for i in range(simulations):\n # Play first stage\n print(\"Result of\", i+1 ,\"simulations\")\n print(\"--------------------------------------------\")\n print(\"This is GROUP STAGE\")\n print(\"\\n\")\n print(\"GROUP A RESULTS\")\n print(\"\\n\")\n groupA = TeamPool(groupA1)\n print(\"\\n\")\n print(\"GROUP B RESULTS\")\n print(\"\\n\")\n groupB = TeamPool(groupB1)\n print(\"\\n\")\n print(\"GROUP C RESULTS\")\n print(\"\\n\")\n groupC = TeamPool(groupC1)\n print(\"\\n\")\n print(\"GROUP D RESULTS\")\n print(\"\\n\")\n groupD = TeamPool(groupD1)\n print(\"\\n\")\n print(\"GROUP E RESULTS\")\n print(\"\\n\")\n groupE = TeamPool(groupE1)\n print(\"\\n\")\n print(\"GROUP F RESULTS\")\n print(\"\\n\")\n groupF = TeamPool(groupF1)\n print(\"\\n\")\n print(\"GROUP G RESULTS\")\n print(\"\\n\")\n groupG = TeamPool(groupG1)\n print(\"\\n\")\n print(\"GROUP H RESULTS\")\n print(\"\\n\")\n groupH = TeamPool(groupH1)\n print(\"Qualifies teams:\", groupH.first_qualified.name)\n\n # Play second stage\n print(\"\\n\")\n print(\"ROUND OF 16\")\n print(\"\\n\")\n\n quarter1 = WorldCupMatch(groupA.first_qualified, groupA.second_qualified, False).winner\n quarter2 = WorldCupMatch(groupB.first_qualified, groupB.second_qualified, False).winner\n quarter3 = WorldCupMatch(groupC.first_qualified, groupC.second_qualified, False).winner\n quarter4 = WorldCupMatch(groupD.first_qualified, groupD.second_qualified, False).winner\n quarter5 = WorldCupMatch(groupE.first_qualified, groupE.second_qualified, False).winner\n quarter6 = WorldCupMatch(groupF.first_qualified, groupF.second_qualified, False).winner\n quarter7 = WorldCupMatch(groupG.first_qualified, groupG.second_qualified, False).winner\n quarter8 = WorldCupMatch(groupH.first_qualified, groupH.second_qualified, False).winner\n\n # Quarters\n print(\"\\n\")\n print(\"QUARTER - FINALS\")\n print(\"\\n\")\n\n semifinalist1 = WorldCupMatch(quarter1, quarter2, False).winner\n semifinalist2 = WorldCupMatch(quarter3, quarter4, False).winner\n semifinalist3 = WorldCupMatch(quarter5, quarter6, False).winner\n semifinalist4 = WorldCupMatch( quarter7, quarter8, False).winner\n\n # Semifinals\n print(\"\\n\")\n print(\"SEMI - FINALS\")\n print(\"\\n\")\n finalist1 = WorldCupMatch(semifinalist1, semifinalist2, False).winner\n finalist2 = WorldCupMatch(semifinalist3, semifinalist4, False).winner\n\n # Final\n print(\"\\n\")\n print(\"WORLD-CUP FINAL\")\n print(\"\\n\")\n winner = WorldCupMatch(finalist1, finalist2, False).winner\n print(\"\\n\")\n\n\n if winner.name in finalresults:\n finalresults[winner.name] += 1\n else:\n finalresults[winner.name] = 1\n\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\n print(key + \": \" + str(finalresults[key] / simulations))\n rou = (finalresults[key] / simulations) * 100\n print(str(rou) + \"% chance of winning the worldcup\")\n print(\"\\n\")\n print(\"\\n\")\n else:\n print(\"Please enter correct input and try again\")\n pass","def scrape_all_world_cup_games():\n\n def scrape_scores_year(year):\n urls = scrape_world_cup_scoreboard(year)\n scores = [scrape_fifa_game(url, 'FIFA World Cup') for url in urls]\n return scores\n\n l = []\n for year in sorted(world_cup_mapping.keys()):\n l.extend(scrape_scores_year(year))\n return l","def fetch_next_match() -> Optional[MatchDict]:\n future_matches = Match.objects.filter(start_date_time__gt=timezone.now())\n\n if not any(future_matches):\n return None\n\n next_match = min(future_matches, key=lambda match: match.start_date_time)\n\n return {\n \"round_number\": next_match.round_number,\n \"season\": next_match.start_date_time.year,\n }","def run_past_matches(self):\n for year in self.store.years:\n # print(year)\n for event in self.store.events[year]:\n for match in self.store.matches[year][event.key]:\n red_alliance = match.red_alliance\n blue_alliance = match.blue_alliance\n\n for team in red_alliance + blue_alliance:\n if team not in self.scores.keys():\n self.scores[team] = self.default_score\n\n prediction = self.predict(red_alliance, blue_alliance, key=match.key)\n self.update_score(red_alliance, blue_alliance, prediction,\n match.red_score, match.blue_score)","def _get_challenge_parts(self):\n query_result = db.session.query(ChallengePart). \\\n filter(ChallengePart.season_id == self.SEASON.id). \\\n order_by(ChallengePart.order.asc()). \\\n all()\n\n result = {}\n dist = 0\n for item in query_result:\n dist += item.distance\n result[dist] = item.target\n\n return result","def coast_to_circ_burn(mission):\n circ_burn = mission.parameters[\"circ_burn\"]\n\n if circ_burn[\"burn_start_time\"] <= mission.ut():\n mission.next()","def swissPairings():\n\n # Ok This is where things get interesting, how in the world should i solve this problem\n # A question to the udacity reviewer. Shouldn't standings be passed in to this function since weve already called it in tournament_test.testPairings\n\n #anyways\n\n nextRoundPlayers = []\n standings = playerStandings()\n \n # since our players are ordered by wins, first place first and we have an even number of players,\n # this seems like a no-brainer to just have every 2 tuples starting from the beginning to be the next match\n # however this needs to to be implemented algorithmically\n \n #loop through our players and when we get to an even index, we get the previous two players and assign their ids and names to the next tuple \n #in nextRoundPlayers\n \n i = 0\n while i < len(standings):\n if i % 2 == 0:\n id1 = standings[i-1][0]\n name1 = standings[i-1][1]\n\n id2 = standings[i-2][0]\n name2 = standings[i-2][1]\n\n nextRoundPlayers.append((id1, name1, id2, name2))\n\n i += 1\n \n return nextRoundPlayers","def covid(covidSet):\n newcovidSet = set()\n for x in covidSet:\n player = playerList[x]\n player.changeImage(1)\n\n #gotta check if anyone is close to this guy\n for idx, y in enumerate(playerList):\n if idx == x or idx in covidSet or idx in newcovidSet:\n continue\n if abs(player.rect.x - y.rect.x) > covidRange or abs(player.rect.y - y.rect.y) > covidRange:\n continue #to reduce amount of astar checks required\n else:\n xx = changeCoord(player.rect.x)\n xy = changeCoord(player.rect.y)\n yx = changeCoord(y.rect.x)\n yy = changeCoord(y.rect.y)\n path = aStar.astar(maze, (xy, xx), (yy, yx))\n if path != None and len(path) <= mazeRange:\n if randChance(covidChance):\n newcovidSet.add(idx)\n print(\"Person\", x, \"infected\", \"Person\", idx)\n edges[\"edges\"].append({\"from\": x, \"to\": idx})\n else:\n y.changeImage(2)\n # uncomment to see astar algo (that was too far apart)\n #else:\n # if path == None:\n # print(\"none\")\n # else:\n # print(len(path), path)\n return set.union(covidSet, newcovidSet)","def reduce_games(game):#game=player_games[0]\n res = []\n res.append(('id', game['id']) )\n res.append(('annulled', game['annulled']))\n res.append(('black', int(game['black'])))\n res.append(('white', int(game['white'])))\n res.append(('white_lost', int(game['white_lost']) ))\n res.append(('black_lost', int(game['black_lost']) ))\n res.append(('outcome', game['outcome']))\n res.append(('handicap', game['handicap']))\n res.append(('komi', game['komi']))\n res.append(('ranked', game['ranked']))\n res.append(('width', game['width']))\n res.append(('height', game['height']))\n res.append(('rules', game['rules']))\n res.append(('started', game['started']))\n res.append(('ended', game['ended']))\n players = game['historical_ratings'].keys()\n res.append(('black_rating', game['historical_ratings']['black']['ratings']['overall']['rating'] if 'black' in players else None))\n res.append(('white_rating', game['historical_ratings']['white']['ratings']['overall']['rating'] if 'white' in players else None))\n res.append(('black_deviation', game['historical_ratings']['black']['ratings']['overall']['deviation'] if 'black' in players else None))\n res.append(('white_deviation', game['historical_ratings']['white']['ratings']['overall']['deviation'] if 'white' in players else None))\n res.append(('black_volatility', game['historical_ratings']['black']['ratings']['overall']['volatility'] if 'black' in players else None))\n res.append(('white_volatility', game['historical_ratings']['white']['ratings']['overall']['volatility'] if 'white' in players else None))\n res.append(('black_ranking', game['historical_ratings']['black']['ranking'] if 'black' in players else None))\n res.append(('white_ranking', game['historical_ratings']['white']['ranking'] if 'white' in players else None))\n res.append(('tournament', not game['tournament'] is None))\n return res","def favorite() -> Tuple[Union[TeamDict, bool], Union[TeamDict, bool], Union[float, bool], Union[int, bool]]:\n if get_time() > 87*60: #if this happens there's no need to look into this match\n time = fav = balan = other = False\n print('Too late into this match. Skipping.')\n return (fav, other, balan, time)\n\n team1, team2, time = collect_info()\n def calc_criteria(team_info, time=time):\n team_info['appm'] = team_info['atp'] / time*60\n team_info['cg'] = team_info['ca'] + team_info['cl'] + team_info['esc']\n team_info['rend'] = team_info['appm'] * team_info['pb']\n\n print(\"Calculating match metrics\")\n calc_criteria(team1)\n calc_criteria(team2)\n\n timer = 10\n while timer:\n try:\n group_tabs = driver.find_elements_by_class_name(\"sip-MarketGroup \")\n both_odds = find_item('resultado final', group_tabs)\n both_odds = both_odds.find_elements_by_class_name('srb-ParticipantStackedBorderless_Odds')\n timer = 0\n except AttributeError: #when \"suspenso\" appears this error is raised\n timer -= 1\n sleep(1)\n \n try:\n assert len(both_odds) == 3\n odds = [float(both_odds[0].text), float(both_odds[2].text)] #[0] team1\n balan = max(odds)/min(odds)\n fav = team1 if max(odds) == odds[1] else team2 #sets who is favorite, i.e., who's got the minor odds of winning\n other = team2 if max(odds) == odds[1] else team1\n except (TypeError, ValueError): \n fav, other = team1, team2\n balan = 0\n fav['balan'] = other['balan'] = balan\n\n return (fav, other, balan, time)","def compute_social_happiness(self):\r\n citizens_happiness = []\r\n for citizen in self.citizens:\r\n citizens_happiness.append(citizen.compute_happiness_level(self.projects_approved))\r\n \r\n return stats.describe(citizens_happiness)[2]","def retrieve_exact_commutes(self):\n for destination in self.tenants:\n try:\n results = retrieve_exact_commute_rent_algorithm(self.homes[:NUMBER_OF_EXACT_COMMUTES_COMPUTED],\n destination,\n destination.commute_type,\n with_traffic=destination.traffic_option)\n\n # Store the results to the homes\n for i in range(len(results)):\n duration_seconds = results[i][0][0]\n distance_meters = results[i][0][1]\n if duration_seconds is not None and distance_meters is not None:\n self.homes[i].exact_commute_times[destination] = int(duration_seconds / 60)\n\n except Distance_Matrix_Exception as e:\n print(\"Caught: \" + e.__class__.__name__)","def tournament():\n return min(sample(population, sample_size)).chromosome[:]","def metcon_leaderboards(cycle):\n for exercise in exercises['metcon']:\n f = f'{DOWNLOADS_DIR}\\\\{cycle}_{clean_name(exercise)}.xlsx'\n if os.path.isfile(f):\n source = pd.read_excel(f)\n\n # reduce to useful columns\n metcon = source[['Athlete', 'Result', 'Is As Prescribed', 'Is Rx Plus']][\n ~source['Athlete'].isin(coaches)]\n\n # Get rid of lowest scores for people that may have tested more than once and sort\n if clean_name(exercise) == 'CFCCindy':\n metcon['Result'] = metcon['Result'].apply(lambda x: x.replace(' + ', '.'))\n metcon['Result'] = metcon['Result'].apply(pd.to_numeric)\n metcon = metcon.sort_values('Result', ascending=False)\n metcon['Result'] = metcon['Result'].astype(str)\n metcon['Result'] = metcon['Result'].apply(lambda x: x.replace('.', ' + '))\n else:\n metcon['Result'] = pd.to_datetime(metcon['Result'], format='%M:%S').dt.time\n metcon = metcon.sort_values('Result', ascending=True) # because lower is better with time duh\n\n metcon['duplicated'] = metcon.duplicated('Athlete', keep='first')\n metcon = metcon[~metcon['duplicated']]\n\n\n # Read in gender data and apply\n users = pd.read_excel(f'{DOWNLOADS_DIR}\\\\{cycle}_Users.xlsx')\n\n # Athlete name\n users['Athlete Name'] = users['First Name'] + ' ' + users['Last Name']\n\n # Look up gender\n metcon['Gender'] = metcon['Athlete'].map(users.set_index('Athlete Name')['Gender'])\n\n # Break out Rx and Rx+\n metcon_rx = metcon[metcon['Is As Prescribed']]\n metcon_rxp = metcon[metcon['Is Rx Plus']]\n\n # Split on gender\n metcon_rx_female = metcon_rx[metcon_rx['Gender'] == 'Female']\n metcon_rx_male = metcon_rx[metcon_rx['Gender'] == 'Male']\n metcon_rxp_female = metcon_rxp[metcon_rxp['Gender'] == 'Female']\n metcon_rxp_male = metcon_rxp[metcon_rxp['Gender'] == 'Male']\n\n # Write out\n\n metcon_dfs = [metcon_rx_female, metcon_rx_male, metcon_rxp_female, metcon_rxp_male]\n\n for df in metcon_dfs:\n if len(df) > 0:\n gender = df.Gender.unique()[0]\n rx = 'rx' if df['Is As Prescribed'].unique()[0] == True else 'rxp'\n df[['Athlete', 'Result']].to_csv(\n f'{RESULTS_DIR}\\\\{cycle}_leaderboard_{clean_name(exercise)}_{gender}_{rx}.csv', index=False)\n print(f'{exercise} results written to {RESULTS_DIR}')\n else:\n print(f'File does not exist for {exercise}.')","def find_games(days_ahead=0):\n headers = {\n 'Host': 'stats.nba.com',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\n 'Accept': 'application/json, text/plain, */*',\n 'Accept-Language': 'en-US,en;q=0.5',\n 'Referer': 'https://stats.nba.com/',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Connection': 'keep-alive',\n 'x-nba-stats-origin': 'stats',\n 'x-nba-stats-token': 'true'\n }\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\n board.replace(id_to_abrv, inplace=True)\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]","def vote_of_citizens():\n\tglobal vote_first_candidate\n\tglobal vote_second_candidate\n\tglobal blank_vote\n\t\n\tfor i in range(NUMBER_OF_CITIZENS):\n\t\tvote = random.randint(1,10)\n\n\t\tif(vote <= 3):\n\t\t\tvote_first_candidate+=1\n\t\telif(vote > 3 and vote <= 6):\n\t\t\tvote_second_candidate+=1\n\t\telse:\n\t\t\tblank_vote+=1","def get_games(date):\n scoreboard = nba_py.Scoreboard(month=date.month,\n day=date.day,\n year=date.year)\n line_score = scoreboard.line_score()\n game_header = scoreboard.game_header()\n\n games = []\n current_game = {}\n game_sequence = 0\n game_sequence_counter = 0\n\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\n for i, value in enumerate(line_score):\n if (value[\"GAME_SEQUENCE\"] != game_sequence):\n game_sequence += 1\n\n current_game[\"GAME_ID\"] = value[\"GAME_ID\"]\n home_team_id = game_header[game_sequence - 1][\"HOME_TEAM_ID\"]\n\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n \n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter += 1\n elif game_sequence_counter == 1:\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n\n current_game[\"GAME_STATUS_TEXT\"] = game_header[game_sequence - 1][\"GAME_STATUS_TEXT\"]\n if not game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]:\n current_game[\"BROADCASTER\"] = \"\"\n else:\n current_game[\"BROADCASTER\"] = game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]\n\n games.append(current_game)\n\n current_game = {}\n\n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter -= 1\n\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return (games, east_standings, west_standings)","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def apply_tournament_selection(individuals, tot_rounds: int):\r\n winner = random.choice(individuals)\r\n\r\n for i in range(0, tot_rounds-1):\r\n ind = random.choice(individuals)\r\n\r\n if ind.fitness.dominates(winner.fitness):\r\n winner = ind\r\n elif not winner.fitness.dominates(ind.fitness):\r\n if ind.fitness.crowding_dist < winner.fitness.crowding_dist:\r\n winner = ind\r\n\r\n return winner","def get_match_features(match, matches):\n\n # Define variables\n date = match.date\n season = match.season\n home_team = match.home_team_api_id\n away_team = match.away_team_api_id\n\n matches_home_ashome = get_last_ashome_matches(matches, date, season, home_team, x=5)\n # print(matches_home_ashome, flush=True)\n matches_home_asaway = get_last_asaway_matches(matches, date, season, home_team, x=5)\n matches_away_ashome = get_last_ashome_matches(matches, date, season, away_team, x=5)\n matches_away_asaway = get_last_asaway_matches(matches, date, season, away_team, x=5)\n # matches_home = pd.concat([matches_home_ashome, matches_home_asaway])\n # matches_away = pd.concat([matches_away_ashome, matches_away_asaway])\n\n # Get last x matches of both teams against each other\n competing_matches_same = get_last_competing_matches(matches, date, home_team, away_team, x=2)\n competing_matches_diff = get_last_reverse_competing_matches(matches, date, home_team, away_team, x=2)\n\n # Define result data frame\n result = pd.Series()\n result['match_api_id'] = match.match_api_id\n result['league_id'] = match.league_id\n result['home_recent_goal_ashome'] = get_goals(matches_home_ashome)\n result['away_recent_goal_asaway'] = get_goals(matches_away_asaway, isAway=True)\n result['home_recent_lost_ashome'] = get_goals_lost(matches_home_ashome)\n result['away_recent_lost_asaway'] = get_goals_lost(matches_away_asaway, isAway=True)\n result['team_ova_diff'] = match.home_ova - match.away_ova\n result['home_recent_score'] \\\n = get_average_league_score(matches_home_ashome) + get_average_league_score(matches_home_asaway, isAway=True)\n result['away_recent_score'] \\\n = get_average_league_score(matches_away_ashome) + get_average_league_score(matches_away_asaway, isAway=True)\n result['home_competing_same_goal'] = get_goals(competing_matches_same)\n result['away_competing_same_goal'] = get_goals(competing_matches_same, isAway=True)\n result['home_competing_diff_goal'] = get_goals(competing_matches_diff, isAway=True)\n result['away_competing_diff_goal'] = get_goals(competing_matches_diff)\n result['home_recent_shoton'] = get_shoton(matches_home_ashome) + 0.8 * get_shoton(matches_home_asaway, isAway=True)\n result['away_recent_shoton'] = get_shoton(matches_away_asaway, isAway=True) + 0.8 * get_shoton(matches_away_ashome)\n result['home_recent_shotoff'] = get_shotoff(matches_home_ashome) + 0.8 * get_shotoff(matches_home_asaway, isAway=True)\n result['away_recent_shotoff'] = get_shotoff(matches_away_asaway, isAway=True) + 0.8 * get_shotoff(matches_away_ashome)\n\n # print(result)\n return result","def runThompson(self):\n \n #Init vars, N number of user sessions, d=number of ads\n N = self.myDS.shape[0] \n d = self.myDS.shape[1] \n total_reward=0\n self.opt_selected=[]\n \n #Declare vars to count to calculate upper bounds\n number_of_rewards_1 = [0] * d\n number_of_rewards_0 = [0] * d\n \n #Calcultate confidance bounds\n for n in range(0,N):\n ad=0\n max_random=0\n for i in range (0,d):\n random_beta = random.betavariate(number_of_rewards_1[i]+1,number_of_rewards_0[i]+1)\n if random_beta>max_random:\n max_random=random_beta\n ad = i\n self.opt_selected.append(ad)\n reward=self.myDS.values[n,ad]\n if (reward==1):\n number_of_rewards_1[ad]=number_of_rewards_1[ad]+1\n else:\n number_of_rewards_0[ad]=number_of_rewards_0[ad]+1 \n total_reward=total_reward+reward\n \n return total_reward","def _tournament(self,probs,n,size):\n participants = np.random.choice(\n self.n_agents,\n size=size,\n replace=False)\n winners = np.argpartition(probs[participants], -n)[-n:]\n return participants[winners]","def career_talent(filtered_dict,WAR_batter_best, player_name='Nap Lajoie', year_proj = 2019):\n \n # Subsetting the dictionary for a particular player\n filtered_dict_new = filtered_dict[player_name]\n \n # Converting it to a dataframe\n df = pd.DataFrame(filtered_dict_new).sort_values(by=['yearID']).reset_index(drop = True)\n \n # Changing the player ID\n df['playerID'] = df['playerID'].astype(str) + '_proj'\n \n # Putting a counter for the number of iteration (we can use this counter later to determine the runtime of each loop)\n # j = 0\n \n # Creating an empty dataframe where we will be merging the rows\n emp_df = pd.DataFrame()\n \n # Iterating over all the years where the selected player has played baseball\n for idx, year in df['yearID'].iteritems():\n #j+=1\n \n # subsetting the Normalized WAR Talent Dataset for the year in which we want to find the projection\n batters_int = WAR_batter_best.loc[WAR_batter_best['yearID'] == year_proj]\n \n # creating a vector with all the WAR values in the projection year\n yy = batters_int.loc[:,'scale_WAR'].sort_values().reset_index(drop = True)\n \n # Finding the length of the vector created above\n n = len(yy)\n \n # Creating a zero vector with one more cell than the vector with all the scaled WAR values\n ytilde = [0] * (n+1)\n \n # Changing the first value in ytilde (Extrapolation logic developed by Shen and Daniel)\n ytilde[0] = yy[0] - 1/(yy[n-1] - yy[0])\n \n # Changing the last value in ytilde\n ytilde[n] = yy[n-1] + 1/(yy[n-1] - yy[n-4])\n \n # Iterating over all the cells of ytilde and updating their values\n # The updated ytilde is the new empirical distribution function of the WAR Talent\n for i in range(1,n):\n ytilde[i] = (yy[i]+yy[i-1])/2\n\n batters_int = pd.concat([batters_int,df.loc[df['yearID'] == year]]).reset_index(drop = True)\n batters_int['pops'][batters_int.shape[0]-1] = batters_int['pops'][0]\n batters_int = batters_int.sort_values(by = ['WAR_talent']).reset_index(drop = True)\n batters_int['foo'] = map_pareto_vals_vec(batters_int['WAR_talent'], batters_int['pops'])\n batters_int['foo'] = batters_int['foo'].reset_index(drop = True)\n batters_int['adj_WAR'] = order_qempirical(batters_int['foo'], ytilde)\n del batters_int['foo']\n batters_int = batters_int.loc[batters_int['playerID'] == df['playerID'].unique()[0]]\n emp_df = pd.concat([emp_df, batters_int])\n emp_df['target_year'] = year_proj\n \n return emp_df","def fold(bot, update):\n user = update.message.from_user\n name = user.first_name\n chat_id = update.message.chat_id\n bot.send_message(chat_id,\n Strings.P_FOLDS % name,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n if chats[chat_id].player1.user == user:\n winner = 2\n elif chats[chat_id].player2.user == user:\n winner = 1\n else:\n return C_R_F\n # we already know the winner, but still need to process the whole match\n result = chats[chat_id].manageResult(chats[chat_id].player1.card_played,\n chats[chat_id].player2.card_played, winner)\n bot.send_message(chat_id,\n result,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n\n if chats[chat_id].player1.lives == 0:\n if chats[chat_id].player1.red_lives == 0:\n bot.send_message(chat_id,\n Strings.END,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n bot.send_message(chat_id,\n Strings.WIN % chats[chat_id].player2.name,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n return ConversationHandler.END\n else:\n # wanna bet your own life?\n red_life_bet(bot, chat_id, chats[chat_id].player1)\n return Y_N\n elif chats[chat_id].player2.lives == 0:\n if chats[chat_id].player2.red_lives == 0:\n bot.send_message(chat_id,\n Strings.END,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n bot.send_message(chat_id,\n Strings.WIN % chats[chat_id].player1.name,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n return ConversationHandler.END\n else:\n # wanna bet your own life\n red_life_bet(bot, chat_id, chats[chat_id].player2)\n return Y_N\n else:\n show_cards(bot, chat_id)\n return CARD","def nextStep(self, game: BJGameMove):\n isFirstChoice = len(game.player.hand) == 2 and not game.splitting\n hand = self.deriveHand(game.player)\n\n if self.game.splitting:\n if not self.game.doneSplitHand:\n splitHand = self.deriveHand(game.splitPlayer)\n splitChoice = self.coach(splitHand, False)\n return self.doCoach(splitChoice+5, game) # small trick: +5 indicates split choices\n else:\n choice = self.coach(hand, False)\n return self.doCoach(choice, game)\n else:\n choice = self.coach(hand, isFirstChoice)\n return self.doCoach(choice, game)","def best_session_weekly(df):\n df = convert_to_datetime(df)\n today = datetime.date.today()\n this_week_start = today - timedelta(days=7)\n last_week_start = today - timedelta(days=14)\n this_week_card_ids = []\n lastweek_card_ids = []\n for index, row in df.iterrows():\n try:\n if str(row['session_start'].date()) >= str(this_week_start):\n this_week_card_ids.append(row['id'])\n elif str(last_week_start) <= str(row['session_start'].date()) < str(this_week_start):\n lastweek_card_ids.append(row['id'])\n except IndexError:\n this_week_card_ids = []\n lastweek_card_ids = []\n thisweek = df[df['id'].isin(this_week_card_ids)]\n lastweek = df[df['id'].isin(lastweek_card_ids)]\n thisweek_best_session = best_session_length(thisweek)\n lastweek_best_session = best_session_length(lastweek)\n\n if thisweek_best_session > lastweek_best_session:\n color_code = \"09B109\"\n arrow = \"\\u2191\"\n elif thisweek_best_session < lastweek_best_session:\n color_code = \"CE2929\"\n arrow = \"\\u2193\"\n else:\n color_code = \"000000\"\n arrow = \"\\u003D\"\n\n if lastweek_best_session > 0:\n difference = abs((thisweek_best_session - lastweek_best_session) / lastweek_best_session) * 100\n else:\n # if no sessions last week, best session is up 100%\n # if both best_sessions are zero, this will display '0 100% =' in black\n difference = 100\n result = make_results_dict(thisweek_best_session, difference, color_code, arrow)\n result['best_session_weekly'] = result.pop('metric')\n return result","def scrape_fifa_game(url, competition):\n # Need to add referee data.\n\n data = scrape_url(url)\n data = data.split(\"<h2>Advertisement</h2>\")[0]\n soup = BeautifulSoup(data)\n \n contents = soup.find(\"div\", {\"id\": \"mainContent\" })\n \n # Really, none of these games have a home team.\n\n #teams = get_contents(contents.find(\"div\", \"bold large teams\"))\n team1 = get_contents(contents.find(\"div\", \"lnupTeam\").find(\"div\", \"bold medium\"))\n team2 = get_contents(contents.find(\"div\", \"lnupTeam away\").find(\"div\", \"bold medium\"))\n\n #import pdb; pdb.set_trace()\n\n \n\n #try:\n # team1, team2 = [e.strip() for e in teams.split(\"-\")]\n #except:\n # import pdb; pdb.set_trace()\n\n score_string = get_contents(contents.find(\"div\", \"bold large result\"))\n\n if 'a.e.t.' in score_string:\n score_string = score_string.split('a.e.t')[0]\n\n team1_score, team2_score = [int(e) for e in score_string.split(\"(\")[0].split(\":\")]\n\n # Implement this if header order is more unpredictable.\n #game_head = contents.findAll(\"thead\")\n #head_teas = game_head.findAll(\"td\", text=True\n\n game_header = contents.find(\"thead\")\n game_info = contents.find(\"tbody\")\n \n game_ths = [get_contents(e) for e in game_header.findAll(\"td\")]\n game_tds = [get_contents(e) for e in game_info.findAll(\"td\")]\n\n game_dict = dict(zip(game_ths, game_tds))\n\n\n match = date_string = time = location = attendance = None\n\n #import pdb; pdb.set_trace()\n\n if 'Match' in game_dict:\n match = game_dict['Match']\n\n if 'Date' in game_dict:\n date_string = game_dict['Date']\n\n #'Time' \n\n if 'Attendance' in game_dict:\n if game_dict['Attendance']:\n attendance = int(game_dict['Attendance'])\n\n if 'Venue / Stadium' in game_dict:\n location = game_dict['Venue / Stadium']\n\n for e in 'Match', 'Date', 'Attendance', 'Venue / Stadium', 'Time': \n if e in game_dict:\n game_dict.pop(e)\n #print(game_dict.keys())\n\n # Standardize city and stadium fields\n try:\n city, stadium = [e.strip() for e in location.rsplit(\"/\", 1)]\n except:\n import pdb; pdb.set_trace()\n\n # Avoid duplication of city name?\n if stadium.endswith(city):\n nlocation = stadium\n else:\n nlocation = \"%s, %s\" % (stadium, city)\n\n date = datetime.datetime.strptime(date_string.strip(), \"%d %B %Y\")\n\n return {\n \"team1\": team1,\n \"team2\": team2,\n 'team1_score': team1_score,\n 'team2_score': team2_score,\n 'competition': competition,\n 'season': str(date.year),\n \"date\": date,\n \"location\": nlocation,\n \"attendance\": attendance,\n \"sources\": [url],\n }","def get_last_competing_matches(matches, date, home_team, away_team, x=2):\n\n # Find matches of both teams\n home_matches = matches[(matches['home_team_api_id'] == home_team) & (matches['away_team_api_id'] == away_team)]\n last_matches = get_most_recent_matches_helper(home_matches, date, x)\n return last_matches","def getgoalies(league, year):\n\n url = 'https://www.eliteprospects.com/league/' + league + '/stats/' + year + '?page-goalie='\n # print('Collects data from ' + 'https://www.eliteprospects.com/league/' + league + '/stats/' + year)\n \n print(\"Beginning scrape of \" + league + \" goalie data from \" + year + \".\")\n \n # Return list with all plyers for season in link \n players = []\n \n page = (requests.get(url + str(1) + \"#goalies\", timeout = 500))\n first_page_string = str(page)\n \n while first_page_string == '<Response [403]>':\n print(\"Just got a 403 Error before entering the page. This means EliteProspects has temporarily blocked your IP address.\")\n print(\"We're going to sleep for 60 seconds, then try again.\")\n time.sleep(100)\n page = (requests.get(url + str(1) + \"#goalies\", timeout = 500))\n first_page_string = str(page)\n print(\"Okay, let's try this again\")\n \n if (first_page_string) == '<Response [404]>':\n print(\"ERROR: \" + first_page_string + \" on league: \" + league + \" in year: \" + year + \". Data doesn't exist for this league and season.\")\n \n else:\n \n for i in range(1,99):\n page = requests.get(url+str(i), timeout = 500)\n page_string = str(page)\n \n while page_string == '<Response [403]>':\n print(\"Just got a 403 Error within the page. Time to Sleep, then re-obtain the link.\")\n time.sleep(100)\n page = (requests.get(url+str(i), timeout = 500))\n page_string = str(page)\n print(\"Changed the string within the page. Let's try again\")\n \n soup = BeautifulSoup(page.content, \"html.parser\")\n\n # Get data for players table\n player_table = soup.find(\"table\", {\"class\":\"table table-striped table-sortable goalie-stats highlight-stats season\"})\n\n try:\n df_players = tableDataText(player_table)\n except AttributeError:\n print(\"BREAK: TABLE NONE ERROR: \" + str(requests.get(url+str(i), timeout = 500)) + \" On League: \" + league + \" In Year: \" + year)\n break\n \n if len(df_players)>0:\n\n if df_players['#'].count()>0:\n # Remove empty rows\n df_players = df_players[df_players['#']!=''].reset_index(drop=True)\n\n # Extract href links in table\n href_row = []\n for link in player_table.find_all('a'):\n href_row.append(link.attrs['href'])\n\n # Create data frame, rename and only keep links to players\n df_links = pd.DataFrame(href_row) \n df_links.rename(columns={ df_links.columns[0]:\"link\"}, inplace=True)\n df_links= df_links[df_links['link'].str.contains(\"/player/\")].reset_index(drop=True) \n\n # Add links to players\n df_players['link']=df_links['link'] \n\n players.append(df_players)\n\n # Wait 3 seconds before going to next\n # time.sleep(1)\n #print(\"Scraped page \" + str(i))\n \n else:\n #print(\"Scraped final page of: \" + league + \" In Year: \" + year)\n break\n\n \n if len(players)!=0:\n df_players = pd.concat(players).reset_index()\n\n df_players.columns = map(str.lower, df_players.columns)\n\n # Clean up dataset\n df_players['season'] = year\n df_players['league'] = league\n\n df_players = df_players.drop(['index','#'], axis=1).reset_index(drop=True)\n \n print(\"Successfully scraped all \" + league + \" goalie data from \" + year + \".\")\n \n df_players = df_players.loc[((df_players.gp!=0) & (~pd.isna(df_players.gp)) & (df_players.gp!=\"0\") & (df_players.gaa!=\"-\"))]\n\n return df_players\n else: print(\"LENGTH 0 ERROR: \" + str(requests.get(url+str(1), timeout = 500)) + \" On League: \" + league + \" In Year: \" + year)","def test_strategy(self):\n self.first_play_test(C)\n for i in range(10):\n history_1 = [C] * i\n history_2 = [C] * i\n self.responses_test(history_1, history_2, [C])\n # Now cooperate 10% less than opponent\n history_1 = [C] * 11\n history_2 = [D] * 11\n self.responses_test(history_1, history_2, [D], random_seed=10)\n history_1 = [C] * 11\n history_2 = [D] * 10 + [C]\n self.responses_test(history_1, history_2, [D], random_seed=10)\n # Test beyond 10 rounds\n history_1 = [C] * 11\n history_2 = [D] * 5 + [C] * 6\n self.responses_test(history_1, history_2, [D, D, D, D], random_seed=20)\n history_1 = [C] * 11\n history_2 = [C] * 9 + [D] * 2\n self.responses_test(history_1, history_2, [C, D, D, C], random_seed=25)","def AmOppCr(_cmp, e87482, e87487, e87492, e87497):\n\n \"\"\"\n This function calculates American Opportunity Credit\n for up to four eligible students\n\n \"\"\"\n\n # Expense should not exceed the cap of $4000.\n if _cmp == 1:\n\n c87482 = max(0., min(e87482, 4000.))\n c87487 = max(0., min(e87487, 4000.))\n c87492 = max(0., min(e87492, 4000.))\n c87497 = max(0., min(e87497, 4000.))\n else:\n c87482, c87487, c87492, c87497 = 0., 0., 0., 0.\n\n # Credit calculated as 100% of the first $2000 expense plus\n # 25% of amount exceeding $2000.\n if max(0, c87482 - 2000) == 0:\n c87483 = c87482\n else:\n c87483 = 2000 + 0.25 * max(0, c87482 - 2000)\n\n if max(0, c87487 - 2000) == 0:\n c87488 = c87487\n else:\n c87488 = 2000 + 0.25 * max(0, c87487 - 2000)\n\n if max(0, c87492 - 2000) == 0:\n c87493 = c87492\n else:\n c87493 = 2000 + 0.25 * max(0, c87492 - 2000)\n\n if max(0, c87497 - 2000) == 0:\n c87498 = c87497\n else:\n c87498 = 2000 + 0.25 * max(0, c87497 - 2000)\n\n # Sum of credits of all four students.\n c87521 = c87483 + c87488 + c87493 + c87498\n\n return (c87482, c87487, c87492, c87497, c87483, c87488, c87493, c87498,\n c87521)","def _accounce_winner(self):\n \n if self._end_time < time.time():\n winner = sorted(((player.get_name(), player.get_last_roll(), player.get_total_score())\n for player in self._players.get_players()),\n key=lambda player: (player[1]),\n reverse=True)[0]\n\n print(\"\\n\\nCongratulations {}, you had the highest score of {} before time ran out. You won the game!\"\n .format(winner[0], winner[2]))\n else:\n super()._accounce_winner()","def playGames(self, num, sonete,secuence,mcts,verbose=False):\n eps_time = AverageMeter()\n bar = Bar('Arena.playGames', max=num)\n end = time.time()\n eps = 0\n maxeps = int(num)\n finalScore1=0\n finalScore2=0\n\n num = int(num/2)\n oneWon = 0\n twoWon = 0\n draws = 0\n gameResults=[]\n global nround\n actions=[]\n self.player1, self.player2 = self.player1, self.player1\n board = self.game.getInitBoard()\n for i in range(100):\n nround = i\n #action,sonete = self.playGame(sonete,sequences,nround,verbose=verbose)\n pi = mcts.getActionProb(sonete, temp=1)\n #actions.append(action)\n\n eps_time.update(time.time() - end)\n end = time.time()\n\n\n return actions#finalScore1, finalScore2#oneWon, twoWon, draws","def greedy_cow_transport(cows,limit=10):\r\n #Casting dictionary into list\r\n list_cows = sorted(([w, n] for n, w in cows.items() if w <= limit), reverse = True)\r\n\r\n\r\n #trip result list\r\n #The algorithm should start with the biggest cow\r\n if list_cows == []:\r\n trip_results = []\r\n else:\r\n trip_results = greedy_helper(list_cows, limit, []) \r\n return trip_results","def strategy_cheap(cookies, cps, history, time_left, build_info):\n pick = None\n cost = float('inf')\n for item in build_info.build_items():\n if build_info.get_cost(item) < cost: \n cost = build_info.get_cost(item)\n if (time_left * cps + cookies) >= cost:\n pick = item\n \n return pick","def second_way(person_to_clubs: Dict[str, List[str]], \n club_to_person: Dict[str, List[str]], \n person: str, club: str) -> int:\n score = 0\n if person not in person_to_clubs:\n return score\n for member in club_to_person[club]:\n for diff_clubs in person_to_clubs[member]:\n if diff_clubs != club and diff_clubs in person_to_clubs[person]:\n score += 1\n return score","def get_best_moves():\n game_data = set()\n\n def next_move(game):\n winner = game.get_winner()\n if winner is not None:\n return {winner}\n next_wins = set()\n future_wins = set()\n future_draws_and_wins = set()\n future_draws = set()\n results = set()\n for row, col in game.valid_spaces():\n next_game = game.move(row, col)\n next_results = next_move(next_game)\n results |= next_results\n if game.turn == 2 and next_game.get_winner() == 2:\n # If Player 2 can win with their move, reject this line\n return {2}\n elif game.turn == 1 and next_game.get_winner() == 1:\n # If Player 1 can win with their move, only accept these lines\n next_wins.add((tuple([tuple(row) for row in game.board]), row * 3 + col))\n elif game.turn == 1:\n if next_results == {1}:\n # Player 1 will only win in this future\n future_wins.add((tuple([tuple(row) for row in game.board]), row * 3 + col)) \n elif next_results == {0, 1}:\n # Player 1 could win or draw in this future\n future_draws_and_wins.add((tuple([tuple(row) for row in game.board]), row * 3 + col))\n elif next_results == {0}:\n # Player 1 could only draw in this future\n future_draws.add((tuple([tuple(row) for row in game.board]), row * 3 + col))\n # We only accept the draws if we don't have any just wins\n if game.turn == 2:\n return results\n\n if next_wins:\n game_data.update(next_wins)\n return {1}\n elif future_wins:\n game_data.update(future_wins)\n return {1}\n elif future_draws_and_wins:\n game_data.update(future_draws_and_wins)\n return {0, 1}\n elif future_draws:\n game_data.update(future_draws)\n return {0}\n return set()\n\n next_move(TTTGame())\n next_move(TTTGame(turn=2))\n return tuple(game_data)","def opponents_score(self):\n if self.opponent_wickets == 10:\n var1 = \"All Out\"\n return str('{0} {1}').format(self.opponent_runs, var1)\n else:\n var1 = self.opponent_wickets\n return str('{0}-{1}').format(self.opponent_runs, var1)","def get_fifa_stats(match, player_stats):\n\n # Define variables\n match_id = int(match.match_api_id)\n date = match['date']\n players = ['home_player_1', 'home_player_2', 'home_player_3', \"home_player_4\", \"home_player_5\",\n \"home_player_6\", \"home_player_7\", \"home_player_8\", \"home_player_9\", \"home_player_10\",\n \"home_player_11\", \"away_player_1\", \"away_player_2\", \"away_player_3\", \"away_player_4\",\n \"away_player_5\", \"away_player_6\", \"away_player_7\", \"away_player_8\", \"away_player_9\",\n \"away_player_10\", \"away_player_11\"]\n\n overall_ratings = np.array([])\n for player in players:\n # Get player ID\n player_id = match[player]\n\n # Get player stats\n stats = player_stats[player_stats.player_api_id == player_id]\n\n # Identify current stats\n current_stats = stats[stats.date < date].sort_values(by='date', ascending=False).iloc[0]\n\n # get overall rating for every player, this cannot be nan since we \"dropna\" in main()\n overall_ratings = np.concatenate((overall_ratings, [current_stats[\"overall_rating\"]]))\n\n colNames = np.core.defchararray.add(players, '_overall_rating')\n player_stats_new = pd.Series(overall_ratings, index=colNames)\n player_stats_new['match_api_id'] = match_id\n # print(player_stats_new)\n return player_stats_new","def scrape_world_cup_scoreboard(year):\n # Replace this with the results logic somehow...\n\n d = world_cup_mapping[year]\n prefix = 'http://www.fifa.com'\n if type(d) == int:\n root_url = '/worldcup/archive/edition=%s/' % d\n else:\n root_url = '/worldcup/archive/%s/' % d\n data = scrape_url(prefix + root_url + \"results/index.html\")\n\n # Find urls in the page.\n match_re = re.compile(root_url + \"results/matches/match=\\d+/report.html\")\n urls = match_re.findall(data)\n return [prefix + e for e in urls]","def show_contest_winner(self, db_session):\n users_contest_list = db_session.query(db.User).filter(db.User.entered_in_contest.isnot(False)).all()\n if len(users_contest_list) > 0:\n winner = random.choice(users_contest_list)\n self._add_to_chat_queue('The winner is {}!'.format(winner.name))\n else:\n self._add_to_chat_queue('There are currently no entrants for the contest.')","def winningTeamPenalty(r):\n \n #Check if home or away had more goals at the 'event' time\n homecheck = int(r['about.goals.home'] > r['about.goals.away'])\n awaycheck = int(r['about.goals.away'] > r['about.goals.home'])\n \n #If home had more goals and the penalty was on the home team, set to 1\n if (homecheck > 0) and (r['against.homeTeam'] == 1):\n return 1\n #If away had more and the penalty was not on home team, set to 1\n if (awaycheck > 0) and (r['against.homeTeam'] == 0):\n return 1\n #Any other situation should be a zero in this column\n else:\n return 0","def collect_century_biomass_all_sites(match_csv, outdir, save_as):\n df_list = []\n site_df = pd.read_csv(match_csv)\n for site in site_df.site_id.unique():\n cent_file = os.path.join(\n outdir, site, 'CENTURY_outputs_iteration0', '{}.lis'.format(site))\n if not os.path.exists(cent_file):\n continue\n cent_df = pd.io.parsers.read_fwf(cent_file, skiprows=[1])\n df_subset = cent_df[(cent_df.time > 2016) & (cent_df.time <= 2017)]\n biomass_df = df_subset[['time', 'aglivc', 'stdedc']]\n live_biomass = biomass_df.aglivc * 2.5 # grams per square m\n dead_biomass = biomass_df.stdedc * 2.5 # grams per square m\n biomass_df['live'] = live_biomass\n biomass_df['standing_dead'] = dead_biomass\n biomass_df['total'] = live_biomass + dead_biomass\n biomass_df['site'] = site\n biomass_df.set_index('time', inplace=True)\n df_list.append(biomass_df)\n combined_df = pd.concat(df_list)\n combined_df.to_csv(save_as)","def WCA_SA(targetMDG, WCAresult):\n hill_climbers = []\n for i in range(NUM_Population):\n hill_climbers.append(SimulatedAnnealing(targetMDG, WCAresult))\n\n completed_climbers = []\n completed_max_climbers = []\n\n # k: int, number of neighbors to be considered\n k = 20\n i = 0\n not_increased = 0\n max_score = 0\n Temperature = 20\n\n while True:\n for climber in hill_climbers[:]:\n result = climber.climb_with_annealing(k, Temperature)\n if not result:\n completed_climbers.append(climber)\n hill_climbers.remove(climber)\n max_completed_climber = SimulatedAnnealing(targetMDG)\n max_completed_climber.result = climber.max_result\n max_completed_climber.update_score()\n completed_max_climbers.append(max_completed_climber)\n\n total_climbers = hill_climbers + completed_climbers + completed_max_climbers\n total_climbers.sort()\n print(\"Iteration \", i, \": \", total_climbers[-1].score)\n\n if total_climbers[-1].score - max_score != 0:\n not_increased = 0\n else:\n not_increased += 1\n\n if len(hill_climbers) == 0 or not_increased == 10:\n break\n i += 1\n max_score = total_climbers[-1].score\n if Temperature > 0:\n Temperature -= 0.5\n\n total_climbers = hill_climbers + completed_climbers + completed_max_climbers\n total_climbers.sort()\n\n max_climber = total_climbers[-1]\n\n print(\"TurboMQ = \", max_climber.score)\n for c in max_climber.result: # print all clusters which are not singleton\n if 1 != len(c.get_nodes()):\n print(c.get_nodes())\n\n max_climber.remove_empty_cluster()\n return max_climber.result","def heuristic_function(self, game_node: ConnectFourGame, search_depth: int):\n\n # Victory or defeat with a higher depth value is more desirable, because it means less moves are used to reach it\n depth_points = 2520 / (10 - search_depth)\n\n if game_node.winner_id is None:\n # Evaluates empty spaces for any player that is one disc away from a victory\n player_four_in_a_rows = self._determine_near_x_in_a_rows(game_node, game_node.victory_condition)\n ai_count = player_four_in_a_rows.get(self.ai_player_id, 0)\n other_player_count = sum([\n player_four_in_a_rows[player_id]\n for player_id in filter(lambda player_id: player_id != self.ai_player_id, player_four_in_a_rows)\n ])\n if ai_count - other_player_count != 0:\n return (ai_count - other_player_count) * 20\n\n # Evaluates grid positioning, granting bonus points for discs closer to the center of the grid\n max_deviation = floor(game_node.grid.width / 4.0)\n mid = ceil(game_node.grid.width / 2.0)\n ai_count = 0\n other_player_count = 0\n for col in range(mid - max_deviation, mid + max_deviation):\n for row in range(0, game_node.grid.height):\n curr_space = game_node.grid.grid_spaces[col][row]\n if curr_space.disc is None:\n break # Short-circuits when empty space in a column is reached\n elif curr_space.disc.player_id == self.ai_player_id:\n ai_count += 1\n else:\n other_player_count += 1\n if ai_count - other_player_count != 0:\n return (ai_count - other_player_count) * 2\n\n return 0\n elif game_node.winner_id == self.ai_player_id:\n return self.winner_heuristic_value + depth_points # Means AI player has won\n else:\n return -self.winner_heuristic_value - depth_points # Means AI player has lost","def calculate_fitness(self,chromo,index):\n #Incase chromosome has been scheduled before\n chromo.fitness = 0\n \n hold_index = index\n \n #Figure out which room you are in\n data_tuple = self.get_room_day_numbers(hold_index)\n room_id = data_tuple[1]\n print room_id\n #Get Room object\n room = self.config.get_room_by_id(room_id)\n\n course = chromo._class\n #Course might not overlap at current position, but could if duration is\n #longer than 1, this checks for that\n if not chromo.overlap:\n if course.duration > 1:\n count = 0\n while count < (course.duration):\n index += 1\n if index < len(self.chromo_list):\n check_list = self.chromo_list[index]\n if check_list:\n chromo.overlap = True\n count += 1\n\n #Class does not overlap EVER\n if not chromo.overlap:\n chromo.fitness += 1\n\n #Room is able to fit the class\n if course.get_room_size() <= room.get_seat_num():\n chromo.fitness += 1\n\n #Course needs lab and room has lab\n if course.needs_lab():\n if room.lab_status():\n chromo.fitness += 1\n\n #Course doesnt need lab and room doesnt have lab\n if not course.needs_lab():\n if not room.lab_status():\n chromo.fitness += 1\n\n #Only way a Professor will have an overlapping class is if the\n #class overlaps with another class\n if chromo.overlap:\n prof_overlap = False\n index = hold_index\n count = 0\n while count < (course.duration):\n index += count\n check_list = self.chromo_list[index]\n for courses in check_list:\n prof_overlap = course.professor_overlap(courses._class)\n if prof_overlap:\n break\n if prof_overlap:\n break\n count += 1\n\n if not prof_overlap:\n chromo.fitness += 1\n else:\n chromo.fitness += 1\n\n if chromo.fitness is self.best_fitness:\n self.best_of[chromo._class] = self.best_fitness","def final_standing_projection(league_no):\n ros_proj_b_list = BatterProjection.objects.all()\n ros_proj_p_list = PitcherProjection.objects.all()\n league_settings = get_league_settings(league_no)\n current_standings = get_standings(league_no, int(league_settings['Max Teams:']))\n team_list = yahoo_teams(league_no)\n final_stats = final_stats_projection(team_list, ros_proj_b_list, ros_proj_p_list, current_standings,\n league_settings)\n volatility_standings = league_volatility(SGP_DICT, final_stats)\n ranked_standings = rank_list(volatility_standings)\n return ranked_standings","def strategy_cheap(cookies, cps, history, time_left, build_info):\n\n items = build_info.build_items()\n costs = [build_info.get_cost(item) for item in items]\n builds = sorted(zip(costs,items))\n\n earnable_cookies = cookies + time_left * cps\n\n if builds[0][0] > earnable_cookies:\n return None\n else:\n return builds[0][1]","def get_upcoming_games(n=10):\n conn, cursor = connect_to_db()\n query = \"\"\"select kickoff_time, t2.team_id home_id, t2.team_name home_name, \n t3.team_id away_id, t3.team_name away_name\n from fpl_fixtures t1 left join fpl_teams t2 on t1.team_h = t2.id left \n join fpl_teams t3 on t1.team_a = t3.id where started = 0 order by \n kickoff_time limit {}\"\"\".format(n)\n df = run_query(cursor, query)\n return df","def strategy_best(cookies, cps, history, time_left, build_info):\n print \"STRATEGY BEGINS\"\n items_available = []\n for item in build_info.build_items():\n items_available.append(item)\n item_to_buy = None\n max_profit = float('-inf')\n for item in items_available:\n if cps * time_left + cookies >= build_info.get_cost(item):\n if cookies >= build_info.get_cost(item):\n time_to_bake = 0.0\n else:\n time_to_bake = math.ceil((build_info.get_cost(item) - cookies) / cps)\n additional_cookies = build_info.get_cps(item) * (time_left - time_to_bake)\n\n profit = additional_cookies - build_info.get_cost(item)\n print \"item:\", item, \", cost:\", build_info.get_cost(item), \", time_to_bake:\", time_to_bake, \", profit:\", profit\n if profit > max_profit:\n max_profit = profit\n item_to_buy = item\n #print \"max_profit:\", max_profit\n print \"STRATEGY ENDS\"\n return item_to_buy","def strategy_cheap(cookies, cps, time_left, build_info):\n items = build_info.build_items()\n result = None\n cost = float('+inf')\n overall = cookies + time_left * cps\n for item in items:\n temp_cost = build_info.get_cost(item)\n if temp_cost <= overall and cost > temp_cost:\n result = item\n cost = temp_cost\n return result","def __get_next_greedy_move(self, game_state): \n best_move = None\n best_score = None\n for free_seat in self.__get_free_seats(game_state):\n next_game_state_score = self.__get_score(game_state, free_seat)\n if best_score is None:\n best_score = next_game_state_score\n best_move = free_seat\n continue\n if next_game_state_score > best_score:\n best_score = next_game_state_score\n best_move = free_seat\n return best_move","def get_convergence_episode(self):\n values = self.stats['return_stats']['episode_totals']\n _, y, (y_lower, _) = self._moving_average(\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\n # The convergence is established as the first time the average return\n # is above the lower bounds of the final return.\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\n return first_episode","def collect_salaries(team_dict):\n salaries = [0, 0, 0, 0, 0]\n for c in team_dict['roster']:\n for i in range(5):\n if (c['contract'].contract_season + i) <= c['contract'].length:\n salaries[i] += c['contract'].salary\n return salaries","def choose_winner(): \r\n max_health = Titan.max_health()\r\n winners = tuple((titan.name for titan in Titan.titans if titan.health == max_health))\r\n return winners","def get_clubs_and_transfers(league_name, league_id, season_id, window):\r\n headers = {\r\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}\r\n base = \"https://www.transfermarkt.it\"\r\n url = base + \"/{league_name}/transfers/wettbewerb/{league_id}/plus/?saison_id={season_id}&s_w={window}\".format(\r\n league_name=league_name, league_id=league_id, season_id=season_id, window=window)\r\n try:\r\n print(\"Connecting...\")\r\n response = requests.get(url, headers=headers)\r\n print(\"Connection successful, status code {}\".format(response.status_code))\r\n except requests.exceptions.RequestException as e:\r\n print(e)\r\n exit()\r\n soup = BeautifulSoup(response.content, 'html.parser')\r\n\r\n clubs = [tag.text for tag in soup.find_all('div', {'class': 'table-header'})][1:]\r\n\r\n tables = [tag.findChild() for tag in soup.find_all('div', {'class': 'responsive-table'})]\r\n table_in_list = tables[::2]\r\n table_out_list = tables[1::2]\r\n\r\n transfer_in_list = []\r\n transfer_out_list = []\r\n column_headers = {'season': season_id, 'window': window, 'league': league_name}\r\n for table_in, table_out in zip(table_in_list, table_out_list):\r\n transfer_in_list.append(get_transfer_info(base, table_in, movement='In', **column_headers))\r\n transfer_out_list.append(get_transfer_info(base, table_out, movement='Out', **column_headers))\r\n\r\n return clubs, transfer_in_list, transfer_out_list","def get_assist_done(self, season=None, stage=None):\n cnt = 0\n for m in self.get_matches(season=season, ordered=True):\n if util.is_None(m.goal):\n continue\n if not util.is_None(stage) and m.stage >= stage:\n return cnt\n soup = BeautifulSoup(m.goal, \"html.parser\")\n for player1 in soup.find_all('player2'):\n if int(str(player1.string).strip()) == self.player_api_id:\n cnt += 1\n return cnt","def conCor(data):\n \n d = { } \n \n for c in data[0]:\n if c not in d:\n d[c]= 0\n for i in range(len(data[0])):\n if isWinner(data,c,data[0][i]):\n d[c] = d[c] +1 \n \n hi = max(d.values()) \n lenA = len(d) - 1\n \n if hi == lenA:\n for i in d:\n if hi == d[i]:\n return i \n \n return \"No Condorcet Winner exists in this data.\"","def players_onsale(self):\r\n headers = {\"Content-type\": \"application/x-www-form-urlencoded\", \"Accept\": \"text/plain\",\r\n 'Referer': 'http://' + self.domain + '/team_news.phtml', \"User-Agent\": user_agent}\r\n req = self.session.get('http://' + self.domain + '/teamInfo.phtml?tid=' + str(self.community_id),\r\n headers=headers).content\r\n soup = BeautifulSoup(req, \"html.parser\")\r\n\r\n current_year = dt.today().year\r\n current_month = dt.today().month\r\n on_sale = list()\r\n year_flag = 0\r\n for i in soup.find_all('table', {'class', 'tablecontent03'})[2].find_all('tr')[1:]:\r\n columns = i.find_all('td')\r\n player_id = int(re.findall('\\d+', columns[0].img['src'])[0])\r\n playername = columns[1].text.strip()\r\n team_id = int(re.findall('\\d+', columns[2].img['src'])[0])\r\n team = columns[2].a['title'].strip()\r\n min_price = float(columns[3].text.replace(\".\", \"\").strip())\r\n market_price = float(columns[4].text.replace(\".\", \"\").strip())\r\n points = int(columns[5].text.strip().strip())\r\n # Controlamos el cambio de año, ya que comunio no lo dá\r\n if current_month <= 7 < int(columns[6].text[3:5]):\r\n year_flag = 1\r\n date = datetime.strptime(str(current_year - year_flag) + columns[6].text[3:5] + columns[6].text[:2], '%Y%m%d').date()\r\n owner = columns[7].text.strip()\r\n position = self.translate_position(columns[8].text.strip())\r\n # Comprobamos si solamente queremos los de la computadora o no\r\n on_sale.append([player_id, playername, team_id, team, min_price, market_price, points, date, owner, position])\r\n\r\n return on_sale","def jaccard_sim(news_data_dict: dict, song_data_dict: dict):\n jaccard_dict = defaultdict(list)\n\n for news_yr, news_txt in news_data_dict.items():\n news_txt_flat = []\n for nt in news_txt:\n news_txt_flat += nt\n news_txt_flat = set(news_txt_flat)\n if news_yr <= 2011:\n song_txt_flat = set()\n for i in range(5):\n song_yr = news_yr+i\n for st in song_data_dict[song_yr]:\n for stw in st:\n song_txt_flat.add(stw)\n shared_words = news_txt_flat.intersection(song_txt_flat)\n jaccard = len(shared_words) / (len(song_txt_flat) + len(news_txt_flat) - len(shared_words))\n jaccard = round(jaccard, 3)\n jaccard_dict[news_yr].append(jaccard)\n\n return jaccard_dict","def scrape_fifa_goals(url, competition):\n\n # Seems the 2006 world cup report is missing some games for sasa ilic.\n goal_replace = {\n \"(SCG) 20',\": \"Sasa ILIC (SCG) 20',\"\n }\n\n\n data = scrape_url(url)\n data = data.split(\"<h2>Advertisement</h2>\")[0]\n soup = BeautifulSoup(data)\n\n goals_div = soup.find(\"div\", text='Goals scored')\n goals = [get_contents(e) for e in goals_div.parent.parent.findAll(\"li\")]\n goals = [goal_replace.get(e, e) for e in goals]\n\n goal_re = re.compile(\"^(?P<name>.*?) \\((?P<team>[A-Z]+)\\) (?P<minute>\\d+)'?\")\n\n game_data = scrape_fifa_game(url, competition)\n\n\n\n l = []\n\n for s in goals:\n try:\n name, team, minute = goal_re.search(s.strip()).groups()\n except:\n #import pdb; pdb.set_trace()\n print(s)\n continue\n \n team = team.strip()\n team = team_abbrevs.get(team, team)\n\n l.append({\n 'team': team,\n 'competition': competition,\n 'season': game_data['season'],\n 'date': game_data['date'],\n 'goal': name.strip().title(),\n\n\n 'minute': int(minute),\n 'assists': [],\n 'source': url\n })\n\n return l","def assign_win_points(game_dict):\n def win_tuple(deck_dict):\n \"\"\" Return tuple ordered by increasing final standing. \"\"\"\n # negate turns so that max() behaves; points good, turns bad.\n num_normal_turns = sum(not ( (POSSESSION in t and t[POSSESSION]) or \\\n (OUTPOST in t and t[OUTPOST]))\n for t in deck_dict[TURNS])\n return (deck_dict[POINTS], -num_normal_turns)\n\n decks = game_dict[DECKS]\n winner_tuple = max(win_tuple(p) for p in decks)\n winners = [p for p in decks if win_tuple(p) == winner_tuple]\n\n win_points = float(len(decks)) / len(winners)\n for player in decks:\n player[WIN_POINTS] = win_points if player in winners else 0.0"],"string":"[\n \"def get_championship_won_for_each_coach(self):\\n self.championship_team = (\\n self.raw_data_postseason\\n .merge(self.season_max_days,how='left',on=['Season'])\\n .query(\\\"DayNum == season_max_days\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"NumOT\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"NumOT\\\":\\\"is_champion\\\",\\\"WTeamID\\\":\\\"TeamID\\\"})\\n )\",\n \"def get_win_rate_regular_season_for_each_coach(self):\\n self.games_won_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n self.games_lose_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df\\n self.combine_regular_games_won_lose = (\\n self.games_lose_for_coaches\\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"games_lost\\\",\\\"which_coach_for_win\\\":\\\"games_won\\\"})\\n )\",\n \"def get_win_rate_post_season_for_each_coach(self):\\n # get winning games for coaches\\n self.post_games_won_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # get losing games for coaches\\n self.post_games_lose_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df for post season\\n self.combine_post_games_won_lose = (\\n self.post_games_lose_for_coaches\\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"post_games_lost\\\",\\\"which_coach_for_win\\\":\\\"post_games_won\\\"})\\n .fillna(0)\\n )\",\n \"def combine_playoff_championship_for_each_coach(self):\\n self.final_coach_with_postseason_champion_each_year = (\\n self.final_coach_with_postseason_each_year\\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\\n .fillna(0)\\n )\",\n \"def get_tourney_rounds(self, conference, year):\\n ts_dict = self.get_tourney_slots()\\n seed_dict = self.get_tourney_seeds()\\n tr_dict = self.get_tourney_results()\\n \\n round_1 = list()\\n round_2 = list()\\n round_3 = list()\\n round_4 = list()\\n winner = list()\\n \\n round1_winners = list()\\n for seed, team in seed_dict[year].items():\\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n round1_winners.append(seed[1:])\\n #removes duplicates because I did this part weirdly... HEHEH\\n round1_winners = list(set(round1_winners))\\n\\n win_counter = defaultdict(int)\\n for seed, team in seed_dict[year].items(): \\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n win_counter[winning] += 1\\n \\n for slot, matchup in ts_dict[year].items():\\n \\n if conference in slot and \\\"R1\\\" in slot: \\n round_1.append(\\\"{}-{}\\\".format(matchup[1:3], matchup[-2:]))\\n round_1 = sorted(round_1)\\n #for match in round_1:\\n for winner1 in round1_winners:\\n if winner1 in round_1[0]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-1]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[1]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-2]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[2]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-3]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[3]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-4]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_2 = sorted(round_2)\\n\\n round2_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 1:\\n round2_winners.append(seed[1:])\\n \\n for winner1 in round2_winners:\\n if winner1 in round_2[0]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-1]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_2[1]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-2]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_3 = sorted(round_3)\\n\\n round3_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 2:\\n round3_winners.append(seed[1:])\\n\\n for winner1 in round3_winners:\\n if winner1 in round_3[0]:\\n for winner2 in round3_winners:\\n if winner2 in round_3[-1]:\\n round_4.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_4 = sorted(round_4)\\n\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 3:\\n winner.append(seed[1:])\\n\\n conferences = {\\\"W\\\": \\\"East\\\", \\\"X\\\": \\\"Midwest\\\", \\\"Y\\\": \\\"South\\\", \\\"Z\\\": \\\"West\\\"}\\n\\n #print(\\\"CONFERENCE: {}, YEAR: {}\\\".format(conferences[conference], year))\\n #print(\\\"ROUND1:\\\", round_1)\\n #print(\\\"ROUND2:\\\", round_2)\\n #print(\\\"ROUND3:\\\", round_3)\\n #print(\\\"ROUND4:\\\", round_4)\\n #print(\\\"WINNER:\\\", winner)\\n\\n #clearing out the tourney results dictionary\\n #tr_dict.clear()\\n\\n return round_1, round_2, round_3, round_4, winner\",\n \"def find_opponent(standings,odds):\\n\\n # simulate all games\\n for i in range(len(odds)):\\n play_game(odds.loc[i],standings)\\n\\n # update the points and GD tally\\n standings['P']=standings['W']*3 + standings['D']\\n standings['GD']=standings['F']-standings['A']\\n\\n # see if teams have equal amount of points, and award h2h_points for\\n # h2h results against those teams.\\n for group in \\\"ABCDEF\\\":\\n gelijk = standings.loc[standings['Group']==group][standings.loc[standings['Group']==group].duplicated(subset='P',keep=False)]\\n gelijk[\\\"h2h_points\\\"]=np.zeros(len(gelijk))\\n\\n for i in gelijk.index:\\n for team1 in gelijk.loc[i][\\\"h2h\\\"]:\\n for team2 in gelijk[\\\"TEAMS\\\"]:\\n if team1==team2:\\n standings.loc[i,\\\"h2h_points\\\"]+=1\\n\\n # sort the final standings\\n standings = standings.sort_values(by=['Group','P',\\\"h2h_points\\\",'GD','F','W'],ascending=[True,False,False,False,False,False])\\n\\n # determine third placed teams\\n standings = standings.reset_index()\\n third = standings.loc[[2,6,10,14,18,22]]\\n\\n # determine best number threes\\n third = third.sort_values(by=['P','GD','F','W'],ascending=False)\\n\\n groups_of_best_no_3 = \\\"\\\"\\n for i in third.head(4).Group:\\n groups_of_best_no_3+=i\\n groups_of_best_no_3 = ''.join(sorted(groups_of_best_no_3))\\n\\n # look up the opponent of the dutch team\\n a = third.loc[third.Group == opponent_matrix[groups_of_best_no_3]]['TEAMS']\\n\\n return a.reset_index().TEAMS[0]\",\n \"def play_game(game,standings_):\\n rand_nmr = random.random()\\n\\n standings_.loc[standings_.TEAMS==game['Home'],'MP'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'MP'] += 1\\n\\n if rand_nmr < game['Prob Home']:\\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\\n standings_.loc[standings_.TEAMS==game['Home'],'W'] += 1\\n standings_.loc[standings_.TEAMS==game['Home'],'F'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Away'],'L'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'A'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Home']][\\\"h2h\\\"].apply(lambda x:x.append(game['Away']))\\n\\n return 0\\n\\n elif rand_nmr < game['Prob Home'] + game['Prob Draw']:\\n # all draws end in 0-0 this can be improved\\n standings_.loc[standings_.TEAMS==game['Home'],'D'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'D'] += 1\\n\\n return 1\\n\\n else:\\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\\n standings_.loc[standings_.TEAMS==game['Away'],'W'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'F'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Home'],'A'] += 1\\n standings_.loc[standings_.TEAMS==game['Home'],'L'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Away']][\\\"h2h\\\"].apply(lambda x:x.append(game['Home']))\\n\\n return 2\",\n \"def first_round_history(self):\\n self.ts_dict = self.get_tourney_slots()\\n self.tsr_dict = self.match_seeds()\\n first_seed_win = 0\\n second_seed_win = 0\\n third_seed_win = 0\\n fourth_seed_win = 0\\n fifth_seed_win = 0\\n sixth_seed_win = 0\\n seventh_seed_win = 0\\n eighth_seed_win = 0\\n total_games = 128\\n\\n for year1 in self.ts_dict: \\n for slot, match_up in self.ts_dict[year1].items():\\n if slot[:2] == \\\"R1\\\":\\n for year2 in self.tsr_dict:\\n if year1 == year2:\\n for winning, losing in self.tsr_dict[year2].items():\\n if winning[5:] == match_up[:3]:\\n seed = winning[6:] \\n if seed == \\\"01\\\":\\n first_seed_win += 1\\n elif seed == \\\"02\\\":\\n second_seed_win += 1\\n elif seed == \\\"03\\\":\\n third_seed_win += 1\\n elif seed == \\\"04\\\":\\n fourth_seed_win += 1\\n elif seed == \\\"05\\\":\\n fifth_seed_win += 1\\n elif seed == \\\"06\\\":\\n sixth_seed_win += 1\\n elif seed == \\\"07\\\":\\n seventh_seed_win += 1\\n elif seed == \\\"08\\\":\\n eighth_seed_win += 1 \\n \\n #print(first_seed_win, second_seed_win, third_seed_win, fourth_seed_win, fifth_seed_win, sixth_seed_win, seventh_seed_win, eighth_seed_win, total_games)\\n\\n gauge = pygal.SolidGauge(inner_radius=0.70, title=\\\"NCAA First Round Results\\\")\\n ratio_first_seed = int(first_seed_win / total_games * 100)\\n ratio_second_seed = int(second_seed_win / total_games * 100)\\n ratio_third_seed = int(third_seed_win / total_games * 100)\\n ratio_fourth_seed = int(fourth_seed_win / total_games * 100)\\n ratio_fifth_seed = int(fifth_seed_win / total_games * 100)\\n ratio_sixth_seed = int(sixth_seed_win / total_games * 100)\\n ratio_seventh_seed = int(seventh_seed_win / total_games * 100)\\n ratio_eighth_seed = int(eighth_seed_win / total_games * 100) \\n\\n percent_formatter = lambda x: '{:.10g}%'.format(x)\\n gauge.value_formatter = percent_formatter\\n gauge.add('1 vs. 16', [{'value': ratio_first_seed, 'max_value': 100}])\\n gauge.add('2 vs. 15', [{'value': ratio_second_seed, 'max_value': 100}])\\n gauge.add('3 vs. 14', [{'value': ratio_third_seed, 'max_value': 100}])\\n gauge.add('4 vs. 13', [{'value': ratio_fourth_seed, 'max_value': 100}])\\n gauge.add('5 vs. 12', [{'value': ratio_fifth_seed, 'max_value': 100}])\\n gauge.add('6 vs. 11', [{'value': ratio_sixth_seed, 'max_value': 100}])\\n gauge.add('7 vs. 10', [{'value': ratio_seventh_seed, 'max_value': 100}])\\n gauge.add('8 vs. 9', [{'value': ratio_eighth_seed, 'max_value': 100}])\\n \\n gauge.render_to_file('chart.svg')\",\n \"def non_current_championships():\\n current_championships = (Alfa_Romeo+Ferrari+Haas+McLaren+Mercedes+Racing_Point+Red_Bull+Renault+Toro_Rosso+Williams).constructors_championships_years\\n non_current_championships = []\\n year = 1958\\n while year < 2020:\\n if year not in current_championships:\\n non_current_championships.append(year)\\n year += 1\\n return f\\\"The F1 Constructors' Championships won by teams no longer on the grid are: \\\\n{non_current_championships}\\\"\",\n \"def winning_games_stats(self):\\n self.winning_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(winning_num_counts = 1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM3\\\":\\\"sum\\\",\\\"WFGA3\\\":\\\"sum\\\",\\\"WFTM\\\":\\\"sum\\\",\\\"WFTA\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"winning_num_counts\\\":\\\"sum\\\",\\n \\\"WOR\\\":\\\"sum\\\",\\\"WDR\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\n \\\"WAst\\\":\\\"sum\\\",\\\"WTO\\\":\\\"sum\\\",\\\"WStl\\\":\\\"sum\\\",\\\"WBlk\\\":\\\"sum\\\",\\\"WPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"LScore\\\":\\\"losing_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"LFGA\\\":\\\"LFGA_opp\\\",\\\"LFGM\\\":\\\"LFGM_opp\\\"})\\n )\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def marcels_players(goalie, date, df):\\n # 0 = that year, 1 is year b4 ....\\n marcel_weights = [.36, .29, .21, .14]\\n reg_const = 2000\\n reg_avg = 0 # Where to regress to\\n\\n # Use past 3 season to weight games played -> Just take weighted average\\n gp_weights = [8, 4, 2, 0]\\n\\n season = int(helpers.get_season(date))\\n\\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\\n weighted_gp_sum, weights_gp_sum = 0, 0\\n\\n # Past 4 Seasons\\n for i in range(0, 4):\\n if season - i > 2006:\\n # Subset from stats df\\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\\n\\n # Sanity Check\\n if df_goalie.shape[0] > 1:\\n print(\\\"Too many rows!!!!!!!\\\")\\n exit()\\n\\n # If he played that year\\n if not df_goalie.empty:\\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\\n\\n # -> To divide by at end...normalize everything\\n weights_marcel_sum += marcel_weights[i]\\n weights_gp_sum += gp_weights[i]\\n\\n # Normalize weighted sums\\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n\\n # Get Regressed fsv%\\n if weighted_fen_sum != 0:\\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\\n else:\\n weighted_adj_fsv = 0\\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\\n\\n # Get weighted gp\\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\\n\\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}\",\n \"def perc_greedy(population, percentage=80):\\n \\n\\n #initialization\\n res_arr = [2] * 10\\n total_knights = 80\\n\\n medians = get_medians(population, percentage);\\n\\n while(total_knights > 0):\\n \\n # find \\\"easiest\\\" to acheive\\n ind = medians.index(min(medians))\\n\\n # calculate the number of knights to assign to that castle\\n assign = min(total_knights, medians[ind]-res_arr[ind] + 1)\\n\\n # make assignment\\n res_arr[ind] += assign\\n total_knights -= assign\\n\\n # mark that castle as \\\"done\\\"\\n medians[ind] = 100\\n \\n # get the score of result inst against input population\\n res_inst = CBInstance(res_arr)\\n res_score = grade_inst(res_inst, population)\\n \\n return res_inst\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def save_games_copa(season, logging_level=logging.INFO):\\n logging.basicConfig(level=logging_level)\\n logger = logging.getLogger(__name__)\\n\\n logger.info('Starting the download of games...')\\n\\n if season.season == get_current_season():\\n current_game_events_ids = season.get_current_game_events_ids_copa()\\n game_ids_list = list(current_game_events_ids.values())\\n else:\\n game_ids_list=season.get_game_ids_copa()\\n\\n n_checkpoints = 4\\n checkpoints = [round(i * float(len(game_ids_list)) / n_checkpoints) for i in range(n_checkpoints + 1)]\\n for i in range(len(game_ids_list)):\\n\\n game_id=int(game_ids_list[i]) % 1000\\n url2 = BASE_URL + \\\"/fichas/CREY{}.php\\\".format(game_ids_list[i])\\n filename = os.path.join(season.GAMES_COPA_PATH, str(game_id)+\\\"-\\\" +str(game_ids_list[i]) + '.html')\\n\\n open_or_download(file_path=filename, url=url2)\\n if i in checkpoints:\\n logger.info('{}% already downloaded'.format(round(float(i * 100) / len(game_ids_list))))\\n\\n logger.info('Download finished! (new {} games in {})\\\\n'.format(len(game_ids_list), season.GAMES_COPA_PATH))\",\n \"def test_get_player_upcoming_chests(self):\\n pass\",\n \"def strategy_cheap(cookies, cps, history, time_left, build_info):\\n print\\n print \\\"STRATEGY PART BEGIN\\\"\\n print\\n items_available = []\\n for item in build_info.build_items():\\n items_available.append(item)\\n while items_available:\\n min_cost = float('inf')\\n for item in items_available:\\n #print \\\"item:\\\", item, \\\", cost:\\\", build_info.get_cost(item)\\n if build_info.get_cost(item) < min_cost:\\n min_cost = build_info.get_cost(item)\\n cheapest = item\\n print \\\"cheapest:\\\", cheapest\\n # check if time enough\\n print \\\"checking time\\\"\\n print \\\"time left:\\\", time_left\\n print \\\"cost:\\\", min_cost\\n print \\\"cookies can be produced:\\\", cps * time_left\\n if cps * time_left + cookies < min_cost:\\n print \\\"not enough,\\\"\\n return None\\n else:\\n print cheapest, \\\"chosen\\\"\\n print \\\"STRATEGY PART END\\\"\\n print\\n return cheapest\",\n \"def _name_champion(self):\\n # TODO BREAK TIES\\n return max(self.teams, key=lambda team: len(team.wins))\",\n \"def possessions_home_away(data_frame, mode):\\n total_games_dict, total_dict, percentage_dict = dict(), dict(), dict()\\n team_name = 'home_team' if mode == 'home' else 'away_team'\\n for index, row in data_frame.iterrows():\\n if row[team_name] not in total_games_dict:\\n percentage_dict[row[\\\"id\\\"]] = 0\\n else:\\n percentage_dict[row[\\\"id\\\"]] = format(float(total_dict[row[team_name]]) / float(total_games_dict[row[team_name]]), '.2f')\\n\\n if row[team_name] in total_games_dict:\\n total_games_dict[row[team_name]] += 1\\n else:\\n total_games_dict[row[team_name]] = 1\\n\\n fga, orb, fta, to = 'fg_made_attempted_', 'offensive_rebounds_', 'ft_made_attempted_', 'turnovers_'\\n\\n possessions = int(row[fga + team_name.split('_')[0]].split('-')[1]) - int(row[orb + team_name.split('_')[0]]) + \\\\\\n (0.475 * int(row[fta + team_name.split('_')[0]].split('-')[1])) + int(row[to + team_name.split('_')[0]])\\n\\n if row[team_name] in total_dict:\\n total_dict[row[team_name]] += possessions\\n else:\\n total_dict[row[team_name]] = possessions\\n\\n return percentage_dict\",\n \"def mk_champs(cls, year, start_date, end_date):\\n seasons = [\\\"Quad Quandary\\\", \\\"Face Off\\\", \\\"Hot Shot\\\", \\\"Get Over It\\\", \\\"Bowled Over\\\", \\\"Ring It Up\\\", \\\"Block Party\\\",\\n \\\"Cascade Effect\\\", \\\"RES-Q\\\", \\\"Velocity Vortex\\\", \\\"Relic Recovery\\\", \\\"Rover Ruckus\\\"]\\n season_name = seasons[year-2007]\\n start_date = datetime.datetime.strptime(start_date, \\\"%Y-%m-%d\\\")\\n end_date = datetime.datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n season = f\\\"{year % 100:02}{(year + 1) % 100:02}\\\"\\n # fyear = f\\\"{year}-{(year+1)%1000:02d}\\\"\\n if year == 2009:\\n city, state_prov, country = \\\"Atlanta\\\", \\\"Georgia\\\", \\\"USA\\\"\\n venue = \\\"Georgia Dome\\\"\\n address = \\\"1 Georgia Dome Dr, Atlanta, GA 30313\\\"\\n elif year < 2013:\\n city, state_prov, country = \\\"St. Louis\\\", \\\"Missouri\\\", \\\"USA\\\"\\n venue = \\\"Edward Jones Dome\\\"\\n address = \\\"701 Convention Plaza, St. Louis, MO 63101\\\"\\n else:\\n city, state_prov, country = \\\"St. Louis\\\", \\\"Missouri\\\", \\\"USA\\\"\\n venue = \\\"Union Station\\\"\\n address = \\\"1820 Market Street, St. Louis, MO 63103\\\"\\n shared = {\\n \\\"year\\\": year,\\n \\\"city\\\": city,\\n \\\"state_prov\\\": state_prov,\\n \\\"country\\\": country,\\n \\\"end_date\\\": end_date,\\n \\\"event_type\\\": EventType.WORLD_CHAMPIONSHIP,\\n \\\"venue\\\": venue,\\n \\\"address\\\": address,\\n \\\"data_sources\\\": [\\\"USFIRST Website Archives\\\"]\\n }\\n\\n finals = Event(key=f\\\"{season}cmp0\\\",\\n name=f\\\"FTC {season_name} World Championship - Finals\\\",\\n playoff_type=PlayoffType.BO3_FINALS, \\n division_keys=[f\\\"{season}cmp1\\\", f\\\"{season}cmp2\\\"],\\n start_date=end_date,\\n **shared)\\n franklin = Event(key=f\\\"{season}cmp{2}\\\",\\n name=f\\\"FTC {season_name} World Championship - Franklin Division\\\",\\n playoff_type=PlayoffType.STANDARD, \\n parent_event_key=f\\\"{season}cmp0\\\", \\n start_date=start_date,\\n **shared)\\n edison = Event(key=f\\\"{season}cmp{1}\\\",\\n name=f\\\"FTC {season_name} World Championship - Edison Division\\\",\\n playoff_type=PlayoffType.STANDARD,\\n parent_event_key=f\\\"{season}cmp0\\\", \\n start_date=start_date,\\n **shared)\\n return (franklin, edison, finals)\",\n \"def cumulative_stats_for_teams_each_year(self):\\n self.cumulative_stats_for_team_each_year = (\\n self.combine_both_winning_losing_games_stats\\n .sort_values(['WTeamID','Season'])\\n .groupby(['WTeamID'])\\n .cumsum()\\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\\n .drop(['LTeamID','win_rate'],1)\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n # rebounds cumsum stats\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # assists, turnovers, steals, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win or lose by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def comp101_game(points, server):\\n \\n player0_points = 0 # sets initial 'points' of both players\\n player1_points = 0 \\n final0_score = 0 # final 'score' of both players in a manner peculiar to\\n final1_score = 0 # tennis\\n remainder = [] # stores the remaining 'points' if the game has ended\\n tennis_score = {0: 0, 1: 15, 2: 30, 3: 40, 4: 40} # use to convert\\n # 'points' to tennis\\n # 'scores'\\n winner = None # initial winner of the game\\n \\n # tests every 'points' in 'points'\\n for number in points:\\n \\n # finds the 'point' differences between both players and make\\n # sure it is a positive value\\n points_diff = abs(player0_points - player1_points)\\n \\n if (player0_points >= 4 or player1_points >= 4):\\n \\n # the case when a 'winner' is found and stores the \\n # remaining 'points'\\n if points_diff >= 2:\\n if player0_points > player1_points:\\n winner = 0\\n final0_score = \\\"W\\\"\\n \\n else:\\n winner = 1\\n final1_score = \\\"W\\\"\\n remainder.append(number)\\n \\n # the case when there is no 'winner' yet \\n else:\\n \\n if number == 0:\\n player0_points += 1\\n\\n else:\\n player1_points += 1\\n \\n # updates the latest 'point' difference\\n points_diff = abs(player0_points - player1_points)\\n \\n # ONLY runs if a player 'won' the game after exactly getting \\n # his next 'point'\\n if points_diff >= 2:\\n \\n if player0_points > player1_points:\\n winner = 0\\n final0_score = \\\"W\\\"\\n \\n else:\\n winner = 1\\n final1_score = \\\"W\\\"\\n \\n # if one of the player gets an \\\"advantage\\\"\\n elif points_diff == 1:\\n \\n if player0_points > player1_points:\\n final0_score = \\\"Ad\\\"\\n final1_score = 40\\n else:\\n final0_score = 40\\n final1_score = \\\"Ad\\\"\\n \\n # if no players get an \\\"advantage\\\" or 'wins' the game\\n else:\\n final0_score = 40\\n final1_score = 40\\n \\n else:\\n \\n # adds a 'point' to a 'player' and converts player 'points' to \\n # 'scores' in a manner peculiar to tennis\\n if number == 0:\\n player0_points += 1\\n final0_score = tennis_score[player0_points]\\n \\n else:\\n player1_points += 1\\n final1_score = tennis_score[player1_points]\\n \\n # updates the latest score difference\\n points_diff = abs(player0_points - player1_points)\\n \\n # checks if a player gets an \\\"advantage\\\" / 'wins' the game at exactly \\n # his 4th 'point'\\n if (player0_points == 4 or player1_points == 4):\\n \\n # when a player 'won' the game\\n if points_diff >= 2:\\n \\n if player0_points > player1_points:\\n winner = 0\\n final0_score = \\\"W\\\"\\n else:\\n winner = 1\\n final1_score = \\\"W\\\"\\n \\n # when a player gets an \\\"advantage\\\"\\n elif points_diff == 1:\\n \\n if player0_points > player1_points:\\n final0_score = \\\"Ad\\\"\\n else:\\n final1_score = \\\"Ad\\\" \\n \\n # determines which player score is displayed first based on 'server'\\n if server == 0:\\n score = str(final0_score) + \\\"-\\\" + str(final1_score)\\n else:\\n score = str(final1_score) + \\\"-\\\" + str(final0_score)\\n \\n return (score, winner, remainder)\",\n \"def qualifiedteams(self):\\n for i in range(0, len(self.teams)):\\n for j in range(i + 1, len(self.teams)):\\n WorldCupMatch(self.teams[i], self.teams[j], True)\\n self.teams = sorted(self.teams, key=op.attrgetter('points', 'goaldifference', 'scored'))\\n self.first_qualified = self.teams[len(self.teams)-1]\\n self.second_qualified = self.teams[len(self.teams)-2]\",\n \"def review(self):\\n # Compile standings\\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\\n # Name a champion\\n self.champion = self._name_champion()\\n self.league.history.champions_timeline[self.year] = self.champion\\n print \\\"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\\\".format(\\n self.champion.team.name.upper(), self.year, self.league.name.upper()\\n )\\n # Compile league leaders\\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\\n # Have each team review its season, as well\\n for team_season in self.teams:\\n team_season.review()\\n # Send the league into the offseason\\n self.league.season = None\",\n \"def get_game(self, game_id):\\n \\n session = requests.session()\\n response = session.get(self.baseURL + str(game_id), headers=self.headers)\\n soup = BeautifulSoup(response.text)\\n \\n #get teams\\n defeated_by = False \\n game_header = soup.find_all(text=re.compile('defeats'))\\n \\n if len(game_header) == 0:\\n game_header = soup.find_all(text=re.compile('defeated by'))\\n \\n if (len(game_header)) == 0:\\n game_header = soup.find_all(text=re.compile('defeat'))\\n \\n if (len(game_header)) == 0:\\n game_header = soup.find_all(text=re.compile('drew'))\\n defeated_by = True \\n else:\\n defeated_by = True \\n\\n if defeated_by: \\n teams = self.remove_long_names(game_header[1]).replace('\\\\n', '')\\n home_team = teams.split(' ')[0]\\n away_team = teams.split(' ')[3]\\n else:\\n teams = self.remove_long_names(game_header[1]).replace('\\\\n', '')\\n home_team = teams.split(' ')[0]\\n away_team = teams.split(' ')[2]\\n \\n date_string = game_header[0].split(' ')\\n date_string_find = [date.lower() for date in date_string]\\n \\n venue = date_string[date_string_find.index('at') + 1]\\n \\n #get round\\n round_num = None\\n \\n try:\\n date_string_find.remove('')\\n except:\\n pass\\n \\n try:\\n round_num = int(date_string[date_string_find.index('round') + 1])\\n except:\\n try:\\n round_num = date_string_find[date_string_find.index('final') - 1] + ' final'\\n except:\\n round_num = date_string_find[date_string_find.index('semi-final')]\\n \\n date = date_string[-3:]\\n date = ' '.join(date) \\n date = parser.parse(date)\\n \\n #get attendance\\n attend = soup.find_all(text=re.compile('Attendance'))\\n attendance = 0\\n \\n if (len(attend) > 3):\\n attendance = int(attend[1].split(' ')[-1])\\n \\n #get stats \\n away_stats = {}\\n home_stats = {}\\n \\n for stat in stats:\\n stat_row = soup.find_all('td', text=stat)[0].find_parent('tr')\\n elements = stat_row.find_all('td')\\n \\n if elements[0].text == '-':\\n home_stats[stat] = None\\n else:\\n home_stats[stat] = elements[0].text\\n \\n if elements[0].text == '-':\\n away_stats[stat] = None\\n else:\\n away_stats[stat] = elements[2].text\\n \\n return Game(game_id, home_team, away_team, venue, round_num, date, attendance, home_stats, away_stats)\",\n \"def build_home_advantage(matches):\\n D = {}\\n df_goal = matches[[\\\"season\\\", \\\"home_team_goal\\\", \\\"away_team_goal\\\"]]\\n\\n for i in range(len(df_goal)):\\n key_season = df_goal.iloc[i].season\\n if key_season not in D:\\n D[key_season] = [\\n 1,\\n df_goal.iloc[i].home_team_goal,\\n df_goal.iloc[i].away_team_goal,\\n ]\\n else:\\n D[key_season][0] += 1\\n D[key_season][1] += df_goal.iloc[i].home_team_goal\\n D[key_season][2] += df_goal.iloc[i].away_team_goal\\n\\n for key in D:\\n D[key][1] /= D[key][0]\\n D[key][2] /= D[key][0]\\n\\n df_goal_info = pd.DataFrame(D)\\n column_headers = list(df_goal_info.columns.values)\\n\\n fig, ax = plt.subplots(nrows=1, ncols=1)\\n fig.set_size_inches(w=7, h=4)\\n plt.plot(column_headers, df_goal_info.iloc[1], label=\\\"avg_home_goal\\\")\\n plt.plot(column_headers, df_goal_info.iloc[2], label=\\\"avg_away_goal\\\")\\n pl.xticks(rotation=270)\\n plt.xlabel(\\\"Season\\\")\\n plt.ylabel(\\\"Average Goal\\\")\\n plt.legend()\\n plt.show()\",\n \"def find_new_contests(sport):\\n\\n # def get_pst_from_timestamp(timestamp_str):\\n # timestamp = float(re.findall(\\\"[^\\\\d]*(\\\\d+)[^\\\\d]*\\\", timestamp_str)[0])\\n # return datetime.datetime.fromtimestamp(\\n # timestamp / 1000, timezone(\\\"America/Los_Angeles\\\")\\n # )\\n\\n url = f\\\"https://www.draftkings.com/lobby/getcontests?sport={sport}\\\"\\n\\n # response = requests.get(url, headers=HEADERS, cookies=COOKIES).json()\\n response_contests = get_contests(url)\\n\\n # create list of Contest objects\\n contests = [Contest(c) for c in response_contests]\\n # contests = [\\n # get_largest_contest(response[\\\"Contests\\\"], 3),\\n # get_largest_contest(response[\\\"Contests\\\"], 0.25),\\n # get_largest_contest(response[\\\"Contests\\\"], 27),\\n # ] + get_contests_by_entries(response[\\\"Contests\\\"], 3, 50000)\\n target_contests = []\\n entry_fees = []\\n if sport == \\\"NFL\\\":\\n entry_fees = [5, 10, 25, 50]\\n else:\\n entry_fees = [10, 25]\\n\\n for entry_fee in entry_fees:\\n largest_contest = get_largest_contest(contests, entry_fee=entry_fee)\\n # check if largest_contest is None\\n if largest_contest is not None:\\n logger.debug(\\\"Appending contest %s\\\", largest_contest)\\n target_contests.append(largest_contest)\\n\\n for contest in target_contests:\\n date_time = contest.start_dt\\n # make naive datetime aware based on django settings\\n aware_datetime = make_aware(date_time)\\n dkcontest, created = DKContest.objects.update_or_create(\\n dk_id=contest.id,\\n defaults={\\n \\\"date\\\": aware_datetime.date(),\\n \\\"datetime\\\": aware_datetime,\\n \\\"sport\\\": sport,\\n \\\"name\\\": contest.name,\\n \\\"draft_group_id\\\": contest.draft_group,\\n \\\"total_prizes\\\": contest.total_prizes,\\n \\\"entries\\\": contest.entries,\\n \\\"entry_fee\\\": contest.entry_fee,\\n },\\n )\\n if created:\\n logger.info(\\\"Creating DKContest %s\\\", dkcontest)\",\n \"def get_league_goalie_boxcars(league, seasons):\\n\\n if len(set(seasons))==1:\\n scraped_season_list = str(seasons)\\n elif len(set(seasons))>2:\\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[:-1]).replace(\\\"'\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\") + \\\", and \\\" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[-1])\\n else:\\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[:-1]).replace(\\\"'\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\") + \\\" and \\\" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[-1])\\n \\n \\n global hidden_patrick\\n hidden_patrick = 0\\n global error\\n error = 0\\n \\n output = pd.DataFrame()\\n \\n if type(seasons) == str:\\n single = getgoalies(league, seasons)\\n output = output.append(single)\\n print(\\\"Scraping \\\" + league + \\\" data is complete. You scraped goalie data from \\\" + seasons + \\\".\\\")\\n return(output)\\n \\n elif ((type(seasons) == tuple) or (type(seasons) == list)):\\n \\n for i in range(0, len(seasons)):\\n try:\\n single = getgoalies(league, seasons[i])\\n output = output.append(single)\\n except KeyboardInterrupt as e:\\n hidden_patrick = 4\\n error = e\\n return output\\n except (ConnectionError,\\n HTTPError,\\n ReadTimeout,\\n ConnectTimeout) as e:\\n hidden_patrick = 5\\n error = e\\n return output\\n \\n print(\\\"Scraping \\\" + league + \\\" data is complete. You scraped goalie data from \\\" + scraped_season_list + \\\".\\\") \\n return(output)\",\n \"def season_game_logs(team, year):\\n\\n # Check year value\\n if year > 2019 or year < 1950:\\n raise ValueError('Year Value Incorrect')\\n\\n # Rename teams that moved\\n team = scrape_utils.rename_team(team, year)\\n\\n # Get HTML content\\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n season_stats = soup.find(id='tgl_basic')\\n games = season_stats.find('tbody')\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # To find opponent statistics\\n opponent = re.compile('^opp_.*$')\\n\\n # Loop through every game in a team's season\\n for game in games.find_all('tr', {'class': None}):\\n\\n curr_team = {'team': team}\\n opp_team = {}\\n\\n # Loop through each stat\\n for stat in game.find_all('td'):\\n\\n stat_name = stat['data-stat']\\n\\n # These are opponent stats\\n if re.match(opponent, stat_name):\\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\\n else:\\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\\n\\n # Remove unnecessary information\\n del curr_team['game_season']\\n del curr_team['x']\\n\\n # Rename relocated teams\\n curr_team['team'] = scrape_utils.rename_team(team)\\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\\n\\n # Use the same ID as basketball reference\\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \\\"%Y-%m-%d\\\"),\\n 'season': year,\\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\\n '_id': game.find('a')['href'][-17:-5]}\\n\\n # Place the teams in the correct spot depending on who is the home team\\n if curr_team.pop('game_location') == 0:\\n result['home'] = curr_team\\n result['away'] = opp_team\\n else:\\n result['home'] = opp_team\\n result['away'] = curr_team\\n\\n # Insert into database\\n m.insert('game_log', result)\",\n \"def Champs(self, summoner):\\n encrypted_summoner_id = summoner.get('encrypted_summoner_id', '')\\n region = summoner.get('region', DEFAULT_REGION)\\n r = self._rito.ListChampionMasteries(region, encrypted_summoner_id)\\n if r:\\n logging.info('Got champ mastery data for %s/%s [%s]', region,\\n encrypted_summoner_id, summoner['summoner'])\\n # Calculate total number of chests received\\n total_chests = sum(1 for x in r.champion_masteries if x.chest_granted)\\n\\n top_champs = []\\n for champ in r.champion_masteries[:3]:\\n top_champs.append(self._game.champion_id_to_name[str(\\n champ.champion_id)])\\n top_champ_lvl = r.champion_masteries[0].champion_level\\n\\n chest_verb = ''\\n chest_verb_dict = {\\n (0, 2): 'receiving',\\n (2, 4): 'collecting',\\n (4, 8): 'earning',\\n (8, 16): 'amassing',\\n (16, 32): 'hoarding'\\n }\\n for range_spec, verb in chest_verb_dict.items():\\n if total_chests in range(*range_spec):\\n chest_verb = verb\\n break\\n\\n if chest_verb:\\n chest_str = '%s %s' % (chest_verb,\\n inflect_lib.Plural(total_chests, 'chest'))\\n else:\\n chest_str = 'with a boatload of chests (%d)' % total_chests\\n\\n return (u'{0} is a L{1} {2[0]} main, but sometimes likes to play {2[1]} '\\n 'and {2[2]}, {3} this season.').format(summoner['summoner'],\\n top_champ_lvl, top_champs,\\n chest_str)\",\n \"def generate_matching_courses(self,goal):\\n\\n searchstring = self.preprocess(goal.goal)\\n wordlist = nltk.word_tokenize(searchstring)\\n relevant_words = []\\n mystopwords = stopwords.words(\\\"english\\\") + stopwords.words(\\\"german\\\")\\n for word in wordlist:\\n if word not in mystopwords:\\n relevant_words.append(word)\\n # TODO: Activate in production\\n # TODO: For testing find workaround to make local courses available for local test systems\\n #user_origin = goal.user.origin\\n # TODO: The following two lines have to be exchanged to filter courses according to origin\\n #courses = models.Course.objects.filter(origin=user_origin)\\n courses = models.Course.objects.all()\\n\\n matches = {}\\n for course in courses:\\n if course == None:\\n print(\\\"Course is None\\\")\\n if course.TF_IDF_scores == {}:\\n continue\\n score = 0.0\\n for word in relevant_words:\\n if word in course.TF_IDF_scores:\\n if word in course.TF_IDF_scores:\\n score += course.TF_IDF_scores[word]\\n if score > 0.0:\\n if score in matches.keys():\\n matches[score].append(course)\\n else:\\n matches[score] = []\\n matches[score].append(course)\\n scores = list(matches.keys())\\n scores.sort()\\n\\n bestcourses = []\\n\\n i = 0\\n for score in scores:\\n for course in matches[score]:\\n bestcourses.append(course)\\n i += 1\\n if i >= COURSES_TO_DISPLAY:\\n break\\n\\n if len(bestcourses) == 0:\\n a = models.Activity.objects.get_or_create(\\n title=\\\"Keine passenden Lehrveranstaltungen\\\",\\n description=\\\"Aktuell gibt es zu Ihrem Interesse keine passenden Lehrveranstaltungen. \\\" \\\\\\n \\\"Siddata wird regelmäßig nach neuen passenden Kursen suchen und diese ggf. hier anzeigen. \\\",\\n type=\\\"todo\\\",\\n goal=goal,\\n image=\\\"idea.png\\\",\\n status=\\\"new\\\"\\n )[0]\\n a.save()\\n i = 0\\n for course in bestcourses:\\n\\n a = models.Activity.objects.get_or_create(\\n title=course.title,\\n description=course.description,\\n type=\\\"course\\\",\\n goal=goal,\\n image=\\\"%s.png\\\" %random.choice([\\\"world\\\",\\\"idea\\\",\\\"library\\\",\\\"cat\\\",\\\"brainbulb\\\",\\\"braincloud\\\",\\\"friends\\\"]),\\n course=course,\\n status=\\\"new\\\"\\n )[0]\\n a.save()\\n i += 1\\n if i == COURSE_MAX:\\n break\",\n \"def calc_winner(self):\\n pass\",\n \"def nth_iteration(Iterations, Moves_ahead, GA_iterations, n_samples,\\n current_gen_spectra, next_gen_conc, x_test,\\n conc_array_actual, spectra_array_actual, seed,\\n median_fitness_list, max_fitness_list,\\n iteration, mutation_rate_list, fitness_multiplier_list):\\n set_seed(seed)\\n mutation_rate, fitness_multiplier, best_move, best_move_turn, \\\\\\n max_fitness, surrogate_score, desired_1, current_gen_spectra_1, \\\\\\n best_conc_array, \\\\\\n dictionary_of_moves = MCTS(Iterations, Moves_ahead,\\n GA_iterations, current_gen_spectra,\\n next_gen_conc, x_test, conc_array_actual,\\n spectra_array_actual, seed, n_samples)\\n print('The best move has a fitness value of', max_fitness)\\n print('The best move occurs in', best_move_turn, 'turns.')\\n print()\\n print('The surrogate model has a score of:', surrogate_score)\\n print()\\n mutation_rate_list.append(mutation_rate)\\n fitness_multiplier_list.append(fitness_multiplier)\\n current_gen_spectra = current_gen_spectra.T\\n current_gen_spectra = MinMaxScaler().fit(current_gen_spectra). \\\\\\n transform(current_gen_spectra).T\\n next_gen_conc, median_fitness, max_fitness = perform_iteration(\\n current_gen_spectra, next_gen_conc, x_test, 20,\\n n_samples, mutation_rate, fitness_multiplier)\\n best_conc_array = \\\\\\n best_conc_array[np.argsort(best_conc_array[:, -1])][-1, :]\\n print(next_gen_conc)\\n return mutation_rate, fitness_multiplier, mutation_rate_list, \\\\\\n fitness_multiplier_list, best_move, best_move_turn, \\\\\\n max_fitness, surrogate_score, next_gen_conc, \\\\\\n best_conc_array, dictionary_of_moves\",\n \"def tournament(self):\\n pass\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def scrape_all_world_cup_goals():\\n def scrape_goals_year(year):\\n urls = scrape_world_cup_scoreboard(year)\\n goals = []\\n for url in urls:\\n goals.extend(scrape_fifa_goals(url, 'FIFA World Cup'))\\n return goals\\n\\n l = []\\n for year in sorted(world_cup_mapping.keys()):\\n l.extend(scrape_goals_year(year))\\n return l\",\n \"def get_smmry_data(soup, game_dict):\\n\\n # Get date and time data.\\n try:\\n date_soup = soup.find(\\\"div\\\", {\\\"class\\\": \\\"spielbericht_tipp_status\\\"})\\n league = date_soup.div.span.text\\n date_string = date_soup.div.text\\n date = re.search(r'\\\\d{2}.\\\\d{2}.\\\\d{2}', date_string).group(0)\\n time = re.search(r'\\\\d{2}:\\\\d{2}', date_string).group(0)\\n matchday = re.search(r'[|]\\\\d+', date_string).group(0)[1:]\\n\\n game_dict[\\\"league\\\"] = league\\n game_dict[\\\"fb_date\\\"] = date\\n game_dict[\\\"fb_time\\\"] = time\\n game_dict[\\\"matchday\\\"] = matchday\\n except AttributeError:\\n pass\\n\\n # Get game result.\\n try:\\n result = soup.find(\\\"div\\\", {\\\"class\\\": \\\"stand\\\"}).text\\n game_dict[\\\"result\\\"] = result\\n except AttributeError:\\n pass\\n\\n # Try to get the referee name.\\n try:\\n referee = soup.find(\\\"span\\\", {\\\"class\\\": \\\"schiri_link\\\"}).text\\n game_dict[\\\"referee\\\"] = referee\\n except AttributeError:\\n pass\\n\\n # Get team, club name and repective url by team.\\n try:\\n smmry_soup = soup.find(\\n \\\"div\\\", {\\\"class\\\": \\\"spielbericht_ergebnis_wrapper\\\"})\\n club_title = smmry_soup.find_all(\\\"img\\\")\\n team_title = smmry_soup.findAll(\\\"div\\\", {\\\"class\\\": \\\"teaminfo\\\"})\\n\\n # Loop through teams.\\n for j, team in enumerate([\\\"home_\\\", \\\"away_\\\"]):\\n game_dict[team + \\\"team\\\"] = team_title[j].a[\\\"title\\\"]\\n game_dict[team + \\\"team_url\\\"] = team_title[j].a[\\\"href\\\"]\\n game_dict[team + \\\"club\\\"] = club_title[j][\\\"title\\\"]\\n except (AttributeError, TypeError):\\n pass\\n\\n return game_dict\",\n \"async def get_league_players(season): \\n if season == '2021-22':\\n get_epl = 2021\\n if season == '2020-21':\\n get_epl = 2020\\n if season == '2019-20':\\n get_epl = 2019\\n async with aiohttp.ClientSession() as session:\\n understat = Understat(session)\\n player = await understat.get_league_players(\\\"epl\\\", get_epl)\\n # print(json.dumps(player))\\n return player\",\n \"def main(simulations, userschoice):\\n # The teams data are obtained from FIFA statistics\\n # Team Name, Attack, Defence\\n quarters = ['quarter1', 'quarter2', 'quarter3', 'quarter4', 'quarter5', 'quarter6', 'quarter7', 'quarter8']\\n semifinalists = ['semifinalist1', 'semifinalist2', 'semifinalist3', 'semifinalist4']\\n finalists = ['finalist1', 'finalist2']\\n\\n df = pd.read_csv('FifaRankings.csv', index_col=\\\"Ranking\\\")\\n a_set = set()\\n while True:\\n a_set.add(randint(42, 85))\\n if len(a_set) == 32:\\n break\\n lst1 = sorted(list(a_set), reverse=True)\\n\\n a_set = set()\\n while True:\\n a_set.add(randint(38, 83))\\n if len(a_set) == 32:\\n break\\n lst2 = sorted(list(a_set), reverse=True)\\n print(\\\"\\\\n\\\")\\n df['Attack'] = lst1\\n df['Defence'] = lst2\\n a = list(df[\\\"Team\\\"])\\n\\n avgScored = 0\\n avgConceded = 0\\n avgScored = df['Attack'].sum()\\n avgConceded = df['Defence'].sum()\\n\\n avgScored = avgScored / len(df)\\n avgConceded = avgConceded / len(df)\\n print(\\\"\\\\n\\\")\\n avgattack = []\\n avgdefense = []\\n\\n for i in range(1, 33):\\n if df['Matches Played'][i] != 0:\\n win_rate = (df['WorldCup Wins'][i] / df['Matches Played'][i])\\n else:\\n win_rate = 0\\n avgattack.append((df['Attack'][i] / avgScored) + win_rate)\\n avgdefense.append((df['Defence'][i] / avgConceded) + win_rate)\\n\\n df['Avg Attack'] = avgattack\\n df['Avg Defense'] = avgdefense\\n\\n\\n teamstats=[]\\n for i in range(1,len(df)+1):\\n teaminfo=[]\\n teaminfo = (df[\\\"Team\\\"][i], df['Avg Attack'][i], df['Avg Defense'][i])\\n teaminfo=list(teaminfo)\\n teamstats.append(teaminfo)\\n\\n germany = WorldCupTeam(\\\"GERMANY\\\", teamstats)\\n brazil = WorldCupTeam(\\\"BRAZIL\\\", teamstats)\\n belgium = WorldCupTeam(\\\"BELGIUM\\\", teamstats)\\n portugal = WorldCupTeam(\\\"PORTUGAL\\\", teamstats)\\n argentina = WorldCupTeam(\\\"ARGENTINA\\\", teamstats)\\n france = WorldCupTeam(\\\"FRANCE\\\", teamstats)\\n switzerland = WorldCupTeam(\\\"SWITZERLAND\\\", teamstats)\\n spain = WorldCupTeam(\\\"SPAIN\\\", teamstats)\\n russia = WorldCupTeam(\\\"RUSSIA\\\", teamstats)\\n japan = WorldCupTeam(\\\"JAPAN\\\", teamstats)\\n polland=WorldCupTeam(\\\"POLLAND\\\", teamstats)\\n korea_republic = WorldCupTeam(\\\"KOREA REPUBLIC\\\", teamstats)\\n england = WorldCupTeam(\\\"ENGLAND\\\", teamstats)\\n denmark= WorldCupTeam(\\\"DENMARK\\\", teamstats)\\n peru= WorldCupTeam(\\\"PERU\\\", teamstats)\\n tunisia=WorldCupTeam(\\\"TUNISIA\\\", teamstats)\\n mexico = WorldCupTeam(\\\"MEXICO\\\", teamstats)\\n colombia = WorldCupTeam(\\\"COLOMBIA\\\", teamstats)\\n uruguay = WorldCupTeam(\\\"URUGUAY\\\", teamstats)\\n croatia = WorldCupTeam(\\\"CROATIA\\\", teamstats)\\n australia = WorldCupTeam(\\\"AUSTRALIA\\\", teamstats)\\n iceland=WorldCupTeam(\\\"ICELAND\\\", teamstats)\\n sweden=WorldCupTeam(\\\"SWEDEN\\\", teamstats)\\n costa_rica = WorldCupTeam(\\\"COSTA RICA\\\", teamstats)\\n senegal=WorldCupTeam(\\\"SENEGAL\\\", teamstats)\\n serbia=WorldCupTeam(\\\"SERBIA\\\", teamstats)\\n morrocco=WorldCupTeam(\\\"MORROCCO\\\", teamstats)\\n egypt=WorldCupTeam(\\\"EGYPT\\\", teamstats)\\n nigeria = WorldCupTeam(\\\"NIGERIA\\\", teamstats)\\n saudi_arabia=WorldCupTeam(\\\"SAUDI ARABIA\\\", teamstats)\\n panama=WorldCupTeam(\\\"PANAMA\\\", teamstats)\\n iran = WorldCupTeam(\\\"IRAN\\\", teamstats)\\n\\n\\n #INPUT USERS CHOICE FOR FIXED CHOICE\\n choices= [\\\"random\\\", \\\"Random\\\", \\\"RANDOM\\\"]\\n choicess = [\\\"fixed\\\", \\\"Fixed\\\", \\\"FIXED\\\"]\\n if userschoice in choices:\\n countries = [germany, brazil, belgium, portugal, argentina, france, switzerland, spain, russia, japan, polland,\\n korea_republic, england, denmark, peru, tunisia, mexico, colombia, uruguay, croatia, australia,\\n iceland, sweden, costa_rica, senegal, serbia, morrocco, egypt, nigeria, saudi_arabia, panama, iran]\\n finalresults = {}\\n\\n GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH = ([] for i in range(8))\\n\\n Groups = [GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH]\\n for i in Groups:\\n for j in range(4):\\n teamname = choice(countries)\\n i.append(teamname)\\n countries.remove(teamname)\\n\\n print(\\\"DRAWS for the WorldCup 2018 are:\\\")\\n print(\\\"\\\\n\\\")\\n for i in range(simulations):\\n # Play first stage\\n print(\\\"Result of\\\", i + 1, \\\"simulations\\\")\\n print(\\\"--------------------------------------------\\\")\\n print(\\\"This is GROUP STAGE\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP A RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupA = TeamPool(Groups[0])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP B RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupB = TeamPool(Groups[1])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP C RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupC = TeamPool(Groups[2])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP D RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupD = TeamPool(Groups[3])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP E RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupE = TeamPool(Groups[4])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP F RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupF = TeamPool(Groups[5])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP G RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupG = TeamPool(Groups[6])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP H RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupH = TeamPool(Groups[7])\\n\\n # Play second stage\\n print(\\\"\\\\n\\\")\\n print(\\\"ROUND OF 16\\\")\\n print(\\\"\\\\n\\\")\\n r16 = [groupA.first_qualified, groupA.second_qualified, groupB.first_qualified, groupB.second_qualified,\\n groupC.first_qualified, groupC.second_qualified, groupD.first_qualified, groupD.second_qualified,\\n groupE.first_qualified, groupE.second_qualified, groupF.first_qualified, groupF.second_qualified,\\n groupG.first_qualified, groupG.second_qualified, groupH.first_qualified, groupH.second_qualified]\\n\\n\\n GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW =([] for i in range(8))\\n\\n round16groups = [GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW]\\n\\n for k in round16groups:\\n for j in range(2):\\n teamname = choice(r16)\\n k.append(teamname)\\n r16.remove(teamname)\\n\\n for i in range(8):\\n quarters[i]=WorldCupMatch(round16groups[i][0], round16groups[i][1], False).winner\\n\\n # Quarters\\n print(\\\"\\\\n\\\")\\n print(\\\"QUARTER - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n quarterfinal = [quarters[0], quarters[1], quarters[2], quarters[3], quarters[4], quarters[5], quarters[6],\\n quarters[7]]\\n GroupA1, GroupB1, GroupC1, GroupD1 = ([] for i in range(4))\\n\\n quarterfinalgroups = [GroupA1, GroupB1, GroupC1, GroupD1]\\n\\n i = 0\\n for i in quarterfinalgroups:\\n for j in range(2):\\n teamname = choice(quarterfinal)\\n i.append(teamname)\\n quarterfinal.remove(teamname)\\n\\n for i in range(4):\\n semifinalists[i] = WorldCupMatch(quarterfinalgroups[i][0], quarterfinalgroups[i][1], False).winner\\n\\n # Semifinals\\n print(\\\"\\\\n\\\")\\n print(\\\"SEMI - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n\\n semifinal = [semifinalists[0], semifinalists[1], semifinalists[2], semifinalists[3]]\\n GroupP1, GroupQ1 = ([] for i in range(2))\\n semifinalgroups = [GroupP1, GroupQ1]\\n\\n i = 0\\n for i in semifinalgroups:\\n for j in range(2):\\n teamname = choice(semifinal)\\n i.append(teamname)\\n semifinal.remove(teamname)\\n\\n for i in range(2):\\n finalists[i] = WorldCupMatch(semifinalgroups[i][0], semifinalgroups[i][1], False).winner\\n # Finals\\n print(\\\"\\\\n\\\")\\n print(\\\"WORLD-CUP FINAL\\\")\\n print(\\\"\\\\n\\\")\\n winner = WorldCupMatch(finalists[0], finalists[1], False).winner\\n print(\\\"\\\\n\\\")\\n\\n if winner.name in finalresults:\\n finalresults[winner.name] += 1\\n else:\\n finalresults[winner.name] = 1\\n\\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\\n print(key + \\\": \\\" + str(finalresults[key] / simulations))\\n ro=(finalresults[key] / simulations) * 100\\n print(str(ro) + \\\"% chance of winning the worldcup\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"\\\\n\\\")\\n\\n\\n elif userschoice in choicess:\\n\\n print(\\\"\\\\n\\\")\\n finalresults = {}\\n groupA1 = [russia , saudi_arabia,egypt, uruguay]\\n groupB1 = [portugal, spain, morrocco, iran]\\n groupC1 = [france, australia, peru, denmark]\\n groupD1 = [argentina, iceland, croatia, nigeria]\\n groupE1 = [brazil, switzerland, costa_rica, serbia]\\n groupF1 = [germany, mexico, sweden, korea_republic]\\n groupG1 = [belgium, panama, tunisia, england]\\n groupH1 = [polland, senegal, colombia, japan]\\n print(\\\"\\\\n\\\")\\n for i in range(simulations):\\n # Play first stage\\n print(\\\"Result of\\\", i+1 ,\\\"simulations\\\")\\n print(\\\"--------------------------------------------\\\")\\n print(\\\"This is GROUP STAGE\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP A RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupA = TeamPool(groupA1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP B RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupB = TeamPool(groupB1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP C RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupC = TeamPool(groupC1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP D RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupD = TeamPool(groupD1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP E RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupE = TeamPool(groupE1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP F RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupF = TeamPool(groupF1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP G RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupG = TeamPool(groupG1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP H RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupH = TeamPool(groupH1)\\n print(\\\"Qualifies teams:\\\", groupH.first_qualified.name)\\n\\n # Play second stage\\n print(\\\"\\\\n\\\")\\n print(\\\"ROUND OF 16\\\")\\n print(\\\"\\\\n\\\")\\n\\n quarter1 = WorldCupMatch(groupA.first_qualified, groupA.second_qualified, False).winner\\n quarter2 = WorldCupMatch(groupB.first_qualified, groupB.second_qualified, False).winner\\n quarter3 = WorldCupMatch(groupC.first_qualified, groupC.second_qualified, False).winner\\n quarter4 = WorldCupMatch(groupD.first_qualified, groupD.second_qualified, False).winner\\n quarter5 = WorldCupMatch(groupE.first_qualified, groupE.second_qualified, False).winner\\n quarter6 = WorldCupMatch(groupF.first_qualified, groupF.second_qualified, False).winner\\n quarter7 = WorldCupMatch(groupG.first_qualified, groupG.second_qualified, False).winner\\n quarter8 = WorldCupMatch(groupH.first_qualified, groupH.second_qualified, False).winner\\n\\n # Quarters\\n print(\\\"\\\\n\\\")\\n print(\\\"QUARTER - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n\\n semifinalist1 = WorldCupMatch(quarter1, quarter2, False).winner\\n semifinalist2 = WorldCupMatch(quarter3, quarter4, False).winner\\n semifinalist3 = WorldCupMatch(quarter5, quarter6, False).winner\\n semifinalist4 = WorldCupMatch( quarter7, quarter8, False).winner\\n\\n # Semifinals\\n print(\\\"\\\\n\\\")\\n print(\\\"SEMI - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n finalist1 = WorldCupMatch(semifinalist1, semifinalist2, False).winner\\n finalist2 = WorldCupMatch(semifinalist3, semifinalist4, False).winner\\n\\n # Final\\n print(\\\"\\\\n\\\")\\n print(\\\"WORLD-CUP FINAL\\\")\\n print(\\\"\\\\n\\\")\\n winner = WorldCupMatch(finalist1, finalist2, False).winner\\n print(\\\"\\\\n\\\")\\n\\n\\n if winner.name in finalresults:\\n finalresults[winner.name] += 1\\n else:\\n finalresults[winner.name] = 1\\n\\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\\n print(key + \\\": \\\" + str(finalresults[key] / simulations))\\n rou = (finalresults[key] / simulations) * 100\\n print(str(rou) + \\\"% chance of winning the worldcup\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"\\\\n\\\")\\n else:\\n print(\\\"Please enter correct input and try again\\\")\\n pass\",\n \"def scrape_all_world_cup_games():\\n\\n def scrape_scores_year(year):\\n urls = scrape_world_cup_scoreboard(year)\\n scores = [scrape_fifa_game(url, 'FIFA World Cup') for url in urls]\\n return scores\\n\\n l = []\\n for year in sorted(world_cup_mapping.keys()):\\n l.extend(scrape_scores_year(year))\\n return l\",\n \"def fetch_next_match() -> Optional[MatchDict]:\\n future_matches = Match.objects.filter(start_date_time__gt=timezone.now())\\n\\n if not any(future_matches):\\n return None\\n\\n next_match = min(future_matches, key=lambda match: match.start_date_time)\\n\\n return {\\n \\\"round_number\\\": next_match.round_number,\\n \\\"season\\\": next_match.start_date_time.year,\\n }\",\n \"def run_past_matches(self):\\n for year in self.store.years:\\n # print(year)\\n for event in self.store.events[year]:\\n for match in self.store.matches[year][event.key]:\\n red_alliance = match.red_alliance\\n blue_alliance = match.blue_alliance\\n\\n for team in red_alliance + blue_alliance:\\n if team not in self.scores.keys():\\n self.scores[team] = self.default_score\\n\\n prediction = self.predict(red_alliance, blue_alliance, key=match.key)\\n self.update_score(red_alliance, blue_alliance, prediction,\\n match.red_score, match.blue_score)\",\n \"def _get_challenge_parts(self):\\n query_result = db.session.query(ChallengePart). \\\\\\n filter(ChallengePart.season_id == self.SEASON.id). \\\\\\n order_by(ChallengePart.order.asc()). \\\\\\n all()\\n\\n result = {}\\n dist = 0\\n for item in query_result:\\n dist += item.distance\\n result[dist] = item.target\\n\\n return result\",\n \"def coast_to_circ_burn(mission):\\n circ_burn = mission.parameters[\\\"circ_burn\\\"]\\n\\n if circ_burn[\\\"burn_start_time\\\"] <= mission.ut():\\n mission.next()\",\n \"def swissPairings():\\n\\n # Ok This is where things get interesting, how in the world should i solve this problem\\n # A question to the udacity reviewer. Shouldn't standings be passed in to this function since weve already called it in tournament_test.testPairings\\n\\n #anyways\\n\\n nextRoundPlayers = []\\n standings = playerStandings()\\n \\n # since our players are ordered by wins, first place first and we have an even number of players,\\n # this seems like a no-brainer to just have every 2 tuples starting from the beginning to be the next match\\n # however this needs to to be implemented algorithmically\\n \\n #loop through our players and when we get to an even index, we get the previous two players and assign their ids and names to the next tuple \\n #in nextRoundPlayers\\n \\n i = 0\\n while i < len(standings):\\n if i % 2 == 0:\\n id1 = standings[i-1][0]\\n name1 = standings[i-1][1]\\n\\n id2 = standings[i-2][0]\\n name2 = standings[i-2][1]\\n\\n nextRoundPlayers.append((id1, name1, id2, name2))\\n\\n i += 1\\n \\n return nextRoundPlayers\",\n \"def covid(covidSet):\\n newcovidSet = set()\\n for x in covidSet:\\n player = playerList[x]\\n player.changeImage(1)\\n\\n #gotta check if anyone is close to this guy\\n for idx, y in enumerate(playerList):\\n if idx == x or idx in covidSet or idx in newcovidSet:\\n continue\\n if abs(player.rect.x - y.rect.x) > covidRange or abs(player.rect.y - y.rect.y) > covidRange:\\n continue #to reduce amount of astar checks required\\n else:\\n xx = changeCoord(player.rect.x)\\n xy = changeCoord(player.rect.y)\\n yx = changeCoord(y.rect.x)\\n yy = changeCoord(y.rect.y)\\n path = aStar.astar(maze, (xy, xx), (yy, yx))\\n if path != None and len(path) <= mazeRange:\\n if randChance(covidChance):\\n newcovidSet.add(idx)\\n print(\\\"Person\\\", x, \\\"infected\\\", \\\"Person\\\", idx)\\n edges[\\\"edges\\\"].append({\\\"from\\\": x, \\\"to\\\": idx})\\n else:\\n y.changeImage(2)\\n # uncomment to see astar algo (that was too far apart)\\n #else:\\n # if path == None:\\n # print(\\\"none\\\")\\n # else:\\n # print(len(path), path)\\n return set.union(covidSet, newcovidSet)\",\n \"def reduce_games(game):#game=player_games[0]\\n res = []\\n res.append(('id', game['id']) )\\n res.append(('annulled', game['annulled']))\\n res.append(('black', int(game['black'])))\\n res.append(('white', int(game['white'])))\\n res.append(('white_lost', int(game['white_lost']) ))\\n res.append(('black_lost', int(game['black_lost']) ))\\n res.append(('outcome', game['outcome']))\\n res.append(('handicap', game['handicap']))\\n res.append(('komi', game['komi']))\\n res.append(('ranked', game['ranked']))\\n res.append(('width', game['width']))\\n res.append(('height', game['height']))\\n res.append(('rules', game['rules']))\\n res.append(('started', game['started']))\\n res.append(('ended', game['ended']))\\n players = game['historical_ratings'].keys()\\n res.append(('black_rating', game['historical_ratings']['black']['ratings']['overall']['rating'] if 'black' in players else None))\\n res.append(('white_rating', game['historical_ratings']['white']['ratings']['overall']['rating'] if 'white' in players else None))\\n res.append(('black_deviation', game['historical_ratings']['black']['ratings']['overall']['deviation'] if 'black' in players else None))\\n res.append(('white_deviation', game['historical_ratings']['white']['ratings']['overall']['deviation'] if 'white' in players else None))\\n res.append(('black_volatility', game['historical_ratings']['black']['ratings']['overall']['volatility'] if 'black' in players else None))\\n res.append(('white_volatility', game['historical_ratings']['white']['ratings']['overall']['volatility'] if 'white' in players else None))\\n res.append(('black_ranking', game['historical_ratings']['black']['ranking'] if 'black' in players else None))\\n res.append(('white_ranking', game['historical_ratings']['white']['ranking'] if 'white' in players else None))\\n res.append(('tournament', not game['tournament'] is None))\\n return res\",\n \"def favorite() -> Tuple[Union[TeamDict, bool], Union[TeamDict, bool], Union[float, bool], Union[int, bool]]:\\n if get_time() > 87*60: #if this happens there's no need to look into this match\\n time = fav = balan = other = False\\n print('Too late into this match. Skipping.')\\n return (fav, other, balan, time)\\n\\n team1, team2, time = collect_info()\\n def calc_criteria(team_info, time=time):\\n team_info['appm'] = team_info['atp'] / time*60\\n team_info['cg'] = team_info['ca'] + team_info['cl'] + team_info['esc']\\n team_info['rend'] = team_info['appm'] * team_info['pb']\\n\\n print(\\\"Calculating match metrics\\\")\\n calc_criteria(team1)\\n calc_criteria(team2)\\n\\n timer = 10\\n while timer:\\n try:\\n group_tabs = driver.find_elements_by_class_name(\\\"sip-MarketGroup \\\")\\n both_odds = find_item('resultado final', group_tabs)\\n both_odds = both_odds.find_elements_by_class_name('srb-ParticipantStackedBorderless_Odds')\\n timer = 0\\n except AttributeError: #when \\\"suspenso\\\" appears this error is raised\\n timer -= 1\\n sleep(1)\\n \\n try:\\n assert len(both_odds) == 3\\n odds = [float(both_odds[0].text), float(both_odds[2].text)] #[0] team1\\n balan = max(odds)/min(odds)\\n fav = team1 if max(odds) == odds[1] else team2 #sets who is favorite, i.e., who's got the minor odds of winning\\n other = team2 if max(odds) == odds[1] else team1\\n except (TypeError, ValueError): \\n fav, other = team1, team2\\n balan = 0\\n fav['balan'] = other['balan'] = balan\\n\\n return (fav, other, balan, time)\",\n \"def compute_social_happiness(self):\\r\\n citizens_happiness = []\\r\\n for citizen in self.citizens:\\r\\n citizens_happiness.append(citizen.compute_happiness_level(self.projects_approved))\\r\\n \\r\\n return stats.describe(citizens_happiness)[2]\",\n \"def retrieve_exact_commutes(self):\\n for destination in self.tenants:\\n try:\\n results = retrieve_exact_commute_rent_algorithm(self.homes[:NUMBER_OF_EXACT_COMMUTES_COMPUTED],\\n destination,\\n destination.commute_type,\\n with_traffic=destination.traffic_option)\\n\\n # Store the results to the homes\\n for i in range(len(results)):\\n duration_seconds = results[i][0][0]\\n distance_meters = results[i][0][1]\\n if duration_seconds is not None and distance_meters is not None:\\n self.homes[i].exact_commute_times[destination] = int(duration_seconds / 60)\\n\\n except Distance_Matrix_Exception as e:\\n print(\\\"Caught: \\\" + e.__class__.__name__)\",\n \"def tournament():\\n return min(sample(population, sample_size)).chromosome[:]\",\n \"def metcon_leaderboards(cycle):\\n for exercise in exercises['metcon']:\\n f = f'{DOWNLOADS_DIR}\\\\\\\\{cycle}_{clean_name(exercise)}.xlsx'\\n if os.path.isfile(f):\\n source = pd.read_excel(f)\\n\\n # reduce to useful columns\\n metcon = source[['Athlete', 'Result', 'Is As Prescribed', 'Is Rx Plus']][\\n ~source['Athlete'].isin(coaches)]\\n\\n # Get rid of lowest scores for people that may have tested more than once and sort\\n if clean_name(exercise) == 'CFCCindy':\\n metcon['Result'] = metcon['Result'].apply(lambda x: x.replace(' + ', '.'))\\n metcon['Result'] = metcon['Result'].apply(pd.to_numeric)\\n metcon = metcon.sort_values('Result', ascending=False)\\n metcon['Result'] = metcon['Result'].astype(str)\\n metcon['Result'] = metcon['Result'].apply(lambda x: x.replace('.', ' + '))\\n else:\\n metcon['Result'] = pd.to_datetime(metcon['Result'], format='%M:%S').dt.time\\n metcon = metcon.sort_values('Result', ascending=True) # because lower is better with time duh\\n\\n metcon['duplicated'] = metcon.duplicated('Athlete', keep='first')\\n metcon = metcon[~metcon['duplicated']]\\n\\n\\n # Read in gender data and apply\\n users = pd.read_excel(f'{DOWNLOADS_DIR}\\\\\\\\{cycle}_Users.xlsx')\\n\\n # Athlete name\\n users['Athlete Name'] = users['First Name'] + ' ' + users['Last Name']\\n\\n # Look up gender\\n metcon['Gender'] = metcon['Athlete'].map(users.set_index('Athlete Name')['Gender'])\\n\\n # Break out Rx and Rx+\\n metcon_rx = metcon[metcon['Is As Prescribed']]\\n metcon_rxp = metcon[metcon['Is Rx Plus']]\\n\\n # Split on gender\\n metcon_rx_female = metcon_rx[metcon_rx['Gender'] == 'Female']\\n metcon_rx_male = metcon_rx[metcon_rx['Gender'] == 'Male']\\n metcon_rxp_female = metcon_rxp[metcon_rxp['Gender'] == 'Female']\\n metcon_rxp_male = metcon_rxp[metcon_rxp['Gender'] == 'Male']\\n\\n # Write out\\n\\n metcon_dfs = [metcon_rx_female, metcon_rx_male, metcon_rxp_female, metcon_rxp_male]\\n\\n for df in metcon_dfs:\\n if len(df) > 0:\\n gender = df.Gender.unique()[0]\\n rx = 'rx' if df['Is As Prescribed'].unique()[0] == True else 'rxp'\\n df[['Athlete', 'Result']].to_csv(\\n f'{RESULTS_DIR}\\\\\\\\{cycle}_leaderboard_{clean_name(exercise)}_{gender}_{rx}.csv', index=False)\\n print(f'{exercise} results written to {RESULTS_DIR}')\\n else:\\n print(f'File does not exist for {exercise}.')\",\n \"def find_games(days_ahead=0):\\n headers = {\\n 'Host': 'stats.nba.com',\\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\\n 'Accept': 'application/json, text/plain, */*',\\n 'Accept-Language': 'en-US,en;q=0.5',\\n 'Referer': 'https://stats.nba.com/',\\n 'Accept-Encoding': 'gzip, deflate, br',\\n 'Connection': 'keep-alive',\\n 'x-nba-stats-origin': 'stats',\\n 'x-nba-stats-token': 'true'\\n }\\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\\n board.replace(id_to_abrv, inplace=True)\\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]\",\n \"def vote_of_citizens():\\n\\tglobal vote_first_candidate\\n\\tglobal vote_second_candidate\\n\\tglobal blank_vote\\n\\t\\n\\tfor i in range(NUMBER_OF_CITIZENS):\\n\\t\\tvote = random.randint(1,10)\\n\\n\\t\\tif(vote <= 3):\\n\\t\\t\\tvote_first_candidate+=1\\n\\t\\telif(vote > 3 and vote <= 6):\\n\\t\\t\\tvote_second_candidate+=1\\n\\t\\telse:\\n\\t\\t\\tblank_vote+=1\",\n \"def get_games(date):\\n scoreboard = nba_py.Scoreboard(month=date.month,\\n day=date.day,\\n year=date.year)\\n line_score = scoreboard.line_score()\\n game_header = scoreboard.game_header()\\n\\n games = []\\n current_game = {}\\n game_sequence = 0\\n game_sequence_counter = 0\\n\\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\\n for i, value in enumerate(line_score):\\n if (value[\\\"GAME_SEQUENCE\\\"] != game_sequence):\\n game_sequence += 1\\n\\n current_game[\\\"GAME_ID\\\"] = value[\\\"GAME_ID\\\"]\\n home_team_id = game_header[game_sequence - 1][\\\"HOME_TEAM_ID\\\"]\\n\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n \\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter += 1\\n elif game_sequence_counter == 1:\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n\\n current_game[\\\"GAME_STATUS_TEXT\\\"] = game_header[game_sequence - 1][\\\"GAME_STATUS_TEXT\\\"]\\n if not game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]:\\n current_game[\\\"BROADCASTER\\\"] = \\\"\\\"\\n else:\\n current_game[\\\"BROADCASTER\\\"] = game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]\\n\\n games.append(current_game)\\n\\n current_game = {}\\n\\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter -= 1\\n\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return (games, east_standings, west_standings)\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def apply_tournament_selection(individuals, tot_rounds: int):\\r\\n winner = random.choice(individuals)\\r\\n\\r\\n for i in range(0, tot_rounds-1):\\r\\n ind = random.choice(individuals)\\r\\n\\r\\n if ind.fitness.dominates(winner.fitness):\\r\\n winner = ind\\r\\n elif not winner.fitness.dominates(ind.fitness):\\r\\n if ind.fitness.crowding_dist < winner.fitness.crowding_dist:\\r\\n winner = ind\\r\\n\\r\\n return winner\",\n \"def get_match_features(match, matches):\\n\\n # Define variables\\n date = match.date\\n season = match.season\\n home_team = match.home_team_api_id\\n away_team = match.away_team_api_id\\n\\n matches_home_ashome = get_last_ashome_matches(matches, date, season, home_team, x=5)\\n # print(matches_home_ashome, flush=True)\\n matches_home_asaway = get_last_asaway_matches(matches, date, season, home_team, x=5)\\n matches_away_ashome = get_last_ashome_matches(matches, date, season, away_team, x=5)\\n matches_away_asaway = get_last_asaway_matches(matches, date, season, away_team, x=5)\\n # matches_home = pd.concat([matches_home_ashome, matches_home_asaway])\\n # matches_away = pd.concat([matches_away_ashome, matches_away_asaway])\\n\\n # Get last x matches of both teams against each other\\n competing_matches_same = get_last_competing_matches(matches, date, home_team, away_team, x=2)\\n competing_matches_diff = get_last_reverse_competing_matches(matches, date, home_team, away_team, x=2)\\n\\n # Define result data frame\\n result = pd.Series()\\n result['match_api_id'] = match.match_api_id\\n result['league_id'] = match.league_id\\n result['home_recent_goal_ashome'] = get_goals(matches_home_ashome)\\n result['away_recent_goal_asaway'] = get_goals(matches_away_asaway, isAway=True)\\n result['home_recent_lost_ashome'] = get_goals_lost(matches_home_ashome)\\n result['away_recent_lost_asaway'] = get_goals_lost(matches_away_asaway, isAway=True)\\n result['team_ova_diff'] = match.home_ova - match.away_ova\\n result['home_recent_score'] \\\\\\n = get_average_league_score(matches_home_ashome) + get_average_league_score(matches_home_asaway, isAway=True)\\n result['away_recent_score'] \\\\\\n = get_average_league_score(matches_away_ashome) + get_average_league_score(matches_away_asaway, isAway=True)\\n result['home_competing_same_goal'] = get_goals(competing_matches_same)\\n result['away_competing_same_goal'] = get_goals(competing_matches_same, isAway=True)\\n result['home_competing_diff_goal'] = get_goals(competing_matches_diff, isAway=True)\\n result['away_competing_diff_goal'] = get_goals(competing_matches_diff)\\n result['home_recent_shoton'] = get_shoton(matches_home_ashome) + 0.8 * get_shoton(matches_home_asaway, isAway=True)\\n result['away_recent_shoton'] = get_shoton(matches_away_asaway, isAway=True) + 0.8 * get_shoton(matches_away_ashome)\\n result['home_recent_shotoff'] = get_shotoff(matches_home_ashome) + 0.8 * get_shotoff(matches_home_asaway, isAway=True)\\n result['away_recent_shotoff'] = get_shotoff(matches_away_asaway, isAway=True) + 0.8 * get_shotoff(matches_away_ashome)\\n\\n # print(result)\\n return result\",\n \"def runThompson(self):\\n \\n #Init vars, N number of user sessions, d=number of ads\\n N = self.myDS.shape[0] \\n d = self.myDS.shape[1] \\n total_reward=0\\n self.opt_selected=[]\\n \\n #Declare vars to count to calculate upper bounds\\n number_of_rewards_1 = [0] * d\\n number_of_rewards_0 = [0] * d\\n \\n #Calcultate confidance bounds\\n for n in range(0,N):\\n ad=0\\n max_random=0\\n for i in range (0,d):\\n random_beta = random.betavariate(number_of_rewards_1[i]+1,number_of_rewards_0[i]+1)\\n if random_beta>max_random:\\n max_random=random_beta\\n ad = i\\n self.opt_selected.append(ad)\\n reward=self.myDS.values[n,ad]\\n if (reward==1):\\n number_of_rewards_1[ad]=number_of_rewards_1[ad]+1\\n else:\\n number_of_rewards_0[ad]=number_of_rewards_0[ad]+1 \\n total_reward=total_reward+reward\\n \\n return total_reward\",\n \"def _tournament(self,probs,n,size):\\n participants = np.random.choice(\\n self.n_agents,\\n size=size,\\n replace=False)\\n winners = np.argpartition(probs[participants], -n)[-n:]\\n return participants[winners]\",\n \"def career_talent(filtered_dict,WAR_batter_best, player_name='Nap Lajoie', year_proj = 2019):\\n \\n # Subsetting the dictionary for a particular player\\n filtered_dict_new = filtered_dict[player_name]\\n \\n # Converting it to a dataframe\\n df = pd.DataFrame(filtered_dict_new).sort_values(by=['yearID']).reset_index(drop = True)\\n \\n # Changing the player ID\\n df['playerID'] = df['playerID'].astype(str) + '_proj'\\n \\n # Putting a counter for the number of iteration (we can use this counter later to determine the runtime of each loop)\\n # j = 0\\n \\n # Creating an empty dataframe where we will be merging the rows\\n emp_df = pd.DataFrame()\\n \\n # Iterating over all the years where the selected player has played baseball\\n for idx, year in df['yearID'].iteritems():\\n #j+=1\\n \\n # subsetting the Normalized WAR Talent Dataset for the year in which we want to find the projection\\n batters_int = WAR_batter_best.loc[WAR_batter_best['yearID'] == year_proj]\\n \\n # creating a vector with all the WAR values in the projection year\\n yy = batters_int.loc[:,'scale_WAR'].sort_values().reset_index(drop = True)\\n \\n # Finding the length of the vector created above\\n n = len(yy)\\n \\n # Creating a zero vector with one more cell than the vector with all the scaled WAR values\\n ytilde = [0] * (n+1)\\n \\n # Changing the first value in ytilde (Extrapolation logic developed by Shen and Daniel)\\n ytilde[0] = yy[0] - 1/(yy[n-1] - yy[0])\\n \\n # Changing the last value in ytilde\\n ytilde[n] = yy[n-1] + 1/(yy[n-1] - yy[n-4])\\n \\n # Iterating over all the cells of ytilde and updating their values\\n # The updated ytilde is the new empirical distribution function of the WAR Talent\\n for i in range(1,n):\\n ytilde[i] = (yy[i]+yy[i-1])/2\\n\\n batters_int = pd.concat([batters_int,df.loc[df['yearID'] == year]]).reset_index(drop = True)\\n batters_int['pops'][batters_int.shape[0]-1] = batters_int['pops'][0]\\n batters_int = batters_int.sort_values(by = ['WAR_talent']).reset_index(drop = True)\\n batters_int['foo'] = map_pareto_vals_vec(batters_int['WAR_talent'], batters_int['pops'])\\n batters_int['foo'] = batters_int['foo'].reset_index(drop = True)\\n batters_int['adj_WAR'] = order_qempirical(batters_int['foo'], ytilde)\\n del batters_int['foo']\\n batters_int = batters_int.loc[batters_int['playerID'] == df['playerID'].unique()[0]]\\n emp_df = pd.concat([emp_df, batters_int])\\n emp_df['target_year'] = year_proj\\n \\n return emp_df\",\n \"def fold(bot, update):\\n user = update.message.from_user\\n name = user.first_name\\n chat_id = update.message.chat_id\\n bot.send_message(chat_id,\\n Strings.P_FOLDS % name,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n if chats[chat_id].player1.user == user:\\n winner = 2\\n elif chats[chat_id].player2.user == user:\\n winner = 1\\n else:\\n return C_R_F\\n # we already know the winner, but still need to process the whole match\\n result = chats[chat_id].manageResult(chats[chat_id].player1.card_played,\\n chats[chat_id].player2.card_played, winner)\\n bot.send_message(chat_id,\\n result,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n\\n if chats[chat_id].player1.lives == 0:\\n if chats[chat_id].player1.red_lives == 0:\\n bot.send_message(chat_id,\\n Strings.END,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n bot.send_message(chat_id,\\n Strings.WIN % chats[chat_id].player2.name,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n return ConversationHandler.END\\n else:\\n # wanna bet your own life?\\n red_life_bet(bot, chat_id, chats[chat_id].player1)\\n return Y_N\\n elif chats[chat_id].player2.lives == 0:\\n if chats[chat_id].player2.red_lives == 0:\\n bot.send_message(chat_id,\\n Strings.END,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n bot.send_message(chat_id,\\n Strings.WIN % chats[chat_id].player1.name,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n return ConversationHandler.END\\n else:\\n # wanna bet your own life\\n red_life_bet(bot, chat_id, chats[chat_id].player2)\\n return Y_N\\n else:\\n show_cards(bot, chat_id)\\n return CARD\",\n \"def nextStep(self, game: BJGameMove):\\n isFirstChoice = len(game.player.hand) == 2 and not game.splitting\\n hand = self.deriveHand(game.player)\\n\\n if self.game.splitting:\\n if not self.game.doneSplitHand:\\n splitHand = self.deriveHand(game.splitPlayer)\\n splitChoice = self.coach(splitHand, False)\\n return self.doCoach(splitChoice+5, game) # small trick: +5 indicates split choices\\n else:\\n choice = self.coach(hand, False)\\n return self.doCoach(choice, game)\\n else:\\n choice = self.coach(hand, isFirstChoice)\\n return self.doCoach(choice, game)\",\n \"def best_session_weekly(df):\\n df = convert_to_datetime(df)\\n today = datetime.date.today()\\n this_week_start = today - timedelta(days=7)\\n last_week_start = today - timedelta(days=14)\\n this_week_card_ids = []\\n lastweek_card_ids = []\\n for index, row in df.iterrows():\\n try:\\n if str(row['session_start'].date()) >= str(this_week_start):\\n this_week_card_ids.append(row['id'])\\n elif str(last_week_start) <= str(row['session_start'].date()) < str(this_week_start):\\n lastweek_card_ids.append(row['id'])\\n except IndexError:\\n this_week_card_ids = []\\n lastweek_card_ids = []\\n thisweek = df[df['id'].isin(this_week_card_ids)]\\n lastweek = df[df['id'].isin(lastweek_card_ids)]\\n thisweek_best_session = best_session_length(thisweek)\\n lastweek_best_session = best_session_length(lastweek)\\n\\n if thisweek_best_session > lastweek_best_session:\\n color_code = \\\"09B109\\\"\\n arrow = \\\"\\\\u2191\\\"\\n elif thisweek_best_session < lastweek_best_session:\\n color_code = \\\"CE2929\\\"\\n arrow = \\\"\\\\u2193\\\"\\n else:\\n color_code = \\\"000000\\\"\\n arrow = \\\"\\\\u003D\\\"\\n\\n if lastweek_best_session > 0:\\n difference = abs((thisweek_best_session - lastweek_best_session) / lastweek_best_session) * 100\\n else:\\n # if no sessions last week, best session is up 100%\\n # if both best_sessions are zero, this will display '0 100% =' in black\\n difference = 100\\n result = make_results_dict(thisweek_best_session, difference, color_code, arrow)\\n result['best_session_weekly'] = result.pop('metric')\\n return result\",\n \"def scrape_fifa_game(url, competition):\\n # Need to add referee data.\\n\\n data = scrape_url(url)\\n data = data.split(\\\"<h2>Advertisement</h2>\\\")[0]\\n soup = BeautifulSoup(data)\\n \\n contents = soup.find(\\\"div\\\", {\\\"id\\\": \\\"mainContent\\\" })\\n \\n # Really, none of these games have a home team.\\n\\n #teams = get_contents(contents.find(\\\"div\\\", \\\"bold large teams\\\"))\\n team1 = get_contents(contents.find(\\\"div\\\", \\\"lnupTeam\\\").find(\\\"div\\\", \\\"bold medium\\\"))\\n team2 = get_contents(contents.find(\\\"div\\\", \\\"lnupTeam away\\\").find(\\\"div\\\", \\\"bold medium\\\"))\\n\\n #import pdb; pdb.set_trace()\\n\\n \\n\\n #try:\\n # team1, team2 = [e.strip() for e in teams.split(\\\"-\\\")]\\n #except:\\n # import pdb; pdb.set_trace()\\n\\n score_string = get_contents(contents.find(\\\"div\\\", \\\"bold large result\\\"))\\n\\n if 'a.e.t.' in score_string:\\n score_string = score_string.split('a.e.t')[0]\\n\\n team1_score, team2_score = [int(e) for e in score_string.split(\\\"(\\\")[0].split(\\\":\\\")]\\n\\n # Implement this if header order is more unpredictable.\\n #game_head = contents.findAll(\\\"thead\\\")\\n #head_teas = game_head.findAll(\\\"td\\\", text=True\\n\\n game_header = contents.find(\\\"thead\\\")\\n game_info = contents.find(\\\"tbody\\\")\\n \\n game_ths = [get_contents(e) for e in game_header.findAll(\\\"td\\\")]\\n game_tds = [get_contents(e) for e in game_info.findAll(\\\"td\\\")]\\n\\n game_dict = dict(zip(game_ths, game_tds))\\n\\n\\n match = date_string = time = location = attendance = None\\n\\n #import pdb; pdb.set_trace()\\n\\n if 'Match' in game_dict:\\n match = game_dict['Match']\\n\\n if 'Date' in game_dict:\\n date_string = game_dict['Date']\\n\\n #'Time' \\n\\n if 'Attendance' in game_dict:\\n if game_dict['Attendance']:\\n attendance = int(game_dict['Attendance'])\\n\\n if 'Venue / Stadium' in game_dict:\\n location = game_dict['Venue / Stadium']\\n\\n for e in 'Match', 'Date', 'Attendance', 'Venue / Stadium', 'Time': \\n if e in game_dict:\\n game_dict.pop(e)\\n #print(game_dict.keys())\\n\\n # Standardize city and stadium fields\\n try:\\n city, stadium = [e.strip() for e in location.rsplit(\\\"/\\\", 1)]\\n except:\\n import pdb; pdb.set_trace()\\n\\n # Avoid duplication of city name?\\n if stadium.endswith(city):\\n nlocation = stadium\\n else:\\n nlocation = \\\"%s, %s\\\" % (stadium, city)\\n\\n date = datetime.datetime.strptime(date_string.strip(), \\\"%d %B %Y\\\")\\n\\n return {\\n \\\"team1\\\": team1,\\n \\\"team2\\\": team2,\\n 'team1_score': team1_score,\\n 'team2_score': team2_score,\\n 'competition': competition,\\n 'season': str(date.year),\\n \\\"date\\\": date,\\n \\\"location\\\": nlocation,\\n \\\"attendance\\\": attendance,\\n \\\"sources\\\": [url],\\n }\",\n \"def get_last_competing_matches(matches, date, home_team, away_team, x=2):\\n\\n # Find matches of both teams\\n home_matches = matches[(matches['home_team_api_id'] == home_team) & (matches['away_team_api_id'] == away_team)]\\n last_matches = get_most_recent_matches_helper(home_matches, date, x)\\n return last_matches\",\n \"def getgoalies(league, year):\\n\\n url = 'https://www.eliteprospects.com/league/' + league + '/stats/' + year + '?page-goalie='\\n # print('Collects data from ' + 'https://www.eliteprospects.com/league/' + league + '/stats/' + year)\\n \\n print(\\\"Beginning scrape of \\\" + league + \\\" goalie data from \\\" + year + \\\".\\\")\\n \\n # Return list with all plyers for season in link \\n players = []\\n \\n page = (requests.get(url + str(1) + \\\"#goalies\\\", timeout = 500))\\n first_page_string = str(page)\\n \\n while first_page_string == '<Response [403]>':\\n print(\\\"Just got a 403 Error before entering the page. This means EliteProspects has temporarily blocked your IP address.\\\")\\n print(\\\"We're going to sleep for 60 seconds, then try again.\\\")\\n time.sleep(100)\\n page = (requests.get(url + str(1) + \\\"#goalies\\\", timeout = 500))\\n first_page_string = str(page)\\n print(\\\"Okay, let's try this again\\\")\\n \\n if (first_page_string) == '<Response [404]>':\\n print(\\\"ERROR: \\\" + first_page_string + \\\" on league: \\\" + league + \\\" in year: \\\" + year + \\\". Data doesn't exist for this league and season.\\\")\\n \\n else:\\n \\n for i in range(1,99):\\n page = requests.get(url+str(i), timeout = 500)\\n page_string = str(page)\\n \\n while page_string == '<Response [403]>':\\n print(\\\"Just got a 403 Error within the page. Time to Sleep, then re-obtain the link.\\\")\\n time.sleep(100)\\n page = (requests.get(url+str(i), timeout = 500))\\n page_string = str(page)\\n print(\\\"Changed the string within the page. Let's try again\\\")\\n \\n soup = BeautifulSoup(page.content, \\\"html.parser\\\")\\n\\n # Get data for players table\\n player_table = soup.find(\\\"table\\\", {\\\"class\\\":\\\"table table-striped table-sortable goalie-stats highlight-stats season\\\"})\\n\\n try:\\n df_players = tableDataText(player_table)\\n except AttributeError:\\n print(\\\"BREAK: TABLE NONE ERROR: \\\" + str(requests.get(url+str(i), timeout = 500)) + \\\" On League: \\\" + league + \\\" In Year: \\\" + year)\\n break\\n \\n if len(df_players)>0:\\n\\n if df_players['#'].count()>0:\\n # Remove empty rows\\n df_players = df_players[df_players['#']!=''].reset_index(drop=True)\\n\\n # Extract href links in table\\n href_row = []\\n for link in player_table.find_all('a'):\\n href_row.append(link.attrs['href'])\\n\\n # Create data frame, rename and only keep links to players\\n df_links = pd.DataFrame(href_row) \\n df_links.rename(columns={ df_links.columns[0]:\\\"link\\\"}, inplace=True)\\n df_links= df_links[df_links['link'].str.contains(\\\"/player/\\\")].reset_index(drop=True) \\n\\n # Add links to players\\n df_players['link']=df_links['link'] \\n\\n players.append(df_players)\\n\\n # Wait 3 seconds before going to next\\n # time.sleep(1)\\n #print(\\\"Scraped page \\\" + str(i))\\n \\n else:\\n #print(\\\"Scraped final page of: \\\" + league + \\\" In Year: \\\" + year)\\n break\\n\\n \\n if len(players)!=0:\\n df_players = pd.concat(players).reset_index()\\n\\n df_players.columns = map(str.lower, df_players.columns)\\n\\n # Clean up dataset\\n df_players['season'] = year\\n df_players['league'] = league\\n\\n df_players = df_players.drop(['index','#'], axis=1).reset_index(drop=True)\\n \\n print(\\\"Successfully scraped all \\\" + league + \\\" goalie data from \\\" + year + \\\".\\\")\\n \\n df_players = df_players.loc[((df_players.gp!=0) & (~pd.isna(df_players.gp)) & (df_players.gp!=\\\"0\\\") & (df_players.gaa!=\\\"-\\\"))]\\n\\n return df_players\\n else: print(\\\"LENGTH 0 ERROR: \\\" + str(requests.get(url+str(1), timeout = 500)) + \\\" On League: \\\" + league + \\\" In Year: \\\" + year)\",\n \"def test_strategy(self):\\n self.first_play_test(C)\\n for i in range(10):\\n history_1 = [C] * i\\n history_2 = [C] * i\\n self.responses_test(history_1, history_2, [C])\\n # Now cooperate 10% less than opponent\\n history_1 = [C] * 11\\n history_2 = [D] * 11\\n self.responses_test(history_1, history_2, [D], random_seed=10)\\n history_1 = [C] * 11\\n history_2 = [D] * 10 + [C]\\n self.responses_test(history_1, history_2, [D], random_seed=10)\\n # Test beyond 10 rounds\\n history_1 = [C] * 11\\n history_2 = [D] * 5 + [C] * 6\\n self.responses_test(history_1, history_2, [D, D, D, D], random_seed=20)\\n history_1 = [C] * 11\\n history_2 = [C] * 9 + [D] * 2\\n self.responses_test(history_1, history_2, [C, D, D, C], random_seed=25)\",\n \"def AmOppCr(_cmp, e87482, e87487, e87492, e87497):\\n\\n \\\"\\\"\\\"\\n This function calculates American Opportunity Credit\\n for up to four eligible students\\n\\n \\\"\\\"\\\"\\n\\n # Expense should not exceed the cap of $4000.\\n if _cmp == 1:\\n\\n c87482 = max(0., min(e87482, 4000.))\\n c87487 = max(0., min(e87487, 4000.))\\n c87492 = max(0., min(e87492, 4000.))\\n c87497 = max(0., min(e87497, 4000.))\\n else:\\n c87482, c87487, c87492, c87497 = 0., 0., 0., 0.\\n\\n # Credit calculated as 100% of the first $2000 expense plus\\n # 25% of amount exceeding $2000.\\n if max(0, c87482 - 2000) == 0:\\n c87483 = c87482\\n else:\\n c87483 = 2000 + 0.25 * max(0, c87482 - 2000)\\n\\n if max(0, c87487 - 2000) == 0:\\n c87488 = c87487\\n else:\\n c87488 = 2000 + 0.25 * max(0, c87487 - 2000)\\n\\n if max(0, c87492 - 2000) == 0:\\n c87493 = c87492\\n else:\\n c87493 = 2000 + 0.25 * max(0, c87492 - 2000)\\n\\n if max(0, c87497 - 2000) == 0:\\n c87498 = c87497\\n else:\\n c87498 = 2000 + 0.25 * max(0, c87497 - 2000)\\n\\n # Sum of credits of all four students.\\n c87521 = c87483 + c87488 + c87493 + c87498\\n\\n return (c87482, c87487, c87492, c87497, c87483, c87488, c87493, c87498,\\n c87521)\",\n \"def _accounce_winner(self):\\n \\n if self._end_time < time.time():\\n winner = sorted(((player.get_name(), player.get_last_roll(), player.get_total_score())\\n for player in self._players.get_players()),\\n key=lambda player: (player[1]),\\n reverse=True)[0]\\n\\n print(\\\"\\\\n\\\\nCongratulations {}, you had the highest score of {} before time ran out. You won the game!\\\"\\n .format(winner[0], winner[2]))\\n else:\\n super()._accounce_winner()\",\n \"def playGames(self, num, sonete,secuence,mcts,verbose=False):\\n eps_time = AverageMeter()\\n bar = Bar('Arena.playGames', max=num)\\n end = time.time()\\n eps = 0\\n maxeps = int(num)\\n finalScore1=0\\n finalScore2=0\\n\\n num = int(num/2)\\n oneWon = 0\\n twoWon = 0\\n draws = 0\\n gameResults=[]\\n global nround\\n actions=[]\\n self.player1, self.player2 = self.player1, self.player1\\n board = self.game.getInitBoard()\\n for i in range(100):\\n nround = i\\n #action,sonete = self.playGame(sonete,sequences,nround,verbose=verbose)\\n pi = mcts.getActionProb(sonete, temp=1)\\n #actions.append(action)\\n\\n eps_time.update(time.time() - end)\\n end = time.time()\\n\\n\\n return actions#finalScore1, finalScore2#oneWon, twoWon, draws\",\n \"def greedy_cow_transport(cows,limit=10):\\r\\n #Casting dictionary into list\\r\\n list_cows = sorted(([w, n] for n, w in cows.items() if w <= limit), reverse = True)\\r\\n\\r\\n\\r\\n #trip result list\\r\\n #The algorithm should start with the biggest cow\\r\\n if list_cows == []:\\r\\n trip_results = []\\r\\n else:\\r\\n trip_results = greedy_helper(list_cows, limit, []) \\r\\n return trip_results\",\n \"def strategy_cheap(cookies, cps, history, time_left, build_info):\\n pick = None\\n cost = float('inf')\\n for item in build_info.build_items():\\n if build_info.get_cost(item) < cost: \\n cost = build_info.get_cost(item)\\n if (time_left * cps + cookies) >= cost:\\n pick = item\\n \\n return pick\",\n \"def second_way(person_to_clubs: Dict[str, List[str]], \\n club_to_person: Dict[str, List[str]], \\n person: str, club: str) -> int:\\n score = 0\\n if person not in person_to_clubs:\\n return score\\n for member in club_to_person[club]:\\n for diff_clubs in person_to_clubs[member]:\\n if diff_clubs != club and diff_clubs in person_to_clubs[person]:\\n score += 1\\n return score\",\n \"def get_best_moves():\\n game_data = set()\\n\\n def next_move(game):\\n winner = game.get_winner()\\n if winner is not None:\\n return {winner}\\n next_wins = set()\\n future_wins = set()\\n future_draws_and_wins = set()\\n future_draws = set()\\n results = set()\\n for row, col in game.valid_spaces():\\n next_game = game.move(row, col)\\n next_results = next_move(next_game)\\n results |= next_results\\n if game.turn == 2 and next_game.get_winner() == 2:\\n # If Player 2 can win with their move, reject this line\\n return {2}\\n elif game.turn == 1 and next_game.get_winner() == 1:\\n # If Player 1 can win with their move, only accept these lines\\n next_wins.add((tuple([tuple(row) for row in game.board]), row * 3 + col))\\n elif game.turn == 1:\\n if next_results == {1}:\\n # Player 1 will only win in this future\\n future_wins.add((tuple([tuple(row) for row in game.board]), row * 3 + col)) \\n elif next_results == {0, 1}:\\n # Player 1 could win or draw in this future\\n future_draws_and_wins.add((tuple([tuple(row) for row in game.board]), row * 3 + col))\\n elif next_results == {0}:\\n # Player 1 could only draw in this future\\n future_draws.add((tuple([tuple(row) for row in game.board]), row * 3 + col))\\n # We only accept the draws if we don't have any just wins\\n if game.turn == 2:\\n return results\\n\\n if next_wins:\\n game_data.update(next_wins)\\n return {1}\\n elif future_wins:\\n game_data.update(future_wins)\\n return {1}\\n elif future_draws_and_wins:\\n game_data.update(future_draws_and_wins)\\n return {0, 1}\\n elif future_draws:\\n game_data.update(future_draws)\\n return {0}\\n return set()\\n\\n next_move(TTTGame())\\n next_move(TTTGame(turn=2))\\n return tuple(game_data)\",\n \"def opponents_score(self):\\n if self.opponent_wickets == 10:\\n var1 = \\\"All Out\\\"\\n return str('{0} {1}').format(self.opponent_runs, var1)\\n else:\\n var1 = self.opponent_wickets\\n return str('{0}-{1}').format(self.opponent_runs, var1)\",\n \"def get_fifa_stats(match, player_stats):\\n\\n # Define variables\\n match_id = int(match.match_api_id)\\n date = match['date']\\n players = ['home_player_1', 'home_player_2', 'home_player_3', \\\"home_player_4\\\", \\\"home_player_5\\\",\\n \\\"home_player_6\\\", \\\"home_player_7\\\", \\\"home_player_8\\\", \\\"home_player_9\\\", \\\"home_player_10\\\",\\n \\\"home_player_11\\\", \\\"away_player_1\\\", \\\"away_player_2\\\", \\\"away_player_3\\\", \\\"away_player_4\\\",\\n \\\"away_player_5\\\", \\\"away_player_6\\\", \\\"away_player_7\\\", \\\"away_player_8\\\", \\\"away_player_9\\\",\\n \\\"away_player_10\\\", \\\"away_player_11\\\"]\\n\\n overall_ratings = np.array([])\\n for player in players:\\n # Get player ID\\n player_id = match[player]\\n\\n # Get player stats\\n stats = player_stats[player_stats.player_api_id == player_id]\\n\\n # Identify current stats\\n current_stats = stats[stats.date < date].sort_values(by='date', ascending=False).iloc[0]\\n\\n # get overall rating for every player, this cannot be nan since we \\\"dropna\\\" in main()\\n overall_ratings = np.concatenate((overall_ratings, [current_stats[\\\"overall_rating\\\"]]))\\n\\n colNames = np.core.defchararray.add(players, '_overall_rating')\\n player_stats_new = pd.Series(overall_ratings, index=colNames)\\n player_stats_new['match_api_id'] = match_id\\n # print(player_stats_new)\\n return player_stats_new\",\n \"def scrape_world_cup_scoreboard(year):\\n # Replace this with the results logic somehow...\\n\\n d = world_cup_mapping[year]\\n prefix = 'http://www.fifa.com'\\n if type(d) == int:\\n root_url = '/worldcup/archive/edition=%s/' % d\\n else:\\n root_url = '/worldcup/archive/%s/' % d\\n data = scrape_url(prefix + root_url + \\\"results/index.html\\\")\\n\\n # Find urls in the page.\\n match_re = re.compile(root_url + \\\"results/matches/match=\\\\d+/report.html\\\")\\n urls = match_re.findall(data)\\n return [prefix + e for e in urls]\",\n \"def show_contest_winner(self, db_session):\\n users_contest_list = db_session.query(db.User).filter(db.User.entered_in_contest.isnot(False)).all()\\n if len(users_contest_list) > 0:\\n winner = random.choice(users_contest_list)\\n self._add_to_chat_queue('The winner is {}!'.format(winner.name))\\n else:\\n self._add_to_chat_queue('There are currently no entrants for the contest.')\",\n \"def winningTeamPenalty(r):\\n \\n #Check if home or away had more goals at the 'event' time\\n homecheck = int(r['about.goals.home'] > r['about.goals.away'])\\n awaycheck = int(r['about.goals.away'] > r['about.goals.home'])\\n \\n #If home had more goals and the penalty was on the home team, set to 1\\n if (homecheck > 0) and (r['against.homeTeam'] == 1):\\n return 1\\n #If away had more and the penalty was not on home team, set to 1\\n if (awaycheck > 0) and (r['against.homeTeam'] == 0):\\n return 1\\n #Any other situation should be a zero in this column\\n else:\\n return 0\",\n \"def collect_century_biomass_all_sites(match_csv, outdir, save_as):\\n df_list = []\\n site_df = pd.read_csv(match_csv)\\n for site in site_df.site_id.unique():\\n cent_file = os.path.join(\\n outdir, site, 'CENTURY_outputs_iteration0', '{}.lis'.format(site))\\n if not os.path.exists(cent_file):\\n continue\\n cent_df = pd.io.parsers.read_fwf(cent_file, skiprows=[1])\\n df_subset = cent_df[(cent_df.time > 2016) & (cent_df.time <= 2017)]\\n biomass_df = df_subset[['time', 'aglivc', 'stdedc']]\\n live_biomass = biomass_df.aglivc * 2.5 # grams per square m\\n dead_biomass = biomass_df.stdedc * 2.5 # grams per square m\\n biomass_df['live'] = live_biomass\\n biomass_df['standing_dead'] = dead_biomass\\n biomass_df['total'] = live_biomass + dead_biomass\\n biomass_df['site'] = site\\n biomass_df.set_index('time', inplace=True)\\n df_list.append(biomass_df)\\n combined_df = pd.concat(df_list)\\n combined_df.to_csv(save_as)\",\n \"def WCA_SA(targetMDG, WCAresult):\\n hill_climbers = []\\n for i in range(NUM_Population):\\n hill_climbers.append(SimulatedAnnealing(targetMDG, WCAresult))\\n\\n completed_climbers = []\\n completed_max_climbers = []\\n\\n # k: int, number of neighbors to be considered\\n k = 20\\n i = 0\\n not_increased = 0\\n max_score = 0\\n Temperature = 20\\n\\n while True:\\n for climber in hill_climbers[:]:\\n result = climber.climb_with_annealing(k, Temperature)\\n if not result:\\n completed_climbers.append(climber)\\n hill_climbers.remove(climber)\\n max_completed_climber = SimulatedAnnealing(targetMDG)\\n max_completed_climber.result = climber.max_result\\n max_completed_climber.update_score()\\n completed_max_climbers.append(max_completed_climber)\\n\\n total_climbers = hill_climbers + completed_climbers + completed_max_climbers\\n total_climbers.sort()\\n print(\\\"Iteration \\\", i, \\\": \\\", total_climbers[-1].score)\\n\\n if total_climbers[-1].score - max_score != 0:\\n not_increased = 0\\n else:\\n not_increased += 1\\n\\n if len(hill_climbers) == 0 or not_increased == 10:\\n break\\n i += 1\\n max_score = total_climbers[-1].score\\n if Temperature > 0:\\n Temperature -= 0.5\\n\\n total_climbers = hill_climbers + completed_climbers + completed_max_climbers\\n total_climbers.sort()\\n\\n max_climber = total_climbers[-1]\\n\\n print(\\\"TurboMQ = \\\", max_climber.score)\\n for c in max_climber.result: # print all clusters which are not singleton\\n if 1 != len(c.get_nodes()):\\n print(c.get_nodes())\\n\\n max_climber.remove_empty_cluster()\\n return max_climber.result\",\n \"def heuristic_function(self, game_node: ConnectFourGame, search_depth: int):\\n\\n # Victory or defeat with a higher depth value is more desirable, because it means less moves are used to reach it\\n depth_points = 2520 / (10 - search_depth)\\n\\n if game_node.winner_id is None:\\n # Evaluates empty spaces for any player that is one disc away from a victory\\n player_four_in_a_rows = self._determine_near_x_in_a_rows(game_node, game_node.victory_condition)\\n ai_count = player_four_in_a_rows.get(self.ai_player_id, 0)\\n other_player_count = sum([\\n player_four_in_a_rows[player_id]\\n for player_id in filter(lambda player_id: player_id != self.ai_player_id, player_four_in_a_rows)\\n ])\\n if ai_count - other_player_count != 0:\\n return (ai_count - other_player_count) * 20\\n\\n # Evaluates grid positioning, granting bonus points for discs closer to the center of the grid\\n max_deviation = floor(game_node.grid.width / 4.0)\\n mid = ceil(game_node.grid.width / 2.0)\\n ai_count = 0\\n other_player_count = 0\\n for col in range(mid - max_deviation, mid + max_deviation):\\n for row in range(0, game_node.grid.height):\\n curr_space = game_node.grid.grid_spaces[col][row]\\n if curr_space.disc is None:\\n break # Short-circuits when empty space in a column is reached\\n elif curr_space.disc.player_id == self.ai_player_id:\\n ai_count += 1\\n else:\\n other_player_count += 1\\n if ai_count - other_player_count != 0:\\n return (ai_count - other_player_count) * 2\\n\\n return 0\\n elif game_node.winner_id == self.ai_player_id:\\n return self.winner_heuristic_value + depth_points # Means AI player has won\\n else:\\n return -self.winner_heuristic_value - depth_points # Means AI player has lost\",\n \"def calculate_fitness(self,chromo,index):\\n #Incase chromosome has been scheduled before\\n chromo.fitness = 0\\n \\n hold_index = index\\n \\n #Figure out which room you are in\\n data_tuple = self.get_room_day_numbers(hold_index)\\n room_id = data_tuple[1]\\n print room_id\\n #Get Room object\\n room = self.config.get_room_by_id(room_id)\\n\\n course = chromo._class\\n #Course might not overlap at current position, but could if duration is\\n #longer than 1, this checks for that\\n if not chromo.overlap:\\n if course.duration > 1:\\n count = 0\\n while count < (course.duration):\\n index += 1\\n if index < len(self.chromo_list):\\n check_list = self.chromo_list[index]\\n if check_list:\\n chromo.overlap = True\\n count += 1\\n\\n #Class does not overlap EVER\\n if not chromo.overlap:\\n chromo.fitness += 1\\n\\n #Room is able to fit the class\\n if course.get_room_size() <= room.get_seat_num():\\n chromo.fitness += 1\\n\\n #Course needs lab and room has lab\\n if course.needs_lab():\\n if room.lab_status():\\n chromo.fitness += 1\\n\\n #Course doesnt need lab and room doesnt have lab\\n if not course.needs_lab():\\n if not room.lab_status():\\n chromo.fitness += 1\\n\\n #Only way a Professor will have an overlapping class is if the\\n #class overlaps with another class\\n if chromo.overlap:\\n prof_overlap = False\\n index = hold_index\\n count = 0\\n while count < (course.duration):\\n index += count\\n check_list = self.chromo_list[index]\\n for courses in check_list:\\n prof_overlap = course.professor_overlap(courses._class)\\n if prof_overlap:\\n break\\n if prof_overlap:\\n break\\n count += 1\\n\\n if not prof_overlap:\\n chromo.fitness += 1\\n else:\\n chromo.fitness += 1\\n\\n if chromo.fitness is self.best_fitness:\\n self.best_of[chromo._class] = self.best_fitness\",\n \"def final_standing_projection(league_no):\\n ros_proj_b_list = BatterProjection.objects.all()\\n ros_proj_p_list = PitcherProjection.objects.all()\\n league_settings = get_league_settings(league_no)\\n current_standings = get_standings(league_no, int(league_settings['Max Teams:']))\\n team_list = yahoo_teams(league_no)\\n final_stats = final_stats_projection(team_list, ros_proj_b_list, ros_proj_p_list, current_standings,\\n league_settings)\\n volatility_standings = league_volatility(SGP_DICT, final_stats)\\n ranked_standings = rank_list(volatility_standings)\\n return ranked_standings\",\n \"def strategy_cheap(cookies, cps, history, time_left, build_info):\\n\\n items = build_info.build_items()\\n costs = [build_info.get_cost(item) for item in items]\\n builds = sorted(zip(costs,items))\\n\\n earnable_cookies = cookies + time_left * cps\\n\\n if builds[0][0] > earnable_cookies:\\n return None\\n else:\\n return builds[0][1]\",\n \"def get_upcoming_games(n=10):\\n conn, cursor = connect_to_db()\\n query = \\\"\\\"\\\"select kickoff_time, t2.team_id home_id, t2.team_name home_name, \\n t3.team_id away_id, t3.team_name away_name\\n from fpl_fixtures t1 left join fpl_teams t2 on t1.team_h = t2.id left \\n join fpl_teams t3 on t1.team_a = t3.id where started = 0 order by \\n kickoff_time limit {}\\\"\\\"\\\".format(n)\\n df = run_query(cursor, query)\\n return df\",\n \"def strategy_best(cookies, cps, history, time_left, build_info):\\n print \\\"STRATEGY BEGINS\\\"\\n items_available = []\\n for item in build_info.build_items():\\n items_available.append(item)\\n item_to_buy = None\\n max_profit = float('-inf')\\n for item in items_available:\\n if cps * time_left + cookies >= build_info.get_cost(item):\\n if cookies >= build_info.get_cost(item):\\n time_to_bake = 0.0\\n else:\\n time_to_bake = math.ceil((build_info.get_cost(item) - cookies) / cps)\\n additional_cookies = build_info.get_cps(item) * (time_left - time_to_bake)\\n\\n profit = additional_cookies - build_info.get_cost(item)\\n print \\\"item:\\\", item, \\\", cost:\\\", build_info.get_cost(item), \\\", time_to_bake:\\\", time_to_bake, \\\", profit:\\\", profit\\n if profit > max_profit:\\n max_profit = profit\\n item_to_buy = item\\n #print \\\"max_profit:\\\", max_profit\\n print \\\"STRATEGY ENDS\\\"\\n return item_to_buy\",\n \"def strategy_cheap(cookies, cps, time_left, build_info):\\n items = build_info.build_items()\\n result = None\\n cost = float('+inf')\\n overall = cookies + time_left * cps\\n for item in items:\\n temp_cost = build_info.get_cost(item)\\n if temp_cost <= overall and cost > temp_cost:\\n result = item\\n cost = temp_cost\\n return result\",\n \"def __get_next_greedy_move(self, game_state): \\n best_move = None\\n best_score = None\\n for free_seat in self.__get_free_seats(game_state):\\n next_game_state_score = self.__get_score(game_state, free_seat)\\n if best_score is None:\\n best_score = next_game_state_score\\n best_move = free_seat\\n continue\\n if next_game_state_score > best_score:\\n best_score = next_game_state_score\\n best_move = free_seat\\n return best_move\",\n \"def get_convergence_episode(self):\\n values = self.stats['return_stats']['episode_totals']\\n _, y, (y_lower, _) = self._moving_average(\\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\\n # The convergence is established as the first time the average return\\n # is above the lower bounds of the final return.\\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\\n return first_episode\",\n \"def collect_salaries(team_dict):\\n salaries = [0, 0, 0, 0, 0]\\n for c in team_dict['roster']:\\n for i in range(5):\\n if (c['contract'].contract_season + i) <= c['contract'].length:\\n salaries[i] += c['contract'].salary\\n return salaries\",\n \"def choose_winner(): \\r\\n max_health = Titan.max_health()\\r\\n winners = tuple((titan.name for titan in Titan.titans if titan.health == max_health))\\r\\n return winners\",\n \"def get_clubs_and_transfers(league_name, league_id, season_id, window):\\r\\n headers = {\\r\\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}\\r\\n base = \\\"https://www.transfermarkt.it\\\"\\r\\n url = base + \\\"/{league_name}/transfers/wettbewerb/{league_id}/plus/?saison_id={season_id}&s_w={window}\\\".format(\\r\\n league_name=league_name, league_id=league_id, season_id=season_id, window=window)\\r\\n try:\\r\\n print(\\\"Connecting...\\\")\\r\\n response = requests.get(url, headers=headers)\\r\\n print(\\\"Connection successful, status code {}\\\".format(response.status_code))\\r\\n except requests.exceptions.RequestException as e:\\r\\n print(e)\\r\\n exit()\\r\\n soup = BeautifulSoup(response.content, 'html.parser')\\r\\n\\r\\n clubs = [tag.text for tag in soup.find_all('div', {'class': 'table-header'})][1:]\\r\\n\\r\\n tables = [tag.findChild() for tag in soup.find_all('div', {'class': 'responsive-table'})]\\r\\n table_in_list = tables[::2]\\r\\n table_out_list = tables[1::2]\\r\\n\\r\\n transfer_in_list = []\\r\\n transfer_out_list = []\\r\\n column_headers = {'season': season_id, 'window': window, 'league': league_name}\\r\\n for table_in, table_out in zip(table_in_list, table_out_list):\\r\\n transfer_in_list.append(get_transfer_info(base, table_in, movement='In', **column_headers))\\r\\n transfer_out_list.append(get_transfer_info(base, table_out, movement='Out', **column_headers))\\r\\n\\r\\n return clubs, transfer_in_list, transfer_out_list\",\n \"def get_assist_done(self, season=None, stage=None):\\n cnt = 0\\n for m in self.get_matches(season=season, ordered=True):\\n if util.is_None(m.goal):\\n continue\\n if not util.is_None(stage) and m.stage >= stage:\\n return cnt\\n soup = BeautifulSoup(m.goal, \\\"html.parser\\\")\\n for player1 in soup.find_all('player2'):\\n if int(str(player1.string).strip()) == self.player_api_id:\\n cnt += 1\\n return cnt\",\n \"def conCor(data):\\n \\n d = { } \\n \\n for c in data[0]:\\n if c not in d:\\n d[c]= 0\\n for i in range(len(data[0])):\\n if isWinner(data,c,data[0][i]):\\n d[c] = d[c] +1 \\n \\n hi = max(d.values()) \\n lenA = len(d) - 1\\n \\n if hi == lenA:\\n for i in d:\\n if hi == d[i]:\\n return i \\n \\n return \\\"No Condorcet Winner exists in this data.\\\"\",\n \"def players_onsale(self):\\r\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\", \\\"Accept\\\": \\\"text/plain\\\",\\r\\n 'Referer': 'http://' + self.domain + '/team_news.phtml', \\\"User-Agent\\\": user_agent}\\r\\n req = self.session.get('http://' + self.domain + '/teamInfo.phtml?tid=' + str(self.community_id),\\r\\n headers=headers).content\\r\\n soup = BeautifulSoup(req, \\\"html.parser\\\")\\r\\n\\r\\n current_year = dt.today().year\\r\\n current_month = dt.today().month\\r\\n on_sale = list()\\r\\n year_flag = 0\\r\\n for i in soup.find_all('table', {'class', 'tablecontent03'})[2].find_all('tr')[1:]:\\r\\n columns = i.find_all('td')\\r\\n player_id = int(re.findall('\\\\d+', columns[0].img['src'])[0])\\r\\n playername = columns[1].text.strip()\\r\\n team_id = int(re.findall('\\\\d+', columns[2].img['src'])[0])\\r\\n team = columns[2].a['title'].strip()\\r\\n min_price = float(columns[3].text.replace(\\\".\\\", \\\"\\\").strip())\\r\\n market_price = float(columns[4].text.replace(\\\".\\\", \\\"\\\").strip())\\r\\n points = int(columns[5].text.strip().strip())\\r\\n # Controlamos el cambio de año, ya que comunio no lo dá\\r\\n if current_month <= 7 < int(columns[6].text[3:5]):\\r\\n year_flag = 1\\r\\n date = datetime.strptime(str(current_year - year_flag) + columns[6].text[3:5] + columns[6].text[:2], '%Y%m%d').date()\\r\\n owner = columns[7].text.strip()\\r\\n position = self.translate_position(columns[8].text.strip())\\r\\n # Comprobamos si solamente queremos los de la computadora o no\\r\\n on_sale.append([player_id, playername, team_id, team, min_price, market_price, points, date, owner, position])\\r\\n\\r\\n return on_sale\",\n \"def jaccard_sim(news_data_dict: dict, song_data_dict: dict):\\n jaccard_dict = defaultdict(list)\\n\\n for news_yr, news_txt in news_data_dict.items():\\n news_txt_flat = []\\n for nt in news_txt:\\n news_txt_flat += nt\\n news_txt_flat = set(news_txt_flat)\\n if news_yr <= 2011:\\n song_txt_flat = set()\\n for i in range(5):\\n song_yr = news_yr+i\\n for st in song_data_dict[song_yr]:\\n for stw in st:\\n song_txt_flat.add(stw)\\n shared_words = news_txt_flat.intersection(song_txt_flat)\\n jaccard = len(shared_words) / (len(song_txt_flat) + len(news_txt_flat) - len(shared_words))\\n jaccard = round(jaccard, 3)\\n jaccard_dict[news_yr].append(jaccard)\\n\\n return jaccard_dict\",\n \"def scrape_fifa_goals(url, competition):\\n\\n # Seems the 2006 world cup report is missing some games for sasa ilic.\\n goal_replace = {\\n \\\"(SCG) 20',\\\": \\\"Sasa ILIC (SCG) 20',\\\"\\n }\\n\\n\\n data = scrape_url(url)\\n data = data.split(\\\"<h2>Advertisement</h2>\\\")[0]\\n soup = BeautifulSoup(data)\\n\\n goals_div = soup.find(\\\"div\\\", text='Goals scored')\\n goals = [get_contents(e) for e in goals_div.parent.parent.findAll(\\\"li\\\")]\\n goals = [goal_replace.get(e, e) for e in goals]\\n\\n goal_re = re.compile(\\\"^(?P<name>.*?) \\\\((?P<team>[A-Z]+)\\\\) (?P<minute>\\\\d+)'?\\\")\\n\\n game_data = scrape_fifa_game(url, competition)\\n\\n\\n\\n l = []\\n\\n for s in goals:\\n try:\\n name, team, minute = goal_re.search(s.strip()).groups()\\n except:\\n #import pdb; pdb.set_trace()\\n print(s)\\n continue\\n \\n team = team.strip()\\n team = team_abbrevs.get(team, team)\\n\\n l.append({\\n 'team': team,\\n 'competition': competition,\\n 'season': game_data['season'],\\n 'date': game_data['date'],\\n 'goal': name.strip().title(),\\n\\n\\n 'minute': int(minute),\\n 'assists': [],\\n 'source': url\\n })\\n\\n return l\",\n \"def assign_win_points(game_dict):\\n def win_tuple(deck_dict):\\n \\\"\\\"\\\" Return tuple ordered by increasing final standing. \\\"\\\"\\\"\\n # negate turns so that max() behaves; points good, turns bad.\\n num_normal_turns = sum(not ( (POSSESSION in t and t[POSSESSION]) or \\\\\\n (OUTPOST in t and t[OUTPOST]))\\n for t in deck_dict[TURNS])\\n return (deck_dict[POINTS], -num_normal_turns)\\n\\n decks = game_dict[DECKS]\\n winner_tuple = max(win_tuple(p) for p in decks)\\n winners = [p for p in decks if win_tuple(p) == winner_tuple]\\n\\n win_points = float(len(decks)) / len(winners)\\n for player in decks:\\n player[WIN_POINTS] = win_points if player in winners else 0.0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69078004","0.66235226","0.65739715","0.64645517","0.54283714","0.53823304","0.5381159","0.5307479","0.5282068","0.52351725","0.51759636","0.51322395","0.5129283","0.50992453","0.5090998","0.5084592","0.5073128","0.5072545","0.5070027","0.50515336","0.5047289","0.50461006","0.503849","0.50384164","0.5027115","0.5018327","0.50121963","0.49724916","0.4972198","0.496628","0.49636334","0.4954485","0.49539784","0.49493584","0.49284747","0.49206564","0.49182016","0.48977852","0.48967144","0.48871547","0.48829225","0.48821834","0.4879712","0.48772737","0.48747247","0.4872872","0.4866084","0.4861147","0.48606548","0.48599494","0.48591512","0.48548934","0.48523566","0.48323268","0.4823426","0.48229223","0.4815915","0.48128754","0.48079488","0.48034844","0.47923538","0.47904873","0.47884414","0.4787674","0.47854114","0.47833836","0.47833627","0.4779896","0.47785646","0.47741422","0.47733617","0.47726145","0.4768082","0.47679928","0.47668043","0.47624952","0.4759151","0.47569808","0.47569752","0.47547585","0.4754471","0.47523433","0.47450662","0.47446057","0.47413665","0.47412235","0.4736994","0.47366092","0.47359276","0.47304767","0.4727766","0.47196233","0.47191498","0.4715088","0.4713639","0.471286","0.47041276","0.47037014","0.47035772","0.47028753"],"string":"[\n \"0.69078004\",\n \"0.66235226\",\n \"0.65739715\",\n \"0.64645517\",\n \"0.54283714\",\n \"0.53823304\",\n \"0.5381159\",\n \"0.5307479\",\n \"0.5282068\",\n \"0.52351725\",\n \"0.51759636\",\n \"0.51322395\",\n \"0.5129283\",\n \"0.50992453\",\n \"0.5090998\",\n \"0.5084592\",\n \"0.5073128\",\n \"0.5072545\",\n \"0.5070027\",\n \"0.50515336\",\n \"0.5047289\",\n \"0.50461006\",\n \"0.503849\",\n \"0.50384164\",\n \"0.5027115\",\n \"0.5018327\",\n \"0.50121963\",\n \"0.49724916\",\n \"0.4972198\",\n \"0.496628\",\n \"0.49636334\",\n \"0.4954485\",\n \"0.49539784\",\n \"0.49493584\",\n \"0.49284747\",\n \"0.49206564\",\n \"0.49182016\",\n \"0.48977852\",\n \"0.48967144\",\n \"0.48871547\",\n \"0.48829225\",\n \"0.48821834\",\n \"0.4879712\",\n \"0.48772737\",\n \"0.48747247\",\n \"0.4872872\",\n \"0.4866084\",\n \"0.4861147\",\n \"0.48606548\",\n \"0.48599494\",\n \"0.48591512\",\n \"0.48548934\",\n \"0.48523566\",\n \"0.48323268\",\n \"0.4823426\",\n \"0.48229223\",\n \"0.4815915\",\n \"0.48128754\",\n \"0.48079488\",\n \"0.48034844\",\n \"0.47923538\",\n \"0.47904873\",\n \"0.47884414\",\n \"0.4787674\",\n \"0.47854114\",\n \"0.47833836\",\n \"0.47833627\",\n \"0.4779896\",\n \"0.47785646\",\n \"0.47741422\",\n \"0.47733617\",\n \"0.47726145\",\n \"0.4768082\",\n \"0.47679928\",\n \"0.47668043\",\n \"0.47624952\",\n \"0.4759151\",\n \"0.47569808\",\n \"0.47569752\",\n \"0.47547585\",\n \"0.4754471\",\n \"0.47523433\",\n \"0.47450662\",\n \"0.47446057\",\n \"0.47413665\",\n \"0.47412235\",\n \"0.4736994\",\n \"0.47366092\",\n \"0.47359276\",\n \"0.47304767\",\n \"0.4727766\",\n \"0.47196233\",\n \"0.47191498\",\n \"0.4715088\",\n \"0.4713639\",\n \"0.471286\",\n \"0.47041276\",\n \"0.47037014\",\n \"0.47035772\",\n \"0.47028753\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.757074"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14995,"cells":{"query":{"kind":"string","value":"Get teams who won the championship for each year"},"document":{"kind":"string","value":"def get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"count\"})\n .reset_index()\n .rename(columns={\"NumOT\":\"is_champion\",\"WTeamID\":\"TeamID\"})\n )"},"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 get_player_games(self, year, use_local=True):","def collect_teams(year):\n\n team_list = Team.objects.filter(year=year).order_by('location')\n teams = []\n for t in team_list:\n team = {\n 'id': t.abbreviation,\n 'team': t,\n }\n teams.append(team)\n return teams","def get_teams():","def collect_teams(year: int = 2005) -> None:\n\n\twith open('../resources/config.json') as config_file, open('../resources/secrets.json') as secrets_file:\n\t\tconfig_json = json.load(config_file)\n\t\tsecrets_json = json.load(secrets_file)\n\n\t\turl = '/'.join(['http:', '', config_json['base_url'], config_json['fbs_teams_endpoint']])\n\t\tapi_key = secrets_json['api_key']\n\n\theaders = {'Authorization': api_key}\n\tparams = {'year': year}\n\n\tresponse = requests.get(url, headers = headers, params = params).json()\n\n\t# dict of one array for json dump\n\tteam_names = {'teamNames': list(map(lambda r: r['school'], response))}\n\n\twith open('../resources/teams.json', 'w') as teams_file:\n\t\tjson.dump(team_names, teams_file)","def get_all_cfb_teams(year):\n \n url = BASE_URL + '/cfb/years/{}-standings.html'.format(year)\n soup = get_soup(url)\n \n table = soup.find('table', attrs={'id':'standings'})\n rows = table.find_all('tr')\n \n team_list = []\n \n for row in rows:\n team = row.find('td', attrs={'data-stat':'school_name'})\n \n if not team:\n continue\n \n team_link = team.find('a').get('href') \n team_list.append(team_link)\n \n return team_list","def wins(self):\n return [g for g in self.games if g.winner is self.team]","def test_get_teams_in_year_names():\n assert sorted(gtiy(2008)) == sorted(team_2008)\n assert sorted(gtiy(2009)) == sorted(team_2009)\n assert sorted(gtiy(2010)) == sorted(team_2010)\n assert sorted(gtiy(2011)) == sorted(team_2011)\n assert sorted(gtiy(2012)) == sorted(team_2012)\n assert sorted(gtiy(2013)) == sorted(team_2013)\n assert sorted(gtiy(2014)) == sorted(team_2014)\n assert sorted(gtiy(2015)) == sorted(team_2015)\n assert sorted(gtiy(2016)) == sorted(team_2016)\n assert sorted(gtiy(2017)) == sorted(team_2017)","def non_current_championships():\n current_championships = (Alfa_Romeo+Ferrari+Haas+McLaren+Mercedes+Racing_Point+Red_Bull+Renault+Toro_Rosso+Williams).constructors_championships_years\n non_current_championships = []\n year = 1958\n while year < 2020:\n if year not in current_championships:\n non_current_championships.append(year)\n year += 1\n return f\"The F1 Constructors' Championships won by teams no longer on the grid are: \\n{non_current_championships}\"","def test_get_teams_in_year_len():\n assert len(gtiy(2008)) == 8\n assert len(gtiy(2009)) == 8\n assert len(gtiy(2010)) == 8\n assert len(gtiy(2011)) == 10\n assert len(gtiy(2012)) == 9\n assert len(gtiy(2013)) == 9\n assert len(gtiy(2014)) == 8\n assert len(gtiy(2015)) == 8\n assert len(gtiy(2016)) == 8\n assert len(gtiy(2017)) == 8","def get_all_cfb_teams_for_year_range(start_year, end_year):\n \n full_team_list = []\n \n for year in range(start_year, end_year + 1):\n print(year)\n \n team_list = get_all_cfb_teams(year)\n full_team_list += team_list\n \n wait_time = random.randint(3, 15)\n time.sleep(wait_time)\n \n return full_team_list","def get_player_stats_from_game(team, year, week):","def get_companies_and_people(team):","def teams(self):\n t = [e[0] for e in self.pick_set.all().values_list(\"team\")]\n return Team.objects.filter(id__in=set(t))","def get_teams_from_league(teams, league):\n\t\n\tteams_1 = []\n\tfor i in range(0,len(teams)):\n\t\tif teams[i].league == 1:\n\t\t\tteams_1.append(teams[i])\n\n\treturn teams_1","def get_available_companies(team):","def get_available_companies_and_people(team):","def getTeamStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\r\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\r\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n team_df = df[[\"TEAM_ID\",\"TEAM_NAME\",\"GP\",\"W\",\"L\",\"W_PCT\",\"MIN\",\"FGM\",\r\n \"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\r\n \"OREB\",\"DREB\",\"REB\",\"AST\",\"TOV\",\"STL\",\"BLK\",\"BLKA\",\"PF\",\r\n \"PFD\",\"PTS\",\"PLUS_MINUS\"]]\r\n \r\n return team_df","def winning_games_stats(self):\n self.winning_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(winning_num_counts = 1))\n .query(\"Season <= 2013\")\n .groupby(['Season','WTeamID'])\n .agg({\"WScore\":\"sum\",\"WFGM\":\"sum\",\"WFGA\":\"sum\",\"WFGM3\":\"sum\",\"WFGA3\":\"sum\",\"WFTM\":\"sum\",\"WFTA\":\"sum\",\"LScore\":\"sum\",\"winning_num_counts\":\"sum\",\n \"WOR\":\"sum\",\"WDR\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\n \"WAst\":\"sum\",\"WTO\":\"sum\",\"WStl\":\"sum\",\"WBlk\":\"sum\",\"WPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"LScore\":\"losing_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"LFGA\":\"LFGA_opp\",\"LFGM\":\"LFGM_opp\"})\n )","def get_team_names(self, year1, year2):\n\n base_url = 'http://www.sports-reference.com/cbb/schools/'\n response = urllib2.urlopen(base_url)\n content = response.read()\n soup = BeautifulSoup(content, 'html.parser')\n soup_results = soup.findAll('tr', {'class':''})\n extract_name = lambda name: name.split('/')[3]\n team_names = []\n \n for result in soup_results[1::]:\n year_span = result.findAll('td', {'align':'center'}) \n year_span = map(int, [year.string for year in year_span])\n\n if year_span[0] <= year1 and year_span[1] >= year2:\n team_name = result.find('a', href = True).get('href')\n team_name = extract_name(team_name)\n team_names.append(str(team_name))\n\n self.team_names = team_names","def scrape_all_world_cup_games():\n\n def scrape_scores_year(year):\n urls = scrape_world_cup_scoreboard(year)\n scores = [scrape_fifa_game(url, 'FIFA World Cup') for url in urls]\n return scores\n\n l = []\n for year in sorted(world_cup_mapping.keys()):\n l.extend(scrape_scores_year(year))\n return l","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )","def get_leagues_and_countries(source=utils.get_native_source):\n if not isinstance(source, games.models.Source):\n # If I used source=native_source() or if native_source was a global variable then\n # during db initialization (running command initialize) you would get an error since\n # it gets its value when the database is empty.\n source = source()\n logger.info(\"getting leagues and countries from source %s...\", source)\n if not source:\n return [], []\n data, meta, status_code = sportmonks.countries.all(include='leagues.seasons')\n if not data:\n # if the status code is not 200 data and meta are None\n return [], []\n # with open('sportmonks/response_texts/aws_01.txt', 'w') as outfile:\n # json.dump(meta, outfile, indent=4)\n # json.dump(data, outfile, indent=4)\n\n pre_countries, pre_competitions = [], []\n\n try:\n # Notice that only the first supported sport will be processed (currently this is is acceptable since we only\n # support football and so the first supported sport will always be football)\n sport_sids = parse_sport(meta)\n sports = []\n for sport_sid in sport_sids:\n sport = games.models.Sport.by_sid(sid=sport_sid, source=source)\n if not sport:\n logger.info(\"Sport contained in the response with sid {} is not supported\".format(sport_sid))\n continue\n sports.append(sport)\n if not sports:\n logger.error(\"No supported sport in the response\")\n return [], []\n football_gname = games.naming.sport_names.get('football', None)\n football = games.models.Sport.objects.get(name=football_gname)\n if football not in sports:\n logger.info(\"Football is not in response\")\n return [], []\n # logger.debug(\"Trying to get sport from source: %s and sid: %s\", source, sport_sid)\n sport_gname = football_gname\n for item in data:\n try:\n country_sid = item.get('id')\n # logger.debug('country_sid: %s', country_sid)\n country_sname = item.get('name')\n # logger.debug('country_sname: %s', country_sname)\n extra = item.get('extra')\n # logger.debug('extra: %s', extra)\n leagues = item.get('leagues').get('data')\n # logger.debug('leagues: %s', leagues)\n try:\n fifa_code = extra.get('fifa') # some countries might lack extra information\n except AttributeError:\n fifa_code = None\n except Exception as e:\n logger.data_error('%s', e)\n continue\n pre_country = pre_models.PreCountry(source=source, sname=country_sname, sid=country_sid, fifa_code=fifa_code)\n pre_countries.append(pre_country)\n for league in leagues:\n try:\n # sportmonks uses sgname for leagues. I use this sgname as an sname (comp_season_specific name)\n competition_sname = league.get('name')\n # logger.debug('competition_sname: %s', competition_sname)\n sid = league.get('id')\n # logger.debug('sid: %s', sid)\n seasons = league.get('seasons').get('data')\n # logger.debug('seasons: %s', seasons)\n except Exception as e:\n logger.data_error('%s', e)\n continue\n competition_season_utils = []\n # comp_seas_sids = []\n for season in seasons:\n try:\n season_name = season.get('name')\n # logger.debug('season_name: %s', season_name)\n # season_name = seasons_special_treatment(season_name)\n competition_season_sid = season.get('id')\n # logger.debug('competition_season_sid: %s', competition_season_sid)\n is_current_season = season.get('is_current_season', False)\n # logger.debug('is_current_season: %s', is_current_season)\n except Exception as e:\n logger.data_error('%s', e)\n continue\n # comp_seas_sids.append(competition_season_sid)\n zak_season_name = games.models.Season.zakandify_season_string(season_name)\n season = zakanda.utils.season_from_season_name(zak_season_name)\n competition_season_type = get_competition_season_type(season)\n competition_season_util = pre_models.CompetitionSeasonUtil(season, competition_season_sid, competition_sname, competition_season_type)\n competition_season_utils.append(competition_season_util)\n # logger.debug(\"competition season sids: %s\", comp_seas_sids)\n pre_competition = pre_models.PreCompetition(\n source=source, sname=competition_sname, sid=sid, sport_name=sport_gname,\n competition_season_utils=competition_season_utils, pre_country=pre_country)\n pre_competitions.append(pre_competition)\n\n except Exception as e:\n logger.error('%s Unexpected problem with sportmonks.countries.all from source %s', e, source)\n logger.info(\"%s pre countries and %s pre competitions were created\", len(pre_countries), len(pre_competitions))\n return pre_countries, pre_competitions","def get_people(team):","def getAllTeams(self):\n return []","def get_all_matches_by_league(self):\n raise NotImplementedError","def cumulative_stats_for_teams_each_year(self):\n self.cumulative_stats_for_team_each_year = (\n self.combine_both_winning_losing_games_stats\n .sort_values(['WTeamID','Season'])\n .groupby(['WTeamID'])\n .cumsum()\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\n .drop(['LTeamID','win_rate'],1)\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n # rebounds cumsum stats\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # assists, turnovers, steals, blocks and personal fouls\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win or lose by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def get_teams():\n teams = []\n for teamId in range(1, 68):\n t = requests.get(TEAMS_URL.format(teamId)).json()\n team_list = t.get('teams')\n if team_list is None or len(team_list) == 0:\n continue\n teams.append(Team.from_json(team_list[0]))\n return teams","def teams(self, game_id: int) -> DataFrame[Any]:","def fa_finder(league_no, team_name):\n ros_proj_b_list = BatterProjection.objects.all()\n ros_proj_p_list = PitcherProjection.objects.all()\n player_comp = {}\n pitching_fa_list = yahoo_players(league_no, \"P\")\n batting_fa_list = yahoo_players(LEAGUE_NO, \"B\")\n avail_pitching_fas = rate_avail_players(pitching_fa_list, ros_proj_p_list)\n yahoo_team = get_single_yahoo_team(league_no, team_name)\n team_pitching_values = rate_team(yahoo_team, ros_proj_p_list)\n avail_batting_fas = rate_avail_players(batting_fa_list, ros_proj_b_list)\n team_batting_values = rate_team(yahoo_team, ros_proj_b_list)\n\n player_comp['Team Name'] = yahoo_team['team_name']\n player_comp['Pitching FAs'] = avail_pitching_fas\n player_comp['Pitching Team'] = team_pitching_values\n player_comp['Batting FAs'] = avail_batting_fas\n player_comp['Batting Team'] = team_batting_values\n\n return player_comp","def teams(self):\n return self._get_by_class(Team)","def get_teams(self):\n url = 'teams'\n result = self.get(url)\n return result.get('teams', result)","def scrape_all_world_cup_goals():\n def scrape_goals_year(year):\n urls = scrape_world_cup_scoreboard(year)\n goals = []\n for url in urls:\n goals.extend(scrape_fifa_goals(url, 'FIFA World Cup'))\n return goals\n\n l = []\n for year in sorted(world_cup_mapping.keys()):\n l.extend(scrape_goals_year(year))\n return l","def build_teams(self):\n # get all nations\n all_nations = self.games.get_all_nations()\n\n # build teams for all participating nations in FIFA World Cup 2018\n bt = BuildTeams(self.squad_size, self.selected_attrs)\n bt.read_data()\n\n # a dict with a nations' name as a key and players' data as value\n self.teams = {}\n\n # build squad for every nation\n for nation in all_nations:\n team = bt.build_team(nation)\n # if we got enough players, add team\n if team.shape[0] >= bt.squad_size:\n #print(team)\n # convert pandas dataframe to matrix and flatten it\n self.teams[nation] = team.as_matrix().flatten()","def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\n\tLOG.debug('Concurrency = %d', concurrency)\n\tgames = season_games(year)\n\tif week is not None:\n\t\tgames = games[games.week == week]\n\texpected_n = len(games)\n\ttables, futures_to_args, failures = [], {}, []\n\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\n\twith futures.ThreadPoolExecutor(concurrency) as pool:\n\t\tfor arg in zip(games.hometeam, games.awayteam, games.week):\n\t\t\targ = arg + (year,)\n\t\t\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\n\t\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\n\t\t\targs = futures_to_args[future]\n\t\t\ttry:\n\t\t\t\ttable = future.result()\n\t\t\texcept Exception as exc:\n\t\t\t\tLOG.exception('Error from %s: %s', args, exc)\n\t\t\telse:\n\t\t\t\tif table is None:\n\t\t\t\t\tLOG.error('Failure: %s', args)\n\t\t\t\t\tfailures.append(args)\n\t\t\t\telse:\n\t\t\t\t\tLOG.info('Success: %s', args)\n\t\t\t\t\ttables.append(table)\n\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\n\tif __debug__:\n\t\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\n\t\tassert n == expected_n, \"Expected %d games, got %d\" % (expected_n, n)\n\treturn tables, failures","def determine_winners(self, players=None):\n players_and_cards = [(holding.player.id, holding.codes) for holding in self.live_holdings]\n if players:\n player_ids = [p.id for p in players]\n players_and_cards = [d for d in players_and_cards if d[0] in player_ids]\n winners = determine_winners(players_and_cards, self.board.codes)\n return [Player.query.get(winner) for winner in winners]","def teams(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'teams')\r\n\r\n return http.Request('GET', url), parsers.parse_json","def get_players_by_team(self, team, year=None):\n year = self._year if not year else year\n return self._db.Players.find({'{}.Team'.format(year) : team})","def __init__(self, name=\"team_name\", races=0, wins=0, drivers_championships_years=[], constructors_championships_years=[]):\n self.name = name\n self.races = races\n self.wins = wins\n self.drivers_championships_years = drivers_championships_years\n self.constructors_championships_years = constructors_championships_years","def get_team_stats(self, team_name, year):\n \n base_url = 'http://www.sports-reference.com/cbb/schools/' + \\\n team_name + '/' + str(year) + '.html'\n\n response = urllib2.urlopen(base_url)\n content = response.read()\n soup = BeautifulSoup(content)\n soup_results = soup.find('td', text='Team')\n team_stats = []\n \n if soup_results:\n soup_results = soup_results.parent()\n \n for result in soup_results[1::]:\n if result.string:\n team_stats.append(float(result.string))\n else:\n team_stats.append(None)\n else:\n team_stats += [None]*21\n\n return team_stats","def find_games(days_ahead=0):\n headers = {\n 'Host': 'stats.nba.com',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\n 'Accept': 'application/json, text/plain, */*',\n 'Accept-Language': 'en-US,en;q=0.5',\n 'Referer': 'https://stats.nba.com/',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Connection': 'keep-alive',\n 'x-nba-stats-origin': 'stats',\n 'x-nba-stats-token': 'true'\n }\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\n board.replace(id_to_abrv, inplace=True)\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]","def test_get_free_agents_season_start(league, season_start_date):\n # equals all players minus drafted players\n # make sure none of the draft players in list\n free_agents = league.as_of(season_start_date).free_agents()\n drafted = league.draft_results(format='Pandas')\n assert(len(free_agents.index.intersection(drafted.index)) == 0), \"Should be no drafted players as free agents\"\n # could make sure all 'all_players' that weren't drafted are here","def get_all_teams(self):\n return self._db.Teams.find({})","def get_sim_winners(teamNames,league,pokedex):\n bData, wHtml = loadBattleData(league)\n similarities = loadSims() \n\n results = []\n\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\n for d in bData:\n winner, loser = determineWinner(d)\n\n score = 0\n for pkm in myTeam:\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\n\n results.append((winner,score))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n \n cutoff = min(len(results),5)\n\n htmls = []\n for win,score in results[:cutoff]:\n htmls.extend(wHtml[str(sorted(win))])\n\n return htmls","def test_get_teams(self):\n pass","def test_get_teams(self):\n pass","def all_match_data(year):\n year_match_data = []\n match_year_data = pm()\n for count in range(len(match_year_data)):\n if year == match_year_data[count][1]:\n year_match_data.append(match_year_data[count])\n for count in range(len(year_match_data)):\n print(\n f'Game ID: {count + 1} Match date: {year_match_data[count][3]} {year_match_data[count][4]} vs '\n f'{year_match_data[count][5]}')\n\n return year_match_data","def getAllTeams(self):\n if self._teams is None:\n self._teams = []\n for match in self._matches.items():\n self._teams.append(match.homeTeam)\n return self._teams","def get_teams(event):\n teams_raw = tba_session.get(BASE_URL + '/event/%s/teams/keys' % event).json()\n teams = []\n for team_raw in teams_raw:\n teams.append(team_raw[3:])\n return teams","def collect_team(year, abbreviation):\n\n team = Team.objects.filter(year=year, abbreviation=abbreviation)[0]\n team = {\n 'id': abbreviation,\n 'team': team,\n }\n return team","def qualifiedteams(self):\n for i in range(0, len(self.teams)):\n for j in range(i + 1, len(self.teams)):\n WorldCupMatch(self.teams[i], self.teams[j], True)\n self.teams = sorted(self.teams, key=op.attrgetter('points', 'goaldifference', 'scored'))\n self.first_qualified = self.teams[len(self.teams)-1]\n self.second_qualified = self.teams[len(self.teams)-2]","def combine_playoff_championship_for_each_coach(self):\n self.final_coach_with_postseason_champion_each_year = (\n self.final_coach_with_postseason_each_year\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\n .fillna(0)\n )","def get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n # get losing games for coaches\n self.post_games_lose_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df for post season\n self.combine_post_games_won_lose = (\n self.post_games_lose_for_coaches\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"post_games_lost\",\"which_coach_for_win\":\"post_games_won\"})\n .fillna(0)\n )","def _get_teams(unstacked, min_games=0):\n count_col = 'game_id'\n home_gb = unstacked.groupby('hteam_id').count()[[count_col]]\n away_gb = unstacked.groupby('ateam_id').count()[[count_col]]\n merged = home_gb.merge(away_gb, how='outer', left_index=True, right_index=True)\n merged[count_col] = merged[count_col + '_x'] + merged[count_col + '_y']\n merged.index.name = 'index'\n teams = merged.reset_index().rename(columns={'index': 'team_id'})\n teams = teams.sort('team_id')\n teams['i_team'] = np.arange(teams.shape[0])\n team_index = {}\n for team, idx in teams[['team_id', 'i_team']].values:\n team_index[int(team)] = int(idx)\n return teams[['team_id', 'i_team']], team_index","def generate_teams(self):\n for ath in self.__extract_athletes():\n squad_id = dl.get_squad_id(ath)\n self.data_engine[squad_id][\"team_members\"].append(ath)\n\n \"\"\" We have already meet all criteria. \"\"\"\n if self.__have_team(squad_id):\n yield (squad_id,\n dl.get_squad_name_by_id(self.data, squad_id),\n self.data_engine[squad_id]['team_members'])","def scraping_league_stats():\n url=\"https://www.basketball-reference.com/teams/\"\n html = urlopen(url)\n bs = BeautifulSoup(html, 'html.parser')\n teams = bs.find(\"div\", {'id':'div_teams_active'}).findAll(\"a\")\n teams_url = [team.get(\"href\") for team in teams]\n team_players_list = []\n for team in teams_url:\n time.sleep(3)\n teamurl = domain + team\n print(teamurl) #fafaf\n html = urlopen(teamurl)\n bs = BeautifulSoup(html, 'html.parser')\n div_team = \"div_\" + teamurl[-4:-1]\n season = bs.find(\"div\", {'id':div_team}).find(\"a\").get(\"href\") #tengo que cambiarlo para que este guay\n #esto tiene la URL de la ultima season\n seasonurl = domain + season\n print(seasonurl)\n html = urlopen(seasonurl)\n bs = BeautifulSoup(html, 'html.parser')\n players = bs.find(\"table\", {'id':'roster'}).findAll(\"td\", {\"data-stat\":\"player\"})\n player_url_list = [player.find(\"a\").get(\"href\") for player in players]\n for player in player_url_list:\n player_url = domain + player\n time.sleep(3)\n print(player_url)\n html = urlopen(player_url)\n bs = BeautifulSoup(html, 'html.parser')\n try:\n tabla = pd.read_html(str(bs.find(\"div\", {'id':'all_per_poss'})).replace(\"<!--\", \"\"))[0] \n tabla[\"Player\"] = bs.find(\"h1\", {\"itemprop\" : \"name\"}).text.strip()\n indice = tabla[tabla[\"Season\"]==\"Career\"].index[0]\n tabla = tabla[0:indice]\n tabla = tabla.drop(axis= 1,columns = \"Unnamed: 29\")\n #no me encuentra tablas para uno del college que es el darlina01\n print(player_url)\n team_players_list.append(tabla)\n except:\n pass\n return pd.concat(team_players_list)","def get_players_df(year: int) -> pd.DataFrame:\n endpoint = _BASE_ENDPOINT + _PLAYER_ENDPOINT\n r = requests.get(endpoint.format(year=year)).json()\n standard_stats = r.get(\"league\").get(\"standard\")\n result_dict = [{\n \"FirstName\": s.get(\"firstName\"),\n \"LastName\": s.get(\"lastName\"),\n \"PlayerID\": s.get(\"personId\"),\n \"TeamID\": s.get(\"teamId\"),\n } for s in standard_stats]\n return pd.DataFrame(result_dict)","def test_teams_get_teams_v1(self):\n pass","def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\n battleData, htmlData = loadBattleData(league)\n similarities = loadSims() \n \n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\n if len(oppTeam) == 0:\n picks = set([])\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\n picks.add(random.randint(0,len(battleData)-1))\n\n losers = []\n loserDict = {}\n for i in picks:\n entry = battleData[i]\n winner,loser = determineWinner(entry)\n loserDict[str(loser)] = [winner]\n losers.append( (loser,0) )\n\n #Given opponent team then find similar teams\n else:\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\n\n #create dictionary from losers team to the team that beat them.\n loserDict = {}\n sims = []\n for d in battleData:\n winner, loser = determineWinner(d)\n\n wTeam = teamToArray(winner,pokedex)\n lTeam = np.array(teamToArray(loser, pokedex))\n\n score = 0\n for oppNp in oppTeam:\n score+= np.amax(lTeam*oppNp) \n\n if str(loser) in loserDict:\n loserDict[str(loser)].append(winner)\n else:\n #new to dictonary\n loserDict[str(loser)] = [winner]\n\n sims.append((loser, score))\n\n\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\n\n cutoff = min(len(sims),NUMLOSINGTEAMS)\n losers = sims[:cutoff]\n\n #Gather winners to losing teams\n winnersComp = []\n for loser,_ in losers:\n for winner in loserDict[str(loser)]:\n winnersComp.append(teamToArray(winner,pokedex))\n \n topScore = len(winnersComp)*6 #pkmn team size\n\n results = []\n inverted_idx = {}\n\n existsSet = []\n\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\n for i in range(len(curTeams)):\n team = curTeams[i]\n results.append((team,0))\n sTeam = set(team)\n if not (sTeam in existsSet):\n existsSet.append(sTeam)\n for pkm in team:\n if pkm != EMPTY:\n if pkm in inverted_idx:\n inverted_idx[pkm].append(i)\n else:\n inverted_idx[pkm] = [i]\n \n #Giving the similiarity scores to the winners based off of the inverted index.\n for pkm in inverted_idx:\n for winner in winnersComp:\n wArr = np.array(winner)\n #tArr = getSimPokemon(pkm,similarities)\n tArr = similarities[pkm]\n \n vals = wArr * tArr\n\n score = np.amax(vals)\n\n for i in inverted_idx[pkm]:\n results[i] = (results[i][0],results[i][1]+(score/topScore))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n\n if len(results) < NUMTEAMSRETURN:\n if len(results) == 0:\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\n\n else:\n returnTeams = [result[0] for result in results]\n teamScores = [result[1] for result in results]\n else:\n firstResult, firstScore = results[0]\n returnTeams = [firstResult]\n teamScores = [round(firstScore*100,1)]\n returnSets = [set(firstResult)]\n \n i = 1\n\n #Loops through results and adds teams with the proper edit distance away.\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\n teamToConsider,teamToConsiderScore = results[i]\n \n considerSet = set(teamToConsider)\n add = True\n ##checks the edit distance of teams is above wanted\n for team in returnSets:\n if len(team.union(considerSet)) < len(team)+minDistWanted:\n add = False\n\n ##If indeed above wanted levels then add\n if add:\n returnTeams.append(teamToConsider)\n returnSets.append(considerSet)\n teamScores.append(round(teamToConsiderScore*100,1))\n \n i+=1\n\n if i >= len(results):\n i = 1\n minDistWanted -= 1 \n \n winHtmls = []\n if htmlData != None:\n for team,_ in losers:\n for winner in loserDict[str(team)]:\n winHtmls.extend(htmlData[str(sorted(winner))])\n \n\n return returnTeams, teamScores, winHtmls","def organizations_owned(self):\n return sorted(set([team.org for team in self.teams if team.org.owners == team]),\n key=lambda o: o.title)","def get_games(date):\n scoreboard = nba_py.Scoreboard(month=date.month,\n day=date.day,\n year=date.year)\n line_score = scoreboard.line_score()\n game_header = scoreboard.game_header()\n\n games = []\n current_game = {}\n game_sequence = 0\n game_sequence_counter = 0\n\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\n for i, value in enumerate(line_score):\n if (value[\"GAME_SEQUENCE\"] != game_sequence):\n game_sequence += 1\n\n current_game[\"GAME_ID\"] = value[\"GAME_ID\"]\n home_team_id = game_header[game_sequence - 1][\"HOME_TEAM_ID\"]\n\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n \n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter += 1\n elif game_sequence_counter == 1:\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n\n current_game[\"GAME_STATUS_TEXT\"] = game_header[game_sequence - 1][\"GAME_STATUS_TEXT\"]\n if not game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]:\n current_game[\"BROADCASTER\"] = \"\"\n else:\n current_game[\"BROADCASTER\"] = game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]\n\n games.append(current_game)\n\n current_game = {}\n\n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter -= 1\n\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return (games, east_standings, west_standings)","def teams(teamid):\n team_summary = team.TeamSummary(teamid)\n team_summary_info = team_summary.info()\n team_season_ranks = team_summary.season_ranks()\n\n team_common_roster = team.TeamCommonRoster(teamid)\n roster = team_common_roster.roster()\n coaches = team_common_roster.coaches()\n\n season = team_summary_info[0][\"SEASON_YEAR\"]\n\n team_game_log = team.TeamGameLogs(teamid,\n season=season)\n team_games = team_game_log.info()\n\n playoffs_teamgamelogs = team.TeamGameLogs(teamid,\n season=season,\n season_type=\"Playoffs\")\n playoffs_team_games = playoffs_teamgamelogs.info()\n\n team_season = team.TeamSeasons(teamid)\n team_season_info = team_season.info()\n\n for i in team_season_info:\n if (i[\"YEAR\"] == season):\n current_season_info = i\n\n return render_template(\"teams.html\",\n title=team_summary_info[0][\"TEAM_CITY\"] + \" \" + team_summary_info[0][\"TEAM_NAME\"],\n teamid=teamid,\n team_summary_info=team_summary_info,\n team_season_ranks=team_season_ranks,\n season=season,\n team_games=team_games,\n playoffs_team_games=playoffs_team_games,\n team_season=team_season_info,\n roster=roster,\n coaches=coaches,\n current_season_info=current_season_info,\n team_img=TEAM_ID_DATA)","def mk_champs(cls, year, start_date, end_date):\n seasons = [\"Quad Quandary\", \"Face Off\", \"Hot Shot\", \"Get Over It\", \"Bowled Over\", \"Ring It Up\", \"Block Party\",\n \"Cascade Effect\", \"RES-Q\", \"Velocity Vortex\", \"Relic Recovery\", \"Rover Ruckus\"]\n season_name = seasons[year-2007]\n start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d\")\n end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n season = f\"{year % 100:02}{(year + 1) % 100:02}\"\n # fyear = f\"{year}-{(year+1)%1000:02d}\"\n if year == 2009:\n city, state_prov, country = \"Atlanta\", \"Georgia\", \"USA\"\n venue = \"Georgia Dome\"\n address = \"1 Georgia Dome Dr, Atlanta, GA 30313\"\n elif year < 2013:\n city, state_prov, country = \"St. Louis\", \"Missouri\", \"USA\"\n venue = \"Edward Jones Dome\"\n address = \"701 Convention Plaza, St. Louis, MO 63101\"\n else:\n city, state_prov, country = \"St. Louis\", \"Missouri\", \"USA\"\n venue = \"Union Station\"\n address = \"1820 Market Street, St. Louis, MO 63103\"\n shared = {\n \"year\": year,\n \"city\": city,\n \"state_prov\": state_prov,\n \"country\": country,\n \"end_date\": end_date,\n \"event_type\": EventType.WORLD_CHAMPIONSHIP,\n \"venue\": venue,\n \"address\": address,\n \"data_sources\": [\"USFIRST Website Archives\"]\n }\n\n finals = Event(key=f\"{season}cmp0\",\n name=f\"FTC {season_name} World Championship - Finals\",\n playoff_type=PlayoffType.BO3_FINALS, \n division_keys=[f\"{season}cmp1\", f\"{season}cmp2\"],\n start_date=end_date,\n **shared)\n franklin = Event(key=f\"{season}cmp{2}\",\n name=f\"FTC {season_name} World Championship - Franklin Division\",\n playoff_type=PlayoffType.STANDARD, \n parent_event_key=f\"{season}cmp0\", \n start_date=start_date,\n **shared)\n edison = Event(key=f\"{season}cmp{1}\",\n name=f\"FTC {season_name} World Championship - Edison Division\",\n playoff_type=PlayoffType.STANDARD,\n parent_event_key=f\"{season}cmp0\", \n start_date=start_date,\n **shared)\n return (franklin, edison, finals)","def find_players_for_team(self, team, src='roster', season=None):\n # creating class wide variable to hold current team\n if type(team) is str:\n team = Team.find(team)\n\n print(\"+ Searching %s players for %s\" % (src, team))\n\n if src == 'roster':\n players = self.get_roster_players_via_api(team, season)\n elif src == 'system':\n players = self.get_system_players(team)\n elif src == 'contract':\n players = self.get_contracted_players(team)\n\n return players","def opponent_games(df, team, gameids):\n games = df[df.gameid.isin(gameids)]\n opponent_games = games[games.team != team].drop_duplicates(subset=['gameid'])[['gameid', 'team']]\n opponent_games.columns = ['gameid', 'opponent']\n\n return opponent_games","def get_all_games(season):\n url = BASE_URL.format(season)\n json_data = requests.get(url, headers=HEADERS).json()\n all_games = json_data[\"resultSets\"][0][\"rowSet\"]\n return all_games","def test_teams_get_teams_v2(self):\n pass","def players(self):\n return Player.objects.filter(team=self)","def get_fb_team_rankings(self):\n\n ranks = []\n self._logger.debug(\"Getting foosball team rankings\")\n\n try:\n self.check_if_db_connected()\n cursor = self._db_conn.cursor()\n cursor.execute(\"SELECT team_id, team_name FROM team\")\n teams = cursor.fetchall()\n\n for team_id, team_name in teams:\n cursor.execute(\"SELECT fb_team_rating FROM \\\nteam WHERE team_id = {0}\".format(team_id))\n team_rating = cursor.fetchall()[0]\n\n cursor.execute(\"SELECT mu, sigma FROM rating WHERE rating_id \\\n= {0}\".format(team_rating[0]))\n mu, sigma = cursor.fetchall()[0]\n\n team_rank = float(mu) - (3 * float(sigma))\n\n # get player_ids\n cursor.execute(\"SELECT player from player_team_xref \\\nWHERE team = {0}\".format(team_id))\n players = cursor.fetchall()\n player_one = players[0]\n player_two = players[1]\n\n cursor.execute(\"SELECT first_name FROM player WHERE \\\nplayer_id = {0}\".format(player_one[0]))\n player_one_name = cursor.fetchone()[0]\n\n cursor.execute(\"SELECT first_name FROM player WHERE \\\nplayer_id = {0}\".format(player_two[0]))\n player_two_name = cursor.fetchone()[0]\n\n cursor.execute(\"SELECT COUNT(result_id) FROM fb_result WHERE \\\n(offense_winner = {0} AND defense_winner = {1}) OR (offense_winner = {1} \\\nAND defense_winner = {0})\".format(player_one[0], player_two[0]))\n team_win_count = cursor.fetchone()[0]\n\n cursor.execute(\"SELECT COUNT(result_id) FROM fb_result WHERE \\\n(offense_loser = {0} AND defense_loser = {1}) OR (offense_loser = {1} \\\nAND defense_loser = {0})\".format(player_one[0], player_two[0]))\n team_loss_count = cursor.fetchone()[0]\n\n intermediate_rank = (team_name, round(team_rank, 4),\n team_win_count, team_loss_count, player_one_name,\n player_two_name)\n ranks.append(intermediate_rank)\n del intermediate_rank\n\n except MySQLdb.OperationalError:\n self._logger.error(\"MySQL operational error occured\")\n traceback.print_exc()\n raise exceptions.DBConnectionError(\"Cannot connect to MySQL server\")\n\n except MySQLdb.ProgrammingError:\n self._logger.error(\"MySQL programming error\")\n traceback.print_exc()\n raise exceptions.DBSyntaxError(\"MySQL syntax error\")\n\n else:\n return ranks","def team_season_stats(team):\n\n # Get HTML Content\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # Team's yearly stats are displayed in a table\n season_stats = soup.find(id='stats').find('tbody')\n\n # Iterate through each year\n for year in season_stats.find_all('tr', {'class': None}):\n\n season_year = year.find('th').text[0:4]\n season_year = int(season_year) + 1\n season = {'year': season_year}\n\n # Loop through each stat\n for stat in year.find_all('td'):\n season[stat['data-stat']] = stat.string\n\n # Rename relocated teams\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\n season['_id'] = season['team_id'] + '_' + str(season_year)\n\n # Remove unwanted stats\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\n for k in to_remove:\n season.pop(k, None)\n\n # Add to MongoDB\n m.insert('team_season', season)","def get_final_coach_for_each_season(self):\n self.final_coach_for_season = (\n self.num_days_coach_for_season\n .groupby(['Season','TeamID'])\n .agg({\"CoachName\":\"count\"})\n .reset_index()\n .rename(columns={\"CoachName\":\"coach_counts\"})\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \"ignore\")))\n [['Season','TeamID','final_coach']]\n )","def test_fantasy_status_nov_1(league):\n nov_1 = datetime.datetime(2019,11,1)\n players = league.as_of(nov_1).all_players()\n # make sure sammy blais is not a free agent, he was picked up oct 31\n assert(players.loc[6544, 'fantasy_status'] != 'FA')","def get_list_team_scores(self):\n scores = defaultdict(lambda: {\n \"scored_xg\": [],\n \"conceded_xg\": [],\n \"home_adv\": 0,\n \"expected_points\": 0\n })\n\n for g in self.games:\n scores[g.HomeTeam][\"scored_xg\"].append(g.FTHG)\n scores[g.HomeTeam][\"conceded_xg\"].append(g.FTAG)\n scores[g.AwayTeam][\"scored_xg\"].append(g.FTAG)\n scores[g.AwayTeam][\"conceded_xg\"].append(g.FTHG)\n\n for team in scores.keys():\n scores[team][\"expected_points\"] = (self.get_table(metric='points')[team] /\n len(scores[team][\"scored_xg\"]))\n\n return scores","def get_all_teams(group):\n base_url = 'http://worldcup.kimonolabs.com/api/teams'\n url = (base_url + '?apikey={key}&group={group}&sort={sort}'\n .format(group=group,\n key='KERbxAUfDYovbQnn9pR3pbLWEMRp47AQ',\n sort='groupRank'))\n r = requests.get(url)\n return r.json()","def get_game_urls(year, week):\n base_url = \"https://www.pro-football-reference.com\"\n\n # URL for the summary of all games for a given week\n # This page has the each match up for the week and\n # a link to the detailed game stats \n url = \"https://www.pro-football-reference.com/years/{}/week_{}.htm\".format(\n year, week)\n\n response = get(url)\n\n week_summary = BeautifulSoup(response.content, 'html.parser')\n\n # Get just the game summary content\n week_summary_games = week_summary.find_all(\n attrs={\"class\": \"game_summary expanded nohover\"})\n\n losing_teams = []\n winning_teams = []\n game_link = []\n\n # For each game played, extract the losing team,\n # the winning team, and the detailed game stats link\n for game in week_summary_games:\n try:\n losing_team_strings = [text for text in game.find(\n attrs={\"class\": \"loser\"}).stripped_strings]\n losing_teams.append(losing_team_strings[0])\n except AttributeError:\n pass\n\n try:\n winning_team_strings = [text for text in game.find(\n attrs={\"class\": \"winner\"}).stripped_strings]\n winning_teams.append(winning_team_strings[0])\n except AttributeError:\n pass\n\n for link in game.find_all(href=re.compile(\"boxscores\")):\n game_link.append(base_url + link.get('href'))\n\n\n game_urls = dict(zip(losing_teams, game_link))\n game_urls.update(dict(zip(winning_teams, game_link)))\n\n return game_urls","def get_teams(self, *teams):\n teams = [convert_name(team, how='abbr') for team in teams]\n return self._db.Teams.find({'Tm' : {'$in' : teams}})","def get_tourney_rounds(self, conference, year):\n ts_dict = self.get_tourney_slots()\n seed_dict = self.get_tourney_seeds()\n tr_dict = self.get_tourney_results()\n \n round_1 = list()\n round_2 = list()\n round_3 = list()\n round_4 = list()\n winner = list()\n \n round1_winners = list()\n for seed, team in seed_dict[year].items():\n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n round1_winners.append(seed[1:])\n #removes duplicates because I did this part weirdly... HEHEH\n round1_winners = list(set(round1_winners))\n\n win_counter = defaultdict(int)\n for seed, team in seed_dict[year].items(): \n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n win_counter[winning] += 1\n \n for slot, matchup in ts_dict[year].items():\n \n if conference in slot and \"R1\" in slot: \n round_1.append(\"{}-{}\".format(matchup[1:3], matchup[-2:]))\n round_1 = sorted(round_1)\n #for match in round_1:\n for winner1 in round1_winners:\n if winner1 in round_1[0]:\n for winner2 in round1_winners:\n if winner2 in round_1[-1]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[1]:\n for winner2 in round1_winners:\n if winner2 in round_1[-2]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[2]:\n for winner2 in round1_winners:\n if winner2 in round_1[-3]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[3]:\n for winner2 in round1_winners:\n if winner2 in round_1[-4]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n round_2 = sorted(round_2)\n\n round2_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 1:\n round2_winners.append(seed[1:])\n \n for winner1 in round2_winners:\n if winner1 in round_2[0]:\n for winner2 in round2_winners:\n if winner2 in round_2[-1]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_2[1]:\n for winner2 in round2_winners:\n if winner2 in round_2[-2]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n round_3 = sorted(round_3)\n\n round3_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 2:\n round3_winners.append(seed[1:])\n\n for winner1 in round3_winners:\n if winner1 in round_3[0]:\n for winner2 in round3_winners:\n if winner2 in round_3[-1]:\n round_4.append(\"{}-{}\".format(winner1, winner2))\n round_4 = sorted(round_4)\n\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 3:\n winner.append(seed[1:])\n\n conferences = {\"W\": \"East\", \"X\": \"Midwest\", \"Y\": \"South\", \"Z\": \"West\"}\n\n #print(\"CONFERENCE: {}, YEAR: {}\".format(conferences[conference], year))\n #print(\"ROUND1:\", round_1)\n #print(\"ROUND2:\", round_2)\n #print(\"ROUND3:\", round_3)\n #print(\"ROUND4:\", round_4)\n #print(\"WINNER:\", winner)\n\n #clearing out the tourney results dictionary\n #tr_dict.clear()\n\n return round_1, round_2, round_3, round_4, winner","def scrape_world_cup_scoreboard(year):\n # Replace this with the results logic somehow...\n\n d = world_cup_mapping[year]\n prefix = 'http://www.fifa.com'\n if type(d) == int:\n root_url = '/worldcup/archive/edition=%s/' % d\n else:\n root_url = '/worldcup/archive/%s/' % d\n data = scrape_url(prefix + root_url + \"results/index.html\")\n\n # Find urls in the page.\n match_re = re.compile(root_url + \"results/matches/match=\\d+/report.html\")\n urls = match_re.findall(data)\n return [prefix + e for e in urls]","def get_season_data(year):\n # Make page request from PFR.\n url = f'https://www.pro-football-reference.com/years/{year}/fantasy.htm'\n res = requests.get(url)\n\n # Create soup object out of request.\n soup = BeautifulSoup(res.content, 'lxml')\n\n try:\n # Extract table of player data from soup object.\n table = soup.find('table', {'id': 'fantasy'})\n\n # Extract all rows from table\n rows = table.find('tbody').find_all('tr')\n\n # Many rows in the table are just header rows, so we need to extract\n # only the rows with player information.\n player_rows = [row for row in rows if row.get('class') != ['thead']]\n\n return player_rows\n except AttributeError:\n return None","def create_teams(a_season):\n\n team_a = Team(name=\"Team A\", season=a_season)\n team_a.save()\n for p in create_players(7, 0):\n team_a.players.add(p)\n team_a.save()\n team_b = Team(name=\"Team B\", season=a_season)\n team_b.save()\n for p in create_players(7, 16):\n team_b.players.add(p)\n team_b.save()","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def scraping_actual_team_players(team_abbreviation):\n starting_point = domain + \"/teams/\"\n teamurl = starting_point + team_abbreviation + \"/\"\n team_id = \"div_\" + team_abbreviation\n html = urlopen(teamurl)\n bs = BeautifulSoup(html, 'html.parser')\n actual_team_url = domain + str(bs.find(\"div\", {'id': team_id}).find(\"a\").get(\"href\"))\n html = urlopen(actual_team_url)\n bs = BeautifulSoup(html, 'html.parser')\n players = bs.find(\"table\", {'id':'roster'}).findAll(\"td\", {\"data-stat\":\"player\"})\n players_url = [player.find(\"a\").get(\"href\") for player in players]\n team_players_list = []\n for player_url in players_url:\n time.sleep(3)\n url = domain + player_url\n html = urlopen(url)\n bs = BeautifulSoup(html, 'html.parser')\n print(player_url)\n try:\n tabla = pd.read_html(str(bs.find(\"div\", {'id':'all_per_poss'})).replace(\"<!--\", \"\"))[0] \n tabla[\"Player\"] = bs.find(\"h1\", {\"itemprop\" : \"name\"}).text.strip()\n indice = tabla[tabla[\"Season\"]==\"Career\"].index[0]\n tabla = tabla[0:indice]\n tabla = tabla.drop(axis= 1,columns = \"Unnamed: 29\")\n #no me encuentra tablas para uno del college que es el darlina01\n print(player_url)\n team_players_list.append(tabla)\n except:\n pass\n return pd.concat(team_players_list)","def get_contracted_players(self, team):\n # setting up empty list of players\n players = list()\n\n # getting html document with team's contracted players\n doc = self.get_html_document(team, 'contracts')\n\n # returning empty list if no system page could be found\n if doc is None:\n return players\n\n # collecting player names and links to capfriendly pages for different\n # player groups\n cf_links = doc.xpath(\n \"//table[@id='team']/tr[@class='column_head c']/td/parent::tr/following-sibling::tr/td[1]/a/@href\")\n cf_names = doc.xpath(\n \"//table[@id='team']/tr[@class='column_head c']/td/parent::tr/following-sibling::tr/td[1]/a/text()\")\n\n for lnk, name in zip(cf_links, cf_names):\n # retrieving capfriendly id from player page link\n cf_id = lnk.split(\"/\")[-1]\n # trying to find player in database\n plr = Player.find_by_capfriendly_id(cf_id)\n # trying to find player using suggestions\n if plr is None:\n last_name, first_name = name.split(\", \")\n suggested_players = self.get_suggested_players(\n last_name, first_name)\n for suggested_player in suggested_players:\n (\n sugg_plr_id, sugg_pos,\n sugg_last_name, sugg_first_name, _\n ) = (\n suggested_player\n )\n if (last_name, first_name) == (\n sugg_last_name, sugg_first_name):\n plr = Player.find_by_id(sugg_plr_id)\n if plr is None:\n plr = self.create_player(\n sugg_plr_id, last_name, first_name, sugg_pos)\n\n if plr is None:\n print(\"Unable to find player with name %s\" % name)\n else:\n players.append(plr)\n\n return players","def test_get_player_stats_from_game():\n team = \"Titans\"\n year = \"2018\"\n week = \"1\"\n expected_team_stats = {}\n\n assert ff_team.get_player_stats_from_game(\n team, year, week) == expected_team_stats","def list_chassis_per_season(start_year=1950, end_year=2020, file_changed=False):\n\n def _get_chassis_names(years):\n req = requests.get(WIKIPEDIA_F1_URL)\n soup = BeautifulSoup(req.content, 'html.parser')\n links = soup.find_all('a')\n\n for link in links:\n link_text = ['Formula', 'One', 'season', 'cars']\n if str(link.get('href')).split('_')[-4:] == link_text:\n link_season = str(WIKIPEDIA + link.get('href')).replace(' ', '')\n season = str(link.get('href')).replace(':', '_').split('_')[-5:-4][0]\n\n if int(season) in years:\n\n req = requests.get(link_season)\n soup = BeautifulSoup(req.content, 'html.parser')\n team_divs = soup.findAll('div', attrs={'class': 'mw-category-group'})\n teams_season_list = []\n\n for team_div in team_divs:\n team_links = team_div.findAll('a')\n for team_link in team_links:\n teams_season_list.append(team_link.get('title'))\n\n\n # Checking missing teams\n missing_teams = MISSING_CHASSIS.get(season)\n for missing_team in missing_teams:\n if missing_team not in teams_season_list:\n teams_season_list.append(missing_team)\n\n seasons_chassis[season] = teams_season_list\n\n save_json(seasons_chassis)\n\n return seasons_chassis\n\n seasons_chassis = load_json(F1_CHASSIS)\n\n # Check if we got the chassis names for all seasons listed\n update_list = []\n\n for year in range(start_year, end_year + 1):\n if str(year) not in seasons_chassis.keys():\n update_list.append(year)\n\n chassis = _get_chassis_names(update_list) if update_list else seasons_chassis\n\n return chassis","def test_teams_list(self):\n pass","def autocomplete_teams():\n school_name = request.args.get('school_name')\n school = School.query.filter(School.name.ilike(school_name)).first()\n if not school:\n return jsonify(**{})\n return jsonify(json_list=[t.name for t in school.teams if len(t.users) < 2])","def split_by_wins(matches, team_id):\n\n sorted_matches = {\n \"wins\": [],\n \"losses\": [],\n \"ties\": []\n }\n\n scores = {}\n\n for match_id, match in matches.items():\n this_team_score = 0\n other_team_score = 0\n if match.hometeam.team_id == team_id:\n this_team_score = match.homegoals\n other_team_score = match.awaygoals\n elif match.awayteam.team_id == team_id:\n this_team_score = match.awaygoals\n other_team_score = match.homegoals\n else:\n continue\n\n scores[match_id] = (this_team_score, other_team_score)\n\n if this_team_score > other_team_score:\n sorted_matches[\"wins\"].append(match_id)\n elif this_team_score < other_team_score:\n sorted_matches[\"losses\"].append(match_id)\n else:\n sorted_matches[\"ties\"].append(match_id)\n\n return sorted_matches, scores","def teams():\n print 'Getting Teams'\n\n substring = \"%\" + request.args.get('t') + \"%\"\n\n team_list = datastore.get_teams_typeahead(engine, substring, max_teams=10)\n\n print 'Teams:', team_list\n return jsonify(team_list)","def season_game_logs(team, year):\n\n # Check year value\n if year > 2019 or year < 1950:\n raise ValueError('Year Value Incorrect')\n\n # Rename teams that moved\n team = scrape_utils.rename_team(team, year)\n\n # Get HTML content\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n season_stats = soup.find(id='tgl_basic')\n games = season_stats.find('tbody')\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # To find opponent statistics\n opponent = re.compile('^opp_.*$')\n\n # Loop through every game in a team's season\n for game in games.find_all('tr', {'class': None}):\n\n curr_team = {'team': team}\n opp_team = {}\n\n # Loop through each stat\n for stat in game.find_all('td'):\n\n stat_name = stat['data-stat']\n\n # These are opponent stats\n if re.match(opponent, stat_name):\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\n else:\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\n\n # Remove unnecessary information\n del curr_team['game_season']\n del curr_team['x']\n\n # Rename relocated teams\n curr_team['team'] = scrape_utils.rename_team(team)\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\n\n # Use the same ID as basketball reference\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \"%Y-%m-%d\"),\n 'season': year,\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\n '_id': game.find('a')['href'][-17:-5]}\n\n # Place the teams in the correct spot depending on who is the home team\n if curr_team.pop('game_location') == 0:\n result['home'] = curr_team\n result['away'] = opp_team\n else:\n result['home'] = opp_team\n result['away'] = curr_team\n\n # Insert into database\n m.insert('game_log', result)","def teams(self):\n return self.V().is_team()","def get_matchup_history(self, *teams):\n abbrA = convert_name(teams[0], how='abbr')\n abbrB = convert_name(teams[1], how='abbr')\n\n return self._db.Games.find({'$or' : [{'home' : abbrA,\n 'away' : abbrB},\n {'home' : abbrB,\n 'away' : abbrA}]})","def get_matches_with_teams():\n\tf = open(\"raw_tba.json\")\n\tjsonvar = json.loads(f.read())\n\n\treturn_val = []\n\tfor i in jsonvar:\n\t\t# print i\n\t\tif \"score_breakdown\" in i and i[\"score_breakdown\"] != None:\n\t\t\treturn_val.append(FullTBAMatch(i))\n\n\treturn return_val","def get_team_roster(league):\n pass","def get_team_stats(players: list[Player]) -> dict[int]:\n\n team_stats = {}\n\n total_reaction = 0\n total_mechanical_skill = 0\n total_tactical_skill = 0\n total_game_knowledge = 0\n total_xp = 0\n\n for player in players:\n total_reaction += player.reaction\n total_mechanical_skill += player.mechanical_skill\n total_tactical_skill += player.tactical_skill\n total_game_knowledge += player.game_knowledge\n total_xp += player.xp\n\n team_stats.update(\n {\"reaction\": total_reaction,\n \"mechanical_skill\": total_mechanical_skill,\n \"tactical_skill\": total_tactical_skill,\n \"game_knowledge\": total_game_knowledge,\n \"xp\": total_xp})\n\n return team_stats","def get_available_teams(self):\n teams = self.request.user.team_set.filter(competition__is_open=True)\n if not teams.exists():\n msg = \"Can't send invites at this time. You're not\"\n msg += \" registered for any open competitions\"\n messages.error(self.request, msg)\n raise Http404(msg)\n return teams","def get_player_stats(season_start_year, csv_file_name = None):\n season_year_full = convert_season_start_to_season_years(\n starting_year = season_start_year\n )\n \n players_df = playergamelogs.PlayerGameLogs(\n season_nullable = season_year_full\n ).player_game_logs.get_data_frame()\n \n # Keep the relevant columns\n players_df = players_df[[\n \"SEASON_YEAR\", \"PLAYER_ID\", \"PLAYER_NAME\", \"TEAM_NAME\",\n \"GAME_ID\", \"GAME_DATE\", \"MATCHUP\", \"WL\", \"MIN\",\n \"FGM\", \"FGA\", \"FTM\", \"FTA\", \"FG3M\", \"PTS\", \"REB\",\n \"AST\", \"STL\", \"BLK\", \"TOV\"]]\n \n # Convert GAME_DATE to datetime\n players_df[\"GAME_DATE\"] = pd.to_datetime(\n players_df[\"GAME_DATE\"]\n )\n \n # Save the data frame to a csv if a file name exists\n if csv_file_name != None:\n # Save to current directory\n csv_path = Path(\"./data/\" + csv_file_name + \".csv\")\n players_df.to_csv(path_or_buf = csv_path,\n index = False,\n na_rep = 'NULL')\n \n return players_df","def losses(self):\n return [g for g in self.games if g.winner is not self.team]","def get_teams(self, *args, **kwargs):\n\n teams_data = api.get_teams(\n *args,\n api_key=self.__creds.api_key_v2,\n **kwargs)\n return [en.Team(creds=self.__creds, **team_data) for team_data in teams_data]","def playerStandings():\n # place all players in a dictionary\n player_dict = {}\n conn, c = connect()\n c.execute(\"\"\"SELECT * FROM players;\"\"\")\n for row in c.fetchall():\n player_dict[row[0]] = [row[1], 0, 0]\n\n # count the number of win and matches in for all matches\n c.execute(\"\"\"SELECT winner, loser FROM matches;\"\"\")\n for row in c.fetchall():\n if row[0] in player_dict:\n player_dict[row[0]][1] += 1\n player_dict[row[0]][2] += 1\n if row[1] in player_dict:\n player_dict[row[1]][2] += 1\n\n # compile win counts as the key to dictionary\n win_count = {}\n for i in player_dict:\n wins = player_dict[i][1]\n if wins in win_count:\n win_count[wins].append((i, player_dict[i][0],\n wins, player_dict[i][2]))\n else:\n win_count[wins] = [(i, player_dict[i][0],\n wins, player_dict[i][2])]\n\n # compile output list\n output_list = []\n for i in sorted(win_count.keys(), reverse=True):\n for j in win_count[i]:\n output_list.append(j)\n\n return output_list"],"string":"[\n \"def get_player_games(self, year, use_local=True):\",\n \"def collect_teams(year):\\n\\n team_list = Team.objects.filter(year=year).order_by('location')\\n teams = []\\n for t in team_list:\\n team = {\\n 'id': t.abbreviation,\\n 'team': t,\\n }\\n teams.append(team)\\n return teams\",\n \"def get_teams():\",\n \"def collect_teams(year: int = 2005) -> None:\\n\\n\\twith open('../resources/config.json') as config_file, open('../resources/secrets.json') as secrets_file:\\n\\t\\tconfig_json = json.load(config_file)\\n\\t\\tsecrets_json = json.load(secrets_file)\\n\\n\\t\\turl = '/'.join(['http:', '', config_json['base_url'], config_json['fbs_teams_endpoint']])\\n\\t\\tapi_key = secrets_json['api_key']\\n\\n\\theaders = {'Authorization': api_key}\\n\\tparams = {'year': year}\\n\\n\\tresponse = requests.get(url, headers = headers, params = params).json()\\n\\n\\t# dict of one array for json dump\\n\\tteam_names = {'teamNames': list(map(lambda r: r['school'], response))}\\n\\n\\twith open('../resources/teams.json', 'w') as teams_file:\\n\\t\\tjson.dump(team_names, teams_file)\",\n \"def get_all_cfb_teams(year):\\n \\n url = BASE_URL + '/cfb/years/{}-standings.html'.format(year)\\n soup = get_soup(url)\\n \\n table = soup.find('table', attrs={'id':'standings'})\\n rows = table.find_all('tr')\\n \\n team_list = []\\n \\n for row in rows:\\n team = row.find('td', attrs={'data-stat':'school_name'})\\n \\n if not team:\\n continue\\n \\n team_link = team.find('a').get('href') \\n team_list.append(team_link)\\n \\n return team_list\",\n \"def wins(self):\\n return [g for g in self.games if g.winner is self.team]\",\n \"def test_get_teams_in_year_names():\\n assert sorted(gtiy(2008)) == sorted(team_2008)\\n assert sorted(gtiy(2009)) == sorted(team_2009)\\n assert sorted(gtiy(2010)) == sorted(team_2010)\\n assert sorted(gtiy(2011)) == sorted(team_2011)\\n assert sorted(gtiy(2012)) == sorted(team_2012)\\n assert sorted(gtiy(2013)) == sorted(team_2013)\\n assert sorted(gtiy(2014)) == sorted(team_2014)\\n assert sorted(gtiy(2015)) == sorted(team_2015)\\n assert sorted(gtiy(2016)) == sorted(team_2016)\\n assert sorted(gtiy(2017)) == sorted(team_2017)\",\n \"def non_current_championships():\\n current_championships = (Alfa_Romeo+Ferrari+Haas+McLaren+Mercedes+Racing_Point+Red_Bull+Renault+Toro_Rosso+Williams).constructors_championships_years\\n non_current_championships = []\\n year = 1958\\n while year < 2020:\\n if year not in current_championships:\\n non_current_championships.append(year)\\n year += 1\\n return f\\\"The F1 Constructors' Championships won by teams no longer on the grid are: \\\\n{non_current_championships}\\\"\",\n \"def test_get_teams_in_year_len():\\n assert len(gtiy(2008)) == 8\\n assert len(gtiy(2009)) == 8\\n assert len(gtiy(2010)) == 8\\n assert len(gtiy(2011)) == 10\\n assert len(gtiy(2012)) == 9\\n assert len(gtiy(2013)) == 9\\n assert len(gtiy(2014)) == 8\\n assert len(gtiy(2015)) == 8\\n assert len(gtiy(2016)) == 8\\n assert len(gtiy(2017)) == 8\",\n \"def get_all_cfb_teams_for_year_range(start_year, end_year):\\n \\n full_team_list = []\\n \\n for year in range(start_year, end_year + 1):\\n print(year)\\n \\n team_list = get_all_cfb_teams(year)\\n full_team_list += team_list\\n \\n wait_time = random.randint(3, 15)\\n time.sleep(wait_time)\\n \\n return full_team_list\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def get_companies_and_people(team):\",\n \"def teams(self):\\n t = [e[0] for e in self.pick_set.all().values_list(\\\"team\\\")]\\n return Team.objects.filter(id__in=set(t))\",\n \"def get_teams_from_league(teams, league):\\n\\t\\n\\tteams_1 = []\\n\\tfor i in range(0,len(teams)):\\n\\t\\tif teams[i].league == 1:\\n\\t\\t\\tteams_1.append(teams[i])\\n\\n\\treturn teams_1\",\n \"def get_available_companies(team):\",\n \"def get_available_companies_and_people(team):\",\n \"def getTeamStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\\\\r\\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\\\\r\\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n team_df = df[[\\\"TEAM_ID\\\",\\\"TEAM_NAME\\\",\\\"GP\\\",\\\"W\\\",\\\"L\\\",\\\"W_PCT\\\",\\\"MIN\\\",\\\"FGM\\\",\\r\\n \\\"FGA\\\",\\\"FG_PCT\\\",\\\"FG3M\\\",\\\"FG3A\\\",\\\"FG3_PCT\\\",\\\"FTM\\\",\\\"FTA\\\",\\\"FT_PCT\\\",\\r\\n \\\"OREB\\\",\\\"DREB\\\",\\\"REB\\\",\\\"AST\\\",\\\"TOV\\\",\\\"STL\\\",\\\"BLK\\\",\\\"BLKA\\\",\\\"PF\\\",\\r\\n \\\"PFD\\\",\\\"PTS\\\",\\\"PLUS_MINUS\\\"]]\\r\\n \\r\\n return team_df\",\n \"def winning_games_stats(self):\\n self.winning_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(winning_num_counts = 1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM3\\\":\\\"sum\\\",\\\"WFGA3\\\":\\\"sum\\\",\\\"WFTM\\\":\\\"sum\\\",\\\"WFTA\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"winning_num_counts\\\":\\\"sum\\\",\\n \\\"WOR\\\":\\\"sum\\\",\\\"WDR\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\n \\\"WAst\\\":\\\"sum\\\",\\\"WTO\\\":\\\"sum\\\",\\\"WStl\\\":\\\"sum\\\",\\\"WBlk\\\":\\\"sum\\\",\\\"WPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"LScore\\\":\\\"losing_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"LFGA\\\":\\\"LFGA_opp\\\",\\\"LFGM\\\":\\\"LFGM_opp\\\"})\\n )\",\n \"def get_team_names(self, year1, year2):\\n\\n base_url = 'http://www.sports-reference.com/cbb/schools/'\\n response = urllib2.urlopen(base_url)\\n content = response.read()\\n soup = BeautifulSoup(content, 'html.parser')\\n soup_results = soup.findAll('tr', {'class':''})\\n extract_name = lambda name: name.split('/')[3]\\n team_names = []\\n \\n for result in soup_results[1::]:\\n year_span = result.findAll('td', {'align':'center'}) \\n year_span = map(int, [year.string for year in year_span])\\n\\n if year_span[0] <= year1 and year_span[1] >= year2:\\n team_name = result.find('a', href = True).get('href')\\n team_name = extract_name(team_name)\\n team_names.append(str(team_name))\\n\\n self.team_names = team_names\",\n \"def scrape_all_world_cup_games():\\n\\n def scrape_scores_year(year):\\n urls = scrape_world_cup_scoreboard(year)\\n scores = [scrape_fifa_game(url, 'FIFA World Cup') for url in urls]\\n return scores\\n\\n l = []\\n for year in sorted(world_cup_mapping.keys()):\\n l.extend(scrape_scores_year(year))\\n return l\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def get_win_rate_regular_season_for_each_coach(self):\\n self.games_won_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n self.games_lose_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df\\n self.combine_regular_games_won_lose = (\\n self.games_lose_for_coaches\\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"games_lost\\\",\\\"which_coach_for_win\\\":\\\"games_won\\\"})\\n )\",\n \"def get_leagues_and_countries(source=utils.get_native_source):\\n if not isinstance(source, games.models.Source):\\n # If I used source=native_source() or if native_source was a global variable then\\n # during db initialization (running command initialize) you would get an error since\\n # it gets its value when the database is empty.\\n source = source()\\n logger.info(\\\"getting leagues and countries from source %s...\\\", source)\\n if not source:\\n return [], []\\n data, meta, status_code = sportmonks.countries.all(include='leagues.seasons')\\n if not data:\\n # if the status code is not 200 data and meta are None\\n return [], []\\n # with open('sportmonks/response_texts/aws_01.txt', 'w') as outfile:\\n # json.dump(meta, outfile, indent=4)\\n # json.dump(data, outfile, indent=4)\\n\\n pre_countries, pre_competitions = [], []\\n\\n try:\\n # Notice that only the first supported sport will be processed (currently this is is acceptable since we only\\n # support football and so the first supported sport will always be football)\\n sport_sids = parse_sport(meta)\\n sports = []\\n for sport_sid in sport_sids:\\n sport = games.models.Sport.by_sid(sid=sport_sid, source=source)\\n if not sport:\\n logger.info(\\\"Sport contained in the response with sid {} is not supported\\\".format(sport_sid))\\n continue\\n sports.append(sport)\\n if not sports:\\n logger.error(\\\"No supported sport in the response\\\")\\n return [], []\\n football_gname = games.naming.sport_names.get('football', None)\\n football = games.models.Sport.objects.get(name=football_gname)\\n if football not in sports:\\n logger.info(\\\"Football is not in response\\\")\\n return [], []\\n # logger.debug(\\\"Trying to get sport from source: %s and sid: %s\\\", source, sport_sid)\\n sport_gname = football_gname\\n for item in data:\\n try:\\n country_sid = item.get('id')\\n # logger.debug('country_sid: %s', country_sid)\\n country_sname = item.get('name')\\n # logger.debug('country_sname: %s', country_sname)\\n extra = item.get('extra')\\n # logger.debug('extra: %s', extra)\\n leagues = item.get('leagues').get('data')\\n # logger.debug('leagues: %s', leagues)\\n try:\\n fifa_code = extra.get('fifa') # some countries might lack extra information\\n except AttributeError:\\n fifa_code = None\\n except Exception as e:\\n logger.data_error('%s', e)\\n continue\\n pre_country = pre_models.PreCountry(source=source, sname=country_sname, sid=country_sid, fifa_code=fifa_code)\\n pre_countries.append(pre_country)\\n for league in leagues:\\n try:\\n # sportmonks uses sgname for leagues. I use this sgname as an sname (comp_season_specific name)\\n competition_sname = league.get('name')\\n # logger.debug('competition_sname: %s', competition_sname)\\n sid = league.get('id')\\n # logger.debug('sid: %s', sid)\\n seasons = league.get('seasons').get('data')\\n # logger.debug('seasons: %s', seasons)\\n except Exception as e:\\n logger.data_error('%s', e)\\n continue\\n competition_season_utils = []\\n # comp_seas_sids = []\\n for season in seasons:\\n try:\\n season_name = season.get('name')\\n # logger.debug('season_name: %s', season_name)\\n # season_name = seasons_special_treatment(season_name)\\n competition_season_sid = season.get('id')\\n # logger.debug('competition_season_sid: %s', competition_season_sid)\\n is_current_season = season.get('is_current_season', False)\\n # logger.debug('is_current_season: %s', is_current_season)\\n except Exception as e:\\n logger.data_error('%s', e)\\n continue\\n # comp_seas_sids.append(competition_season_sid)\\n zak_season_name = games.models.Season.zakandify_season_string(season_name)\\n season = zakanda.utils.season_from_season_name(zak_season_name)\\n competition_season_type = get_competition_season_type(season)\\n competition_season_util = pre_models.CompetitionSeasonUtil(season, competition_season_sid, competition_sname, competition_season_type)\\n competition_season_utils.append(competition_season_util)\\n # logger.debug(\\\"competition season sids: %s\\\", comp_seas_sids)\\n pre_competition = pre_models.PreCompetition(\\n source=source, sname=competition_sname, sid=sid, sport_name=sport_gname,\\n competition_season_utils=competition_season_utils, pre_country=pre_country)\\n pre_competitions.append(pre_competition)\\n\\n except Exception as e:\\n logger.error('%s Unexpected problem with sportmonks.countries.all from source %s', e, source)\\n logger.info(\\\"%s pre countries and %s pre competitions were created\\\", len(pre_countries), len(pre_competitions))\\n return pre_countries, pre_competitions\",\n \"def get_people(team):\",\n \"def getAllTeams(self):\\n return []\",\n \"def get_all_matches_by_league(self):\\n raise NotImplementedError\",\n \"def cumulative_stats_for_teams_each_year(self):\\n self.cumulative_stats_for_team_each_year = (\\n self.combine_both_winning_losing_games_stats\\n .sort_values(['WTeamID','Season'])\\n .groupby(['WTeamID'])\\n .cumsum()\\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\\n .drop(['LTeamID','win_rate'],1)\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n # rebounds cumsum stats\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # assists, turnovers, steals, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win or lose by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def get_teams():\\n teams = []\\n for teamId in range(1, 68):\\n t = requests.get(TEAMS_URL.format(teamId)).json()\\n team_list = t.get('teams')\\n if team_list is None or len(team_list) == 0:\\n continue\\n teams.append(Team.from_json(team_list[0]))\\n return teams\",\n \"def teams(self, game_id: int) -> DataFrame[Any]:\",\n \"def fa_finder(league_no, team_name):\\n ros_proj_b_list = BatterProjection.objects.all()\\n ros_proj_p_list = PitcherProjection.objects.all()\\n player_comp = {}\\n pitching_fa_list = yahoo_players(league_no, \\\"P\\\")\\n batting_fa_list = yahoo_players(LEAGUE_NO, \\\"B\\\")\\n avail_pitching_fas = rate_avail_players(pitching_fa_list, ros_proj_p_list)\\n yahoo_team = get_single_yahoo_team(league_no, team_name)\\n team_pitching_values = rate_team(yahoo_team, ros_proj_p_list)\\n avail_batting_fas = rate_avail_players(batting_fa_list, ros_proj_b_list)\\n team_batting_values = rate_team(yahoo_team, ros_proj_b_list)\\n\\n player_comp['Team Name'] = yahoo_team['team_name']\\n player_comp['Pitching FAs'] = avail_pitching_fas\\n player_comp['Pitching Team'] = team_pitching_values\\n player_comp['Batting FAs'] = avail_batting_fas\\n player_comp['Batting Team'] = team_batting_values\\n\\n return player_comp\",\n \"def teams(self):\\n return self._get_by_class(Team)\",\n \"def get_teams(self):\\n url = 'teams'\\n result = self.get(url)\\n return result.get('teams', result)\",\n \"def scrape_all_world_cup_goals():\\n def scrape_goals_year(year):\\n urls = scrape_world_cup_scoreboard(year)\\n goals = []\\n for url in urls:\\n goals.extend(scrape_fifa_goals(url, 'FIFA World Cup'))\\n return goals\\n\\n l = []\\n for year in sorted(world_cup_mapping.keys()):\\n l.extend(scrape_goals_year(year))\\n return l\",\n \"def build_teams(self):\\n # get all nations\\n all_nations = self.games.get_all_nations()\\n\\n # build teams for all participating nations in FIFA World Cup 2018\\n bt = BuildTeams(self.squad_size, self.selected_attrs)\\n bt.read_data()\\n\\n # a dict with a nations' name as a key and players' data as value\\n self.teams = {}\\n\\n # build squad for every nation\\n for nation in all_nations:\\n team = bt.build_team(nation)\\n # if we got enough players, add team\\n if team.shape[0] >= bt.squad_size:\\n #print(team)\\n # convert pandas dataframe to matrix and flatten it\\n self.teams[nation] = team.as_matrix().flatten()\",\n \"def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\\n\\tLOG.debug('Concurrency = %d', concurrency)\\n\\tgames = season_games(year)\\n\\tif week is not None:\\n\\t\\tgames = games[games.week == week]\\n\\texpected_n = len(games)\\n\\ttables, futures_to_args, failures = [], {}, []\\n\\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\\n\\twith futures.ThreadPoolExecutor(concurrency) as pool:\\n\\t\\tfor arg in zip(games.hometeam, games.awayteam, games.week):\\n\\t\\t\\targ = arg + (year,)\\n\\t\\t\\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\\n\\t\\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\\n\\t\\t\\targs = futures_to_args[future]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ttable = future.result()\\n\\t\\t\\texcept Exception as exc:\\n\\t\\t\\t\\tLOG.exception('Error from %s: %s', args, exc)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif table is None:\\n\\t\\t\\t\\t\\tLOG.error('Failure: %s', args)\\n\\t\\t\\t\\t\\tfailures.append(args)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tLOG.info('Success: %s', args)\\n\\t\\t\\t\\t\\ttables.append(table)\\n\\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\\n\\tif __debug__:\\n\\t\\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\\n\\t\\tassert n == expected_n, \\\"Expected %d games, got %d\\\" % (expected_n, n)\\n\\treturn tables, failures\",\n \"def determine_winners(self, players=None):\\n players_and_cards = [(holding.player.id, holding.codes) for holding in self.live_holdings]\\n if players:\\n player_ids = [p.id for p in players]\\n players_and_cards = [d for d in players_and_cards if d[0] in player_ids]\\n winners = determine_winners(players_and_cards, self.board.codes)\\n return [Player.query.get(winner) for winner in winners]\",\n \"def teams(self):\\r\\n url = '{0}/{1}'.format(self.get_url(), 'teams')\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def get_players_by_team(self, team, year=None):\\n year = self._year if not year else year\\n return self._db.Players.find({'{}.Team'.format(year) : team})\",\n \"def __init__(self, name=\\\"team_name\\\", races=0, wins=0, drivers_championships_years=[], constructors_championships_years=[]):\\n self.name = name\\n self.races = races\\n self.wins = wins\\n self.drivers_championships_years = drivers_championships_years\\n self.constructors_championships_years = constructors_championships_years\",\n \"def get_team_stats(self, team_name, year):\\n \\n base_url = 'http://www.sports-reference.com/cbb/schools/' + \\\\\\n team_name + '/' + str(year) + '.html'\\n\\n response = urllib2.urlopen(base_url)\\n content = response.read()\\n soup = BeautifulSoup(content)\\n soup_results = soup.find('td', text='Team')\\n team_stats = []\\n \\n if soup_results:\\n soup_results = soup_results.parent()\\n \\n for result in soup_results[1::]:\\n if result.string:\\n team_stats.append(float(result.string))\\n else:\\n team_stats.append(None)\\n else:\\n team_stats += [None]*21\\n\\n return team_stats\",\n \"def find_games(days_ahead=0):\\n headers = {\\n 'Host': 'stats.nba.com',\\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\\n 'Accept': 'application/json, text/plain, */*',\\n 'Accept-Language': 'en-US,en;q=0.5',\\n 'Referer': 'https://stats.nba.com/',\\n 'Accept-Encoding': 'gzip, deflate, br',\\n 'Connection': 'keep-alive',\\n 'x-nba-stats-origin': 'stats',\\n 'x-nba-stats-token': 'true'\\n }\\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\\n board.replace(id_to_abrv, inplace=True)\\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]\",\n \"def test_get_free_agents_season_start(league, season_start_date):\\n # equals all players minus drafted players\\n # make sure none of the draft players in list\\n free_agents = league.as_of(season_start_date).free_agents()\\n drafted = league.draft_results(format='Pandas')\\n assert(len(free_agents.index.intersection(drafted.index)) == 0), \\\"Should be no drafted players as free agents\\\"\\n # could make sure all 'all_players' that weren't drafted are here\",\n \"def get_all_teams(self):\\n return self._db.Teams.find({})\",\n \"def get_sim_winners(teamNames,league,pokedex):\\n bData, wHtml = loadBattleData(league)\\n similarities = loadSims() \\n\\n results = []\\n\\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\\n for d in bData:\\n winner, loser = determineWinner(d)\\n\\n score = 0\\n for pkm in myTeam:\\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\\n\\n results.append((winner,score))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n \\n cutoff = min(len(results),5)\\n\\n htmls = []\\n for win,score in results[:cutoff]:\\n htmls.extend(wHtml[str(sorted(win))])\\n\\n return htmls\",\n \"def test_get_teams(self):\\n pass\",\n \"def test_get_teams(self):\\n pass\",\n \"def all_match_data(year):\\n year_match_data = []\\n match_year_data = pm()\\n for count in range(len(match_year_data)):\\n if year == match_year_data[count][1]:\\n year_match_data.append(match_year_data[count])\\n for count in range(len(year_match_data)):\\n print(\\n f'Game ID: {count + 1} Match date: {year_match_data[count][3]} {year_match_data[count][4]} vs '\\n f'{year_match_data[count][5]}')\\n\\n return year_match_data\",\n \"def getAllTeams(self):\\n if self._teams is None:\\n self._teams = []\\n for match in self._matches.items():\\n self._teams.append(match.homeTeam)\\n return self._teams\",\n \"def get_teams(event):\\n teams_raw = tba_session.get(BASE_URL + '/event/%s/teams/keys' % event).json()\\n teams = []\\n for team_raw in teams_raw:\\n teams.append(team_raw[3:])\\n return teams\",\n \"def collect_team(year, abbreviation):\\n\\n team = Team.objects.filter(year=year, abbreviation=abbreviation)[0]\\n team = {\\n 'id': abbreviation,\\n 'team': team,\\n }\\n return team\",\n \"def qualifiedteams(self):\\n for i in range(0, len(self.teams)):\\n for j in range(i + 1, len(self.teams)):\\n WorldCupMatch(self.teams[i], self.teams[j], True)\\n self.teams = sorted(self.teams, key=op.attrgetter('points', 'goaldifference', 'scored'))\\n self.first_qualified = self.teams[len(self.teams)-1]\\n self.second_qualified = self.teams[len(self.teams)-2]\",\n \"def combine_playoff_championship_for_each_coach(self):\\n self.final_coach_with_postseason_champion_each_year = (\\n self.final_coach_with_postseason_each_year\\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\\n .fillna(0)\\n )\",\n \"def get_win_rate_post_season_for_each_coach(self):\\n # get winning games for coaches\\n self.post_games_won_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # get losing games for coaches\\n self.post_games_lose_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df for post season\\n self.combine_post_games_won_lose = (\\n self.post_games_lose_for_coaches\\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"post_games_lost\\\",\\\"which_coach_for_win\\\":\\\"post_games_won\\\"})\\n .fillna(0)\\n )\",\n \"def _get_teams(unstacked, min_games=0):\\n count_col = 'game_id'\\n home_gb = unstacked.groupby('hteam_id').count()[[count_col]]\\n away_gb = unstacked.groupby('ateam_id').count()[[count_col]]\\n merged = home_gb.merge(away_gb, how='outer', left_index=True, right_index=True)\\n merged[count_col] = merged[count_col + '_x'] + merged[count_col + '_y']\\n merged.index.name = 'index'\\n teams = merged.reset_index().rename(columns={'index': 'team_id'})\\n teams = teams.sort('team_id')\\n teams['i_team'] = np.arange(teams.shape[0])\\n team_index = {}\\n for team, idx in teams[['team_id', 'i_team']].values:\\n team_index[int(team)] = int(idx)\\n return teams[['team_id', 'i_team']], team_index\",\n \"def generate_teams(self):\\n for ath in self.__extract_athletes():\\n squad_id = dl.get_squad_id(ath)\\n self.data_engine[squad_id][\\\"team_members\\\"].append(ath)\\n\\n \\\"\\\"\\\" We have already meet all criteria. \\\"\\\"\\\"\\n if self.__have_team(squad_id):\\n yield (squad_id,\\n dl.get_squad_name_by_id(self.data, squad_id),\\n self.data_engine[squad_id]['team_members'])\",\n \"def scraping_league_stats():\\n url=\\\"https://www.basketball-reference.com/teams/\\\"\\n html = urlopen(url)\\n bs = BeautifulSoup(html, 'html.parser')\\n teams = bs.find(\\\"div\\\", {'id':'div_teams_active'}).findAll(\\\"a\\\")\\n teams_url = [team.get(\\\"href\\\") for team in teams]\\n team_players_list = []\\n for team in teams_url:\\n time.sleep(3)\\n teamurl = domain + team\\n print(teamurl) #fafaf\\n html = urlopen(teamurl)\\n bs = BeautifulSoup(html, 'html.parser')\\n div_team = \\\"div_\\\" + teamurl[-4:-1]\\n season = bs.find(\\\"div\\\", {'id':div_team}).find(\\\"a\\\").get(\\\"href\\\") #tengo que cambiarlo para que este guay\\n #esto tiene la URL de la ultima season\\n seasonurl = domain + season\\n print(seasonurl)\\n html = urlopen(seasonurl)\\n bs = BeautifulSoup(html, 'html.parser')\\n players = bs.find(\\\"table\\\", {'id':'roster'}).findAll(\\\"td\\\", {\\\"data-stat\\\":\\\"player\\\"})\\n player_url_list = [player.find(\\\"a\\\").get(\\\"href\\\") for player in players]\\n for player in player_url_list:\\n player_url = domain + player\\n time.sleep(3)\\n print(player_url)\\n html = urlopen(player_url)\\n bs = BeautifulSoup(html, 'html.parser')\\n try:\\n tabla = pd.read_html(str(bs.find(\\\"div\\\", {'id':'all_per_poss'})).replace(\\\"<!--\\\", \\\"\\\"))[0] \\n tabla[\\\"Player\\\"] = bs.find(\\\"h1\\\", {\\\"itemprop\\\" : \\\"name\\\"}).text.strip()\\n indice = tabla[tabla[\\\"Season\\\"]==\\\"Career\\\"].index[0]\\n tabla = tabla[0:indice]\\n tabla = tabla.drop(axis= 1,columns = \\\"Unnamed: 29\\\")\\n #no me encuentra tablas para uno del college que es el darlina01\\n print(player_url)\\n team_players_list.append(tabla)\\n except:\\n pass\\n return pd.concat(team_players_list)\",\n \"def get_players_df(year: int) -> pd.DataFrame:\\n endpoint = _BASE_ENDPOINT + _PLAYER_ENDPOINT\\n r = requests.get(endpoint.format(year=year)).json()\\n standard_stats = r.get(\\\"league\\\").get(\\\"standard\\\")\\n result_dict = [{\\n \\\"FirstName\\\": s.get(\\\"firstName\\\"),\\n \\\"LastName\\\": s.get(\\\"lastName\\\"),\\n \\\"PlayerID\\\": s.get(\\\"personId\\\"),\\n \\\"TeamID\\\": s.get(\\\"teamId\\\"),\\n } for s in standard_stats]\\n return pd.DataFrame(result_dict)\",\n \"def test_teams_get_teams_v1(self):\\n pass\",\n \"def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\\n battleData, htmlData = loadBattleData(league)\\n similarities = loadSims() \\n \\n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\\n if len(oppTeam) == 0:\\n picks = set([])\\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\\n picks.add(random.randint(0,len(battleData)-1))\\n\\n losers = []\\n loserDict = {}\\n for i in picks:\\n entry = battleData[i]\\n winner,loser = determineWinner(entry)\\n loserDict[str(loser)] = [winner]\\n losers.append( (loser,0) )\\n\\n #Given opponent team then find similar teams\\n else:\\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\\n\\n #create dictionary from losers team to the team that beat them.\\n loserDict = {}\\n sims = []\\n for d in battleData:\\n winner, loser = determineWinner(d)\\n\\n wTeam = teamToArray(winner,pokedex)\\n lTeam = np.array(teamToArray(loser, pokedex))\\n\\n score = 0\\n for oppNp in oppTeam:\\n score+= np.amax(lTeam*oppNp) \\n\\n if str(loser) in loserDict:\\n loserDict[str(loser)].append(winner)\\n else:\\n #new to dictonary\\n loserDict[str(loser)] = [winner]\\n\\n sims.append((loser, score))\\n\\n\\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\\n\\n cutoff = min(len(sims),NUMLOSINGTEAMS)\\n losers = sims[:cutoff]\\n\\n #Gather winners to losing teams\\n winnersComp = []\\n for loser,_ in losers:\\n for winner in loserDict[str(loser)]:\\n winnersComp.append(teamToArray(winner,pokedex))\\n \\n topScore = len(winnersComp)*6 #pkmn team size\\n\\n results = []\\n inverted_idx = {}\\n\\n existsSet = []\\n\\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\\n for i in range(len(curTeams)):\\n team = curTeams[i]\\n results.append((team,0))\\n sTeam = set(team)\\n if not (sTeam in existsSet):\\n existsSet.append(sTeam)\\n for pkm in team:\\n if pkm != EMPTY:\\n if pkm in inverted_idx:\\n inverted_idx[pkm].append(i)\\n else:\\n inverted_idx[pkm] = [i]\\n \\n #Giving the similiarity scores to the winners based off of the inverted index.\\n for pkm in inverted_idx:\\n for winner in winnersComp:\\n wArr = np.array(winner)\\n #tArr = getSimPokemon(pkm,similarities)\\n tArr = similarities[pkm]\\n \\n vals = wArr * tArr\\n\\n score = np.amax(vals)\\n\\n for i in inverted_idx[pkm]:\\n results[i] = (results[i][0],results[i][1]+(score/topScore))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n\\n if len(results) < NUMTEAMSRETURN:\\n if len(results) == 0:\\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\\n\\n else:\\n returnTeams = [result[0] for result in results]\\n teamScores = [result[1] for result in results]\\n else:\\n firstResult, firstScore = results[0]\\n returnTeams = [firstResult]\\n teamScores = [round(firstScore*100,1)]\\n returnSets = [set(firstResult)]\\n \\n i = 1\\n\\n #Loops through results and adds teams with the proper edit distance away.\\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\\n teamToConsider,teamToConsiderScore = results[i]\\n \\n considerSet = set(teamToConsider)\\n add = True\\n ##checks the edit distance of teams is above wanted\\n for team in returnSets:\\n if len(team.union(considerSet)) < len(team)+minDistWanted:\\n add = False\\n\\n ##If indeed above wanted levels then add\\n if add:\\n returnTeams.append(teamToConsider)\\n returnSets.append(considerSet)\\n teamScores.append(round(teamToConsiderScore*100,1))\\n \\n i+=1\\n\\n if i >= len(results):\\n i = 1\\n minDistWanted -= 1 \\n \\n winHtmls = []\\n if htmlData != None:\\n for team,_ in losers:\\n for winner in loserDict[str(team)]:\\n winHtmls.extend(htmlData[str(sorted(winner))])\\n \\n\\n return returnTeams, teamScores, winHtmls\",\n \"def organizations_owned(self):\\n return sorted(set([team.org for team in self.teams if team.org.owners == team]),\\n key=lambda o: o.title)\",\n \"def get_games(date):\\n scoreboard = nba_py.Scoreboard(month=date.month,\\n day=date.day,\\n year=date.year)\\n line_score = scoreboard.line_score()\\n game_header = scoreboard.game_header()\\n\\n games = []\\n current_game = {}\\n game_sequence = 0\\n game_sequence_counter = 0\\n\\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\\n for i, value in enumerate(line_score):\\n if (value[\\\"GAME_SEQUENCE\\\"] != game_sequence):\\n game_sequence += 1\\n\\n current_game[\\\"GAME_ID\\\"] = value[\\\"GAME_ID\\\"]\\n home_team_id = game_header[game_sequence - 1][\\\"HOME_TEAM_ID\\\"]\\n\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n \\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter += 1\\n elif game_sequence_counter == 1:\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n\\n current_game[\\\"GAME_STATUS_TEXT\\\"] = game_header[game_sequence - 1][\\\"GAME_STATUS_TEXT\\\"]\\n if not game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]:\\n current_game[\\\"BROADCASTER\\\"] = \\\"\\\"\\n else:\\n current_game[\\\"BROADCASTER\\\"] = game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]\\n\\n games.append(current_game)\\n\\n current_game = {}\\n\\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter -= 1\\n\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return (games, east_standings, west_standings)\",\n \"def teams(teamid):\\n team_summary = team.TeamSummary(teamid)\\n team_summary_info = team_summary.info()\\n team_season_ranks = team_summary.season_ranks()\\n\\n team_common_roster = team.TeamCommonRoster(teamid)\\n roster = team_common_roster.roster()\\n coaches = team_common_roster.coaches()\\n\\n season = team_summary_info[0][\\\"SEASON_YEAR\\\"]\\n\\n team_game_log = team.TeamGameLogs(teamid,\\n season=season)\\n team_games = team_game_log.info()\\n\\n playoffs_teamgamelogs = team.TeamGameLogs(teamid,\\n season=season,\\n season_type=\\\"Playoffs\\\")\\n playoffs_team_games = playoffs_teamgamelogs.info()\\n\\n team_season = team.TeamSeasons(teamid)\\n team_season_info = team_season.info()\\n\\n for i in team_season_info:\\n if (i[\\\"YEAR\\\"] == season):\\n current_season_info = i\\n\\n return render_template(\\\"teams.html\\\",\\n title=team_summary_info[0][\\\"TEAM_CITY\\\"] + \\\" \\\" + team_summary_info[0][\\\"TEAM_NAME\\\"],\\n teamid=teamid,\\n team_summary_info=team_summary_info,\\n team_season_ranks=team_season_ranks,\\n season=season,\\n team_games=team_games,\\n playoffs_team_games=playoffs_team_games,\\n team_season=team_season_info,\\n roster=roster,\\n coaches=coaches,\\n current_season_info=current_season_info,\\n team_img=TEAM_ID_DATA)\",\n \"def mk_champs(cls, year, start_date, end_date):\\n seasons = [\\\"Quad Quandary\\\", \\\"Face Off\\\", \\\"Hot Shot\\\", \\\"Get Over It\\\", \\\"Bowled Over\\\", \\\"Ring It Up\\\", \\\"Block Party\\\",\\n \\\"Cascade Effect\\\", \\\"RES-Q\\\", \\\"Velocity Vortex\\\", \\\"Relic Recovery\\\", \\\"Rover Ruckus\\\"]\\n season_name = seasons[year-2007]\\n start_date = datetime.datetime.strptime(start_date, \\\"%Y-%m-%d\\\")\\n end_date = datetime.datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n season = f\\\"{year % 100:02}{(year + 1) % 100:02}\\\"\\n # fyear = f\\\"{year}-{(year+1)%1000:02d}\\\"\\n if year == 2009:\\n city, state_prov, country = \\\"Atlanta\\\", \\\"Georgia\\\", \\\"USA\\\"\\n venue = \\\"Georgia Dome\\\"\\n address = \\\"1 Georgia Dome Dr, Atlanta, GA 30313\\\"\\n elif year < 2013:\\n city, state_prov, country = \\\"St. Louis\\\", \\\"Missouri\\\", \\\"USA\\\"\\n venue = \\\"Edward Jones Dome\\\"\\n address = \\\"701 Convention Plaza, St. Louis, MO 63101\\\"\\n else:\\n city, state_prov, country = \\\"St. Louis\\\", \\\"Missouri\\\", \\\"USA\\\"\\n venue = \\\"Union Station\\\"\\n address = \\\"1820 Market Street, St. Louis, MO 63103\\\"\\n shared = {\\n \\\"year\\\": year,\\n \\\"city\\\": city,\\n \\\"state_prov\\\": state_prov,\\n \\\"country\\\": country,\\n \\\"end_date\\\": end_date,\\n \\\"event_type\\\": EventType.WORLD_CHAMPIONSHIP,\\n \\\"venue\\\": venue,\\n \\\"address\\\": address,\\n \\\"data_sources\\\": [\\\"USFIRST Website Archives\\\"]\\n }\\n\\n finals = Event(key=f\\\"{season}cmp0\\\",\\n name=f\\\"FTC {season_name} World Championship - Finals\\\",\\n playoff_type=PlayoffType.BO3_FINALS, \\n division_keys=[f\\\"{season}cmp1\\\", f\\\"{season}cmp2\\\"],\\n start_date=end_date,\\n **shared)\\n franklin = Event(key=f\\\"{season}cmp{2}\\\",\\n name=f\\\"FTC {season_name} World Championship - Franklin Division\\\",\\n playoff_type=PlayoffType.STANDARD, \\n parent_event_key=f\\\"{season}cmp0\\\", \\n start_date=start_date,\\n **shared)\\n edison = Event(key=f\\\"{season}cmp{1}\\\",\\n name=f\\\"FTC {season_name} World Championship - Edison Division\\\",\\n playoff_type=PlayoffType.STANDARD,\\n parent_event_key=f\\\"{season}cmp0\\\", \\n start_date=start_date,\\n **shared)\\n return (franklin, edison, finals)\",\n \"def find_players_for_team(self, team, src='roster', season=None):\\n # creating class wide variable to hold current team\\n if type(team) is str:\\n team = Team.find(team)\\n\\n print(\\\"+ Searching %s players for %s\\\" % (src, team))\\n\\n if src == 'roster':\\n players = self.get_roster_players_via_api(team, season)\\n elif src == 'system':\\n players = self.get_system_players(team)\\n elif src == 'contract':\\n players = self.get_contracted_players(team)\\n\\n return players\",\n \"def opponent_games(df, team, gameids):\\n games = df[df.gameid.isin(gameids)]\\n opponent_games = games[games.team != team].drop_duplicates(subset=['gameid'])[['gameid', 'team']]\\n opponent_games.columns = ['gameid', 'opponent']\\n\\n return opponent_games\",\n \"def get_all_games(season):\\n url = BASE_URL.format(season)\\n json_data = requests.get(url, headers=HEADERS).json()\\n all_games = json_data[\\\"resultSets\\\"][0][\\\"rowSet\\\"]\\n return all_games\",\n \"def test_teams_get_teams_v2(self):\\n pass\",\n \"def players(self):\\n return Player.objects.filter(team=self)\",\n \"def get_fb_team_rankings(self):\\n\\n ranks = []\\n self._logger.debug(\\\"Getting foosball team rankings\\\")\\n\\n try:\\n self.check_if_db_connected()\\n cursor = self._db_conn.cursor()\\n cursor.execute(\\\"SELECT team_id, team_name FROM team\\\")\\n teams = cursor.fetchall()\\n\\n for team_id, team_name in teams:\\n cursor.execute(\\\"SELECT fb_team_rating FROM \\\\\\nteam WHERE team_id = {0}\\\".format(team_id))\\n team_rating = cursor.fetchall()[0]\\n\\n cursor.execute(\\\"SELECT mu, sigma FROM rating WHERE rating_id \\\\\\n= {0}\\\".format(team_rating[0]))\\n mu, sigma = cursor.fetchall()[0]\\n\\n team_rank = float(mu) - (3 * float(sigma))\\n\\n # get player_ids\\n cursor.execute(\\\"SELECT player from player_team_xref \\\\\\nWHERE team = {0}\\\".format(team_id))\\n players = cursor.fetchall()\\n player_one = players[0]\\n player_two = players[1]\\n\\n cursor.execute(\\\"SELECT first_name FROM player WHERE \\\\\\nplayer_id = {0}\\\".format(player_one[0]))\\n player_one_name = cursor.fetchone()[0]\\n\\n cursor.execute(\\\"SELECT first_name FROM player WHERE \\\\\\nplayer_id = {0}\\\".format(player_two[0]))\\n player_two_name = cursor.fetchone()[0]\\n\\n cursor.execute(\\\"SELECT COUNT(result_id) FROM fb_result WHERE \\\\\\n(offense_winner = {0} AND defense_winner = {1}) OR (offense_winner = {1} \\\\\\nAND defense_winner = {0})\\\".format(player_one[0], player_two[0]))\\n team_win_count = cursor.fetchone()[0]\\n\\n cursor.execute(\\\"SELECT COUNT(result_id) FROM fb_result WHERE \\\\\\n(offense_loser = {0} AND defense_loser = {1}) OR (offense_loser = {1} \\\\\\nAND defense_loser = {0})\\\".format(player_one[0], player_two[0]))\\n team_loss_count = cursor.fetchone()[0]\\n\\n intermediate_rank = (team_name, round(team_rank, 4),\\n team_win_count, team_loss_count, player_one_name,\\n player_two_name)\\n ranks.append(intermediate_rank)\\n del intermediate_rank\\n\\n except MySQLdb.OperationalError:\\n self._logger.error(\\\"MySQL operational error occured\\\")\\n traceback.print_exc()\\n raise exceptions.DBConnectionError(\\\"Cannot connect to MySQL server\\\")\\n\\n except MySQLdb.ProgrammingError:\\n self._logger.error(\\\"MySQL programming error\\\")\\n traceback.print_exc()\\n raise exceptions.DBSyntaxError(\\\"MySQL syntax error\\\")\\n\\n else:\\n return ranks\",\n \"def team_season_stats(team):\\n\\n # Get HTML Content\\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # Team's yearly stats are displayed in a table\\n season_stats = soup.find(id='stats').find('tbody')\\n\\n # Iterate through each year\\n for year in season_stats.find_all('tr', {'class': None}):\\n\\n season_year = year.find('th').text[0:4]\\n season_year = int(season_year) + 1\\n season = {'year': season_year}\\n\\n # Loop through each stat\\n for stat in year.find_all('td'):\\n season[stat['data-stat']] = stat.string\\n\\n # Rename relocated teams\\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\\n season['_id'] = season['team_id'] + '_' + str(season_year)\\n\\n # Remove unwanted stats\\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\\n for k in to_remove:\\n season.pop(k, None)\\n\\n # Add to MongoDB\\n m.insert('team_season', season)\",\n \"def get_final_coach_for_each_season(self):\\n self.final_coach_for_season = (\\n self.num_days_coach_for_season\\n .groupby(['Season','TeamID'])\\n .agg({\\\"CoachName\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"CoachName\\\":\\\"coach_counts\\\"})\\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \\\"ignore\\\")))\\n [['Season','TeamID','final_coach']]\\n )\",\n \"def test_fantasy_status_nov_1(league):\\n nov_1 = datetime.datetime(2019,11,1)\\n players = league.as_of(nov_1).all_players()\\n # make sure sammy blais is not a free agent, he was picked up oct 31\\n assert(players.loc[6544, 'fantasy_status'] != 'FA')\",\n \"def get_list_team_scores(self):\\n scores = defaultdict(lambda: {\\n \\\"scored_xg\\\": [],\\n \\\"conceded_xg\\\": [],\\n \\\"home_adv\\\": 0,\\n \\\"expected_points\\\": 0\\n })\\n\\n for g in self.games:\\n scores[g.HomeTeam][\\\"scored_xg\\\"].append(g.FTHG)\\n scores[g.HomeTeam][\\\"conceded_xg\\\"].append(g.FTAG)\\n scores[g.AwayTeam][\\\"scored_xg\\\"].append(g.FTAG)\\n scores[g.AwayTeam][\\\"conceded_xg\\\"].append(g.FTHG)\\n\\n for team in scores.keys():\\n scores[team][\\\"expected_points\\\"] = (self.get_table(metric='points')[team] /\\n len(scores[team][\\\"scored_xg\\\"]))\\n\\n return scores\",\n \"def get_all_teams(group):\\n base_url = 'http://worldcup.kimonolabs.com/api/teams'\\n url = (base_url + '?apikey={key}&group={group}&sort={sort}'\\n .format(group=group,\\n key='KERbxAUfDYovbQnn9pR3pbLWEMRp47AQ',\\n sort='groupRank'))\\n r = requests.get(url)\\n return r.json()\",\n \"def get_game_urls(year, week):\\n base_url = \\\"https://www.pro-football-reference.com\\\"\\n\\n # URL for the summary of all games for a given week\\n # This page has the each match up for the week and\\n # a link to the detailed game stats \\n url = \\\"https://www.pro-football-reference.com/years/{}/week_{}.htm\\\".format(\\n year, week)\\n\\n response = get(url)\\n\\n week_summary = BeautifulSoup(response.content, 'html.parser')\\n\\n # Get just the game summary content\\n week_summary_games = week_summary.find_all(\\n attrs={\\\"class\\\": \\\"game_summary expanded nohover\\\"})\\n\\n losing_teams = []\\n winning_teams = []\\n game_link = []\\n\\n # For each game played, extract the losing team,\\n # the winning team, and the detailed game stats link\\n for game in week_summary_games:\\n try:\\n losing_team_strings = [text for text in game.find(\\n attrs={\\\"class\\\": \\\"loser\\\"}).stripped_strings]\\n losing_teams.append(losing_team_strings[0])\\n except AttributeError:\\n pass\\n\\n try:\\n winning_team_strings = [text for text in game.find(\\n attrs={\\\"class\\\": \\\"winner\\\"}).stripped_strings]\\n winning_teams.append(winning_team_strings[0])\\n except AttributeError:\\n pass\\n\\n for link in game.find_all(href=re.compile(\\\"boxscores\\\")):\\n game_link.append(base_url + link.get('href'))\\n\\n\\n game_urls = dict(zip(losing_teams, game_link))\\n game_urls.update(dict(zip(winning_teams, game_link)))\\n\\n return game_urls\",\n \"def get_teams(self, *teams):\\n teams = [convert_name(team, how='abbr') for team in teams]\\n return self._db.Teams.find({'Tm' : {'$in' : teams}})\",\n \"def get_tourney_rounds(self, conference, year):\\n ts_dict = self.get_tourney_slots()\\n seed_dict = self.get_tourney_seeds()\\n tr_dict = self.get_tourney_results()\\n \\n round_1 = list()\\n round_2 = list()\\n round_3 = list()\\n round_4 = list()\\n winner = list()\\n \\n round1_winners = list()\\n for seed, team in seed_dict[year].items():\\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n round1_winners.append(seed[1:])\\n #removes duplicates because I did this part weirdly... HEHEH\\n round1_winners = list(set(round1_winners))\\n\\n win_counter = defaultdict(int)\\n for seed, team in seed_dict[year].items(): \\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n win_counter[winning] += 1\\n \\n for slot, matchup in ts_dict[year].items():\\n \\n if conference in slot and \\\"R1\\\" in slot: \\n round_1.append(\\\"{}-{}\\\".format(matchup[1:3], matchup[-2:]))\\n round_1 = sorted(round_1)\\n #for match in round_1:\\n for winner1 in round1_winners:\\n if winner1 in round_1[0]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-1]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[1]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-2]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[2]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-3]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[3]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-4]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_2 = sorted(round_2)\\n\\n round2_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 1:\\n round2_winners.append(seed[1:])\\n \\n for winner1 in round2_winners:\\n if winner1 in round_2[0]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-1]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_2[1]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-2]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_3 = sorted(round_3)\\n\\n round3_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 2:\\n round3_winners.append(seed[1:])\\n\\n for winner1 in round3_winners:\\n if winner1 in round_3[0]:\\n for winner2 in round3_winners:\\n if winner2 in round_3[-1]:\\n round_4.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_4 = sorted(round_4)\\n\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 3:\\n winner.append(seed[1:])\\n\\n conferences = {\\\"W\\\": \\\"East\\\", \\\"X\\\": \\\"Midwest\\\", \\\"Y\\\": \\\"South\\\", \\\"Z\\\": \\\"West\\\"}\\n\\n #print(\\\"CONFERENCE: {}, YEAR: {}\\\".format(conferences[conference], year))\\n #print(\\\"ROUND1:\\\", round_1)\\n #print(\\\"ROUND2:\\\", round_2)\\n #print(\\\"ROUND3:\\\", round_3)\\n #print(\\\"ROUND4:\\\", round_4)\\n #print(\\\"WINNER:\\\", winner)\\n\\n #clearing out the tourney results dictionary\\n #tr_dict.clear()\\n\\n return round_1, round_2, round_3, round_4, winner\",\n \"def scrape_world_cup_scoreboard(year):\\n # Replace this with the results logic somehow...\\n\\n d = world_cup_mapping[year]\\n prefix = 'http://www.fifa.com'\\n if type(d) == int:\\n root_url = '/worldcup/archive/edition=%s/' % d\\n else:\\n root_url = '/worldcup/archive/%s/' % d\\n data = scrape_url(prefix + root_url + \\\"results/index.html\\\")\\n\\n # Find urls in the page.\\n match_re = re.compile(root_url + \\\"results/matches/match=\\\\d+/report.html\\\")\\n urls = match_re.findall(data)\\n return [prefix + e for e in urls]\",\n \"def get_season_data(year):\\n # Make page request from PFR.\\n url = f'https://www.pro-football-reference.com/years/{year}/fantasy.htm'\\n res = requests.get(url)\\n\\n # Create soup object out of request.\\n soup = BeautifulSoup(res.content, 'lxml')\\n\\n try:\\n # Extract table of player data from soup object.\\n table = soup.find('table', {'id': 'fantasy'})\\n\\n # Extract all rows from table\\n rows = table.find('tbody').find_all('tr')\\n\\n # Many rows in the table are just header rows, so we need to extract\\n # only the rows with player information.\\n player_rows = [row for row in rows if row.get('class') != ['thead']]\\n\\n return player_rows\\n except AttributeError:\\n return None\",\n \"def create_teams(a_season):\\n\\n team_a = Team(name=\\\"Team A\\\", season=a_season)\\n team_a.save()\\n for p in create_players(7, 0):\\n team_a.players.add(p)\\n team_a.save()\\n team_b = Team(name=\\\"Team B\\\", season=a_season)\\n team_b.save()\\n for p in create_players(7, 16):\\n team_b.players.add(p)\\n team_b.save()\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def scraping_actual_team_players(team_abbreviation):\\n starting_point = domain + \\\"/teams/\\\"\\n teamurl = starting_point + team_abbreviation + \\\"/\\\"\\n team_id = \\\"div_\\\" + team_abbreviation\\n html = urlopen(teamurl)\\n bs = BeautifulSoup(html, 'html.parser')\\n actual_team_url = domain + str(bs.find(\\\"div\\\", {'id': team_id}).find(\\\"a\\\").get(\\\"href\\\"))\\n html = urlopen(actual_team_url)\\n bs = BeautifulSoup(html, 'html.parser')\\n players = bs.find(\\\"table\\\", {'id':'roster'}).findAll(\\\"td\\\", {\\\"data-stat\\\":\\\"player\\\"})\\n players_url = [player.find(\\\"a\\\").get(\\\"href\\\") for player in players]\\n team_players_list = []\\n for player_url in players_url:\\n time.sleep(3)\\n url = domain + player_url\\n html = urlopen(url)\\n bs = BeautifulSoup(html, 'html.parser')\\n print(player_url)\\n try:\\n tabla = pd.read_html(str(bs.find(\\\"div\\\", {'id':'all_per_poss'})).replace(\\\"<!--\\\", \\\"\\\"))[0] \\n tabla[\\\"Player\\\"] = bs.find(\\\"h1\\\", {\\\"itemprop\\\" : \\\"name\\\"}).text.strip()\\n indice = tabla[tabla[\\\"Season\\\"]==\\\"Career\\\"].index[0]\\n tabla = tabla[0:indice]\\n tabla = tabla.drop(axis= 1,columns = \\\"Unnamed: 29\\\")\\n #no me encuentra tablas para uno del college que es el darlina01\\n print(player_url)\\n team_players_list.append(tabla)\\n except:\\n pass\\n return pd.concat(team_players_list)\",\n \"def get_contracted_players(self, team):\\n # setting up empty list of players\\n players = list()\\n\\n # getting html document with team's contracted players\\n doc = self.get_html_document(team, 'contracts')\\n\\n # returning empty list if no system page could be found\\n if doc is None:\\n return players\\n\\n # collecting player names and links to capfriendly pages for different\\n # player groups\\n cf_links = doc.xpath(\\n \\\"//table[@id='team']/tr[@class='column_head c']/td/parent::tr/following-sibling::tr/td[1]/a/@href\\\")\\n cf_names = doc.xpath(\\n \\\"//table[@id='team']/tr[@class='column_head c']/td/parent::tr/following-sibling::tr/td[1]/a/text()\\\")\\n\\n for lnk, name in zip(cf_links, cf_names):\\n # retrieving capfriendly id from player page link\\n cf_id = lnk.split(\\\"/\\\")[-1]\\n # trying to find player in database\\n plr = Player.find_by_capfriendly_id(cf_id)\\n # trying to find player using suggestions\\n if plr is None:\\n last_name, first_name = name.split(\\\", \\\")\\n suggested_players = self.get_suggested_players(\\n last_name, first_name)\\n for suggested_player in suggested_players:\\n (\\n sugg_plr_id, sugg_pos,\\n sugg_last_name, sugg_first_name, _\\n ) = (\\n suggested_player\\n )\\n if (last_name, first_name) == (\\n sugg_last_name, sugg_first_name):\\n plr = Player.find_by_id(sugg_plr_id)\\n if plr is None:\\n plr = self.create_player(\\n sugg_plr_id, last_name, first_name, sugg_pos)\\n\\n if plr is None:\\n print(\\\"Unable to find player with name %s\\\" % name)\\n else:\\n players.append(plr)\\n\\n return players\",\n \"def test_get_player_stats_from_game():\\n team = \\\"Titans\\\"\\n year = \\\"2018\\\"\\n week = \\\"1\\\"\\n expected_team_stats = {}\\n\\n assert ff_team.get_player_stats_from_game(\\n team, year, week) == expected_team_stats\",\n \"def list_chassis_per_season(start_year=1950, end_year=2020, file_changed=False):\\n\\n def _get_chassis_names(years):\\n req = requests.get(WIKIPEDIA_F1_URL)\\n soup = BeautifulSoup(req.content, 'html.parser')\\n links = soup.find_all('a')\\n\\n for link in links:\\n link_text = ['Formula', 'One', 'season', 'cars']\\n if str(link.get('href')).split('_')[-4:] == link_text:\\n link_season = str(WIKIPEDIA + link.get('href')).replace(' ', '')\\n season = str(link.get('href')).replace(':', '_').split('_')[-5:-4][0]\\n\\n if int(season) in years:\\n\\n req = requests.get(link_season)\\n soup = BeautifulSoup(req.content, 'html.parser')\\n team_divs = soup.findAll('div', attrs={'class': 'mw-category-group'})\\n teams_season_list = []\\n\\n for team_div in team_divs:\\n team_links = team_div.findAll('a')\\n for team_link in team_links:\\n teams_season_list.append(team_link.get('title'))\\n\\n\\n # Checking missing teams\\n missing_teams = MISSING_CHASSIS.get(season)\\n for missing_team in missing_teams:\\n if missing_team not in teams_season_list:\\n teams_season_list.append(missing_team)\\n\\n seasons_chassis[season] = teams_season_list\\n\\n save_json(seasons_chassis)\\n\\n return seasons_chassis\\n\\n seasons_chassis = load_json(F1_CHASSIS)\\n\\n # Check if we got the chassis names for all seasons listed\\n update_list = []\\n\\n for year in range(start_year, end_year + 1):\\n if str(year) not in seasons_chassis.keys():\\n update_list.append(year)\\n\\n chassis = _get_chassis_names(update_list) if update_list else seasons_chassis\\n\\n return chassis\",\n \"def test_teams_list(self):\\n pass\",\n \"def autocomplete_teams():\\n school_name = request.args.get('school_name')\\n school = School.query.filter(School.name.ilike(school_name)).first()\\n if not school:\\n return jsonify(**{})\\n return jsonify(json_list=[t.name for t in school.teams if len(t.users) < 2])\",\n \"def split_by_wins(matches, team_id):\\n\\n sorted_matches = {\\n \\\"wins\\\": [],\\n \\\"losses\\\": [],\\n \\\"ties\\\": []\\n }\\n\\n scores = {}\\n\\n for match_id, match in matches.items():\\n this_team_score = 0\\n other_team_score = 0\\n if match.hometeam.team_id == team_id:\\n this_team_score = match.homegoals\\n other_team_score = match.awaygoals\\n elif match.awayteam.team_id == team_id:\\n this_team_score = match.awaygoals\\n other_team_score = match.homegoals\\n else:\\n continue\\n\\n scores[match_id] = (this_team_score, other_team_score)\\n\\n if this_team_score > other_team_score:\\n sorted_matches[\\\"wins\\\"].append(match_id)\\n elif this_team_score < other_team_score:\\n sorted_matches[\\\"losses\\\"].append(match_id)\\n else:\\n sorted_matches[\\\"ties\\\"].append(match_id)\\n\\n return sorted_matches, scores\",\n \"def teams():\\n print 'Getting Teams'\\n\\n substring = \\\"%\\\" + request.args.get('t') + \\\"%\\\"\\n\\n team_list = datastore.get_teams_typeahead(engine, substring, max_teams=10)\\n\\n print 'Teams:', team_list\\n return jsonify(team_list)\",\n \"def season_game_logs(team, year):\\n\\n # Check year value\\n if year > 2019 or year < 1950:\\n raise ValueError('Year Value Incorrect')\\n\\n # Rename teams that moved\\n team = scrape_utils.rename_team(team, year)\\n\\n # Get HTML content\\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n season_stats = soup.find(id='tgl_basic')\\n games = season_stats.find('tbody')\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # To find opponent statistics\\n opponent = re.compile('^opp_.*$')\\n\\n # Loop through every game in a team's season\\n for game in games.find_all('tr', {'class': None}):\\n\\n curr_team = {'team': team}\\n opp_team = {}\\n\\n # Loop through each stat\\n for stat in game.find_all('td'):\\n\\n stat_name = stat['data-stat']\\n\\n # These are opponent stats\\n if re.match(opponent, stat_name):\\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\\n else:\\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\\n\\n # Remove unnecessary information\\n del curr_team['game_season']\\n del curr_team['x']\\n\\n # Rename relocated teams\\n curr_team['team'] = scrape_utils.rename_team(team)\\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\\n\\n # Use the same ID as basketball reference\\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \\\"%Y-%m-%d\\\"),\\n 'season': year,\\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\\n '_id': game.find('a')['href'][-17:-5]}\\n\\n # Place the teams in the correct spot depending on who is the home team\\n if curr_team.pop('game_location') == 0:\\n result['home'] = curr_team\\n result['away'] = opp_team\\n else:\\n result['home'] = opp_team\\n result['away'] = curr_team\\n\\n # Insert into database\\n m.insert('game_log', result)\",\n \"def teams(self):\\n return self.V().is_team()\",\n \"def get_matchup_history(self, *teams):\\n abbrA = convert_name(teams[0], how='abbr')\\n abbrB = convert_name(teams[1], how='abbr')\\n\\n return self._db.Games.find({'$or' : [{'home' : abbrA,\\n 'away' : abbrB},\\n {'home' : abbrB,\\n 'away' : abbrA}]})\",\n \"def get_matches_with_teams():\\n\\tf = open(\\\"raw_tba.json\\\")\\n\\tjsonvar = json.loads(f.read())\\n\\n\\treturn_val = []\\n\\tfor i in jsonvar:\\n\\t\\t# print i\\n\\t\\tif \\\"score_breakdown\\\" in i and i[\\\"score_breakdown\\\"] != None:\\n\\t\\t\\treturn_val.append(FullTBAMatch(i))\\n\\n\\treturn return_val\",\n \"def get_team_roster(league):\\n pass\",\n \"def get_team_stats(players: list[Player]) -> dict[int]:\\n\\n team_stats = {}\\n\\n total_reaction = 0\\n total_mechanical_skill = 0\\n total_tactical_skill = 0\\n total_game_knowledge = 0\\n total_xp = 0\\n\\n for player in players:\\n total_reaction += player.reaction\\n total_mechanical_skill += player.mechanical_skill\\n total_tactical_skill += player.tactical_skill\\n total_game_knowledge += player.game_knowledge\\n total_xp += player.xp\\n\\n team_stats.update(\\n {\\\"reaction\\\": total_reaction,\\n \\\"mechanical_skill\\\": total_mechanical_skill,\\n \\\"tactical_skill\\\": total_tactical_skill,\\n \\\"game_knowledge\\\": total_game_knowledge,\\n \\\"xp\\\": total_xp})\\n\\n return team_stats\",\n \"def get_available_teams(self):\\n teams = self.request.user.team_set.filter(competition__is_open=True)\\n if not teams.exists():\\n msg = \\\"Can't send invites at this time. You're not\\\"\\n msg += \\\" registered for any open competitions\\\"\\n messages.error(self.request, msg)\\n raise Http404(msg)\\n return teams\",\n \"def get_player_stats(season_start_year, csv_file_name = None):\\n season_year_full = convert_season_start_to_season_years(\\n starting_year = season_start_year\\n )\\n \\n players_df = playergamelogs.PlayerGameLogs(\\n season_nullable = season_year_full\\n ).player_game_logs.get_data_frame()\\n \\n # Keep the relevant columns\\n players_df = players_df[[\\n \\\"SEASON_YEAR\\\", \\\"PLAYER_ID\\\", \\\"PLAYER_NAME\\\", \\\"TEAM_NAME\\\",\\n \\\"GAME_ID\\\", \\\"GAME_DATE\\\", \\\"MATCHUP\\\", \\\"WL\\\", \\\"MIN\\\",\\n \\\"FGM\\\", \\\"FGA\\\", \\\"FTM\\\", \\\"FTA\\\", \\\"FG3M\\\", \\\"PTS\\\", \\\"REB\\\",\\n \\\"AST\\\", \\\"STL\\\", \\\"BLK\\\", \\\"TOV\\\"]]\\n \\n # Convert GAME_DATE to datetime\\n players_df[\\\"GAME_DATE\\\"] = pd.to_datetime(\\n players_df[\\\"GAME_DATE\\\"]\\n )\\n \\n # Save the data frame to a csv if a file name exists\\n if csv_file_name != None:\\n # Save to current directory\\n csv_path = Path(\\\"./data/\\\" + csv_file_name + \\\".csv\\\")\\n players_df.to_csv(path_or_buf = csv_path,\\n index = False,\\n na_rep = 'NULL')\\n \\n return players_df\",\n \"def losses(self):\\n return [g for g in self.games if g.winner is not self.team]\",\n \"def get_teams(self, *args, **kwargs):\\n\\n teams_data = api.get_teams(\\n *args,\\n api_key=self.__creds.api_key_v2,\\n **kwargs)\\n return [en.Team(creds=self.__creds, **team_data) for team_data in teams_data]\",\n \"def playerStandings():\\n # place all players in a dictionary\\n player_dict = {}\\n conn, c = connect()\\n c.execute(\\\"\\\"\\\"SELECT * FROM players;\\\"\\\"\\\")\\n for row in c.fetchall():\\n player_dict[row[0]] = [row[1], 0, 0]\\n\\n # count the number of win and matches in for all matches\\n c.execute(\\\"\\\"\\\"SELECT winner, loser FROM matches;\\\"\\\"\\\")\\n for row in c.fetchall():\\n if row[0] in player_dict:\\n player_dict[row[0]][1] += 1\\n player_dict[row[0]][2] += 1\\n if row[1] in player_dict:\\n player_dict[row[1]][2] += 1\\n\\n # compile win counts as the key to dictionary\\n win_count = {}\\n for i in player_dict:\\n wins = player_dict[i][1]\\n if wins in win_count:\\n win_count[wins].append((i, player_dict[i][0],\\n wins, player_dict[i][2]))\\n else:\\n win_count[wins] = [(i, player_dict[i][0],\\n wins, player_dict[i][2])]\\n\\n # compile output list\\n output_list = []\\n for i in sorted(win_count.keys(), reverse=True):\\n for j in win_count[i]:\\n output_list.append(j)\\n\\n return output_list\"\n]","isConversational":false},"negative_scores":{"kind":"list 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one dataframe"},"document":{"kind":"string","value":"def combine_playoff_championship_for_each_coach(self):\n self.final_coach_with_postseason_champion_each_year = (\n self.final_coach_with_postseason_each_year\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\n .fillna(0)\n )"},"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 get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"count\"})\n .reset_index()\n .rename(columns={\"NumOT\":\"is_champion\",\"WTeamID\":\"TeamID\"})\n )","def teams(self, game_id: int) -> DataFrame[Any]:","def opponent_games(df, team, gameids):\n games = df[df.gameid.isin(gameids)]\n opponent_games = games[games.team != team].drop_duplicates(subset=['gameid'])[['gameid', 'team']]\n opponent_games.columns = ['gameid', 'opponent']\n\n return opponent_games","def winning_games_stats(self):\n self.winning_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(winning_num_counts = 1))\n .query(\"Season <= 2013\")\n .groupby(['Season','WTeamID'])\n .agg({\"WScore\":\"sum\",\"WFGM\":\"sum\",\"WFGA\":\"sum\",\"WFGM3\":\"sum\",\"WFGA3\":\"sum\",\"WFTM\":\"sum\",\"WFTA\":\"sum\",\"LScore\":\"sum\",\"winning_num_counts\":\"sum\",\n \"WOR\":\"sum\",\"WDR\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\n \"WAst\":\"sum\",\"WTO\":\"sum\",\"WStl\":\"sum\",\"WBlk\":\"sum\",\"WPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"LScore\":\"losing_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"LFGA\":\"LFGA_opp\",\"LFGM\":\"LFGM_opp\"})\n )","def get_teams_table(games_table: pd.DataFrame) -> pd.DataFrame:\n team_name_series = games_table[\"team\"].unique()\n teams_table = pd.DataFrame(data=team_name_series)\n teams_table = teams_table.reset_index()\n teams_table.columns = [\"id\", \"team\"]\n return teams_table","def construct_team_dataframe(game_dict):\n from .models import Game\n\n logger = logging.getLogger(__name__)\n\n # WHEN NOT SPECIFIED ALL STATS RELATE TO US ONLY\n\n # create list of 'vs opponent' row titles to use as index\n # indexlist = [str('vs ' + game.opposing_team.team_name) if game.opposing_team else game.game_ID for game in Game.objects.filter(pk__in=game_dict.keys())]\n\n indexlist = [game.game_ID for game in Game.objects.filter(pk__in=game_dict.keys())]\n\n tf = pd.DataFrame(index=indexlist)\n\n # final score of each game\n scores = [str(frame.iloc[-1]['ourscore_EOP']) + '-' + str(frame.iloc[-1]['theirscore_EOP']) for gameid, frame in game_dict.items()]\n score = pd.Series(scores, index=tf.index)\n\n # total passes per game\n passes = [frame['passes'].sum() for gameid, frame in game_dict.items()]\n passes = pd.Series(passes, index=tf.index)\n\n # turnovers we had\n turnovers = [frame['turnovers'].sum() for gameid, frame in game_dict.items()]\n t_series = pd.Series(turnovers, index=tf.index)\n\n completion = []\n for g_pass, g_turn in zip(passes, turnovers):\n if g_pass:\n comp_pct = round((g_pass - g_turn) / g_pass, 2)*100\n else:\n comp_pct = None\n\n completion.append(comp_pct)\n # completion %\n completion = pd.Series(completion, index=tf.index)\n\n # total possessions we had\n possessions = pd.Series([frame['possessions'].sum() for gameid, frame in game_dict.items()], index=tf.index)\n\n # turnovers our opponent committed\n opp_turns = pd.Series([frame['opp_turns'].sum() for gameid, frame in game_dict.items()], index=tf.index)\n\n # % of our possessions we scored on\n # ( goals / our_possessions ) * 100%\n goals = [frame.iloc[-1]['ourscore_EOP'] for gameid, frame in game_dict.items()]\n goals = pd.Series(goals, index=tf.index)\n score_pct = round((goals / possessions) * 100, 2)\n\n # number of possessions our opponents had\n opp_poss = [frame['opp_poss'].sum() for gameid, frame in game_dict.items()]\n opp_poss = pd.Series(opp_poss, index=tf.index)\n\n # number of blocks we got\n blocks = [frame['blocks'].sum() for gameid, frame in game_dict.items()]\n blocks = pd.Series(blocks, index=tf.index)\n\n # % of our opponents possessions where we got a block\n # ( blocks / opp_possessions ) * 100%\n block_pct = round((blocks / opp_poss) * 100, 2)\n\n tf = tf.assign(score=score)\n tf = tf.assign(passes=passes)\n tf = tf.assign(completion_pct=completion)\n tf = tf.assign(possessions=possessions)\n tf = tf.assign(score_pct=score_pct)\n tf = tf.assign(blocks=blocks)\n\n tf = tf.assign(turns=t_series)\n tf = tf.assign(opp_turns=opp_turns)\n tf = tf.assign(opp_poss=opp_poss)\n\n tf = tf.assign(block_pct=block_pct)\n\n return tf","def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )","def get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n # get losing games for coaches\n self.post_games_lose_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df for post season\n self.combine_post_games_won_lose = (\n self.post_games_lose_for_coaches\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"post_games_lost\",\"which_coach_for_win\":\"post_games_won\"})\n .fillna(0)\n )","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def create_table_per_team(self) -> None:\n for team_id in self.teams:\n home = self.file.loc[(self.file[\"IdHomeTeam\"] == team_id)].reset_index(drop=True)\n home = home.rename(columns={\"FTHG\": \"FTG_asH\", \"FTR\": \"FT_RESULT\", \"HTHG\": \"HTG_asH\",\n \"HTR\": \"HT_RESULT\", \"HS\": \"Shoot_asH\", \"HST\": \"ShootTarget_asH\",\n \"HF\": \"Fouls_asH\", \"HC\": \"Corner_asH\", \"HY\": \"YellowC_asH\", \"HR\": \"RedC_asH\",\n \"FTAG\": \"FT_against_H\", \"HTAG\": \"HT_against_H\",\n \"AS\": \"Shoot_against_H\", \"AST\": \"ShootTarget_against_H\",\n \"AF\": \"Fouls_against_H\",\n \"AC\": \"Corner_against_H\", \"AR\": \"RedC_againts_H\", \"AY\": \"YellowC_against_H\"})\n home[\"nWeekHome\"] = home.index + 1\n home.drop([\"nWeekAway\", \"IdAwayTeam\"], axis=1,\n inplace=True)\n home.loc[(home[\"FT_RESULT\"] == \"H\"), \"FT_RESULT\"] = \"Winn\"\n home.loc[(home[\"FT_RESULT\"] == \"D\"), \"FT_RESULT\"] = \"Draw\"\n home.loc[(home[\"FT_RESULT\"] == \"A\"), \"FT_RESULT\"] = \"Lost\"\n home.loc[(home[\"HT_RESULT\"] == \"H\"), \"HT_RESULT\"] = \"Winn\"\n home.loc[(home[\"HT_RESULT\"] == \"D\"), \"HT_RESULT\"] = \"Draw\"\n home.loc[(home[\"HT_RESULT\"] == \"A\"), \"HT_RESULT\"] = \"Lost\"\n\n away = self.file.loc[(self.file[\"IdAwayTeam\"] == team_id)].reset_index(drop=True)\n away = away.rename(columns={\"FTAG\": \"FTG_asA\", \"FTR\": \"FT_RESULT\", \"HTAG\": \"HTG_asA\",\n \"HTR\": \"HT_RESULT\", \"AS\": \"Shoot_asA\", \"AST\": \"ShootTarget_asA\",\n \"AF\": \"Fouls_asA\", \"AC\": \"Corner_asA\", \"AY\": \"YellowC_asA\", \"AR\": \"RedC_asA\",\n \"FTHG\": \"FT_against_A\", \"HTHG\": \"HT_against_A\",\n \"HS\": \"Shoot_against_A\", \"HST\": \"ShootTarget_against_A\",\n \"HF\": \"Fouls_against_A\",\n \"HC\": \"Corner_against_A\", \"HR\": \"RedC_againts_A\", \"HY\": \"YellowC_against_A\"})\n away[\"nWeekAway\"] = away.index + 1\n away.drop([\"nWeekHome\", \"IdHomeTeam\"], axis=1,\n inplace=True)\n away.loc[(away[\"FT_RESULT\"] == \"H\"), \"FT_RESULT\"] = \"Lost\"\n away.loc[(away[\"FT_RESULT\"] == \"D\"), \"FT_RESULT\"] = \"Draw\"\n away.loc[(away[\"FT_RESULT\"] == \"A\"), \"FT_RESULT\"] = \"Winn\"\n away.loc[(away[\"HT_RESULT\"] == \"H\"), \"HT_RESULT\"] = \"Lost\"\n away.loc[(away[\"HT_RESULT\"] == \"D\"), \"HT_RESULT\"] = \"Draw\"\n away.loc[(away[\"HT_RESULT\"] == \"A\"), \"HT_RESULT\"] = \"Winn\"\n\n self.home = self.home.append(home, ignore_index=True)\n self.away = self.away.append(away, ignore_index=True)\n\n self.home[\"Date\"] = pd.to_datetime(self.home[\"Date\"], format=\"%Y-%m-%d\")\n self.away[\"Date\"] = pd.to_datetime(self.away[\"Date\"], format=\"%Y-%m-%d\")\n self.home.reset_index(drop=True)\n self.away.reset_index(drop=True)","def history(df, team, opponent):\n if opponent:\n games = df[(df.team == team) & (df.opponent == opponent)]#team_games(df, team)\n else:\n games = df[df.team == team]#team_games(df, team)\n\n games['dragkills'] = (games['teamdragkills'] + games['oppdragkills'])\n games['turrkills'] = (games['teamtowerkills'] + games['opptowerkills'])\n result = games[['team', 'opponent', 'player', 'champion', 'fb', 'fd', 'ft', 'fbaron', 'result', 'turrkills', 'dragkills', 'gamelength']]\n\n result = result[result.player == 'Team'].sort_values('gamelength')\n\n return result.round(2)","def get_teams():","def get_current_lineups():\n out = []\n pf = players[players[\"team\"].isin(top30Teams)]\n for index, row in pf.iterrows():\n # Make sure that we only use player data where a player is\n # playing for their current team\n if(row[\"name\"] in top30Obj[row[\"team\"]]):\n out.append(row)\n return pd.DataFrame(out)","def build_teams(self):\n # get all nations\n all_nations = self.games.get_all_nations()\n\n # build teams for all participating nations in FIFA World Cup 2018\n bt = BuildTeams(self.squad_size, self.selected_attrs)\n bt.read_data()\n\n # a dict with a nations' name as a key and players' data as value\n self.teams = {}\n\n # build squad for every nation\n for nation in all_nations:\n team = bt.build_team(nation)\n # if we got enough players, add team\n if team.shape[0] >= bt.squad_size:\n #print(team)\n # convert pandas dataframe to matrix and flatten it\n self.teams[nation] = team.as_matrix().flatten()","def win(self) -> pd.DataFrame:\n if not all([self.league, self.feature_set, self.team, self.opponent, self.variable]):\n return pd.DataFrame().from_records([])\n\n # Get win probability from the team and opponent's perspectives\n df_team = self._win(is_opponent=False)\n df_opp = self._win(is_opponent=True)\n\n # Merge\n df = df_team.drop('RandomEffect', axis=1).merge(df_opp.drop('RandomEffect', axis=1),\n on=self.variable, how='inner')\n\n # Normalize so that P(Win) + P(Lose) = 1.0 calculated from each perspective\n df['Win'] = df['Win_team'] / (df['Win_team'] + (1 - df['Win_opp']))\n df['WinUB'] = abs(df['WinUB_team'] / (df['WinUB_team'] + (1 - df['WinLB_opp'])) - df['Win'])\n df['WinLB'] = abs(df['Win'] - df['WinLB_team'] / (df['WinLB_team'] + (1 - df['WinUB_opp'])))\n\n return df","def get_opponents(df):\n opponents = []\n games = games_only(df)\n for idx, row in df.iterrows():\n op = games[(games.gameid == row.gameid) & (games.team != row.team)].team.values[0]\n opponents.append(op)\n\n return opponents","def plays(df):\n tp = (\n df.query('play_type in @OFFENSE_PLAY_TYPES')\n .pivot_table(index=['game_id', 'posteam'], \n columns=['play_type'], \n values=['play_id'], \n aggfunc='count',\n fill_value=0)\n .pipe(lambda x: x.set_axis([f'{b}_plays' for a, b in x.columns], axis=1, inplace=False))\n .reset_index()\n ) \n tp['tot_plays'] = tp.loc[:, [c for c in tp.columns if '_plays' in c]].sum(axis=1)\n tp['run_pct'] = tp['run_plays'] / (tp['run_plays'] + tp['pass_plays'])\n tp['pass_pct'] = tp['pass_plays'] / (tp['run_plays'] + tp['pass_plays'])\n return tp.join(time_of_possession(df), on=['game_id', 'posteam'], how='left')","def combine_both_winning_losing_games_stats(self):\n self.combine_both_winning_losing_games_stats = (\n self.winning_games_up_to_2013\n .merge(self.losing_games_up_to_2013, how='left',left_on=['Season','WTeamID'],right_on=['Season','LTeamID'])\n # on field goal percentage and winning counts\n .pipe(lambda x:x.assign(total_score = x.WScore + x.LScore))\n .pipe(lambda x:x.assign(total_opponent_score = x.winning_opponent_score + x.losing_opponent_score))\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\n .pipe(lambda x:x.assign(total_fga = x.WFGA + x.LFGA))\n .pipe(lambda x:x.assign(total_fg3m = x.WFGM3 + x.LFGM3))\n .pipe(lambda x:x.assign(total_fg3a = x.WFGA3 + x.LFGA3))\n .pipe(lambda x:x.assign(total_ftm = x.WFTM + x.LFTM))\n .pipe(lambda x:x.assign(total_fta = x.WFTA + x.LFTA))\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .sort_values(['WTeamID','Season'])\n # on offensive and defensive rebounds\n .pipe(lambda x:x.assign(total_rebounds = x.total_winning_rebounds + x.total_losing_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds = x.WOR + x.LOR))\n .pipe(lambda x:x.assign(total_def_rebounds = x.WDR + x.LDR))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_team_missed_attempts = x.team_missed_attempts + x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(total_opp_team_missed_attempts = x.opp_team_missed_attempts + x.winning_opp_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # on steals, turnovers, assists, blocks and personal fouls\n .pipe(lambda x:x.assign(total_blocks = x.WBlk + x.LBlk))\n .pipe(lambda x:x.assign(total_assists = x.WAst + x.LAst))\n .pipe(lambda x:x.assign(total_steals = x.WStl + x.LStl))\n .pipe(lambda x:x.assign(total_turnover = x.WTO + x.LTO))\n .pipe(lambda x:x.assign(total_personalfoul = x.WPF + x.LPF))\n .pipe(lambda x:x.assign(total_opp_fga = x.LFGA_opp + x.WFGA_opp))\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def players(self, game_id: int) -> DataFrame[Any]:","def wins(self):\n return [g for g in self.games if g.winner is self.team]","def pivot_team_matches_to_matches(team_match_df: pd.DataFrame) -> pd.DataFrame:\n home_data_frame = _home_away_data_frame(team_match_df, \"home\")\n away_data_frame = _home_away_data_frame(team_match_df, \"away\")\n\n return home_data_frame.merge(\n away_data_frame,\n on=[\"home_team\", \"away_team\", \"year\", \"round_number\", \"ml_model\"],\n how=\"inner\",\n )","def TeamScores(level,team_N):\r\n \r\n groupresults = Combined_Non_Compound_Results(level).groupby('Club') \r\n # groups clubs together in a big list just for NMR\r\n # will need to generalise for all categories\r\n\r\n LoR = [ frame for LoRs, frame in groupresults ]\r\n \r\n TeamTable = pd.DataFrame({},columns=['Club','Total Score', # initial empty\r\n 'Total Golds', 'Total Hits']) # dataframe\r\n \r\n# Uni = pd.DataFrame({},columns=['Name','Club','Score','Golds', 'Hits'])\r\n TeamComposition = [[],[],[],[]]\r\n for j in range(4): # only four clubs in the dataframe\r\n\r\n Uni = LoR[j][0:team_N] # jth club in index, gets top team_N archers\r\n Uni = Uni.reset_index(drop=True) # resets the index for UCL sublist\r\n UniName = Uni.loc[0,'Club']\r\n\r\n Scores=0\r\n Golds=0\r\n Hits=0\r\n \r\n TeamComposition[j].append(UniName)\r\n\r\n for i in range(team_N): # sums the score,golds and hits for uni club j\r\n Scores += Uni.loc[i,'Score']\r\n Golds += Uni.loc[i,'Golds']\r\n Hits += Uni.loc[i,'Hits']\r\n\r\n TeamComposition[j].append(Uni.loc[i,'Name'])\r\n \r\n TeamTable2 = pd.DataFrame({'Club': [UniName], \r\n 'Total Score': [Scores],\r\n 'Total Golds': [Golds], \r\n 'Total Hits': [Hits]},\r\n columns=['Club','Total Score', \r\n 'Total Golds', 'Total Hits'])\r\n \r\n TeamTable = TeamTable.append(TeamTable2) # appends each club data\r\n\r\n TeamTable = TeamTable.sort_values(['Total Score','Total Golds',\r\n 'Total Hits'],ascending=[False,False,\r\n False],na_position='last')\r\n TeamTable = TeamTable.reset_index(drop=True)\r\n print()\r\n print(TeamTable)\r\n print()\r\n \r\n \r\n FinalList = [[],[],[],[]]\r\n \r\n for h in range(4):\r\n for g in range(4):\r\n if TeamTable.iloc[h,0] == TeamComposition[g][0]:\r\n FinalList[h] = TeamComposition[g]\r\n\r\n \r\n for k in range(4):\r\n print(FinalList[k])\r\n print()\r\n\r\n if level == NovCategories:\r\n \r\n return print(\"----- End of Novice Team Scores -----\")\r\n \r\n if level == AllCategories:\r\n \r\n return print(\"----- End of Experienced Team Scores -----\")","def load_and_process_team_data(self,\n dataset: Dataset,\n teamid: int,\n team_fixtures_idx: List[int]) -> pd.DataFrame:\n df = self._dbmanager.query_team_data(self._seasons, params=(teamid, teamid, teamid, *self._seasons))\n\n # Current win-or-draw target from team's POV\n df[\"wd\"] = np.select(\n condlist=[\n (df[\"homeTeamID\"] == teamid) & (df[\"winner\"] == 1),\n (df[\"homeTeamID\"] == teamid) & (df[\"winner\"] == 2),\n (df[\"awayTeamID\"] == teamid) & (df[\"winner\"] == 2),\n (df[\"awayTeamID\"] == teamid) & (df[\"winner\"] == 1),\n df[\"winner\"] == 0\n ],\n choicelist=[\n 1,\n 0,\n 1,\n 0,\n 1\n ],\n default=np.nan\n )\n # Odds for wd from team's POV\n df[\"odds_wd\"] = np.select(\n condlist=[\n df[\"homeTeamID\"] == teamid,\n df[\"awayTeamID\"] == teamid\n ],\n choicelist=[\n df[\"oddsDC_1X\"],\n df[\"oddsDC_X2\"]\n ],\n default=np.nan\n )\n # `team` is just name of current team\n df[\"team\"] = np.select(\n condlist=[\n df[\"homeTeamID\"] == teamid,\n df[\"awayTeamID\"] == teamid\n ],\n choicelist=[\n df[\"home\"],\n df[\"away\"]\n ],\n default=np.nan\n )\n # `opponent` is the opposite team to `team`\n df[\"opponent\"] = np.select(\n condlist=[\n df[\"homeTeamID\"] == teamid,\n df[\"awayTeamID\"] == teamid\n ],\n choicelist=[\n df[\"away\"],\n df[\"home\"]\n ],\n default=np.nan\n )\n # Whether the current team plays as home = 1, or away = 0\n df[\"ashome\"] = np.select(\n condlist=[\n df[\"homeTeamID\"] == teamid,\n df[\"awayTeamID\"] == teamid,\n ],\n choicelist=[\n 1,\n 0\n ],\n default=np.nan\n )\n # Encode league: PL = 1, CH = 0\n df[\"league\"] = np.select(\n condlist=[\n df[\"league\"] == \"PL\",\n df[\"league\"] == \"CH\"\n ],\n choicelist=[\n 1,\n 0\n ],\n default=np.nan\n )\n\n # Create future values of features which are known in advance.\n # They are created by shifting original values by one row.\n df[\"future_season\"] = df[\"season\"].shift(-1)\n df[\"future_league\"] = df[\"league\"].shift(-1)\n df[\"future_ashome\"] = df[\"ashome\"].shift(-1)\n df[\"future_opponent\"] = df[\"opponent\"].shift(-1)\n df[\"future_wd\"] = df[\"wd\"].shift(-1)\n df[\"future_odds_wd\"] = df[\"odds_wd\"].shift(-1)\n\n # Dataframe needs to be filtered according to current dataset to contain only corresponding\n # matches. Also index needs to be reset (to correctly access data for test and predict sets).\n df = df.loc[df[\"id\"].isin(team_fixtures_idx)].copy()\n df.reset_index(inplace=True, drop=True)\n\n # If there are nans for train dataset, use ffill method to fill them.\n # This can happen if team occurs only in train dataset and does not have more data in later\n # seasons, thus it does not matter that we fill these nans with incorrect data as they wo not be\n # used for testing anyway.\n if dataset == Dataset.Train:\n # Fill nan values of future opponent with empty string\n df[\"future_opponent\"].fillna(\"\", inplace=True)\n df.fillna(method=\"ffill\", inplace=True)\n\n df = self._scale_team_data(df)\n\n return df","def make_player_stats(tournament, lineups):\n positions = find_player_positions(lineups)\n positions = positions.set_index('player_id')[['player_position']]\n player_stats = load_player_stats(tournament)\n player_stats.set_index('player_id', inplace=True)\n mask = player_stats['goals'] > player_stats['shots']\n player_stats.loc[mask, 'shots'] = player_stats[mask]['goals']\n res = player_stats.join(positions)\n res = res[pd.notna(res['player_position'])]\n return res","def get_final_coach_for_each_season(self):\n self.final_coach_for_season = (\n self.num_days_coach_for_season\n .groupby(['Season','TeamID'])\n .agg({\"CoachName\":\"count\"})\n .reset_index()\n .rename(columns={\"CoachName\":\"coach_counts\"})\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \"ignore\")))\n [['Season','TeamID','final_coach']]\n )","def _win(self, is_opponent: bool) -> pd.DataFrame:\n records = []\n for var in self.variable_vals:\n # Add variable to parameters\n self.parameters[self.variable] = var\n # Add derived features\n self._derived_features()\n # Configure inputs\n input_set = {\n 'random_effect': 'team' if not is_opponent else 'opponent',\n 'feature_set': self.feature_set,\n 'inputs': {'RandomEffect': self.team if not is_opponent else self.opponent}\n }\n input_set['inputs'].update(self.parameters)\n # Predict\n output = self.predictor.predict(**input_set)[\n ('team' if not is_opponent else 'opponent', self.feature_set, 'Win')\n ]\n # Wrangle output\n record = {\n 'RandomEffect': self.team if is_opponent else self.opponent,\n self.variable: var,\n 'WinLB_opp' if is_opponent else 'WinLB_team': expit(output['mu']['lb']),\n 'Win_opp' if is_opponent else 'Win_team': expit(output['mu']['mean']),\n 'WinUB_opp' if is_opponent else 'WinUB_team': expit(output['mu']['ub'])\n }\n records.append(record)\n\n return pd.DataFrame().from_records(records)","def gamesmeta(sched=None):\n if sched is None or sched.empty:\n sched = schedule()\n h = sched.copy()\n a = sched.copy()\n h = h.rename(columns={'home_team': 'team', 'away_team': 'opp'})\n a = a.rename(columns={'away_team': 'team', 'home_team': 'opp'})\n return pd.concat([a, h]).sort_values('game_id')","def _get_teams(unstacked, min_games=0):\n count_col = 'game_id'\n home_gb = unstacked.groupby('hteam_id').count()[[count_col]]\n away_gb = unstacked.groupby('ateam_id').count()[[count_col]]\n merged = home_gb.merge(away_gb, how='outer', left_index=True, right_index=True)\n merged[count_col] = merged[count_col + '_x'] + merged[count_col + '_y']\n merged.index.name = 'index'\n teams = merged.reset_index().rename(columns={'index': 'team_id'})\n teams = teams.sort('team_id')\n teams['i_team'] = np.arange(teams.shape[0])\n team_index = {}\n for team, idx in teams[['team_id', 'i_team']].values:\n team_index[int(team)] = int(idx)\n return teams[['team_id', 'i_team']], team_index","def team_games(df, team):\n print(df[\n ( df['team'] == team ) & \n ( df['player'] == 'Team' )\n ].drop_duplicates(subset=['gameid']).shape)\n return df[\n ( df['team'] == team ) & \n ( df['player'] == 'Team' )\n ].drop_duplicates(subset=['gameid'])","def get_past_matches_data(team):\n matches = team.get_past_matches()\n match_list = []\n for match in matches:\n match_dict = {}\n match_dict['match_date'] = match.match_date\n match_dict['match_name'] = match.__str__()\n match_dict['id'] = match.id\n innings = match.get_innings()\n if len(innings):\n if innings[0].runs > innings[1].runs:\n match_dict['winner_team'] = innings[0].bat_team\n match_dict['win_margin'] = innings[0].runs - innings[1].runs\n match_dict['win_type'] = 'Runs'\n match_dict['winner_score'] = str(innings[0].runs) + '/' + str(innings[0].wickets)\n else:\n match_dict['winner_team'] = innings[1].bat_team\n match_dict['win_margin'] = 10 - innings[1].wickets\n match_dict['win_type'] = 'Wickets'\n match_dict['winner_score'] = str(innings[1].runs) + '/' + str(innings[1].wickets)\n match_list.append(match_dict)\n return match_list","def get_player_stats_from_game(team, year, week):","def cumulative_stats_for_teams_each_year(self):\n self.cumulative_stats_for_team_each_year = (\n self.combine_both_winning_losing_games_stats\n .sort_values(['WTeamID','Season'])\n .groupby(['WTeamID'])\n .cumsum()\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\n .drop(['LTeamID','win_rate'],1)\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n # rebounds cumsum stats\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # assists, turnovers, steals, blocks and personal fouls\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win or lose by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def get_upcoming_games(n=10):\n conn, cursor = connect_to_db()\n query = \"\"\"select kickoff_time, t2.team_id home_id, t2.team_name home_name, \n t3.team_id away_id, t3.team_name away_name\n from fpl_fixtures t1 left join fpl_teams t2 on t1.team_h = t2.id left \n join fpl_teams t3 on t1.team_a = t3.id where started = 0 order by \n kickoff_time limit {}\"\"\".format(n)\n df = run_query(cursor, query)\n return df","def get_fb_team_rankings(self):\n\n ranks = []\n self._logger.debug(\"Getting foosball team rankings\")\n\n try:\n self.check_if_db_connected()\n cursor = self._db_conn.cursor()\n cursor.execute(\"SELECT team_id, team_name FROM team\")\n teams = cursor.fetchall()\n\n for team_id, team_name in teams:\n cursor.execute(\"SELECT fb_team_rating FROM \\\nteam WHERE team_id = {0}\".format(team_id))\n team_rating = cursor.fetchall()[0]\n\n cursor.execute(\"SELECT mu, sigma FROM rating WHERE rating_id \\\n= {0}\".format(team_rating[0]))\n mu, sigma = cursor.fetchall()[0]\n\n team_rank = float(mu) - (3 * float(sigma))\n\n # get player_ids\n cursor.execute(\"SELECT player from player_team_xref \\\nWHERE team = {0}\".format(team_id))\n players = cursor.fetchall()\n player_one = players[0]\n player_two = players[1]\n\n cursor.execute(\"SELECT first_name FROM player WHERE \\\nplayer_id = {0}\".format(player_one[0]))\n player_one_name = cursor.fetchone()[0]\n\n cursor.execute(\"SELECT first_name FROM player WHERE \\\nplayer_id = {0}\".format(player_two[0]))\n player_two_name = cursor.fetchone()[0]\n\n cursor.execute(\"SELECT COUNT(result_id) FROM fb_result WHERE \\\n(offense_winner = {0} AND defense_winner = {1}) OR (offense_winner = {1} \\\nAND defense_winner = {0})\".format(player_one[0], player_two[0]))\n team_win_count = cursor.fetchone()[0]\n\n cursor.execute(\"SELECT COUNT(result_id) FROM fb_result WHERE \\\n(offense_loser = {0} AND defense_loser = {1}) OR (offense_loser = {1} \\\nAND defense_loser = {0})\".format(player_one[0], player_two[0]))\n team_loss_count = cursor.fetchone()[0]\n\n intermediate_rank = (team_name, round(team_rank, 4),\n team_win_count, team_loss_count, player_one_name,\n player_two_name)\n ranks.append(intermediate_rank)\n del intermediate_rank\n\n except MySQLdb.OperationalError:\n self._logger.error(\"MySQL operational error occured\")\n traceback.print_exc()\n raise exceptions.DBConnectionError(\"Cannot connect to MySQL server\")\n\n except MySQLdb.ProgrammingError:\n self._logger.error(\"MySQL programming error\")\n traceback.print_exc()\n raise exceptions.DBSyntaxError(\"MySQL syntax error\")\n\n else:\n return ranks","def gamestats(self, table, curr_team):\n\n # Drop unneeded header \n tmp = table.iloc[1:,]\n # Fix the column names by reading line 0\n tmp.columns = [x.replace(\" \", \"\").replace(\"/\",\"\").replace(\".\",\"\") for x in tmp.iloc[0]]\n # Drop row zero which held the header row\n tmp = tmp.drop(tmp.index[0])\n # Forward fill the dates for defensive split later \n tmp['Date'].fillna(method='ffill', inplace = True)\n # Add in the team \n tmp['Team'] = curr_team\n # Create an offense/defense variable\n tmp['OffenseDefense'] = tmp['Opponent']\n # If it's not a defensive total then it's offense - set that in the offensedefense variable\n tmp['OffenseDefense'] = tmp['OffenseDefense'].apply(lambda x: \"Defense\" if x == \"Defensive Totals\" else \"Offense\")\n # Set the defensive totals in the opponent varaible to nullls\n tmp['Opponent'] = tmp['Opponent'].apply(lambda x: None if x == \"Defensive Totals\" else x)\n # Forward fill the opponents in for analysis later\n tmp['Opponent'].fillna(method='ffill', inplace = True)\n # Forward fill the results in for analysis later \n tmp['Result'].fillna(method='ffill', inplace = True)\n return tmp","def combine_games(all_data):\n\tcombined_fields = concat_data_fields(all_data)\n\tcombined_data = pd.DataFrame(columns=combined_fields)\n\tgids = np.unique(all_data['Id'])\n\tfor gid in gids:\n\t\tgames = all_data[all_data['Id'] == gid]\n\t\tassert games.shape[0] == 2, \"Should have found 2 games here\"\n\t\tconcat_games = concat_game_rows(games, combined_fields)\n\t\tcombined_data = combined_data.append(pd.Series(concat_games[0]), ignore_index=True)\n\t\tcombined_data = combined_data.append(pd.Series(concat_games[1]), ignore_index=True)\n\treturn combined_data","def losing_games_stats(self):\n self.losing_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(losing_num_counts=1))\n .query(\"Season <= 2013\")\n .groupby(['Season','LTeamID'])\n .agg({\"WScore\":\"sum\",\"LScore\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\"LFGM3\":\"sum\",\"LFGA3\":\"sum\",\"LFTM\":\"sum\",\"LFTA\":\"sum\",\"losing_num_counts\":\"sum\",\n \"LOR\":\"sum\",\"LDR\":\"sum\",\"WFGA\":\"sum\",\"WFGM\":\"sum\",\n \"LAst\":\"sum\",\"LTO\":\"sum\",\"LStl\":\"sum\",\"LBlk\":\"sum\",\"LPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"WScore\":\"winning_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"WFGA\":\"WFGA_opp\",\"WFGM\":\"WFGM_opp\"})\n )","def get_games_table() -> pd.DataFrame:\n games_list = []\n for filename in os.listdir(DATA_DIR / \"raw_games_data\"):\n games_table = pd.read_csv(DATA_DIR / \"raw_games_data\" / filename, index_col=0)\n games_list.append(games_table)\n games_table = pd.concat(games_list)\n games_table[\"player_name\"] = games_table[\"player_name\"].apply(lambda x: _capitalize_name(x))\n return games_table","def get_match_features(match, matches):\n\n # Define variables\n date = match.date\n season = match.season\n home_team = match.home_team_api_id\n away_team = match.away_team_api_id\n\n matches_home_ashome = get_last_ashome_matches(matches, date, season, home_team, x=5)\n # print(matches_home_ashome, flush=True)\n matches_home_asaway = get_last_asaway_matches(matches, date, season, home_team, x=5)\n matches_away_ashome = get_last_ashome_matches(matches, date, season, away_team, x=5)\n matches_away_asaway = get_last_asaway_matches(matches, date, season, away_team, x=5)\n # matches_home = pd.concat([matches_home_ashome, matches_home_asaway])\n # matches_away = pd.concat([matches_away_ashome, matches_away_asaway])\n\n # Get last x matches of both teams against each other\n competing_matches_same = get_last_competing_matches(matches, date, home_team, away_team, x=2)\n competing_matches_diff = get_last_reverse_competing_matches(matches, date, home_team, away_team, x=2)\n\n # Define result data frame\n result = pd.Series()\n result['match_api_id'] = match.match_api_id\n result['league_id'] = match.league_id\n result['home_recent_goal_ashome'] = get_goals(matches_home_ashome)\n result['away_recent_goal_asaway'] = get_goals(matches_away_asaway, isAway=True)\n result['home_recent_lost_ashome'] = get_goals_lost(matches_home_ashome)\n result['away_recent_lost_asaway'] = get_goals_lost(matches_away_asaway, isAway=True)\n result['team_ova_diff'] = match.home_ova - match.away_ova\n result['home_recent_score'] \\\n = get_average_league_score(matches_home_ashome) + get_average_league_score(matches_home_asaway, isAway=True)\n result['away_recent_score'] \\\n = get_average_league_score(matches_away_ashome) + get_average_league_score(matches_away_asaway, isAway=True)\n result['home_competing_same_goal'] = get_goals(competing_matches_same)\n result['away_competing_same_goal'] = get_goals(competing_matches_same, isAway=True)\n result['home_competing_diff_goal'] = get_goals(competing_matches_diff, isAway=True)\n result['away_competing_diff_goal'] = get_goals(competing_matches_diff)\n result['home_recent_shoton'] = get_shoton(matches_home_ashome) + 0.8 * get_shoton(matches_home_asaway, isAway=True)\n result['away_recent_shoton'] = get_shoton(matches_away_asaway, isAway=True) + 0.8 * get_shoton(matches_away_ashome)\n result['home_recent_shotoff'] = get_shotoff(matches_home_ashome) + 0.8 * get_shotoff(matches_home_asaway, isAway=True)\n result['away_recent_shotoff'] = get_shotoff(matches_away_asaway, isAway=True) + 0.8 * get_shotoff(matches_away_ashome)\n\n # print(result)\n return result","def get_player_data(responses) -> pd.DataFrame:\n result = []\n for p in responses:\n data = p.decode(\"utf8\").replace(\"'\", '\"')\n data = json.loads(data)\n latest_stats = data.get(\"league\").get(\"standard\").get(\"stats\").get(\n \"latest\")\n stats_dict = {\n \"FGP\":\n latest_stats.get(\"fgp\"),\n \"FTP\":\n latest_stats.get(\"ftp\"),\n \"3PM\":\n round(\n int(latest_stats.get(\"tpm\")) / int(\n latest_stats.get(\"gamesPlayed\")), 1),\n \"PPG\":\n latest_stats.get(\"ppg\"),\n \"APG\":\n latest_stats.get(\"apg\"),\n \"RPG\":\n latest_stats.get(\"rpg\"),\n \"SPG\":\n latest_stats.get(\"spg\"),\n \"BPG\":\n latest_stats.get(\"bpg\"),\n \"TPG\":\n latest_stats.get(\"topg\"),\n \"MPG\":\n round(\n int(latest_stats.get(\"min\")) / int(\n latest_stats.get(\"gamesPlayed\")), 1)\n }\n result.append(stats_dict)\n return pd.DataFrame(result)","def get_matchup_history(self, *teams):\n abbrA = convert_name(teams[0], how='abbr')\n abbrB = convert_name(teams[1], how='abbr')\n\n return self._db.Games.find({'$or' : [{'home' : abbrA,\n 'away' : abbrB},\n {'home' : abbrB,\n 'away' : abbrA}]})","def split_by_wins(matches, team_id):\n\n sorted_matches = {\n \"wins\": [],\n \"losses\": [],\n \"ties\": []\n }\n\n scores = {}\n\n for match_id, match in matches.items():\n this_team_score = 0\n other_team_score = 0\n if match.hometeam.team_id == team_id:\n this_team_score = match.homegoals\n other_team_score = match.awaygoals\n elif match.awayteam.team_id == team_id:\n this_team_score = match.awaygoals\n other_team_score = match.homegoals\n else:\n continue\n\n scores[match_id] = (this_team_score, other_team_score)\n\n if this_team_score > other_team_score:\n sorted_matches[\"wins\"].append(match_id)\n elif this_team_score < other_team_score:\n sorted_matches[\"losses\"].append(match_id)\n else:\n sorted_matches[\"ties\"].append(match_id)\n\n return sorted_matches, scores","async def teams(self, ctx, teamsize: int, *member: discord.Member):\r\n d = dict()\r\n for x in member:\r\n if x.name in player.keys():\r\n d[x.name] = player[x.name]\r\n else:\r\n await ctx.send('{} is not a registered player'.format(x.name))\r\n return\r\n\r\n teams = itertools.combinations(d.items(), teamsize) # divide the players up into all possible teams: AB, AC...\r\n matchups = itertools.combinations(teams, 2) # all possible match ups from all possible teams\r\n\r\n report = '```md' + u\"\\u000A\"\r\n\r\n for x in matchups:\r\n t1 = x[0] # first team\r\n t2 = x[1] # second team\r\n t1_ratings, t2_ratings, t1_names, t2_names = ([] for n in range(4))\r\n for i in range(teamsize):\r\n t1_ratings.append(t1[i][1])\r\n t2_ratings.append(t2[i][1])\r\n t1_names.append(t1[i][0])\r\n t2_names.append(t2[i][0])\r\n qual = quality([t1_ratings, t2_ratings])\r\n # ignore all match ups with players on the same team...\r\n if len(set(t1_names).intersection(set(t2_names))) == 0:\r\n report += ', '.join(t1_names) + ' <vs> ' + ', '.join(t2_names) + u\"\\u000A\" + '> Quality: ' + str(\r\n round(qual, 3)) + u\"\\u000A\"\r\n\r\n await ctx.send(report + u\"\\u000A\" + '```')","def makeMatchupData(fantasyTeams, week):\n\t# this parsing will get messed up if a team doesn't have a full bench.\n\tmatchups = []\n\tmatchupsNicknames = []\n\tfor team in fantasyTeams:\n\t\tif team not in matchups:\n\t\t\tmatchups.append(team)\n\t\t\tmatchupsNicknames.append(fantasyTeams[team].nickname)\n\t\t\tmatchups.append(fantasyTeams[team].matchup[week])\n\t\t\tmatchupsNicknames.append(fantasyTeams[fantasyTeams[team].matchup[week]].nickname)\n\tteamsPoints = []\n\tfor team in matchups:\n\t\tteamPoints = []\n\t\tteamRoster = fantasyTeams[team].roster[week]\n\t\t#print(team, len(teamRoster))\n\t\tteamPoints.append(teamRoster[0].points[week]) # qb\n\t\tteamPoints.append(teamRoster[1].points[week] + teamRoster[2].points[week] + teamRoster[3].points[week]) # wrs\n\t\tteamPoints.append(teamRoster[4].points[week] + teamRoster[5].points[week]) # rbs\n\t\tteamPoints.append(teamRoster[6].points[week]) # te\n\t\tteamPoints.append(teamRoster[7].points[week]) # flx\n\t\tif teamRoster[len(teamRoster)-1].position == 'DEF':\n\t\t\tteamPoints.append(teamRoster[len(teamRoster)-2].points[week]) # k\n\t\t\tteamPoints.append(teamRoster[len(teamRoster)-1].points[week]) # def\n\t\telse: # if there's an IR spot\n\t\t\tteamPoints.append(teamRoster[len(teamRoster)-3].points[week]) # k\n\t\t\tteamPoints.append(teamRoster[len(teamRoster)-2].points[week]) # def\n\t\tteamsPoints.append(teamPoints)\n\tfor i in range(len(teamsPoints)):\n\t\tfor j in range(len(teamsPoints[0])):\n\t\t\tif teamsPoints[i][j]<-20:\n\t\t\t\tteamsPoints[i][j]=-20\n\treturn matchups, matchupsNicknames, teamsPoints","def merge_player_dfs(basic_stats_df, adv_stats_df, cols_to_strip=7):\n return pd.merge(basic_stats_df, _strip_left_cols(adv_stats_df, cols_to_strip), left_index=True, right_index=True)","def format_df(df, winner, match_id):\n df['match_id'] = match_id\n try:\n df = df.set_index('hero_id')\n except KeyError:\n pass\n df = df.rename(index=lambda x:\n _hero_id_to_names.get(int(x),\n str(x)))\n df.index.set_names(['hero'], inplace=True)\n\n df['team'] = df['player_slot'].apply(DetailsResponse.rep_team)\n df['win'] = df['team'] == winner\n df = df.sort('team', ascending=False)\n\n df = df.reset_index().set_index(['match_id', 'team', 'hero']).sort_index()\n return df","def processed_overall(self):\n self.processed_overall = (\n self.combine_both_winning_losing_games_stats\n .rename(columns={\"WTeamID\":\"TeamID\"})\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n [['Season','TeamID','win_rate','total_score','total_opponent_score','fgp','fg3p','ftp', 'total_rebounds','total_off_rebounds','total_def_rebounds',\n 'total_off_rebounds_percent','total_def_rebounds_percent','total_rebound_possession_percent','total_rebound_possessiongain_percent','total_blocks',\n 'total_assists','total_steals','total_turnover','total_personalfoul','total_block_opp_FGA_percent','total_assist_per_fgm','total_assist_turnover_ratio',\n 'expectation_per_game','avg_lose_score_by','avg_win_score_by']]\n )","def create_match(team_id, current_matches, match_details, round_number, stats, targets):\n\n current_previous_matches = match_details.loc[\n ((match_details['home_id'] == team_id) | (match_details['away_id'] == team_id)) &\n (match_details['round'] < round_number)]\n\n # Only take the previous 3 matches and sum those stats together\n previous_matches = current_previous_matches.iloc[-3:]\n\n # Find CUR_TEAM's stats\n match_id, team_id, team_name, scheduled, is_home, total_points, goals_for, goals_against, goal_diff, goal_efficiency, \\\n played, win, loss, recent_wins, recent_losses, prev_opp, opp_id, points, goals, opp_goals, \\\n current_formation, opp_formation, game_features = \\\n calculate_stats(team_id, current_matches, previous_matches, stats, targets)\n\n # Calculate the OPPONENTS stats\n if stats:\n print('Current Opponent ID : {0}'.format(opp_id))\n\n # Find OPP_TEAM's stats\n opp_previous_matches = match_details.loc[\n ((match_details['home_id'] == opp_id) | (match_details['away_id'] == opp_id)) &\n (match_details['round'] < round_number)]\n\n opp_previous_matches = opp_previous_matches.iloc[-3:]\n\n _, opp_team_id, opp_team_name, _, opp_is_home, opp_total_points, opp_goals_for, opp_goals_against, opp_goal_diff, opp_goal_efficiency, \\\n opp_played, opp_win, opp_loss, opp_recent_wins, opp_recent_losses, opp_opp, _, _, _, _, _, _, opp_game_features = calculate_stats(opp_id, current_matches, opp_previous_matches, False, False)\n\n if stats:\n print('Previous Opponents of Current Team : {0}'.format(prev_opp))\n\n prev_opp_won_total = 0\n prev_opp_lost_total = 0\n\n for prev_opp_id in prev_opp:\n prev_opp_previous_matches = match_details.loc[\n ((match_details['home_id'] == prev_opp_id) | (match_details['away_id'] == prev_opp_id)) &\n (match_details['round'] < round_number)]\n\n # Only take the previous 3 matches and sum those stats together\n prev_opp_previous_matches = prev_opp_previous_matches.iloc[-3:]\n\n _, _, _, _, _, _, prev_opp_goals_for, prev_opp_goals_against, prev_opp_goal_diff, prev_opp_goal_efficiency, \\\n prev_opp_played, prev_opp_win, prev_opp_loss, _, _, opp_prev_opp, _, prev_opp_points, prev_opp_goals, opp_prev_opp_goals, _, _, \\\n prev_opp_game_features = calculate_stats(prev_opp_id, current_matches, prev_opp_previous_matches, False, False)\n\n prev_opp_won_total += prev_opp_win\n prev_opp_lost_total += prev_opp_loss\n\n opp_opp_won_total = 0\n opp_opp_lost_total = 0\n\n if stats:\n print('Current Opponents of Opponent : {0}'.format(opp_opp))\n\n # Calculate OPPONENTS of the OPPONENTS stats\n for opp_opp_id in opp_opp:\n\n opp_opp_previous_matches = match_details.loc[\n ((match_details['home_id'] == opp_opp_id) | (match_details['away_id'] == opp_opp_id)) &\n (match_details['round'] < round_number)]\n\n # Only take the previous 3 matches and sum those stats together\n opp_opp_previous_matches = opp_opp_previous_matches.iloc[-3:]\n\n opp_opp_match_id, opp_opp_team_id, opp_opp_team_name, scheduled, opp_opp_is_home, opp_opp_total_points, opp_opp_goals_for, opp_opp_goals_against, opp_opp_goal_diff, opp_opp_goal_efficiency, \\\n opp_opp_played, opp_opp_win, opp_opp_loss, opp_opp_recent_wins, opp_opp_recent_losses, opp_opp_opp, _, _, _, _, _, _, opp_opp_game_features = calculate_stats(opp_opp_id, current_matches, opp_opp_previous_matches, False, False)\n opp_opp_won_total += opp_opp_win\n opp_opp_lost_total += opp_opp_loss\n\n if stats:\n print('Opponents of Previous Opponents : {0}'.format(opp_prev_opp))\n\n opp_prev_opp_won_total = 0\n opp_prev_opp_lost_total = 0\n\n # Calculate OPPONENTS of the PREVIOUS OPPONENTS stats\n for opp_prev_opp_id in opp_prev_opp:\n\n opp_prev_opp_previous_matches = match_details.loc[\n ((match_details['home_id'] == opp_prev_opp_id) | (match_details['away_id'] == opp_prev_opp_id)) &\n (match_details['round'] < round_number)]\n\n # Only take the previous 3 matches and sum those stats together\n opp_prev_opp_previous_matches = opp_prev_opp_previous_matches.iloc[-3:]\n\n opp_prev_opp_match_id, opp_prev_opp_team_id, opp_prev_opp_team_name, scheduled, opp_prev_opp_is_home, opp_prev_opp_total_points, opp_prev_opp_goals_for, opp_prev_opp_goals_against, opp_prev_opp_goal_diff, opp_prev_opp_goal_efficiency, \\\n opp_prev_opp_played, opp_prev_opp_win, opp_prev_opp_loss, opp_prev_opp_recent_wins, opp_prev_opp_recent_losses, _, _, _, _, _, _, _, opp_prev_opp_game_features = calculate_stats(\n opp_prev_opp_id, current_matches, opp_prev_opp_previous_matches, False, False)\n opp_prev_opp_won_total += opp_prev_opp_win\n opp_prev_opp_lost_total += opp_prev_opp_loss\n\n if stats:\n print('Opponents of Opponents Opponents : {0}'.format(opp_opp_opp))\n\n opp_opp_opp_won_total = 0\n opp_opp_opp_lost_total = 0\n\n # Calculate OPPONENTS of the OPPONENTS' OPPONENTS' stats\n for opp_opp_opp_id in opp_opp_opp:\n opp_opp_opp_previous_matches = match_details.loc[\n ((match_details['home_id'] == opp_opp_opp_id) | (match_details['away_id'] == opp_opp_opp_id)) &\n (match_details['round'] < round_number)]\n\n opp_opp_opp_match_id, opp_opp_opp_team_id, opp_opp_opp_team_name, scheduled, opp_opp_opp_is_home, opp_opp_opp_total_points, opp_opp_opp_goals_for, opp_opp_opp_goals_against, opp_opp_opp_goal_diff, opp_opp_opp_goal_efficiency, \\\n opp_opp_opp_played, opp_opp_opp_win, opp_opp_opp_loss, opp_opp_opp_recent_wins, opp_opp_opp_recent_losses, _, _, _, _, _, _, _, opp_opp_opp_game_features = calculate_stats(\n opp_opp_opp_id, current_matches, opp_opp_opp_previous_matches, False, False)\n opp_opp_opp_won_total += opp_opp_opp_win\n opp_opp_opp_lost_total += opp_opp_opp_loss\n\n \"\"\" //////////////////////////////////////////////////////////////////////////////////////////////////// \"\"\"\n \"\"\" Collected all the information from relevant matches. Now send through all what we have. \"\"\"\n \"\"\" //////////////////////////////////////////////////////////////////////////////////////////////////// \"\"\"\n # Only calculate SOS + RPI here since they include previous matches\n current_record = np.divide(win, (win + loss))\n opp_record = np.divide(opp_win, (opp_win + opp_loss))\n prev_opp_record = np.divide(prev_opp_win, (prev_opp_win + prev_opp_loss))\n opp_prev_opp_record = np.divide(opp_prev_opp_won_total, (opp_prev_opp_won_total + opp_prev_opp_lost_total))\n sos = np.divide((2 * prev_opp_record) + opp_prev_opp_record, 3)\n rpi = (current_record * .25) + (sos * .75)\n\n feature = {'match_id': match_id, 'team_id': team_id, 'team_name': team_name, 'opp_id': opp_team_id,\n 'opp_name': opp_team_name, 'scheduled': scheduled, 'round': round_number, 'games_played': played,\n 'is_home': is_home, 'current_formation': current_formation, 'current_record': current_record,\n 'opp_record': opp_record, 'goals_for': goals_for, 'opp_goals_for': opp_goals_for,\n 'goals_against': goals_against, 'opp_goals_against': opp_goals_against, 'rpi': rpi,\n 'goals': goals, 'points': points}\n\n game_features = {'current_team': game_features, 'opp_team': opp_game_features }\n\n if stats:\n print(\"//////////////////////////////////////////////////\")\n\n return feature, game_features","def scraping_actual_team_players(team_abbreviation):\n starting_point = domain + \"/teams/\"\n teamurl = starting_point + team_abbreviation + \"/\"\n team_id = \"div_\" + team_abbreviation\n html = urlopen(teamurl)\n bs = BeautifulSoup(html, 'html.parser')\n actual_team_url = domain + str(bs.find(\"div\", {'id': team_id}).find(\"a\").get(\"href\"))\n html = urlopen(actual_team_url)\n bs = BeautifulSoup(html, 'html.parser')\n players = bs.find(\"table\", {'id':'roster'}).findAll(\"td\", {\"data-stat\":\"player\"})\n players_url = [player.find(\"a\").get(\"href\") for player in players]\n team_players_list = []\n for player_url in players_url:\n time.sleep(3)\n url = domain + player_url\n html = urlopen(url)\n bs = BeautifulSoup(html, 'html.parser')\n print(player_url)\n try:\n tabla = pd.read_html(str(bs.find(\"div\", {'id':'all_per_poss'})).replace(\"<!--\", \"\"))[0] \n tabla[\"Player\"] = bs.find(\"h1\", {\"itemprop\" : \"name\"}).text.strip()\n indice = tabla[tabla[\"Season\"]==\"Career\"].index[0]\n tabla = tabla[0:indice]\n tabla = tabla.drop(axis= 1,columns = \"Unnamed: 29\")\n #no me encuentra tablas para uno del college que es el darlina01\n print(player_url)\n team_players_list.append(tabla)\n except:\n pass\n return pd.concat(team_players_list)","def get_companies_and_people(team):","def teams(self):\n t = [e[0] for e in self.pick_set.all().values_list(\"team\")]\n return Team.objects.filter(id__in=set(t))","def gamePenaltyDf(plays_home_away):\n \n #Load the read plays data into a dataframe\n playsdf = pd.io.json.json_normalize(plays_home_away[0])\n #Also grab the tricodes\n home_tricode = plays_home_away[1]\n away_tricode = plays_home_away[2]\n \n #Define the columns I want from the data\n relevant_columns = ['about.dateTime', 'about.eventId', 'about.goals.away', 'about.goals.home', 'about.period', 'about.periodTimeRemaining', 'result.penaltyMinutes', 'result.penaltySeverity', 'result.secondaryType', 'team.triCode']\n #Confirm that there are actually penalty minutes in the game\n if 'result.penaltyMinutes' in list(playsdf.columns):\n #Subset down to only plays which resulted in non-nan penalty minutes\n pdf = playsdf[playsdf['result.penaltyMinutes'].isna()==False]\n pdf.reset_index(drop=True, inplace=True)\n #Reduce the columns down to the relevant ones\n relevantdf = pdf[relevant_columns].copy()\n #Some manufactured columns\n relevantdf['against.homeTeam'] = relevantdf['team.triCode'].apply(lambda code: int(code == home_tricode))\n relevantdf['against.awayTeam'] = relevantdf['team.triCode'].apply(lambda code: int(code == away_tricode))\n relevantdf['committed.playingAgainst'] = relevantdf['team.triCode'].apply(lambda code: away_tricode if code == home_tricode else home_tricode)\n relevantdf['winning.teamPenalty'] = relevantdf.apply(lambda x: winningTeamPenalty(x), 1)\n #Markov Chain type columns - recording the state of the game and most recent state\n relevantdf['prev.against.home'] = relevantdf['against.homeTeam'].shift(1).fillna(0)\n relevantdf['prev.against.away'] = relevantdf['against.awayTeam'].shift(1).fillna(0)\n relevantdf['goal.diff'] = relevantdf['about.goals.home'] - relevantdf['about.goals.away']\n relevantdf['penalty.diff'] = relevantdf['against.homeTeam'].cumsum() - relevantdf['against.awayTeam'].cumsum()\n \n return relevantdf\n else:\n return 0","def get_list_team_scores(self):\n scores = defaultdict(lambda: {\n \"scored_xg\": [],\n \"conceded_xg\": [],\n \"home_adv\": 0,\n \"expected_points\": 0\n })\n\n for g in self.games:\n scores[g.HomeTeam][\"scored_xg\"].append(g.FTHG)\n scores[g.HomeTeam][\"conceded_xg\"].append(g.FTAG)\n scores[g.AwayTeam][\"scored_xg\"].append(g.FTAG)\n scores[g.AwayTeam][\"conceded_xg\"].append(g.FTHG)\n\n for team in scores.keys():\n scores[team][\"expected_points\"] = (self.get_table(metric='points')[team] /\n len(scores[team][\"scored_xg\"]))\n\n return scores","def qualifiedteams(self):\n for i in range(0, len(self.teams)):\n for j in range(i + 1, len(self.teams)):\n WorldCupMatch(self.teams[i], self.teams[j], True)\n self.teams = sorted(self.teams, key=op.attrgetter('points', 'goaldifference', 'scored'))\n self.first_qualified = self.teams[len(self.teams)-1]\n self.second_qualified = self.teams[len(self.teams)-2]","def get_single_game_team_data(game, grouped_shot_data, pp_sit_data):\n game_stat_lines = list()\n game_id = game['game_id']\n home_id = game['home_id']\n road_id = game['road_id']\n game_type = get_game_type_from_season_type(game)\n\n home_stats_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_team_stats',\n str(game['season']), str(game_type), \"%d_%d.json\" % (game_id, home_id))\n road_stats_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_team_stats',\n str(game['season']), str(game_type), \"%d_%d.json\" % (game_id, road_id))\n\n # loading raw team game stats (if available)\n raw_stats = dict()\n if os.path.isfile(home_stats_src_path):\n raw_stats['home'] = json.loads(open(home_stats_src_path).read())\n else:\n raw_stats['home'] = dict()\n if os.path.isfile(road_stats_src_path):\n raw_stats['road'] = json.loads(open(road_stats_src_path).read())\n else:\n raw_stats['road'] = dict()\n\n # counting penalties per team\n penalty_counts = get_penalty_counts(game)\n\n for key in ['home', 'road']:\n opp_key = 'road' if key == 'home' else 'home'\n game_stat_line = dict()\n # basic game information\n game_stat_line['game_date'] = game['date']\n game_stat_line['weekday'] = game['weekday']\n game_stat_line['season'] = game['season']\n game_stat_line['season_type'] = game['season_type']\n game_stat_line['round'] = game['round']\n game_stat_line['game_id'] = game_id\n game_stat_line['team_id'] = game[\"%s_id\" % key]\n game_stat_line['team'] = game[\"%s_abbr\" % key]\n game_stat_line['opp_team_id'] = game[\"%s_id\" % opp_key]\n game_stat_line['opp_team'] = game[\"%s_abbr\" % opp_key]\n # identifying team's and opposing team's division (if\n # applicable for current season and season type)\n if (game['season'], game['season_type']) in divisions:\n current_divisions = divisions[game['season'], game['season_type']]\n game_stat_line['division'] = current_divisions[game_stat_line['team']]\n game_stat_line['opp_division'] = current_divisions[game_stat_line['opp_team']]\n # TODO: reactivate when schedule game id is available again\n # game_stat_line['schedule_game_id'] = game['schedule_game_id']\n game_stat_line['arena'] = correct_name(game['arena'])\n game_stat_line['attendance'] = game['attendance']\n if game_stat_line['arena'] in capacities:\n game_stat_line['capacity'] = capacities[game_stat_line['arena']]\n else:\n print(\n \"\\t+ Unable to retrieve capacity \" +\n \"for '%s'\" % game_stat_line['arena'])\n game_stat_line['capacity'] = 0\n # coaches and referees\n if \"%s_coach\" % key in game:\n game_stat_line['coach'] = correct_name(\n game[\"%s_coach\" % key], game['date'])\n if game_stat_line['coach'] not in coaches:\n print(\"+ Unknown coach '%s'\" % game_stat_line['coach'])\n else:\n print(\"\\t+ No coach information found for %s in game %d\" % (\n game_stat_line['team'], game_id))\n game_stat_line['coach'] = correct_name(\n \"%d_%s\" % (game_id, game_stat_line['team']))\n print(\"\\t+ Adjusted to '%s'\" % game_stat_line['coach'])\n if \"%s_coach\" % opp_key in game:\n game_stat_line['opp_coach'] = correct_name(\n game[\"%s_coach\" % opp_key], game['date'])\n if game_stat_line['opp_coach'] not in coaches:\n print(\"+ Unknown coach '%s'\" % game_stat_line['opp_coach'])\n else:\n print(\n \"\\t+ No opposition coach information found \" +\n \"for %s in game %d\" % (game_stat_line['opp_team'], game_id))\n game_stat_line['opp_coach'] = correct_name(\n \"%d_%s\" % (game_id, game_stat_line['opp_team']))\n print(\"\\t+ Adjusted to '%s'\" % game_stat_line['opp_coach'])\n game_stat_line['ref_1'] = correct_name(game['referee_1'])\n game_stat_line['ref_2'] = correct_name(game['referee_2'])\n game_stat_line['lma_1'] = correct_name(game['linesman_1'])\n game_stat_line['lma_2'] = correct_name(game['linesman_2'])\n # outcomes\n game_stat_line['games_played'] = 1\n game_stat_line['home_road'] = key\n game_stat_line['score'] = game[\"%s_score\" % key]\n game_stat_line['goals'] = game[\"%s_score\" % key]\n game_stat_line['opp_score'] = game[\"%s_score\" % opp_key]\n game_stat_line['opp_goals'] = game[\"%s_score\" % opp_key]\n # optionally correcting game scores\n if game_id in game_score_corrections:\n for team_abbr in game_score_corrections[game_id]:\n if game_stat_line['team'] == team_abbr:\n game_stat_line['score'] = game_score_corrections[game_id][team_abbr]\n if game_stat_line['opp_team'] == team_abbr:\n game_stat_line['opp_score'] = game_score_corrections[game_id][team_abbr]\n if game['shootout_game']:\n game_stat_line['game_type'] = 'SO'\n elif game['overtime_game']:\n game_stat_line['game_type'] = 'OT'\n else:\n game_stat_line['game_type'] = ''\n for gsl_key in ['w', 'rw', 'ow', 'sw', 'l', 'rl', 'ol', 'sl']:\n game_stat_line[gsl_key] = 0\n if game_stat_line['score'] > game_stat_line['opp_score']:\n game_stat_line['w'] += 1\n if game['shootout_game']:\n game_stat_line['sw'] += 1\n game_stat_line['goals'] -= 1\n elif game['overtime_game']:\n game_stat_line['ow'] += 1\n else:\n game_stat_line['rw'] += 1\n else:\n game_stat_line['l'] += 1\n if game['shootout_game']:\n game_stat_line['sl'] += 1\n game_stat_line['opp_goals'] -= 1\n elif game['overtime_game']:\n game_stat_line['ol'] += 1\n else:\n game_stat_line['rl'] += 1\n game_stat_line['points'] = (\n game_stat_line['rw'] * 3 + game_stat_line['ow'] * 2 +\n game_stat_line['sw'] * 2 + game_stat_line['sl'] * 1 +\n game_stat_line['ol'] * 1)\n # per-period goals\n for period in [1, 2, 3]:\n game_stat_line[\"goals_%d\" % period] = game[\n \"%s_goals_%d\" % (key, period)]\n game_stat_line[\"opp_goals_%d\" % period] = game[\n \"%s_goals_%d\" % (opp_key, period)]\n # empty-net and extra-attacker goals\n game_stat_line['en_goals'] = game[\"%s_en_goals\" % key]\n game_stat_line['ea_goals'] = game[\"%s_ea_goals\" % key]\n game_stat_line['opp_en_goals'] = game[\"%s_en_goals\" % opp_key]\n game_stat_line['opp_ea_goals'] = game[\"%s_ea_goals\" % opp_key]\n # situation after 20 and 40 minutes respectively\n for situation in [\n 'tied20', 'lead20', 'trail20', 'tied40', 'lead40', 'trail40'\n ]:\n game_stat_line[situation] = False\n if game_stat_line['goals_1'] == game_stat_line['opp_goals_1']:\n game_stat_line['tied20'] = True\n elif game_stat_line['goals_1'] > game_stat_line['opp_goals_1']:\n game_stat_line['lead20'] = True\n else:\n game_stat_line['trail20'] = True\n goals40 = game_stat_line['goals_1'] + game_stat_line['goals_2']\n opp_goals40 = (\n game_stat_line['opp_goals_1'] + game_stat_line['opp_goals_2'])\n if goals40 == opp_goals40:\n game_stat_line['tied40'] = True\n elif goals40 > opp_goals40:\n game_stat_line['lead40'] = True\n else:\n game_stat_line['trail40'] = True\n # scored first?\n if game['first_goal'] == game_stat_line['team']:\n game_stat_line['scored_first'] = True\n game_stat_line['trailed_first'] = False\n elif game['first_goal'] == game_stat_line['opp_team']:\n game_stat_line['scored_first'] = False\n game_stat_line['trailed_first'] = True\n # one-goal, two-goal, three-goal, four-goal-game?\n for goal_game in ['one_goal', 'two_goal', 'three_goal', 'four_goal']:\n game_stat_line[goal_game] = False\n score_diff = abs(\n (game_stat_line['score'] - game_stat_line['en_goals']) -\n (game_stat_line['opp_score'] - game_stat_line['opp_en_goals']))\n # in case the right amount of empty-net goals have been scored, we\n # may end up with a score differential of zero, see game between STR\n # and ING on Mar 3, 2019\n if not score_diff:\n game_stat_line['zero_goal'] = True\n if score_diff == 1:\n game_stat_line['one_goal'] = True\n elif score_diff == 2:\n game_stat_line['two_goal'] = True\n elif score_diff == 3:\n game_stat_line['three_goal'] = True\n elif score_diff > 3:\n game_stat_line['four_goal'] = True\n\n # retrieving score state time spans for current team\n game_stat_line['time_played'] = game['time_played']\n game_stat_line['tied'] = game['tied']\n game_stat_line['tied_pctg'] = round(\n game['tied'] / game['time_played'] * 100, 2)\n if key == 'home':\n game_stat_line['leading'] = game['home_leading']\n game_stat_line['trailing'] = game['road_leading']\n else:\n game_stat_line['leading'] = game['road_leading']\n game_stat_line['trailing'] = game['home_leading']\n game_stat_line['leading_pctg'] = round(\n game_stat_line['leading'] / game['time_played'] * 100, 2)\n game_stat_line['trailing_pctg'] = round(\n game_stat_line['trailing'] / game['time_played'] * 100, 2)\n\n # retrieving raw stats for team and opposing team\n for category, raw_category in RAW_STATS_MAPPING:\n game_stat_line[category] = raw_stats[key].get(raw_category, None)\n game_stat_line[\"opp_%s\" % category] = raw_stats[opp_key].get(raw_category, None)\n\n # checking number of power play goals retrieved from team stats with those registered in event data\n game_stat_line = check_pp_goals(game, key, opp_key, game_stat_line)\n\n # calculating shooting percentages\n if game_stat_line['shots_on_goal']:\n game_stat_line['shot_pctg'] = round(\n game_stat_line['goals'] /\n game_stat_line['shots_on_goal'] * 100., 2)\n else:\n game_stat_line['shot_pctg'] = None\n if game_stat_line['opp_shots_on_goal']:\n game_stat_line['opp_shot_pctg'] = round(\n game_stat_line['opp_goals'] /\n game_stat_line['opp_shots_on_goal'] * 100., 2)\n else:\n game_stat_line['opp_shot_pctg'] = None\n # calculating save percentages\n if game_stat_line['opp_shots_on_goal']:\n game_stat_line['save_pctg'] = round(\n 100 - game_stat_line['opp_goals'] /\n game_stat_line['opp_shots_on_goal'] * 100., 2)\n else:\n game_stat_line['save_pctg'] = None\n if game_stat_line['shots_on_goal']:\n game_stat_line['opp_save_pctg'] = round(\n 100 - game_stat_line['goals'] /\n game_stat_line['shots_on_goal'] * 100., 2)\n else:\n game_stat_line['opp_save_pctg'] = None\n # calculating pdo values\n if (\n game_stat_line['shot_pctg'] is not None and\n game_stat_line['save_pctg'] is not None\n ):\n game_stat_line['pdo'] = round((\n game_stat_line['shot_pctg'] +\n game_stat_line['save_pctg']), 1)\n game_stat_line['opp_pdo'] = round((\n game_stat_line['opp_shot_pctg'] +\n game_stat_line['opp_save_pctg']), 1)\n # calculating power play percentages\n if game_stat_line['pp_opps']:\n game_stat_line['pp_pctg'] = round((\n game_stat_line['pp_goals'] /\n game_stat_line['pp_opps']) * 100., 1)\n else:\n game_stat_line['pp_pctg'] = 0\n if game_stat_line['opp_pp_opps']:\n game_stat_line['opp_pp_pctg'] = round((\n game_stat_line['opp_pp_goals'] /\n game_stat_line['opp_pp_opps']) * 100., 1)\n else:\n game_stat_line['opp_pp_pctg'] = 0\n # calculating penalty killing percentages\n if game_stat_line['sh_opps']:\n game_stat_line['pk_pctg'] = round(\n 100 - game_stat_line['opp_pp_goals'] /\n game_stat_line['sh_opps'] * 100., 1)\n else:\n game_stat_line['pk_pctg'] = 0\n if game_stat_line['opp_sh_opps']:\n game_stat_line['opp_pk_pctg'] = round(\n 100 - game_stat_line['pp_goals'] /\n game_stat_line['opp_sh_opps'] * 100., 1)\n else:\n game_stat_line['opp_pk_pctg'] = 0\n game_stat_line['ev_goals'] = (\n game_stat_line['goals'] -\n game_stat_line['pp_goals'] -\n game_stat_line['sh_goals'])\n game_stat_line['opp_ev_goals'] = (\n game_stat_line['opp_goals'] -\n game_stat_line['opp_pp_goals'] -\n game_stat_line['opp_sh_goals'])\n # faceoffs are treated separately since each of the team game stats\n # datasets only contains the number of won faceoffs and sometimes this\n # one is stored as a string (wtf?)\n game_stat_line['faceoffs_won'] = int(\n raw_stats[key].get('faceOffsWon', 0))\n game_stat_line['faceoffs_lost'] = int(\n raw_stats[opp_key].get('faceOffsWon', 0))\n # calculating overall number of faceoffs and faceoff percentage\n game_stat_line['faceoffs'] = (\n game_stat_line['faceoffs_won'] + game_stat_line['faceoffs_lost'])\n if game_stat_line['faceoffs']:\n game_stat_line['faceoff_pctg'] = round(\n game_stat_line['faceoffs_won'] /\n game_stat_line['faceoffs'] * 100., 1)\n else:\n game_stat_line['faceoff_pctg'] = 0.\n # best players\n game_stat_line['best_plr_id'] = game.get(\n \"%s_best_player_id\" % key, None)\n game_stat_line['best_plr'] = game.get(\"%s_best_player\" % key, None)\n game_stat_line['opp_best_plr_id'] = game.get(\n \"%s_best_player_id\" % opp_key, None)\n game_stat_line['opp_best_plr'] = game.get(\n \"%s_best_player\" % opp_key, None)\n # game-winning-goal\n game_stat_line['gw_goal_team'] = game['gw_goal']\n game_stat_line['gw_goal_player_id'] = game['gw_goal_player_id']\n game_stat_line['gw_goal_first_name'] = game['gw_goal_first_name']\n game_stat_line['gw_goal_last_name'] = game['gw_goal_last_name']\n\n shot_zones_to_retain = ['slot', 'left', 'right', 'blue_line']\n shot_situations_to_retain = [\n 'shots_ev', 'shots_5v5', 'shots_pp', 'shots_sh', 'shots_unblocked',\n 'shots_unblocked_ev', 'shots_unblocked_5v5', 'shots_unblocked_pp',\n 'shots_unblocked_sh', 'shots_on_goal_ev', 'shots_on_goal_5v5',\n 'shots_on_goal_pp', 'shots_on_goal_sh', 'goals_5v5', 'hit_post']\n\n # retrieving shot data for current game and team\n shot_data = grouped_shot_data.get(\n (game_id, game_stat_line['team']), list())\n for item in shot_data:\n if item.startswith(tuple(shot_zones_to_retain)):\n abbr_item = item\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\n abbr_item = abbr_item.replace(zone_key, replacement)\n game_stat_line[abbr_item] = shot_data[item]\n elif item in shot_situations_to_retain:\n game_stat_line[item] = shot_data[item]\n\n # retrieving shots against data for current game and team\n shot_against_data = grouped_shot_data.get(\n (game_id, game_stat_line['opp_team']), list())\n for item in shot_against_data:\n if item.startswith(tuple(shot_zones_to_retain)):\n abbr_item = item\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\n abbr_item = abbr_item.replace(zone_key, replacement)\n game_stat_line[\"%s_a\" % abbr_item] = shot_against_data[item]\n elif item in shot_situations_to_retain:\n game_stat_line[\"opp_%s\" % item] = shot_against_data[item]\n\n try:\n game_stat_line['ev_cf_pctg'] = round(\n game_stat_line['shots_ev'] / (game_stat_line['shots_ev'] + game_stat_line['opp_shots_ev']) * 100, 2)\n except KeyError:\n print(\"\\t+Unable to calculate even strength shots for percentage\")\n game_stat_line['ev_cf_pctg'] = None\n\n for penalty_duration in [2, 5, 10, 20]:\n if penalty_counts[key] and penalty_duration in penalty_counts[key]:\n game_stat_line[\"penalty_%d\" % penalty_duration] = (\n penalty_counts[key][penalty_duration])\n else:\n game_stat_line[\"penalty_%d\" % penalty_duration] = 0\n\n game_stat_line['pp_5v4'] = pp_sit_data[key]['pp_sits']['5v4']\n game_stat_line['pp_5v3'] = pp_sit_data[key]['pp_sits']['5v3']\n game_stat_line['pp_4v3'] = pp_sit_data[key]['pp_sits']['4v3']\n game_stat_line['ppg_5v4'] = pp_sit_data[key]['pp_goals']['5v4']\n game_stat_line['ppg_5v3'] = pp_sit_data[key]['pp_goals']['5v3']\n game_stat_line['ppg_4v3'] = pp_sit_data[key]['pp_goals']['4v3']\n game_stat_line['opp_pp_5v4'] = pp_sit_data[opp_key]['pp_sits']['5v4']\n game_stat_line['opp_pp_5v3'] = pp_sit_data[opp_key]['pp_sits']['5v3']\n game_stat_line['opp_pp_4v3'] = pp_sit_data[opp_key]['pp_sits']['4v3']\n game_stat_line['opp_ppg_5v4'] = pp_sit_data[opp_key]['pp_goals']['5v4']\n game_stat_line['opp_ppg_5v3'] = pp_sit_data[opp_key]['pp_goals']['5v3']\n game_stat_line['opp_ppg_4v3'] = pp_sit_data[opp_key]['pp_goals']['4v3']\n\n # opp_diff = game_stat_line['pp_opps'] - (\n # game_stat_line['pp_5v4'] +\n # game_stat_line['pp_5v3'] +\n # game_stat_line['pp_4v3']\n # )\n # if opp_diff:\n # print(\"\\tpp opp discrepancy of %d for %s\" % (opp_diff, key))\n\n # registering shootout stats (if applicable)\n shootout_stats = get_shootout_stats(game, key, opp_key)\n if shootout_stats:\n game_stat_line = {**game_stat_line, **shootout_stats}\n\n game_stat_lines.append(game_stat_line)\n\n return game_stat_lines","def main(simulations, userschoice):\n # The teams data are obtained from FIFA statistics\n # Team Name, Attack, Defence\n quarters = ['quarter1', 'quarter2', 'quarter3', 'quarter4', 'quarter5', 'quarter6', 'quarter7', 'quarter8']\n semifinalists = ['semifinalist1', 'semifinalist2', 'semifinalist3', 'semifinalist4']\n finalists = ['finalist1', 'finalist2']\n\n df = pd.read_csv('FifaRankings.csv', index_col=\"Ranking\")\n a_set = set()\n while True:\n a_set.add(randint(42, 85))\n if len(a_set) == 32:\n break\n lst1 = sorted(list(a_set), reverse=True)\n\n a_set = set()\n while True:\n a_set.add(randint(38, 83))\n if len(a_set) == 32:\n break\n lst2 = sorted(list(a_set), reverse=True)\n print(\"\\n\")\n df['Attack'] = lst1\n df['Defence'] = lst2\n a = list(df[\"Team\"])\n\n avgScored = 0\n avgConceded = 0\n avgScored = df['Attack'].sum()\n avgConceded = df['Defence'].sum()\n\n avgScored = avgScored / len(df)\n avgConceded = avgConceded / len(df)\n print(\"\\n\")\n avgattack = []\n avgdefense = []\n\n for i in range(1, 33):\n if df['Matches Played'][i] != 0:\n win_rate = (df['WorldCup Wins'][i] / df['Matches Played'][i])\n else:\n win_rate = 0\n avgattack.append((df['Attack'][i] / avgScored) + win_rate)\n avgdefense.append((df['Defence'][i] / avgConceded) + win_rate)\n\n df['Avg Attack'] = avgattack\n df['Avg Defense'] = avgdefense\n\n\n teamstats=[]\n for i in range(1,len(df)+1):\n teaminfo=[]\n teaminfo = (df[\"Team\"][i], df['Avg Attack'][i], df['Avg Defense'][i])\n teaminfo=list(teaminfo)\n teamstats.append(teaminfo)\n\n germany = WorldCupTeam(\"GERMANY\", teamstats)\n brazil = WorldCupTeam(\"BRAZIL\", teamstats)\n belgium = WorldCupTeam(\"BELGIUM\", teamstats)\n portugal = WorldCupTeam(\"PORTUGAL\", teamstats)\n argentina = WorldCupTeam(\"ARGENTINA\", teamstats)\n france = WorldCupTeam(\"FRANCE\", teamstats)\n switzerland = WorldCupTeam(\"SWITZERLAND\", teamstats)\n spain = WorldCupTeam(\"SPAIN\", teamstats)\n russia = WorldCupTeam(\"RUSSIA\", teamstats)\n japan = WorldCupTeam(\"JAPAN\", teamstats)\n polland=WorldCupTeam(\"POLLAND\", teamstats)\n korea_republic = WorldCupTeam(\"KOREA REPUBLIC\", teamstats)\n england = WorldCupTeam(\"ENGLAND\", teamstats)\n denmark= WorldCupTeam(\"DENMARK\", teamstats)\n peru= WorldCupTeam(\"PERU\", teamstats)\n tunisia=WorldCupTeam(\"TUNISIA\", teamstats)\n mexico = WorldCupTeam(\"MEXICO\", teamstats)\n colombia = WorldCupTeam(\"COLOMBIA\", teamstats)\n uruguay = WorldCupTeam(\"URUGUAY\", teamstats)\n croatia = WorldCupTeam(\"CROATIA\", teamstats)\n australia = WorldCupTeam(\"AUSTRALIA\", teamstats)\n iceland=WorldCupTeam(\"ICELAND\", teamstats)\n sweden=WorldCupTeam(\"SWEDEN\", teamstats)\n costa_rica = WorldCupTeam(\"COSTA RICA\", teamstats)\n senegal=WorldCupTeam(\"SENEGAL\", teamstats)\n serbia=WorldCupTeam(\"SERBIA\", teamstats)\n morrocco=WorldCupTeam(\"MORROCCO\", teamstats)\n egypt=WorldCupTeam(\"EGYPT\", teamstats)\n nigeria = WorldCupTeam(\"NIGERIA\", teamstats)\n saudi_arabia=WorldCupTeam(\"SAUDI ARABIA\", teamstats)\n panama=WorldCupTeam(\"PANAMA\", teamstats)\n iran = WorldCupTeam(\"IRAN\", teamstats)\n\n\n #INPUT USERS CHOICE FOR FIXED CHOICE\n choices= [\"random\", \"Random\", \"RANDOM\"]\n choicess = [\"fixed\", \"Fixed\", \"FIXED\"]\n if userschoice in choices:\n countries = [germany, brazil, belgium, portugal, argentina, france, switzerland, spain, russia, japan, polland,\n korea_republic, england, denmark, peru, tunisia, mexico, colombia, uruguay, croatia, australia,\n iceland, sweden, costa_rica, senegal, serbia, morrocco, egypt, nigeria, saudi_arabia, panama, iran]\n finalresults = {}\n\n GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH = ([] for i in range(8))\n\n Groups = [GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH]\n for i in Groups:\n for j in range(4):\n teamname = choice(countries)\n i.append(teamname)\n countries.remove(teamname)\n\n print(\"DRAWS for the WorldCup 2018 are:\")\n print(\"\\n\")\n for i in range(simulations):\n # Play first stage\n print(\"Result of\", i + 1, \"simulations\")\n print(\"--------------------------------------------\")\n print(\"This is GROUP STAGE\")\n print(\"\\n\")\n print(\"GROUP A RESULTS\")\n print(\"\\n\")\n groupA = TeamPool(Groups[0])\n print(\"\\n\")\n print(\"GROUP B RESULTS\")\n print(\"\\n\")\n groupB = TeamPool(Groups[1])\n print(\"\\n\")\n print(\"GROUP C RESULTS\")\n print(\"\\n\")\n groupC = TeamPool(Groups[2])\n print(\"\\n\")\n print(\"GROUP D RESULTS\")\n print(\"\\n\")\n groupD = TeamPool(Groups[3])\n print(\"\\n\")\n print(\"GROUP E RESULTS\")\n print(\"\\n\")\n groupE = TeamPool(Groups[4])\n print(\"\\n\")\n print(\"GROUP F RESULTS\")\n print(\"\\n\")\n groupF = TeamPool(Groups[5])\n print(\"\\n\")\n print(\"GROUP G RESULTS\")\n print(\"\\n\")\n groupG = TeamPool(Groups[6])\n print(\"\\n\")\n print(\"GROUP H RESULTS\")\n print(\"\\n\")\n groupH = TeamPool(Groups[7])\n\n # Play second stage\n print(\"\\n\")\n print(\"ROUND OF 16\")\n print(\"\\n\")\n r16 = [groupA.first_qualified, groupA.second_qualified, groupB.first_qualified, groupB.second_qualified,\n groupC.first_qualified, groupC.second_qualified, groupD.first_qualified, groupD.second_qualified,\n groupE.first_qualified, groupE.second_qualified, groupF.first_qualified, groupF.second_qualified,\n groupG.first_qualified, groupG.second_qualified, groupH.first_qualified, groupH.second_qualified]\n\n\n GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW =([] for i in range(8))\n\n round16groups = [GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW]\n\n for k in round16groups:\n for j in range(2):\n teamname = choice(r16)\n k.append(teamname)\n r16.remove(teamname)\n\n for i in range(8):\n quarters[i]=WorldCupMatch(round16groups[i][0], round16groups[i][1], False).winner\n\n # Quarters\n print(\"\\n\")\n print(\"QUARTER - FINALS\")\n print(\"\\n\")\n quarterfinal = [quarters[0], quarters[1], quarters[2], quarters[3], quarters[4], quarters[5], quarters[6],\n quarters[7]]\n GroupA1, GroupB1, GroupC1, GroupD1 = ([] for i in range(4))\n\n quarterfinalgroups = [GroupA1, GroupB1, GroupC1, GroupD1]\n\n i = 0\n for i in quarterfinalgroups:\n for j in range(2):\n teamname = choice(quarterfinal)\n i.append(teamname)\n quarterfinal.remove(teamname)\n\n for i in range(4):\n semifinalists[i] = WorldCupMatch(quarterfinalgroups[i][0], quarterfinalgroups[i][1], False).winner\n\n # Semifinals\n print(\"\\n\")\n print(\"SEMI - FINALS\")\n print(\"\\n\")\n\n semifinal = [semifinalists[0], semifinalists[1], semifinalists[2], semifinalists[3]]\n GroupP1, GroupQ1 = ([] for i in range(2))\n semifinalgroups = [GroupP1, GroupQ1]\n\n i = 0\n for i in semifinalgroups:\n for j in range(2):\n teamname = choice(semifinal)\n i.append(teamname)\n semifinal.remove(teamname)\n\n for i in range(2):\n finalists[i] = WorldCupMatch(semifinalgroups[i][0], semifinalgroups[i][1], False).winner\n # Finals\n print(\"\\n\")\n print(\"WORLD-CUP FINAL\")\n print(\"\\n\")\n winner = WorldCupMatch(finalists[0], finalists[1], False).winner\n print(\"\\n\")\n\n if winner.name in finalresults:\n finalresults[winner.name] += 1\n else:\n finalresults[winner.name] = 1\n\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\n print(key + \": \" + str(finalresults[key] / simulations))\n ro=(finalresults[key] / simulations) * 100\n print(str(ro) + \"% chance of winning the worldcup\")\n print(\"\\n\")\n print(\"\\n\")\n\n\n elif userschoice in choicess:\n\n print(\"\\n\")\n finalresults = {}\n groupA1 = [russia , saudi_arabia,egypt, uruguay]\n groupB1 = [portugal, spain, morrocco, iran]\n groupC1 = [france, australia, peru, denmark]\n groupD1 = [argentina, iceland, croatia, nigeria]\n groupE1 = [brazil, switzerland, costa_rica, serbia]\n groupF1 = [germany, mexico, sweden, korea_republic]\n groupG1 = [belgium, panama, tunisia, england]\n groupH1 = [polland, senegal, colombia, japan]\n print(\"\\n\")\n for i in range(simulations):\n # Play first stage\n print(\"Result of\", i+1 ,\"simulations\")\n print(\"--------------------------------------------\")\n print(\"This is GROUP STAGE\")\n print(\"\\n\")\n print(\"GROUP A RESULTS\")\n print(\"\\n\")\n groupA = TeamPool(groupA1)\n print(\"\\n\")\n print(\"GROUP B RESULTS\")\n print(\"\\n\")\n groupB = TeamPool(groupB1)\n print(\"\\n\")\n print(\"GROUP C RESULTS\")\n print(\"\\n\")\n groupC = TeamPool(groupC1)\n print(\"\\n\")\n print(\"GROUP D RESULTS\")\n print(\"\\n\")\n groupD = TeamPool(groupD1)\n print(\"\\n\")\n print(\"GROUP E RESULTS\")\n print(\"\\n\")\n groupE = TeamPool(groupE1)\n print(\"\\n\")\n print(\"GROUP F RESULTS\")\n print(\"\\n\")\n groupF = TeamPool(groupF1)\n print(\"\\n\")\n print(\"GROUP G RESULTS\")\n print(\"\\n\")\n groupG = TeamPool(groupG1)\n print(\"\\n\")\n print(\"GROUP H RESULTS\")\n print(\"\\n\")\n groupH = TeamPool(groupH1)\n print(\"Qualifies teams:\", groupH.first_qualified.name)\n\n # Play second stage\n print(\"\\n\")\n print(\"ROUND OF 16\")\n print(\"\\n\")\n\n quarter1 = WorldCupMatch(groupA.first_qualified, groupA.second_qualified, False).winner\n quarter2 = WorldCupMatch(groupB.first_qualified, groupB.second_qualified, False).winner\n quarter3 = WorldCupMatch(groupC.first_qualified, groupC.second_qualified, False).winner\n quarter4 = WorldCupMatch(groupD.first_qualified, groupD.second_qualified, False).winner\n quarter5 = WorldCupMatch(groupE.first_qualified, groupE.second_qualified, False).winner\n quarter6 = WorldCupMatch(groupF.first_qualified, groupF.second_qualified, False).winner\n quarter7 = WorldCupMatch(groupG.first_qualified, groupG.second_qualified, False).winner\n quarter8 = WorldCupMatch(groupH.first_qualified, groupH.second_qualified, False).winner\n\n # Quarters\n print(\"\\n\")\n print(\"QUARTER - FINALS\")\n print(\"\\n\")\n\n semifinalist1 = WorldCupMatch(quarter1, quarter2, False).winner\n semifinalist2 = WorldCupMatch(quarter3, quarter4, False).winner\n semifinalist3 = WorldCupMatch(quarter5, quarter6, False).winner\n semifinalist4 = WorldCupMatch( quarter7, quarter8, False).winner\n\n # Semifinals\n print(\"\\n\")\n print(\"SEMI - FINALS\")\n print(\"\\n\")\n finalist1 = WorldCupMatch(semifinalist1, semifinalist2, False).winner\n finalist2 = WorldCupMatch(semifinalist3, semifinalist4, False).winner\n\n # Final\n print(\"\\n\")\n print(\"WORLD-CUP FINAL\")\n print(\"\\n\")\n winner = WorldCupMatch(finalist1, finalist2, False).winner\n print(\"\\n\")\n\n\n if winner.name in finalresults:\n finalresults[winner.name] += 1\n else:\n finalresults[winner.name] = 1\n\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\n print(key + \": \" + str(finalresults[key] / simulations))\n rou = (finalresults[key] / simulations) * 100\n print(str(rou) + \"% chance of winning the worldcup\")\n print(\"\\n\")\n print(\"\\n\")\n else:\n print(\"Please enter correct input and try again\")\n pass","def players(self):\n if self.players_cache is None:\n team_df = self.teams()\n self.players_cache = self.ea.players_endpoint(\n team_df[\"id\"].tolist())\n\n columns = [\"teamId\", \"playerId\", \"name\", \"position\"]\n all_players = []\n for team in self.players_cache[\"teams\"]:\n team_id = team[\"id\"]\n for plyr in team[\"roster\"][\"roster\"]:\n player_id = plyr[\"person\"][\"id\"]\n player_name = plyr[\"person\"][\"fullName\"]\n position = plyr[\"position\"][\"abbreviation\"]\n all_players.append({columns[0]: team_id,\n columns[1]: player_id,\n columns[2]: player_name,\n columns[3]: position})\n return pd.DataFrame(data=all_players, columns=columns)","def find_opponent(standings,odds):\n\n # simulate all games\n for i in range(len(odds)):\n play_game(odds.loc[i],standings)\n\n # update the points and GD tally\n standings['P']=standings['W']*3 + standings['D']\n standings['GD']=standings['F']-standings['A']\n\n # see if teams have equal amount of points, and award h2h_points for\n # h2h results against those teams.\n for group in \"ABCDEF\":\n gelijk = standings.loc[standings['Group']==group][standings.loc[standings['Group']==group].duplicated(subset='P',keep=False)]\n gelijk[\"h2h_points\"]=np.zeros(len(gelijk))\n\n for i in gelijk.index:\n for team1 in gelijk.loc[i][\"h2h\"]:\n for team2 in gelijk[\"TEAMS\"]:\n if team1==team2:\n standings.loc[i,\"h2h_points\"]+=1\n\n # sort the final standings\n standings = standings.sort_values(by=['Group','P',\"h2h_points\",'GD','F','W'],ascending=[True,False,False,False,False,False])\n\n # determine third placed teams\n standings = standings.reset_index()\n third = standings.loc[[2,6,10,14,18,22]]\n\n # determine best number threes\n third = third.sort_values(by=['P','GD','F','W'],ascending=False)\n\n groups_of_best_no_3 = \"\"\n for i in third.head(4).Group:\n groups_of_best_no_3+=i\n groups_of_best_no_3 = ''.join(sorted(groups_of_best_no_3))\n\n # look up the opponent of the dutch team\n a = third.loc[third.Group == opponent_matrix[groups_of_best_no_3]]['TEAMS']\n\n return a.reset_index().TEAMS[0]","def scraping_league_stats():\n url=\"https://www.basketball-reference.com/teams/\"\n html = urlopen(url)\n bs = BeautifulSoup(html, 'html.parser')\n teams = bs.find(\"div\", {'id':'div_teams_active'}).findAll(\"a\")\n teams_url = [team.get(\"href\") for team in teams]\n team_players_list = []\n for team in teams_url:\n time.sleep(3)\n teamurl = domain + team\n print(teamurl) #fafaf\n html = urlopen(teamurl)\n bs = BeautifulSoup(html, 'html.parser')\n div_team = \"div_\" + teamurl[-4:-1]\n season = bs.find(\"div\", {'id':div_team}).find(\"a\").get(\"href\") #tengo que cambiarlo para que este guay\n #esto tiene la URL de la ultima season\n seasonurl = domain + season\n print(seasonurl)\n html = urlopen(seasonurl)\n bs = BeautifulSoup(html, 'html.parser')\n players = bs.find(\"table\", {'id':'roster'}).findAll(\"td\", {\"data-stat\":\"player\"})\n player_url_list = [player.find(\"a\").get(\"href\") for player in players]\n for player in player_url_list:\n player_url = domain + player\n time.sleep(3)\n print(player_url)\n html = urlopen(player_url)\n bs = BeautifulSoup(html, 'html.parser')\n try:\n tabla = pd.read_html(str(bs.find(\"div\", {'id':'all_per_poss'})).replace(\"<!--\", \"\"))[0] \n tabla[\"Player\"] = bs.find(\"h1\", {\"itemprop\" : \"name\"}).text.strip()\n indice = tabla[tabla[\"Season\"]==\"Career\"].index[0]\n tabla = tabla[0:indice]\n tabla = tabla.drop(axis= 1,columns = \"Unnamed: 29\")\n #no me encuentra tablas para uno del college que es el darlina01\n print(player_url)\n team_players_list.append(tabla)\n except:\n pass\n return pd.concat(team_players_list)","def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\n battleData, htmlData = loadBattleData(league)\n similarities = loadSims() \n \n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\n if len(oppTeam) == 0:\n picks = set([])\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\n picks.add(random.randint(0,len(battleData)-1))\n\n losers = []\n loserDict = {}\n for i in picks:\n entry = battleData[i]\n winner,loser = determineWinner(entry)\n loserDict[str(loser)] = [winner]\n losers.append( (loser,0) )\n\n #Given opponent team then find similar teams\n else:\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\n\n #create dictionary from losers team to the team that beat them.\n loserDict = {}\n sims = []\n for d in battleData:\n winner, loser = determineWinner(d)\n\n wTeam = teamToArray(winner,pokedex)\n lTeam = np.array(teamToArray(loser, pokedex))\n\n score = 0\n for oppNp in oppTeam:\n score+= np.amax(lTeam*oppNp) \n\n if str(loser) in loserDict:\n loserDict[str(loser)].append(winner)\n else:\n #new to dictonary\n loserDict[str(loser)] = [winner]\n\n sims.append((loser, score))\n\n\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\n\n cutoff = min(len(sims),NUMLOSINGTEAMS)\n losers = sims[:cutoff]\n\n #Gather winners to losing teams\n winnersComp = []\n for loser,_ in losers:\n for winner in loserDict[str(loser)]:\n winnersComp.append(teamToArray(winner,pokedex))\n \n topScore = len(winnersComp)*6 #pkmn team size\n\n results = []\n inverted_idx = {}\n\n existsSet = []\n\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\n for i in range(len(curTeams)):\n team = curTeams[i]\n results.append((team,0))\n sTeam = set(team)\n if not (sTeam in existsSet):\n existsSet.append(sTeam)\n for pkm in team:\n if pkm != EMPTY:\n if pkm in inverted_idx:\n inverted_idx[pkm].append(i)\n else:\n inverted_idx[pkm] = [i]\n \n #Giving the similiarity scores to the winners based off of the inverted index.\n for pkm in inverted_idx:\n for winner in winnersComp:\n wArr = np.array(winner)\n #tArr = getSimPokemon(pkm,similarities)\n tArr = similarities[pkm]\n \n vals = wArr * tArr\n\n score = np.amax(vals)\n\n for i in inverted_idx[pkm]:\n results[i] = (results[i][0],results[i][1]+(score/topScore))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n\n if len(results) < NUMTEAMSRETURN:\n if len(results) == 0:\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\n\n else:\n returnTeams = [result[0] for result in results]\n teamScores = [result[1] for result in results]\n else:\n firstResult, firstScore = results[0]\n returnTeams = [firstResult]\n teamScores = [round(firstScore*100,1)]\n returnSets = [set(firstResult)]\n \n i = 1\n\n #Loops through results and adds teams with the proper edit distance away.\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\n teamToConsider,teamToConsiderScore = results[i]\n \n considerSet = set(teamToConsider)\n add = True\n ##checks the edit distance of teams is above wanted\n for team in returnSets:\n if len(team.union(considerSet)) < len(team)+minDistWanted:\n add = False\n\n ##If indeed above wanted levels then add\n if add:\n returnTeams.append(teamToConsider)\n returnSets.append(considerSet)\n teamScores.append(round(teamToConsiderScore*100,1))\n \n i+=1\n\n if i >= len(results):\n i = 1\n minDistWanted -= 1 \n \n winHtmls = []\n if htmlData != None:\n for team,_ in losers:\n for winner in loserDict[str(team)]:\n winHtmls.extend(htmlData[str(sorted(winner))])\n \n\n return returnTeams, teamScores, winHtmls","def get_players_table(games_table: pd.DataFrame) -> pd.DataFrame:\n player_name_series = games_table[\"player_name\"].unique()\n player_name_series = pd.Series(player_name_series)\n # In case a player name includes digits, it should be removed\n invalid_element_mask = player_name_series.str.contains(r\"[0-9]\")\n player_name_series = player_name_series[~invalid_element_mask]\n players_data = pd.DataFrame(data=player_name_series)\n players_data = players_data.reset_index()\n players_data.columns = [\"id\", \"player_name\"]\n return players_data","def hometeamify(t):\n\tt = t.copy()\n\t# Winner/loser based columns\n\thw, aw = t.hometeam == t.winner, t.awayteam == t.winner\n\tassert (hw == ~aw).all()\n\t# Suffix for keys = W for winner L for loser. Values are new names\n\tfor old, new in {'Pts': 'points', 'Yds': 'yards', 'TO': 'turn_overs'}.items():\n\t\tt[new + '_home'] = (hw * t[old + 'W'] + aw * t[old + 'L'])\n\t\tt[new + '_away'] = (aw * t[old + 'W'] + hw * t[old + 'L'])\n\t\tdel t[old + 'L'], t[old + 'W']\n\tdel t['winner']\n\t# Favored-team based columns\n\tto_swap, to_keep = t.favored == t.awayteam, t.favored == t.hometeam\n\tassert (to_keep == ~to_swap).all()\n\tfor col in 'pinnacle_spread', 'betonline_spread', 'bookmaker_spread':\n\t\tt[col] = -1 * to_swap * t[col] + to_keep * t[col]\n\tdel t['favored']\n\treturn t","def fetch_top_games(twitch: Twitch, n: int = 100) -> pd.DataFrame:\n top_games = fetch_twitch_data(twitch, 'get_top_games', first=n)\n\n return top_games","def fetch_game_streams(twitch: Twitch,\n game_id: str,\n n: int = 100) -> pd.DataFrame:\n kwargs = {'first': n, 'game_id': [game_id]}\n game_streams = fetch_twitch_data(twitch, 'get_streams', **kwargs)\n # get user id to dig more data\n try:\n total_user_ids = game_streams['user_id'].tolist()\n user_ids_num = len(total_user_ids)\n ephoch = user_ids_num // 100\n if user_ids_num % 100 != 0:\n ephoch += 1\n except:\n print('game_streams')\n cprint('Error: ' + game_id + ' data broken. Jump over it.', 'red')\n return None\n else:\n total_users_data = pd.DataFrame(\n columns=['broadcaster_type', 'description', 'type'])\n for i in range(ephoch):\n user_ids = total_user_ids[i * 100:i * 100 + 100]\n users_data = twitch.get_users(user_ids=user_ids)\n users_data = turn_into_df(users_data)\n # select needed columns\n users_data = users_data[[\n 'broadcaster_type', 'description', 'type'\n ]]\n total_users_data = total_users_data.append(users_data,\n ignore_index=True)\n\n total_users_data.reset_index(drop=True, inplace=True)\n game_streams.reset_index(drop=True, inplace=True)\n game_streams = pd.concat([game_streams, total_users_data], axis=1)\n return game_streams","def playerStandings():\n cur4 = conn.cursor()\n \n # This playerStandings() should return in format (id,name,wins,matches) \n query =\"\"\"SELECT id, name, sum(wincount) as wins, sum(lose_count)+sum(wincount) as total\n from\n (((\n select p.id, p.name, count(winner) as wincount, '0' as lose_count\n from players p left join matches on p.id=winner group by p.id, p.name order by count(winner) desc)\n UNION\n (select p.id, p.name, '0' as wincount, count(loser) as lose_count\n from players p left join matches on p.id=loser group by p.id, p.name order by count(loser) desc\n )))\n as standings group by id, name order by wins desc, total asc;\n \"\"\"\n cur4.execute(query)\n rows = cur4.fetchall()\n\n return rows","def get_player_stats(df_players, url, headers):\n\tfor i, pid in enumerate(df_players['PERSON_ID']):\n\t\tif i==0:\n\t\t\tdf_stats=get_player_career_reg_season_stats(pid, url, headers)\n\t\telse:\n\t\t\tdf_stats=df_stats.append(\n\t\t\t\tget_player_career_reg_season_stats(pid, url, headers)\n\t\t\t)\n\t\tprint('i={} Added player stats for ID={}'.format(i, pid))\n\t\ttime.sleep(2) # sleep so we don't get blocked\n\n\treturn df_players.merge(df_stats, left_on=\"PERSON_ID\", right_on=\"PLAYER_ID\", how='left')","def swissPairings():\n #get all players, sort by number of wins.create matches with the 2 adjacent players\n c.execute(\"\"\"SELECT id, playerName, num_wins\n FROM (SELECT winner_id, count(match_id) as num_wins \n FROM wins \n group by winner_id \n )as R1 right join tournament on R1.winner_id= tournament.id order by num_wins\"\"\")\n result= c.fetchall()\n for row in result:\n print row","def find_games(days_ahead=0):\n headers = {\n 'Host': 'stats.nba.com',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\n 'Accept': 'application/json, text/plain, */*',\n 'Accept-Language': 'en-US,en;q=0.5',\n 'Referer': 'https://stats.nba.com/',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Connection': 'keep-alive',\n 'x-nba-stats-origin': 'stats',\n 'x-nba-stats-token': 'true'\n }\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\n board.replace(id_to_abrv, inplace=True)\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]","def getAllTeams(self):\n return []","def get_team_metrics(file_prefix, game_period, teams, overall_dataframes, number_of_teams):\n runs = overall_dataframes[0]['run'].unique()\n\n consolidated_result = []\n\n logger.info(\"Dataframes under analysis: \" + str(len(overall_dataframes)) + \". Number of runs: \" + str(\n len(runs)) + \" Number of teams: \" + str(teams))\n\n for run in runs:\n\n team_results = {}\n for team in range(teams):\n\n for index, overall_dataframe in enumerate(overall_dataframes):\n\n period_reports = overall_dataframe[overall_dataframe['period'] == game_period]\n reports_in_run = period_reports[period_reports['run'] == run]\n\n team_run_reports = reports_in_run[reports_in_run['reporter_team'] == team]\n if len(team_run_reports.index) > 0:\n team_resolved = team_run_reports['reported_completed'].sum()\n team_reported = team_run_reports['reported'].sum()\n team_score = team_run_reports['payoff_score'].sum()\n\n team_results[team] = {\"team_resolved\": team_resolved,\n \"team_reported\": team_reported,\n \"team_score\": team_score}\n\n simulation_result = {\"run\": run}\n\n for team_index in range(number_of_teams):\n team_prefix = \"team_\" + str(team_index + 1) + \"_\"\n\n simulation_result[team_prefix + \"results\"] = team_results[team_index]['team_resolved']\n simulation_result[team_prefix + \"reports\"] = team_results[team_index]['team_reported']\n simulation_result[team_prefix + \"score\"] = team_results[team_index]['team_score']\n\n consolidated_result.append(simulation_result)\n\n consolidated_dataframe = pd.DataFrame(consolidated_result)\n consolidated_dataframe.to_csv(\"csv/\" + file_prefix + \"_consolidated_result.csv\", index=False)\n\n team_averages = []\n\n for team_index in range(number_of_teams):\n score_column = \"team_\" + str(team_index + 1) + \"_score\"\n\n mean = consolidated_dataframe[score_column].mean()\n team_averages.append(int(mean))\n\n # This is the procedure found -and validated- on Chapter 2 of Introduction to Discrete Event Simulation by\n # Theodore Allen\n sem = st.sem(consolidated_dataframe[score_column])\n df = consolidated_dataframe[score_column].count() - 1\n alpha = 0.95\n\n interval = st.t.interval(alpha=alpha, df=df, loc=mean, scale=sem)\n logger.info(file_prefix + \": Confidence Interval Analysis for Team \" + str(team_index) + \" mean=\" + str(\n mean) + \" sem=\" + str(sem) + \" df=\" + str(df) + \" alpha=\" + str(alpha) + \" interval=\" + str(interval))\n\n return [str(team_avg) for team_avg in team_averages]","def getTeams(db):\n #teamAo = firebase.get('https://decent-league.firebaseio.com/teamA', None)\n teamAo = db.child(\"teamA\")\n names, forms, descs = [], [], []\n playersA = teamAo.child('players').get()\n for user in playersA.each():\n details = user.val()\n names.append(details['name'])\n forms.append(details['form'])\n descs.append(details['desc'])\n teamA = pd.concat([pd.Series(names), pd.Series(forms)], axis=1) \n teamA.columns = ['name', 'form']\n #teamBo = firebase.get('https://decent-league.firebaseio.com/teamB', None)\n teamBo = db.child('teamB')\n names, forms, descs = [], [], []\n playersB = teamBo.child('players').get()\n for user in playersB.each():\n details = user.val()\n names.append(details['name'])\n forms.append(details['form'])\n descs.append(details['desc'])\n teamB = pd.concat([pd.Series(names), pd.Series(forms)], axis=1)\n teamB.columns = ['name', 'form']\n #print(teamB)\n return teamA, teamB, teamAo, teamBo","def compare_game_against_opponents(self, home, away, date):\n match = get_match(self, home, away, date)\n home_stats = self.team_stats[match.HomeTeam]\n away_stats = self.team_stats[match.AwayTeam]\n display_table(['Measure',\n 'Average Per Game for',\n 'This game for',\n 'Average Per Game against opponents'],\n [['{0} Shots'.format(home),\n home_stats.average_stat('shots_for'),\n shots_for_in_game(home, match),\n away_stats.average_stat('shots_against')],\n ['{0} Shots'.format(away),\n away_stats.average_stat('shots_for'),\n shots_for_in_game(away, match),\n home_stats.average_stat('shots_against')],\n\n ['{0} SOT'.format(home),\n home_stats.average_stat('sot_for'),\n sot_for_in_game(home, match),\n away_stats.average_stat('sot_against')],\n ['{0} SOT'.format(away),\n away_stats.average_stat('sot_for'),\n sot_for_in_game(away, match),\n home_stats.average_stat('sot_against')]])","def possessions_home_away(data_frame, mode):\n total_games_dict, total_dict, percentage_dict = dict(), dict(), dict()\n team_name = 'home_team' if mode == 'home' else 'away_team'\n for index, row in data_frame.iterrows():\n if row[team_name] not in total_games_dict:\n percentage_dict[row[\"id\"]] = 0\n else:\n percentage_dict[row[\"id\"]] = format(float(total_dict[row[team_name]]) / float(total_games_dict[row[team_name]]), '.2f')\n\n if row[team_name] in total_games_dict:\n total_games_dict[row[team_name]] += 1\n else:\n total_games_dict[row[team_name]] = 1\n\n fga, orb, fta, to = 'fg_made_attempted_', 'offensive_rebounds_', 'ft_made_attempted_', 'turnovers_'\n\n possessions = int(row[fga + team_name.split('_')[0]].split('-')[1]) - int(row[orb + team_name.split('_')[0]]) + \\\n (0.475 * int(row[fta + team_name.split('_')[0]].split('-')[1])) + int(row[to + team_name.split('_')[0]])\n\n if row[team_name] in total_dict:\n total_dict[row[team_name]] += possessions\n else:\n total_dict[row[team_name]] = possessions\n\n return percentage_dict","def compile_games(all_games):\n compiled_games = {}\n for game_data in all_games:\n game_id = game_data[4]\n team_data = extract_team_data(game_data)\n home_team_abbrev = get_home_team(game_data[6])\n if team_data[\"team_abbrev\"] == home_team_abbrev:\n home_team = True\n team_data = rename_features(team_data, \"home\")\n else:\n home_team = False\n team_data = rename_features(team_data, \"away\")\n\n if game_id in compiled_games:\n compiled_games[game_id] = {\n **compiled_games[game_id],\n **team_data\n }\n else:\n if home_team:\n winner = 1 if game_data[7] == \"W\" else 0\n else:\n winner = 0 if game_data[7] == \"W\" else 1\n # game_date, win/loss, matchup all needed exactly once\n compiled_games[game_id] = team_data\n compiled_games[game_id][\"winner\"] = winner\n compiled_games[game_id][\"game_date\"] = game_data[5]\n compiled_games[game_id][\"game_id\"] = game_id\n compiled_games[game_id][\"matchup_name\"] = game_data[6]\n\n return compiled_games","def toSeasonAggFormat(self):\n df_a = self.toHomeAwayFormat().copy()\n df_b = df_a.copy()\n\n for col in list(df_a.columns).copy():\n if col[0] == \"H\":\n df_a = df_a.rename(columns={col:(col[1:])})\n\n if col[0] == \"A\":\n df_a = df_a.rename(columns={col:('Opp'+col[1:])})\n\n #df_a['Win'] = 1\n\n for col in list(df_b.columns).copy():\n if col[0] == \"H\":\n df_b = df_b.rename(columns={col:('Opp'+col[1:])})\n\n if col[0] == \"A\":\n df_b = df_b.rename(columns={col:(col[1:])})\n\n df_c = df_a.append(df_b, sort=True)\n\n return df_c","def getTeamStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\r\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\r\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n team_df = df[[\"TEAM_ID\",\"TEAM_NAME\",\"GP\",\"W\",\"L\",\"W_PCT\",\"MIN\",\"FGM\",\r\n \"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\r\n \"OREB\",\"DREB\",\"REB\",\"AST\",\"TOV\",\"STL\",\"BLK\",\"BLKA\",\"PF\",\r\n \"PFD\",\"PTS\",\"PLUS_MINUS\"]]\r\n \r\n return team_df","def touchdowns(df):\n return (\n df\n .loc[df.play_type.isin(['run', 'pass']), :]\n .assign(player=lambda x: np.where(pd.isna(x['receiver_player_name']), x['rusher_player_name'], x['receiver_player_name']))\n .loc[:, ['play_id', 'player', 'play_type', 'yards_gained']]\n .sort_values('yards_gained', ascending=False)\n )","def get_sim_winners(teamNames,league,pokedex):\n bData, wHtml = loadBattleData(league)\n similarities = loadSims() \n\n results = []\n\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\n for d in bData:\n winner, loser = determineWinner(d)\n\n score = 0\n for pkm in myTeam:\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\n\n results.append((winner,score))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n \n cutoff = min(len(results),5)\n\n htmls = []\n for win,score in results[:cutoff]:\n htmls.extend(wHtml[str(sorted(win))])\n\n return htmls","def rename_teams(teams, fpl, cols = ['team', 'opponent_team', 'team_a', 'team_h']):\n fpl = fpl.copy()\n for col in cols:\n for i in np.arange(start = 0, stop = len(teams)):\n id = teams['id'][i]\n team_name = teams['name'][i]\n fpl[col].loc[fpl[col] == id] = team_name\n return fpl","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def game(hometeam, awayteam, week, year):\n\twith urlopen(spread_url(hometeam, awayteam, week, year)) as connection:\n\t\tspreads_page = connection.read()\n\t# Note that infer_types is deprecated and won't work starting in Pandas 0.14\n\tLOG.debug('Getting game %s', (hometeam, awayteam, week, year))\n\tsp = read_html(io=spreads_page.decode('utf-8'),\n\t\t\t\t\t match=\"History\", attrs={'id': 'table-000'},\n\t\t\t\t\t infer_types=False, header=0,\n\t\t\t\t\t skiprows=[1, 2, 3])\n\tif len(sp) != 1:\n\t\traise CantFindTheRightTable\n\tsp = sp.pop()\n\n\t# Get the over-under page\n\tou = read_html(io=over_under_url(hometeam, awayteam, week, year),\n\t\t\t\t match=\"History\", attrs={'cellspacing': 0},\n\t\t\t\t infer_types=False, header=0,\n\t\t\t\t skiprows=[1, 2, 3])\n\tif len(ou) != 1:\n\t\traise CantFindTheRightTable\n\tou = ou.pop()\n\n\t# Cleaning.\n\tfor t, name, date_col in (sp, 'spread', 'Unnamed: 0'), (ou, 'over_under', '\\xa0'):\n\t\tdatetime = pd.to_datetime(\n\t\t\tt[date_col]\n\t\t\t.replace(r'(\\d\\d?/\\d\\d?)', r'\\1/%d' % year, regex=True)\n\t\t\t.replace(r'(01|02)/(\\d\\d?)/\\d{4}', r'\\1/\\2/%d' % (year + 1),\n\t\t\t\t\t regex=True))\n\t\tdel t[date_col]\n\n\t\t# Replace all the '--' as missing so we can convert numbers to floats.\n\t\tfor column in t.keys():\n\t\t\tt[column] = (t[column]\n\t\t\t\t\t\t .replace('--', 'nan')\n\t\t\t\t\t\t .replace('(Pick)', 0)\n\t\t\t\t\t\t .apply(float))\n\n\t\t# Add datetime back in after the str-to-float conversion so we don't do\n\t\t# it for the datetime.\n\t\tt['datetime'] = datetime\n\n\t\t# Lowercase column names for ease of programming later\n\t\tt.columns = [h.lower() for h in t.columns]\n\n\t\t# Give spreads/over-under their suffixes\n\t\tfor col in 'pinnacle', 'betonline', 'bookmaker':\n\t\t\tt[col + '_' + name] = t[col]\n\t\t\tdel t[col]\n\n\tdata = sp.merge(ou, on=['datetime'], how='outer')\n\tassert set(data.datetime) == (set(sp.datetime) | set(ou.datetime))\n\n\t# Add this function's arguments to the table.\n\tdata['hometeam'] = hometeam\n\tdata['awayteam'] = awayteam\n\tdata['week'] = week\n\n\t# Get favored team from the big \"Odds: Washington by 4,\" that shows up at the\n\t# top of the page.\n\tsoup = BeautifulSoup(spreads_page)\n\tsubheader = soup.find('p', attrs={'class': 'h1-sub'}).find('strong')\n\tm = _FAVORED_RE.search(subheader.contents[0])\n\tif m is None or not m.group('city'):\n\t\traise ValueError(\"Couldn't figure out who was favored: %r\" %\n\t\t\t\t\t\t (subheader.contents))\n\tcity = m.group('city').replace(' ', '-').replace('.', '').lower()\n\t# city will be something like 'san-francisco' after the transformations\n\t# above. Find what team that is by looking for the links to the teams that\n\t# are also in that subheader.\n\tfor link in subheader.findAll('a'):\n\t\tlink = link['href']\n\t\tif city in link:\n\t\t\tdata['favored'] = link.split('-')[-1]\n\t\t\tbreak\n\telse:\n\t\traise ValueError(\"couldn't figure out who %s is\" % city)\n\n\treturn data","def get_games(date):\n scoreboard = nba_py.Scoreboard(month=date.month,\n day=date.day,\n year=date.year)\n line_score = scoreboard.line_score()\n game_header = scoreboard.game_header()\n\n games = []\n current_game = {}\n game_sequence = 0\n game_sequence_counter = 0\n\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\n for i, value in enumerate(line_score):\n if (value[\"GAME_SEQUENCE\"] != game_sequence):\n game_sequence += 1\n\n current_game[\"GAME_ID\"] = value[\"GAME_ID\"]\n home_team_id = game_header[game_sequence - 1][\"HOME_TEAM_ID\"]\n\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n \n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter += 1\n elif game_sequence_counter == 1:\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n\n current_game[\"GAME_STATUS_TEXT\"] = game_header[game_sequence - 1][\"GAME_STATUS_TEXT\"]\n if not game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]:\n current_game[\"BROADCASTER\"] = \"\"\n else:\n current_game[\"BROADCASTER\"] = game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]\n\n games.append(current_game)\n\n current_game = {}\n\n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter -= 1\n\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return (games, east_standings, west_standings)","def get_team_stats(players: list[Player]) -> dict[int]:\n\n team_stats = {}\n\n total_reaction = 0\n total_mechanical_skill = 0\n total_tactical_skill = 0\n total_game_knowledge = 0\n total_xp = 0\n\n for player in players:\n total_reaction += player.reaction\n total_mechanical_skill += player.mechanical_skill\n total_tactical_skill += player.tactical_skill\n total_game_knowledge += player.game_knowledge\n total_xp += player.xp\n\n team_stats.update(\n {\"reaction\": total_reaction,\n \"mechanical_skill\": total_mechanical_skill,\n \"tactical_skill\": total_tactical_skill,\n \"game_knowledge\": total_game_knowledge,\n \"xp\": total_xp})\n\n return team_stats","def teams_to_SQL (self, cursor, gid, games):\n\n\t\tvalues = []\n\t\tquery_with = \"WITH teams AS (\"\n\t\tfor game in games.findall('.'):\n\t\t\tquery_with += \"SELECT ? AS id, ? AS abbreviation, ? AS file_code, ? AS city, ? AS name UNION SELECT ? AS id, ? AS abbreviation, ? AS file_code, ? AS city, ? AS name UNION \"\n\t\t\tvalues.append(game.attrib['away_team_id'])\n\t\t\tvalues.append(game.attrib['away_name_abbrev'])\n\t\t\tvalues.append(game.attrib['away_file_code'])\n\t\t\tvalues.append(game.attrib['away_team_city'])\n\t\t\tvalues.append(game.attrib['away_team_name'])\n\t\t\tvalues.append(game.attrib['home_team_id'])\n\t\t\tvalues.append(game.attrib['home_name_abbrev'])\n\t\t\tvalues.append(game.attrib['home_file_code'])\n\t\t\tvalues.append(game.attrib['home_team_city'])\n\t\t\tvalues.append(game.attrib['home_team_name'])\n\t\tquery_with = query_with[:-7] + \")\"\n\n\t\tmerge = query_with + \"MERGE team AS t USING teams AS s ON t.id = s.id \"\n\t\tmerge += \"WHEN NOT MATCHED BY TARGET THEN INSERT (id, abbreviation, file_code, city, name) VALUES (s.id, s.abbreviation, s.file_code, s.city, s.name) \"\n\t\tmerge += \"WHEN MATCHED AND ((t.abbreviation IS NULL AND s.abbreviation IS NOT NULL) OR (t.file_code IS NULL AND s.file_code IS NOT NULL) OR (t.city IS NULL AND s.city IS NOT NULL) OR (t.name IS NULL AND s.name IS NOT NULL)) THEN UPDATE SET t.abbreviation = s.abbreviation, t.file_code = s.file_code, t.city = s.city, t.name = s.name;\"\n\t\t\n\t\tcursor.execute(merge, values)","def own_games(self):\r\n return sorted(self.games + self.finals, key=lambda g: (g.datetime, g.pitch.rank))","def evaluate_team(self, team, mode):\n if mode == Config.RESTRICTIONS['mode']['training']:\n point_population = self.point_population_\n opponent_population = self.training_opponent_population()\n elif mode == Config.RESTRICTIONS['mode']['validation']:\n point_population = self.validation_point_population_\n opponent_population = self.validation_opponent_population_\n elif mode == Config.RESTRICTIONS['mode']['champion']:\n point_population = self.champion_population()\n opponent_population = self.champion_opponent_population()\n else:\n raise ValueError(\"Invalid mode\")\n results = []\n extra_metrics_opponents = defaultdict(list)\n match_id = 0\n\n if len(point_population) == 0:\n raise ValueError(\"Error: Nothing in point population. Probably the population size is too small.\")\n if len(opponent_population) == 0:\n raise ValueError(\"Error: Nothing in opponent population. Probably the population size is too small.\")\n\n if mode == Config.RESTRICTIONS['mode']['training']:\n for point, opponent in zip(point_population, opponent_population):\n match_id += 1\n result = self._play_match(team, opponent, point, mode, match_id)\n team.reset_registers()\n extra_metrics_opponents[opponent.opponent_id].append(result)\n team.results_per_points_[point.point_id_] = result\n results.append(result)\n if opponent.opponent_id == 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\n extra_metrics_opponents[opponent.__repr__()].append(result)\n \n opponent_type = 'training_opponents'\n team.fitness_ = numpy.mean(results)\n else:\n extra_metrics_points = self._initialize_extra_metrics_for_points()\n for point, opponent in zip(point_population, opponent_population):\n match_id += 1\n result = self._play_match(team, opponent, point, mode, match_id)\n team.reset_registers()\n extra_metrics_opponents[opponent.opponent_id].append(result)\n extra_metrics_points = self._update_extra_metrics_for_points(extra_metrics_points, point, result)\n if mode == Config.RESTRICTIONS['mode']['validation']:\n team.results_per_points_for_validation_[point.point_id_] = result\n results.append(result)\n elif mode == Config.RESTRICTIONS['mode']['champion']:\n if opponent.opponent_id != 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\n results.append(result)\n else:\n extra_metrics_opponents[opponent.__repr__()].append(result)\n \n opponent_type = 'opponents'\n for key in extra_metrics_points:\n for subkey in extra_metrics_points[key]:\n extra_metrics_points[key][subkey] = round_value(numpy.mean(extra_metrics_points[key][subkey]))\n team.extra_metrics_['points'] = extra_metrics_points\n if mode == Config.RESTRICTIONS['mode']['validation']:\n team.score_validation_ = round_value(numpy.mean(results))\n else:\n team.score_champion_ = round_value(numpy.mean(results))\n\n for key in extra_metrics_opponents:\n extra_metrics_opponents[key] = round_value(numpy.mean(extra_metrics_opponents[key]))\n team.extra_metrics_[opponent_type] = extra_metrics_opponents","def get_baseball_data():\n\n baseball_data = [\n {\n 'team_name': 'Boston', 'wins': 103, 'losses': 47,\n 'wins_in_last_ten': 7, 'streak': 'W2'\n },\n {\n 'team_name': 'NY Yankees', 'wins': 91, 'losses': 58,\n 'wins_in_last_ten': 4, 'streak': 'L2'\n },\n {\n 'team_name': 'Tampa Bay', 'wins': 83, 'losses': 66,\n 'wins_in_last_ten': 8, 'streak': 'W3'\n },\n {\n 'team_name': 'Toronto', 'wins': 68, 'losses': 82,\n 'wins_in_last_ten': 5, 'streak': 'W3'\n },\n {\n 'team_name': 'Baltimore', 'wins': 43, 'losses': 107,\n 'wins_in_last_ten': 2, 'streak': 'L1'\n },\n {\n 'team_name': 'Cleveland', 'wins': 83, 'losses': 66,\n 'wins_in_last_ten': 4, 'streak': 'L1'\n },\n {\n 'team_name': 'Minnesota', 'wins': 69, 'losses': 81,\n 'wins_in_last_ten': 5, 'streak': 'W2'\n },\n {\n 'team_name': 'Detroit', 'wins': 61, 'losses': 89,\n 'wins_in_last_ten': 4, 'streak': 'L1'\n },\n {\n 'team_name': 'Chi White Sox', 'wins': 59, 'losses': 90,\n 'wins_in_last_ten': 3, 'streak': 'L1'\n },\n {\n 'team_name': 'Kansas City', 'wins': 52, 'losses': 98,\n 'wins_in_last_ten': 6, 'streak': 'L2'\n },\n {\n 'team_name': 'Houston', 'wins': 94, 'losses': 56,\n 'wins_in_last_ten': 7, 'streak': 'L1'\n },\n {\n 'team_name': 'Oakland', 'wins': 90, 'losses': 60,\n 'wins_in_last_ten': 7, 'streak': 'L2'\n },\n {\n 'team_name': 'Seattle', 'wins': 83, 'losses': 67,\n 'wins_in_last_ten': 5, 'streak': 'W1'\n },\n {\n 'team_name': 'LA Angels', 'wins': 74, 'losses': 76,\n 'wins_in_last_ten': 6, 'streak': 'W1'\n },\n {\n 'team_name': 'Texas', 'wins': 64, 'losses': 86,\n 'wins_in_last_ten': 3, 'streak': 'L2'\n }\n ]\n\n return baseball_data","def teams(teamid):\n team_summary = team.TeamSummary(teamid)\n team_summary_info = team_summary.info()\n team_season_ranks = team_summary.season_ranks()\n\n team_common_roster = team.TeamCommonRoster(teamid)\n roster = team_common_roster.roster()\n coaches = team_common_roster.coaches()\n\n season = team_summary_info[0][\"SEASON_YEAR\"]\n\n team_game_log = team.TeamGameLogs(teamid,\n season=season)\n team_games = team_game_log.info()\n\n playoffs_teamgamelogs = team.TeamGameLogs(teamid,\n season=season,\n season_type=\"Playoffs\")\n playoffs_team_games = playoffs_teamgamelogs.info()\n\n team_season = team.TeamSeasons(teamid)\n team_season_info = team_season.info()\n\n for i in team_season_info:\n if (i[\"YEAR\"] == season):\n current_season_info = i\n\n return render_template(\"teams.html\",\n title=team_summary_info[0][\"TEAM_CITY\"] + \" \" + team_summary_info[0][\"TEAM_NAME\"],\n teamid=teamid,\n team_summary_info=team_summary_info,\n team_season_ranks=team_season_ranks,\n season=season,\n team_games=team_games,\n playoffs_team_games=playoffs_team_games,\n team_season=team_season_info,\n roster=roster,\n coaches=coaches,\n current_season_info=current_season_info,\n team_img=TEAM_ID_DATA)","def breakingteam_set_competing(self):\n return self.breakingteam_set.filter(break_rank__isnull=False)","def review(self):\n # Compile standings\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\n # Name a champion\n self.champion = self._name_champion()\n self.league.history.champions_timeline[self.year] = self.champion\n print \"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\".format(\n self.champion.team.name.upper(), self.year, self.league.name.upper()\n )\n # Compile league leaders\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\n # Have each team review its season, as well\n for team_season in self.teams:\n team_season.review()\n # Send the league into the offseason\n self.league.season = None","def get_learning_data(self):\n if self.teams is None:\n self.build_teams()\n\n # now put together X and y for every match\n return self.games.get_games(self.teams)","def create_lineups(self):\n\t\tnum_skaters = len(self.skaters_df.index)\n\t\tnum_goalies = len(self.goalies_df.index)\n\t\tteams = list(set(self.skaters_df['team'].values))\n\t\tnum_teams = len(teams)\n\n\t\t#Create player position indicators so you know which position they are playing\n\t\tpositions = {'C':[], 'W':[], 'D':[]}\n\t\tfor pos in self.skaters_df.loc[:, 'pos']:\n\t\t\tfor key in positions:\n\t\t\t\tpositions[key].append(1 if key in pos else 0)\n\t\t\n\t\t#Create player line indicators so you know which line by their team they are on\n\t\tteam_lines = []\n\t\tfor i, line in enumerate(self.skaters_df.loc[:, 'line']):\n\t\t\tplayer_line = []\n\t\t\tif int(line) == 1:\n\t\t\t\tplayer_line.extend((1, 0, 0, 0))\n\t\t\telif int(line) == 2:\n\t\t\t\tplayer_line.extend((0, 1, 0, 0))\n\t\t\telif int(line) == 3:\n\t\t\t\tplayer_line.extend((0, 0, 1, 0))\n\t\t\telif int(line) == 4:\n\t\t\t\tplayer_line.extend((0, 0, 0, 1))\n\t\t\telse:\n\t\t\t\tplayer_line.extend((0, 0, 0, 0))\n\t\t\tplayer_lines = []\n\t\t\tfor team in teams:\n\t\t\t\tif self.skaters_df.loc[i, 'team'] == team:\n\t\t\t\t\tplayer_lines.extend(player_line)\n\t\t\t\telse:\n\t\t\t\t\tplayer_lines.extend((0, 0, 0, 0))\n\t\t\tteam_lines.append(player_lines)\n\t\tnum_lines = len(team_lines[0])\n\t\t\n\t\t#NOTE: Maybe add PP line indicators\n\n\t\t#Create player team indicators so you know which team they are on\n\t\tskaters_teams = []\n\t\tfor player_team in self.skaters_df.loc[:, 'team']:\n\t\t\tskaters_teams.append([1 if player_team == team else 0 for team in teams])\n\n\t\t#Create goalie opponent indicators so you know who the goalie is opposing\n\t\tgoalies_opponents = []\n\t\tfor player_opp in self.skaters_df.loc[:, 'opp']:\n\t\t\tgoalies_opponents.append([1 if player_opp == team else 0 for team in self.goalies_df.loc[:, 'team']])\n\n\t\t#Generate the lineups\n\t\tlineups = []\n\t\tfor _ in tqdm(range(1, self.num_lineups+1)):\n\t\t\tlineup = self.type_1(lineups, positions, team_lines, skaters_teams, goalies_opponents, num_skaters, num_goalies, num_teams, num_lines)\n\t\t\tif lineup:\n\t\t\t\tlineups.append(lineup)\n\t\t\telse:\n\t\t\t\tbreak\n\n\t\t#Fill the lineups with player names\n\t\tself.fill_lineups(lineups, positions, num_skaters, num_goalies)","def sum_by_team(self,df):\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n df_team = df.groupby(['Team']).sum()\n #print(df_team[['PB', 'AP1', 'ST']])\n \n\n #for a visual, barh of pro-bowls by drafting team\n df_team1 = df_team['PB']\n team_plot = df_team1.plot(kind=\"barh\", fontsize=4)\n team_plot.set_xlabel(\"Pro bowl appearances\")\n team_plot.set_title(\"PRO BOWL APPEARANCES, BY NFL DRAFTING TEAM, 2010-2020\")\n plt.show()\n \n #return the new df(by team)\n return df_team","def get_teams(self):\n url = 'teams'\n result = self.get(url)\n return result.get('teams', result)","def calculate_stats(team_id, current_matches, prev_matches, stats, targets):\n\n # Features\n current_formation = None\n opp_formation = None\n win = 0\n loss = 0\n recent_wins = 0\n recent_losses = 0\n prev_opp = []\n current_opp = 0\n total_goals = 0\n goals_for = 0\n goals_against = 0\n goal_diff = 0\n total_points = 0\n count = 1\n played = float(0)\n goal_efficiency = 0\n\n # Game Features\n game_features = {'possession': [], 'attacks': [], 'dangerous_attacks': [], 'yellow_cards': [],\n 'corner_kicks': [], 'shots_on_target': [], 'shots_total': [], 'ball_safe': [],\n 'goal_attempts': [], 'goal_attempts_allowed': [], 'saves': [], 'first_half_goals': [], 'sec_half_goals': [], 'goal_kicks': []}\n\n # Targets\n points = 0\n goals = 0\n opp_goals = 0\n\n goals_home = 0\n goals_away = 0\n opp_goals_at_home = 0 # Opponent goals when CURRENT team is AT Home\n opp_goals_at_away = 0 # Opponent goals when CURRENT team is Away\n\n total_games = len(prev_matches)\n # Pulling Data for PREVIOUS Matches\n for index, game in prev_matches.iterrows():\n\n # Home Wins and Road Losses are .8 while Road Wins and Home Losses are 1.2 / Draws remain the same\n if team_id == game['home_id']:\n\n if game['home_points'] == 3:\n win += .8\n\n elif game['home_points'] == 1:\n win += .5\n loss += .5\n\n else:\n loss += 1.2\n\n total_goals += game['home_score']\n goals_for += game['home_score']\n goals_against += game['away_score']\n goal_diff += game['home_score'] - game['away_score']\n total_points += game['home_points']\n\n goals_home += game['home_points']\n opp_goals_at_away += game['away_points']\n\n current_formation = game['home_formation']\n opp_formation = game['away_formation']\n\n prev_opp.append(game['away_id'])\n\n game_features['possession'].append(game['home_possession'])\n game_features['attacks'].append(game['home_attacks'])\n game_features['dangerous_attacks'].append(game['home_dangerous_attacks'])\n game_features['yellow_cards'].append(game['home_yellow_card'])\n game_features['corner_kicks'].append(game['home_corner_kicks'])\n game_features['shots_on_target'].append(game['home_shots_on_target'])\n game_features['shots_total'].append(game['home_shots_total'])\n game_features['ball_safe'].append(game['home_ball_safe'])\n game_features['goal_attempts'].append(game['home_goal_attempts'])\n game_features['goal_attempts_allowed'].append(game['away_goal_attempts'])\n game_features['saves'].append(game['home_saves'])\n game_features['first_half_goals'].append(game['home_first_half_score'])\n game_features['sec_half_goals'].append(game['home_second_half_score'])\n game_features['goal_kicks'].append(game['home_goal_kicks'])\n\n else:\n team_name = game[\"away_team\"]\n\n if game['away_points'] == 3:\n win += 1.2\n\n elif game['away_points'] == 1:\n win += .5\n loss += .5\n\n else:\n loss += .8\n\n total_goals += game['away_score']\n goals_for += game['away_score']\n goals_against += game['home_score']\n goal_diff += game['away_score'] - game['home_score']\n total_points += game['away_points']\n\n goals_away += game['away_points']\n opp_goals_at_home += game['home_points']\n\n current_formation = game['away_formation']\n opp_formation = game['home_formation']\n\n prev_opp.append(game['home_id'])\n\n game_features['possession'].append(game['away_possession'])\n game_features['attacks'].append(game['away_attacks'])\n game_features['dangerous_attacks'].append(game['away_dangerous_attacks'])\n game_features['yellow_cards'].append(game['away_yellow_card'])\n game_features['corner_kicks'].append(game['away_corner_kicks'])\n game_features['shots_on_target'].append(game['away_shots_on_target'])\n game_features['shots_total'].append(game['away_shots_total'])\n game_features['ball_safe'].append(game['away_ball_safe'])\n game_features['goal_attempts'].append(game['away_goal_attempts'])\n game_features['goal_attempts_allowed'].append(game['home_goal_attempts'])\n game_features['saves'].append(game['away_saves'])\n game_features['first_half_goals'].append(game['away_first_half_score'])\n game_features['sec_half_goals'].append(game['away_second_half_score'])\n game_features['goal_kicks'].append(game['away_goal_kicks'])\n\n played += 1\n count += 1\n\n # Pulling the data for the CURRENT MATCH\n for index, cur_game in current_matches.iterrows():\n\n match_id = cur_game[\"match_id\"]\n scheduled = cur_game[\"scheduled\"]\n if team_id == cur_game['home_id']:\n is_home = True\n team_name = cur_game[\"home_team\"]\n current_opp = cur_game['away_id']\n\n if targets:\n # Targets\n points = cur_game['home_points']\n goals = cur_game['home_score']\n opp_goals = cur_game['away_score']\n\n else:\n\n is_home = False\n team_name = cur_game[\"away_team\"]\n current_opp = cur_game['home_id']\n\n if targets:\n # Targets\n points = cur_game['away_points']\n goals = cur_game['away_score']\n opp_goals = cur_game['home_score']\n\n for k, v in game_features.items():\n game_features[k] = np.sum(np.array(v))\n \"\"\"if len(v) != 0:\n game_features[k] = np.nanmean(np.array(v))\n else:\n game_features[k] = np.nan\"\"\"\n\n\n if stats:\n print(\" ========================== \")\n print(\"Team Id : {} - Name : {}\".format(team_id, team_name))\n print(\"Prev Opponent Ids : {}\".format(prev_opp))\n print(\"FEATURES (Stats from * 3 Previous Matches)\")\n print(\"Total Goals : {}\".format(total_goals))\n print(\"Total Points : {}\".format(total_points))\n # print(\"Win Points : {}\".format(win))\n # print(\"Loss Points : {}\".format(loss))\n print(\"Played : {}\".format(played))\n print(\"Goal Diff : {}\".format(goal_diff))\n print(\"Margin : {}\".format(np.divide(goal_diff, played)))\n print(\"\")\n\n if targets:\n # Still can weight games more on most recent games\n print(\"\\nTARGETS (RESULTS OF CURRENT MATCH)\")\n print(\"Points : {}\".format(points))\n print(\"Goals : {}\".format(goals))\n print(\"Opp_Goals : {}\".format(opp_goals))\n\n return match_id, team_id, team_name, scheduled, int(is_home == True), total_points, \\\n goals_for, goals_against, goal_diff, goal_efficiency, played, win, loss, recent_wins, recent_losses, prev_opp, \\\n current_opp, points, goals, opp_goals, current_formation, opp_formation, game_features","def get_teams_from_league(teams, league):\n\t\n\tteams_1 = []\n\tfor i in range(0,len(teams)):\n\t\tif teams[i].league == 1:\n\t\t\tteams_1.append(teams[i])\n\n\treturn teams_1","def play_game(game,standings_):\n rand_nmr = random.random()\n\n standings_.loc[standings_.TEAMS==game['Home'],'MP'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'MP'] += 1\n\n if rand_nmr < game['Prob Home']:\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\n standings_.loc[standings_.TEAMS==game['Home'],'W'] += 1\n standings_.loc[standings_.TEAMS==game['Home'],'F'] += n_goals\n standings_.loc[standings_.TEAMS==game['Away'],'L'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'A'] += n_goals\n standings_.loc[standings_.TEAMS==game['Home']][\"h2h\"].apply(lambda x:x.append(game['Away']))\n\n return 0\n\n elif rand_nmr < game['Prob Home'] + game['Prob Draw']:\n # all draws end in 0-0 this can be improved\n standings_.loc[standings_.TEAMS==game['Home'],'D'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'D'] += 1\n\n return 1\n\n else:\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\n standings_.loc[standings_.TEAMS==game['Away'],'W'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'F'] += n_goals\n standings_.loc[standings_.TEAMS==game['Home'],'A'] += 1\n standings_.loc[standings_.TEAMS==game['Home'],'L'] += n_goals\n standings_.loc[standings_.TEAMS==game['Away']][\"h2h\"].apply(lambda x:x.append(game['Home']))\n\n return 2","def extract_team_data(game_data):\n team_id = game_data[1]\n team_abbrev = game_data[2]\n team_name = game_data[3]\n total_minutes_all_players = game_data[8]\n field_goals_made = game_data[9]\n field_goals_attempted = game_data[10]\n three_ptr_made = game_data[12]\n three_ptr_attempted = game_data[13]\n free_throws_made = game_data[15]\n free_throws_attempted = game_data[16]\n offensive_rebounds = game_data[18]\n defensive_rebounds = game_data[19]\n rebounds = game_data[20]\n assists = game_data[21]\n steals = game_data[22]\n blocks = game_data[23]\n turnovers = game_data[24]\n personal_fouls = game_data[25]\n points = game_data[26]\n plus_minus = game_data[27]\n\n final_dict = {\n \"team_id\": team_id,\n \"team_abbrev\": team_abbrev,\n \"team_name\": team_name,\n \"total_minutes_all_players\": total_minutes_all_players,\n \"field_goals_made\": field_goals_made,\n \"field_goals_attempted\": field_goals_attempted,\n \"three_ptr_made\": three_ptr_made,\n \"three_ptr_attempted\": three_ptr_attempted,\n \"free_throws_made\": free_throws_made,\n \"free_throws_attempted\": free_throws_attempted,\n \"offensive_rebounds\": offensive_rebounds,\n \"defensive_rebounds\": defensive_rebounds,\n \"rebounds\": rebounds,\n \"assists\": assists,\n \"steals\": steals,\n \"blocks\": blocks,\n \"turnovers\": turnovers,\n \"personal_fouls\": personal_fouls,\n \"points\": points,\n \"plus_minus\": plus_minus\n }\n return final_dict","def fa_finder(league_no, team_name):\n ros_proj_b_list = BatterProjection.objects.all()\n ros_proj_p_list = PitcherProjection.objects.all()\n player_comp = {}\n pitching_fa_list = yahoo_players(league_no, \"P\")\n batting_fa_list = yahoo_players(LEAGUE_NO, \"B\")\n avail_pitching_fas = rate_avail_players(pitching_fa_list, ros_proj_p_list)\n yahoo_team = get_single_yahoo_team(league_no, team_name)\n team_pitching_values = rate_team(yahoo_team, ros_proj_p_list)\n avail_batting_fas = rate_avail_players(batting_fa_list, ros_proj_b_list)\n team_batting_values = rate_team(yahoo_team, ros_proj_b_list)\n\n player_comp['Team Name'] = yahoo_team['team_name']\n player_comp['Pitching FAs'] = avail_pitching_fas\n player_comp['Pitching Team'] = team_pitching_values\n player_comp['Batting FAs'] = avail_batting_fas\n player_comp['Batting Team'] = team_batting_values\n\n return player_comp","def get_team_game_previews(self, team, dates):\n abbr = convert_name(team, how='abbr')\n return self._db.Games.find({'date' : {'$in' : dates},\n '$or' : [{'home' : abbr},\n {'away' : abbr}]})"],"string":"[\n \"def get_championship_won_for_each_coach(self):\\n self.championship_team = (\\n self.raw_data_postseason\\n .merge(self.season_max_days,how='left',on=['Season'])\\n .query(\\\"DayNum == season_max_days\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"NumOT\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"NumOT\\\":\\\"is_champion\\\",\\\"WTeamID\\\":\\\"TeamID\\\"})\\n )\",\n \"def teams(self, game_id: int) -> DataFrame[Any]:\",\n \"def opponent_games(df, team, gameids):\\n games = df[df.gameid.isin(gameids)]\\n opponent_games = games[games.team != team].drop_duplicates(subset=['gameid'])[['gameid', 'team']]\\n opponent_games.columns = ['gameid', 'opponent']\\n\\n return opponent_games\",\n \"def winning_games_stats(self):\\n self.winning_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(winning_num_counts = 1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM3\\\":\\\"sum\\\",\\\"WFGA3\\\":\\\"sum\\\",\\\"WFTM\\\":\\\"sum\\\",\\\"WFTA\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"winning_num_counts\\\":\\\"sum\\\",\\n \\\"WOR\\\":\\\"sum\\\",\\\"WDR\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\n \\\"WAst\\\":\\\"sum\\\",\\\"WTO\\\":\\\"sum\\\",\\\"WStl\\\":\\\"sum\\\",\\\"WBlk\\\":\\\"sum\\\",\\\"WPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"LScore\\\":\\\"losing_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"LFGA\\\":\\\"LFGA_opp\\\",\\\"LFGM\\\":\\\"LFGM_opp\\\"})\\n )\",\n \"def get_teams_table(games_table: pd.DataFrame) -> pd.DataFrame:\\n team_name_series = games_table[\\\"team\\\"].unique()\\n teams_table = pd.DataFrame(data=team_name_series)\\n teams_table = teams_table.reset_index()\\n teams_table.columns = [\\\"id\\\", \\\"team\\\"]\\n return teams_table\",\n \"def construct_team_dataframe(game_dict):\\n from .models import Game\\n\\n logger = logging.getLogger(__name__)\\n\\n # WHEN NOT SPECIFIED ALL STATS RELATE TO US ONLY\\n\\n # create list of 'vs opponent' row titles to use as index\\n # indexlist = [str('vs ' + game.opposing_team.team_name) if game.opposing_team else game.game_ID for game in Game.objects.filter(pk__in=game_dict.keys())]\\n\\n indexlist = [game.game_ID for game in Game.objects.filter(pk__in=game_dict.keys())]\\n\\n tf = pd.DataFrame(index=indexlist)\\n\\n # final score of each game\\n scores = [str(frame.iloc[-1]['ourscore_EOP']) + '-' + str(frame.iloc[-1]['theirscore_EOP']) for gameid, frame in game_dict.items()]\\n score = pd.Series(scores, index=tf.index)\\n\\n # total passes per game\\n passes = [frame['passes'].sum() for gameid, frame in game_dict.items()]\\n passes = pd.Series(passes, index=tf.index)\\n\\n # turnovers we had\\n turnovers = [frame['turnovers'].sum() for gameid, frame in game_dict.items()]\\n t_series = pd.Series(turnovers, index=tf.index)\\n\\n completion = []\\n for g_pass, g_turn in zip(passes, turnovers):\\n if g_pass:\\n comp_pct = round((g_pass - g_turn) / g_pass, 2)*100\\n else:\\n comp_pct = None\\n\\n completion.append(comp_pct)\\n # completion %\\n completion = pd.Series(completion, index=tf.index)\\n\\n # total possessions we had\\n possessions = pd.Series([frame['possessions'].sum() for gameid, frame in game_dict.items()], index=tf.index)\\n\\n # turnovers our opponent committed\\n opp_turns = pd.Series([frame['opp_turns'].sum() for gameid, frame in game_dict.items()], index=tf.index)\\n\\n # % of our possessions we scored on\\n # ( goals / our_possessions ) * 100%\\n goals = [frame.iloc[-1]['ourscore_EOP'] for gameid, frame in game_dict.items()]\\n goals = pd.Series(goals, index=tf.index)\\n score_pct = round((goals / possessions) * 100, 2)\\n\\n # number of possessions our opponents had\\n opp_poss = [frame['opp_poss'].sum() for gameid, frame in game_dict.items()]\\n opp_poss = pd.Series(opp_poss, index=tf.index)\\n\\n # number of blocks we got\\n blocks = [frame['blocks'].sum() for gameid, frame in game_dict.items()]\\n blocks = pd.Series(blocks, index=tf.index)\\n\\n # % of our opponents possessions where we got a block\\n # ( blocks / opp_possessions ) * 100%\\n block_pct = round((blocks / opp_poss) * 100, 2)\\n\\n tf = tf.assign(score=score)\\n tf = tf.assign(passes=passes)\\n tf = tf.assign(completion_pct=completion)\\n tf = tf.assign(possessions=possessions)\\n tf = tf.assign(score_pct=score_pct)\\n tf = tf.assign(blocks=blocks)\\n\\n tf = tf.assign(turns=t_series)\\n tf = tf.assign(opp_turns=opp_turns)\\n tf = tf.assign(opp_poss=opp_poss)\\n\\n tf = tf.assign(block_pct=block_pct)\\n\\n return tf\",\n \"def get_win_rate_regular_season_for_each_coach(self):\\n self.games_won_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n self.games_lose_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df\\n self.combine_regular_games_won_lose = (\\n self.games_lose_for_coaches\\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"games_lost\\\",\\\"which_coach_for_win\\\":\\\"games_won\\\"})\\n )\",\n \"def get_win_rate_post_season_for_each_coach(self):\\n # get winning games for coaches\\n self.post_games_won_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # get losing games for coaches\\n self.post_games_lose_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df for post season\\n self.combine_post_games_won_lose = (\\n self.post_games_lose_for_coaches\\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"post_games_lost\\\",\\\"which_coach_for_win\\\":\\\"post_games_won\\\"})\\n .fillna(0)\\n )\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def create_table_per_team(self) -> None:\\n for team_id in self.teams:\\n home = self.file.loc[(self.file[\\\"IdHomeTeam\\\"] == team_id)].reset_index(drop=True)\\n home = home.rename(columns={\\\"FTHG\\\": \\\"FTG_asH\\\", \\\"FTR\\\": \\\"FT_RESULT\\\", \\\"HTHG\\\": \\\"HTG_asH\\\",\\n \\\"HTR\\\": \\\"HT_RESULT\\\", \\\"HS\\\": \\\"Shoot_asH\\\", \\\"HST\\\": \\\"ShootTarget_asH\\\",\\n \\\"HF\\\": \\\"Fouls_asH\\\", \\\"HC\\\": \\\"Corner_asH\\\", \\\"HY\\\": \\\"YellowC_asH\\\", \\\"HR\\\": \\\"RedC_asH\\\",\\n \\\"FTAG\\\": \\\"FT_against_H\\\", \\\"HTAG\\\": \\\"HT_against_H\\\",\\n \\\"AS\\\": \\\"Shoot_against_H\\\", \\\"AST\\\": \\\"ShootTarget_against_H\\\",\\n \\\"AF\\\": \\\"Fouls_against_H\\\",\\n \\\"AC\\\": \\\"Corner_against_H\\\", \\\"AR\\\": \\\"RedC_againts_H\\\", \\\"AY\\\": \\\"YellowC_against_H\\\"})\\n home[\\\"nWeekHome\\\"] = home.index + 1\\n home.drop([\\\"nWeekAway\\\", \\\"IdAwayTeam\\\"], axis=1,\\n inplace=True)\\n home.loc[(home[\\\"FT_RESULT\\\"] == \\\"H\\\"), \\\"FT_RESULT\\\"] = \\\"Winn\\\"\\n home.loc[(home[\\\"FT_RESULT\\\"] == \\\"D\\\"), \\\"FT_RESULT\\\"] = \\\"Draw\\\"\\n home.loc[(home[\\\"FT_RESULT\\\"] == \\\"A\\\"), \\\"FT_RESULT\\\"] = \\\"Lost\\\"\\n home.loc[(home[\\\"HT_RESULT\\\"] == \\\"H\\\"), \\\"HT_RESULT\\\"] = \\\"Winn\\\"\\n home.loc[(home[\\\"HT_RESULT\\\"] == \\\"D\\\"), \\\"HT_RESULT\\\"] = \\\"Draw\\\"\\n home.loc[(home[\\\"HT_RESULT\\\"] == \\\"A\\\"), \\\"HT_RESULT\\\"] = \\\"Lost\\\"\\n\\n away = self.file.loc[(self.file[\\\"IdAwayTeam\\\"] == team_id)].reset_index(drop=True)\\n away = away.rename(columns={\\\"FTAG\\\": \\\"FTG_asA\\\", \\\"FTR\\\": \\\"FT_RESULT\\\", \\\"HTAG\\\": \\\"HTG_asA\\\",\\n \\\"HTR\\\": \\\"HT_RESULT\\\", \\\"AS\\\": \\\"Shoot_asA\\\", \\\"AST\\\": \\\"ShootTarget_asA\\\",\\n \\\"AF\\\": \\\"Fouls_asA\\\", \\\"AC\\\": \\\"Corner_asA\\\", \\\"AY\\\": \\\"YellowC_asA\\\", \\\"AR\\\": \\\"RedC_asA\\\",\\n \\\"FTHG\\\": \\\"FT_against_A\\\", \\\"HTHG\\\": \\\"HT_against_A\\\",\\n \\\"HS\\\": \\\"Shoot_against_A\\\", \\\"HST\\\": \\\"ShootTarget_against_A\\\",\\n \\\"HF\\\": \\\"Fouls_against_A\\\",\\n \\\"HC\\\": \\\"Corner_against_A\\\", \\\"HR\\\": \\\"RedC_againts_A\\\", \\\"HY\\\": \\\"YellowC_against_A\\\"})\\n away[\\\"nWeekAway\\\"] = away.index + 1\\n away.drop([\\\"nWeekHome\\\", \\\"IdHomeTeam\\\"], axis=1,\\n inplace=True)\\n away.loc[(away[\\\"FT_RESULT\\\"] == \\\"H\\\"), \\\"FT_RESULT\\\"] = \\\"Lost\\\"\\n away.loc[(away[\\\"FT_RESULT\\\"] == \\\"D\\\"), \\\"FT_RESULT\\\"] = \\\"Draw\\\"\\n away.loc[(away[\\\"FT_RESULT\\\"] == \\\"A\\\"), \\\"FT_RESULT\\\"] = \\\"Winn\\\"\\n away.loc[(away[\\\"HT_RESULT\\\"] == \\\"H\\\"), \\\"HT_RESULT\\\"] = \\\"Lost\\\"\\n away.loc[(away[\\\"HT_RESULT\\\"] == \\\"D\\\"), \\\"HT_RESULT\\\"] = \\\"Draw\\\"\\n away.loc[(away[\\\"HT_RESULT\\\"] == \\\"A\\\"), \\\"HT_RESULT\\\"] = \\\"Winn\\\"\\n\\n self.home = self.home.append(home, ignore_index=True)\\n self.away = self.away.append(away, ignore_index=True)\\n\\n self.home[\\\"Date\\\"] = pd.to_datetime(self.home[\\\"Date\\\"], format=\\\"%Y-%m-%d\\\")\\n self.away[\\\"Date\\\"] = pd.to_datetime(self.away[\\\"Date\\\"], format=\\\"%Y-%m-%d\\\")\\n self.home.reset_index(drop=True)\\n self.away.reset_index(drop=True)\",\n \"def history(df, team, opponent):\\n if opponent:\\n games = df[(df.team == team) & (df.opponent == opponent)]#team_games(df, team)\\n else:\\n games = df[df.team == team]#team_games(df, team)\\n\\n games['dragkills'] = (games['teamdragkills'] + games['oppdragkills'])\\n games['turrkills'] = (games['teamtowerkills'] + games['opptowerkills'])\\n result = games[['team', 'opponent', 'player', 'champion', 'fb', 'fd', 'ft', 'fbaron', 'result', 'turrkills', 'dragkills', 'gamelength']]\\n\\n result = result[result.player == 'Team'].sort_values('gamelength')\\n\\n return result.round(2)\",\n \"def get_teams():\",\n \"def get_current_lineups():\\n out = []\\n pf = players[players[\\\"team\\\"].isin(top30Teams)]\\n for index, row in pf.iterrows():\\n # Make sure that we only use player data where a player is\\n # playing for their current team\\n if(row[\\\"name\\\"] in top30Obj[row[\\\"team\\\"]]):\\n out.append(row)\\n return pd.DataFrame(out)\",\n \"def build_teams(self):\\n # get all nations\\n all_nations = self.games.get_all_nations()\\n\\n # build teams for all participating nations in FIFA World Cup 2018\\n bt = BuildTeams(self.squad_size, self.selected_attrs)\\n bt.read_data()\\n\\n # a dict with a nations' name as a key and players' data as value\\n self.teams = {}\\n\\n # build squad for every nation\\n for nation in all_nations:\\n team = bt.build_team(nation)\\n # if we got enough players, add team\\n if team.shape[0] >= bt.squad_size:\\n #print(team)\\n # convert pandas dataframe to matrix and flatten it\\n self.teams[nation] = team.as_matrix().flatten()\",\n \"def win(self) -> pd.DataFrame:\\n if not all([self.league, self.feature_set, self.team, self.opponent, self.variable]):\\n return pd.DataFrame().from_records([])\\n\\n # Get win probability from the team and opponent's perspectives\\n df_team = self._win(is_opponent=False)\\n df_opp = self._win(is_opponent=True)\\n\\n # Merge\\n df = df_team.drop('RandomEffect', axis=1).merge(df_opp.drop('RandomEffect', axis=1),\\n on=self.variable, how='inner')\\n\\n # Normalize so that P(Win) + P(Lose) = 1.0 calculated from each perspective\\n df['Win'] = df['Win_team'] / (df['Win_team'] + (1 - df['Win_opp']))\\n df['WinUB'] = abs(df['WinUB_team'] / (df['WinUB_team'] + (1 - df['WinLB_opp'])) - df['Win'])\\n df['WinLB'] = abs(df['Win'] - df['WinLB_team'] / (df['WinLB_team'] + (1 - df['WinUB_opp'])))\\n\\n return df\",\n \"def get_opponents(df):\\n opponents = []\\n games = games_only(df)\\n for idx, row in df.iterrows():\\n op = games[(games.gameid == row.gameid) & (games.team != row.team)].team.values[0]\\n opponents.append(op)\\n\\n return opponents\",\n \"def plays(df):\\n tp = (\\n df.query('play_type in @OFFENSE_PLAY_TYPES')\\n .pivot_table(index=['game_id', 'posteam'], \\n columns=['play_type'], \\n values=['play_id'], \\n aggfunc='count',\\n fill_value=0)\\n .pipe(lambda x: x.set_axis([f'{b}_plays' for a, b in x.columns], axis=1, inplace=False))\\n .reset_index()\\n ) \\n tp['tot_plays'] = tp.loc[:, [c for c in tp.columns if '_plays' in c]].sum(axis=1)\\n tp['run_pct'] = tp['run_plays'] / (tp['run_plays'] + tp['pass_plays'])\\n tp['pass_pct'] = tp['pass_plays'] / (tp['run_plays'] + tp['pass_plays'])\\n return tp.join(time_of_possession(df), on=['game_id', 'posteam'], how='left')\",\n \"def combine_both_winning_losing_games_stats(self):\\n self.combine_both_winning_losing_games_stats = (\\n self.winning_games_up_to_2013\\n .merge(self.losing_games_up_to_2013, how='left',left_on=['Season','WTeamID'],right_on=['Season','LTeamID'])\\n # on field goal percentage and winning counts\\n .pipe(lambda x:x.assign(total_score = x.WScore + x.LScore))\\n .pipe(lambda x:x.assign(total_opponent_score = x.winning_opponent_score + x.losing_opponent_score))\\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\\n .pipe(lambda x:x.assign(total_fga = x.WFGA + x.LFGA))\\n .pipe(lambda x:x.assign(total_fg3m = x.WFGM3 + x.LFGM3))\\n .pipe(lambda x:x.assign(total_fg3a = x.WFGA3 + x.LFGA3))\\n .pipe(lambda x:x.assign(total_ftm = x.WFTM + x.LFTM))\\n .pipe(lambda x:x.assign(total_fta = x.WFTA + x.LFTA))\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .sort_values(['WTeamID','Season'])\\n # on offensive and defensive rebounds\\n .pipe(lambda x:x.assign(total_rebounds = x.total_winning_rebounds + x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds = x.WOR + x.LOR))\\n .pipe(lambda x:x.assign(total_def_rebounds = x.WDR + x.LDR))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_team_missed_attempts = x.team_missed_attempts + x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_opp_team_missed_attempts = x.opp_team_missed_attempts + x.winning_opp_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # on steals, turnovers, assists, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_blocks = x.WBlk + x.LBlk))\\n .pipe(lambda x:x.assign(total_assists = x.WAst + x.LAst))\\n .pipe(lambda x:x.assign(total_steals = x.WStl + x.LStl))\\n .pipe(lambda x:x.assign(total_turnover = x.WTO + x.LTO))\\n .pipe(lambda x:x.assign(total_personalfoul = x.WPF + x.LPF))\\n .pipe(lambda x:x.assign(total_opp_fga = x.LFGA_opp + x.WFGA_opp))\\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def players(self, game_id: int) -> DataFrame[Any]:\",\n \"def wins(self):\\n return [g for g in self.games if g.winner is self.team]\",\n \"def pivot_team_matches_to_matches(team_match_df: pd.DataFrame) -> pd.DataFrame:\\n home_data_frame = _home_away_data_frame(team_match_df, \\\"home\\\")\\n away_data_frame = _home_away_data_frame(team_match_df, \\\"away\\\")\\n\\n return home_data_frame.merge(\\n away_data_frame,\\n on=[\\\"home_team\\\", \\\"away_team\\\", \\\"year\\\", \\\"round_number\\\", \\\"ml_model\\\"],\\n how=\\\"inner\\\",\\n )\",\n \"def TeamScores(level,team_N):\\r\\n \\r\\n groupresults = Combined_Non_Compound_Results(level).groupby('Club') \\r\\n # groups clubs together in a big list just for NMR\\r\\n # will need to generalise for all categories\\r\\n\\r\\n LoR = [ frame for LoRs, frame in groupresults ]\\r\\n \\r\\n TeamTable = pd.DataFrame({},columns=['Club','Total Score', # initial empty\\r\\n 'Total Golds', 'Total Hits']) # dataframe\\r\\n \\r\\n# Uni = pd.DataFrame({},columns=['Name','Club','Score','Golds', 'Hits'])\\r\\n TeamComposition = [[],[],[],[]]\\r\\n for j in range(4): # only four clubs in the dataframe\\r\\n\\r\\n Uni = LoR[j][0:team_N] # jth club in index, gets top team_N archers\\r\\n Uni = Uni.reset_index(drop=True) # resets the index for UCL sublist\\r\\n UniName = Uni.loc[0,'Club']\\r\\n\\r\\n Scores=0\\r\\n Golds=0\\r\\n Hits=0\\r\\n \\r\\n TeamComposition[j].append(UniName)\\r\\n\\r\\n for i in range(team_N): # sums the score,golds and hits for uni club j\\r\\n Scores += Uni.loc[i,'Score']\\r\\n Golds += Uni.loc[i,'Golds']\\r\\n Hits += Uni.loc[i,'Hits']\\r\\n\\r\\n TeamComposition[j].append(Uni.loc[i,'Name'])\\r\\n \\r\\n TeamTable2 = pd.DataFrame({'Club': [UniName], \\r\\n 'Total Score': [Scores],\\r\\n 'Total Golds': [Golds], \\r\\n 'Total Hits': [Hits]},\\r\\n columns=['Club','Total Score', \\r\\n 'Total Golds', 'Total Hits'])\\r\\n \\r\\n TeamTable = TeamTable.append(TeamTable2) # appends each club data\\r\\n\\r\\n TeamTable = TeamTable.sort_values(['Total Score','Total Golds',\\r\\n 'Total Hits'],ascending=[False,False,\\r\\n False],na_position='last')\\r\\n TeamTable = TeamTable.reset_index(drop=True)\\r\\n print()\\r\\n print(TeamTable)\\r\\n print()\\r\\n \\r\\n \\r\\n FinalList = [[],[],[],[]]\\r\\n \\r\\n for h in range(4):\\r\\n for g in range(4):\\r\\n if TeamTable.iloc[h,0] == TeamComposition[g][0]:\\r\\n FinalList[h] = TeamComposition[g]\\r\\n\\r\\n \\r\\n for k in range(4):\\r\\n print(FinalList[k])\\r\\n print()\\r\\n\\r\\n if level == NovCategories:\\r\\n \\r\\n return print(\\\"----- End of Novice Team Scores -----\\\")\\r\\n \\r\\n if level == AllCategories:\\r\\n \\r\\n return print(\\\"----- End of Experienced Team Scores -----\\\")\",\n \"def load_and_process_team_data(self,\\n dataset: Dataset,\\n teamid: int,\\n team_fixtures_idx: List[int]) -> pd.DataFrame:\\n df = self._dbmanager.query_team_data(self._seasons, params=(teamid, teamid, teamid, *self._seasons))\\n\\n # Current win-or-draw target from team's POV\\n df[\\\"wd\\\"] = np.select(\\n condlist=[\\n (df[\\\"homeTeamID\\\"] == teamid) & (df[\\\"winner\\\"] == 1),\\n (df[\\\"homeTeamID\\\"] == teamid) & (df[\\\"winner\\\"] == 2),\\n (df[\\\"awayTeamID\\\"] == teamid) & (df[\\\"winner\\\"] == 2),\\n (df[\\\"awayTeamID\\\"] == teamid) & (df[\\\"winner\\\"] == 1),\\n df[\\\"winner\\\"] == 0\\n ],\\n choicelist=[\\n 1,\\n 0,\\n 1,\\n 0,\\n 1\\n ],\\n default=np.nan\\n )\\n # Odds for wd from team's POV\\n df[\\\"odds_wd\\\"] = np.select(\\n condlist=[\\n df[\\\"homeTeamID\\\"] == teamid,\\n df[\\\"awayTeamID\\\"] == teamid\\n ],\\n choicelist=[\\n df[\\\"oddsDC_1X\\\"],\\n df[\\\"oddsDC_X2\\\"]\\n ],\\n default=np.nan\\n )\\n # `team` is just name of current team\\n df[\\\"team\\\"] = np.select(\\n condlist=[\\n df[\\\"homeTeamID\\\"] == teamid,\\n df[\\\"awayTeamID\\\"] == teamid\\n ],\\n choicelist=[\\n df[\\\"home\\\"],\\n df[\\\"away\\\"]\\n ],\\n default=np.nan\\n )\\n # `opponent` is the opposite team to `team`\\n df[\\\"opponent\\\"] = np.select(\\n condlist=[\\n df[\\\"homeTeamID\\\"] == teamid,\\n df[\\\"awayTeamID\\\"] == teamid\\n ],\\n choicelist=[\\n df[\\\"away\\\"],\\n df[\\\"home\\\"]\\n ],\\n default=np.nan\\n )\\n # Whether the current team plays as home = 1, or away = 0\\n df[\\\"ashome\\\"] = np.select(\\n condlist=[\\n df[\\\"homeTeamID\\\"] == teamid,\\n df[\\\"awayTeamID\\\"] == teamid,\\n ],\\n choicelist=[\\n 1,\\n 0\\n ],\\n default=np.nan\\n )\\n # Encode league: PL = 1, CH = 0\\n df[\\\"league\\\"] = np.select(\\n condlist=[\\n df[\\\"league\\\"] == \\\"PL\\\",\\n df[\\\"league\\\"] == \\\"CH\\\"\\n ],\\n choicelist=[\\n 1,\\n 0\\n ],\\n default=np.nan\\n )\\n\\n # Create future values of features which are known in advance.\\n # They are created by shifting original values by one row.\\n df[\\\"future_season\\\"] = df[\\\"season\\\"].shift(-1)\\n df[\\\"future_league\\\"] = df[\\\"league\\\"].shift(-1)\\n df[\\\"future_ashome\\\"] = df[\\\"ashome\\\"].shift(-1)\\n df[\\\"future_opponent\\\"] = df[\\\"opponent\\\"].shift(-1)\\n df[\\\"future_wd\\\"] = df[\\\"wd\\\"].shift(-1)\\n df[\\\"future_odds_wd\\\"] = df[\\\"odds_wd\\\"].shift(-1)\\n\\n # Dataframe needs to be filtered according to current dataset to contain only corresponding\\n # matches. Also index needs to be reset (to correctly access data for test and predict sets).\\n df = df.loc[df[\\\"id\\\"].isin(team_fixtures_idx)].copy()\\n df.reset_index(inplace=True, drop=True)\\n\\n # If there are nans for train dataset, use ffill method to fill them.\\n # This can happen if team occurs only in train dataset and does not have more data in later\\n # seasons, thus it does not matter that we fill these nans with incorrect data as they wo not be\\n # used for testing anyway.\\n if dataset == Dataset.Train:\\n # Fill nan values of future opponent with empty string\\n df[\\\"future_opponent\\\"].fillna(\\\"\\\", inplace=True)\\n df.fillna(method=\\\"ffill\\\", inplace=True)\\n\\n df = self._scale_team_data(df)\\n\\n return df\",\n \"def make_player_stats(tournament, lineups):\\n positions = find_player_positions(lineups)\\n positions = positions.set_index('player_id')[['player_position']]\\n player_stats = load_player_stats(tournament)\\n player_stats.set_index('player_id', inplace=True)\\n mask = player_stats['goals'] > player_stats['shots']\\n player_stats.loc[mask, 'shots'] = player_stats[mask]['goals']\\n res = player_stats.join(positions)\\n res = res[pd.notna(res['player_position'])]\\n return res\",\n \"def get_final_coach_for_each_season(self):\\n self.final_coach_for_season = (\\n self.num_days_coach_for_season\\n .groupby(['Season','TeamID'])\\n .agg({\\\"CoachName\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"CoachName\\\":\\\"coach_counts\\\"})\\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \\\"ignore\\\")))\\n [['Season','TeamID','final_coach']]\\n )\",\n \"def _win(self, is_opponent: bool) -> pd.DataFrame:\\n records = []\\n for var in self.variable_vals:\\n # Add variable to parameters\\n self.parameters[self.variable] = var\\n # Add derived features\\n self._derived_features()\\n # Configure inputs\\n input_set = {\\n 'random_effect': 'team' if not is_opponent else 'opponent',\\n 'feature_set': self.feature_set,\\n 'inputs': {'RandomEffect': self.team if not is_opponent else self.opponent}\\n }\\n input_set['inputs'].update(self.parameters)\\n # Predict\\n output = self.predictor.predict(**input_set)[\\n ('team' if not is_opponent else 'opponent', self.feature_set, 'Win')\\n ]\\n # Wrangle output\\n record = {\\n 'RandomEffect': self.team if is_opponent else self.opponent,\\n self.variable: var,\\n 'WinLB_opp' if is_opponent else 'WinLB_team': expit(output['mu']['lb']),\\n 'Win_opp' if is_opponent else 'Win_team': expit(output['mu']['mean']),\\n 'WinUB_opp' if is_opponent else 'WinUB_team': expit(output['mu']['ub'])\\n }\\n records.append(record)\\n\\n return pd.DataFrame().from_records(records)\",\n \"def gamesmeta(sched=None):\\n if sched is None or sched.empty:\\n sched = schedule()\\n h = sched.copy()\\n a = sched.copy()\\n h = h.rename(columns={'home_team': 'team', 'away_team': 'opp'})\\n a = a.rename(columns={'away_team': 'team', 'home_team': 'opp'})\\n return pd.concat([a, h]).sort_values('game_id')\",\n \"def _get_teams(unstacked, min_games=0):\\n count_col = 'game_id'\\n home_gb = unstacked.groupby('hteam_id').count()[[count_col]]\\n away_gb = unstacked.groupby('ateam_id').count()[[count_col]]\\n merged = home_gb.merge(away_gb, how='outer', left_index=True, right_index=True)\\n merged[count_col] = merged[count_col + '_x'] + merged[count_col + '_y']\\n merged.index.name = 'index'\\n teams = merged.reset_index().rename(columns={'index': 'team_id'})\\n teams = teams.sort('team_id')\\n teams['i_team'] = np.arange(teams.shape[0])\\n team_index = {}\\n for team, idx in teams[['team_id', 'i_team']].values:\\n team_index[int(team)] = int(idx)\\n return teams[['team_id', 'i_team']], team_index\",\n \"def team_games(df, team):\\n print(df[\\n ( df['team'] == team ) & \\n ( df['player'] == 'Team' )\\n ].drop_duplicates(subset=['gameid']).shape)\\n return df[\\n ( df['team'] == team ) & \\n ( df['player'] == 'Team' )\\n ].drop_duplicates(subset=['gameid'])\",\n \"def get_past_matches_data(team):\\n matches = team.get_past_matches()\\n match_list = []\\n for match in matches:\\n match_dict = {}\\n match_dict['match_date'] = match.match_date\\n match_dict['match_name'] = match.__str__()\\n match_dict['id'] = match.id\\n innings = match.get_innings()\\n if len(innings):\\n if innings[0].runs > innings[1].runs:\\n match_dict['winner_team'] = innings[0].bat_team\\n match_dict['win_margin'] = innings[0].runs - innings[1].runs\\n match_dict['win_type'] = 'Runs'\\n match_dict['winner_score'] = str(innings[0].runs) + '/' + str(innings[0].wickets)\\n else:\\n match_dict['winner_team'] = innings[1].bat_team\\n match_dict['win_margin'] = 10 - innings[1].wickets\\n match_dict['win_type'] = 'Wickets'\\n match_dict['winner_score'] = str(innings[1].runs) + '/' + str(innings[1].wickets)\\n match_list.append(match_dict)\\n return match_list\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def cumulative_stats_for_teams_each_year(self):\\n self.cumulative_stats_for_team_each_year = (\\n self.combine_both_winning_losing_games_stats\\n .sort_values(['WTeamID','Season'])\\n .groupby(['WTeamID'])\\n .cumsum()\\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\\n .drop(['LTeamID','win_rate'],1)\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n # rebounds cumsum stats\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # assists, turnovers, steals, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win or lose by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def get_upcoming_games(n=10):\\n conn, cursor = connect_to_db()\\n query = \\\"\\\"\\\"select kickoff_time, t2.team_id home_id, t2.team_name home_name, \\n t3.team_id away_id, t3.team_name away_name\\n from fpl_fixtures t1 left join fpl_teams t2 on t1.team_h = t2.id left \\n join fpl_teams t3 on t1.team_a = t3.id where started = 0 order by \\n kickoff_time limit {}\\\"\\\"\\\".format(n)\\n df = run_query(cursor, query)\\n return df\",\n \"def get_fb_team_rankings(self):\\n\\n ranks = []\\n self._logger.debug(\\\"Getting foosball team rankings\\\")\\n\\n try:\\n self.check_if_db_connected()\\n cursor = self._db_conn.cursor()\\n cursor.execute(\\\"SELECT team_id, team_name FROM team\\\")\\n teams = cursor.fetchall()\\n\\n for team_id, team_name in teams:\\n cursor.execute(\\\"SELECT fb_team_rating FROM \\\\\\nteam WHERE team_id = {0}\\\".format(team_id))\\n team_rating = cursor.fetchall()[0]\\n\\n cursor.execute(\\\"SELECT mu, sigma FROM rating WHERE rating_id \\\\\\n= {0}\\\".format(team_rating[0]))\\n mu, sigma = cursor.fetchall()[0]\\n\\n team_rank = float(mu) - (3 * float(sigma))\\n\\n # get player_ids\\n cursor.execute(\\\"SELECT player from player_team_xref \\\\\\nWHERE team = {0}\\\".format(team_id))\\n players = cursor.fetchall()\\n player_one = players[0]\\n player_two = players[1]\\n\\n cursor.execute(\\\"SELECT first_name FROM player WHERE \\\\\\nplayer_id = {0}\\\".format(player_one[0]))\\n player_one_name = cursor.fetchone()[0]\\n\\n cursor.execute(\\\"SELECT first_name FROM player WHERE \\\\\\nplayer_id = {0}\\\".format(player_two[0]))\\n player_two_name = cursor.fetchone()[0]\\n\\n cursor.execute(\\\"SELECT COUNT(result_id) FROM fb_result WHERE \\\\\\n(offense_winner = {0} AND defense_winner = {1}) OR (offense_winner = {1} \\\\\\nAND defense_winner = {0})\\\".format(player_one[0], player_two[0]))\\n team_win_count = cursor.fetchone()[0]\\n\\n cursor.execute(\\\"SELECT COUNT(result_id) FROM fb_result WHERE \\\\\\n(offense_loser = {0} AND defense_loser = {1}) OR (offense_loser = {1} \\\\\\nAND defense_loser = {0})\\\".format(player_one[0], player_two[0]))\\n team_loss_count = cursor.fetchone()[0]\\n\\n intermediate_rank = (team_name, round(team_rank, 4),\\n team_win_count, team_loss_count, player_one_name,\\n player_two_name)\\n ranks.append(intermediate_rank)\\n del intermediate_rank\\n\\n except MySQLdb.OperationalError:\\n self._logger.error(\\\"MySQL operational error occured\\\")\\n traceback.print_exc()\\n raise exceptions.DBConnectionError(\\\"Cannot connect to MySQL server\\\")\\n\\n except MySQLdb.ProgrammingError:\\n self._logger.error(\\\"MySQL programming error\\\")\\n traceback.print_exc()\\n raise exceptions.DBSyntaxError(\\\"MySQL syntax error\\\")\\n\\n else:\\n return ranks\",\n \"def gamestats(self, table, curr_team):\\n\\n # Drop unneeded header \\n tmp = table.iloc[1:,]\\n # Fix the column names by reading line 0\\n tmp.columns = [x.replace(\\\" \\\", \\\"\\\").replace(\\\"/\\\",\\\"\\\").replace(\\\".\\\",\\\"\\\") for x in tmp.iloc[0]]\\n # Drop row zero which held the header row\\n tmp = tmp.drop(tmp.index[0])\\n # Forward fill the dates for defensive split later \\n tmp['Date'].fillna(method='ffill', inplace = True)\\n # Add in the team \\n tmp['Team'] = curr_team\\n # Create an offense/defense variable\\n tmp['OffenseDefense'] = tmp['Opponent']\\n # If it's not a defensive total then it's offense - set that in the offensedefense variable\\n tmp['OffenseDefense'] = tmp['OffenseDefense'].apply(lambda x: \\\"Defense\\\" if x == \\\"Defensive Totals\\\" else \\\"Offense\\\")\\n # Set the defensive totals in the opponent varaible to nullls\\n tmp['Opponent'] = tmp['Opponent'].apply(lambda x: None if x == \\\"Defensive Totals\\\" else x)\\n # Forward fill the opponents in for analysis later\\n tmp['Opponent'].fillna(method='ffill', inplace = True)\\n # Forward fill the results in for analysis later \\n tmp['Result'].fillna(method='ffill', inplace = True)\\n return tmp\",\n \"def combine_games(all_data):\\n\\tcombined_fields = concat_data_fields(all_data)\\n\\tcombined_data = pd.DataFrame(columns=combined_fields)\\n\\tgids = np.unique(all_data['Id'])\\n\\tfor gid in gids:\\n\\t\\tgames = all_data[all_data['Id'] == gid]\\n\\t\\tassert games.shape[0] == 2, \\\"Should have found 2 games here\\\"\\n\\t\\tconcat_games = concat_game_rows(games, combined_fields)\\n\\t\\tcombined_data = combined_data.append(pd.Series(concat_games[0]), ignore_index=True)\\n\\t\\tcombined_data = combined_data.append(pd.Series(concat_games[1]), ignore_index=True)\\n\\treturn combined_data\",\n \"def losing_games_stats(self):\\n self.losing_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(losing_num_counts=1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','LTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\\"LFGM3\\\":\\\"sum\\\",\\\"LFGA3\\\":\\\"sum\\\",\\\"LFTM\\\":\\\"sum\\\",\\\"LFTA\\\":\\\"sum\\\",\\\"losing_num_counts\\\":\\\"sum\\\",\\n \\\"LOR\\\":\\\"sum\\\",\\\"LDR\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\n \\\"LAst\\\":\\\"sum\\\",\\\"LTO\\\":\\\"sum\\\",\\\"LStl\\\":\\\"sum\\\",\\\"LBlk\\\":\\\"sum\\\",\\\"LPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"WScore\\\":\\\"winning_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"WFGA\\\":\\\"WFGA_opp\\\",\\\"WFGM\\\":\\\"WFGM_opp\\\"})\\n )\",\n \"def get_games_table() -> pd.DataFrame:\\n games_list = []\\n for filename in os.listdir(DATA_DIR / \\\"raw_games_data\\\"):\\n games_table = pd.read_csv(DATA_DIR / \\\"raw_games_data\\\" / filename, index_col=0)\\n games_list.append(games_table)\\n games_table = pd.concat(games_list)\\n games_table[\\\"player_name\\\"] = games_table[\\\"player_name\\\"].apply(lambda x: _capitalize_name(x))\\n return games_table\",\n \"def get_match_features(match, matches):\\n\\n # Define variables\\n date = match.date\\n season = match.season\\n home_team = match.home_team_api_id\\n away_team = match.away_team_api_id\\n\\n matches_home_ashome = get_last_ashome_matches(matches, date, season, home_team, x=5)\\n # print(matches_home_ashome, flush=True)\\n matches_home_asaway = get_last_asaway_matches(matches, date, season, home_team, x=5)\\n matches_away_ashome = get_last_ashome_matches(matches, date, season, away_team, x=5)\\n matches_away_asaway = get_last_asaway_matches(matches, date, season, away_team, x=5)\\n # matches_home = pd.concat([matches_home_ashome, matches_home_asaway])\\n # matches_away = pd.concat([matches_away_ashome, matches_away_asaway])\\n\\n # Get last x matches of both teams against each other\\n competing_matches_same = get_last_competing_matches(matches, date, home_team, away_team, x=2)\\n competing_matches_diff = get_last_reverse_competing_matches(matches, date, home_team, away_team, x=2)\\n\\n # Define result data frame\\n result = pd.Series()\\n result['match_api_id'] = match.match_api_id\\n result['league_id'] = match.league_id\\n result['home_recent_goal_ashome'] = get_goals(matches_home_ashome)\\n result['away_recent_goal_asaway'] = get_goals(matches_away_asaway, isAway=True)\\n result['home_recent_lost_ashome'] = get_goals_lost(matches_home_ashome)\\n result['away_recent_lost_asaway'] = get_goals_lost(matches_away_asaway, isAway=True)\\n result['team_ova_diff'] = match.home_ova - match.away_ova\\n result['home_recent_score'] \\\\\\n = get_average_league_score(matches_home_ashome) + get_average_league_score(matches_home_asaway, isAway=True)\\n result['away_recent_score'] \\\\\\n = get_average_league_score(matches_away_ashome) + get_average_league_score(matches_away_asaway, isAway=True)\\n result['home_competing_same_goal'] = get_goals(competing_matches_same)\\n result['away_competing_same_goal'] = get_goals(competing_matches_same, isAway=True)\\n result['home_competing_diff_goal'] = get_goals(competing_matches_diff, isAway=True)\\n result['away_competing_diff_goal'] = get_goals(competing_matches_diff)\\n result['home_recent_shoton'] = get_shoton(matches_home_ashome) + 0.8 * get_shoton(matches_home_asaway, isAway=True)\\n result['away_recent_shoton'] = get_shoton(matches_away_asaway, isAway=True) + 0.8 * get_shoton(matches_away_ashome)\\n result['home_recent_shotoff'] = get_shotoff(matches_home_ashome) + 0.8 * get_shotoff(matches_home_asaway, isAway=True)\\n result['away_recent_shotoff'] = get_shotoff(matches_away_asaway, isAway=True) + 0.8 * get_shotoff(matches_away_ashome)\\n\\n # print(result)\\n return result\",\n \"def get_player_data(responses) -> pd.DataFrame:\\n result = []\\n for p in responses:\\n data = p.decode(\\\"utf8\\\").replace(\\\"'\\\", '\\\"')\\n data = json.loads(data)\\n latest_stats = data.get(\\\"league\\\").get(\\\"standard\\\").get(\\\"stats\\\").get(\\n \\\"latest\\\")\\n stats_dict = {\\n \\\"FGP\\\":\\n latest_stats.get(\\\"fgp\\\"),\\n \\\"FTP\\\":\\n latest_stats.get(\\\"ftp\\\"),\\n \\\"3PM\\\":\\n round(\\n int(latest_stats.get(\\\"tpm\\\")) / int(\\n latest_stats.get(\\\"gamesPlayed\\\")), 1),\\n \\\"PPG\\\":\\n latest_stats.get(\\\"ppg\\\"),\\n \\\"APG\\\":\\n latest_stats.get(\\\"apg\\\"),\\n \\\"RPG\\\":\\n latest_stats.get(\\\"rpg\\\"),\\n \\\"SPG\\\":\\n latest_stats.get(\\\"spg\\\"),\\n \\\"BPG\\\":\\n latest_stats.get(\\\"bpg\\\"),\\n \\\"TPG\\\":\\n latest_stats.get(\\\"topg\\\"),\\n \\\"MPG\\\":\\n round(\\n int(latest_stats.get(\\\"min\\\")) / int(\\n latest_stats.get(\\\"gamesPlayed\\\")), 1)\\n }\\n result.append(stats_dict)\\n return pd.DataFrame(result)\",\n \"def get_matchup_history(self, *teams):\\n abbrA = convert_name(teams[0], how='abbr')\\n abbrB = convert_name(teams[1], how='abbr')\\n\\n return self._db.Games.find({'$or' : [{'home' : abbrA,\\n 'away' : abbrB},\\n {'home' : abbrB,\\n 'away' : abbrA}]})\",\n \"def split_by_wins(matches, team_id):\\n\\n sorted_matches = {\\n \\\"wins\\\": [],\\n \\\"losses\\\": [],\\n \\\"ties\\\": []\\n }\\n\\n scores = {}\\n\\n for match_id, match in matches.items():\\n this_team_score = 0\\n other_team_score = 0\\n if match.hometeam.team_id == team_id:\\n this_team_score = match.homegoals\\n other_team_score = match.awaygoals\\n elif match.awayteam.team_id == team_id:\\n this_team_score = match.awaygoals\\n other_team_score = match.homegoals\\n else:\\n continue\\n\\n scores[match_id] = (this_team_score, other_team_score)\\n\\n if this_team_score > other_team_score:\\n sorted_matches[\\\"wins\\\"].append(match_id)\\n elif this_team_score < other_team_score:\\n sorted_matches[\\\"losses\\\"].append(match_id)\\n else:\\n sorted_matches[\\\"ties\\\"].append(match_id)\\n\\n return sorted_matches, scores\",\n \"async def teams(self, ctx, teamsize: int, *member: discord.Member):\\r\\n d = dict()\\r\\n for x in member:\\r\\n if x.name in player.keys():\\r\\n d[x.name] = player[x.name]\\r\\n else:\\r\\n await ctx.send('{} is not a registered player'.format(x.name))\\r\\n return\\r\\n\\r\\n teams = itertools.combinations(d.items(), teamsize) # divide the players up into all possible teams: AB, AC...\\r\\n matchups = itertools.combinations(teams, 2) # all possible match ups from all possible teams\\r\\n\\r\\n report = '```md' + u\\\"\\\\u000A\\\"\\r\\n\\r\\n for x in matchups:\\r\\n t1 = x[0] # first team\\r\\n t2 = x[1] # second team\\r\\n t1_ratings, t2_ratings, t1_names, t2_names = ([] for n in range(4))\\r\\n for i in range(teamsize):\\r\\n t1_ratings.append(t1[i][1])\\r\\n t2_ratings.append(t2[i][1])\\r\\n t1_names.append(t1[i][0])\\r\\n t2_names.append(t2[i][0])\\r\\n qual = quality([t1_ratings, t2_ratings])\\r\\n # ignore all match ups with players on the same team...\\r\\n if len(set(t1_names).intersection(set(t2_names))) == 0:\\r\\n report += ', '.join(t1_names) + ' <vs> ' + ', '.join(t2_names) + u\\\"\\\\u000A\\\" + '> Quality: ' + str(\\r\\n round(qual, 3)) + u\\\"\\\\u000A\\\"\\r\\n\\r\\n await ctx.send(report + u\\\"\\\\u000A\\\" + '```')\",\n \"def makeMatchupData(fantasyTeams, week):\\n\\t# this parsing will get messed up if a team doesn't have a full bench.\\n\\tmatchups = []\\n\\tmatchupsNicknames = []\\n\\tfor team in fantasyTeams:\\n\\t\\tif team not in matchups:\\n\\t\\t\\tmatchups.append(team)\\n\\t\\t\\tmatchupsNicknames.append(fantasyTeams[team].nickname)\\n\\t\\t\\tmatchups.append(fantasyTeams[team].matchup[week])\\n\\t\\t\\tmatchupsNicknames.append(fantasyTeams[fantasyTeams[team].matchup[week]].nickname)\\n\\tteamsPoints = []\\n\\tfor team in matchups:\\n\\t\\tteamPoints = []\\n\\t\\tteamRoster = fantasyTeams[team].roster[week]\\n\\t\\t#print(team, len(teamRoster))\\n\\t\\tteamPoints.append(teamRoster[0].points[week]) # qb\\n\\t\\tteamPoints.append(teamRoster[1].points[week] + teamRoster[2].points[week] + teamRoster[3].points[week]) # wrs\\n\\t\\tteamPoints.append(teamRoster[4].points[week] + teamRoster[5].points[week]) # rbs\\n\\t\\tteamPoints.append(teamRoster[6].points[week]) # te\\n\\t\\tteamPoints.append(teamRoster[7].points[week]) # flx\\n\\t\\tif teamRoster[len(teamRoster)-1].position == 'DEF':\\n\\t\\t\\tteamPoints.append(teamRoster[len(teamRoster)-2].points[week]) # k\\n\\t\\t\\tteamPoints.append(teamRoster[len(teamRoster)-1].points[week]) # def\\n\\t\\telse: # if there's an IR spot\\n\\t\\t\\tteamPoints.append(teamRoster[len(teamRoster)-3].points[week]) # k\\n\\t\\t\\tteamPoints.append(teamRoster[len(teamRoster)-2].points[week]) # def\\n\\t\\tteamsPoints.append(teamPoints)\\n\\tfor i in range(len(teamsPoints)):\\n\\t\\tfor j in range(len(teamsPoints[0])):\\n\\t\\t\\tif teamsPoints[i][j]<-20:\\n\\t\\t\\t\\tteamsPoints[i][j]=-20\\n\\treturn matchups, matchupsNicknames, teamsPoints\",\n \"def merge_player_dfs(basic_stats_df, adv_stats_df, cols_to_strip=7):\\n return pd.merge(basic_stats_df, _strip_left_cols(adv_stats_df, cols_to_strip), left_index=True, right_index=True)\",\n \"def format_df(df, winner, match_id):\\n df['match_id'] = match_id\\n try:\\n df = df.set_index('hero_id')\\n except KeyError:\\n pass\\n df = df.rename(index=lambda x:\\n _hero_id_to_names.get(int(x),\\n str(x)))\\n df.index.set_names(['hero'], inplace=True)\\n\\n df['team'] = df['player_slot'].apply(DetailsResponse.rep_team)\\n df['win'] = df['team'] == winner\\n df = df.sort('team', ascending=False)\\n\\n df = df.reset_index().set_index(['match_id', 'team', 'hero']).sort_index()\\n return df\",\n \"def processed_overall(self):\\n self.processed_overall = (\\n self.combine_both_winning_losing_games_stats\\n .rename(columns={\\\"WTeamID\\\":\\\"TeamID\\\"})\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n [['Season','TeamID','win_rate','total_score','total_opponent_score','fgp','fg3p','ftp', 'total_rebounds','total_off_rebounds','total_def_rebounds',\\n 'total_off_rebounds_percent','total_def_rebounds_percent','total_rebound_possession_percent','total_rebound_possessiongain_percent','total_blocks',\\n 'total_assists','total_steals','total_turnover','total_personalfoul','total_block_opp_FGA_percent','total_assist_per_fgm','total_assist_turnover_ratio',\\n 'expectation_per_game','avg_lose_score_by','avg_win_score_by']]\\n )\",\n \"def create_match(team_id, current_matches, match_details, round_number, stats, targets):\\n\\n current_previous_matches = match_details.loc[\\n ((match_details['home_id'] == team_id) | (match_details['away_id'] == team_id)) &\\n (match_details['round'] < round_number)]\\n\\n # Only take the previous 3 matches and sum those stats together\\n previous_matches = current_previous_matches.iloc[-3:]\\n\\n # Find CUR_TEAM's stats\\n match_id, team_id, team_name, scheduled, is_home, total_points, goals_for, goals_against, goal_diff, goal_efficiency, \\\\\\n played, win, loss, recent_wins, recent_losses, prev_opp, opp_id, points, goals, opp_goals, \\\\\\n current_formation, opp_formation, game_features = \\\\\\n calculate_stats(team_id, current_matches, previous_matches, stats, targets)\\n\\n # Calculate the OPPONENTS stats\\n if stats:\\n print('Current Opponent ID : {0}'.format(opp_id))\\n\\n # Find OPP_TEAM's stats\\n opp_previous_matches = match_details.loc[\\n ((match_details['home_id'] == opp_id) | (match_details['away_id'] == opp_id)) &\\n (match_details['round'] < round_number)]\\n\\n opp_previous_matches = opp_previous_matches.iloc[-3:]\\n\\n _, opp_team_id, opp_team_name, _, opp_is_home, opp_total_points, opp_goals_for, opp_goals_against, opp_goal_diff, opp_goal_efficiency, \\\\\\n opp_played, opp_win, opp_loss, opp_recent_wins, opp_recent_losses, opp_opp, _, _, _, _, _, _, opp_game_features = calculate_stats(opp_id, current_matches, opp_previous_matches, False, False)\\n\\n if stats:\\n print('Previous Opponents of Current Team : {0}'.format(prev_opp))\\n\\n prev_opp_won_total = 0\\n prev_opp_lost_total = 0\\n\\n for prev_opp_id in prev_opp:\\n prev_opp_previous_matches = match_details.loc[\\n ((match_details['home_id'] == prev_opp_id) | (match_details['away_id'] == prev_opp_id)) &\\n (match_details['round'] < round_number)]\\n\\n # Only take the previous 3 matches and sum those stats together\\n prev_opp_previous_matches = prev_opp_previous_matches.iloc[-3:]\\n\\n _, _, _, _, _, _, prev_opp_goals_for, prev_opp_goals_against, prev_opp_goal_diff, prev_opp_goal_efficiency, \\\\\\n prev_opp_played, prev_opp_win, prev_opp_loss, _, _, opp_prev_opp, _, prev_opp_points, prev_opp_goals, opp_prev_opp_goals, _, _, \\\\\\n prev_opp_game_features = calculate_stats(prev_opp_id, current_matches, prev_opp_previous_matches, False, False)\\n\\n prev_opp_won_total += prev_opp_win\\n prev_opp_lost_total += prev_opp_loss\\n\\n opp_opp_won_total = 0\\n opp_opp_lost_total = 0\\n\\n if stats:\\n print('Current Opponents of Opponent : {0}'.format(opp_opp))\\n\\n # Calculate OPPONENTS of the OPPONENTS stats\\n for opp_opp_id in opp_opp:\\n\\n opp_opp_previous_matches = match_details.loc[\\n ((match_details['home_id'] == opp_opp_id) | (match_details['away_id'] == opp_opp_id)) &\\n (match_details['round'] < round_number)]\\n\\n # Only take the previous 3 matches and sum those stats together\\n opp_opp_previous_matches = opp_opp_previous_matches.iloc[-3:]\\n\\n opp_opp_match_id, opp_opp_team_id, opp_opp_team_name, scheduled, opp_opp_is_home, opp_opp_total_points, opp_opp_goals_for, opp_opp_goals_against, opp_opp_goal_diff, opp_opp_goal_efficiency, \\\\\\n opp_opp_played, opp_opp_win, opp_opp_loss, opp_opp_recent_wins, opp_opp_recent_losses, opp_opp_opp, _, _, _, _, _, _, opp_opp_game_features = calculate_stats(opp_opp_id, current_matches, opp_opp_previous_matches, False, False)\\n opp_opp_won_total += opp_opp_win\\n opp_opp_lost_total += opp_opp_loss\\n\\n if stats:\\n print('Opponents of Previous Opponents : {0}'.format(opp_prev_opp))\\n\\n opp_prev_opp_won_total = 0\\n opp_prev_opp_lost_total = 0\\n\\n # Calculate OPPONENTS of the PREVIOUS OPPONENTS stats\\n for opp_prev_opp_id in opp_prev_opp:\\n\\n opp_prev_opp_previous_matches = match_details.loc[\\n ((match_details['home_id'] == opp_prev_opp_id) | (match_details['away_id'] == opp_prev_opp_id)) &\\n (match_details['round'] < round_number)]\\n\\n # Only take the previous 3 matches and sum those stats together\\n opp_prev_opp_previous_matches = opp_prev_opp_previous_matches.iloc[-3:]\\n\\n opp_prev_opp_match_id, opp_prev_opp_team_id, opp_prev_opp_team_name, scheduled, opp_prev_opp_is_home, opp_prev_opp_total_points, opp_prev_opp_goals_for, opp_prev_opp_goals_against, opp_prev_opp_goal_diff, opp_prev_opp_goal_efficiency, \\\\\\n opp_prev_opp_played, opp_prev_opp_win, opp_prev_opp_loss, opp_prev_opp_recent_wins, opp_prev_opp_recent_losses, _, _, _, _, _, _, _, opp_prev_opp_game_features = calculate_stats(\\n opp_prev_opp_id, current_matches, opp_prev_opp_previous_matches, False, False)\\n opp_prev_opp_won_total += opp_prev_opp_win\\n opp_prev_opp_lost_total += opp_prev_opp_loss\\n\\n if stats:\\n print('Opponents of Opponents Opponents : {0}'.format(opp_opp_opp))\\n\\n opp_opp_opp_won_total = 0\\n opp_opp_opp_lost_total = 0\\n\\n # Calculate OPPONENTS of the OPPONENTS' OPPONENTS' stats\\n for opp_opp_opp_id in opp_opp_opp:\\n opp_opp_opp_previous_matches = match_details.loc[\\n ((match_details['home_id'] == opp_opp_opp_id) | (match_details['away_id'] == opp_opp_opp_id)) &\\n (match_details['round'] < round_number)]\\n\\n opp_opp_opp_match_id, opp_opp_opp_team_id, opp_opp_opp_team_name, scheduled, opp_opp_opp_is_home, opp_opp_opp_total_points, opp_opp_opp_goals_for, opp_opp_opp_goals_against, opp_opp_opp_goal_diff, opp_opp_opp_goal_efficiency, \\\\\\n opp_opp_opp_played, opp_opp_opp_win, opp_opp_opp_loss, opp_opp_opp_recent_wins, opp_opp_opp_recent_losses, _, _, _, _, _, _, _, opp_opp_opp_game_features = calculate_stats(\\n opp_opp_opp_id, current_matches, opp_opp_opp_previous_matches, False, False)\\n opp_opp_opp_won_total += opp_opp_opp_win\\n opp_opp_opp_lost_total += opp_opp_opp_loss\\n\\n \\\"\\\"\\\" //////////////////////////////////////////////////////////////////////////////////////////////////// \\\"\\\"\\\"\\n \\\"\\\"\\\" Collected all the information from relevant matches. Now send through all what we have. \\\"\\\"\\\"\\n \\\"\\\"\\\" //////////////////////////////////////////////////////////////////////////////////////////////////// \\\"\\\"\\\"\\n # Only calculate SOS + RPI here since they include previous matches\\n current_record = np.divide(win, (win + loss))\\n opp_record = np.divide(opp_win, (opp_win + opp_loss))\\n prev_opp_record = np.divide(prev_opp_win, (prev_opp_win + prev_opp_loss))\\n opp_prev_opp_record = np.divide(opp_prev_opp_won_total, (opp_prev_opp_won_total + opp_prev_opp_lost_total))\\n sos = np.divide((2 * prev_opp_record) + opp_prev_opp_record, 3)\\n rpi = (current_record * .25) + (sos * .75)\\n\\n feature = {'match_id': match_id, 'team_id': team_id, 'team_name': team_name, 'opp_id': opp_team_id,\\n 'opp_name': opp_team_name, 'scheduled': scheduled, 'round': round_number, 'games_played': played,\\n 'is_home': is_home, 'current_formation': current_formation, 'current_record': current_record,\\n 'opp_record': opp_record, 'goals_for': goals_for, 'opp_goals_for': opp_goals_for,\\n 'goals_against': goals_against, 'opp_goals_against': opp_goals_against, 'rpi': rpi,\\n 'goals': goals, 'points': points}\\n\\n game_features = {'current_team': game_features, 'opp_team': opp_game_features }\\n\\n if stats:\\n print(\\\"//////////////////////////////////////////////////\\\")\\n\\n return feature, game_features\",\n \"def scraping_actual_team_players(team_abbreviation):\\n starting_point = domain + \\\"/teams/\\\"\\n teamurl = starting_point + team_abbreviation + \\\"/\\\"\\n team_id = \\\"div_\\\" + team_abbreviation\\n html = urlopen(teamurl)\\n bs = BeautifulSoup(html, 'html.parser')\\n actual_team_url = domain + str(bs.find(\\\"div\\\", {'id': team_id}).find(\\\"a\\\").get(\\\"href\\\"))\\n html = urlopen(actual_team_url)\\n bs = BeautifulSoup(html, 'html.parser')\\n players = bs.find(\\\"table\\\", {'id':'roster'}).findAll(\\\"td\\\", {\\\"data-stat\\\":\\\"player\\\"})\\n players_url = [player.find(\\\"a\\\").get(\\\"href\\\") for player in players]\\n team_players_list = []\\n for player_url in players_url:\\n time.sleep(3)\\n url = domain + player_url\\n html = urlopen(url)\\n bs = BeautifulSoup(html, 'html.parser')\\n print(player_url)\\n try:\\n tabla = pd.read_html(str(bs.find(\\\"div\\\", {'id':'all_per_poss'})).replace(\\\"<!--\\\", \\\"\\\"))[0] \\n tabla[\\\"Player\\\"] = bs.find(\\\"h1\\\", {\\\"itemprop\\\" : \\\"name\\\"}).text.strip()\\n indice = tabla[tabla[\\\"Season\\\"]==\\\"Career\\\"].index[0]\\n tabla = tabla[0:indice]\\n tabla = tabla.drop(axis= 1,columns = \\\"Unnamed: 29\\\")\\n #no me encuentra tablas para uno del college que es el darlina01\\n print(player_url)\\n team_players_list.append(tabla)\\n except:\\n pass\\n return pd.concat(team_players_list)\",\n \"def get_companies_and_people(team):\",\n \"def teams(self):\\n t = [e[0] for e in self.pick_set.all().values_list(\\\"team\\\")]\\n return Team.objects.filter(id__in=set(t))\",\n \"def gamePenaltyDf(plays_home_away):\\n \\n #Load the read plays data into a dataframe\\n playsdf = pd.io.json.json_normalize(plays_home_away[0])\\n #Also grab the tricodes\\n home_tricode = plays_home_away[1]\\n away_tricode = plays_home_away[2]\\n \\n #Define the columns I want from the data\\n relevant_columns = ['about.dateTime', 'about.eventId', 'about.goals.away', 'about.goals.home', 'about.period', 'about.periodTimeRemaining', 'result.penaltyMinutes', 'result.penaltySeverity', 'result.secondaryType', 'team.triCode']\\n #Confirm that there are actually penalty minutes in the game\\n if 'result.penaltyMinutes' in list(playsdf.columns):\\n #Subset down to only plays which resulted in non-nan penalty minutes\\n pdf = playsdf[playsdf['result.penaltyMinutes'].isna()==False]\\n pdf.reset_index(drop=True, inplace=True)\\n #Reduce the columns down to the relevant ones\\n relevantdf = pdf[relevant_columns].copy()\\n #Some manufactured columns\\n relevantdf['against.homeTeam'] = relevantdf['team.triCode'].apply(lambda code: int(code == home_tricode))\\n relevantdf['against.awayTeam'] = relevantdf['team.triCode'].apply(lambda code: int(code == away_tricode))\\n relevantdf['committed.playingAgainst'] = relevantdf['team.triCode'].apply(lambda code: away_tricode if code == home_tricode else home_tricode)\\n relevantdf['winning.teamPenalty'] = relevantdf.apply(lambda x: winningTeamPenalty(x), 1)\\n #Markov Chain type columns - recording the state of the game and most recent state\\n relevantdf['prev.against.home'] = relevantdf['against.homeTeam'].shift(1).fillna(0)\\n relevantdf['prev.against.away'] = relevantdf['against.awayTeam'].shift(1).fillna(0)\\n relevantdf['goal.diff'] = relevantdf['about.goals.home'] - relevantdf['about.goals.away']\\n relevantdf['penalty.diff'] = relevantdf['against.homeTeam'].cumsum() - relevantdf['against.awayTeam'].cumsum()\\n \\n return relevantdf\\n else:\\n return 0\",\n \"def get_list_team_scores(self):\\n scores = defaultdict(lambda: {\\n \\\"scored_xg\\\": [],\\n \\\"conceded_xg\\\": [],\\n \\\"home_adv\\\": 0,\\n \\\"expected_points\\\": 0\\n })\\n\\n for g in self.games:\\n scores[g.HomeTeam][\\\"scored_xg\\\"].append(g.FTHG)\\n scores[g.HomeTeam][\\\"conceded_xg\\\"].append(g.FTAG)\\n scores[g.AwayTeam][\\\"scored_xg\\\"].append(g.FTAG)\\n scores[g.AwayTeam][\\\"conceded_xg\\\"].append(g.FTHG)\\n\\n for team in scores.keys():\\n scores[team][\\\"expected_points\\\"] = (self.get_table(metric='points')[team] /\\n len(scores[team][\\\"scored_xg\\\"]))\\n\\n return scores\",\n \"def qualifiedteams(self):\\n for i in range(0, len(self.teams)):\\n for j in range(i + 1, len(self.teams)):\\n WorldCupMatch(self.teams[i], self.teams[j], True)\\n self.teams = sorted(self.teams, key=op.attrgetter('points', 'goaldifference', 'scored'))\\n self.first_qualified = self.teams[len(self.teams)-1]\\n self.second_qualified = self.teams[len(self.teams)-2]\",\n \"def get_single_game_team_data(game, grouped_shot_data, pp_sit_data):\\n game_stat_lines = list()\\n game_id = game['game_id']\\n home_id = game['home_id']\\n road_id = game['road_id']\\n game_type = get_game_type_from_season_type(game)\\n\\n home_stats_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_team_stats',\\n str(game['season']), str(game_type), \\\"%d_%d.json\\\" % (game_id, home_id))\\n road_stats_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_team_stats',\\n str(game['season']), str(game_type), \\\"%d_%d.json\\\" % (game_id, road_id))\\n\\n # loading raw team game stats (if available)\\n raw_stats = dict()\\n if os.path.isfile(home_stats_src_path):\\n raw_stats['home'] = json.loads(open(home_stats_src_path).read())\\n else:\\n raw_stats['home'] = dict()\\n if os.path.isfile(road_stats_src_path):\\n raw_stats['road'] = json.loads(open(road_stats_src_path).read())\\n else:\\n raw_stats['road'] = dict()\\n\\n # counting penalties per team\\n penalty_counts = get_penalty_counts(game)\\n\\n for key in ['home', 'road']:\\n opp_key = 'road' if key == 'home' else 'home'\\n game_stat_line = dict()\\n # basic game information\\n game_stat_line['game_date'] = game['date']\\n game_stat_line['weekday'] = game['weekday']\\n game_stat_line['season'] = game['season']\\n game_stat_line['season_type'] = game['season_type']\\n game_stat_line['round'] = game['round']\\n game_stat_line['game_id'] = game_id\\n game_stat_line['team_id'] = game[\\\"%s_id\\\" % key]\\n game_stat_line['team'] = game[\\\"%s_abbr\\\" % key]\\n game_stat_line['opp_team_id'] = game[\\\"%s_id\\\" % opp_key]\\n game_stat_line['opp_team'] = game[\\\"%s_abbr\\\" % opp_key]\\n # identifying team's and opposing team's division (if\\n # applicable for current season and season type)\\n if (game['season'], game['season_type']) in divisions:\\n current_divisions = divisions[game['season'], game['season_type']]\\n game_stat_line['division'] = current_divisions[game_stat_line['team']]\\n game_stat_line['opp_division'] = current_divisions[game_stat_line['opp_team']]\\n # TODO: reactivate when schedule game id is available again\\n # game_stat_line['schedule_game_id'] = game['schedule_game_id']\\n game_stat_line['arena'] = correct_name(game['arena'])\\n game_stat_line['attendance'] = game['attendance']\\n if game_stat_line['arena'] in capacities:\\n game_stat_line['capacity'] = capacities[game_stat_line['arena']]\\n else:\\n print(\\n \\\"\\\\t+ Unable to retrieve capacity \\\" +\\n \\\"for '%s'\\\" % game_stat_line['arena'])\\n game_stat_line['capacity'] = 0\\n # coaches and referees\\n if \\\"%s_coach\\\" % key in game:\\n game_stat_line['coach'] = correct_name(\\n game[\\\"%s_coach\\\" % key], game['date'])\\n if game_stat_line['coach'] not in coaches:\\n print(\\\"+ Unknown coach '%s'\\\" % game_stat_line['coach'])\\n else:\\n print(\\\"\\\\t+ No coach information found for %s in game %d\\\" % (\\n game_stat_line['team'], game_id))\\n game_stat_line['coach'] = correct_name(\\n \\\"%d_%s\\\" % (game_id, game_stat_line['team']))\\n print(\\\"\\\\t+ Adjusted to '%s'\\\" % game_stat_line['coach'])\\n if \\\"%s_coach\\\" % opp_key in game:\\n game_stat_line['opp_coach'] = correct_name(\\n game[\\\"%s_coach\\\" % opp_key], game['date'])\\n if game_stat_line['opp_coach'] not in coaches:\\n print(\\\"+ Unknown coach '%s'\\\" % game_stat_line['opp_coach'])\\n else:\\n print(\\n \\\"\\\\t+ No opposition coach information found \\\" +\\n \\\"for %s in game %d\\\" % (game_stat_line['opp_team'], game_id))\\n game_stat_line['opp_coach'] = correct_name(\\n \\\"%d_%s\\\" % (game_id, game_stat_line['opp_team']))\\n print(\\\"\\\\t+ Adjusted to '%s'\\\" % game_stat_line['opp_coach'])\\n game_stat_line['ref_1'] = correct_name(game['referee_1'])\\n game_stat_line['ref_2'] = correct_name(game['referee_2'])\\n game_stat_line['lma_1'] = correct_name(game['linesman_1'])\\n game_stat_line['lma_2'] = correct_name(game['linesman_2'])\\n # outcomes\\n game_stat_line['games_played'] = 1\\n game_stat_line['home_road'] = key\\n game_stat_line['score'] = game[\\\"%s_score\\\" % key]\\n game_stat_line['goals'] = game[\\\"%s_score\\\" % key]\\n game_stat_line['opp_score'] = game[\\\"%s_score\\\" % opp_key]\\n game_stat_line['opp_goals'] = game[\\\"%s_score\\\" % opp_key]\\n # optionally correcting game scores\\n if game_id in game_score_corrections:\\n for team_abbr in game_score_corrections[game_id]:\\n if game_stat_line['team'] == team_abbr:\\n game_stat_line['score'] = game_score_corrections[game_id][team_abbr]\\n if game_stat_line['opp_team'] == team_abbr:\\n game_stat_line['opp_score'] = game_score_corrections[game_id][team_abbr]\\n if game['shootout_game']:\\n game_stat_line['game_type'] = 'SO'\\n elif game['overtime_game']:\\n game_stat_line['game_type'] = 'OT'\\n else:\\n game_stat_line['game_type'] = ''\\n for gsl_key in ['w', 'rw', 'ow', 'sw', 'l', 'rl', 'ol', 'sl']:\\n game_stat_line[gsl_key] = 0\\n if game_stat_line['score'] > game_stat_line['opp_score']:\\n game_stat_line['w'] += 1\\n if game['shootout_game']:\\n game_stat_line['sw'] += 1\\n game_stat_line['goals'] -= 1\\n elif game['overtime_game']:\\n game_stat_line['ow'] += 1\\n else:\\n game_stat_line['rw'] += 1\\n else:\\n game_stat_line['l'] += 1\\n if game['shootout_game']:\\n game_stat_line['sl'] += 1\\n game_stat_line['opp_goals'] -= 1\\n elif game['overtime_game']:\\n game_stat_line['ol'] += 1\\n else:\\n game_stat_line['rl'] += 1\\n game_stat_line['points'] = (\\n game_stat_line['rw'] * 3 + game_stat_line['ow'] * 2 +\\n game_stat_line['sw'] * 2 + game_stat_line['sl'] * 1 +\\n game_stat_line['ol'] * 1)\\n # per-period goals\\n for period in [1, 2, 3]:\\n game_stat_line[\\\"goals_%d\\\" % period] = game[\\n \\\"%s_goals_%d\\\" % (key, period)]\\n game_stat_line[\\\"opp_goals_%d\\\" % period] = game[\\n \\\"%s_goals_%d\\\" % (opp_key, period)]\\n # empty-net and extra-attacker goals\\n game_stat_line['en_goals'] = game[\\\"%s_en_goals\\\" % key]\\n game_stat_line['ea_goals'] = game[\\\"%s_ea_goals\\\" % key]\\n game_stat_line['opp_en_goals'] = game[\\\"%s_en_goals\\\" % opp_key]\\n game_stat_line['opp_ea_goals'] = game[\\\"%s_ea_goals\\\" % opp_key]\\n # situation after 20 and 40 minutes respectively\\n for situation in [\\n 'tied20', 'lead20', 'trail20', 'tied40', 'lead40', 'trail40'\\n ]:\\n game_stat_line[situation] = False\\n if game_stat_line['goals_1'] == game_stat_line['opp_goals_1']:\\n game_stat_line['tied20'] = True\\n elif game_stat_line['goals_1'] > game_stat_line['opp_goals_1']:\\n game_stat_line['lead20'] = True\\n else:\\n game_stat_line['trail20'] = True\\n goals40 = game_stat_line['goals_1'] + game_stat_line['goals_2']\\n opp_goals40 = (\\n game_stat_line['opp_goals_1'] + game_stat_line['opp_goals_2'])\\n if goals40 == opp_goals40:\\n game_stat_line['tied40'] = True\\n elif goals40 > opp_goals40:\\n game_stat_line['lead40'] = True\\n else:\\n game_stat_line['trail40'] = True\\n # scored first?\\n if game['first_goal'] == game_stat_line['team']:\\n game_stat_line['scored_first'] = True\\n game_stat_line['trailed_first'] = False\\n elif game['first_goal'] == game_stat_line['opp_team']:\\n game_stat_line['scored_first'] = False\\n game_stat_line['trailed_first'] = True\\n # one-goal, two-goal, three-goal, four-goal-game?\\n for goal_game in ['one_goal', 'two_goal', 'three_goal', 'four_goal']:\\n game_stat_line[goal_game] = False\\n score_diff = abs(\\n (game_stat_line['score'] - game_stat_line['en_goals']) -\\n (game_stat_line['opp_score'] - game_stat_line['opp_en_goals']))\\n # in case the right amount of empty-net goals have been scored, we\\n # may end up with a score differential of zero, see game between STR\\n # and ING on Mar 3, 2019\\n if not score_diff:\\n game_stat_line['zero_goal'] = True\\n if score_diff == 1:\\n game_stat_line['one_goal'] = True\\n elif score_diff == 2:\\n game_stat_line['two_goal'] = True\\n elif score_diff == 3:\\n game_stat_line['three_goal'] = True\\n elif score_diff > 3:\\n game_stat_line['four_goal'] = True\\n\\n # retrieving score state time spans for current team\\n game_stat_line['time_played'] = game['time_played']\\n game_stat_line['tied'] = game['tied']\\n game_stat_line['tied_pctg'] = round(\\n game['tied'] / game['time_played'] * 100, 2)\\n if key == 'home':\\n game_stat_line['leading'] = game['home_leading']\\n game_stat_line['trailing'] = game['road_leading']\\n else:\\n game_stat_line['leading'] = game['road_leading']\\n game_stat_line['trailing'] = game['home_leading']\\n game_stat_line['leading_pctg'] = round(\\n game_stat_line['leading'] / game['time_played'] * 100, 2)\\n game_stat_line['trailing_pctg'] = round(\\n game_stat_line['trailing'] / game['time_played'] * 100, 2)\\n\\n # retrieving raw stats for team and opposing team\\n for category, raw_category in RAW_STATS_MAPPING:\\n game_stat_line[category] = raw_stats[key].get(raw_category, None)\\n game_stat_line[\\\"opp_%s\\\" % category] = raw_stats[opp_key].get(raw_category, None)\\n\\n # checking number of power play goals retrieved from team stats with those registered in event data\\n game_stat_line = check_pp_goals(game, key, opp_key, game_stat_line)\\n\\n # calculating shooting percentages\\n if game_stat_line['shots_on_goal']:\\n game_stat_line['shot_pctg'] = round(\\n game_stat_line['goals'] /\\n game_stat_line['shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['shot_pctg'] = None\\n if game_stat_line['opp_shots_on_goal']:\\n game_stat_line['opp_shot_pctg'] = round(\\n game_stat_line['opp_goals'] /\\n game_stat_line['opp_shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['opp_shot_pctg'] = None\\n # calculating save percentages\\n if game_stat_line['opp_shots_on_goal']:\\n game_stat_line['save_pctg'] = round(\\n 100 - game_stat_line['opp_goals'] /\\n game_stat_line['opp_shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['save_pctg'] = None\\n if game_stat_line['shots_on_goal']:\\n game_stat_line['opp_save_pctg'] = round(\\n 100 - game_stat_line['goals'] /\\n game_stat_line['shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['opp_save_pctg'] = None\\n # calculating pdo values\\n if (\\n game_stat_line['shot_pctg'] is not None and\\n game_stat_line['save_pctg'] is not None\\n ):\\n game_stat_line['pdo'] = round((\\n game_stat_line['shot_pctg'] +\\n game_stat_line['save_pctg']), 1)\\n game_stat_line['opp_pdo'] = round((\\n game_stat_line['opp_shot_pctg'] +\\n game_stat_line['opp_save_pctg']), 1)\\n # calculating power play percentages\\n if game_stat_line['pp_opps']:\\n game_stat_line['pp_pctg'] = round((\\n game_stat_line['pp_goals'] /\\n game_stat_line['pp_opps']) * 100., 1)\\n else:\\n game_stat_line['pp_pctg'] = 0\\n if game_stat_line['opp_pp_opps']:\\n game_stat_line['opp_pp_pctg'] = round((\\n game_stat_line['opp_pp_goals'] /\\n game_stat_line['opp_pp_opps']) * 100., 1)\\n else:\\n game_stat_line['opp_pp_pctg'] = 0\\n # calculating penalty killing percentages\\n if game_stat_line['sh_opps']:\\n game_stat_line['pk_pctg'] = round(\\n 100 - game_stat_line['opp_pp_goals'] /\\n game_stat_line['sh_opps'] * 100., 1)\\n else:\\n game_stat_line['pk_pctg'] = 0\\n if game_stat_line['opp_sh_opps']:\\n game_stat_line['opp_pk_pctg'] = round(\\n 100 - game_stat_line['pp_goals'] /\\n game_stat_line['opp_sh_opps'] * 100., 1)\\n else:\\n game_stat_line['opp_pk_pctg'] = 0\\n game_stat_line['ev_goals'] = (\\n game_stat_line['goals'] -\\n game_stat_line['pp_goals'] -\\n game_stat_line['sh_goals'])\\n game_stat_line['opp_ev_goals'] = (\\n game_stat_line['opp_goals'] -\\n game_stat_line['opp_pp_goals'] -\\n game_stat_line['opp_sh_goals'])\\n # faceoffs are treated separately since each of the team game stats\\n # datasets only contains the number of won faceoffs and sometimes this\\n # one is stored as a string (wtf?)\\n game_stat_line['faceoffs_won'] = int(\\n raw_stats[key].get('faceOffsWon', 0))\\n game_stat_line['faceoffs_lost'] = int(\\n raw_stats[opp_key].get('faceOffsWon', 0))\\n # calculating overall number of faceoffs and faceoff percentage\\n game_stat_line['faceoffs'] = (\\n game_stat_line['faceoffs_won'] + game_stat_line['faceoffs_lost'])\\n if game_stat_line['faceoffs']:\\n game_stat_line['faceoff_pctg'] = round(\\n game_stat_line['faceoffs_won'] /\\n game_stat_line['faceoffs'] * 100., 1)\\n else:\\n game_stat_line['faceoff_pctg'] = 0.\\n # best players\\n game_stat_line['best_plr_id'] = game.get(\\n \\\"%s_best_player_id\\\" % key, None)\\n game_stat_line['best_plr'] = game.get(\\\"%s_best_player\\\" % key, None)\\n game_stat_line['opp_best_plr_id'] = game.get(\\n \\\"%s_best_player_id\\\" % opp_key, None)\\n game_stat_line['opp_best_plr'] = game.get(\\n \\\"%s_best_player\\\" % opp_key, None)\\n # game-winning-goal\\n game_stat_line['gw_goal_team'] = game['gw_goal']\\n game_stat_line['gw_goal_player_id'] = game['gw_goal_player_id']\\n game_stat_line['gw_goal_first_name'] = game['gw_goal_first_name']\\n game_stat_line['gw_goal_last_name'] = game['gw_goal_last_name']\\n\\n shot_zones_to_retain = ['slot', 'left', 'right', 'blue_line']\\n shot_situations_to_retain = [\\n 'shots_ev', 'shots_5v5', 'shots_pp', 'shots_sh', 'shots_unblocked',\\n 'shots_unblocked_ev', 'shots_unblocked_5v5', 'shots_unblocked_pp',\\n 'shots_unblocked_sh', 'shots_on_goal_ev', 'shots_on_goal_5v5',\\n 'shots_on_goal_pp', 'shots_on_goal_sh', 'goals_5v5', 'hit_post']\\n\\n # retrieving shot data for current game and team\\n shot_data = grouped_shot_data.get(\\n (game_id, game_stat_line['team']), list())\\n for item in shot_data:\\n if item.startswith(tuple(shot_zones_to_retain)):\\n abbr_item = item\\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\\n abbr_item = abbr_item.replace(zone_key, replacement)\\n game_stat_line[abbr_item] = shot_data[item]\\n elif item in shot_situations_to_retain:\\n game_stat_line[item] = shot_data[item]\\n\\n # retrieving shots against data for current game and team\\n shot_against_data = grouped_shot_data.get(\\n (game_id, game_stat_line['opp_team']), list())\\n for item in shot_against_data:\\n if item.startswith(tuple(shot_zones_to_retain)):\\n abbr_item = item\\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\\n abbr_item = abbr_item.replace(zone_key, replacement)\\n game_stat_line[\\\"%s_a\\\" % abbr_item] = shot_against_data[item]\\n elif item in shot_situations_to_retain:\\n game_stat_line[\\\"opp_%s\\\" % item] = shot_against_data[item]\\n\\n try:\\n game_stat_line['ev_cf_pctg'] = round(\\n game_stat_line['shots_ev'] / (game_stat_line['shots_ev'] + game_stat_line['opp_shots_ev']) * 100, 2)\\n except KeyError:\\n print(\\\"\\\\t+Unable to calculate even strength shots for percentage\\\")\\n game_stat_line['ev_cf_pctg'] = None\\n\\n for penalty_duration in [2, 5, 10, 20]:\\n if penalty_counts[key] and penalty_duration in penalty_counts[key]:\\n game_stat_line[\\\"penalty_%d\\\" % penalty_duration] = (\\n penalty_counts[key][penalty_duration])\\n else:\\n game_stat_line[\\\"penalty_%d\\\" % penalty_duration] = 0\\n\\n game_stat_line['pp_5v4'] = pp_sit_data[key]['pp_sits']['5v4']\\n game_stat_line['pp_5v3'] = pp_sit_data[key]['pp_sits']['5v3']\\n game_stat_line['pp_4v3'] = pp_sit_data[key]['pp_sits']['4v3']\\n game_stat_line['ppg_5v4'] = pp_sit_data[key]['pp_goals']['5v4']\\n game_stat_line['ppg_5v3'] = pp_sit_data[key]['pp_goals']['5v3']\\n game_stat_line['ppg_4v3'] = pp_sit_data[key]['pp_goals']['4v3']\\n game_stat_line['opp_pp_5v4'] = pp_sit_data[opp_key]['pp_sits']['5v4']\\n game_stat_line['opp_pp_5v3'] = pp_sit_data[opp_key]['pp_sits']['5v3']\\n game_stat_line['opp_pp_4v3'] = pp_sit_data[opp_key]['pp_sits']['4v3']\\n game_stat_line['opp_ppg_5v4'] = pp_sit_data[opp_key]['pp_goals']['5v4']\\n game_stat_line['opp_ppg_5v3'] = pp_sit_data[opp_key]['pp_goals']['5v3']\\n game_stat_line['opp_ppg_4v3'] = pp_sit_data[opp_key]['pp_goals']['4v3']\\n\\n # opp_diff = game_stat_line['pp_opps'] - (\\n # game_stat_line['pp_5v4'] +\\n # game_stat_line['pp_5v3'] +\\n # game_stat_line['pp_4v3']\\n # )\\n # if opp_diff:\\n # print(\\\"\\\\tpp opp discrepancy of %d for %s\\\" % (opp_diff, key))\\n\\n # registering shootout stats (if applicable)\\n shootout_stats = get_shootout_stats(game, key, opp_key)\\n if shootout_stats:\\n game_stat_line = {**game_stat_line, **shootout_stats}\\n\\n game_stat_lines.append(game_stat_line)\\n\\n return game_stat_lines\",\n \"def main(simulations, userschoice):\\n # The teams data are obtained from FIFA statistics\\n # Team Name, Attack, Defence\\n quarters = ['quarter1', 'quarter2', 'quarter3', 'quarter4', 'quarter5', 'quarter6', 'quarter7', 'quarter8']\\n semifinalists = ['semifinalist1', 'semifinalist2', 'semifinalist3', 'semifinalist4']\\n finalists = ['finalist1', 'finalist2']\\n\\n df = pd.read_csv('FifaRankings.csv', index_col=\\\"Ranking\\\")\\n a_set = set()\\n while True:\\n a_set.add(randint(42, 85))\\n if len(a_set) == 32:\\n break\\n lst1 = sorted(list(a_set), reverse=True)\\n\\n a_set = set()\\n while True:\\n a_set.add(randint(38, 83))\\n if len(a_set) == 32:\\n break\\n lst2 = sorted(list(a_set), reverse=True)\\n print(\\\"\\\\n\\\")\\n df['Attack'] = lst1\\n df['Defence'] = lst2\\n a = list(df[\\\"Team\\\"])\\n\\n avgScored = 0\\n avgConceded = 0\\n avgScored = df['Attack'].sum()\\n avgConceded = df['Defence'].sum()\\n\\n avgScored = avgScored / len(df)\\n avgConceded = avgConceded / len(df)\\n print(\\\"\\\\n\\\")\\n avgattack = []\\n avgdefense = []\\n\\n for i in range(1, 33):\\n if df['Matches Played'][i] != 0:\\n win_rate = (df['WorldCup Wins'][i] / df['Matches Played'][i])\\n else:\\n win_rate = 0\\n avgattack.append((df['Attack'][i] / avgScored) + win_rate)\\n avgdefense.append((df['Defence'][i] / avgConceded) + win_rate)\\n\\n df['Avg Attack'] = avgattack\\n df['Avg Defense'] = avgdefense\\n\\n\\n teamstats=[]\\n for i in range(1,len(df)+1):\\n teaminfo=[]\\n teaminfo = (df[\\\"Team\\\"][i], df['Avg Attack'][i], df['Avg Defense'][i])\\n teaminfo=list(teaminfo)\\n teamstats.append(teaminfo)\\n\\n germany = WorldCupTeam(\\\"GERMANY\\\", teamstats)\\n brazil = WorldCupTeam(\\\"BRAZIL\\\", teamstats)\\n belgium = WorldCupTeam(\\\"BELGIUM\\\", teamstats)\\n portugal = WorldCupTeam(\\\"PORTUGAL\\\", teamstats)\\n argentina = WorldCupTeam(\\\"ARGENTINA\\\", teamstats)\\n france = WorldCupTeam(\\\"FRANCE\\\", teamstats)\\n switzerland = WorldCupTeam(\\\"SWITZERLAND\\\", teamstats)\\n spain = WorldCupTeam(\\\"SPAIN\\\", teamstats)\\n russia = WorldCupTeam(\\\"RUSSIA\\\", teamstats)\\n japan = WorldCupTeam(\\\"JAPAN\\\", teamstats)\\n polland=WorldCupTeam(\\\"POLLAND\\\", teamstats)\\n korea_republic = WorldCupTeam(\\\"KOREA REPUBLIC\\\", teamstats)\\n england = WorldCupTeam(\\\"ENGLAND\\\", teamstats)\\n denmark= WorldCupTeam(\\\"DENMARK\\\", teamstats)\\n peru= WorldCupTeam(\\\"PERU\\\", teamstats)\\n tunisia=WorldCupTeam(\\\"TUNISIA\\\", teamstats)\\n mexico = WorldCupTeam(\\\"MEXICO\\\", teamstats)\\n colombia = WorldCupTeam(\\\"COLOMBIA\\\", teamstats)\\n uruguay = WorldCupTeam(\\\"URUGUAY\\\", teamstats)\\n croatia = WorldCupTeam(\\\"CROATIA\\\", teamstats)\\n australia = WorldCupTeam(\\\"AUSTRALIA\\\", teamstats)\\n iceland=WorldCupTeam(\\\"ICELAND\\\", teamstats)\\n sweden=WorldCupTeam(\\\"SWEDEN\\\", teamstats)\\n costa_rica = WorldCupTeam(\\\"COSTA RICA\\\", teamstats)\\n senegal=WorldCupTeam(\\\"SENEGAL\\\", teamstats)\\n serbia=WorldCupTeam(\\\"SERBIA\\\", teamstats)\\n morrocco=WorldCupTeam(\\\"MORROCCO\\\", teamstats)\\n egypt=WorldCupTeam(\\\"EGYPT\\\", teamstats)\\n nigeria = WorldCupTeam(\\\"NIGERIA\\\", teamstats)\\n saudi_arabia=WorldCupTeam(\\\"SAUDI ARABIA\\\", teamstats)\\n panama=WorldCupTeam(\\\"PANAMA\\\", teamstats)\\n iran = WorldCupTeam(\\\"IRAN\\\", teamstats)\\n\\n\\n #INPUT USERS CHOICE FOR FIXED CHOICE\\n choices= [\\\"random\\\", \\\"Random\\\", \\\"RANDOM\\\"]\\n choicess = [\\\"fixed\\\", \\\"Fixed\\\", \\\"FIXED\\\"]\\n if userschoice in choices:\\n countries = [germany, brazil, belgium, portugal, argentina, france, switzerland, spain, russia, japan, polland,\\n korea_republic, england, denmark, peru, tunisia, mexico, colombia, uruguay, croatia, australia,\\n iceland, sweden, costa_rica, senegal, serbia, morrocco, egypt, nigeria, saudi_arabia, panama, iran]\\n finalresults = {}\\n\\n GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH = ([] for i in range(8))\\n\\n Groups = [GroupA, GroupB, GroupC, GroupD, GroupE, GroupF, GroupG, GroupH]\\n for i in Groups:\\n for j in range(4):\\n teamname = choice(countries)\\n i.append(teamname)\\n countries.remove(teamname)\\n\\n print(\\\"DRAWS for the WorldCup 2018 are:\\\")\\n print(\\\"\\\\n\\\")\\n for i in range(simulations):\\n # Play first stage\\n print(\\\"Result of\\\", i + 1, \\\"simulations\\\")\\n print(\\\"--------------------------------------------\\\")\\n print(\\\"This is GROUP STAGE\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP A RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupA = TeamPool(Groups[0])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP B RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupB = TeamPool(Groups[1])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP C RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupC = TeamPool(Groups[2])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP D RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupD = TeamPool(Groups[3])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP E RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupE = TeamPool(Groups[4])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP F RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupF = TeamPool(Groups[5])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP G RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupG = TeamPool(Groups[6])\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP H RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupH = TeamPool(Groups[7])\\n\\n # Play second stage\\n print(\\\"\\\\n\\\")\\n print(\\\"ROUND OF 16\\\")\\n print(\\\"\\\\n\\\")\\n r16 = [groupA.first_qualified, groupA.second_qualified, groupB.first_qualified, groupB.second_qualified,\\n groupC.first_qualified, groupC.second_qualified, groupD.first_qualified, groupD.second_qualified,\\n groupE.first_qualified, groupE.second_qualified, groupF.first_qualified, groupF.second_qualified,\\n groupG.first_qualified, groupG.second_qualified, groupH.first_qualified, groupH.second_qualified]\\n\\n\\n GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW =([] for i in range(8))\\n\\n round16groups = [GroupP, GroupQ, GroupR, GroupS, GroupT, GroupU, GroupV, GroupW]\\n\\n for k in round16groups:\\n for j in range(2):\\n teamname = choice(r16)\\n k.append(teamname)\\n r16.remove(teamname)\\n\\n for i in range(8):\\n quarters[i]=WorldCupMatch(round16groups[i][0], round16groups[i][1], False).winner\\n\\n # Quarters\\n print(\\\"\\\\n\\\")\\n print(\\\"QUARTER - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n quarterfinal = [quarters[0], quarters[1], quarters[2], quarters[3], quarters[4], quarters[5], quarters[6],\\n quarters[7]]\\n GroupA1, GroupB1, GroupC1, GroupD1 = ([] for i in range(4))\\n\\n quarterfinalgroups = [GroupA1, GroupB1, GroupC1, GroupD1]\\n\\n i = 0\\n for i in quarterfinalgroups:\\n for j in range(2):\\n teamname = choice(quarterfinal)\\n i.append(teamname)\\n quarterfinal.remove(teamname)\\n\\n for i in range(4):\\n semifinalists[i] = WorldCupMatch(quarterfinalgroups[i][0], quarterfinalgroups[i][1], False).winner\\n\\n # Semifinals\\n print(\\\"\\\\n\\\")\\n print(\\\"SEMI - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n\\n semifinal = [semifinalists[0], semifinalists[1], semifinalists[2], semifinalists[3]]\\n GroupP1, GroupQ1 = ([] for i in range(2))\\n semifinalgroups = [GroupP1, GroupQ1]\\n\\n i = 0\\n for i in semifinalgroups:\\n for j in range(2):\\n teamname = choice(semifinal)\\n i.append(teamname)\\n semifinal.remove(teamname)\\n\\n for i in range(2):\\n finalists[i] = WorldCupMatch(semifinalgroups[i][0], semifinalgroups[i][1], False).winner\\n # Finals\\n print(\\\"\\\\n\\\")\\n print(\\\"WORLD-CUP FINAL\\\")\\n print(\\\"\\\\n\\\")\\n winner = WorldCupMatch(finalists[0], finalists[1], False).winner\\n print(\\\"\\\\n\\\")\\n\\n if winner.name in finalresults:\\n finalresults[winner.name] += 1\\n else:\\n finalresults[winner.name] = 1\\n\\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\\n print(key + \\\": \\\" + str(finalresults[key] / simulations))\\n ro=(finalresults[key] / simulations) * 100\\n print(str(ro) + \\\"% chance of winning the worldcup\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"\\\\n\\\")\\n\\n\\n elif userschoice in choicess:\\n\\n print(\\\"\\\\n\\\")\\n finalresults = {}\\n groupA1 = [russia , saudi_arabia,egypt, uruguay]\\n groupB1 = [portugal, spain, morrocco, iran]\\n groupC1 = [france, australia, peru, denmark]\\n groupD1 = [argentina, iceland, croatia, nigeria]\\n groupE1 = [brazil, switzerland, costa_rica, serbia]\\n groupF1 = [germany, mexico, sweden, korea_republic]\\n groupG1 = [belgium, panama, tunisia, england]\\n groupH1 = [polland, senegal, colombia, japan]\\n print(\\\"\\\\n\\\")\\n for i in range(simulations):\\n # Play first stage\\n print(\\\"Result of\\\", i+1 ,\\\"simulations\\\")\\n print(\\\"--------------------------------------------\\\")\\n print(\\\"This is GROUP STAGE\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP A RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupA = TeamPool(groupA1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP B RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupB = TeamPool(groupB1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP C RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupC = TeamPool(groupC1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP D RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupD = TeamPool(groupD1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP E RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupE = TeamPool(groupE1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP F RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupF = TeamPool(groupF1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP G RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupG = TeamPool(groupG1)\\n print(\\\"\\\\n\\\")\\n print(\\\"GROUP H RESULTS\\\")\\n print(\\\"\\\\n\\\")\\n groupH = TeamPool(groupH1)\\n print(\\\"Qualifies teams:\\\", groupH.first_qualified.name)\\n\\n # Play second stage\\n print(\\\"\\\\n\\\")\\n print(\\\"ROUND OF 16\\\")\\n print(\\\"\\\\n\\\")\\n\\n quarter1 = WorldCupMatch(groupA.first_qualified, groupA.second_qualified, False).winner\\n quarter2 = WorldCupMatch(groupB.first_qualified, groupB.second_qualified, False).winner\\n quarter3 = WorldCupMatch(groupC.first_qualified, groupC.second_qualified, False).winner\\n quarter4 = WorldCupMatch(groupD.first_qualified, groupD.second_qualified, False).winner\\n quarter5 = WorldCupMatch(groupE.first_qualified, groupE.second_qualified, False).winner\\n quarter6 = WorldCupMatch(groupF.first_qualified, groupF.second_qualified, False).winner\\n quarter7 = WorldCupMatch(groupG.first_qualified, groupG.second_qualified, False).winner\\n quarter8 = WorldCupMatch(groupH.first_qualified, groupH.second_qualified, False).winner\\n\\n # Quarters\\n print(\\\"\\\\n\\\")\\n print(\\\"QUARTER - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n\\n semifinalist1 = WorldCupMatch(quarter1, quarter2, False).winner\\n semifinalist2 = WorldCupMatch(quarter3, quarter4, False).winner\\n semifinalist3 = WorldCupMatch(quarter5, quarter6, False).winner\\n semifinalist4 = WorldCupMatch( quarter7, quarter8, False).winner\\n\\n # Semifinals\\n print(\\\"\\\\n\\\")\\n print(\\\"SEMI - FINALS\\\")\\n print(\\\"\\\\n\\\")\\n finalist1 = WorldCupMatch(semifinalist1, semifinalist2, False).winner\\n finalist2 = WorldCupMatch(semifinalist3, semifinalist4, False).winner\\n\\n # Final\\n print(\\\"\\\\n\\\")\\n print(\\\"WORLD-CUP FINAL\\\")\\n print(\\\"\\\\n\\\")\\n winner = WorldCupMatch(finalist1, finalist2, False).winner\\n print(\\\"\\\\n\\\")\\n\\n\\n if winner.name in finalresults:\\n finalresults[winner.name] += 1\\n else:\\n finalresults[winner.name] = 1\\n\\n for key in sorted(finalresults, key=finalresults.get, reverse=True):\\n print(key + \\\": \\\" + str(finalresults[key] / simulations))\\n rou = (finalresults[key] / simulations) * 100\\n print(str(rou) + \\\"% chance of winning the worldcup\\\")\\n print(\\\"\\\\n\\\")\\n print(\\\"\\\\n\\\")\\n else:\\n print(\\\"Please enter correct input and try again\\\")\\n pass\",\n \"def players(self):\\n if self.players_cache is None:\\n team_df = self.teams()\\n self.players_cache = self.ea.players_endpoint(\\n team_df[\\\"id\\\"].tolist())\\n\\n columns = [\\\"teamId\\\", \\\"playerId\\\", \\\"name\\\", \\\"position\\\"]\\n all_players = []\\n for team in self.players_cache[\\\"teams\\\"]:\\n team_id = team[\\\"id\\\"]\\n for plyr in team[\\\"roster\\\"][\\\"roster\\\"]:\\n player_id = plyr[\\\"person\\\"][\\\"id\\\"]\\n player_name = plyr[\\\"person\\\"][\\\"fullName\\\"]\\n position = plyr[\\\"position\\\"][\\\"abbreviation\\\"]\\n all_players.append({columns[0]: team_id,\\n columns[1]: player_id,\\n columns[2]: player_name,\\n columns[3]: position})\\n return pd.DataFrame(data=all_players, columns=columns)\",\n \"def find_opponent(standings,odds):\\n\\n # simulate all games\\n for i in range(len(odds)):\\n play_game(odds.loc[i],standings)\\n\\n # update the points and GD tally\\n standings['P']=standings['W']*3 + standings['D']\\n standings['GD']=standings['F']-standings['A']\\n\\n # see if teams have equal amount of points, and award h2h_points for\\n # h2h results against those teams.\\n for group in \\\"ABCDEF\\\":\\n gelijk = standings.loc[standings['Group']==group][standings.loc[standings['Group']==group].duplicated(subset='P',keep=False)]\\n gelijk[\\\"h2h_points\\\"]=np.zeros(len(gelijk))\\n\\n for i in gelijk.index:\\n for team1 in gelijk.loc[i][\\\"h2h\\\"]:\\n for team2 in gelijk[\\\"TEAMS\\\"]:\\n if team1==team2:\\n standings.loc[i,\\\"h2h_points\\\"]+=1\\n\\n # sort the final standings\\n standings = standings.sort_values(by=['Group','P',\\\"h2h_points\\\",'GD','F','W'],ascending=[True,False,False,False,False,False])\\n\\n # determine third placed teams\\n standings = standings.reset_index()\\n third = standings.loc[[2,6,10,14,18,22]]\\n\\n # determine best number threes\\n third = third.sort_values(by=['P','GD','F','W'],ascending=False)\\n\\n groups_of_best_no_3 = \\\"\\\"\\n for i in third.head(4).Group:\\n groups_of_best_no_3+=i\\n groups_of_best_no_3 = ''.join(sorted(groups_of_best_no_3))\\n\\n # look up the opponent of the dutch team\\n a = third.loc[third.Group == opponent_matrix[groups_of_best_no_3]]['TEAMS']\\n\\n return a.reset_index().TEAMS[0]\",\n \"def scraping_league_stats():\\n url=\\\"https://www.basketball-reference.com/teams/\\\"\\n html = urlopen(url)\\n bs = BeautifulSoup(html, 'html.parser')\\n teams = bs.find(\\\"div\\\", {'id':'div_teams_active'}).findAll(\\\"a\\\")\\n teams_url = [team.get(\\\"href\\\") for team in teams]\\n team_players_list = []\\n for team in teams_url:\\n time.sleep(3)\\n teamurl = domain + team\\n print(teamurl) #fafaf\\n html = urlopen(teamurl)\\n bs = BeautifulSoup(html, 'html.parser')\\n div_team = \\\"div_\\\" + teamurl[-4:-1]\\n season = bs.find(\\\"div\\\", {'id':div_team}).find(\\\"a\\\").get(\\\"href\\\") #tengo que cambiarlo para que este guay\\n #esto tiene la URL de la ultima season\\n seasonurl = domain + season\\n print(seasonurl)\\n html = urlopen(seasonurl)\\n bs = BeautifulSoup(html, 'html.parser')\\n players = bs.find(\\\"table\\\", {'id':'roster'}).findAll(\\\"td\\\", {\\\"data-stat\\\":\\\"player\\\"})\\n player_url_list = [player.find(\\\"a\\\").get(\\\"href\\\") for player in players]\\n for player in player_url_list:\\n player_url = domain + player\\n time.sleep(3)\\n print(player_url)\\n html = urlopen(player_url)\\n bs = BeautifulSoup(html, 'html.parser')\\n try:\\n tabla = pd.read_html(str(bs.find(\\\"div\\\", {'id':'all_per_poss'})).replace(\\\"<!--\\\", \\\"\\\"))[0] \\n tabla[\\\"Player\\\"] = bs.find(\\\"h1\\\", {\\\"itemprop\\\" : \\\"name\\\"}).text.strip()\\n indice = tabla[tabla[\\\"Season\\\"]==\\\"Career\\\"].index[0]\\n tabla = tabla[0:indice]\\n tabla = tabla.drop(axis= 1,columns = \\\"Unnamed: 29\\\")\\n #no me encuentra tablas para uno del college que es el darlina01\\n print(player_url)\\n team_players_list.append(tabla)\\n except:\\n pass\\n return pd.concat(team_players_list)\",\n \"def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\\n battleData, htmlData = loadBattleData(league)\\n similarities = loadSims() \\n \\n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\\n if len(oppTeam) == 0:\\n picks = set([])\\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\\n picks.add(random.randint(0,len(battleData)-1))\\n\\n losers = []\\n loserDict = {}\\n for i in picks:\\n entry = battleData[i]\\n winner,loser = determineWinner(entry)\\n loserDict[str(loser)] = [winner]\\n losers.append( (loser,0) )\\n\\n #Given opponent team then find similar teams\\n else:\\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\\n\\n #create dictionary from losers team to the team that beat them.\\n loserDict = {}\\n sims = []\\n for d in battleData:\\n winner, loser = determineWinner(d)\\n\\n wTeam = teamToArray(winner,pokedex)\\n lTeam = np.array(teamToArray(loser, pokedex))\\n\\n score = 0\\n for oppNp in oppTeam:\\n score+= np.amax(lTeam*oppNp) \\n\\n if str(loser) in loserDict:\\n loserDict[str(loser)].append(winner)\\n else:\\n #new to dictonary\\n loserDict[str(loser)] = [winner]\\n\\n sims.append((loser, score))\\n\\n\\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\\n\\n cutoff = min(len(sims),NUMLOSINGTEAMS)\\n losers = sims[:cutoff]\\n\\n #Gather winners to losing teams\\n winnersComp = []\\n for loser,_ in losers:\\n for winner in loserDict[str(loser)]:\\n winnersComp.append(teamToArray(winner,pokedex))\\n \\n topScore = len(winnersComp)*6 #pkmn team size\\n\\n results = []\\n inverted_idx = {}\\n\\n existsSet = []\\n\\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\\n for i in range(len(curTeams)):\\n team = curTeams[i]\\n results.append((team,0))\\n sTeam = set(team)\\n if not (sTeam in existsSet):\\n existsSet.append(sTeam)\\n for pkm in team:\\n if pkm != EMPTY:\\n if pkm in inverted_idx:\\n inverted_idx[pkm].append(i)\\n else:\\n inverted_idx[pkm] = [i]\\n \\n #Giving the similiarity scores to the winners based off of the inverted index.\\n for pkm in inverted_idx:\\n for winner in winnersComp:\\n wArr = np.array(winner)\\n #tArr = getSimPokemon(pkm,similarities)\\n tArr = similarities[pkm]\\n \\n vals = wArr * tArr\\n\\n score = np.amax(vals)\\n\\n for i in inverted_idx[pkm]:\\n results[i] = (results[i][0],results[i][1]+(score/topScore))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n\\n if len(results) < NUMTEAMSRETURN:\\n if len(results) == 0:\\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\\n\\n else:\\n returnTeams = [result[0] for result in results]\\n teamScores = [result[1] for result in results]\\n else:\\n firstResult, firstScore = results[0]\\n returnTeams = [firstResult]\\n teamScores = [round(firstScore*100,1)]\\n returnSets = [set(firstResult)]\\n \\n i = 1\\n\\n #Loops through results and adds teams with the proper edit distance away.\\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\\n teamToConsider,teamToConsiderScore = results[i]\\n \\n considerSet = set(teamToConsider)\\n add = True\\n ##checks the edit distance of teams is above wanted\\n for team in returnSets:\\n if len(team.union(considerSet)) < len(team)+minDistWanted:\\n add = False\\n\\n ##If indeed above wanted levels then add\\n if add:\\n returnTeams.append(teamToConsider)\\n returnSets.append(considerSet)\\n teamScores.append(round(teamToConsiderScore*100,1))\\n \\n i+=1\\n\\n if i >= len(results):\\n i = 1\\n minDistWanted -= 1 \\n \\n winHtmls = []\\n if htmlData != None:\\n for team,_ in losers:\\n for winner in loserDict[str(team)]:\\n winHtmls.extend(htmlData[str(sorted(winner))])\\n \\n\\n return returnTeams, teamScores, winHtmls\",\n \"def get_players_table(games_table: pd.DataFrame) -> pd.DataFrame:\\n player_name_series = games_table[\\\"player_name\\\"].unique()\\n player_name_series = pd.Series(player_name_series)\\n # In case a player name includes digits, it should be removed\\n invalid_element_mask = player_name_series.str.contains(r\\\"[0-9]\\\")\\n player_name_series = player_name_series[~invalid_element_mask]\\n players_data = pd.DataFrame(data=player_name_series)\\n players_data = players_data.reset_index()\\n players_data.columns = [\\\"id\\\", \\\"player_name\\\"]\\n return players_data\",\n \"def hometeamify(t):\\n\\tt = t.copy()\\n\\t# Winner/loser based columns\\n\\thw, aw = t.hometeam == t.winner, t.awayteam == t.winner\\n\\tassert (hw == ~aw).all()\\n\\t# Suffix for keys = W for winner L for loser. Values are new names\\n\\tfor old, new in {'Pts': 'points', 'Yds': 'yards', 'TO': 'turn_overs'}.items():\\n\\t\\tt[new + '_home'] = (hw * t[old + 'W'] + aw * t[old + 'L'])\\n\\t\\tt[new + '_away'] = (aw * t[old + 'W'] + hw * t[old + 'L'])\\n\\t\\tdel t[old + 'L'], t[old + 'W']\\n\\tdel t['winner']\\n\\t# Favored-team based columns\\n\\tto_swap, to_keep = t.favored == t.awayteam, t.favored == t.hometeam\\n\\tassert (to_keep == ~to_swap).all()\\n\\tfor col in 'pinnacle_spread', 'betonline_spread', 'bookmaker_spread':\\n\\t\\tt[col] = -1 * to_swap * t[col] + to_keep * t[col]\\n\\tdel t['favored']\\n\\treturn t\",\n \"def fetch_top_games(twitch: Twitch, n: int = 100) -> pd.DataFrame:\\n top_games = fetch_twitch_data(twitch, 'get_top_games', first=n)\\n\\n return top_games\",\n \"def fetch_game_streams(twitch: Twitch,\\n game_id: str,\\n n: int = 100) -> pd.DataFrame:\\n kwargs = {'first': n, 'game_id': [game_id]}\\n game_streams = fetch_twitch_data(twitch, 'get_streams', **kwargs)\\n # get user id to dig more data\\n try:\\n total_user_ids = game_streams['user_id'].tolist()\\n user_ids_num = len(total_user_ids)\\n ephoch = user_ids_num // 100\\n if user_ids_num % 100 != 0:\\n ephoch += 1\\n except:\\n print('game_streams')\\n cprint('Error: ' + game_id + ' data broken. Jump over it.', 'red')\\n return None\\n else:\\n total_users_data = pd.DataFrame(\\n columns=['broadcaster_type', 'description', 'type'])\\n for i in range(ephoch):\\n user_ids = total_user_ids[i * 100:i * 100 + 100]\\n users_data = twitch.get_users(user_ids=user_ids)\\n users_data = turn_into_df(users_data)\\n # select needed columns\\n users_data = users_data[[\\n 'broadcaster_type', 'description', 'type'\\n ]]\\n total_users_data = total_users_data.append(users_data,\\n ignore_index=True)\\n\\n total_users_data.reset_index(drop=True, inplace=True)\\n game_streams.reset_index(drop=True, inplace=True)\\n game_streams = pd.concat([game_streams, total_users_data], axis=1)\\n return game_streams\",\n \"def playerStandings():\\n cur4 = conn.cursor()\\n \\n # This playerStandings() should return in format (id,name,wins,matches) \\n query =\\\"\\\"\\\"SELECT id, name, sum(wincount) as wins, sum(lose_count)+sum(wincount) as total\\n from\\n (((\\n select p.id, p.name, count(winner) as wincount, '0' as lose_count\\n from players p left join matches on p.id=winner group by p.id, p.name order by count(winner) desc)\\n UNION\\n (select p.id, p.name, '0' as wincount, count(loser) as lose_count\\n from players p left join matches on p.id=loser group by p.id, p.name order by count(loser) desc\\n )))\\n as standings group by id, name order by wins desc, total asc;\\n \\\"\\\"\\\"\\n cur4.execute(query)\\n rows = cur4.fetchall()\\n\\n return rows\",\n \"def get_player_stats(df_players, url, headers):\\n\\tfor i, pid in enumerate(df_players['PERSON_ID']):\\n\\t\\tif i==0:\\n\\t\\t\\tdf_stats=get_player_career_reg_season_stats(pid, url, headers)\\n\\t\\telse:\\n\\t\\t\\tdf_stats=df_stats.append(\\n\\t\\t\\t\\tget_player_career_reg_season_stats(pid, url, headers)\\n\\t\\t\\t)\\n\\t\\tprint('i={} Added player stats for ID={}'.format(i, pid))\\n\\t\\ttime.sleep(2) # sleep so we don't get blocked\\n\\n\\treturn df_players.merge(df_stats, left_on=\\\"PERSON_ID\\\", right_on=\\\"PLAYER_ID\\\", how='left')\",\n \"def swissPairings():\\n #get all players, sort by number of wins.create matches with the 2 adjacent players\\n c.execute(\\\"\\\"\\\"SELECT id, playerName, num_wins\\n FROM (SELECT winner_id, count(match_id) as num_wins \\n FROM wins \\n group by winner_id \\n )as R1 right join tournament on R1.winner_id= tournament.id order by num_wins\\\"\\\"\\\")\\n result= c.fetchall()\\n for row in result:\\n print row\",\n \"def find_games(days_ahead=0):\\n headers = {\\n 'Host': 'stats.nba.com',\\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\\n 'Accept': 'application/json, text/plain, */*',\\n 'Accept-Language': 'en-US,en;q=0.5',\\n 'Referer': 'https://stats.nba.com/',\\n 'Accept-Encoding': 'gzip, deflate, br',\\n 'Connection': 'keep-alive',\\n 'x-nba-stats-origin': 'stats',\\n 'x-nba-stats-token': 'true'\\n }\\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\\n board.replace(id_to_abrv, inplace=True)\\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]\",\n \"def getAllTeams(self):\\n return []\",\n \"def get_team_metrics(file_prefix, game_period, teams, overall_dataframes, number_of_teams):\\n runs = overall_dataframes[0]['run'].unique()\\n\\n consolidated_result = []\\n\\n logger.info(\\\"Dataframes under analysis: \\\" + str(len(overall_dataframes)) + \\\". Number of runs: \\\" + str(\\n len(runs)) + \\\" Number of teams: \\\" + str(teams))\\n\\n for run in runs:\\n\\n team_results = {}\\n for team in range(teams):\\n\\n for index, overall_dataframe in enumerate(overall_dataframes):\\n\\n period_reports = overall_dataframe[overall_dataframe['period'] == game_period]\\n reports_in_run = period_reports[period_reports['run'] == run]\\n\\n team_run_reports = reports_in_run[reports_in_run['reporter_team'] == team]\\n if len(team_run_reports.index) > 0:\\n team_resolved = team_run_reports['reported_completed'].sum()\\n team_reported = team_run_reports['reported'].sum()\\n team_score = team_run_reports['payoff_score'].sum()\\n\\n team_results[team] = {\\\"team_resolved\\\": team_resolved,\\n \\\"team_reported\\\": team_reported,\\n \\\"team_score\\\": team_score}\\n\\n simulation_result = {\\\"run\\\": run}\\n\\n for team_index in range(number_of_teams):\\n team_prefix = \\\"team_\\\" + str(team_index + 1) + \\\"_\\\"\\n\\n simulation_result[team_prefix + \\\"results\\\"] = team_results[team_index]['team_resolved']\\n simulation_result[team_prefix + \\\"reports\\\"] = team_results[team_index]['team_reported']\\n simulation_result[team_prefix + \\\"score\\\"] = team_results[team_index]['team_score']\\n\\n consolidated_result.append(simulation_result)\\n\\n consolidated_dataframe = pd.DataFrame(consolidated_result)\\n consolidated_dataframe.to_csv(\\\"csv/\\\" + file_prefix + \\\"_consolidated_result.csv\\\", index=False)\\n\\n team_averages = []\\n\\n for team_index in range(number_of_teams):\\n score_column = \\\"team_\\\" + str(team_index + 1) + \\\"_score\\\"\\n\\n mean = consolidated_dataframe[score_column].mean()\\n team_averages.append(int(mean))\\n\\n # This is the procedure found -and validated- on Chapter 2 of Introduction to Discrete Event Simulation by\\n # Theodore Allen\\n sem = st.sem(consolidated_dataframe[score_column])\\n df = consolidated_dataframe[score_column].count() - 1\\n alpha = 0.95\\n\\n interval = st.t.interval(alpha=alpha, df=df, loc=mean, scale=sem)\\n logger.info(file_prefix + \\\": Confidence Interval Analysis for Team \\\" + str(team_index) + \\\" mean=\\\" + str(\\n mean) + \\\" sem=\\\" + str(sem) + \\\" df=\\\" + str(df) + \\\" alpha=\\\" + str(alpha) + \\\" interval=\\\" + str(interval))\\n\\n return [str(team_avg) for team_avg in team_averages]\",\n \"def getTeams(db):\\n #teamAo = firebase.get('https://decent-league.firebaseio.com/teamA', None)\\n teamAo = db.child(\\\"teamA\\\")\\n names, forms, descs = [], [], []\\n playersA = teamAo.child('players').get()\\n for user in playersA.each():\\n details = user.val()\\n names.append(details['name'])\\n forms.append(details['form'])\\n descs.append(details['desc'])\\n teamA = pd.concat([pd.Series(names), pd.Series(forms)], axis=1) \\n teamA.columns = ['name', 'form']\\n #teamBo = firebase.get('https://decent-league.firebaseio.com/teamB', None)\\n teamBo = db.child('teamB')\\n names, forms, descs = [], [], []\\n playersB = teamBo.child('players').get()\\n for user in playersB.each():\\n details = user.val()\\n names.append(details['name'])\\n forms.append(details['form'])\\n descs.append(details['desc'])\\n teamB = pd.concat([pd.Series(names), pd.Series(forms)], axis=1)\\n teamB.columns = ['name', 'form']\\n #print(teamB)\\n return teamA, teamB, teamAo, teamBo\",\n \"def compare_game_against_opponents(self, home, away, date):\\n match = get_match(self, home, away, date)\\n home_stats = self.team_stats[match.HomeTeam]\\n away_stats = self.team_stats[match.AwayTeam]\\n display_table(['Measure',\\n 'Average Per Game for',\\n 'This game for',\\n 'Average Per Game against opponents'],\\n [['{0} Shots'.format(home),\\n home_stats.average_stat('shots_for'),\\n shots_for_in_game(home, match),\\n away_stats.average_stat('shots_against')],\\n ['{0} Shots'.format(away),\\n away_stats.average_stat('shots_for'),\\n shots_for_in_game(away, match),\\n home_stats.average_stat('shots_against')],\\n\\n ['{0} SOT'.format(home),\\n home_stats.average_stat('sot_for'),\\n sot_for_in_game(home, match),\\n away_stats.average_stat('sot_against')],\\n ['{0} SOT'.format(away),\\n away_stats.average_stat('sot_for'),\\n sot_for_in_game(away, match),\\n home_stats.average_stat('sot_against')]])\",\n \"def possessions_home_away(data_frame, mode):\\n total_games_dict, total_dict, percentage_dict = dict(), dict(), dict()\\n team_name = 'home_team' if mode == 'home' else 'away_team'\\n for index, row in data_frame.iterrows():\\n if row[team_name] not in total_games_dict:\\n percentage_dict[row[\\\"id\\\"]] = 0\\n else:\\n percentage_dict[row[\\\"id\\\"]] = format(float(total_dict[row[team_name]]) / float(total_games_dict[row[team_name]]), '.2f')\\n\\n if row[team_name] in total_games_dict:\\n total_games_dict[row[team_name]] += 1\\n else:\\n total_games_dict[row[team_name]] = 1\\n\\n fga, orb, fta, to = 'fg_made_attempted_', 'offensive_rebounds_', 'ft_made_attempted_', 'turnovers_'\\n\\n possessions = int(row[fga + team_name.split('_')[0]].split('-')[1]) - int(row[orb + team_name.split('_')[0]]) + \\\\\\n (0.475 * int(row[fta + team_name.split('_')[0]].split('-')[1])) + int(row[to + team_name.split('_')[0]])\\n\\n if row[team_name] in total_dict:\\n total_dict[row[team_name]] += possessions\\n else:\\n total_dict[row[team_name]] = possessions\\n\\n return percentage_dict\",\n \"def compile_games(all_games):\\n compiled_games = {}\\n for game_data in all_games:\\n game_id = game_data[4]\\n team_data = extract_team_data(game_data)\\n home_team_abbrev = get_home_team(game_data[6])\\n if team_data[\\\"team_abbrev\\\"] == home_team_abbrev:\\n home_team = True\\n team_data = rename_features(team_data, \\\"home\\\")\\n else:\\n home_team = False\\n team_data = rename_features(team_data, \\\"away\\\")\\n\\n if game_id in compiled_games:\\n compiled_games[game_id] = {\\n **compiled_games[game_id],\\n **team_data\\n }\\n else:\\n if home_team:\\n winner = 1 if game_data[7] == \\\"W\\\" else 0\\n else:\\n winner = 0 if game_data[7] == \\\"W\\\" else 1\\n # game_date, win/loss, matchup all needed exactly once\\n compiled_games[game_id] = team_data\\n compiled_games[game_id][\\\"winner\\\"] = winner\\n compiled_games[game_id][\\\"game_date\\\"] = game_data[5]\\n compiled_games[game_id][\\\"game_id\\\"] = game_id\\n compiled_games[game_id][\\\"matchup_name\\\"] = game_data[6]\\n\\n return compiled_games\",\n \"def toSeasonAggFormat(self):\\n df_a = self.toHomeAwayFormat().copy()\\n df_b = df_a.copy()\\n\\n for col in list(df_a.columns).copy():\\n if col[0] == \\\"H\\\":\\n df_a = df_a.rename(columns={col:(col[1:])})\\n\\n if col[0] == \\\"A\\\":\\n df_a = df_a.rename(columns={col:('Opp'+col[1:])})\\n\\n #df_a['Win'] = 1\\n\\n for col in list(df_b.columns).copy():\\n if col[0] == \\\"H\\\":\\n df_b = df_b.rename(columns={col:('Opp'+col[1:])})\\n\\n if col[0] == \\\"A\\\":\\n df_b = df_b.rename(columns={col:(col[1:])})\\n\\n df_c = df_a.append(df_b, sort=True)\\n\\n return df_c\",\n \"def getTeamStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\\\\r\\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\\\\r\\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n team_df = df[[\\\"TEAM_ID\\\",\\\"TEAM_NAME\\\",\\\"GP\\\",\\\"W\\\",\\\"L\\\",\\\"W_PCT\\\",\\\"MIN\\\",\\\"FGM\\\",\\r\\n \\\"FGA\\\",\\\"FG_PCT\\\",\\\"FG3M\\\",\\\"FG3A\\\",\\\"FG3_PCT\\\",\\\"FTM\\\",\\\"FTA\\\",\\\"FT_PCT\\\",\\r\\n \\\"OREB\\\",\\\"DREB\\\",\\\"REB\\\",\\\"AST\\\",\\\"TOV\\\",\\\"STL\\\",\\\"BLK\\\",\\\"BLKA\\\",\\\"PF\\\",\\r\\n \\\"PFD\\\",\\\"PTS\\\",\\\"PLUS_MINUS\\\"]]\\r\\n \\r\\n return team_df\",\n \"def touchdowns(df):\\n return (\\n df\\n .loc[df.play_type.isin(['run', 'pass']), :]\\n .assign(player=lambda x: np.where(pd.isna(x['receiver_player_name']), x['rusher_player_name'], x['receiver_player_name']))\\n .loc[:, ['play_id', 'player', 'play_type', 'yards_gained']]\\n .sort_values('yards_gained', ascending=False)\\n )\",\n \"def get_sim_winners(teamNames,league,pokedex):\\n bData, wHtml = loadBattleData(league)\\n similarities = loadSims() \\n\\n results = []\\n\\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\\n for d in bData:\\n winner, loser = determineWinner(d)\\n\\n score = 0\\n for pkm in myTeam:\\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\\n\\n results.append((winner,score))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n \\n cutoff = min(len(results),5)\\n\\n htmls = []\\n for win,score in results[:cutoff]:\\n htmls.extend(wHtml[str(sorted(win))])\\n\\n return htmls\",\n \"def rename_teams(teams, fpl, cols = ['team', 'opponent_team', 'team_a', 'team_h']):\\n fpl = fpl.copy()\\n for col in cols:\\n for i in np.arange(start = 0, stop = len(teams)):\\n id = teams['id'][i]\\n team_name = teams['name'][i]\\n fpl[col].loc[fpl[col] == id] = team_name\\n return fpl\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def game(hometeam, awayteam, week, year):\\n\\twith urlopen(spread_url(hometeam, awayteam, week, year)) as connection:\\n\\t\\tspreads_page = connection.read()\\n\\t# Note that infer_types is deprecated and won't work starting in Pandas 0.14\\n\\tLOG.debug('Getting game %s', (hometeam, awayteam, week, year))\\n\\tsp = read_html(io=spreads_page.decode('utf-8'),\\n\\t\\t\\t\\t\\t match=\\\"History\\\", attrs={'id': 'table-000'},\\n\\t\\t\\t\\t\\t infer_types=False, header=0,\\n\\t\\t\\t\\t\\t skiprows=[1, 2, 3])\\n\\tif len(sp) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tsp = sp.pop()\\n\\n\\t# Get the over-under page\\n\\tou = read_html(io=over_under_url(hometeam, awayteam, week, year),\\n\\t\\t\\t\\t match=\\\"History\\\", attrs={'cellspacing': 0},\\n\\t\\t\\t\\t infer_types=False, header=0,\\n\\t\\t\\t\\t skiprows=[1, 2, 3])\\n\\tif len(ou) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tou = ou.pop()\\n\\n\\t# Cleaning.\\n\\tfor t, name, date_col in (sp, 'spread', 'Unnamed: 0'), (ou, 'over_under', '\\\\xa0'):\\n\\t\\tdatetime = pd.to_datetime(\\n\\t\\t\\tt[date_col]\\n\\t\\t\\t.replace(r'(\\\\d\\\\d?/\\\\d\\\\d?)', r'\\\\1/%d' % year, regex=True)\\n\\t\\t\\t.replace(r'(01|02)/(\\\\d\\\\d?)/\\\\d{4}', r'\\\\1/\\\\2/%d' % (year + 1),\\n\\t\\t\\t\\t\\t regex=True))\\n\\t\\tdel t[date_col]\\n\\n\\t\\t# Replace all the '--' as missing so we can convert numbers to floats.\\n\\t\\tfor column in t.keys():\\n\\t\\t\\tt[column] = (t[column]\\n\\t\\t\\t\\t\\t\\t .replace('--', 'nan')\\n\\t\\t\\t\\t\\t\\t .replace('(Pick)', 0)\\n\\t\\t\\t\\t\\t\\t .apply(float))\\n\\n\\t\\t# Add datetime back in after the str-to-float conversion so we don't do\\n\\t\\t# it for the datetime.\\n\\t\\tt['datetime'] = datetime\\n\\n\\t\\t# Lowercase column names for ease of programming later\\n\\t\\tt.columns = [h.lower() for h in t.columns]\\n\\n\\t\\t# Give spreads/over-under their suffixes\\n\\t\\tfor col in 'pinnacle', 'betonline', 'bookmaker':\\n\\t\\t\\tt[col + '_' + name] = t[col]\\n\\t\\t\\tdel t[col]\\n\\n\\tdata = sp.merge(ou, on=['datetime'], how='outer')\\n\\tassert set(data.datetime) == (set(sp.datetime) | set(ou.datetime))\\n\\n\\t# Add this function's arguments to the table.\\n\\tdata['hometeam'] = hometeam\\n\\tdata['awayteam'] = awayteam\\n\\tdata['week'] = week\\n\\n\\t# Get favored team from the big \\\"Odds: Washington by 4,\\\" that shows up at the\\n\\t# top of the page.\\n\\tsoup = BeautifulSoup(spreads_page)\\n\\tsubheader = soup.find('p', attrs={'class': 'h1-sub'}).find('strong')\\n\\tm = _FAVORED_RE.search(subheader.contents[0])\\n\\tif m is None or not m.group('city'):\\n\\t\\traise ValueError(\\\"Couldn't figure out who was favored: %r\\\" %\\n\\t\\t\\t\\t\\t\\t (subheader.contents))\\n\\tcity = m.group('city').replace(' ', '-').replace('.', '').lower()\\n\\t# city will be something like 'san-francisco' after the transformations\\n\\t# above. Find what team that is by looking for the links to the teams that\\n\\t# are also in that subheader.\\n\\tfor link in subheader.findAll('a'):\\n\\t\\tlink = link['href']\\n\\t\\tif city in link:\\n\\t\\t\\tdata['favored'] = link.split('-')[-1]\\n\\t\\t\\tbreak\\n\\telse:\\n\\t\\traise ValueError(\\\"couldn't figure out who %s is\\\" % city)\\n\\n\\treturn data\",\n \"def get_games(date):\\n scoreboard = nba_py.Scoreboard(month=date.month,\\n day=date.day,\\n year=date.year)\\n line_score = scoreboard.line_score()\\n game_header = scoreboard.game_header()\\n\\n games = []\\n current_game = {}\\n game_sequence = 0\\n game_sequence_counter = 0\\n\\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\\n for i, value in enumerate(line_score):\\n if (value[\\\"GAME_SEQUENCE\\\"] != game_sequence):\\n game_sequence += 1\\n\\n current_game[\\\"GAME_ID\\\"] = value[\\\"GAME_ID\\\"]\\n home_team_id = game_header[game_sequence - 1][\\\"HOME_TEAM_ID\\\"]\\n\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n \\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter += 1\\n elif game_sequence_counter == 1:\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n\\n current_game[\\\"GAME_STATUS_TEXT\\\"] = game_header[game_sequence - 1][\\\"GAME_STATUS_TEXT\\\"]\\n if not game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]:\\n current_game[\\\"BROADCASTER\\\"] = \\\"\\\"\\n else:\\n current_game[\\\"BROADCASTER\\\"] = game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]\\n\\n games.append(current_game)\\n\\n current_game = {}\\n\\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter -= 1\\n\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return (games, east_standings, west_standings)\",\n \"def get_team_stats(players: list[Player]) -> dict[int]:\\n\\n team_stats = {}\\n\\n total_reaction = 0\\n total_mechanical_skill = 0\\n total_tactical_skill = 0\\n total_game_knowledge = 0\\n total_xp = 0\\n\\n for player in players:\\n total_reaction += player.reaction\\n total_mechanical_skill += player.mechanical_skill\\n total_tactical_skill += player.tactical_skill\\n total_game_knowledge += player.game_knowledge\\n total_xp += player.xp\\n\\n team_stats.update(\\n {\\\"reaction\\\": total_reaction,\\n \\\"mechanical_skill\\\": total_mechanical_skill,\\n \\\"tactical_skill\\\": total_tactical_skill,\\n \\\"game_knowledge\\\": total_game_knowledge,\\n \\\"xp\\\": total_xp})\\n\\n return team_stats\",\n \"def teams_to_SQL (self, cursor, gid, games):\\n\\n\\t\\tvalues = []\\n\\t\\tquery_with = \\\"WITH teams AS (\\\"\\n\\t\\tfor game in games.findall('.'):\\n\\t\\t\\tquery_with += \\\"SELECT ? AS id, ? AS abbreviation, ? AS file_code, ? AS city, ? AS name UNION SELECT ? AS id, ? AS abbreviation, ? AS file_code, ? AS city, ? AS name UNION \\\"\\n\\t\\t\\tvalues.append(game.attrib['away_team_id'])\\n\\t\\t\\tvalues.append(game.attrib['away_name_abbrev'])\\n\\t\\t\\tvalues.append(game.attrib['away_file_code'])\\n\\t\\t\\tvalues.append(game.attrib['away_team_city'])\\n\\t\\t\\tvalues.append(game.attrib['away_team_name'])\\n\\t\\t\\tvalues.append(game.attrib['home_team_id'])\\n\\t\\t\\tvalues.append(game.attrib['home_name_abbrev'])\\n\\t\\t\\tvalues.append(game.attrib['home_file_code'])\\n\\t\\t\\tvalues.append(game.attrib['home_team_city'])\\n\\t\\t\\tvalues.append(game.attrib['home_team_name'])\\n\\t\\tquery_with = query_with[:-7] + \\\")\\\"\\n\\n\\t\\tmerge = query_with + \\\"MERGE team AS t USING teams AS s ON t.id = s.id \\\"\\n\\t\\tmerge += \\\"WHEN NOT MATCHED BY TARGET THEN INSERT (id, abbreviation, file_code, city, name) VALUES (s.id, s.abbreviation, s.file_code, s.city, s.name) \\\"\\n\\t\\tmerge += \\\"WHEN MATCHED AND ((t.abbreviation IS NULL AND s.abbreviation IS NOT NULL) OR (t.file_code IS NULL AND s.file_code IS NOT NULL) OR (t.city IS NULL AND s.city IS NOT NULL) OR (t.name IS NULL AND s.name IS NOT NULL)) THEN UPDATE SET t.abbreviation = s.abbreviation, t.file_code = s.file_code, t.city = s.city, t.name = s.name;\\\"\\n\\t\\t\\n\\t\\tcursor.execute(merge, values)\",\n \"def own_games(self):\\r\\n return sorted(self.games + self.finals, key=lambda g: (g.datetime, g.pitch.rank))\",\n \"def evaluate_team(self, team, mode):\\n if mode == Config.RESTRICTIONS['mode']['training']:\\n point_population = self.point_population_\\n opponent_population = self.training_opponent_population()\\n elif mode == Config.RESTRICTIONS['mode']['validation']:\\n point_population = self.validation_point_population_\\n opponent_population = self.validation_opponent_population_\\n elif mode == Config.RESTRICTIONS['mode']['champion']:\\n point_population = self.champion_population()\\n opponent_population = self.champion_opponent_population()\\n else:\\n raise ValueError(\\\"Invalid mode\\\")\\n results = []\\n extra_metrics_opponents = defaultdict(list)\\n match_id = 0\\n\\n if len(point_population) == 0:\\n raise ValueError(\\\"Error: Nothing in point population. Probably the population size is too small.\\\")\\n if len(opponent_population) == 0:\\n raise ValueError(\\\"Error: Nothing in opponent population. Probably the population size is too small.\\\")\\n\\n if mode == Config.RESTRICTIONS['mode']['training']:\\n for point, opponent in zip(point_population, opponent_population):\\n match_id += 1\\n result = self._play_match(team, opponent, point, mode, match_id)\\n team.reset_registers()\\n extra_metrics_opponents[opponent.opponent_id].append(result)\\n team.results_per_points_[point.point_id_] = result\\n results.append(result)\\n if opponent.opponent_id == 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\\n extra_metrics_opponents[opponent.__repr__()].append(result)\\n \\n opponent_type = 'training_opponents'\\n team.fitness_ = numpy.mean(results)\\n else:\\n extra_metrics_points = self._initialize_extra_metrics_for_points()\\n for point, opponent in zip(point_population, opponent_population):\\n match_id += 1\\n result = self._play_match(team, opponent, point, mode, match_id)\\n team.reset_registers()\\n extra_metrics_opponents[opponent.opponent_id].append(result)\\n extra_metrics_points = self._update_extra_metrics_for_points(extra_metrics_points, point, result)\\n if mode == Config.RESTRICTIONS['mode']['validation']:\\n team.results_per_points_for_validation_[point.point_id_] = result\\n results.append(result)\\n elif mode == Config.RESTRICTIONS['mode']['champion']:\\n if opponent.opponent_id != 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\\n results.append(result)\\n else:\\n extra_metrics_opponents[opponent.__repr__()].append(result)\\n \\n opponent_type = 'opponents'\\n for key in extra_metrics_points:\\n for subkey in extra_metrics_points[key]:\\n extra_metrics_points[key][subkey] = round_value(numpy.mean(extra_metrics_points[key][subkey]))\\n team.extra_metrics_['points'] = extra_metrics_points\\n if mode == Config.RESTRICTIONS['mode']['validation']:\\n team.score_validation_ = round_value(numpy.mean(results))\\n else:\\n team.score_champion_ = round_value(numpy.mean(results))\\n\\n for key in extra_metrics_opponents:\\n extra_metrics_opponents[key] = round_value(numpy.mean(extra_metrics_opponents[key]))\\n team.extra_metrics_[opponent_type] = extra_metrics_opponents\",\n \"def get_baseball_data():\\n\\n baseball_data = [\\n {\\n 'team_name': 'Boston', 'wins': 103, 'losses': 47,\\n 'wins_in_last_ten': 7, 'streak': 'W2'\\n },\\n {\\n 'team_name': 'NY Yankees', 'wins': 91, 'losses': 58,\\n 'wins_in_last_ten': 4, 'streak': 'L2'\\n },\\n {\\n 'team_name': 'Tampa Bay', 'wins': 83, 'losses': 66,\\n 'wins_in_last_ten': 8, 'streak': 'W3'\\n },\\n {\\n 'team_name': 'Toronto', 'wins': 68, 'losses': 82,\\n 'wins_in_last_ten': 5, 'streak': 'W3'\\n },\\n {\\n 'team_name': 'Baltimore', 'wins': 43, 'losses': 107,\\n 'wins_in_last_ten': 2, 'streak': 'L1'\\n },\\n {\\n 'team_name': 'Cleveland', 'wins': 83, 'losses': 66,\\n 'wins_in_last_ten': 4, 'streak': 'L1'\\n },\\n {\\n 'team_name': 'Minnesota', 'wins': 69, 'losses': 81,\\n 'wins_in_last_ten': 5, 'streak': 'W2'\\n },\\n {\\n 'team_name': 'Detroit', 'wins': 61, 'losses': 89,\\n 'wins_in_last_ten': 4, 'streak': 'L1'\\n },\\n {\\n 'team_name': 'Chi White Sox', 'wins': 59, 'losses': 90,\\n 'wins_in_last_ten': 3, 'streak': 'L1'\\n },\\n {\\n 'team_name': 'Kansas City', 'wins': 52, 'losses': 98,\\n 'wins_in_last_ten': 6, 'streak': 'L2'\\n },\\n {\\n 'team_name': 'Houston', 'wins': 94, 'losses': 56,\\n 'wins_in_last_ten': 7, 'streak': 'L1'\\n },\\n {\\n 'team_name': 'Oakland', 'wins': 90, 'losses': 60,\\n 'wins_in_last_ten': 7, 'streak': 'L2'\\n },\\n {\\n 'team_name': 'Seattle', 'wins': 83, 'losses': 67,\\n 'wins_in_last_ten': 5, 'streak': 'W1'\\n },\\n {\\n 'team_name': 'LA Angels', 'wins': 74, 'losses': 76,\\n 'wins_in_last_ten': 6, 'streak': 'W1'\\n },\\n {\\n 'team_name': 'Texas', 'wins': 64, 'losses': 86,\\n 'wins_in_last_ten': 3, 'streak': 'L2'\\n }\\n ]\\n\\n return baseball_data\",\n \"def teams(teamid):\\n team_summary = team.TeamSummary(teamid)\\n team_summary_info = team_summary.info()\\n team_season_ranks = team_summary.season_ranks()\\n\\n team_common_roster = team.TeamCommonRoster(teamid)\\n roster = team_common_roster.roster()\\n coaches = team_common_roster.coaches()\\n\\n season = team_summary_info[0][\\\"SEASON_YEAR\\\"]\\n\\n team_game_log = team.TeamGameLogs(teamid,\\n season=season)\\n team_games = team_game_log.info()\\n\\n playoffs_teamgamelogs = team.TeamGameLogs(teamid,\\n season=season,\\n season_type=\\\"Playoffs\\\")\\n playoffs_team_games = playoffs_teamgamelogs.info()\\n\\n team_season = team.TeamSeasons(teamid)\\n team_season_info = team_season.info()\\n\\n for i in team_season_info:\\n if (i[\\\"YEAR\\\"] == season):\\n current_season_info = i\\n\\n return render_template(\\\"teams.html\\\",\\n title=team_summary_info[0][\\\"TEAM_CITY\\\"] + \\\" \\\" + team_summary_info[0][\\\"TEAM_NAME\\\"],\\n teamid=teamid,\\n team_summary_info=team_summary_info,\\n team_season_ranks=team_season_ranks,\\n season=season,\\n team_games=team_games,\\n playoffs_team_games=playoffs_team_games,\\n team_season=team_season_info,\\n roster=roster,\\n coaches=coaches,\\n current_season_info=current_season_info,\\n team_img=TEAM_ID_DATA)\",\n \"def breakingteam_set_competing(self):\\n return self.breakingteam_set.filter(break_rank__isnull=False)\",\n \"def review(self):\\n # Compile standings\\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\\n # Name a champion\\n self.champion = self._name_champion()\\n self.league.history.champions_timeline[self.year] = self.champion\\n print \\\"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\\\".format(\\n self.champion.team.name.upper(), self.year, self.league.name.upper()\\n )\\n # Compile league leaders\\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\\n # Have each team review its season, as well\\n for team_season in self.teams:\\n team_season.review()\\n # Send the league into the offseason\\n self.league.season = None\",\n \"def get_learning_data(self):\\n if self.teams is None:\\n self.build_teams()\\n\\n # now put together X and y for every match\\n return self.games.get_games(self.teams)\",\n \"def create_lineups(self):\\n\\t\\tnum_skaters = len(self.skaters_df.index)\\n\\t\\tnum_goalies = len(self.goalies_df.index)\\n\\t\\tteams = list(set(self.skaters_df['team'].values))\\n\\t\\tnum_teams = len(teams)\\n\\n\\t\\t#Create player position indicators so you know which position they are playing\\n\\t\\tpositions = {'C':[], 'W':[], 'D':[]}\\n\\t\\tfor pos in self.skaters_df.loc[:, 'pos']:\\n\\t\\t\\tfor key in positions:\\n\\t\\t\\t\\tpositions[key].append(1 if key in pos else 0)\\n\\t\\t\\n\\t\\t#Create player line indicators so you know which line by their team they are on\\n\\t\\tteam_lines = []\\n\\t\\tfor i, line in enumerate(self.skaters_df.loc[:, 'line']):\\n\\t\\t\\tplayer_line = []\\n\\t\\t\\tif int(line) == 1:\\n\\t\\t\\t\\tplayer_line.extend((1, 0, 0, 0))\\n\\t\\t\\telif int(line) == 2:\\n\\t\\t\\t\\tplayer_line.extend((0, 1, 0, 0))\\n\\t\\t\\telif int(line) == 3:\\n\\t\\t\\t\\tplayer_line.extend((0, 0, 1, 0))\\n\\t\\t\\telif int(line) == 4:\\n\\t\\t\\t\\tplayer_line.extend((0, 0, 0, 1))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tplayer_line.extend((0, 0, 0, 0))\\n\\t\\t\\tplayer_lines = []\\n\\t\\t\\tfor team in teams:\\n\\t\\t\\t\\tif self.skaters_df.loc[i, 'team'] == team:\\n\\t\\t\\t\\t\\tplayer_lines.extend(player_line)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tplayer_lines.extend((0, 0, 0, 0))\\n\\t\\t\\tteam_lines.append(player_lines)\\n\\t\\tnum_lines = len(team_lines[0])\\n\\t\\t\\n\\t\\t#NOTE: Maybe add PP line indicators\\n\\n\\t\\t#Create player team indicators so you know which team they are on\\n\\t\\tskaters_teams = []\\n\\t\\tfor player_team in self.skaters_df.loc[:, 'team']:\\n\\t\\t\\tskaters_teams.append([1 if player_team == team else 0 for team in teams])\\n\\n\\t\\t#Create goalie opponent indicators so you know who the goalie is opposing\\n\\t\\tgoalies_opponents = []\\n\\t\\tfor player_opp in self.skaters_df.loc[:, 'opp']:\\n\\t\\t\\tgoalies_opponents.append([1 if player_opp == team else 0 for team in self.goalies_df.loc[:, 'team']])\\n\\n\\t\\t#Generate the lineups\\n\\t\\tlineups = []\\n\\t\\tfor _ in tqdm(range(1, self.num_lineups+1)):\\n\\t\\t\\tlineup = self.type_1(lineups, positions, team_lines, skaters_teams, goalies_opponents, num_skaters, num_goalies, num_teams, num_lines)\\n\\t\\t\\tif lineup:\\n\\t\\t\\t\\tlineups.append(lineup)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t#Fill the lineups with player names\\n\\t\\tself.fill_lineups(lineups, positions, num_skaters, num_goalies)\",\n \"def sum_by_team(self,df):\\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\\n df_team = df.groupby(['Team']).sum()\\n #print(df_team[['PB', 'AP1', 'ST']])\\n \\n\\n #for a visual, barh of pro-bowls by drafting team\\n df_team1 = df_team['PB']\\n team_plot = df_team1.plot(kind=\\\"barh\\\", fontsize=4)\\n team_plot.set_xlabel(\\\"Pro bowl appearances\\\")\\n team_plot.set_title(\\\"PRO BOWL APPEARANCES, BY NFL DRAFTING TEAM, 2010-2020\\\")\\n plt.show()\\n \\n #return the new df(by team)\\n return df_team\",\n \"def get_teams(self):\\n url = 'teams'\\n result = self.get(url)\\n return result.get('teams', result)\",\n \"def calculate_stats(team_id, current_matches, prev_matches, stats, targets):\\n\\n # Features\\n current_formation = None\\n opp_formation = None\\n win = 0\\n loss = 0\\n recent_wins = 0\\n recent_losses = 0\\n prev_opp = []\\n current_opp = 0\\n total_goals = 0\\n goals_for = 0\\n goals_against = 0\\n goal_diff = 0\\n total_points = 0\\n count = 1\\n played = float(0)\\n goal_efficiency = 0\\n\\n # Game Features\\n game_features = {'possession': [], 'attacks': [], 'dangerous_attacks': [], 'yellow_cards': [],\\n 'corner_kicks': [], 'shots_on_target': [], 'shots_total': [], 'ball_safe': [],\\n 'goal_attempts': [], 'goal_attempts_allowed': [], 'saves': [], 'first_half_goals': [], 'sec_half_goals': [], 'goal_kicks': []}\\n\\n # Targets\\n points = 0\\n goals = 0\\n opp_goals = 0\\n\\n goals_home = 0\\n goals_away = 0\\n opp_goals_at_home = 0 # Opponent goals when CURRENT team is AT Home\\n opp_goals_at_away = 0 # Opponent goals when CURRENT team is Away\\n\\n total_games = len(prev_matches)\\n # Pulling Data for PREVIOUS Matches\\n for index, game in prev_matches.iterrows():\\n\\n # Home Wins and Road Losses are .8 while Road Wins and Home Losses are 1.2 / Draws remain the same\\n if team_id == game['home_id']:\\n\\n if game['home_points'] == 3:\\n win += .8\\n\\n elif game['home_points'] == 1:\\n win += .5\\n loss += .5\\n\\n else:\\n loss += 1.2\\n\\n total_goals += game['home_score']\\n goals_for += game['home_score']\\n goals_against += game['away_score']\\n goal_diff += game['home_score'] - game['away_score']\\n total_points += game['home_points']\\n\\n goals_home += game['home_points']\\n opp_goals_at_away += game['away_points']\\n\\n current_formation = game['home_formation']\\n opp_formation = game['away_formation']\\n\\n prev_opp.append(game['away_id'])\\n\\n game_features['possession'].append(game['home_possession'])\\n game_features['attacks'].append(game['home_attacks'])\\n game_features['dangerous_attacks'].append(game['home_dangerous_attacks'])\\n game_features['yellow_cards'].append(game['home_yellow_card'])\\n game_features['corner_kicks'].append(game['home_corner_kicks'])\\n game_features['shots_on_target'].append(game['home_shots_on_target'])\\n game_features['shots_total'].append(game['home_shots_total'])\\n game_features['ball_safe'].append(game['home_ball_safe'])\\n game_features['goal_attempts'].append(game['home_goal_attempts'])\\n game_features['goal_attempts_allowed'].append(game['away_goal_attempts'])\\n game_features['saves'].append(game['home_saves'])\\n game_features['first_half_goals'].append(game['home_first_half_score'])\\n game_features['sec_half_goals'].append(game['home_second_half_score'])\\n game_features['goal_kicks'].append(game['home_goal_kicks'])\\n\\n else:\\n team_name = game[\\\"away_team\\\"]\\n\\n if game['away_points'] == 3:\\n win += 1.2\\n\\n elif game['away_points'] == 1:\\n win += .5\\n loss += .5\\n\\n else:\\n loss += .8\\n\\n total_goals += game['away_score']\\n goals_for += game['away_score']\\n goals_against += game['home_score']\\n goal_diff += game['away_score'] - game['home_score']\\n total_points += game['away_points']\\n\\n goals_away += game['away_points']\\n opp_goals_at_home += game['home_points']\\n\\n current_formation = game['away_formation']\\n opp_formation = game['home_formation']\\n\\n prev_opp.append(game['home_id'])\\n\\n game_features['possession'].append(game['away_possession'])\\n game_features['attacks'].append(game['away_attacks'])\\n game_features['dangerous_attacks'].append(game['away_dangerous_attacks'])\\n game_features['yellow_cards'].append(game['away_yellow_card'])\\n game_features['corner_kicks'].append(game['away_corner_kicks'])\\n game_features['shots_on_target'].append(game['away_shots_on_target'])\\n game_features['shots_total'].append(game['away_shots_total'])\\n game_features['ball_safe'].append(game['away_ball_safe'])\\n game_features['goal_attempts'].append(game['away_goal_attempts'])\\n game_features['goal_attempts_allowed'].append(game['home_goal_attempts'])\\n game_features['saves'].append(game['away_saves'])\\n game_features['first_half_goals'].append(game['away_first_half_score'])\\n game_features['sec_half_goals'].append(game['away_second_half_score'])\\n game_features['goal_kicks'].append(game['away_goal_kicks'])\\n\\n played += 1\\n count += 1\\n\\n # Pulling the data for the CURRENT MATCH\\n for index, cur_game in current_matches.iterrows():\\n\\n match_id = cur_game[\\\"match_id\\\"]\\n scheduled = cur_game[\\\"scheduled\\\"]\\n if team_id == cur_game['home_id']:\\n is_home = True\\n team_name = cur_game[\\\"home_team\\\"]\\n current_opp = cur_game['away_id']\\n\\n if targets:\\n # Targets\\n points = cur_game['home_points']\\n goals = cur_game['home_score']\\n opp_goals = cur_game['away_score']\\n\\n else:\\n\\n is_home = False\\n team_name = cur_game[\\\"away_team\\\"]\\n current_opp = cur_game['home_id']\\n\\n if targets:\\n # Targets\\n points = cur_game['away_points']\\n goals = cur_game['away_score']\\n opp_goals = cur_game['home_score']\\n\\n for k, v in game_features.items():\\n game_features[k] = np.sum(np.array(v))\\n \\\"\\\"\\\"if len(v) != 0:\\n game_features[k] = np.nanmean(np.array(v))\\n else:\\n game_features[k] = np.nan\\\"\\\"\\\"\\n\\n\\n if stats:\\n print(\\\" ========================== \\\")\\n print(\\\"Team Id : {} - Name : {}\\\".format(team_id, team_name))\\n print(\\\"Prev Opponent Ids : {}\\\".format(prev_opp))\\n print(\\\"FEATURES (Stats from * 3 Previous Matches)\\\")\\n print(\\\"Total Goals : {}\\\".format(total_goals))\\n print(\\\"Total Points : {}\\\".format(total_points))\\n # print(\\\"Win Points : {}\\\".format(win))\\n # print(\\\"Loss Points : {}\\\".format(loss))\\n print(\\\"Played : {}\\\".format(played))\\n print(\\\"Goal Diff : {}\\\".format(goal_diff))\\n print(\\\"Margin : {}\\\".format(np.divide(goal_diff, played)))\\n print(\\\"\\\")\\n\\n if targets:\\n # Still can weight games more on most recent games\\n print(\\\"\\\\nTARGETS (RESULTS OF CURRENT MATCH)\\\")\\n print(\\\"Points : {}\\\".format(points))\\n print(\\\"Goals : {}\\\".format(goals))\\n print(\\\"Opp_Goals : {}\\\".format(opp_goals))\\n\\n return match_id, team_id, team_name, scheduled, int(is_home == True), total_points, \\\\\\n goals_for, goals_against, goal_diff, goal_efficiency, played, win, loss, recent_wins, recent_losses, prev_opp, \\\\\\n current_opp, points, goals, opp_goals, current_formation, opp_formation, game_features\",\n \"def get_teams_from_league(teams, league):\\n\\t\\n\\tteams_1 = []\\n\\tfor i in range(0,len(teams)):\\n\\t\\tif teams[i].league == 1:\\n\\t\\t\\tteams_1.append(teams[i])\\n\\n\\treturn teams_1\",\n \"def play_game(game,standings_):\\n rand_nmr = random.random()\\n\\n standings_.loc[standings_.TEAMS==game['Home'],'MP'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'MP'] += 1\\n\\n if rand_nmr < game['Prob Home']:\\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\\n standings_.loc[standings_.TEAMS==game['Home'],'W'] += 1\\n standings_.loc[standings_.TEAMS==game['Home'],'F'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Away'],'L'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'A'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Home']][\\\"h2h\\\"].apply(lambda x:x.append(game['Away']))\\n\\n return 0\\n\\n elif rand_nmr < game['Prob Home'] + game['Prob Draw']:\\n # all draws end in 0-0 this can be improved\\n standings_.loc[standings_.TEAMS==game['Home'],'D'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'D'] += 1\\n\\n return 1\\n\\n else:\\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\\n standings_.loc[standings_.TEAMS==game['Away'],'W'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'F'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Home'],'A'] += 1\\n standings_.loc[standings_.TEAMS==game['Home'],'L'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Away']][\\\"h2h\\\"].apply(lambda x:x.append(game['Home']))\\n\\n return 2\",\n \"def extract_team_data(game_data):\\n team_id = game_data[1]\\n team_abbrev = game_data[2]\\n team_name = game_data[3]\\n total_minutes_all_players = game_data[8]\\n field_goals_made = game_data[9]\\n field_goals_attempted = game_data[10]\\n three_ptr_made = game_data[12]\\n three_ptr_attempted = game_data[13]\\n free_throws_made = game_data[15]\\n free_throws_attempted = game_data[16]\\n offensive_rebounds = game_data[18]\\n defensive_rebounds = game_data[19]\\n rebounds = game_data[20]\\n assists = game_data[21]\\n steals = game_data[22]\\n blocks = game_data[23]\\n turnovers = game_data[24]\\n personal_fouls = game_data[25]\\n points = game_data[26]\\n plus_minus = game_data[27]\\n\\n final_dict = {\\n \\\"team_id\\\": team_id,\\n \\\"team_abbrev\\\": team_abbrev,\\n \\\"team_name\\\": team_name,\\n \\\"total_minutes_all_players\\\": total_minutes_all_players,\\n \\\"field_goals_made\\\": field_goals_made,\\n \\\"field_goals_attempted\\\": field_goals_attempted,\\n \\\"three_ptr_made\\\": three_ptr_made,\\n \\\"three_ptr_attempted\\\": three_ptr_attempted,\\n \\\"free_throws_made\\\": free_throws_made,\\n \\\"free_throws_attempted\\\": free_throws_attempted,\\n \\\"offensive_rebounds\\\": offensive_rebounds,\\n \\\"defensive_rebounds\\\": defensive_rebounds,\\n \\\"rebounds\\\": rebounds,\\n \\\"assists\\\": assists,\\n \\\"steals\\\": steals,\\n \\\"blocks\\\": blocks,\\n \\\"turnovers\\\": turnovers,\\n \\\"personal_fouls\\\": personal_fouls,\\n \\\"points\\\": points,\\n \\\"plus_minus\\\": plus_minus\\n }\\n return final_dict\",\n \"def fa_finder(league_no, team_name):\\n ros_proj_b_list = BatterProjection.objects.all()\\n ros_proj_p_list = PitcherProjection.objects.all()\\n player_comp = {}\\n pitching_fa_list = yahoo_players(league_no, \\\"P\\\")\\n batting_fa_list = yahoo_players(LEAGUE_NO, \\\"B\\\")\\n avail_pitching_fas = rate_avail_players(pitching_fa_list, ros_proj_p_list)\\n yahoo_team = get_single_yahoo_team(league_no, team_name)\\n team_pitching_values = rate_team(yahoo_team, ros_proj_p_list)\\n avail_batting_fas = rate_avail_players(batting_fa_list, ros_proj_b_list)\\n team_batting_values = rate_team(yahoo_team, ros_proj_b_list)\\n\\n player_comp['Team Name'] = yahoo_team['team_name']\\n player_comp['Pitching FAs'] = avail_pitching_fas\\n player_comp['Pitching Team'] = team_pitching_values\\n player_comp['Batting FAs'] = avail_batting_fas\\n player_comp['Batting Team'] = team_batting_values\\n\\n return player_comp\",\n \"def get_team_game_previews(self, team, dates):\\n abbr = convert_name(team, how='abbr')\\n return self._db.Games.find({'date' : {'$in' : dates},\\n '$or' : [{'home' : abbr},\\n {'away' : abbr}]})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72284454","0.7174533","0.6835919","0.65570146","0.65117526","0.64460844","0.6409053","0.62457347","0.6203352","0.6171146","0.6097149","0.60657734","0.6041406","0.6035894","0.60347044","0.6033802","0.60308903","0.60294217","0.59119236","0.59107935","0.5875944","0.5875147","0.5871516","0.5863404","0.5848215","0.58410895","0.58087164","0.58052707","0.5802783","0.57612103","0.57493293","0.5715657","0.5700155","0.5694803","0.5693757","0.56907415","0.56870824","0.56786585","0.5669064","0.5668537","0.5667535","0.56584734","0.560012","0.5574212","0.5552027","0.55432117","0.5538618","0.5499137","0.5497837","0.54800665","0.5474383","0.5443013","0.5441128","0.5430886","0.5410921","0.54099023","0.53950655","0.5389084","0.5383678","0.538227","0.538222","0.53770256","0.5374549","0.5370069","0.53586197","0.53489786","0.53376704","0.5337422","0.53261185","0.5324281","0.53205967","0.53195894","0.5314931","0.5313349","0.5312807","0.53115433","0.53100836","0.5289034","0.5271623","0.5259735","0.52555144","0.52524316","0.5242239","0.5233342","0.52288467","0.52268076","0.5221468","0.5217933","0.52093107","0.52079266","0.5194514","0.5194408","0.51920885","0.51862496","0.51858234","0.51856893","0.51830524","0.51795745","0.51701134","0.51599544"],"string":"[\n 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coach"},"document":{"kind":"string","value":"def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )"},"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 get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n # get losing games for coaches\n self.post_games_lose_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df for post season\n self.combine_post_games_won_lose = (\n self.post_games_lose_for_coaches\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"post_games_lost\",\"which_coach_for_win\":\"post_games_won\"})\n .fillna(0)\n )","def get_champion_winrate(self, summoner_id, champion_id):\n request = rq.get(\n 'https://{region}.api.pvp.net/api/lol/{region}/v{api_v}/stats/by-summoner/{summ_id}/ranked?season=SEASON{year}&api_key={api_key}'\n .format(\n region=self.region,\n api_v=api_version['stats'],\n summ_id=summoner_id,\n year=dt.today().year,\n api_key=API_KEY\n )\n )\n try:\n check_response(request)\n champions = request.json()['champions']\n if champions is not None:\n for champion in champions:\n if champion['id'] == champion_id:\n total_won = champion['stats']['totalSessionsWon']\n total = total_won + champion['stats']['totalSessionsLost']\n\n winrate = total_won / total\n return [winrate, total]\n return 0, 0\n except ServiceException:\n return 0, 0","def get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"count\"})\n .reset_index()\n .rename(columns={\"NumOT\":\"is_champion\",\"WTeamID\":\"TeamID\"})\n )","def calculateWinRate():\n times = 10\n winRate = 0.0\n for i in range(times):\n game = Game('user', 6, 6)\n winRate += game.play(5, False, True, False, False)\n winRate = winRate/times\n print \"Winrate:\", winRate","def _compute_winrates(synergy, counter, heroes_released):\n for i in range(heroes_released):\n for j in range(heroes_released):\n if i != j and i != 23 and j != 23:\n if synergy['games'][i, j] != 0:\n synergy['winrate'][i, j] = synergy['wins'][i, j] / \\\n float(synergy['games'][i, j])\n\n if counter['games'][i, j] != 0:\n counter['winrate'][i, j] = counter['wins'][i, j] / \\\n float(counter['games'][i, j])","def winning_games_stats(self):\n self.winning_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(winning_num_counts = 1))\n .query(\"Season <= 2013\")\n .groupby(['Season','WTeamID'])\n .agg({\"WScore\":\"sum\",\"WFGM\":\"sum\",\"WFGA\":\"sum\",\"WFGM3\":\"sum\",\"WFGA3\":\"sum\",\"WFTM\":\"sum\",\"WFTA\":\"sum\",\"LScore\":\"sum\",\"winning_num_counts\":\"sum\",\n \"WOR\":\"sum\",\"WDR\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\n \"WAst\":\"sum\",\"WTO\":\"sum\",\"WStl\":\"sum\",\"WBlk\":\"sum\",\"WPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"LScore\":\"losing_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"LFGA\":\"LFGA_opp\",\"LFGM\":\"LFGM_opp\"})\n )","def winrate(matches):\n if not matches:\n print('no matches')\n return None\n\n win_loss = [match['result'] for match in matches]\n return sum(win_loss)/len(win_loss)","def marcels_players(goalie, date, df):\n # 0 = that year, 1 is year b4 ....\n marcel_weights = [.36, .29, .21, .14]\n reg_const = 2000\n reg_avg = 0 # Where to regress to\n\n # Use past 3 season to weight games played -> Just take weighted average\n gp_weights = [8, 4, 2, 0]\n\n season = int(helpers.get_season(date))\n\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\n weighted_gp_sum, weights_gp_sum = 0, 0\n\n # Past 4 Seasons\n for i in range(0, 4):\n if season - i > 2006:\n # Subset from stats df\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\n\n # Sanity Check\n if df_goalie.shape[0] > 1:\n print(\"Too many rows!!!!!!!\")\n exit()\n\n # If he played that year\n if not df_goalie.empty:\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\n\n # -> To divide by at end...normalize everything\n weights_marcel_sum += marcel_weights[i]\n weights_gp_sum += gp_weights[i]\n\n # Normalize weighted sums\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n\n # Get Regressed fsv%\n if weighted_fen_sum != 0:\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\n else:\n weighted_adj_fsv = 0\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\n\n # Get weighted gp\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\n\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}","def get_final_coach_for_each_season(self):\n self.final_coach_for_season = (\n self.num_days_coach_for_season\n .groupby(['Season','TeamID'])\n .agg({\"CoachName\":\"count\"})\n .reset_index()\n .rename(columns={\"CoachName\":\"coach_counts\"})\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \"ignore\")))\n [['Season','TeamID','final_coach']]\n )","def cumulative_stats_for_teams_each_year(self):\n self.cumulative_stats_for_team_each_year = (\n self.combine_both_winning_losing_games_stats\n .sort_values(['WTeamID','Season'])\n .groupby(['WTeamID'])\n .cumsum()\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\n .drop(['LTeamID','win_rate'],1)\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n # rebounds cumsum stats\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # assists, turnovers, steals, blocks and personal fouls\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win or lose by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def get_player_stats_from_game(team, year, week):","def calc_winner(self):\n pass","def getSeasonStats(self):\n df_season_agg = self.toSeasonAggFormat()\n\n # Calculate Possessions for each game\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\n\n # Aggregate to Season Summary Level\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\n\n season_stats = season_stats.rename(columns={'Win':'wins'})\n\n # Season Advanced Stats\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\n\n season_stats.drop('DayNum', axis=1, inplace=True)\n season_stats.drop('OppTeamID', axis=1, inplace=True)\n season_stats.drop('rand', axis=1, inplace=True)\n\n return season_stats","def rate_club(user, club):\n if not user.is_authenticated():\n return None\n if not club.posel_set.exists():\n return None\n return sum(x[1] for x in rank_in_club(user, club)) / club.posel_set.count()","def getWinProbability(team1, team2, r, year=2019):\n\tR1_PROBS = R1_PROBS_2019 if year == 2019 else R1_PROBS_2020\n\tALPHA_VALS = ALPHA_VALS_2019 if year == 2019 else ALPHA_VALS_2020\n\n\t# Currently using Power Model\n\ts1 = team1['seed']\n\ts2 = team2['seed']\n\n\t# Use R1_PROBS for round 1\n\tif r == 1:\n\t\tif not (s1 + s2 == 17):\n\t\t\texit('Invalid round 1 matchup: seeds {0} vs. {1}.'.format(s1, s2))\n\t\treturn R1_PROBS[s1] if s1 < s2 else R1_PROBS[s2]\n\t\n\t# Use ALPHA_VALS for other rounds (unless seeds are same)\n\tif s1 == s2:\n\t\treturn 0.5\n\n\talpha = ALPHA_VALS[r]\n\ts1a = (s1 * 1.0) ** alpha\n\ts2a = (s2 * 1.0) ** alpha\n\treturn s2a / (s1a + s2a)","def get_winpercent(self, game: 'Game' = None, player: 'Player' = None):\n if game and player:\n pass\n elif game:\n play_count = self.play_set.filter(game=game).count()\n win_count = self.play_set.filter(winner=self, game=game).count()\n return win_count / play_count * 100\n elif player:\n pass\n # play_count = self.play_set.filter(players__in=player).count()\n # win_count = self.play_set.filter(\n # winner=self, player=player).count()\n # return win_count / play_count * 100\n else:\n return self.winpercent","def get_team_round_score(self, team: int, match_round: int) -> int:\n assert self.rounds, \"There are no rounds or the requested round doesn't exist\"\n\n team_score = 0\n\n if team == 1:\n for player in self.rounds[match_round - 1].team1.players:\n team_score += player.scores\n else:\n for player in self.rounds[match_round - 1].team2.players:\n team_score += player.scores\n\n return team_score","def get_new_ratings(players, teams):\n nb_players_team0 = len(teams[0])\n nb_players_team1 = len(teams[1])\n winner = players[teams[0][0]]\n loser = players[teams[1][0]]\n if nb_players_team0 == 1 and nb_players_team1 == 1:\n new_r1, new_r3 = rate_1vs1(winner,loser)\n elif nb_players_team0 == 1 and nb_players_team1 > 1:\n team_loser = [loser, players[teams[1][1]]]\n (new_r1), (new_r3, new_r4) = rate([winner, team_loser], ranks=[0, 1]) \n elif nb_players_team0 > 1 and nb_players_team1 == 1:\n team_winner = [winner, players[teams[0][1]]]\n (new_r1, new_r2), (new_r3) = rate([team_winner, loser], ranks=[0, 1]) \n else:\n team_loser = [loser, players[teams[1][1]]]\n team_winner = [winner, players[teams[0][1]]]\n (new_r1, new_r2), (new_r3, new_r4) = rate([team_winner, team_loser], ranks=[0, 1]) \n player1 = {'name': teams[0][0], 'mu': new_r1.mu, 'sigma': new_r1.sigma}\n player3 = {'name': teams[1][0], 'mu': new_r3.mu, 'sigma': new_r3.sigma}\n if nb_players_team0 > 1:\n player2 = {'name': teams[0][1], 'mu': new_r2.mu, 'sigma': new_r2.sigma}\n if nb_players_team1 > 1:\n player4 = {'name': teams[1][1], 'mu': new_r4.mu, 'sigma': new_r4.sigma}\n if nb_players_team0 > 1:\n return [player1, player2, player3, player4]\n return [player1, player2, player4]\n return [player1, player3]","def win_ratio_avg(self):\n win_ratio = 0\n # Adds all the win ratios of team in this conference which will be\n # used to compute the win ratio average.\n for team_obj in self._conf_teams:\n ### INVARIANT: team_obj is a Team class object and\n ### self._conf_teams is a list of Team class objects.\n win_ratio += team_obj._win_ratio\n return win_ratio/len(self._conf_teams)","def winRate(DF):\r\n df = DF[\"return\"]\r\n pos = df[df>1]\r\n neg = df[df<1]\r\n return (len(pos) / len(pos + neg)) * 100","def evaluate(game, player):\n weights = [2, 200, 2000, 20000]\n reward = 0\n opponent = get_opponent(player)\n for length in range(2, 6):\n reward += weights[length - 2] * get_num_series(game, player, length)\n reward -= weights[length - 2] * get_num_series(game, opponent, length)\n return reward","def calc_match_points(self, match):\n if match.winner == match.TIE:\n match.home.tournament_score += 1\n match.away.tournament_score += 1\n else:\n match.winner.tournament_score += 3\n match.loser.tournament_score += 0","def glicko2_summarize(df, min_weeks=4, use_prior=False):\n\n # Loop through seasons and weeks to create full history of ratings by team\n results = pd.DataFrame()\n for season in df['Season'].sort_values().unique():\n for week in df[df['Season']==season]['Week'].sort_values().unique():\n if week > min_weeks:\n if week == min_weeks + 1:\n season_df = df[df['Season']==season].copy()\n uniqueteamids = pd.concat([season_df['VisID'],\n season_df['HomeID']]).unique()\n if use_prior == True and season > df['Season'].min():\n ratings = np.repeat(1500, len(uniqueteamids))\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\n sigma = np.repeat(0.06, len(uniqueteamids))\n glicko_stats = pd.DataFrame({'ratings': ratings,\n 'ratingsdeviance': ratingsdeviance,\n 'sigma': sigma}, index=uniqueteamids)\n prior = results[results['Season']==season-1]\n prior_id_mask = [True if id in uniqueteamids else False for id in prior['TeamID']]\n prior = prior[prior_id_mask]\n prior = prior.sort_values('Week').groupby('TeamID').tail(1)\n prior = prior.drop('Week',1)\n prior = prior.set_index('TeamID')\n glicko_stats.loc[prior.index, 'ratings'] = prior['Glicko_Rating'] - (prior['Glicko_Rating'] - 1500)/2\n glicko_stats.loc[prior.index, 'ratingsdeviance'] = prior['Glicko_Rating_Deviance'] - (prior['Glicko_Rating_Deviance'] - 350)/2\n glicko_stats.loc[prior.index, 'sigma'] = prior['Glicko_Sigma'] - (prior['Glicko_Sigma'] - 0.06)/2\n else:\n ratings = np.repeat(1500, len(uniqueteamids))\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\n sigma = np.repeat(0.06, len(uniqueteamids))\n glicko_stats = pd.DataFrame({'ratings': ratings,\n 'ratingsdeviance': ratingsdeviance,\n 'sigma': sigma}, index=uniqueteamids)\n\n week_df = df[(df['Season']==season) & (df['Week']<week)].copy()\n glicko_stats = glicko2(week_df, uniqueteamids, glicko_stats)\n\n\n glicko_results = glicko_stats.reset_index()\n print(glicko_results.head(), season)\n glicko_results.columns = ['TeamID','Glicko_Rating',\n 'Glicko_Rating_Deviance',\n 'Glicko_Sigma']\n glicko_results['Season'] = season\n glicko_results['Week'] = week\n results = pd.concat([results, glicko_results], axis=0,\n ignore_index=True)\n\n # Join the ratings to the original schedule of games\n df = df.merge(results, left_on=['Season','Week','HomeID'],\n right_on=['Season','Week','TeamID'],\n suffixes=('','_Home'))\n df.drop('TeamID', 1, inplace=True)\n\n df = df.merge(results, left_on=['Season','Week','VisID'],\n right_on=['Season','Week','TeamID'],\n suffixes=('','_Away'))\n df.drop('TeamID', 1, inplace=True)\n\n # Create key and set index to join with n_game summaries dataset.\n df.set_index(['HomeID', 'VisID', 'Season', 'Week'], inplace=True)\n df = df[['Glicko_Rating', 'Glicko_Rating_Deviance', 'Glicko_Sigma',\n 'Glicko_Rating_Away', 'Glicko_Rating_Deviance_Away',\n 'Glicko_Sigma_Away']]\n df.columns = ['Glicko_Rating_Home', 'Glicko_Rating_Deviance_Home',\n 'Glicko_Sigma_Home', 'Glicko_Rating_Away',\n 'Glicko_Rating_Deviance_Away', 'Glicko_Sigma_Away']\n\n return df","def calc_win_lose_ratio(self):\n total = len(self.train_y)\n survived = 0\n for i in self.train_y:\n if i > 0:\n survived += 1\n\n self.survival_sum = [survived, total-survived]","def average_win_rate(strategy, baseline=always_roll(4)):\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\n\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2","def average_win_rate(strategy, baseline=always_roll(4)):\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\n\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2","def average_win_rate(strategy, baseline=always_roll(4)):\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\n\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2","def get_tourney_rounds(self, conference, year):\n ts_dict = self.get_tourney_slots()\n seed_dict = self.get_tourney_seeds()\n tr_dict = self.get_tourney_results()\n \n round_1 = list()\n round_2 = list()\n round_3 = list()\n round_4 = list()\n winner = list()\n \n round1_winners = list()\n for seed, team in seed_dict[year].items():\n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n round1_winners.append(seed[1:])\n #removes duplicates because I did this part weirdly... HEHEH\n round1_winners = list(set(round1_winners))\n\n win_counter = defaultdict(int)\n for seed, team in seed_dict[year].items(): \n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n win_counter[winning] += 1\n \n for slot, matchup in ts_dict[year].items():\n \n if conference in slot and \"R1\" in slot: \n round_1.append(\"{}-{}\".format(matchup[1:3], matchup[-2:]))\n round_1 = sorted(round_1)\n #for match in round_1:\n for winner1 in round1_winners:\n if winner1 in round_1[0]:\n for winner2 in round1_winners:\n if winner2 in round_1[-1]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[1]:\n for winner2 in round1_winners:\n if winner2 in round_1[-2]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[2]:\n for winner2 in round1_winners:\n if winner2 in round_1[-3]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[3]:\n for winner2 in round1_winners:\n if winner2 in round_1[-4]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n round_2 = sorted(round_2)\n\n round2_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 1:\n round2_winners.append(seed[1:])\n \n for winner1 in round2_winners:\n if winner1 in round_2[0]:\n for winner2 in round2_winners:\n if winner2 in round_2[-1]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_2[1]:\n for winner2 in round2_winners:\n if winner2 in round_2[-2]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n round_3 = sorted(round_3)\n\n round3_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 2:\n round3_winners.append(seed[1:])\n\n for winner1 in round3_winners:\n if winner1 in round_3[0]:\n for winner2 in round3_winners:\n if winner2 in round_3[-1]:\n round_4.append(\"{}-{}\".format(winner1, winner2))\n round_4 = sorted(round_4)\n\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 3:\n winner.append(seed[1:])\n\n conferences = {\"W\": \"East\", \"X\": \"Midwest\", \"Y\": \"South\", \"Z\": \"West\"}\n\n #print(\"CONFERENCE: {}, YEAR: {}\".format(conferences[conference], year))\n #print(\"ROUND1:\", round_1)\n #print(\"ROUND2:\", round_2)\n #print(\"ROUND3:\", round_3)\n #print(\"ROUND4:\", round_4)\n #print(\"WINNER:\", winner)\n\n #clearing out the tourney results dictionary\n #tr_dict.clear()\n\n return round_1, round_2, round_3, round_4, winner","def get_win_percentage(self) -> float:\n if self.wins == 0:\n return 0.0\n else:\n return round((self.wins / (self.wins + self.losses)) * 100, 2)","def winning_percentage(self):\n return float(len(self.wins))/len((self.wins+self.losses))","def get_player_career_reg_season_stats(player_id, player_data_url, headers):\n\tplayer_data_json=json.loads(requests.get(player_data_url+str(player_id), headers=headers).content)\n\tcareer_totals=player_data_json['resultSets'][1]\n\tdf_career_totals=pd.DataFrame.from_records(career_totals['rowSet'], columns=career_totals['headers'])\n\tdf_career_totals.PLAYER_ID=df_career_totals.PLAYER_ID.astype(int)\n\treturn df_career_totals","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","def update_scores(self, AI_win):\n self.games_played += 1\n if not AI_win:\n self.games_won += 1\n else:\n self.games_lost += 1","def team_season_stats(team):\n\n # Get HTML Content\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # Team's yearly stats are displayed in a table\n season_stats = soup.find(id='stats').find('tbody')\n\n # Iterate through each year\n for year in season_stats.find_all('tr', {'class': None}):\n\n season_year = year.find('th').text[0:4]\n season_year = int(season_year) + 1\n season = {'year': season_year}\n\n # Loop through each stat\n for stat in year.find_all('td'):\n season[stat['data-stat']] = stat.string\n\n # Rename relocated teams\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\n season['_id'] = season['team_id'] + '_' + str(season_year)\n\n # Remove unwanted stats\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\n for k in to_remove:\n season.pop(k, None)\n\n # Add to MongoDB\n m.insert('team_season', season)","def assign_win_points(game_dict):\n def win_tuple(deck_dict):\n \"\"\" Return tuple ordered by increasing final standing. \"\"\"\n # negate turns so that max() behaves; points good, turns bad.\n num_normal_turns = sum(not ( (POSSESSION in t and t[POSSESSION]) or \\\n (OUTPOST in t and t[OUTPOST]))\n for t in deck_dict[TURNS])\n return (deck_dict[POINTS], -num_normal_turns)\n\n decks = game_dict[DECKS]\n winner_tuple = max(win_tuple(p) for p in decks)\n winners = [p for p in decks if win_tuple(p) == winner_tuple]\n\n win_points = float(len(decks)) / len(winners)\n for player in decks:\n player[WIN_POINTS] = win_points if player in winners else 0.0","def custom_score_2(game, player):\n \n # get avaliable moves for each player\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n \n # shortcut to definite state:\n # 1. my agent win -> return very high score\n if opp_moves == 0:\n return float(\"inf\")\n # 2. opponenent's agent win -> return very low score\n elif own_moves == 0:\n return float(\"-inf\")\n\n # score: avaliable moves ratio\n return float(own_moves/opp_moves)","def getPlayerAdvStat(self, stat, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_call = stat.lower()\r\n stat_dict = {'touch':'Possessions', 'possession':'Possessions',\r\n 'speed':'SpeedDistance', 'distance':'SpeedDistance'}\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashptstats?College=&'\\\r\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\r\n 'DraftPick=&DraftYear=&GameScope=&Height=&LastNGames=0&'\\\r\n 'LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&'\\\r\n 'PORound=0&PerMode=PerGame&PlayerExperience=&PlayerOr'\\\r\n 'Team=Player&PlayerPosition=&PtMeasureType=' + \\\r\n stat_dict[stat_call] + '&Season=' + season + \\\r\n '&SeasonSegment=&SeasonType=Regular+Season&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision=&Weight='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n advStat_df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n return advStat_df","def opponents_score(self):\n if self.opponent_wickets == 10:\n var1 = \"All Out\"\n return str('{0} {1}').format(self.opponent_runs, var1)\n else:\n var1 = self.opponent_wickets\n return str('{0}-{1}').format(self.opponent_runs, var1)","def calc_marcel_weights(df):\n cols = ['toi_on', 'gs60', 'toi/gp']\n\n # Only essential columns\n df = df[[\"player\", \"player_id\", \"season\"] + cols]\n\n # Copy over ALL Data\n predict_df = df.copy()\n\n # To get fours years in a row (3 predicting 1).. we run 'get_previous_year' 3 times\n # Each time we get n-_ by using predict_col\n # NOTE: I'm writing over df here!!!!\n for seasons in range(1, 4):\n df = get_previous_yr(df, predict_df, seasons)\n df = df[~df['toi_on_n-{}'.format(seasons)].isnull()]\n\n # Filter for minimum toi\n # 400 for first 3 and 800 for last\n df = df[(df['toi_on'] >= 800) & (df['toi_on_n-1'] >= 400) & (df['toi_on_n-2'] >= 400) & (df['toi_on_n-3'] >= 400)]\n\n print(\"\\nPlayers: {}\".format(df.shape[0]))\n\n for col in ['gs60', 'toi/gp']:\n print(\"Getting the Weights for: \", col)\n # Prepare shit\n model_features = df[['{}_n-1'.format(col), '{}_n-2'.format(col), '{}_n-3'.format(col)]].values.tolist()\n model_target = df[col].values.tolist()\n model_features, model_target = np.array(model_features), np.array(model_target).ravel()\n\n lr = LinearRegression()\n lr.fit(model_features, model_target)\n\n # Print all the Coefficient neatly\n print(\"Coefficients:\")\n for season, coef in zip(range(1, 4), lr.coef_):\n print(\"Season n-{}:\".format(season), round(coef, 3))\n\n print(\"\")","def compute_score(window, computer_piece):\n score = 0\n if window.count(computer_piece) == 4:\n score += 100\n elif window.count(computer_piece) == 3 and window.count(0) == 1:\n score += 5\n elif window.count(computer_piece) == 2 and window.count(0) == 2:\n score += 2\n if window.count(PLAYER_PIECE) == 2 and window.count(0) == 2:\n score -= 1\n if window.count(PLAYER_PIECE) == 3 and window.count(0) == 1:\n score -= 100\n return score","def play_simulation(self, n_iter=1000):\r\n final_wins = final_wins = {t: 0 for t in self.teams.dteams.keys()}\r\n season_teams_ranked, _ = get_playoffs(self.season_calendar, self.teams_info)\r\n for i in tqdm(range(n_iter)):\r\n if not self.playoffs_only:\r\n self.season.play_regular_season(self.teams)\r\n season_teams_ranked = self.season.playoffs_teams_ranked\r\n\r\n playoffs_sim = Playoffs(season_teams_ranked, self.gsim)\r\n winner_playoff = playoffs_sim.get_winner(self.teams)\r\n\r\n final_wins[winner_playoff] += 1\r\n\r\n return final_wins","def getTeamStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\r\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\r\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n team_df = df[[\"TEAM_ID\",\"TEAM_NAME\",\"GP\",\"W\",\"L\",\"W_PCT\",\"MIN\",\"FGM\",\r\n \"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\r\n \"OREB\",\"DREB\",\"REB\",\"AST\",\"TOV\",\"STL\",\"BLK\",\"BLKA\",\"PF\",\r\n \"PFD\",\"PTS\",\"PLUS_MINUS\"]]\r\n \r\n return team_df","def get_team_scores(self, team, include_home=True, include_away=True):\n if include_away:\n away_games = list(filter(lambda g: team == g.AwayTeam, self.games))\n else:\n away_games = []\n\n if include_home:\n home_games = list(filter(lambda g: team == g.HomeTeam, self.games))\n else:\n home_games = []\n\n scored_h = [g.FTHG for g in home_games]\n scored_a = [g.FTAG for g in away_games]\n\n conceded_h = [g.FTAG for g in home_games]\n conceded_a = [g.FTHG for g in away_games]\n\n try:\n mean_gd = mean(scored_h + scored_a) - mean(conceded_h + conceded_a)\n home_gd = mean(scored_h) - mean(conceded_h)\n home_adv = home_gd - mean_gd\n except Exception:\n home_adv = 0\n\n return {\n \"scored_xg\": scored_h + scored_a,\n \"conceded_xg\": conceded_h + conceded_a,\n \"home_adv\": home_adv,\n \"expected_points\": self.get_table(metric='points')[team] /\n len(home_games + away_games)\n }","def get_result(state, winrate_predictor):\n teamA_picks = state[:, TEAM_A_PICK_INDICES]\n teamB_picks = state[:, TEAM_B_PICK_INDICES]\n team_comp = torch.cat((teamA_picks, teamB_picks), dim=1)\n winrate = winrate_predictor(team_comp)[0, 0]\n \n if winrate >= 0.5:\n return 0\n return 1","def year_cost_rule(_m, y):\r\n\r\n return sum(m.RHO[y, s] * m.SCEN[y, s] for s in m.S)","def ucbScore(self,totalPlayedTimes):\n winRate = self.winRate()\n #print totalPlayedTimes\n #print self.playedTimes\n confidenceInterval = math.sqrt(2 * math.log(totalPlayedTimes,math.e) / self.playedTimes)\n \n return winRate + confidenceInterval","def review(self):\n # Compile standings\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\n # Name a champion\n self.champion = self._name_champion()\n self.league.history.champions_timeline[self.year] = self.champion\n print \"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\".format(\n self.champion.team.name.upper(), self.year, self.league.name.upper()\n )\n # Compile league leaders\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\n # Have each team review its season, as well\n for team_season in self.teams:\n team_season.review()\n # Send the league into the offseason\n self.league.season = None","def win_rate(returns_pred, returns_real, symbol=None, horizon=None):\n # Input processing\n if not horizon:\n horizons = [1, 3, 5, 10, 20, 40, 60, 90, 120]\n elif type(symbol) == str:\n return SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon)\n else:\n horizons = [horizon]\n\n # Data frame skeleton\n win_rate_all = pd.DataFrame(index=horizons)\n\n # Compute win rate for each symbol\n for symbol in returns_pred.columns:\n win_rate = []\n for horizon in horizons:\n win_rate.append(SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon))\n win_rate_all[symbol] = win_rate\n\n # Compute statistics across all symbols\n win_rate_all = win_rate_all.agg(['mean', 'std'], axis=1).merge(win_rate_all, left_index=True, right_index=True)\n\n # Formatting\n cm = sns.light_palette(\"green\", as_cmap=True)\n return win_rate_all.style.background_gradient(cmap=cm).format(\"{:.1%}\")","def first_round_history(self):\n self.ts_dict = self.get_tourney_slots()\n self.tsr_dict = self.match_seeds()\n first_seed_win = 0\n second_seed_win = 0\n third_seed_win = 0\n fourth_seed_win = 0\n fifth_seed_win = 0\n sixth_seed_win = 0\n seventh_seed_win = 0\n eighth_seed_win = 0\n total_games = 128\n\n for year1 in self.ts_dict: \n for slot, match_up in self.ts_dict[year1].items():\n if slot[:2] == \"R1\":\n for year2 in self.tsr_dict:\n if year1 == year2:\n for winning, losing in self.tsr_dict[year2].items():\n if winning[5:] == match_up[:3]:\n seed = winning[6:] \n if seed == \"01\":\n first_seed_win += 1\n elif seed == \"02\":\n second_seed_win += 1\n elif seed == \"03\":\n third_seed_win += 1\n elif seed == \"04\":\n fourth_seed_win += 1\n elif seed == \"05\":\n fifth_seed_win += 1\n elif seed == \"06\":\n sixth_seed_win += 1\n elif seed == \"07\":\n seventh_seed_win += 1\n elif seed == \"08\":\n eighth_seed_win += 1 \n \n #print(first_seed_win, second_seed_win, third_seed_win, fourth_seed_win, fifth_seed_win, sixth_seed_win, seventh_seed_win, eighth_seed_win, total_games)\n\n gauge = pygal.SolidGauge(inner_radius=0.70, title=\"NCAA First Round Results\")\n ratio_first_seed = int(first_seed_win / total_games * 100)\n ratio_second_seed = int(second_seed_win / total_games * 100)\n ratio_third_seed = int(third_seed_win / total_games * 100)\n ratio_fourth_seed = int(fourth_seed_win / total_games * 100)\n ratio_fifth_seed = int(fifth_seed_win / total_games * 100)\n ratio_sixth_seed = int(sixth_seed_win / total_games * 100)\n ratio_seventh_seed = int(seventh_seed_win / total_games * 100)\n ratio_eighth_seed = int(eighth_seed_win / total_games * 100) \n\n percent_formatter = lambda x: '{:.10g}%'.format(x)\n gauge.value_formatter = percent_formatter\n gauge.add('1 vs. 16', [{'value': ratio_first_seed, 'max_value': 100}])\n gauge.add('2 vs. 15', [{'value': ratio_second_seed, 'max_value': 100}])\n gauge.add('3 vs. 14', [{'value': ratio_third_seed, 'max_value': 100}])\n gauge.add('4 vs. 13', [{'value': ratio_fourth_seed, 'max_value': 100}])\n gauge.add('5 vs. 12', [{'value': ratio_fifth_seed, 'max_value': 100}])\n gauge.add('6 vs. 11', [{'value': ratio_sixth_seed, 'max_value': 100}])\n gauge.add('7 vs. 10', [{'value': ratio_seventh_seed, 'max_value': 100}])\n gauge.add('8 vs. 9', [{'value': ratio_eighth_seed, 'max_value': 100}])\n \n gauge.render_to_file('chart.svg')","def _adv_counter(winrate_together, winrate_hero1, winrate_hero2):\n return winrate_together","def calculate_power_ranking(wins, losses, is_winning_streak,\n streak_length, recent_wins, recent_losses):\n pred1 = 0\n pred2 = round( ( ((wins*1.0)/(wins+losses)) - 0.500 ) * 16 * 9 * 10/9 )\n pred3 = recent_wins - recent_losses\n streak_factor = 1 if is_winning_streak else -1\n pred4 = streak_factor * round( ( streak_length - 1 ) / 2.0 )\n print pred1, pred2, pred3, pred4\n return pred1 + pred2 + pred3 + pred4","def scrape():\n league_year = Config.get_property(\"league_year\")\n\n # Create table\n season_data = client.season_schedule(league_year)\n season_data = br_enum_to_string(season_data)\n return season_data","def evaluateWinner(self):\n\t\tif self.pots[-1] == 0:\n\t\t\tself.pots.pop()\n\t\tlivePlayers = self.getLivePlayers()\t\n\t\tfor i in range(len(self.pots)):\n\t\t\tplayers = self.getPlayersInPot(i, livePlayers)\n\t\t\tevaluations = []\n\t\t\tfor x in players:\n\t\t\t\tcombined = x.hand + self.communityCards\n\t\t\t\tevaluations.append((x, self.evaluator.getRankOfSeven(\tcombined[0], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[1], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[2], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[3], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[4], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[5], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[6] )))\n\t\t\twinners = self.getWinners(evaluations, i)\n\t\t\tself.handOutMoney(winners, i)\n\t\t\tself.potwinQ.append(winners[0].name)","def custom_score(game, player):\n \n # get avaliable moves for each player\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n \n # return different between # of my agent's move and oppenent's\n return float(own_moves - opp_moves)","def _get_current_teams_score(self):\n for game in self._get_live_games():\n teams_playing = [x['abbreviation'] for index, x in game['teams'].items()]\n if self.team in teams_playing:\n # Our team is playing in this game, get the score \n return int(game['scores'][self.team])","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def season_rounds(cls, season):\r\n\t\t\r\n\t\tfolder_name = cls.season_folder(season)\r\n\t\tround_list = os.listdir(f'Data/{folder_name}')\r\n\r\n\t\tall_rounds = []\r\n\r\n\t\tfor round_file in round_list:\r\n\t\t\twith open(f'Data/{folder_name}/{round_file}', 'r', encoding='utf-8') as f:\r\n\t\t\t\tround_info = f.read().splitlines()\r\n\r\n\t\t\tround_number = round_file[:-4]\r\n\t\t\tfull_round_name = f\"{season} R{round_number}\"\r\n\r\n\t\t\tround_date = int(round_info[0])\r\n\t\t\tlookup_ind = DATES.month_diff(round_date, DATES.MIN_DATE)\r\n\r\n\t\t\t# If the round isn't actually counted for TWOW Glicko\r\n\t\t\tif full_round_name not in cls.ROUNDS[lookup_ind].keys():\r\n\t\t\t\tcontinue\r\n\r\n\t\t\tcontestant_count = len(round_info) - 1\r\n\r\n\t\t\tstrength = cls.ROUNDS[lookup_ind][full_round_name][0]\r\n\r\n\t\t\tall_rounds.append([\r\n\t\t\t\tround_number,\r\n\t\t\t\tround_date,\r\n\t\t\t\tcontestant_count,\r\n\t\t\t\tstrength\r\n\t\t\t])\r\n\t\t\r\n\t\treturn all_rounds","def eval(self):\n\n ratio_player_win = self.player_wins / self.num_test\n ratio_opponent_win = self.opponent_wins / self.num_test\n ratio_tie = 1.0 - ratio_player_win - ratio_opponent_win\n\n print(\"\\nPlayer Test Results:\")\n print(\"\\tWins {0:.2f}%\".format(100.0 * ratio_player_win))\n print(\"\\tLosses {0:.2f}%\".format(100.0 * ratio_opponent_win))\n print(\"\\tTie {0:.2f}%\".format(100.0 * ratio_tie))\n\n ratio_optimal_win = self.optimal_wins / self.num_test\n ratio_optimal_loose = self.optimal_losses / self.num_test\n ratio_optimal_tie = 1.0 - ratio_optimal_win - ratio_optimal_loose\n\n print(\"\\nOptimal Results:\")\n print(\"\\tPlayer {0:.2f}%\".format(100.0 * ratio_optimal_win))\n print(\"\\tOpponent {0:.2f}%\".format(100.0 * ratio_optimal_loose))\n print(\"\\tTie {0:.2f}%\".format(100.0 * ratio_optimal_tie))\n\n # Ratio of win, loss diff between player and optimal\n # positive if the player beats opponent\n relative_result = ((ratio_player_win - ratio_opponent_win) /\n (ratio_optimal_win - ratio_optimal_loose))\n\n print(\"\\nResults Player Relative Optimal:\")\n print(\"\\tWins {0:.2f}%\".format(100.0 * ratio_player_win / ratio_optimal_win))\n print(\"\\tLosses {0:.2f}%\".format(100.0 * ratio_opponent_win / ratio_optimal_loose))\n print(\"\\tScore {0:.2f}%\".format(100.0 * relative_result))\n\n if self.last_test is not None:\n print(\"Diff from last test score is {0:.2f}%\".format(100.0 * (relative_result - self.last_test)))\n self.last_test = relative_result","def get_smmry_data(soup, game_dict):\n\n # Get date and time data.\n try:\n date_soup = soup.find(\"div\", {\"class\": \"spielbericht_tipp_status\"})\n league = date_soup.div.span.text\n date_string = date_soup.div.text\n date = re.search(r'\\d{2}.\\d{2}.\\d{2}', date_string).group(0)\n time = re.search(r'\\d{2}:\\d{2}', date_string).group(0)\n matchday = re.search(r'[|]\\d+', date_string).group(0)[1:]\n\n game_dict[\"league\"] = league\n game_dict[\"fb_date\"] = date\n game_dict[\"fb_time\"] = time\n game_dict[\"matchday\"] = matchday\n except AttributeError:\n pass\n\n # Get game result.\n try:\n result = soup.find(\"div\", {\"class\": \"stand\"}).text\n game_dict[\"result\"] = result\n except AttributeError:\n pass\n\n # Try to get the referee name.\n try:\n referee = soup.find(\"span\", {\"class\": \"schiri_link\"}).text\n game_dict[\"referee\"] = referee\n except AttributeError:\n pass\n\n # Get team, club name and repective url by team.\n try:\n smmry_soup = soup.find(\n \"div\", {\"class\": \"spielbericht_ergebnis_wrapper\"})\n club_title = smmry_soup.find_all(\"img\")\n team_title = smmry_soup.findAll(\"div\", {\"class\": \"teaminfo\"})\n\n # Loop through teams.\n for j, team in enumerate([\"home_\", \"away_\"]):\n game_dict[team + \"team\"] = team_title[j].a[\"title\"]\n game_dict[team + \"team_url\"] = team_title[j].a[\"href\"]\n game_dict[team + \"club\"] = club_title[j][\"title\"]\n except (AttributeError, TypeError):\n pass\n\n return game_dict","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def season_game_logs(team, year):\n\n # Check year value\n if year > 2019 or year < 1950:\n raise ValueError('Year Value Incorrect')\n\n # Rename teams that moved\n team = scrape_utils.rename_team(team, year)\n\n # Get HTML content\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n season_stats = soup.find(id='tgl_basic')\n games = season_stats.find('tbody')\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # To find opponent statistics\n opponent = re.compile('^opp_.*$')\n\n # Loop through every game in a team's season\n for game in games.find_all('tr', {'class': None}):\n\n curr_team = {'team': team}\n opp_team = {}\n\n # Loop through each stat\n for stat in game.find_all('td'):\n\n stat_name = stat['data-stat']\n\n # These are opponent stats\n if re.match(opponent, stat_name):\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\n else:\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\n\n # Remove unnecessary information\n del curr_team['game_season']\n del curr_team['x']\n\n # Rename relocated teams\n curr_team['team'] = scrape_utils.rename_team(team)\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\n\n # Use the same ID as basketball reference\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \"%Y-%m-%d\"),\n 'season': year,\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\n '_id': game.find('a')['href'][-17:-5]}\n\n # Place the teams in the correct spot depending on who is the home team\n if curr_team.pop('game_location') == 0:\n result['home'] = curr_team\n result['away'] = opp_team\n else:\n result['home'] = opp_team\n result['away'] = curr_team\n\n # Insert into database\n m.insert('game_log', result)","def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\n battleData, htmlData = loadBattleData(league)\n similarities = loadSims() \n \n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\n if len(oppTeam) == 0:\n picks = set([])\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\n picks.add(random.randint(0,len(battleData)-1))\n\n losers = []\n loserDict = {}\n for i in picks:\n entry = battleData[i]\n winner,loser = determineWinner(entry)\n loserDict[str(loser)] = [winner]\n losers.append( (loser,0) )\n\n #Given opponent team then find similar teams\n else:\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\n\n #create dictionary from losers team to the team that beat them.\n loserDict = {}\n sims = []\n for d in battleData:\n winner, loser = determineWinner(d)\n\n wTeam = teamToArray(winner,pokedex)\n lTeam = np.array(teamToArray(loser, pokedex))\n\n score = 0\n for oppNp in oppTeam:\n score+= np.amax(lTeam*oppNp) \n\n if str(loser) in loserDict:\n loserDict[str(loser)].append(winner)\n else:\n #new to dictonary\n loserDict[str(loser)] = [winner]\n\n sims.append((loser, score))\n\n\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\n\n cutoff = min(len(sims),NUMLOSINGTEAMS)\n losers = sims[:cutoff]\n\n #Gather winners to losing teams\n winnersComp = []\n for loser,_ in losers:\n for winner in loserDict[str(loser)]:\n winnersComp.append(teamToArray(winner,pokedex))\n \n topScore = len(winnersComp)*6 #pkmn team size\n\n results = []\n inverted_idx = {}\n\n existsSet = []\n\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\n for i in range(len(curTeams)):\n team = curTeams[i]\n results.append((team,0))\n sTeam = set(team)\n if not (sTeam in existsSet):\n existsSet.append(sTeam)\n for pkm in team:\n if pkm != EMPTY:\n if pkm in inverted_idx:\n inverted_idx[pkm].append(i)\n else:\n inverted_idx[pkm] = [i]\n \n #Giving the similiarity scores to the winners based off of the inverted index.\n for pkm in inverted_idx:\n for winner in winnersComp:\n wArr = np.array(winner)\n #tArr = getSimPokemon(pkm,similarities)\n tArr = similarities[pkm]\n \n vals = wArr * tArr\n\n score = np.amax(vals)\n\n for i in inverted_idx[pkm]:\n results[i] = (results[i][0],results[i][1]+(score/topScore))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n\n if len(results) < NUMTEAMSRETURN:\n if len(results) == 0:\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\n\n else:\n returnTeams = [result[0] for result in results]\n teamScores = [result[1] for result in results]\n else:\n firstResult, firstScore = results[0]\n returnTeams = [firstResult]\n teamScores = [round(firstScore*100,1)]\n returnSets = [set(firstResult)]\n \n i = 1\n\n #Loops through results and adds teams with the proper edit distance away.\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\n teamToConsider,teamToConsiderScore = results[i]\n \n considerSet = set(teamToConsider)\n add = True\n ##checks the edit distance of teams is above wanted\n for team in returnSets:\n if len(team.union(considerSet)) < len(team)+minDistWanted:\n add = False\n\n ##If indeed above wanted levels then add\n if add:\n returnTeams.append(teamToConsider)\n returnSets.append(considerSet)\n teamScores.append(round(teamToConsiderScore*100,1))\n \n i+=1\n\n if i >= len(results):\n i = 1\n minDistWanted -= 1 \n \n winHtmls = []\n if htmlData != None:\n for team,_ in losers:\n for winner in loserDict[str(team)]:\n winHtmls.extend(htmlData[str(sorted(winner))])\n \n\n return returnTeams, teamScores, winHtmls","def calculate(self, prev_scores):\n self.set_scores(prev_scores)\n for match in self.week.matches:\n if match.played:\n # Fetch old scores\n winner_score = float(prev_scores[match.winner])\n loser_score = float(prev_scores[match.loser])\n\n # Update this ranking's scores\n score_delta = loser_score * 0.1\n self.score[match.winner] = winner_score + score_delta\n self.score[match.loser] = loser_score - score_delta","def score(self,player, board):\r\n numPlayer = 0\r\n numOpp = 0\r\n for i in self.squares():\r\n if board[i] == player:\r\n numPlayer+= SQUARE_WEIGHTS[i]\r\n else:\r\n numOpp+=SQUARE_WEIGHTS[i]\r\n return numPlayer-numOpp","def calc_calories(gpx_track, wt = 175, activity='Run'):","def create_dreamteam_count_yearly(player, raw_list):\n num_seasons = 0\n yearly_dreamteam_count = 0\n for i, raw in enumerate(raw_list):\n if i == len(raw_list) - 1:\n break\n if player in raw['name'].values:\n num_seasons += 1\n yearly_dreamteam_count += raw[['dreamteam_count']][raw.name == player].values[0][0]\n if num_seasons == 0:\n return np.nan\n return yearly_dreamteam_count / num_seasons","def calculate_scores(self):\n words = self.walk_board()\n player_scores = {}\n for word in words:\n player = word.get_owning_player()\n if player not in player_scores:\n player_scores[player] = 0\n player_scores[player] += word.get_score()\n return player_scores","def play_game(game,standings_):\n rand_nmr = random.random()\n\n standings_.loc[standings_.TEAMS==game['Home'],'MP'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'MP'] += 1\n\n if rand_nmr < game['Prob Home']:\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\n standings_.loc[standings_.TEAMS==game['Home'],'W'] += 1\n standings_.loc[standings_.TEAMS==game['Home'],'F'] += n_goals\n standings_.loc[standings_.TEAMS==game['Away'],'L'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'A'] += n_goals\n standings_.loc[standings_.TEAMS==game['Home']][\"h2h\"].apply(lambda x:x.append(game['Away']))\n\n return 0\n\n elif rand_nmr < game['Prob Home'] + game['Prob Draw']:\n # all draws end in 0-0 this can be improved\n standings_.loc[standings_.TEAMS==game['Home'],'D'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'D'] += 1\n\n return 1\n\n else:\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\n standings_.loc[standings_.TEAMS==game['Away'],'W'] += 1\n standings_.loc[standings_.TEAMS==game['Away'],'F'] += n_goals\n standings_.loc[standings_.TEAMS==game['Home'],'A'] += 1\n standings_.loc[standings_.TEAMS==game['Home'],'L'] += n_goals\n standings_.loc[standings_.TEAMS==game['Away']][\"h2h\"].apply(lambda x:x.append(game['Home']))\n\n return 2","def evaulate_monster(generikmon):\r\n score = generikmon['headA']\r\n #score = generikmon['chinA']\r\n return score","def get_winner(self):\n winner: Player = Player('none')\n points_winner = 0\n for player in self.players:\n for key, value in player.get_stats().items():\n print('{}: {}'.format(key, value))\n if key == 'points':\n if value >= points_winner:\n winner = player\n print()\n\n print('The winner is: ' + winner.get_name())\n return winner","def winningTeamPenalty(r):\n \n #Check if home or away had more goals at the 'event' time\n homecheck = int(r['about.goals.home'] > r['about.goals.away'])\n awaycheck = int(r['about.goals.away'] > r['about.goals.home'])\n \n #If home had more goals and the penalty was on the home team, set to 1\n if (homecheck > 0) and (r['against.homeTeam'] == 1):\n return 1\n #If away had more and the penalty was not on home team, set to 1\n if (awaycheck > 0) and (r['against.homeTeam'] == 0):\n return 1\n #Any other situation should be a zero in this column\n else:\n return 0","def get_team_stats(players: list[Player]) -> dict[int]:\n\n team_stats = {}\n\n total_reaction = 0\n total_mechanical_skill = 0\n total_tactical_skill = 0\n total_game_knowledge = 0\n total_xp = 0\n\n for player in players:\n total_reaction += player.reaction\n total_mechanical_skill += player.mechanical_skill\n total_tactical_skill += player.tactical_skill\n total_game_knowledge += player.game_knowledge\n total_xp += player.xp\n\n team_stats.update(\n {\"reaction\": total_reaction,\n \"mechanical_skill\": total_mechanical_skill,\n \"tactical_skill\": total_tactical_skill,\n \"game_knowledge\": total_game_knowledge,\n \"xp\": total_xp})\n\n return team_stats","async def player_ratio(self, ctx):\r\n player = ctx.message.content.split(' ')[1]\r\n if os.environ.get(\"WoW_Token\") is None:\r\n return\r\n else:\r\n async with aiohttp.ClientSession().get('https://us.api.battle.net/wow/character/zul\\'jin/' + player + '?fields=pvp&locale=en_US&apikey=' + os.environ.get(\"WoW_Token\")) as res:\r\n if res.status == 200:\r\n data = await res.json()\r\n player_pvp_stats = data['pvp']['brackets']['ARENA_BRACKET_3v3']\r\n await ctx.message.channel.send(u\"Player: {:s}\").format(player)\r\n await ctx.message.channel.send(\"Rating: {:d}\".format(player_pvp_stats['rating']))\r\n await ctx.message.channel.send(\"Season Wins: {:d}\".format(player_pvp_stats['seasonWon']))\r\n await ctx.message.channel.send(\"Season Losses: {:d}\".format(player_pvp_stats['seasonLost']))\r\n\r\n if player_pvp_stats['seasonWon'] == 0 or player_pvp_stats['seasonLost'] == 0:\r\n await ctx.message.channel.send(\"Ratio: 0\")\r\n else:\r\n await ctx.message.channel.send(\"Ratio: {:.4f}\".format(\r\n float(player_pvp_stats['seasonWon'])/\r\n float(player_pvp_stats['seasonLost']))\r\n )","def get_sim_winners(teamNames,league,pokedex):\n bData, wHtml = loadBattleData(league)\n similarities = loadSims() \n\n results = []\n\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\n for d in bData:\n winner, loser = determineWinner(d)\n\n score = 0\n for pkm in myTeam:\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\n\n results.append((winner,score))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n \n cutoff = min(len(results),5)\n\n htmls = []\n for win,score in results[:cutoff]:\n htmls.extend(wHtml[str(sorted(win))])\n\n return htmls","def get_revenue_per_hour(self):\n tp = self.target_profile\n p_driver_ticketed = self.cops.p_driver_ticketed(tp.mins_between_drivers)\n return p_driver_ticketed * tp.revenue_opportunity_per_hour - self.cops.get_cost_per_hour()","def wins(self):\n return [g for g in self.games if g.winner is self.team]","def Winner(self, whichPlayer, tResult, score, gameCount):\n\n if whichPlayer == 0:\n tResult[0] = tResult[0] + score[0]\n else:\n tResult[1] = tResult[1] + score[1]\n print(data['tResult'],\"player1 \", tResult[0],\"player2 \",tResult[1])\n if gameCount == 3:\n if tResult[0] > tResult[1]:\n print(data['mplayer1'],tResult[0] - tResult[1])\n else:\n print(data['mplayer2'],tResult[1] - tResult[0])\n return whichPlayer, score, gameCount, tResult","def get_score(self, player):\n\n df = pd.read_csv('RPSscores.csv')\n if not str(player) in df['Name'].to_dict().values():\n df.loc[len(df.index)] = [str(player),\n 0, 0, 0]\n player_index = int(df.loc[df['Name'] == str(player)].index[0])\n result = 'wins: ' + str(df.iloc[player_index, 1]) + '\\n' + \\\n 'draws: ' + str(df.iloc[player_index, 2]) + '\\n' + \\\n 'losses: ' + str(df.iloc[player_index, 3])\n return result","def losing_games_stats(self):\n self.losing_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(losing_num_counts=1))\n .query(\"Season <= 2013\")\n .groupby(['Season','LTeamID'])\n .agg({\"WScore\":\"sum\",\"LScore\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\"LFGM3\":\"sum\",\"LFGA3\":\"sum\",\"LFTM\":\"sum\",\"LFTA\":\"sum\",\"losing_num_counts\":\"sum\",\n \"LOR\":\"sum\",\"LDR\":\"sum\",\"WFGA\":\"sum\",\"WFGM\":\"sum\",\n \"LAst\":\"sum\",\"LTO\":\"sum\",\"LStl\":\"sum\",\"LBlk\":\"sum\",\"LPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"WScore\":\"winning_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"WFGA\":\"WFGA_opp\",\"WFGM\":\"WFGM_opp\"})\n )","def custom_score(game, player):\n \n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n \n # First obtain the improved score\n player_legal_moves = game.get_legal_moves(player)\n opponent = game.get_opponent(player)\n opponent_legal_moves = game.get_legal_moves(opponent)\n improved = len(player_legal_moves) - len(opponent_legal_moves)\n if improved != 0:\n return float(improved)\n \n # Second get differences from center\n center_coordinates = center_y, center_x = get_center_coordinates(game)\n player_coordinates = game.get_player_location(player)\n opponent_coordinates = game.get_player_location(opponent)\n player_center_dist = get_distances_from_center(center_coordinates, player_coordinates)\n opponent_center_dist = get_distances_from_center(center_coordinates, opponent_coordinates)\n center_dist_diff = player_center_dist - opponent_center_dist\n \n # Third obtain next_moves\n player_next_moves = [get_next_moves(game, move, list(move)) for move in player_legal_moves]\n opponent_next_moves = [get_next_moves(game, move, list(move)) for move in opponent_legal_moves] \n improved_next = len(player_next_moves) - len(opponent_next_moves)\n \n # Put player and opponent feature differences in a tuple/vector surrogoate\n feature_diff_vector = (improved, center_dist_diff, improved_next)\n \n # Provide a weighting vector for the features of each player-participant\n weight_vector = (1.5,0.1,1.0)\n # Calculate the return value = weighted difference of players' features\n weighted_difference_dot_product = sum(p*q for p,q, \\\n in zip(feature_diff_vector, weight_vector))\n \n return float(weighted_difference_dot_product)","def get_game_score(self):\n if self.game_is_tied():\n return 0\n elif self.is_game_won():\n my_available_steps = self.steps_available(self.loc)\n opp_available_steps = self.steps_available(self.opponent_loc)\n my_score = self.my_score - self.penalty_score if my_available_steps == 0 else self.my_score\n opp_score = self.opponent_score - self.penalty_score if opp_available_steps == 0 else self.opponent_score\n return (my_score - opp_score) / (abs(my_score) + abs(opp_score))\n else:\n if abs(self.my_score) + abs(self.opponent_score) == 0:\n return 0\n return (self.my_score - self.opponent_score) / (abs(self.my_score) + abs(self.opponent_score))","def determine_winner1(self): \r\n sorted_player_rank = self._rank()\r\n print(f\"sorted player rank: {sorted_player_rank}\")\r\n print(f\"winner is player {sorted_player_rank[0]}: with points {sorted_player_rank[0][1]}\")","def evaluate_team(self, team, mode):\n if mode == Config.RESTRICTIONS['mode']['training']:\n point_population = self.point_population_\n opponent_population = self.training_opponent_population()\n elif mode == Config.RESTRICTIONS['mode']['validation']:\n point_population = self.validation_point_population_\n opponent_population = self.validation_opponent_population_\n elif mode == Config.RESTRICTIONS['mode']['champion']:\n point_population = self.champion_population()\n opponent_population = self.champion_opponent_population()\n else:\n raise ValueError(\"Invalid mode\")\n results = []\n extra_metrics_opponents = defaultdict(list)\n match_id = 0\n\n if len(point_population) == 0:\n raise ValueError(\"Error: Nothing in point population. Probably the population size is too small.\")\n if len(opponent_population) == 0:\n raise ValueError(\"Error: Nothing in opponent population. Probably the population size is too small.\")\n\n if mode == Config.RESTRICTIONS['mode']['training']:\n for point, opponent in zip(point_population, opponent_population):\n match_id += 1\n result = self._play_match(team, opponent, point, mode, match_id)\n team.reset_registers()\n extra_metrics_opponents[opponent.opponent_id].append(result)\n team.results_per_points_[point.point_id_] = result\n results.append(result)\n if opponent.opponent_id == 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\n extra_metrics_opponents[opponent.__repr__()].append(result)\n \n opponent_type = 'training_opponents'\n team.fitness_ = numpy.mean(results)\n else:\n extra_metrics_points = self._initialize_extra_metrics_for_points()\n for point, opponent in zip(point_population, opponent_population):\n match_id += 1\n result = self._play_match(team, opponent, point, mode, match_id)\n team.reset_registers()\n extra_metrics_opponents[opponent.opponent_id].append(result)\n extra_metrics_points = self._update_extra_metrics_for_points(extra_metrics_points, point, result)\n if mode == Config.RESTRICTIONS['mode']['validation']:\n team.results_per_points_for_validation_[point.point_id_] = result\n results.append(result)\n elif mode == Config.RESTRICTIONS['mode']['champion']:\n if opponent.opponent_id != 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\n results.append(result)\n else:\n extra_metrics_opponents[opponent.__repr__()].append(result)\n \n opponent_type = 'opponents'\n for key in extra_metrics_points:\n for subkey in extra_metrics_points[key]:\n extra_metrics_points[key][subkey] = round_value(numpy.mean(extra_metrics_points[key][subkey]))\n team.extra_metrics_['points'] = extra_metrics_points\n if mode == Config.RESTRICTIONS['mode']['validation']:\n team.score_validation_ = round_value(numpy.mean(results))\n else:\n team.score_champion_ = round_value(numpy.mean(results))\n\n for key in extra_metrics_opponents:\n extra_metrics_opponents[key] = round_value(numpy.mean(extra_metrics_opponents[key]))\n team.extra_metrics_[opponent_type] = extra_metrics_opponents","def play(self):\n if self.stats['round'] == 0:\n if self.data['personalities'] and self.data['events']:\n self.choose_opponent()\n self.resolve_conflict()\n else:\n self.stats['round'] += 1\n elif self.stats['round'] == 1:\n if self.data['locations']:\n self.choose_location()\n self.resolve_conflict()\n else:\n self.stats['round'] += 1\n else:\n print(\"You've won\")\n self.game_over = True\n return self.stats","def get_winners_of_game(self):\n return self.game_winners","def custom_score_6(game, player):\n \"\"\"custom_score_6 heuristic function aims towards weighted chances heuristics\n \"\"\"\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\n return float(length_my_player_moves*length_my_player_moves - 1.5*length_opp_payer_moves*length_opp_payer_moves)","def get_winner(game):\n return game['winner']","def build_home_advantage(matches):\n D = {}\n df_goal = matches[[\"season\", \"home_team_goal\", \"away_team_goal\"]]\n\n for i in range(len(df_goal)):\n key_season = df_goal.iloc[i].season\n if key_season not in D:\n D[key_season] = [\n 1,\n df_goal.iloc[i].home_team_goal,\n df_goal.iloc[i].away_team_goal,\n ]\n else:\n D[key_season][0] += 1\n D[key_season][1] += df_goal.iloc[i].home_team_goal\n D[key_season][2] += df_goal.iloc[i].away_team_goal\n\n for key in D:\n D[key][1] /= D[key][0]\n D[key][2] /= D[key][0]\n\n df_goal_info = pd.DataFrame(D)\n column_headers = list(df_goal_info.columns.values)\n\n fig, ax = plt.subplots(nrows=1, ncols=1)\n fig.set_size_inches(w=7, h=4)\n plt.plot(column_headers, df_goal_info.iloc[1], label=\"avg_home_goal\")\n plt.plot(column_headers, df_goal_info.iloc[2], label=\"avg_away_goal\")\n pl.xticks(rotation=270)\n plt.xlabel(\"Season\")\n plt.ylabel(\"Average Goal\")\n plt.legend()\n plt.show()","def tournament(tournament):\n participants = TournamentParticipant.objects.filter(\n tournament=tournament\n ).order_by('ranking', 'revision__competitor__name').all()\n\n prev_ranking = None\n rankings = []\n for player in participants:\n if prev_ranking is None or player.ranking != prev_ranking['ranking']:\n prev_ranking = {\n 'ranking': player.ranking,\n 'span': 1\n }\n rankings.append(prev_ranking)\n else:\n prev_ranking['span'] += 1\n rankings.append(None)\n\n return {\n 'tournament': tournament,\n 'good_progress_width': 100 * tournament.completed_duels / tournament.total_duels,\n 'bad_progress_width': 100 * tournament.failed_duels / tournament.total_duels,\n 'participants_and_rankings': list(zip(participants, rankings))\n }","def get_fifa_stats(match, player_stats):\n\n # Define variables\n match_id = int(match.match_api_id)\n date = match['date']\n players = ['home_player_1', 'home_player_2', 'home_player_3', \"home_player_4\", \"home_player_5\",\n \"home_player_6\", \"home_player_7\", \"home_player_8\", \"home_player_9\", \"home_player_10\",\n \"home_player_11\", \"away_player_1\", \"away_player_2\", \"away_player_3\", \"away_player_4\",\n \"away_player_5\", \"away_player_6\", \"away_player_7\", \"away_player_8\", \"away_player_9\",\n \"away_player_10\", \"away_player_11\"]\n\n overall_ratings = np.array([])\n for player in players:\n # Get player ID\n player_id = match[player]\n\n # Get player stats\n stats = player_stats[player_stats.player_api_id == player_id]\n\n # Identify current stats\n current_stats = stats[stats.date < date].sort_values(by='date', ascending=False).iloc[0]\n\n # get overall rating for every player, this cannot be nan since we \"dropna\" in main()\n overall_ratings = np.concatenate((overall_ratings, [current_stats[\"overall_rating\"]]))\n\n colNames = np.core.defchararray.add(players, '_overall_rating')\n player_stats_new = pd.Series(overall_ratings, index=colNames)\n player_stats_new['match_api_id'] = match_id\n # print(player_stats_new)\n return player_stats_new","def winner(self):\n\n if self.home_score > self.away_score:\n return HOME\n elif self.home_score < self.away_score:\n return VISITOR\n else:\n return TIE","def survival_score(timeSurvived, duration, winPlace):\n\n\tsurvival = (timeSurvived / duration) * 100\n\tif winPlace == 1:\n\t\twin_place = 100\n\telse:\n\t\twin_place = 100 - winPlace\n\tsurvival_score = int(survival * 0.8 + win_place * 0.2)\n\tif survival_score < 50:\n\t\tsurvival_score = 50\n\n\treturn survival_score","def give_round_scores(list_of_players):\n print(\"\\nThe round has ended !\\nWe shall now unveil the cards and the scores!\")\n\n for player in list_of_players:\n cards = [card.name for card in player.cards]\n cards_string = \" \"\n for card in cards:\n cards_string += card + \", \"\n cards_string = cards_string[:-2]\n print(\"\\n{} has these cards: \".format(player.name), cards_string)\n print(\"{} has a score of {}\".format(player.name, player.score()))\n final_scores = [player.score() for player in list_of_players]\n min_score = min(final_scores)\n winners_index = [i for i, x in enumerate(final_scores) if x == min_score]\n if len(winners_index) == 1:\n index_winner = winners_index[0]\n winner = list_of_players[index_winner]\n print(winner.name, \"won the round with a score of {}\".format(winner.score()))\n if len(winners_index) > 1:\n print(\"It's a tie!\")\n winners_names = \"\"\n winners = [list_of_players[i] for i in winners_index]\n for winner in winners:\n winners_names += winner.name\n print(winners_names, \"won the round with a score of \", str(min_score))","def combine_playoff_championship_for_each_coach(self):\n self.final_coach_with_postseason_champion_each_year = (\n self.final_coach_with_postseason_each_year\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\n .fillna(0)\n )","def calculate_my_win_strength(self):\n self.winStrength = self.strategy(deepcopy(self.currentBoardState))","def counter_opponent_win(self):\n\n # get essential values\n board = self.get_game_space()\n affinity = self.get_opponent().get_affinity()\n \n # pick the right check for the game we are playing\n if isinstance(board, Gomoku):\n \n # get the possible ways for the opponent to win\n possible_wins = board.get_wins(affinity)\n winning_blocks = board.get_winning_blocks(affinity)\n best_move = None\n\n # sort the best win to counter \n for win in possible_wins:\n if best_move is None: best_move = win\n elif win[0] <= best_move[0]: \n if win[1] >= best_move[1]:\n best_move = win\n if best_move is not None: possible_wins.remove(best_move)\n return best_move","def host_result_value(winner: Winner) -> float:\n if winner == Winner.HOME:\n return 1\n if winner == Winner.AWAY:\n return 0\n return 0.5","def get_convergence_episode(self):\n values = self.stats['return_stats']['episode_totals']\n _, y, (y_lower, _) = self._moving_average(\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\n # The convergence is established as the first time the average return\n # is above the lower bounds of the final return.\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\n return first_episode","def getTotals(dataSet):\n\ttotalWins = 0\n\ttotalLosses = 0\n\ttotalGains = 0.0\n\n\tfor row in dataSet:\n\t\tbatchNum = row[0]\n\t\tseasonCode = row[1]\n\t\tresult = row[2]\n\t\tfixtureDate = row[3]\n\t\tworstOdds = row[4]\n\t\tnumFixtures = row[5]\n\t\tif result == 1:\n\t\t\ttotalWins += 1\n\t\t\ttotalGains += (worstOdds-1.0)\n\t\telse:\n\t\t\ttotalLosses += 1\n\t\t\ttotalGains -= 1.0\n\treturn totalGains, totalWins, totalLosses"],"string":"[\n \"def get_win_rate_post_season_for_each_coach(self):\\n # get winning games for coaches\\n self.post_games_won_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # get losing games for coaches\\n self.post_games_lose_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df for post season\\n self.combine_post_games_won_lose = (\\n self.post_games_lose_for_coaches\\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"post_games_lost\\\",\\\"which_coach_for_win\\\":\\\"post_games_won\\\"})\\n .fillna(0)\\n )\",\n \"def get_champion_winrate(self, summoner_id, champion_id):\\n request = rq.get(\\n 'https://{region}.api.pvp.net/api/lol/{region}/v{api_v}/stats/by-summoner/{summ_id}/ranked?season=SEASON{year}&api_key={api_key}'\\n .format(\\n region=self.region,\\n api_v=api_version['stats'],\\n summ_id=summoner_id,\\n year=dt.today().year,\\n api_key=API_KEY\\n )\\n )\\n try:\\n check_response(request)\\n champions = request.json()['champions']\\n if champions is not None:\\n for champion in champions:\\n if champion['id'] == champion_id:\\n total_won = champion['stats']['totalSessionsWon']\\n total = total_won + champion['stats']['totalSessionsLost']\\n\\n winrate = total_won / total\\n return [winrate, total]\\n return 0, 0\\n except ServiceException:\\n return 0, 0\",\n \"def get_championship_won_for_each_coach(self):\\n self.championship_team = (\\n self.raw_data_postseason\\n .merge(self.season_max_days,how='left',on=['Season'])\\n .query(\\\"DayNum == season_max_days\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"NumOT\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"NumOT\\\":\\\"is_champion\\\",\\\"WTeamID\\\":\\\"TeamID\\\"})\\n )\",\n \"def calculateWinRate():\\n times = 10\\n winRate = 0.0\\n for i in range(times):\\n game = Game('user', 6, 6)\\n winRate += game.play(5, False, True, False, False)\\n winRate = winRate/times\\n print \\\"Winrate:\\\", winRate\",\n \"def _compute_winrates(synergy, counter, heroes_released):\\n for i in range(heroes_released):\\n for j in range(heroes_released):\\n if i != j and i != 23 and j != 23:\\n if synergy['games'][i, j] != 0:\\n synergy['winrate'][i, j] = synergy['wins'][i, j] / \\\\\\n float(synergy['games'][i, j])\\n\\n if counter['games'][i, j] != 0:\\n counter['winrate'][i, j] = counter['wins'][i, j] / \\\\\\n float(counter['games'][i, j])\",\n \"def winning_games_stats(self):\\n self.winning_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(winning_num_counts = 1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM3\\\":\\\"sum\\\",\\\"WFGA3\\\":\\\"sum\\\",\\\"WFTM\\\":\\\"sum\\\",\\\"WFTA\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"winning_num_counts\\\":\\\"sum\\\",\\n \\\"WOR\\\":\\\"sum\\\",\\\"WDR\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\n \\\"WAst\\\":\\\"sum\\\",\\\"WTO\\\":\\\"sum\\\",\\\"WStl\\\":\\\"sum\\\",\\\"WBlk\\\":\\\"sum\\\",\\\"WPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"LScore\\\":\\\"losing_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"LFGA\\\":\\\"LFGA_opp\\\",\\\"LFGM\\\":\\\"LFGM_opp\\\"})\\n )\",\n \"def winrate(matches):\\n if not matches:\\n print('no matches')\\n return None\\n\\n win_loss = [match['result'] for match in matches]\\n return sum(win_loss)/len(win_loss)\",\n \"def marcels_players(goalie, date, df):\\n # 0 = that year, 1 is year b4 ....\\n marcel_weights = [.36, .29, .21, .14]\\n reg_const = 2000\\n reg_avg = 0 # Where to regress to\\n\\n # Use past 3 season to weight games played -> Just take weighted average\\n gp_weights = [8, 4, 2, 0]\\n\\n season = int(helpers.get_season(date))\\n\\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\\n weighted_gp_sum, weights_gp_sum = 0, 0\\n\\n # Past 4 Seasons\\n for i in range(0, 4):\\n if season - i > 2006:\\n # Subset from stats df\\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\\n\\n # Sanity Check\\n if df_goalie.shape[0] > 1:\\n print(\\\"Too many rows!!!!!!!\\\")\\n exit()\\n\\n # If he played that year\\n if not df_goalie.empty:\\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\\n\\n # -> To divide by at end...normalize everything\\n weights_marcel_sum += marcel_weights[i]\\n weights_gp_sum += gp_weights[i]\\n\\n # Normalize weighted sums\\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n\\n # Get Regressed fsv%\\n if weighted_fen_sum != 0:\\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\\n else:\\n weighted_adj_fsv = 0\\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\\n\\n # Get weighted gp\\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\\n\\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}\",\n \"def get_final_coach_for_each_season(self):\\n self.final_coach_for_season = (\\n self.num_days_coach_for_season\\n .groupby(['Season','TeamID'])\\n .agg({\\\"CoachName\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"CoachName\\\":\\\"coach_counts\\\"})\\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \\\"ignore\\\")))\\n [['Season','TeamID','final_coach']]\\n )\",\n \"def cumulative_stats_for_teams_each_year(self):\\n self.cumulative_stats_for_team_each_year = (\\n self.combine_both_winning_losing_games_stats\\n .sort_values(['WTeamID','Season'])\\n .groupby(['WTeamID'])\\n .cumsum()\\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\\n .drop(['LTeamID','win_rate'],1)\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n # rebounds cumsum stats\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # assists, turnovers, steals, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win or lose by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def calc_winner(self):\\n pass\",\n \"def getSeasonStats(self):\\n df_season_agg = self.toSeasonAggFormat()\\n\\n # Calculate Possessions for each game\\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\\\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\\n\\n # Aggregate to Season Summary Level\\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\\n\\n season_stats = season_stats.rename(columns={'Win':'wins'})\\n\\n # Season Advanced Stats\\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\\n\\n season_stats.drop('DayNum', axis=1, inplace=True)\\n season_stats.drop('OppTeamID', axis=1, inplace=True)\\n season_stats.drop('rand', axis=1, inplace=True)\\n\\n return season_stats\",\n \"def rate_club(user, club):\\n if not user.is_authenticated():\\n return None\\n if not club.posel_set.exists():\\n return None\\n return sum(x[1] for x in rank_in_club(user, club)) / club.posel_set.count()\",\n \"def getWinProbability(team1, team2, r, year=2019):\\n\\tR1_PROBS = R1_PROBS_2019 if year == 2019 else R1_PROBS_2020\\n\\tALPHA_VALS = ALPHA_VALS_2019 if year == 2019 else ALPHA_VALS_2020\\n\\n\\t# Currently using Power Model\\n\\ts1 = team1['seed']\\n\\ts2 = team2['seed']\\n\\n\\t# Use R1_PROBS for round 1\\n\\tif r == 1:\\n\\t\\tif not (s1 + s2 == 17):\\n\\t\\t\\texit('Invalid round 1 matchup: seeds {0} vs. {1}.'.format(s1, s2))\\n\\t\\treturn R1_PROBS[s1] if s1 < s2 else R1_PROBS[s2]\\n\\t\\n\\t# Use ALPHA_VALS for other rounds (unless seeds are same)\\n\\tif s1 == s2:\\n\\t\\treturn 0.5\\n\\n\\talpha = ALPHA_VALS[r]\\n\\ts1a = (s1 * 1.0) ** alpha\\n\\ts2a = (s2 * 1.0) ** alpha\\n\\treturn s2a / (s1a + s2a)\",\n \"def get_winpercent(self, game: 'Game' = None, player: 'Player' = None):\\n if game and player:\\n pass\\n elif game:\\n play_count = self.play_set.filter(game=game).count()\\n win_count = self.play_set.filter(winner=self, game=game).count()\\n return win_count / play_count * 100\\n elif player:\\n pass\\n # play_count = self.play_set.filter(players__in=player).count()\\n # win_count = self.play_set.filter(\\n # winner=self, player=player).count()\\n # return win_count / play_count * 100\\n else:\\n return self.winpercent\",\n \"def get_team_round_score(self, team: int, match_round: int) -> int:\\n assert self.rounds, \\\"There are no rounds or the requested round doesn't exist\\\"\\n\\n team_score = 0\\n\\n if team == 1:\\n for player in self.rounds[match_round - 1].team1.players:\\n team_score += player.scores\\n else:\\n for player in self.rounds[match_round - 1].team2.players:\\n team_score += player.scores\\n\\n return team_score\",\n \"def get_new_ratings(players, teams):\\n nb_players_team0 = len(teams[0])\\n nb_players_team1 = len(teams[1])\\n winner = players[teams[0][0]]\\n loser = players[teams[1][0]]\\n if nb_players_team0 == 1 and nb_players_team1 == 1:\\n new_r1, new_r3 = rate_1vs1(winner,loser)\\n elif nb_players_team0 == 1 and nb_players_team1 > 1:\\n team_loser = [loser, players[teams[1][1]]]\\n (new_r1), (new_r3, new_r4) = rate([winner, team_loser], ranks=[0, 1]) \\n elif nb_players_team0 > 1 and nb_players_team1 == 1:\\n team_winner = [winner, players[teams[0][1]]]\\n (new_r1, new_r2), (new_r3) = rate([team_winner, loser], ranks=[0, 1]) \\n else:\\n team_loser = [loser, players[teams[1][1]]]\\n team_winner = [winner, players[teams[0][1]]]\\n (new_r1, new_r2), (new_r3, new_r4) = rate([team_winner, team_loser], ranks=[0, 1]) \\n player1 = {'name': teams[0][0], 'mu': new_r1.mu, 'sigma': new_r1.sigma}\\n player3 = {'name': teams[1][0], 'mu': new_r3.mu, 'sigma': new_r3.sigma}\\n if nb_players_team0 > 1:\\n player2 = {'name': teams[0][1], 'mu': new_r2.mu, 'sigma': new_r2.sigma}\\n if nb_players_team1 > 1:\\n player4 = {'name': teams[1][1], 'mu': new_r4.mu, 'sigma': new_r4.sigma}\\n if nb_players_team0 > 1:\\n return [player1, player2, player3, player4]\\n return [player1, player2, player4]\\n return [player1, player3]\",\n \"def win_ratio_avg(self):\\n win_ratio = 0\\n # Adds all the win ratios of team in this conference which will be\\n # used to compute the win ratio average.\\n for team_obj in self._conf_teams:\\n ### INVARIANT: team_obj is a Team class object and\\n ### self._conf_teams is a list of Team class objects.\\n win_ratio += team_obj._win_ratio\\n return win_ratio/len(self._conf_teams)\",\n \"def winRate(DF):\\r\\n df = DF[\\\"return\\\"]\\r\\n pos = df[df>1]\\r\\n neg = df[df<1]\\r\\n return (len(pos) / len(pos + neg)) * 100\",\n \"def evaluate(game, player):\\n weights = [2, 200, 2000, 20000]\\n reward = 0\\n opponent = get_opponent(player)\\n for length in range(2, 6):\\n reward += weights[length - 2] * get_num_series(game, player, length)\\n reward -= weights[length - 2] * get_num_series(game, opponent, length)\\n return reward\",\n \"def calc_match_points(self, match):\\n if match.winner == match.TIE:\\n match.home.tournament_score += 1\\n match.away.tournament_score += 1\\n else:\\n match.winner.tournament_score += 3\\n match.loser.tournament_score += 0\",\n \"def glicko2_summarize(df, min_weeks=4, use_prior=False):\\n\\n # Loop through seasons and weeks to create full history of ratings by team\\n results = pd.DataFrame()\\n for season in df['Season'].sort_values().unique():\\n for week in df[df['Season']==season]['Week'].sort_values().unique():\\n if week > min_weeks:\\n if week == min_weeks + 1:\\n season_df = df[df['Season']==season].copy()\\n uniqueteamids = pd.concat([season_df['VisID'],\\n season_df['HomeID']]).unique()\\n if use_prior == True and season > df['Season'].min():\\n ratings = np.repeat(1500, len(uniqueteamids))\\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\\n sigma = np.repeat(0.06, len(uniqueteamids))\\n glicko_stats = pd.DataFrame({'ratings': ratings,\\n 'ratingsdeviance': ratingsdeviance,\\n 'sigma': sigma}, index=uniqueteamids)\\n prior = results[results['Season']==season-1]\\n prior_id_mask = [True if id in uniqueteamids else False for id in prior['TeamID']]\\n prior = prior[prior_id_mask]\\n prior = prior.sort_values('Week').groupby('TeamID').tail(1)\\n prior = prior.drop('Week',1)\\n prior = prior.set_index('TeamID')\\n glicko_stats.loc[prior.index, 'ratings'] = prior['Glicko_Rating'] - (prior['Glicko_Rating'] - 1500)/2\\n glicko_stats.loc[prior.index, 'ratingsdeviance'] = prior['Glicko_Rating_Deviance'] - (prior['Glicko_Rating_Deviance'] - 350)/2\\n glicko_stats.loc[prior.index, 'sigma'] = prior['Glicko_Sigma'] - (prior['Glicko_Sigma'] - 0.06)/2\\n else:\\n ratings = np.repeat(1500, len(uniqueteamids))\\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\\n sigma = np.repeat(0.06, len(uniqueteamids))\\n glicko_stats = pd.DataFrame({'ratings': ratings,\\n 'ratingsdeviance': ratingsdeviance,\\n 'sigma': sigma}, index=uniqueteamids)\\n\\n week_df = df[(df['Season']==season) & (df['Week']<week)].copy()\\n glicko_stats = glicko2(week_df, uniqueteamids, glicko_stats)\\n\\n\\n glicko_results = glicko_stats.reset_index()\\n print(glicko_results.head(), season)\\n glicko_results.columns = ['TeamID','Glicko_Rating',\\n 'Glicko_Rating_Deviance',\\n 'Glicko_Sigma']\\n glicko_results['Season'] = season\\n glicko_results['Week'] = week\\n results = pd.concat([results, glicko_results], axis=0,\\n ignore_index=True)\\n\\n # Join the ratings to the original schedule of games\\n df = df.merge(results, left_on=['Season','Week','HomeID'],\\n right_on=['Season','Week','TeamID'],\\n suffixes=('','_Home'))\\n df.drop('TeamID', 1, inplace=True)\\n\\n df = df.merge(results, left_on=['Season','Week','VisID'],\\n right_on=['Season','Week','TeamID'],\\n suffixes=('','_Away'))\\n df.drop('TeamID', 1, inplace=True)\\n\\n # Create key and set index to join with n_game summaries dataset.\\n df.set_index(['HomeID', 'VisID', 'Season', 'Week'], inplace=True)\\n df = df[['Glicko_Rating', 'Glicko_Rating_Deviance', 'Glicko_Sigma',\\n 'Glicko_Rating_Away', 'Glicko_Rating_Deviance_Away',\\n 'Glicko_Sigma_Away']]\\n df.columns = ['Glicko_Rating_Home', 'Glicko_Rating_Deviance_Home',\\n 'Glicko_Sigma_Home', 'Glicko_Rating_Away',\\n 'Glicko_Rating_Deviance_Away', 'Glicko_Sigma_Away']\\n\\n return df\",\n \"def calc_win_lose_ratio(self):\\n total = len(self.train_y)\\n survived = 0\\n for i in self.train_y:\\n if i > 0:\\n survived += 1\\n\\n self.survival_sum = [survived, total-survived]\",\n \"def average_win_rate(strategy, baseline=always_roll(4)):\\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\\n\\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2\",\n \"def average_win_rate(strategy, baseline=always_roll(4)):\\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\\n\\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2\",\n \"def average_win_rate(strategy, baseline=always_roll(4)):\\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\\n\\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2\",\n \"def get_tourney_rounds(self, conference, year):\\n ts_dict = self.get_tourney_slots()\\n seed_dict = self.get_tourney_seeds()\\n tr_dict = self.get_tourney_results()\\n \\n round_1 = list()\\n round_2 = list()\\n round_3 = list()\\n round_4 = list()\\n winner = list()\\n \\n round1_winners = list()\\n for seed, team in seed_dict[year].items():\\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n round1_winners.append(seed[1:])\\n #removes duplicates because I did this part weirdly... HEHEH\\n round1_winners = list(set(round1_winners))\\n\\n win_counter = defaultdict(int)\\n for seed, team in seed_dict[year].items(): \\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n win_counter[winning] += 1\\n \\n for slot, matchup in ts_dict[year].items():\\n \\n if conference in slot and \\\"R1\\\" in slot: \\n round_1.append(\\\"{}-{}\\\".format(matchup[1:3], matchup[-2:]))\\n round_1 = sorted(round_1)\\n #for match in round_1:\\n for winner1 in round1_winners:\\n if winner1 in round_1[0]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-1]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[1]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-2]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[2]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-3]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[3]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-4]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_2 = sorted(round_2)\\n\\n round2_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 1:\\n round2_winners.append(seed[1:])\\n \\n for winner1 in round2_winners:\\n if winner1 in round_2[0]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-1]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_2[1]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-2]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_3 = sorted(round_3)\\n\\n round3_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 2:\\n round3_winners.append(seed[1:])\\n\\n for winner1 in round3_winners:\\n if winner1 in round_3[0]:\\n for winner2 in round3_winners:\\n if winner2 in round_3[-1]:\\n round_4.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_4 = sorted(round_4)\\n\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 3:\\n winner.append(seed[1:])\\n\\n conferences = {\\\"W\\\": \\\"East\\\", \\\"X\\\": \\\"Midwest\\\", \\\"Y\\\": \\\"South\\\", \\\"Z\\\": \\\"West\\\"}\\n\\n #print(\\\"CONFERENCE: {}, YEAR: {}\\\".format(conferences[conference], year))\\n #print(\\\"ROUND1:\\\", round_1)\\n #print(\\\"ROUND2:\\\", round_2)\\n #print(\\\"ROUND3:\\\", round_3)\\n #print(\\\"ROUND4:\\\", round_4)\\n #print(\\\"WINNER:\\\", winner)\\n\\n #clearing out the tourney results dictionary\\n #tr_dict.clear()\\n\\n return round_1, round_2, round_3, round_4, winner\",\n \"def get_win_percentage(self) -> float:\\n if self.wins == 0:\\n return 0.0\\n else:\\n return round((self.wins / (self.wins + self.losses)) * 100, 2)\",\n \"def winning_percentage(self):\\n return float(len(self.wins))/len((self.wins+self.losses))\",\n \"def get_player_career_reg_season_stats(player_id, player_data_url, headers):\\n\\tplayer_data_json=json.loads(requests.get(player_data_url+str(player_id), headers=headers).content)\\n\\tcareer_totals=player_data_json['resultSets'][1]\\n\\tdf_career_totals=pd.DataFrame.from_records(career_totals['rowSet'], columns=career_totals['headers'])\\n\\tdf_career_totals.PLAYER_ID=df_career_totals.PLAYER_ID.astype(int)\\n\\treturn df_career_totals\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"def update_scores(self, AI_win):\\n self.games_played += 1\\n if not AI_win:\\n self.games_won += 1\\n else:\\n self.games_lost += 1\",\n \"def team_season_stats(team):\\n\\n # Get HTML Content\\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # Team's yearly stats are displayed in a table\\n season_stats = soup.find(id='stats').find('tbody')\\n\\n # Iterate through each year\\n for year in season_stats.find_all('tr', {'class': None}):\\n\\n season_year = year.find('th').text[0:4]\\n season_year = int(season_year) + 1\\n season = {'year': season_year}\\n\\n # Loop through each stat\\n for stat in year.find_all('td'):\\n season[stat['data-stat']] = stat.string\\n\\n # Rename relocated teams\\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\\n season['_id'] = season['team_id'] + '_' + str(season_year)\\n\\n # Remove unwanted stats\\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\\n for k in to_remove:\\n season.pop(k, None)\\n\\n # Add to MongoDB\\n m.insert('team_season', season)\",\n \"def assign_win_points(game_dict):\\n def win_tuple(deck_dict):\\n \\\"\\\"\\\" Return tuple ordered by increasing final standing. \\\"\\\"\\\"\\n # negate turns so that max() behaves; points good, turns bad.\\n num_normal_turns = sum(not ( (POSSESSION in t and t[POSSESSION]) or \\\\\\n (OUTPOST in t and t[OUTPOST]))\\n for t in deck_dict[TURNS])\\n return (deck_dict[POINTS], -num_normal_turns)\\n\\n decks = game_dict[DECKS]\\n winner_tuple = max(win_tuple(p) for p in decks)\\n winners = [p for p in decks if win_tuple(p) == winner_tuple]\\n\\n win_points = float(len(decks)) / len(winners)\\n for player in decks:\\n player[WIN_POINTS] = win_points if player in winners else 0.0\",\n \"def custom_score_2(game, player):\\n \\n # get avaliable moves for each player\\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n \\n # shortcut to definite state:\\n # 1. my agent win -> return very high score\\n if opp_moves == 0:\\n return float(\\\"inf\\\")\\n # 2. opponenent's agent win -> return very low score\\n elif own_moves == 0:\\n return float(\\\"-inf\\\")\\n\\n # score: avaliable moves ratio\\n return float(own_moves/opp_moves)\",\n \"def getPlayerAdvStat(self, stat, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_call = stat.lower()\\r\\n stat_dict = {'touch':'Possessions', 'possession':'Possessions',\\r\\n 'speed':'SpeedDistance', 'distance':'SpeedDistance'}\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashptstats?College=&'\\\\\\r\\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\\\\r\\n 'DraftPick=&DraftYear=&GameScope=&Height=&LastNGames=0&'\\\\\\r\\n 'LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&'\\\\\\r\\n 'PORound=0&PerMode=PerGame&PlayerExperience=&PlayerOr'\\\\\\r\\n 'Team=Player&PlayerPosition=&PtMeasureType=' + \\\\\\r\\n stat_dict[stat_call] + '&Season=' + season + \\\\\\r\\n '&SeasonSegment=&SeasonType=Regular+Season&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision=&Weight='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n advStat_df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n return advStat_df\",\n \"def opponents_score(self):\\n if self.opponent_wickets == 10:\\n var1 = \\\"All Out\\\"\\n return str('{0} {1}').format(self.opponent_runs, var1)\\n else:\\n var1 = self.opponent_wickets\\n return str('{0}-{1}').format(self.opponent_runs, var1)\",\n \"def calc_marcel_weights(df):\\n cols = ['toi_on', 'gs60', 'toi/gp']\\n\\n # Only essential columns\\n df = df[[\\\"player\\\", \\\"player_id\\\", \\\"season\\\"] + cols]\\n\\n # Copy over ALL Data\\n predict_df = df.copy()\\n\\n # To get fours years in a row (3 predicting 1).. we run 'get_previous_year' 3 times\\n # Each time we get n-_ by using predict_col\\n # NOTE: I'm writing over df here!!!!\\n for seasons in range(1, 4):\\n df = get_previous_yr(df, predict_df, seasons)\\n df = df[~df['toi_on_n-{}'.format(seasons)].isnull()]\\n\\n # Filter for minimum toi\\n # 400 for first 3 and 800 for last\\n df = df[(df['toi_on'] >= 800) & (df['toi_on_n-1'] >= 400) & (df['toi_on_n-2'] >= 400) & (df['toi_on_n-3'] >= 400)]\\n\\n print(\\\"\\\\nPlayers: {}\\\".format(df.shape[0]))\\n\\n for col in ['gs60', 'toi/gp']:\\n print(\\\"Getting the Weights for: \\\", col)\\n # Prepare shit\\n model_features = df[['{}_n-1'.format(col), '{}_n-2'.format(col), '{}_n-3'.format(col)]].values.tolist()\\n model_target = df[col].values.tolist()\\n model_features, model_target = np.array(model_features), np.array(model_target).ravel()\\n\\n lr = LinearRegression()\\n lr.fit(model_features, model_target)\\n\\n # Print all the Coefficient neatly\\n print(\\\"Coefficients:\\\")\\n for season, coef in zip(range(1, 4), lr.coef_):\\n print(\\\"Season n-{}:\\\".format(season), round(coef, 3))\\n\\n print(\\\"\\\")\",\n \"def compute_score(window, computer_piece):\\n score = 0\\n if window.count(computer_piece) == 4:\\n score += 100\\n elif window.count(computer_piece) == 3 and window.count(0) == 1:\\n score += 5\\n elif window.count(computer_piece) == 2 and window.count(0) == 2:\\n score += 2\\n if window.count(PLAYER_PIECE) == 2 and window.count(0) == 2:\\n score -= 1\\n if window.count(PLAYER_PIECE) == 3 and window.count(0) == 1:\\n score -= 100\\n return score\",\n \"def play_simulation(self, n_iter=1000):\\r\\n final_wins = final_wins = {t: 0 for t in self.teams.dteams.keys()}\\r\\n season_teams_ranked, _ = get_playoffs(self.season_calendar, self.teams_info)\\r\\n for i in tqdm(range(n_iter)):\\r\\n if not self.playoffs_only:\\r\\n self.season.play_regular_season(self.teams)\\r\\n season_teams_ranked = self.season.playoffs_teams_ranked\\r\\n\\r\\n playoffs_sim = Playoffs(season_teams_ranked, self.gsim)\\r\\n winner_playoff = playoffs_sim.get_winner(self.teams)\\r\\n\\r\\n final_wins[winner_playoff] += 1\\r\\n\\r\\n return final_wins\",\n \"def getTeamStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\\\\r\\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\\\\r\\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n team_df = df[[\\\"TEAM_ID\\\",\\\"TEAM_NAME\\\",\\\"GP\\\",\\\"W\\\",\\\"L\\\",\\\"W_PCT\\\",\\\"MIN\\\",\\\"FGM\\\",\\r\\n \\\"FGA\\\",\\\"FG_PCT\\\",\\\"FG3M\\\",\\\"FG3A\\\",\\\"FG3_PCT\\\",\\\"FTM\\\",\\\"FTA\\\",\\\"FT_PCT\\\",\\r\\n \\\"OREB\\\",\\\"DREB\\\",\\\"REB\\\",\\\"AST\\\",\\\"TOV\\\",\\\"STL\\\",\\\"BLK\\\",\\\"BLKA\\\",\\\"PF\\\",\\r\\n \\\"PFD\\\",\\\"PTS\\\",\\\"PLUS_MINUS\\\"]]\\r\\n \\r\\n return team_df\",\n \"def get_team_scores(self, team, include_home=True, include_away=True):\\n if include_away:\\n away_games = list(filter(lambda g: team == g.AwayTeam, self.games))\\n else:\\n away_games = []\\n\\n if include_home:\\n home_games = list(filter(lambda g: team == g.HomeTeam, self.games))\\n else:\\n home_games = []\\n\\n scored_h = [g.FTHG for g in home_games]\\n scored_a = [g.FTAG for g in away_games]\\n\\n conceded_h = [g.FTAG for g in home_games]\\n conceded_a = [g.FTHG for g in away_games]\\n\\n try:\\n mean_gd = mean(scored_h + scored_a) - mean(conceded_h + conceded_a)\\n home_gd = mean(scored_h) - mean(conceded_h)\\n home_adv = home_gd - mean_gd\\n except Exception:\\n home_adv = 0\\n\\n return {\\n \\\"scored_xg\\\": scored_h + scored_a,\\n \\\"conceded_xg\\\": conceded_h + conceded_a,\\n \\\"home_adv\\\": home_adv,\\n \\\"expected_points\\\": self.get_table(metric='points')[team] /\\n len(home_games + away_games)\\n }\",\n \"def get_result(state, winrate_predictor):\\n teamA_picks = state[:, TEAM_A_PICK_INDICES]\\n teamB_picks = state[:, TEAM_B_PICK_INDICES]\\n team_comp = torch.cat((teamA_picks, teamB_picks), dim=1)\\n winrate = winrate_predictor(team_comp)[0, 0]\\n \\n if winrate >= 0.5:\\n return 0\\n return 1\",\n \"def year_cost_rule(_m, y):\\r\\n\\r\\n return sum(m.RHO[y, s] * m.SCEN[y, s] for s in m.S)\",\n \"def ucbScore(self,totalPlayedTimes):\\n winRate = self.winRate()\\n #print totalPlayedTimes\\n #print self.playedTimes\\n confidenceInterval = math.sqrt(2 * math.log(totalPlayedTimes,math.e) / self.playedTimes)\\n \\n return winRate + confidenceInterval\",\n \"def review(self):\\n # Compile standings\\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\\n # Name a champion\\n self.champion = self._name_champion()\\n self.league.history.champions_timeline[self.year] = self.champion\\n print \\\"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\\\".format(\\n self.champion.team.name.upper(), self.year, self.league.name.upper()\\n )\\n # Compile league leaders\\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\\n # Have each team review its season, as well\\n for team_season in self.teams:\\n team_season.review()\\n # Send the league into the offseason\\n self.league.season = None\",\n \"def win_rate(returns_pred, returns_real, symbol=None, horizon=None):\\n # Input processing\\n if not horizon:\\n horizons = [1, 3, 5, 10, 20, 40, 60, 90, 120]\\n elif type(symbol) == str:\\n return SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon)\\n else:\\n horizons = [horizon]\\n\\n # Data frame skeleton\\n win_rate_all = pd.DataFrame(index=horizons)\\n\\n # Compute win rate for each symbol\\n for symbol in returns_pred.columns:\\n win_rate = []\\n for horizon in horizons:\\n win_rate.append(SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon))\\n win_rate_all[symbol] = win_rate\\n\\n # Compute statistics across all symbols\\n win_rate_all = win_rate_all.agg(['mean', 'std'], axis=1).merge(win_rate_all, left_index=True, right_index=True)\\n\\n # Formatting\\n cm = sns.light_palette(\\\"green\\\", as_cmap=True)\\n return win_rate_all.style.background_gradient(cmap=cm).format(\\\"{:.1%}\\\")\",\n \"def first_round_history(self):\\n self.ts_dict = self.get_tourney_slots()\\n self.tsr_dict = self.match_seeds()\\n first_seed_win = 0\\n second_seed_win = 0\\n third_seed_win = 0\\n fourth_seed_win = 0\\n fifth_seed_win = 0\\n sixth_seed_win = 0\\n seventh_seed_win = 0\\n eighth_seed_win = 0\\n total_games = 128\\n\\n for year1 in self.ts_dict: \\n for slot, match_up in self.ts_dict[year1].items():\\n if slot[:2] == \\\"R1\\\":\\n for year2 in self.tsr_dict:\\n if year1 == year2:\\n for winning, losing in self.tsr_dict[year2].items():\\n if winning[5:] == match_up[:3]:\\n seed = winning[6:] \\n if seed == \\\"01\\\":\\n first_seed_win += 1\\n elif seed == \\\"02\\\":\\n second_seed_win += 1\\n elif seed == \\\"03\\\":\\n third_seed_win += 1\\n elif seed == \\\"04\\\":\\n fourth_seed_win += 1\\n elif seed == \\\"05\\\":\\n fifth_seed_win += 1\\n elif seed == \\\"06\\\":\\n sixth_seed_win += 1\\n elif seed == \\\"07\\\":\\n seventh_seed_win += 1\\n elif seed == \\\"08\\\":\\n eighth_seed_win += 1 \\n \\n #print(first_seed_win, second_seed_win, third_seed_win, fourth_seed_win, fifth_seed_win, sixth_seed_win, seventh_seed_win, eighth_seed_win, total_games)\\n\\n gauge = pygal.SolidGauge(inner_radius=0.70, title=\\\"NCAA First Round Results\\\")\\n ratio_first_seed = int(first_seed_win / total_games * 100)\\n ratio_second_seed = int(second_seed_win / total_games * 100)\\n ratio_third_seed = int(third_seed_win / total_games * 100)\\n ratio_fourth_seed = int(fourth_seed_win / total_games * 100)\\n ratio_fifth_seed = int(fifth_seed_win / total_games * 100)\\n ratio_sixth_seed = int(sixth_seed_win / total_games * 100)\\n ratio_seventh_seed = int(seventh_seed_win / total_games * 100)\\n ratio_eighth_seed = int(eighth_seed_win / total_games * 100) \\n\\n percent_formatter = lambda x: '{:.10g}%'.format(x)\\n gauge.value_formatter = percent_formatter\\n gauge.add('1 vs. 16', [{'value': ratio_first_seed, 'max_value': 100}])\\n gauge.add('2 vs. 15', [{'value': ratio_second_seed, 'max_value': 100}])\\n gauge.add('3 vs. 14', [{'value': ratio_third_seed, 'max_value': 100}])\\n gauge.add('4 vs. 13', [{'value': ratio_fourth_seed, 'max_value': 100}])\\n gauge.add('5 vs. 12', [{'value': ratio_fifth_seed, 'max_value': 100}])\\n gauge.add('6 vs. 11', [{'value': ratio_sixth_seed, 'max_value': 100}])\\n gauge.add('7 vs. 10', [{'value': ratio_seventh_seed, 'max_value': 100}])\\n gauge.add('8 vs. 9', [{'value': ratio_eighth_seed, 'max_value': 100}])\\n \\n gauge.render_to_file('chart.svg')\",\n \"def _adv_counter(winrate_together, winrate_hero1, winrate_hero2):\\n return winrate_together\",\n \"def calculate_power_ranking(wins, losses, is_winning_streak,\\n streak_length, recent_wins, recent_losses):\\n pred1 = 0\\n pred2 = round( ( ((wins*1.0)/(wins+losses)) - 0.500 ) * 16 * 9 * 10/9 )\\n pred3 = recent_wins - recent_losses\\n streak_factor = 1 if is_winning_streak else -1\\n pred4 = streak_factor * round( ( streak_length - 1 ) / 2.0 )\\n print pred1, pred2, pred3, pred4\\n return pred1 + pred2 + pred3 + pred4\",\n \"def scrape():\\n league_year = Config.get_property(\\\"league_year\\\")\\n\\n # Create table\\n season_data = client.season_schedule(league_year)\\n season_data = br_enum_to_string(season_data)\\n return season_data\",\n \"def evaluateWinner(self):\\n\\t\\tif self.pots[-1] == 0:\\n\\t\\t\\tself.pots.pop()\\n\\t\\tlivePlayers = self.getLivePlayers()\\t\\n\\t\\tfor i in range(len(self.pots)):\\n\\t\\t\\tplayers = self.getPlayersInPot(i, livePlayers)\\n\\t\\t\\tevaluations = []\\n\\t\\t\\tfor x in players:\\n\\t\\t\\t\\tcombined = x.hand + self.communityCards\\n\\t\\t\\t\\tevaluations.append((x, self.evaluator.getRankOfSeven(\\tcombined[0], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[1], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[2], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[3], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[4], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[5], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[6] )))\\n\\t\\t\\twinners = self.getWinners(evaluations, i)\\n\\t\\t\\tself.handOutMoney(winners, i)\\n\\t\\t\\tself.potwinQ.append(winners[0].name)\",\n \"def custom_score(game, player):\\n \\n # get avaliable moves for each player\\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n \\n # return different between # of my agent's move and oppenent's\\n return float(own_moves - opp_moves)\",\n \"def _get_current_teams_score(self):\\n for game in self._get_live_games():\\n teams_playing = [x['abbreviation'] for index, x in game['teams'].items()]\\n if self.team in teams_playing:\\n # Our team is playing in this game, get the score \\n return int(game['scores'][self.team])\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def season_rounds(cls, season):\\r\\n\\t\\t\\r\\n\\t\\tfolder_name = cls.season_folder(season)\\r\\n\\t\\tround_list = os.listdir(f'Data/{folder_name}')\\r\\n\\r\\n\\t\\tall_rounds = []\\r\\n\\r\\n\\t\\tfor round_file in round_list:\\r\\n\\t\\t\\twith open(f'Data/{folder_name}/{round_file}', 'r', encoding='utf-8') as f:\\r\\n\\t\\t\\t\\tround_info = f.read().splitlines()\\r\\n\\r\\n\\t\\t\\tround_number = round_file[:-4]\\r\\n\\t\\t\\tfull_round_name = f\\\"{season} R{round_number}\\\"\\r\\n\\r\\n\\t\\t\\tround_date = int(round_info[0])\\r\\n\\t\\t\\tlookup_ind = DATES.month_diff(round_date, DATES.MIN_DATE)\\r\\n\\r\\n\\t\\t\\t# If the round isn't actually counted for TWOW Glicko\\r\\n\\t\\t\\tif full_round_name not in cls.ROUNDS[lookup_ind].keys():\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\r\\n\\t\\t\\tcontestant_count = len(round_info) - 1\\r\\n\\r\\n\\t\\t\\tstrength = cls.ROUNDS[lookup_ind][full_round_name][0]\\r\\n\\r\\n\\t\\t\\tall_rounds.append([\\r\\n\\t\\t\\t\\tround_number,\\r\\n\\t\\t\\t\\tround_date,\\r\\n\\t\\t\\t\\tcontestant_count,\\r\\n\\t\\t\\t\\tstrength\\r\\n\\t\\t\\t])\\r\\n\\t\\t\\r\\n\\t\\treturn all_rounds\",\n \"def eval(self):\\n\\n ratio_player_win = self.player_wins / self.num_test\\n ratio_opponent_win = self.opponent_wins / self.num_test\\n ratio_tie = 1.0 - ratio_player_win - ratio_opponent_win\\n\\n print(\\\"\\\\nPlayer Test Results:\\\")\\n print(\\\"\\\\tWins {0:.2f}%\\\".format(100.0 * ratio_player_win))\\n print(\\\"\\\\tLosses {0:.2f}%\\\".format(100.0 * ratio_opponent_win))\\n print(\\\"\\\\tTie {0:.2f}%\\\".format(100.0 * ratio_tie))\\n\\n ratio_optimal_win = self.optimal_wins / self.num_test\\n ratio_optimal_loose = self.optimal_losses / self.num_test\\n ratio_optimal_tie = 1.0 - ratio_optimal_win - ratio_optimal_loose\\n\\n print(\\\"\\\\nOptimal Results:\\\")\\n print(\\\"\\\\tPlayer {0:.2f}%\\\".format(100.0 * ratio_optimal_win))\\n print(\\\"\\\\tOpponent {0:.2f}%\\\".format(100.0 * ratio_optimal_loose))\\n print(\\\"\\\\tTie {0:.2f}%\\\".format(100.0 * ratio_optimal_tie))\\n\\n # Ratio of win, loss diff between player and optimal\\n # positive if the player beats opponent\\n relative_result = ((ratio_player_win - ratio_opponent_win) /\\n (ratio_optimal_win - ratio_optimal_loose))\\n\\n print(\\\"\\\\nResults Player Relative Optimal:\\\")\\n print(\\\"\\\\tWins {0:.2f}%\\\".format(100.0 * ratio_player_win / ratio_optimal_win))\\n print(\\\"\\\\tLosses {0:.2f}%\\\".format(100.0 * ratio_opponent_win / ratio_optimal_loose))\\n print(\\\"\\\\tScore {0:.2f}%\\\".format(100.0 * relative_result))\\n\\n if self.last_test is not None:\\n print(\\\"Diff from last test score is {0:.2f}%\\\".format(100.0 * (relative_result - self.last_test)))\\n self.last_test = relative_result\",\n \"def get_smmry_data(soup, game_dict):\\n\\n # Get date and time data.\\n try:\\n date_soup = soup.find(\\\"div\\\", {\\\"class\\\": \\\"spielbericht_tipp_status\\\"})\\n league = date_soup.div.span.text\\n date_string = date_soup.div.text\\n date = re.search(r'\\\\d{2}.\\\\d{2}.\\\\d{2}', date_string).group(0)\\n time = re.search(r'\\\\d{2}:\\\\d{2}', date_string).group(0)\\n matchday = re.search(r'[|]\\\\d+', date_string).group(0)[1:]\\n\\n game_dict[\\\"league\\\"] = league\\n game_dict[\\\"fb_date\\\"] = date\\n game_dict[\\\"fb_time\\\"] = time\\n game_dict[\\\"matchday\\\"] = matchday\\n except AttributeError:\\n pass\\n\\n # Get game result.\\n try:\\n result = soup.find(\\\"div\\\", {\\\"class\\\": \\\"stand\\\"}).text\\n game_dict[\\\"result\\\"] = result\\n except AttributeError:\\n pass\\n\\n # Try to get the referee name.\\n try:\\n referee = soup.find(\\\"span\\\", {\\\"class\\\": \\\"schiri_link\\\"}).text\\n game_dict[\\\"referee\\\"] = referee\\n except AttributeError:\\n pass\\n\\n # Get team, club name and repective url by team.\\n try:\\n smmry_soup = soup.find(\\n \\\"div\\\", {\\\"class\\\": \\\"spielbericht_ergebnis_wrapper\\\"})\\n club_title = smmry_soup.find_all(\\\"img\\\")\\n team_title = smmry_soup.findAll(\\\"div\\\", {\\\"class\\\": \\\"teaminfo\\\"})\\n\\n # Loop through teams.\\n for j, team in enumerate([\\\"home_\\\", \\\"away_\\\"]):\\n game_dict[team + \\\"team\\\"] = team_title[j].a[\\\"title\\\"]\\n game_dict[team + \\\"team_url\\\"] = team_title[j].a[\\\"href\\\"]\\n game_dict[team + \\\"club\\\"] = club_title[j][\\\"title\\\"]\\n except (AttributeError, TypeError):\\n pass\\n\\n return game_dict\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def season_game_logs(team, year):\\n\\n # Check year value\\n if year > 2019 or year < 1950:\\n raise ValueError('Year Value Incorrect')\\n\\n # Rename teams that moved\\n team = scrape_utils.rename_team(team, year)\\n\\n # Get HTML content\\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n season_stats = soup.find(id='tgl_basic')\\n games = season_stats.find('tbody')\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # To find opponent statistics\\n opponent = re.compile('^opp_.*$')\\n\\n # Loop through every game in a team's season\\n for game in games.find_all('tr', {'class': None}):\\n\\n curr_team = {'team': team}\\n opp_team = {}\\n\\n # Loop through each stat\\n for stat in game.find_all('td'):\\n\\n stat_name = stat['data-stat']\\n\\n # These are opponent stats\\n if re.match(opponent, stat_name):\\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\\n else:\\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\\n\\n # Remove unnecessary information\\n del curr_team['game_season']\\n del curr_team['x']\\n\\n # Rename relocated teams\\n curr_team['team'] = scrape_utils.rename_team(team)\\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\\n\\n # Use the same ID as basketball reference\\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \\\"%Y-%m-%d\\\"),\\n 'season': year,\\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\\n '_id': game.find('a')['href'][-17:-5]}\\n\\n # Place the teams in the correct spot depending on who is the home team\\n if curr_team.pop('game_location') == 0:\\n result['home'] = curr_team\\n result['away'] = opp_team\\n else:\\n result['home'] = opp_team\\n result['away'] = curr_team\\n\\n # Insert into database\\n m.insert('game_log', result)\",\n \"def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\\n battleData, htmlData = loadBattleData(league)\\n similarities = loadSims() \\n \\n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\\n if len(oppTeam) == 0:\\n picks = set([])\\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\\n picks.add(random.randint(0,len(battleData)-1))\\n\\n losers = []\\n loserDict = {}\\n for i in picks:\\n entry = battleData[i]\\n winner,loser = determineWinner(entry)\\n loserDict[str(loser)] = [winner]\\n losers.append( (loser,0) )\\n\\n #Given opponent team then find similar teams\\n else:\\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\\n\\n #create dictionary from losers team to the team that beat them.\\n loserDict = {}\\n sims = []\\n for d in battleData:\\n winner, loser = determineWinner(d)\\n\\n wTeam = teamToArray(winner,pokedex)\\n lTeam = np.array(teamToArray(loser, pokedex))\\n\\n score = 0\\n for oppNp in oppTeam:\\n score+= np.amax(lTeam*oppNp) \\n\\n if str(loser) in loserDict:\\n loserDict[str(loser)].append(winner)\\n else:\\n #new to dictonary\\n loserDict[str(loser)] = [winner]\\n\\n sims.append((loser, score))\\n\\n\\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\\n\\n cutoff = min(len(sims),NUMLOSINGTEAMS)\\n losers = sims[:cutoff]\\n\\n #Gather winners to losing teams\\n winnersComp = []\\n for loser,_ in losers:\\n for winner in loserDict[str(loser)]:\\n winnersComp.append(teamToArray(winner,pokedex))\\n \\n topScore = len(winnersComp)*6 #pkmn team size\\n\\n results = []\\n inverted_idx = {}\\n\\n existsSet = []\\n\\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\\n for i in range(len(curTeams)):\\n team = curTeams[i]\\n results.append((team,0))\\n sTeam = set(team)\\n if not (sTeam in existsSet):\\n existsSet.append(sTeam)\\n for pkm in team:\\n if pkm != EMPTY:\\n if pkm in inverted_idx:\\n inverted_idx[pkm].append(i)\\n else:\\n inverted_idx[pkm] = [i]\\n \\n #Giving the similiarity scores to the winners based off of the inverted index.\\n for pkm in inverted_idx:\\n for winner in winnersComp:\\n wArr = np.array(winner)\\n #tArr = getSimPokemon(pkm,similarities)\\n tArr = similarities[pkm]\\n \\n vals = wArr * tArr\\n\\n score = np.amax(vals)\\n\\n for i in inverted_idx[pkm]:\\n results[i] = (results[i][0],results[i][1]+(score/topScore))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n\\n if len(results) < NUMTEAMSRETURN:\\n if len(results) == 0:\\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\\n\\n else:\\n returnTeams = [result[0] for result in results]\\n teamScores = [result[1] for result in results]\\n else:\\n firstResult, firstScore = results[0]\\n returnTeams = [firstResult]\\n teamScores = [round(firstScore*100,1)]\\n returnSets = [set(firstResult)]\\n \\n i = 1\\n\\n #Loops through results and adds teams with the proper edit distance away.\\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\\n teamToConsider,teamToConsiderScore = results[i]\\n \\n considerSet = set(teamToConsider)\\n add = True\\n ##checks the edit distance of teams is above wanted\\n for team in returnSets:\\n if len(team.union(considerSet)) < len(team)+minDistWanted:\\n add = False\\n\\n ##If indeed above wanted levels then add\\n if add:\\n returnTeams.append(teamToConsider)\\n returnSets.append(considerSet)\\n teamScores.append(round(teamToConsiderScore*100,1))\\n \\n i+=1\\n\\n if i >= len(results):\\n i = 1\\n minDistWanted -= 1 \\n \\n winHtmls = []\\n if htmlData != None:\\n for team,_ in losers:\\n for winner in loserDict[str(team)]:\\n winHtmls.extend(htmlData[str(sorted(winner))])\\n \\n\\n return returnTeams, teamScores, winHtmls\",\n \"def calculate(self, prev_scores):\\n self.set_scores(prev_scores)\\n for match in self.week.matches:\\n if match.played:\\n # Fetch old scores\\n winner_score = float(prev_scores[match.winner])\\n loser_score = float(prev_scores[match.loser])\\n\\n # Update this ranking's scores\\n score_delta = loser_score * 0.1\\n self.score[match.winner] = winner_score + score_delta\\n self.score[match.loser] = loser_score - score_delta\",\n \"def score(self,player, board):\\r\\n numPlayer = 0\\r\\n numOpp = 0\\r\\n for i in self.squares():\\r\\n if board[i] == player:\\r\\n numPlayer+= SQUARE_WEIGHTS[i]\\r\\n else:\\r\\n numOpp+=SQUARE_WEIGHTS[i]\\r\\n return numPlayer-numOpp\",\n \"def calc_calories(gpx_track, wt = 175, activity='Run'):\",\n \"def create_dreamteam_count_yearly(player, raw_list):\\n num_seasons = 0\\n yearly_dreamteam_count = 0\\n for i, raw in enumerate(raw_list):\\n if i == len(raw_list) - 1:\\n break\\n if player in raw['name'].values:\\n num_seasons += 1\\n yearly_dreamteam_count += raw[['dreamteam_count']][raw.name == player].values[0][0]\\n if num_seasons == 0:\\n return np.nan\\n return yearly_dreamteam_count / num_seasons\",\n \"def calculate_scores(self):\\n words = self.walk_board()\\n player_scores = {}\\n for word in words:\\n player = word.get_owning_player()\\n if player not in player_scores:\\n player_scores[player] = 0\\n player_scores[player] += word.get_score()\\n return player_scores\",\n \"def play_game(game,standings_):\\n rand_nmr = random.random()\\n\\n standings_.loc[standings_.TEAMS==game['Home'],'MP'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'MP'] += 1\\n\\n if rand_nmr < game['Prob Home']:\\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\\n standings_.loc[standings_.TEAMS==game['Home'],'W'] += 1\\n standings_.loc[standings_.TEAMS==game['Home'],'F'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Away'],'L'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'A'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Home']][\\\"h2h\\\"].apply(lambda x:x.append(game['Away']))\\n\\n return 0\\n\\n elif rand_nmr < game['Prob Home'] + game['Prob Draw']:\\n # all draws end in 0-0 this can be improved\\n standings_.loc[standings_.TEAMS==game['Home'],'D'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'D'] += 1\\n\\n return 1\\n\\n else:\\n n_goals = goals() # a random number of goals is added to the goal tally, all games and in 1-0,2-0,3-0 or 4-0. This can be improved\\n standings_.loc[standings_.TEAMS==game['Away'],'W'] += 1\\n standings_.loc[standings_.TEAMS==game['Away'],'F'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Home'],'A'] += 1\\n standings_.loc[standings_.TEAMS==game['Home'],'L'] += n_goals\\n standings_.loc[standings_.TEAMS==game['Away']][\\\"h2h\\\"].apply(lambda x:x.append(game['Home']))\\n\\n return 2\",\n \"def evaulate_monster(generikmon):\\r\\n score = generikmon['headA']\\r\\n #score = generikmon['chinA']\\r\\n return score\",\n \"def get_winner(self):\\n winner: Player = Player('none')\\n points_winner = 0\\n for player in self.players:\\n for key, value in player.get_stats().items():\\n print('{}: {}'.format(key, value))\\n if key == 'points':\\n if value >= points_winner:\\n winner = player\\n print()\\n\\n print('The winner is: ' + winner.get_name())\\n return winner\",\n \"def winningTeamPenalty(r):\\n \\n #Check if home or away had more goals at the 'event' time\\n homecheck = int(r['about.goals.home'] > r['about.goals.away'])\\n awaycheck = int(r['about.goals.away'] > r['about.goals.home'])\\n \\n #If home had more goals and the penalty was on the home team, set to 1\\n if (homecheck > 0) and (r['against.homeTeam'] == 1):\\n return 1\\n #If away had more and the penalty was not on home team, set to 1\\n if (awaycheck > 0) and (r['against.homeTeam'] == 0):\\n return 1\\n #Any other situation should be a zero in this column\\n else:\\n return 0\",\n \"def get_team_stats(players: list[Player]) -> dict[int]:\\n\\n team_stats = {}\\n\\n total_reaction = 0\\n total_mechanical_skill = 0\\n total_tactical_skill = 0\\n total_game_knowledge = 0\\n total_xp = 0\\n\\n for player in players:\\n total_reaction += player.reaction\\n total_mechanical_skill += player.mechanical_skill\\n total_tactical_skill += player.tactical_skill\\n total_game_knowledge += player.game_knowledge\\n total_xp += player.xp\\n\\n team_stats.update(\\n {\\\"reaction\\\": total_reaction,\\n \\\"mechanical_skill\\\": total_mechanical_skill,\\n \\\"tactical_skill\\\": total_tactical_skill,\\n \\\"game_knowledge\\\": total_game_knowledge,\\n \\\"xp\\\": total_xp})\\n\\n return team_stats\",\n \"async def player_ratio(self, ctx):\\r\\n player = ctx.message.content.split(' ')[1]\\r\\n if os.environ.get(\\\"WoW_Token\\\") is None:\\r\\n return\\r\\n else:\\r\\n async with aiohttp.ClientSession().get('https://us.api.battle.net/wow/character/zul\\\\'jin/' + player + '?fields=pvp&locale=en_US&apikey=' + os.environ.get(\\\"WoW_Token\\\")) as res:\\r\\n if res.status == 200:\\r\\n data = await res.json()\\r\\n player_pvp_stats = data['pvp']['brackets']['ARENA_BRACKET_3v3']\\r\\n await ctx.message.channel.send(u\\\"Player: {:s}\\\").format(player)\\r\\n await ctx.message.channel.send(\\\"Rating: {:d}\\\".format(player_pvp_stats['rating']))\\r\\n await ctx.message.channel.send(\\\"Season Wins: {:d}\\\".format(player_pvp_stats['seasonWon']))\\r\\n await ctx.message.channel.send(\\\"Season Losses: {:d}\\\".format(player_pvp_stats['seasonLost']))\\r\\n\\r\\n if player_pvp_stats['seasonWon'] == 0 or player_pvp_stats['seasonLost'] == 0:\\r\\n await ctx.message.channel.send(\\\"Ratio: 0\\\")\\r\\n else:\\r\\n await ctx.message.channel.send(\\\"Ratio: {:.4f}\\\".format(\\r\\n float(player_pvp_stats['seasonWon'])/\\r\\n float(player_pvp_stats['seasonLost']))\\r\\n )\",\n \"def get_sim_winners(teamNames,league,pokedex):\\n bData, wHtml = loadBattleData(league)\\n similarities = loadSims() \\n\\n results = []\\n\\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\\n for d in bData:\\n winner, loser = determineWinner(d)\\n\\n score = 0\\n for pkm in myTeam:\\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\\n\\n results.append((winner,score))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n \\n cutoff = min(len(results),5)\\n\\n htmls = []\\n for win,score in results[:cutoff]:\\n htmls.extend(wHtml[str(sorted(win))])\\n\\n return htmls\",\n \"def get_revenue_per_hour(self):\\n tp = self.target_profile\\n p_driver_ticketed = self.cops.p_driver_ticketed(tp.mins_between_drivers)\\n return p_driver_ticketed * tp.revenue_opportunity_per_hour - self.cops.get_cost_per_hour()\",\n \"def wins(self):\\n return [g for g in self.games if g.winner is self.team]\",\n \"def Winner(self, whichPlayer, tResult, score, gameCount):\\n\\n if whichPlayer == 0:\\n tResult[0] = tResult[0] + score[0]\\n else:\\n tResult[1] = tResult[1] + score[1]\\n print(data['tResult'],\\\"player1 \\\", tResult[0],\\\"player2 \\\",tResult[1])\\n if gameCount == 3:\\n if tResult[0] > tResult[1]:\\n print(data['mplayer1'],tResult[0] - tResult[1])\\n else:\\n print(data['mplayer2'],tResult[1] - tResult[0])\\n return whichPlayer, score, gameCount, tResult\",\n \"def get_score(self, player):\\n\\n df = pd.read_csv('RPSscores.csv')\\n if not str(player) in df['Name'].to_dict().values():\\n df.loc[len(df.index)] = [str(player),\\n 0, 0, 0]\\n player_index = int(df.loc[df['Name'] == str(player)].index[0])\\n result = 'wins: ' + str(df.iloc[player_index, 1]) + '\\\\n' + \\\\\\n 'draws: ' + str(df.iloc[player_index, 2]) + '\\\\n' + \\\\\\n 'losses: ' + str(df.iloc[player_index, 3])\\n return result\",\n \"def losing_games_stats(self):\\n self.losing_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(losing_num_counts=1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','LTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\\"LFGM3\\\":\\\"sum\\\",\\\"LFGA3\\\":\\\"sum\\\",\\\"LFTM\\\":\\\"sum\\\",\\\"LFTA\\\":\\\"sum\\\",\\\"losing_num_counts\\\":\\\"sum\\\",\\n \\\"LOR\\\":\\\"sum\\\",\\\"LDR\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\n \\\"LAst\\\":\\\"sum\\\",\\\"LTO\\\":\\\"sum\\\",\\\"LStl\\\":\\\"sum\\\",\\\"LBlk\\\":\\\"sum\\\",\\\"LPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"WScore\\\":\\\"winning_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"WFGA\\\":\\\"WFGA_opp\\\",\\\"WFGM\\\":\\\"WFGM_opp\\\"})\\n )\",\n \"def custom_score(game, player):\\n \\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n \\n # First obtain the improved score\\n player_legal_moves = game.get_legal_moves(player)\\n opponent = game.get_opponent(player)\\n opponent_legal_moves = game.get_legal_moves(opponent)\\n improved = len(player_legal_moves) - len(opponent_legal_moves)\\n if improved != 0:\\n return float(improved)\\n \\n # Second get differences from center\\n center_coordinates = center_y, center_x = get_center_coordinates(game)\\n player_coordinates = game.get_player_location(player)\\n opponent_coordinates = game.get_player_location(opponent)\\n player_center_dist = get_distances_from_center(center_coordinates, player_coordinates)\\n opponent_center_dist = get_distances_from_center(center_coordinates, opponent_coordinates)\\n center_dist_diff = player_center_dist - opponent_center_dist\\n \\n # Third obtain next_moves\\n player_next_moves = [get_next_moves(game, move, list(move)) for move in player_legal_moves]\\n opponent_next_moves = [get_next_moves(game, move, list(move)) for move in opponent_legal_moves] \\n improved_next = len(player_next_moves) - len(opponent_next_moves)\\n \\n # Put player and opponent feature differences in a tuple/vector surrogoate\\n feature_diff_vector = (improved, center_dist_diff, improved_next)\\n \\n # Provide a weighting vector for the features of each player-participant\\n weight_vector = (1.5,0.1,1.0)\\n # Calculate the return value = weighted difference of players' features\\n weighted_difference_dot_product = sum(p*q for p,q, \\\\\\n in zip(feature_diff_vector, weight_vector))\\n \\n return float(weighted_difference_dot_product)\",\n \"def get_game_score(self):\\n if self.game_is_tied():\\n return 0\\n elif self.is_game_won():\\n my_available_steps = self.steps_available(self.loc)\\n opp_available_steps = self.steps_available(self.opponent_loc)\\n my_score = self.my_score - self.penalty_score if my_available_steps == 0 else self.my_score\\n opp_score = self.opponent_score - self.penalty_score if opp_available_steps == 0 else self.opponent_score\\n return (my_score - opp_score) / (abs(my_score) + abs(opp_score))\\n else:\\n if abs(self.my_score) + abs(self.opponent_score) == 0:\\n return 0\\n return (self.my_score - self.opponent_score) / (abs(self.my_score) + abs(self.opponent_score))\",\n \"def determine_winner1(self): \\r\\n sorted_player_rank = self._rank()\\r\\n print(f\\\"sorted player rank: {sorted_player_rank}\\\")\\r\\n print(f\\\"winner is player {sorted_player_rank[0]}: with points {sorted_player_rank[0][1]}\\\")\",\n \"def evaluate_team(self, team, mode):\\n if mode == Config.RESTRICTIONS['mode']['training']:\\n point_population = self.point_population_\\n opponent_population = self.training_opponent_population()\\n elif mode == Config.RESTRICTIONS['mode']['validation']:\\n point_population = self.validation_point_population_\\n opponent_population = self.validation_opponent_population_\\n elif mode == Config.RESTRICTIONS['mode']['champion']:\\n point_population = self.champion_population()\\n opponent_population = self.champion_opponent_population()\\n else:\\n raise ValueError(\\\"Invalid mode\\\")\\n results = []\\n extra_metrics_opponents = defaultdict(list)\\n match_id = 0\\n\\n if len(point_population) == 0:\\n raise ValueError(\\\"Error: Nothing in point population. Probably the population size is too small.\\\")\\n if len(opponent_population) == 0:\\n raise ValueError(\\\"Error: Nothing in opponent population. Probably the population size is too small.\\\")\\n\\n if mode == Config.RESTRICTIONS['mode']['training']:\\n for point, opponent in zip(point_population, opponent_population):\\n match_id += 1\\n result = self._play_match(team, opponent, point, mode, match_id)\\n team.reset_registers()\\n extra_metrics_opponents[opponent.opponent_id].append(result)\\n team.results_per_points_[point.point_id_] = result\\n results.append(result)\\n if opponent.opponent_id == 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\\n extra_metrics_opponents[opponent.__repr__()].append(result)\\n \\n opponent_type = 'training_opponents'\\n team.fitness_ = numpy.mean(results)\\n else:\\n extra_metrics_points = self._initialize_extra_metrics_for_points()\\n for point, opponent in zip(point_population, opponent_population):\\n match_id += 1\\n result = self._play_match(team, opponent, point, mode, match_id)\\n team.reset_registers()\\n extra_metrics_opponents[opponent.opponent_id].append(result)\\n extra_metrics_points = self._update_extra_metrics_for_points(extra_metrics_points, point, result)\\n if mode == Config.RESTRICTIONS['mode']['validation']:\\n team.results_per_points_for_validation_[point.point_id_] = result\\n results.append(result)\\n elif mode == Config.RESTRICTIONS['mode']['champion']:\\n if opponent.opponent_id != 'hall_of_fame': # since the hall of fame changes over time, it is better to dont use it to get the champion score, since you wouldnt be able to track the score improvement\\n results.append(result)\\n else:\\n extra_metrics_opponents[opponent.__repr__()].append(result)\\n \\n opponent_type = 'opponents'\\n for key in extra_metrics_points:\\n for subkey in extra_metrics_points[key]:\\n extra_metrics_points[key][subkey] = round_value(numpy.mean(extra_metrics_points[key][subkey]))\\n team.extra_metrics_['points'] = extra_metrics_points\\n if mode == Config.RESTRICTIONS['mode']['validation']:\\n team.score_validation_ = round_value(numpy.mean(results))\\n else:\\n team.score_champion_ = round_value(numpy.mean(results))\\n\\n for key in extra_metrics_opponents:\\n extra_metrics_opponents[key] = round_value(numpy.mean(extra_metrics_opponents[key]))\\n team.extra_metrics_[opponent_type] = extra_metrics_opponents\",\n \"def play(self):\\n if self.stats['round'] == 0:\\n if self.data['personalities'] and self.data['events']:\\n self.choose_opponent()\\n self.resolve_conflict()\\n else:\\n self.stats['round'] += 1\\n elif self.stats['round'] == 1:\\n if self.data['locations']:\\n self.choose_location()\\n self.resolve_conflict()\\n else:\\n self.stats['round'] += 1\\n else:\\n print(\\\"You've won\\\")\\n self.game_over = True\\n return self.stats\",\n \"def get_winners_of_game(self):\\n return self.game_winners\",\n \"def custom_score_6(game, player):\\n \\\"\\\"\\\"custom_score_6 heuristic function aims towards weighted chances heuristics\\n \\\"\\\"\\\"\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\\n return float(length_my_player_moves*length_my_player_moves - 1.5*length_opp_payer_moves*length_opp_payer_moves)\",\n \"def get_winner(game):\\n return game['winner']\",\n \"def build_home_advantage(matches):\\n D = {}\\n df_goal = matches[[\\\"season\\\", \\\"home_team_goal\\\", \\\"away_team_goal\\\"]]\\n\\n for i in range(len(df_goal)):\\n key_season = df_goal.iloc[i].season\\n if key_season not in D:\\n D[key_season] = [\\n 1,\\n df_goal.iloc[i].home_team_goal,\\n df_goal.iloc[i].away_team_goal,\\n ]\\n else:\\n D[key_season][0] += 1\\n D[key_season][1] += df_goal.iloc[i].home_team_goal\\n D[key_season][2] += df_goal.iloc[i].away_team_goal\\n\\n for key in D:\\n D[key][1] /= D[key][0]\\n D[key][2] /= D[key][0]\\n\\n df_goal_info = pd.DataFrame(D)\\n column_headers = list(df_goal_info.columns.values)\\n\\n fig, ax = plt.subplots(nrows=1, ncols=1)\\n fig.set_size_inches(w=7, h=4)\\n plt.plot(column_headers, df_goal_info.iloc[1], label=\\\"avg_home_goal\\\")\\n plt.plot(column_headers, df_goal_info.iloc[2], label=\\\"avg_away_goal\\\")\\n pl.xticks(rotation=270)\\n plt.xlabel(\\\"Season\\\")\\n plt.ylabel(\\\"Average Goal\\\")\\n plt.legend()\\n plt.show()\",\n \"def tournament(tournament):\\n participants = TournamentParticipant.objects.filter(\\n tournament=tournament\\n ).order_by('ranking', 'revision__competitor__name').all()\\n\\n prev_ranking = None\\n rankings = []\\n for player in participants:\\n if prev_ranking is None or player.ranking != prev_ranking['ranking']:\\n prev_ranking = {\\n 'ranking': player.ranking,\\n 'span': 1\\n }\\n rankings.append(prev_ranking)\\n else:\\n prev_ranking['span'] += 1\\n rankings.append(None)\\n\\n return {\\n 'tournament': tournament,\\n 'good_progress_width': 100 * tournament.completed_duels / tournament.total_duels,\\n 'bad_progress_width': 100 * tournament.failed_duels / tournament.total_duels,\\n 'participants_and_rankings': list(zip(participants, rankings))\\n }\",\n \"def get_fifa_stats(match, player_stats):\\n\\n # Define variables\\n match_id = int(match.match_api_id)\\n date = match['date']\\n players = ['home_player_1', 'home_player_2', 'home_player_3', \\\"home_player_4\\\", \\\"home_player_5\\\",\\n \\\"home_player_6\\\", \\\"home_player_7\\\", \\\"home_player_8\\\", \\\"home_player_9\\\", \\\"home_player_10\\\",\\n \\\"home_player_11\\\", \\\"away_player_1\\\", \\\"away_player_2\\\", \\\"away_player_3\\\", \\\"away_player_4\\\",\\n \\\"away_player_5\\\", \\\"away_player_6\\\", \\\"away_player_7\\\", \\\"away_player_8\\\", \\\"away_player_9\\\",\\n \\\"away_player_10\\\", \\\"away_player_11\\\"]\\n\\n overall_ratings = np.array([])\\n for player in players:\\n # Get player ID\\n player_id = match[player]\\n\\n # Get player stats\\n stats = player_stats[player_stats.player_api_id == player_id]\\n\\n # Identify current stats\\n current_stats = stats[stats.date < date].sort_values(by='date', ascending=False).iloc[0]\\n\\n # get overall rating for every player, this cannot be nan since we \\\"dropna\\\" in main()\\n overall_ratings = np.concatenate((overall_ratings, [current_stats[\\\"overall_rating\\\"]]))\\n\\n colNames = np.core.defchararray.add(players, '_overall_rating')\\n player_stats_new = pd.Series(overall_ratings, index=colNames)\\n player_stats_new['match_api_id'] = match_id\\n # print(player_stats_new)\\n return player_stats_new\",\n \"def winner(self):\\n\\n if self.home_score > self.away_score:\\n return HOME\\n elif self.home_score < self.away_score:\\n return VISITOR\\n else:\\n return TIE\",\n \"def survival_score(timeSurvived, duration, winPlace):\\n\\n\\tsurvival = (timeSurvived / duration) * 100\\n\\tif winPlace == 1:\\n\\t\\twin_place = 100\\n\\telse:\\n\\t\\twin_place = 100 - winPlace\\n\\tsurvival_score = int(survival * 0.8 + win_place * 0.2)\\n\\tif survival_score < 50:\\n\\t\\tsurvival_score = 50\\n\\n\\treturn survival_score\",\n \"def give_round_scores(list_of_players):\\n print(\\\"\\\\nThe round has ended !\\\\nWe shall now unveil the cards and the scores!\\\")\\n\\n for player in list_of_players:\\n cards = [card.name for card in player.cards]\\n cards_string = \\\" \\\"\\n for card in cards:\\n cards_string += card + \\\", \\\"\\n cards_string = cards_string[:-2]\\n print(\\\"\\\\n{} has these cards: \\\".format(player.name), cards_string)\\n print(\\\"{} has a score of {}\\\".format(player.name, player.score()))\\n final_scores = [player.score() for player in list_of_players]\\n min_score = min(final_scores)\\n winners_index = [i for i, x in enumerate(final_scores) if x == min_score]\\n if len(winners_index) == 1:\\n index_winner = winners_index[0]\\n winner = list_of_players[index_winner]\\n print(winner.name, \\\"won the round with a score of {}\\\".format(winner.score()))\\n if len(winners_index) > 1:\\n print(\\\"It's a tie!\\\")\\n winners_names = \\\"\\\"\\n winners = [list_of_players[i] for i in winners_index]\\n for winner in winners:\\n winners_names += winner.name\\n print(winners_names, \\\"won the round with a score of \\\", str(min_score))\",\n \"def combine_playoff_championship_for_each_coach(self):\\n self.final_coach_with_postseason_champion_each_year = (\\n self.final_coach_with_postseason_each_year\\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\\n .fillna(0)\\n )\",\n \"def calculate_my_win_strength(self):\\n self.winStrength = self.strategy(deepcopy(self.currentBoardState))\",\n \"def counter_opponent_win(self):\\n\\n # get essential values\\n board = self.get_game_space()\\n affinity = self.get_opponent().get_affinity()\\n \\n # pick the right check for the game we are playing\\n if isinstance(board, Gomoku):\\n \\n # get the possible ways for the opponent to win\\n possible_wins = board.get_wins(affinity)\\n winning_blocks = board.get_winning_blocks(affinity)\\n best_move = None\\n\\n # sort the best win to counter \\n for win in possible_wins:\\n if best_move is None: best_move = win\\n elif win[0] <= best_move[0]: \\n if win[1] >= best_move[1]:\\n best_move = win\\n if best_move is not None: possible_wins.remove(best_move)\\n return best_move\",\n \"def host_result_value(winner: Winner) -> float:\\n if winner == Winner.HOME:\\n return 1\\n if winner == Winner.AWAY:\\n return 0\\n return 0.5\",\n \"def get_convergence_episode(self):\\n values = self.stats['return_stats']['episode_totals']\\n _, y, (y_lower, _) = self._moving_average(\\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\\n # The convergence is established as the first time the average return\\n # is above the lower bounds of the final return.\\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\\n return first_episode\",\n \"def getTotals(dataSet):\\n\\ttotalWins = 0\\n\\ttotalLosses = 0\\n\\ttotalGains = 0.0\\n\\n\\tfor row in dataSet:\\n\\t\\tbatchNum = row[0]\\n\\t\\tseasonCode = row[1]\\n\\t\\tresult = row[2]\\n\\t\\tfixtureDate = row[3]\\n\\t\\tworstOdds = row[4]\\n\\t\\tnumFixtures = row[5]\\n\\t\\tif result == 1:\\n\\t\\t\\ttotalWins += 1\\n\\t\\t\\ttotalGains += (worstOdds-1.0)\\n\\t\\telse:\\n\\t\\t\\ttotalLosses += 1\\n\\t\\t\\ttotalGains -= 1.0\\n\\treturn totalGains, totalWins, totalLosses\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7728886","0.6802758","0.66279066","0.6522059","0.64248955","0.6424082","0.6392534","0.62645185","0.6220719","0.6031486","0.6006317","0.5837831","0.5770262","0.57343024","0.57129455","0.5707701","0.56960624","0.5694389","0.56909186","0.56840575","0.56737727","0.5627025","0.56268334","0.5622924","0.5618245","0.5618245","0.5618245","0.56104475","0.56102824","0.5572643","0.5569494","0.5562584","0.5550734","0.552518","0.5486642","0.5478433","0.5476181","0.5459538","0.54590386","0.5450128","0.5427965","0.54200685","0.54174995","0.54129","0.5375299","0.53746104","0.5373135","0.5368086","0.53562415","0.5345866","0.53352684","0.5334908","0.533003","0.5322865","0.531639","0.53163207","0.53156835","0.5309005","0.53058904","0.53035647","0.5296336","0.5283084","0.5276483","0.52747005","0.5274367","0.5266125","0.5255552","0.52512085","0.52448916","0.52409583","0.5239203","0.52306294","0.5228862","0.5213718","0.5212658","0.51994103","0.5197331","0.51936907","0.5193678","0.51922554","0.51879996","0.51867735","0.5186187","0.5186157","0.51851326","0.5183455","0.51764953","0.51749796","0.51706886","0.5168374","0.51655596","0.51649237","0.5158843","0.51584244","0.51542145","0.5152875","0.5146842","0.5143942","0.5138452","0.5135018"],"string":"[\n \"0.7728886\",\n \"0.6802758\",\n \"0.66279066\",\n \"0.6522059\",\n \"0.64248955\",\n \"0.6424082\",\n \"0.6392534\",\n \"0.62645185\",\n \"0.6220719\",\n \"0.6031486\",\n \"0.6006317\",\n \"0.5837831\",\n \"0.5770262\",\n \"0.57343024\",\n \"0.57129455\",\n \"0.5707701\",\n \"0.56960624\",\n \"0.5694389\",\n \"0.56909186\",\n \"0.56840575\",\n \"0.56737727\",\n \"0.5627025\",\n \"0.56268334\",\n \"0.5622924\",\n \"0.5618245\",\n \"0.5618245\",\n \"0.5618245\",\n \"0.56104475\",\n \"0.56102824\",\n \"0.5572643\",\n \"0.5569494\",\n \"0.5562584\",\n \"0.5550734\",\n \"0.552518\",\n \"0.5486642\",\n \"0.5478433\",\n \"0.5476181\",\n \"0.5459538\",\n \"0.54590386\",\n \"0.5450128\",\n \"0.5427965\",\n \"0.54200685\",\n \"0.54174995\",\n \"0.54129\",\n \"0.5375299\",\n \"0.53746104\",\n \"0.5373135\",\n \"0.5368086\",\n \"0.53562415\",\n \"0.5345866\",\n \"0.53352684\",\n \"0.5334908\",\n \"0.533003\",\n \"0.5322865\",\n \"0.531639\",\n \"0.53163207\",\n \"0.53156835\",\n \"0.5309005\",\n \"0.53058904\",\n \"0.53035647\",\n \"0.5296336\",\n \"0.5283084\",\n \"0.5276483\",\n \"0.52747005\",\n \"0.5274367\",\n \"0.5266125\",\n \"0.5255552\",\n \"0.52512085\",\n \"0.52448916\",\n \"0.52409583\",\n \"0.5239203\",\n \"0.52306294\",\n \"0.5228862\",\n \"0.5213718\",\n \"0.5212658\",\n \"0.51994103\",\n \"0.5197331\",\n \"0.51936907\",\n \"0.5193678\",\n \"0.51922554\",\n \"0.51879996\",\n \"0.51867735\",\n \"0.5186187\",\n \"0.5186157\",\n \"0.51851326\",\n \"0.5183455\",\n \"0.51764953\",\n \"0.51749796\",\n \"0.51706886\",\n \"0.5168374\",\n \"0.51655596\",\n \"0.51649237\",\n \"0.5158843\",\n \"0.51584244\",\n \"0.51542145\",\n \"0.5152875\",\n \"0.5146842\",\n \"0.5143942\",\n \"0.5138452\",\n \"0.5135018\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8015784"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14998,"cells":{"query":{"kind":"string","value":"Get win rate for post season for each coach"},"document":{"kind":"string","value":"def get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n # get losing games for coaches\n self.post_games_lose_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df for post season\n self.combine_post_games_won_lose = (\n self.post_games_lose_for_coaches\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"post_games_lost\",\"which_coach_for_win\":\"post_games_won\"})\n .fillna(0)\n )"},"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 get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )","def get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"count\"})\n .reset_index()\n .rename(columns={\"NumOT\":\"is_champion\",\"WTeamID\":\"TeamID\"})\n )","def get_champion_winrate(self, summoner_id, champion_id):\n request = rq.get(\n 'https://{region}.api.pvp.net/api/lol/{region}/v{api_v}/stats/by-summoner/{summ_id}/ranked?season=SEASON{year}&api_key={api_key}'\n .format(\n region=self.region,\n api_v=api_version['stats'],\n summ_id=summoner_id,\n year=dt.today().year,\n api_key=API_KEY\n )\n )\n try:\n check_response(request)\n champions = request.json()['champions']\n if champions is not None:\n for champion in champions:\n if champion['id'] == champion_id:\n total_won = champion['stats']['totalSessionsWon']\n total = total_won + champion['stats']['totalSessionsLost']\n\n winrate = total_won / total\n return [winrate, total]\n return 0, 0\n except ServiceException:\n return 0, 0","def winning_games_stats(self):\n self.winning_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(winning_num_counts = 1))\n .query(\"Season <= 2013\")\n .groupby(['Season','WTeamID'])\n .agg({\"WScore\":\"sum\",\"WFGM\":\"sum\",\"WFGA\":\"sum\",\"WFGM3\":\"sum\",\"WFGA3\":\"sum\",\"WFTM\":\"sum\",\"WFTA\":\"sum\",\"LScore\":\"sum\",\"winning_num_counts\":\"sum\",\n \"WOR\":\"sum\",\"WDR\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\n \"WAst\":\"sum\",\"WTO\":\"sum\",\"WStl\":\"sum\",\"WBlk\":\"sum\",\"WPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"LScore\":\"losing_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"LFGA\":\"LFGA_opp\",\"LFGM\":\"LFGM_opp\"})\n )","def _compute_winrates(synergy, counter, heroes_released):\n for i in range(heroes_released):\n for j in range(heroes_released):\n if i != j and i != 23 and j != 23:\n if synergy['games'][i, j] != 0:\n synergy['winrate'][i, j] = synergy['wins'][i, j] / \\\n float(synergy['games'][i, j])\n\n if counter['games'][i, j] != 0:\n counter['winrate'][i, j] = counter['wins'][i, j] / \\\n float(counter['games'][i, j])","def calculateWinRate():\n times = 10\n winRate = 0.0\n for i in range(times):\n game = Game('user', 6, 6)\n winRate += game.play(5, False, True, False, False)\n winRate = winRate/times\n print \"Winrate:\", winRate","def get_final_coach_for_each_season(self):\n self.final_coach_for_season = (\n self.num_days_coach_for_season\n .groupby(['Season','TeamID'])\n .agg({\"CoachName\":\"count\"})\n .reset_index()\n .rename(columns={\"CoachName\":\"coach_counts\"})\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \"ignore\")))\n [['Season','TeamID','final_coach']]\n )","def winrate(matches):\n if not matches:\n print('no matches')\n return None\n\n win_loss = [match['result'] for match in matches]\n return sum(win_loss)/len(win_loss)","def marcels_players(goalie, date, df):\n # 0 = that year, 1 is year b4 ....\n marcel_weights = [.36, .29, .21, .14]\n reg_const = 2000\n reg_avg = 0 # Where to regress to\n\n # Use past 3 season to weight games played -> Just take weighted average\n gp_weights = [8, 4, 2, 0]\n\n season = int(helpers.get_season(date))\n\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\n weighted_gp_sum, weights_gp_sum = 0, 0\n\n # Past 4 Seasons\n for i in range(0, 4):\n if season - i > 2006:\n # Subset from stats df\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\n\n # Sanity Check\n if df_goalie.shape[0] > 1:\n print(\"Too many rows!!!!!!!\")\n exit()\n\n # If he played that year\n if not df_goalie.empty:\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\n\n # -> To divide by at end...normalize everything\n weights_marcel_sum += marcel_weights[i]\n weights_gp_sum += gp_weights[i]\n\n # Normalize weighted sums\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n\n # Get Regressed fsv%\n if weighted_fen_sum != 0:\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\n else:\n weighted_adj_fsv = 0\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\n\n # Get weighted gp\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\n\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}","def getSeasonStats(self):\n df_season_agg = self.toSeasonAggFormat()\n\n # Calculate Possessions for each game\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\n\n # Aggregate to Season Summary Level\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\n\n season_stats = season_stats.rename(columns={'Win':'wins'})\n\n # Season Advanced Stats\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\n\n season_stats.drop('DayNum', axis=1, inplace=True)\n season_stats.drop('OppTeamID', axis=1, inplace=True)\n season_stats.drop('rand', axis=1, inplace=True)\n\n return season_stats","def get_player_stats_from_game(team, year, week):","def cumulative_stats_for_teams_each_year(self):\n self.cumulative_stats_for_team_each_year = (\n self.combine_both_winning_losing_games_stats\n .sort_values(['WTeamID','Season'])\n .groupby(['WTeamID'])\n .cumsum()\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\n .drop(['LTeamID','win_rate'],1)\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n # rebounds cumsum stats\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # assists, turnovers, steals, blocks and personal fouls\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win or lose by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def get_team_round_score(self, team: int, match_round: int) -> int:\n assert self.rounds, \"There are no rounds or the requested round doesn't exist\"\n\n team_score = 0\n\n if team == 1:\n for player in self.rounds[match_round - 1].team1.players:\n team_score += player.scores\n else:\n for player in self.rounds[match_round - 1].team2.players:\n team_score += player.scores\n\n return team_score","def team_season_stats(team):\n\n # Get HTML Content\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # Team's yearly stats are displayed in a table\n season_stats = soup.find(id='stats').find('tbody')\n\n # Iterate through each year\n for year in season_stats.find_all('tr', {'class': None}):\n\n season_year = year.find('th').text[0:4]\n season_year = int(season_year) + 1\n season = {'year': season_year}\n\n # Loop through each stat\n for stat in year.find_all('td'):\n season[stat['data-stat']] = stat.string\n\n # Rename relocated teams\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\n season['_id'] = season['team_id'] + '_' + str(season_year)\n\n # Remove unwanted stats\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\n for k in to_remove:\n season.pop(k, None)\n\n # Add to MongoDB\n m.insert('team_season', season)","def get_winpercent(self, game: 'Game' = None, player: 'Player' = None):\n if game and player:\n pass\n elif game:\n play_count = self.play_set.filter(game=game).count()\n win_count = self.play_set.filter(winner=self, game=game).count()\n return win_count / play_count * 100\n elif player:\n pass\n # play_count = self.play_set.filter(players__in=player).count()\n # win_count = self.play_set.filter(\n # winner=self, player=player).count()\n # return win_count / play_count * 100\n else:\n return self.winpercent","def glicko2_summarize(df, min_weeks=4, use_prior=False):\n\n # Loop through seasons and weeks to create full history of ratings by team\n results = pd.DataFrame()\n for season in df['Season'].sort_values().unique():\n for week in df[df['Season']==season]['Week'].sort_values().unique():\n if week > min_weeks:\n if week == min_weeks + 1:\n season_df = df[df['Season']==season].copy()\n uniqueteamids = pd.concat([season_df['VisID'],\n season_df['HomeID']]).unique()\n if use_prior == True and season > df['Season'].min():\n ratings = np.repeat(1500, len(uniqueteamids))\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\n sigma = np.repeat(0.06, len(uniqueteamids))\n glicko_stats = pd.DataFrame({'ratings': ratings,\n 'ratingsdeviance': ratingsdeviance,\n 'sigma': sigma}, index=uniqueteamids)\n prior = results[results['Season']==season-1]\n prior_id_mask = [True if id in uniqueteamids else False for id in prior['TeamID']]\n prior = prior[prior_id_mask]\n prior = prior.sort_values('Week').groupby('TeamID').tail(1)\n prior = prior.drop('Week',1)\n prior = prior.set_index('TeamID')\n glicko_stats.loc[prior.index, 'ratings'] = prior['Glicko_Rating'] - (prior['Glicko_Rating'] - 1500)/2\n glicko_stats.loc[prior.index, 'ratingsdeviance'] = prior['Glicko_Rating_Deviance'] - (prior['Glicko_Rating_Deviance'] - 350)/2\n glicko_stats.loc[prior.index, 'sigma'] = prior['Glicko_Sigma'] - (prior['Glicko_Sigma'] - 0.06)/2\n else:\n ratings = np.repeat(1500, len(uniqueteamids))\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\n sigma = np.repeat(0.06, len(uniqueteamids))\n glicko_stats = pd.DataFrame({'ratings': ratings,\n 'ratingsdeviance': ratingsdeviance,\n 'sigma': sigma}, index=uniqueteamids)\n\n week_df = df[(df['Season']==season) & (df['Week']<week)].copy()\n glicko_stats = glicko2(week_df, uniqueteamids, glicko_stats)\n\n\n glicko_results = glicko_stats.reset_index()\n print(glicko_results.head(), season)\n glicko_results.columns = ['TeamID','Glicko_Rating',\n 'Glicko_Rating_Deviance',\n 'Glicko_Sigma']\n glicko_results['Season'] = season\n glicko_results['Week'] = week\n results = pd.concat([results, glicko_results], axis=0,\n ignore_index=True)\n\n # Join the ratings to the original schedule of games\n df = df.merge(results, left_on=['Season','Week','HomeID'],\n right_on=['Season','Week','TeamID'],\n suffixes=('','_Home'))\n df.drop('TeamID', 1, inplace=True)\n\n df = df.merge(results, left_on=['Season','Week','VisID'],\n right_on=['Season','Week','TeamID'],\n suffixes=('','_Away'))\n df.drop('TeamID', 1, inplace=True)\n\n # Create key and set index to join with n_game summaries dataset.\n df.set_index(['HomeID', 'VisID', 'Season', 'Week'], inplace=True)\n df = df[['Glicko_Rating', 'Glicko_Rating_Deviance', 'Glicko_Sigma',\n 'Glicko_Rating_Away', 'Glicko_Rating_Deviance_Away',\n 'Glicko_Sigma_Away']]\n df.columns = ['Glicko_Rating_Home', 'Glicko_Rating_Deviance_Home',\n 'Glicko_Sigma_Home', 'Glicko_Rating_Away',\n 'Glicko_Rating_Deviance_Away', 'Glicko_Sigma_Away']\n\n return df","def calc_winner(self):\n pass","def rate_club(user, club):\n if not user.is_authenticated():\n return None\n if not club.posel_set.exists():\n return None\n return sum(x[1] for x in rank_in_club(user, club)) / club.posel_set.count()","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","def get_new_ratings(players, teams):\n nb_players_team0 = len(teams[0])\n nb_players_team1 = len(teams[1])\n winner = players[teams[0][0]]\n loser = players[teams[1][0]]\n if nb_players_team0 == 1 and nb_players_team1 == 1:\n new_r1, new_r3 = rate_1vs1(winner,loser)\n elif nb_players_team0 == 1 and nb_players_team1 > 1:\n team_loser = [loser, players[teams[1][1]]]\n (new_r1), (new_r3, new_r4) = rate([winner, team_loser], ranks=[0, 1]) \n elif nb_players_team0 > 1 and nb_players_team1 == 1:\n team_winner = [winner, players[teams[0][1]]]\n (new_r1, new_r2), (new_r3) = rate([team_winner, loser], ranks=[0, 1]) \n else:\n team_loser = [loser, players[teams[1][1]]]\n team_winner = [winner, players[teams[0][1]]]\n (new_r1, new_r2), (new_r3, new_r4) = rate([team_winner, team_loser], ranks=[0, 1]) \n player1 = {'name': teams[0][0], 'mu': new_r1.mu, 'sigma': new_r1.sigma}\n player3 = {'name': teams[1][0], 'mu': new_r3.mu, 'sigma': new_r3.sigma}\n if nb_players_team0 > 1:\n player2 = {'name': teams[0][1], 'mu': new_r2.mu, 'sigma': new_r2.sigma}\n if nb_players_team1 > 1:\n player4 = {'name': teams[1][1], 'mu': new_r4.mu, 'sigma': new_r4.sigma}\n if nb_players_team0 > 1:\n return [player1, player2, player3, player4]\n return [player1, player2, player4]\n return [player1, player3]","def combine_playoff_championship_for_each_coach(self):\n self.final_coach_with_postseason_champion_each_year = (\n self.final_coach_with_postseason_each_year\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\n .fillna(0)\n )","def season_game_logs(team, year):\n\n # Check year value\n if year > 2019 or year < 1950:\n raise ValueError('Year Value Incorrect')\n\n # Rename teams that moved\n team = scrape_utils.rename_team(team, year)\n\n # Get HTML content\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n season_stats = soup.find(id='tgl_basic')\n games = season_stats.find('tbody')\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # To find opponent statistics\n opponent = re.compile('^opp_.*$')\n\n # Loop through every game in a team's season\n for game in games.find_all('tr', {'class': None}):\n\n curr_team = {'team': team}\n opp_team = {}\n\n # Loop through each stat\n for stat in game.find_all('td'):\n\n stat_name = stat['data-stat']\n\n # These are opponent stats\n if re.match(opponent, stat_name):\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\n else:\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\n\n # Remove unnecessary information\n del curr_team['game_season']\n del curr_team['x']\n\n # Rename relocated teams\n curr_team['team'] = scrape_utils.rename_team(team)\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\n\n # Use the same ID as basketball reference\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \"%Y-%m-%d\"),\n 'season': year,\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\n '_id': game.find('a')['href'][-17:-5]}\n\n # Place the teams in the correct spot depending on who is the home team\n if curr_team.pop('game_location') == 0:\n result['home'] = curr_team\n result['away'] = opp_team\n else:\n result['home'] = opp_team\n result['away'] = curr_team\n\n # Insert into database\n m.insert('game_log', result)","def season_rounds(cls, season):\r\n\t\t\r\n\t\tfolder_name = cls.season_folder(season)\r\n\t\tround_list = os.listdir(f'Data/{folder_name}')\r\n\r\n\t\tall_rounds = []\r\n\r\n\t\tfor round_file in round_list:\r\n\t\t\twith open(f'Data/{folder_name}/{round_file}', 'r', encoding='utf-8') as f:\r\n\t\t\t\tround_info = f.read().splitlines()\r\n\r\n\t\t\tround_number = round_file[:-4]\r\n\t\t\tfull_round_name = f\"{season} R{round_number}\"\r\n\r\n\t\t\tround_date = int(round_info[0])\r\n\t\t\tlookup_ind = DATES.month_diff(round_date, DATES.MIN_DATE)\r\n\r\n\t\t\t# If the round isn't actually counted for TWOW Glicko\r\n\t\t\tif full_round_name not in cls.ROUNDS[lookup_ind].keys():\r\n\t\t\t\tcontinue\r\n\r\n\t\t\tcontestant_count = len(round_info) - 1\r\n\r\n\t\t\tstrength = cls.ROUNDS[lookup_ind][full_round_name][0]\r\n\r\n\t\t\tall_rounds.append([\r\n\t\t\t\tround_number,\r\n\t\t\t\tround_date,\r\n\t\t\t\tcontestant_count,\r\n\t\t\t\tstrength\r\n\t\t\t])\r\n\t\t\r\n\t\treturn all_rounds","def get_tourney_rounds(self, conference, year):\n ts_dict = self.get_tourney_slots()\n seed_dict = self.get_tourney_seeds()\n tr_dict = self.get_tourney_results()\n \n round_1 = list()\n round_2 = list()\n round_3 = list()\n round_4 = list()\n winner = list()\n \n round1_winners = list()\n for seed, team in seed_dict[year].items():\n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n round1_winners.append(seed[1:])\n #removes duplicates because I did this part weirdly... HEHEH\n round1_winners = list(set(round1_winners))\n\n win_counter = defaultdict(int)\n for seed, team in seed_dict[year].items(): \n for winning, losing in tr_dict[year]:\n if team == winning and conference in seed:\n win_counter[winning] += 1\n \n for slot, matchup in ts_dict[year].items():\n \n if conference in slot and \"R1\" in slot: \n round_1.append(\"{}-{}\".format(matchup[1:3], matchup[-2:]))\n round_1 = sorted(round_1)\n #for match in round_1:\n for winner1 in round1_winners:\n if winner1 in round_1[0]:\n for winner2 in round1_winners:\n if winner2 in round_1[-1]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[1]:\n for winner2 in round1_winners:\n if winner2 in round_1[-2]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[2]:\n for winner2 in round1_winners:\n if winner2 in round_1[-3]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_1[3]:\n for winner2 in round1_winners:\n if winner2 in round_1[-4]:\n round_2.append(\"{}-{}\".format(winner1, winner2))\n round_2 = sorted(round_2)\n\n round2_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 1:\n round2_winners.append(seed[1:])\n \n for winner1 in round2_winners:\n if winner1 in round_2[0]:\n for winner2 in round2_winners:\n if winner2 in round_2[-1]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n if winner1 in round_2[1]:\n for winner2 in round2_winners:\n if winner2 in round_2[-2]:\n round_3.append(\"{}-{}\".format(winner1, winner2))\n round_3 = sorted(round_3)\n\n round3_winners = list()\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 2:\n round3_winners.append(seed[1:])\n\n for winner1 in round3_winners:\n if winner1 in round_3[0]:\n for winner2 in round3_winners:\n if winner2 in round_3[-1]:\n round_4.append(\"{}-{}\".format(winner1, winner2))\n round_4 = sorted(round_4)\n\n for seed, team in seed_dict[year].items():\n for team2, count in win_counter.items():\n if team == team2 and count > 3:\n winner.append(seed[1:])\n\n conferences = {\"W\": \"East\", \"X\": \"Midwest\", \"Y\": \"South\", \"Z\": \"West\"}\n\n #print(\"CONFERENCE: {}, YEAR: {}\".format(conferences[conference], year))\n #print(\"ROUND1:\", round_1)\n #print(\"ROUND2:\", round_2)\n #print(\"ROUND3:\", round_3)\n #print(\"ROUND4:\", round_4)\n #print(\"WINNER:\", winner)\n\n #clearing out the tourney results dictionary\n #tr_dict.clear()\n\n return round_1, round_2, round_3, round_4, winner","def assign_win_points(game_dict):\n def win_tuple(deck_dict):\n \"\"\" Return tuple ordered by increasing final standing. \"\"\"\n # negate turns so that max() behaves; points good, turns bad.\n num_normal_turns = sum(not ( (POSSESSION in t and t[POSSESSION]) or \\\n (OUTPOST in t and t[OUTPOST]))\n for t in deck_dict[TURNS])\n return (deck_dict[POINTS], -num_normal_turns)\n\n decks = game_dict[DECKS]\n winner_tuple = max(win_tuple(p) for p in decks)\n winners = [p for p in decks if win_tuple(p) == winner_tuple]\n\n win_points = float(len(decks)) / len(winners)\n for player in decks:\n player[WIN_POINTS] = win_points if player in winners else 0.0","def compute_score(window, computer_piece):\n score = 0\n if window.count(computer_piece) == 4:\n score += 100\n elif window.count(computer_piece) == 3 and window.count(0) == 1:\n score += 5\n elif window.count(computer_piece) == 2 and window.count(0) == 2:\n score += 2\n if window.count(PLAYER_PIECE) == 2 and window.count(0) == 2:\n score -= 1\n if window.count(PLAYER_PIECE) == 3 and window.count(0) == 1:\n score -= 100\n return score","def get_smmry_data(soup, game_dict):\n\n # Get date and time data.\n try:\n date_soup = soup.find(\"div\", {\"class\": \"spielbericht_tipp_status\"})\n league = date_soup.div.span.text\n date_string = date_soup.div.text\n date = re.search(r'\\d{2}.\\d{2}.\\d{2}', date_string).group(0)\n time = re.search(r'\\d{2}:\\d{2}', date_string).group(0)\n matchday = re.search(r'[|]\\d+', date_string).group(0)[1:]\n\n game_dict[\"league\"] = league\n game_dict[\"fb_date\"] = date\n game_dict[\"fb_time\"] = time\n game_dict[\"matchday\"] = matchday\n except AttributeError:\n pass\n\n # Get game result.\n try:\n result = soup.find(\"div\", {\"class\": \"stand\"}).text\n game_dict[\"result\"] = result\n except AttributeError:\n pass\n\n # Try to get the referee name.\n try:\n referee = soup.find(\"span\", {\"class\": \"schiri_link\"}).text\n game_dict[\"referee\"] = referee\n except AttributeError:\n pass\n\n # Get team, club name and repective url by team.\n try:\n smmry_soup = soup.find(\n \"div\", {\"class\": \"spielbericht_ergebnis_wrapper\"})\n club_title = smmry_soup.find_all(\"img\")\n team_title = smmry_soup.findAll(\"div\", {\"class\": \"teaminfo\"})\n\n # Loop through teams.\n for j, team in enumerate([\"home_\", \"away_\"]):\n game_dict[team + \"team\"] = team_title[j].a[\"title\"]\n game_dict[team + \"team_url\"] = team_title[j].a[\"href\"]\n game_dict[team + \"club\"] = club_title[j][\"title\"]\n except (AttributeError, TypeError):\n pass\n\n return game_dict","def winRate(DF):\r\n df = DF[\"return\"]\r\n pos = df[df>1]\r\n neg = df[df<1]\r\n return (len(pos) / len(pos + neg)) * 100","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def get_result(state, winrate_predictor):\n teamA_picks = state[:, TEAM_A_PICK_INDICES]\n teamB_picks = state[:, TEAM_B_PICK_INDICES]\n team_comp = torch.cat((teamA_picks, teamB_picks), dim=1)\n winrate = winrate_predictor(team_comp)[0, 0]\n \n if winrate >= 0.5:\n return 0\n return 1","def win_ratio_avg(self):\n win_ratio = 0\n # Adds all the win ratios of team in this conference which will be\n # used to compute the win ratio average.\n for team_obj in self._conf_teams:\n ### INVARIANT: team_obj is a Team class object and\n ### self._conf_teams is a list of Team class objects.\n win_ratio += team_obj._win_ratio\n return win_ratio/len(self._conf_teams)","def review(self):\n # Compile standings\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\n # Name a champion\n self.champion = self._name_champion()\n self.league.history.champions_timeline[self.year] = self.champion\n print \"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\".format(\n self.champion.team.name.upper(), self.year, self.league.name.upper()\n )\n # Compile league leaders\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\n # Have each team review its season, as well\n for team_season in self.teams:\n team_season.review()\n # Send the league into the offseason\n self.league.season = None","def opponents_score(self):\n if self.opponent_wickets == 10:\n var1 = \"All Out\"\n return str('{0} {1}').format(self.opponent_runs, var1)\n else:\n var1 = self.opponent_wickets\n return str('{0}-{1}').format(self.opponent_runs, var1)","def getWinProbability(team1, team2, r, year=2019):\n\tR1_PROBS = R1_PROBS_2019 if year == 2019 else R1_PROBS_2020\n\tALPHA_VALS = ALPHA_VALS_2019 if year == 2019 else ALPHA_VALS_2020\n\n\t# Currently using Power Model\n\ts1 = team1['seed']\n\ts2 = team2['seed']\n\n\t# Use R1_PROBS for round 1\n\tif r == 1:\n\t\tif not (s1 + s2 == 17):\n\t\t\texit('Invalid round 1 matchup: seeds {0} vs. {1}.'.format(s1, s2))\n\t\treturn R1_PROBS[s1] if s1 < s2 else R1_PROBS[s2]\n\t\n\t# Use ALPHA_VALS for other rounds (unless seeds are same)\n\tif s1 == s2:\n\t\treturn 0.5\n\n\talpha = ALPHA_VALS[r]\n\ts1a = (s1 * 1.0) ** alpha\n\ts2a = (s2 * 1.0) ** alpha\n\treturn s2a / (s1a + s2a)","def calc_match_points(self, match):\n if match.winner == match.TIE:\n match.home.tournament_score += 1\n match.away.tournament_score += 1\n else:\n match.winner.tournament_score += 3\n match.loser.tournament_score += 0","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def evaluate(game, player):\n weights = [2, 200, 2000, 20000]\n reward = 0\n opponent = get_opponent(player)\n for length in range(2, 6):\n reward += weights[length - 2] * get_num_series(game, player, length)\n reward -= weights[length - 2] * get_num_series(game, opponent, length)\n return reward","def RankSeason(Featuresfile: str, Placementsfile: str, week: int) -> None:\r\n Features = pd.read_csv(Featuresfile, encoding = \"ISO-8859-1\")\r\n Placements = pd.read_csv(Placementsfile, encoding = \"ISO-8859-1\")\r\n RF.PlacementPointsSeason(Placements, week)\r\n RF.FormulaTotalSeason(Features, Placements)\r\n Features['Rank'] = (Features['Points'] * -1).rank(method = 'min')\r\n Features.to_csv(Featuresfile, index = False, encoding = \"ISO-8859-1\")","def get_player_career_reg_season_stats(player_id, player_data_url, headers):\n\tplayer_data_json=json.loads(requests.get(player_data_url+str(player_id), headers=headers).content)\n\tcareer_totals=player_data_json['resultSets'][1]\n\tdf_career_totals=pd.DataFrame.from_records(career_totals['rowSet'], columns=career_totals['headers'])\n\tdf_career_totals.PLAYER_ID=df_career_totals.PLAYER_ID.astype(int)\n\treturn df_career_totals","def calculate_power_ranking(wins, losses, is_winning_streak,\n streak_length, recent_wins, recent_losses):\n pred1 = 0\n pred2 = round( ( ((wins*1.0)/(wins+losses)) - 0.500 ) * 16 * 9 * 10/9 )\n pred3 = recent_wins - recent_losses\n streak_factor = 1 if is_winning_streak else -1\n pred4 = streak_factor * round( ( streak_length - 1 ) / 2.0 )\n print pred1, pred2, pred3, pred4\n return pred1 + pred2 + pred3 + pred4","def play_simulation(self, n_iter=1000):\r\n final_wins = final_wins = {t: 0 for t in self.teams.dteams.keys()}\r\n season_teams_ranked, _ = get_playoffs(self.season_calendar, self.teams_info)\r\n for i in tqdm(range(n_iter)):\r\n if not self.playoffs_only:\r\n self.season.play_regular_season(self.teams)\r\n season_teams_ranked = self.season.playoffs_teams_ranked\r\n\r\n playoffs_sim = Playoffs(season_teams_ranked, self.gsim)\r\n winner_playoff = playoffs_sim.get_winner(self.teams)\r\n\r\n final_wins[winner_playoff] += 1\r\n\r\n return final_wins","def average_win_rate(strategy, baseline=always_roll(4)):\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\n\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2","def average_win_rate(strategy, baseline=always_roll(4)):\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\n\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2","def average_win_rate(strategy, baseline=always_roll(4)):\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\n\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2","def score(entry):\n score = time_seconds(entry['Swim'])\n score += time_seconds(entry['Run'])\n score -= (int(entry['Push-ups']) * 2)\n score -= int(entry['Sit-ups'])\n score -= int(entry['Pull-ups']) * 6\n return score","def get_win_percentage(self) -> float:\n if self.wins == 0:\n return 0.0\n else:\n return round((self.wins / (self.wins + self.losses)) * 100, 2)","def calc_win_lose_ratio(self):\n total = len(self.train_y)\n survived = 0\n for i in self.train_y:\n if i > 0:\n survived += 1\n\n self.survival_sum = [survived, total-survived]","def get_team_scores(self, team, include_home=True, include_away=True):\n if include_away:\n away_games = list(filter(lambda g: team == g.AwayTeam, self.games))\n else:\n away_games = []\n\n if include_home:\n home_games = list(filter(lambda g: team == g.HomeTeam, self.games))\n else:\n home_games = []\n\n scored_h = [g.FTHG for g in home_games]\n scored_a = [g.FTAG for g in away_games]\n\n conceded_h = [g.FTAG for g in home_games]\n conceded_a = [g.FTHG for g in away_games]\n\n try:\n mean_gd = mean(scored_h + scored_a) - mean(conceded_h + conceded_a)\n home_gd = mean(scored_h) - mean(conceded_h)\n home_adv = home_gd - mean_gd\n except Exception:\n home_adv = 0\n\n return {\n \"scored_xg\": scored_h + scored_a,\n \"conceded_xg\": conceded_h + conceded_a,\n \"home_adv\": home_adv,\n \"expected_points\": self.get_table(metric='points')[team] /\n len(home_games + away_games)\n }","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def winning_percentage(self):\n return float(len(self.wins))/len((self.wins+self.losses))","def compute_score(self, n_episodes=5):\n\n # Score is computed via aggregate over multiple episodes\n score = 0\n\n for _ in range(n_episodes):\n score += play_episode(self.model, self.env)\n\n return score / n_episodes","def tally_results(self, verbose=False):\n\t\tself.tally={\n\t\t\tself.player1.name: {\n\t\t\t\t'wins': 0,\n\t\t\t\t'draws': 0,\n\t\t\t\t'losses': 0\n\t\t\t},\n\t\t\tself.player2.name: {\n\t\t\t\t'wins': 0,\n\t\t\t\t'draws': 0,\n\t\t\t\t'losses': 0\n\t\t\t}\n\t\t}\n\t\tfor key, value in self.winner_dict.items():\n\t\t\tself.tally[self.player1.name]['wins']+=1 if value == self.player1.name else 0\n\t\t\tself.tally[self.player1.name]['draws']+=1 if value is None else 0\n\t\t\tself.tally[self.player1.name]['losses']+=1 if value == self.player2.name else 0\n\t\t\tself.tally[self.player2.name]['wins']+=1 if value == self.player2.name else 0\n\t\t\tself.tally[self.player2.name]['draws']+=1 if value is None else 0\n\t\t\tself.tally[self.player2.name]['losses']+=1 if value == self.player1.name else 0\n\t\tif verbose:\n\t\t\tprint('\\n--- FINAL RESULT ---\\n')\n\t\t\ttally_pretty=pd.DataFrame(self.tally).to_markdown()\n\t\t\tprint(tally_pretty)\n\t\t\tif self.tally[self.player1.name]['wins'] == self.tally[self.player2.name]['wins']:\n\t\t\t\tprint('\\nIt\\'s a draw!\\n')\n\t\t\telse:\n\t\t\t\twinner=self.player1.name if self.tally[self.player1.name]['wins'] > self.tally[self.player2.name]['wins'] else self.player2.name\n\t\t\t\tprint('\\n{player} wins the game!\\n'.format(player=winner))","def get_team_stats(players: list[Player]) -> dict[int]:\n\n team_stats = {}\n\n total_reaction = 0\n total_mechanical_skill = 0\n total_tactical_skill = 0\n total_game_knowledge = 0\n total_xp = 0\n\n for player in players:\n total_reaction += player.reaction\n total_mechanical_skill += player.mechanical_skill\n total_tactical_skill += player.tactical_skill\n total_game_knowledge += player.game_knowledge\n total_xp += player.xp\n\n team_stats.update(\n {\"reaction\": total_reaction,\n \"mechanical_skill\": total_mechanical_skill,\n \"tactical_skill\": total_tactical_skill,\n \"game_knowledge\": total_game_knowledge,\n \"xp\": total_xp})\n\n return team_stats","def create_dreamteam_count_yearly(player, raw_list):\n num_seasons = 0\n yearly_dreamteam_count = 0\n for i, raw in enumerate(raw_list):\n if i == len(raw_list) - 1:\n break\n if player in raw['name'].values:\n num_seasons += 1\n yearly_dreamteam_count += raw[['dreamteam_count']][raw.name == player].values[0][0]\n if num_seasons == 0:\n return np.nan\n return yearly_dreamteam_count / num_seasons","def calculate_scores(self):\n words = self.walk_board()\n player_scores = {}\n for word in words:\n player = word.get_owning_player()\n if player not in player_scores:\n player_scores[player] = 0\n player_scores[player] += word.get_score()\n return player_scores","def scrape():\n league_year = Config.get_property(\"league_year\")\n\n # Create table\n season_data = client.season_schedule(league_year)\n season_data = br_enum_to_string(season_data)\n return season_data","def calc_marcel_weights(df):\n cols = ['toi_on', 'gs60', 'toi/gp']\n\n # Only essential columns\n df = df[[\"player\", \"player_id\", \"season\"] + cols]\n\n # Copy over ALL Data\n predict_df = df.copy()\n\n # To get fours years in a row (3 predicting 1).. we run 'get_previous_year' 3 times\n # Each time we get n-_ by using predict_col\n # NOTE: I'm writing over df here!!!!\n for seasons in range(1, 4):\n df = get_previous_yr(df, predict_df, seasons)\n df = df[~df['toi_on_n-{}'.format(seasons)].isnull()]\n\n # Filter for minimum toi\n # 400 for first 3 and 800 for last\n df = df[(df['toi_on'] >= 800) & (df['toi_on_n-1'] >= 400) & (df['toi_on_n-2'] >= 400) & (df['toi_on_n-3'] >= 400)]\n\n print(\"\\nPlayers: {}\".format(df.shape[0]))\n\n for col in ['gs60', 'toi/gp']:\n print(\"Getting the Weights for: \", col)\n # Prepare shit\n model_features = df[['{}_n-1'.format(col), '{}_n-2'.format(col), '{}_n-3'.format(col)]].values.tolist()\n model_target = df[col].values.tolist()\n model_features, model_target = np.array(model_features), np.array(model_target).ravel()\n\n lr = LinearRegression()\n lr.fit(model_features, model_target)\n\n # Print all the Coefficient neatly\n print(\"Coefficients:\")\n for season, coef in zip(range(1, 4), lr.coef_):\n print(\"Season n-{}:\".format(season), round(coef, 3))\n\n print(\"\")","def update_scores(self, AI_win):\n self.games_played += 1\n if not AI_win:\n self.games_won += 1\n else:\n self.games_lost += 1","def tournament(tournament):\n participants = TournamentParticipant.objects.filter(\n tournament=tournament\n ).order_by('ranking', 'revision__competitor__name').all()\n\n prev_ranking = None\n rankings = []\n for player in participants:\n if prev_ranking is None or player.ranking != prev_ranking['ranking']:\n prev_ranking = {\n 'ranking': player.ranking,\n 'span': 1\n }\n rankings.append(prev_ranking)\n else:\n prev_ranking['span'] += 1\n rankings.append(None)\n\n return {\n 'tournament': tournament,\n 'good_progress_width': 100 * tournament.completed_duels / tournament.total_duels,\n 'bad_progress_width': 100 * tournament.failed_duels / tournament.total_duels,\n 'participants_and_rankings': list(zip(participants, rankings))\n }","def get_winner(game):\n return game['winner']","def getPlayerAdvStat(self, stat, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_call = stat.lower()\r\n stat_dict = {'touch':'Possessions', 'possession':'Possessions',\r\n 'speed':'SpeedDistance', 'distance':'SpeedDistance'}\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashptstats?College=&'\\\r\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\r\n 'DraftPick=&DraftYear=&GameScope=&Height=&LastNGames=0&'\\\r\n 'LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&'\\\r\n 'PORound=0&PerMode=PerGame&PlayerExperience=&PlayerOr'\\\r\n 'Team=Player&PlayerPosition=&PtMeasureType=' + \\\r\n stat_dict[stat_call] + '&Season=' + season + \\\r\n '&SeasonSegment=&SeasonType=Regular+Season&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision=&Weight='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n advStat_df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n return advStat_df","def losing_games_stats(self):\n self.losing_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(losing_num_counts=1))\n .query(\"Season <= 2013\")\n .groupby(['Season','LTeamID'])\n .agg({\"WScore\":\"sum\",\"LScore\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\"LFGM3\":\"sum\",\"LFGA3\":\"sum\",\"LFTM\":\"sum\",\"LFTA\":\"sum\",\"losing_num_counts\":\"sum\",\n \"LOR\":\"sum\",\"LDR\":\"sum\",\"WFGA\":\"sum\",\"WFGM\":\"sum\",\n \"LAst\":\"sum\",\"LTO\":\"sum\",\"LStl\":\"sum\",\"LBlk\":\"sum\",\"LPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"WScore\":\"winning_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"WFGA\":\"WFGA_opp\",\"WFGM\":\"WFGM_opp\"})\n )","def Winner(self, whichPlayer, tResult, score, gameCount):\n\n if whichPlayer == 0:\n tResult[0] = tResult[0] + score[0]\n else:\n tResult[1] = tResult[1] + score[1]\n print(data['tResult'],\"player1 \", tResult[0],\"player2 \",tResult[1])\n if gameCount == 3:\n if tResult[0] > tResult[1]:\n print(data['mplayer1'],tResult[0] - tResult[1])\n else:\n print(data['mplayer2'],tResult[1] - tResult[0])\n return whichPlayer, score, gameCount, tResult","def _adv_counter(winrate_together, winrate_hero1, winrate_hero2):\n return winrate_together","def evaluateWinner(self):\n\t\tif self.pots[-1] == 0:\n\t\t\tself.pots.pop()\n\t\tlivePlayers = self.getLivePlayers()\t\n\t\tfor i in range(len(self.pots)):\n\t\t\tplayers = self.getPlayersInPot(i, livePlayers)\n\t\t\tevaluations = []\n\t\t\tfor x in players:\n\t\t\t\tcombined = x.hand + self.communityCards\n\t\t\t\tevaluations.append((x, self.evaluator.getRankOfSeven(\tcombined[0], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[1], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[2], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[3], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[4], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[5], \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcombined[6] )))\n\t\t\twinners = self.getWinners(evaluations, i)\n\t\t\tself.handOutMoney(winners, i)\n\t\t\tself.potwinQ.append(winners[0].name)","def score(self,player, board):\r\n numPlayer = 0\r\n numOpp = 0\r\n for i in self.squares():\r\n if board[i] == player:\r\n numPlayer+= SQUARE_WEIGHTS[i]\r\n else:\r\n numOpp+=SQUARE_WEIGHTS[i]\r\n return numPlayer-numOpp","def stats_strategy_response(data: OTreeSessionData, ws=None):\n\n rounds = [data.get_round(i + 1) for i in range(data.num_rounds())]\n rnd_stats = {}\n for rnd, (pr, ne) in enumerate(zip(rounds[:-1], rounds[1:]), 2):\n stats = {k1: {k2: 0 for k2 in product(('Node', 'Edge'), repeat=2)}\n for k1 in product(list('CD'), repeat=2)}\n print(f'\\rCalculating strategy response (round {rnd}) ... ', end='')\n for pid in range(1, ne.num_players() + 1):\n nep = ne.loc[pid]\n prp = pr.loc[pid]\n stats[(nep.player.choice_L, prp.player.choice_nei_L)][(nep.player.type, prp.player.type_L)] += 1\n stats[(nep.player.choice_U, prp.player.choice_nei_U)][(nep.player.type, prp.player.type_U)] += 1\n stats[(nep.player.choice_R, prp.player.choice_nei_R)][(nep.player.type, prp.player.type_R)] += 1\n stats[(nep.player.choice_D, prp.player.choice_nei_D)][(nep.player.type, prp.player.type_D)] += 1\n if ws:\n ws.append((rnd, pid, nep.player.type,\n nep.player.choice_L, nep.player.type_L, prp.player.choice_nei_L,\n nep.player.choice_U, nep.player.type_U, prp.player.choice_nei_U,\n nep.player.choice_R, nep.player.type_R, prp.player.choice_nei_R,\n nep.player.choice_D, nep.player.type_D, prp.player.choice_nei_D))\n rnd_stats[rnd] = stats\n print('Done')\n global_vars['rnd_stats'] = rnd_stats\n return rnd_stats","def get_winner(self):\n winner: Player = Player('none')\n points_winner = 0\n for player in self.players:\n for key, value in player.get_stats().items():\n print('{}: {}'.format(key, value))\n if key == 'points':\n if value >= points_winner:\n winner = player\n print()\n\n print('The winner is: ' + winner.get_name())\n return winner","def determine_winner1(self): \r\n sorted_player_rank = self._rank()\r\n print(f\"sorted player rank: {sorted_player_rank}\")\r\n print(f\"winner is player {sorted_player_rank[0]}: with points {sorted_player_rank[0][1]}\")","def tournament(submissions, num_rounds):\n strategies = [Strategy(submission) for submission in submissions]\n game_num = 1\n games = []\n for i in range(len(strategies)):\n for j in range(i+1, len(strategies)):\n #print(strategies[i].name, strategies[j].name)\n g = Game(strategies[i], strategies[j], num_rounds, game_num)\n score1, score2 = g.play()\n strategies[i].update_score(score1)\n strategies[j].update_score(score2)\n game_num += 1\n games.append(g)\n \n for strat in strategies:\n print(\"Final score for {} submitted by {} is {}\".format(strat.name, strat.author, strat.score))\n write_results(strategies, games)","def getTotals(dataSet):\n\ttotalWins = 0\n\ttotalLosses = 0\n\ttotalGains = 0.0\n\n\tfor row in dataSet:\n\t\tbatchNum = row[0]\n\t\tseasonCode = row[1]\n\t\tresult = row[2]\n\t\tfixtureDate = row[3]\n\t\tworstOdds = row[4]\n\t\tnumFixtures = row[5]\n\t\tif result == 1:\n\t\t\ttotalWins += 1\n\t\t\ttotalGains += (worstOdds-1.0)\n\t\telse:\n\t\t\ttotalLosses += 1\n\t\t\ttotalGains -= 1.0\n\treturn totalGains, totalWins, totalLosses","def add_dreamteam_count(df, raw_seasons):\n df17 = df.loc[df['season'] == 17].copy()\n df18 = df.loc[df['season'] == 18].copy()\n df19 = df.loc[df['season'] == 19].copy()\n df20 = df.loc[df['season'] == 20].copy()\n df21 = df.loc[df['season'] == 21].copy()\n\n dreamteam_count_yearly_average = []\n\n for _, row in df21.iterrows():\n dreamteam_count_yearly_average.append(create_dreamteam_count_yearly(row['name'], raw_seasons))\n df21['dreamteam_yearly_average'] = dreamteam_count_yearly_average\n\n complete_data = pd.concat([df17, df18, df19, df20, df21])\n\n return complete_data","def evaulate_monster(generikmon):\r\n score = generikmon['headA']\r\n #score = generikmon['chinA']\r\n return score","def get_sim_winners(teamNames,league,pokedex):\n bData, wHtml = loadBattleData(league)\n similarities = loadSims() \n\n results = []\n\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\n for d in bData:\n winner, loser = determineWinner(d)\n\n score = 0\n for pkm in myTeam:\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\n\n results.append((winner,score))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n \n cutoff = min(len(results),5)\n\n htmls = []\n for win,score in results[:cutoff]:\n htmls.extend(wHtml[str(sorted(win))])\n\n return htmls","def collect_stats(games: List[BaseGame], date_min = None, date_max = None):\n if not games: games = self.games\n\n under2_5 = len(list(filter(lambda g: g.is_total_under(), games)))\n under3_5 = len(list(filter(lambda g: g.is_total_under(3.5), games)))\n under1_5 = len(list(filter(lambda g: g.is_total_under(1.5), games)))\n\n home_score = sum([g.FTHG for g in games])\n away_score = sum([g.FTAG for g in games])\n\n home_wins = sum(1 for _ in filter(lambda g: g.is_home_win(), games))\n away_wins = sum(1 for _ in filter(lambda g: g.is_away_win(), games))\n draws = sum(1 for _ in filter(lambda g: g.is_draw(), games))\n\n return {\n 'under2.5': float(under2_5) / len(games),\n 'under3.5': float(under3_5) / len(games),\n 'under1.5': float(under1_5) / len(games),\n 'avgScoredHome': float(home_score) / len(games),\n 'avgScoredAway': float(away_score) / len(games),\n \"home_wins\": float(home_wins) / len(games),\n \"away_wins\": float(away_wins) / len(games),\n \"draws\": float(draws) / len(games),\n }","def _get_current_teams_score(self):\n for game in self._get_live_games():\n teams_playing = [x['abbreviation'] for index, x in game['teams'].items()]\n if self.team in teams_playing:\n # Our team is playing in this game, get the score \n return int(game['scores'][self.team])","def get_winners_of_game(self):\n return self.game_winners","def standings_by_season(season):\n season = int(season) + 1\n scoreboard = nba_py.Scoreboard(month=7,\n day=1,\n year=season)\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return render_template(\"standings.html\",\n title=\"standings\",\n east_standings=enumerate(east_standings, 1),\n west_standings=enumerate(west_standings, 1),\n team=CITY_TO_TEAM)","def calculate(self, prev_scores):\n self.set_scores(prev_scores)\n for match in self.week.matches:\n if match.played:\n # Fetch old scores\n winner_score = float(prev_scores[match.winner])\n loser_score = float(prev_scores[match.loser])\n\n # Update this ranking's scores\n score_delta = loser_score * 0.1\n self.score[match.winner] = winner_score + score_delta\n self.score[match.loser] = loser_score - score_delta","def combine_both_winning_losing_games_stats(self):\n self.combine_both_winning_losing_games_stats = (\n self.winning_games_up_to_2013\n .merge(self.losing_games_up_to_2013, how='left',left_on=['Season','WTeamID'],right_on=['Season','LTeamID'])\n # on field goal percentage and winning counts\n .pipe(lambda x:x.assign(total_score = x.WScore + x.LScore))\n .pipe(lambda x:x.assign(total_opponent_score = x.winning_opponent_score + x.losing_opponent_score))\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\n .pipe(lambda x:x.assign(total_fga = x.WFGA + x.LFGA))\n .pipe(lambda x:x.assign(total_fg3m = x.WFGM3 + x.LFGM3))\n .pipe(lambda x:x.assign(total_fg3a = x.WFGA3 + x.LFGA3))\n .pipe(lambda x:x.assign(total_ftm = x.WFTM + x.LFTM))\n .pipe(lambda x:x.assign(total_fta = x.WFTA + x.LFTA))\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .sort_values(['WTeamID','Season'])\n # on offensive and defensive rebounds\n .pipe(lambda x:x.assign(total_rebounds = x.total_winning_rebounds + x.total_losing_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds = x.WOR + x.LOR))\n .pipe(lambda x:x.assign(total_def_rebounds = x.WDR + x.LDR))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_team_missed_attempts = x.team_missed_attempts + x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(total_opp_team_missed_attempts = x.opp_team_missed_attempts + x.winning_opp_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # on steals, turnovers, assists, blocks and personal fouls\n .pipe(lambda x:x.assign(total_blocks = x.WBlk + x.LBlk))\n .pipe(lambda x:x.assign(total_assists = x.WAst + x.LAst))\n .pipe(lambda x:x.assign(total_steals = x.WStl + x.LStl))\n .pipe(lambda x:x.assign(total_turnover = x.WTO + x.LTO))\n .pipe(lambda x:x.assign(total_personalfoul = x.WPF + x.LPF))\n .pipe(lambda x:x.assign(total_opp_fga = x.LFGA_opp + x.WFGA_opp))\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def custom_score_2(game, player):\n \n # get avaliable moves for each player\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n \n # shortcut to definite state:\n # 1. my agent win -> return very high score\n if opp_moves == 0:\n return float(\"inf\")\n # 2. opponenent's agent win -> return very low score\n elif own_moves == 0:\n return float(\"-inf\")\n\n # score: avaliable moves ratio\n return float(own_moves/opp_moves)","def get_wr_season_totals():\n wr_totals = redis_cache('wr_games_key', NFL_WR_Game_2015.query.filter_by(is_season_totals=True).all)\n return wr_totals","async def player_ratio(self, ctx):\r\n player = ctx.message.content.split(' ')[1]\r\n if os.environ.get(\"WoW_Token\") is None:\r\n return\r\n else:\r\n async with aiohttp.ClientSession().get('https://us.api.battle.net/wow/character/zul\\'jin/' + player + '?fields=pvp&locale=en_US&apikey=' + os.environ.get(\"WoW_Token\")) as res:\r\n if res.status == 200:\r\n data = await res.json()\r\n player_pvp_stats = data['pvp']['brackets']['ARENA_BRACKET_3v3']\r\n await ctx.message.channel.send(u\"Player: {:s}\").format(player)\r\n await ctx.message.channel.send(\"Rating: {:d}\".format(player_pvp_stats['rating']))\r\n await ctx.message.channel.send(\"Season Wins: {:d}\".format(player_pvp_stats['seasonWon']))\r\n await ctx.message.channel.send(\"Season Losses: {:d}\".format(player_pvp_stats['seasonLost']))\r\n\r\n if player_pvp_stats['seasonWon'] == 0 or player_pvp_stats['seasonLost'] == 0:\r\n await ctx.message.channel.send(\"Ratio: 0\")\r\n else:\r\n await ctx.message.channel.send(\"Ratio: {:.4f}\".format(\r\n float(player_pvp_stats['seasonWon'])/\r\n float(player_pvp_stats['seasonLost']))\r\n )","def count_wins(outcomes):\n counter = {x[1]:0 for x in outcomes[0]}\n numPlayers = len(outcomes[0])\n \n for game in outcomes:\n \n index = 0\n toScore = numPlayers\n counter[game[index][1]] += toScore\n index += 1\n while index < numPlayers:\n if game[index][0] == game[index - 1][0]:\n counter[game[index][1]] += toScore\n else:\n toScore -= 1\n counter[game[index][1]] += toScore\n index += 1\n\n return [(x, counter[x]) for x in sorted(counter,\n reverse = True, \n key = lambda y: counter[y])]","def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\n battleData, htmlData = loadBattleData(league)\n similarities = loadSims() \n \n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\n if len(oppTeam) == 0:\n picks = set([])\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\n picks.add(random.randint(0,len(battleData)-1))\n\n losers = []\n loserDict = {}\n for i in picks:\n entry = battleData[i]\n winner,loser = determineWinner(entry)\n loserDict[str(loser)] = [winner]\n losers.append( (loser,0) )\n\n #Given opponent team then find similar teams\n else:\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\n\n #create dictionary from losers team to the team that beat them.\n loserDict = {}\n sims = []\n for d in battleData:\n winner, loser = determineWinner(d)\n\n wTeam = teamToArray(winner,pokedex)\n lTeam = np.array(teamToArray(loser, pokedex))\n\n score = 0\n for oppNp in oppTeam:\n score+= np.amax(lTeam*oppNp) \n\n if str(loser) in loserDict:\n loserDict[str(loser)].append(winner)\n else:\n #new to dictonary\n loserDict[str(loser)] = [winner]\n\n sims.append((loser, score))\n\n\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\n\n cutoff = min(len(sims),NUMLOSINGTEAMS)\n losers = sims[:cutoff]\n\n #Gather winners to losing teams\n winnersComp = []\n for loser,_ in losers:\n for winner in loserDict[str(loser)]:\n winnersComp.append(teamToArray(winner,pokedex))\n \n topScore = len(winnersComp)*6 #pkmn team size\n\n results = []\n inverted_idx = {}\n\n existsSet = []\n\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\n for i in range(len(curTeams)):\n team = curTeams[i]\n results.append((team,0))\n sTeam = set(team)\n if not (sTeam in existsSet):\n existsSet.append(sTeam)\n for pkm in team:\n if pkm != EMPTY:\n if pkm in inverted_idx:\n inverted_idx[pkm].append(i)\n else:\n inverted_idx[pkm] = [i]\n \n #Giving the similiarity scores to the winners based off of the inverted index.\n for pkm in inverted_idx:\n for winner in winnersComp:\n wArr = np.array(winner)\n #tArr = getSimPokemon(pkm,similarities)\n tArr = similarities[pkm]\n \n vals = wArr * tArr\n\n score = np.amax(vals)\n\n for i in inverted_idx[pkm]:\n results[i] = (results[i][0],results[i][1]+(score/topScore))\n\n results = sorted(results, key = lambda x : x[1], reverse = True)\n\n if len(results) < NUMTEAMSRETURN:\n if len(results) == 0:\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\n\n else:\n returnTeams = [result[0] for result in results]\n teamScores = [result[1] for result in results]\n else:\n firstResult, firstScore = results[0]\n returnTeams = [firstResult]\n teamScores = [round(firstScore*100,1)]\n returnSets = [set(firstResult)]\n \n i = 1\n\n #Loops through results and adds teams with the proper edit distance away.\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\n teamToConsider,teamToConsiderScore = results[i]\n \n considerSet = set(teamToConsider)\n add = True\n ##checks the edit distance of teams is above wanted\n for team in returnSets:\n if len(team.union(considerSet)) < len(team)+minDistWanted:\n add = False\n\n ##If indeed above wanted levels then add\n if add:\n returnTeams.append(teamToConsider)\n returnSets.append(considerSet)\n teamScores.append(round(teamToConsiderScore*100,1))\n \n i+=1\n\n if i >= len(results):\n i = 1\n minDistWanted -= 1 \n \n winHtmls = []\n if htmlData != None:\n for team,_ in losers:\n for winner in loserDict[str(team)]:\n winHtmls.extend(htmlData[str(sorted(winner))])\n \n\n return returnTeams, teamScores, winHtmls","def give_round_scores(list_of_players):\n print(\"\\nThe round has ended !\\nWe shall now unveil the cards and the scores!\")\n\n for player in list_of_players:\n cards = [card.name for card in player.cards]\n cards_string = \" \"\n for card in cards:\n cards_string += card + \", \"\n cards_string = cards_string[:-2]\n print(\"\\n{} has these cards: \".format(player.name), cards_string)\n print(\"{} has a score of {}\".format(player.name, player.score()))\n final_scores = [player.score() for player in list_of_players]\n min_score = min(final_scores)\n winners_index = [i for i, x in enumerate(final_scores) if x == min_score]\n if len(winners_index) == 1:\n index_winner = winners_index[0]\n winner = list_of_players[index_winner]\n print(winner.name, \"won the round with a score of {}\".format(winner.score()))\n if len(winners_index) > 1:\n print(\"It's a tie!\")\n winners_names = \"\"\n winners = [list_of_players[i] for i in winners_index]\n for winner in winners:\n winners_names += winner.name\n print(winners_names, \"won the round with a score of \", str(min_score))","def custom_score(game, player):\n \n # get avaliable moves for each player\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n \n # return different between # of my agent's move and oppenent's\n return float(own_moves - opp_moves)","def getTeamStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\r\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\r\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n team_df = df[[\"TEAM_ID\",\"TEAM_NAME\",\"GP\",\"W\",\"L\",\"W_PCT\",\"MIN\",\"FGM\",\r\n \"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\r\n \"OREB\",\"DREB\",\"REB\",\"AST\",\"TOV\",\"STL\",\"BLK\",\"BLKA\",\"PF\",\r\n \"PFD\",\"PTS\",\"PLUS_MINUS\"]]\r\n \r\n return team_df","def _win(self, is_opponent: bool) -> pd.DataFrame:\n records = []\n for var in self.variable_vals:\n # Add variable to parameters\n self.parameters[self.variable] = var\n # Add derived features\n self._derived_features()\n # Configure inputs\n input_set = {\n 'random_effect': 'team' if not is_opponent else 'opponent',\n 'feature_set': self.feature_set,\n 'inputs': {'RandomEffect': self.team if not is_opponent else self.opponent}\n }\n input_set['inputs'].update(self.parameters)\n # Predict\n output = self.predictor.predict(**input_set)[\n ('team' if not is_opponent else 'opponent', self.feature_set, 'Win')\n ]\n # Wrangle output\n record = {\n 'RandomEffect': self.team if is_opponent else self.opponent,\n self.variable: var,\n 'WinLB_opp' if is_opponent else 'WinLB_team': expit(output['mu']['lb']),\n 'Win_opp' if is_opponent else 'Win_team': expit(output['mu']['mean']),\n 'WinUB_opp' if is_opponent else 'WinUB_team': expit(output['mu']['ub'])\n }\n records.append(record)\n\n return pd.DataFrame().from_records(records)","def get_convergence_episode(self):\n values = self.stats['return_stats']['episode_totals']\n _, y, (y_lower, _) = self._moving_average(\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\n # The convergence is established as the first time the average return\n # is above the lower bounds of the final return.\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\n return first_episode","def wins(self):\n return [g for g in self.games if g.winner is self.team]","def first_round_history(self):\n self.ts_dict = self.get_tourney_slots()\n self.tsr_dict = self.match_seeds()\n first_seed_win = 0\n second_seed_win = 0\n third_seed_win = 0\n fourth_seed_win = 0\n fifth_seed_win = 0\n sixth_seed_win = 0\n seventh_seed_win = 0\n eighth_seed_win = 0\n total_games = 128\n\n for year1 in self.ts_dict: \n for slot, match_up in self.ts_dict[year1].items():\n if slot[:2] == \"R1\":\n for year2 in self.tsr_dict:\n if year1 == year2:\n for winning, losing in self.tsr_dict[year2].items():\n if winning[5:] == match_up[:3]:\n seed = winning[6:] \n if seed == \"01\":\n first_seed_win += 1\n elif seed == \"02\":\n second_seed_win += 1\n elif seed == \"03\":\n third_seed_win += 1\n elif seed == \"04\":\n fourth_seed_win += 1\n elif seed == \"05\":\n fifth_seed_win += 1\n elif seed == \"06\":\n sixth_seed_win += 1\n elif seed == \"07\":\n seventh_seed_win += 1\n elif seed == \"08\":\n eighth_seed_win += 1 \n \n #print(first_seed_win, second_seed_win, third_seed_win, fourth_seed_win, fifth_seed_win, sixth_seed_win, seventh_seed_win, eighth_seed_win, total_games)\n\n gauge = pygal.SolidGauge(inner_radius=0.70, title=\"NCAA First Round Results\")\n ratio_first_seed = int(first_seed_win / total_games * 100)\n ratio_second_seed = int(second_seed_win / total_games * 100)\n ratio_third_seed = int(third_seed_win / total_games * 100)\n ratio_fourth_seed = int(fourth_seed_win / total_games * 100)\n ratio_fifth_seed = int(fifth_seed_win / total_games * 100)\n ratio_sixth_seed = int(sixth_seed_win / total_games * 100)\n ratio_seventh_seed = int(seventh_seed_win / total_games * 100)\n ratio_eighth_seed = int(eighth_seed_win / total_games * 100) \n\n percent_formatter = lambda x: '{:.10g}%'.format(x)\n gauge.value_formatter = percent_formatter\n gauge.add('1 vs. 16', [{'value': ratio_first_seed, 'max_value': 100}])\n gauge.add('2 vs. 15', [{'value': ratio_second_seed, 'max_value': 100}])\n gauge.add('3 vs. 14', [{'value': ratio_third_seed, 'max_value': 100}])\n gauge.add('4 vs. 13', [{'value': ratio_fourth_seed, 'max_value': 100}])\n gauge.add('5 vs. 12', [{'value': ratio_fifth_seed, 'max_value': 100}])\n gauge.add('6 vs. 11', [{'value': ratio_sixth_seed, 'max_value': 100}])\n gauge.add('7 vs. 10', [{'value': ratio_seventh_seed, 'max_value': 100}])\n gauge.add('8 vs. 9', [{'value': ratio_eighth_seed, 'max_value': 100}])\n \n gauge.render_to_file('chart.svg')","def snowfall_average_for_date(resort_name, date):\n resort_table = resort_table_dict[resort_name]\n\n date_month = int(date[4:6])\n date_day = int(date[6:8])\n query = \"SELECT snowfall FROM %s WHERE CAST(EXTRACT(MONTH FROM status_date) AS INTEGER) = %d AND CAST(EXTRACT(DAY FROM status_date) AS INTEGER) = %d\" %(resort_table, date_month, date_day)\n connection = get_connection()\n total = 0\n counter = 0\n for row in get_select_query_results(connection, query):\n counter += 1\n total += int(row[0])\n if (counter != 0):\n snowfall_to_return = int(total/counter)\n else:\n snowfall_to_return = 0\n return json.dumps(snowfall_to_return)","def host_result_value(winner: Winner) -> float:\n if winner == Winner.HOME:\n return 1\n if winner == Winner.AWAY:\n return 0\n return 0.5","def win_rate(returns_pred, returns_real, symbol=None, horizon=None):\n # Input processing\n if not horizon:\n horizons = [1, 3, 5, 10, 20, 40, 60, 90, 120]\n elif type(symbol) == str:\n return SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon)\n else:\n horizons = [horizon]\n\n # Data frame skeleton\n win_rate_all = pd.DataFrame(index=horizons)\n\n # Compute win rate for each symbol\n for symbol in returns_pred.columns:\n win_rate = []\n for horizon in horizons:\n win_rate.append(SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon))\n win_rate_all[symbol] = win_rate\n\n # Compute statistics across all symbols\n win_rate_all = win_rate_all.agg(['mean', 'std'], axis=1).merge(win_rate_all, left_index=True, right_index=True)\n\n # Formatting\n cm = sns.light_palette(\"green\", as_cmap=True)\n return win_rate_all.style.background_gradient(cmap=cm).format(\"{:.1%}\")","def get_winner_df(row):\n\tteam1 = row['team1']\n\tteam2 = row['team2']\n\tprob = row['prob']\n\trand_num = float(randint(0,1000)/1000)\n\tprint('prob: %s rand %s' % (str(prob), str(rand_num)) )\n\tif rand_num <= prob:\n\t\twinner = team1\n\telse:\n\t\twinner = team2\n\n\treturn winner","def find_winner(players_dict: dict) -> list:\n\n podium = []\n for player, points in players_dict.items():\n podium.append((player, sum_points(points)))\n\n podium.sort(key=lambda x: x[1], reverse=True)\n\n return podium","def run_tournament(agents, randomize_round_num=True):\r\n\r\n for a in agents:\r\n try:\r\n a.load_payoff_conditions(payoffs)\r\n except Exception as e:\r\n pass\r\n\r\n score_dict = {}\r\n\r\n for a in agents:\r\n score_dict[a.name] = 0\r\n\r\n for (a1, a2) in itertools.product(agents, agents):\r\n\r\n if randomize_round_num:\r\n match_rounds = random.randint(min_rounds, max_rounds)\r\n else:\r\n match_rounds = rounds\r\n\r\n (a1_score, a2_score) = run_match(a1, a2, match_rounds, payoffs)\r\n\r\n score_dict[a1.name] += a1_score\r\n score_dict[a2.name] += a2_score\r\n\r\n return score_dict","def play(self):\n if self.stats['round'] == 0:\n if self.data['personalities'] and self.data['events']:\n self.choose_opponent()\n self.resolve_conflict()\n else:\n self.stats['round'] += 1\n elif self.stats['round'] == 1:\n if self.data['locations']:\n self.choose_location()\n self.resolve_conflict()\n else:\n self.stats['round'] += 1\n else:\n print(\"You've won\")\n self.game_over = True\n return self.stats","def get_team_rating(pdo, tsotr, tsr):\n normalised_tsr = (tsr - 0.5) * 2.0\n rating = (0.82 * normalised_tsr) + (0.45 * tsotr) + (0.4 * pdo)\n return rating"],"string":"[\n \"def get_win_rate_regular_season_for_each_coach(self):\\n self.games_won_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n self.games_lose_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df\\n self.combine_regular_games_won_lose = (\\n self.games_lose_for_coaches\\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"games_lost\\\",\\\"which_coach_for_win\\\":\\\"games_won\\\"})\\n )\",\n \"def get_championship_won_for_each_coach(self):\\n self.championship_team = (\\n self.raw_data_postseason\\n .merge(self.season_max_days,how='left',on=['Season'])\\n .query(\\\"DayNum == season_max_days\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"NumOT\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"NumOT\\\":\\\"is_champion\\\",\\\"WTeamID\\\":\\\"TeamID\\\"})\\n )\",\n \"def get_champion_winrate(self, summoner_id, champion_id):\\n request = rq.get(\\n 'https://{region}.api.pvp.net/api/lol/{region}/v{api_v}/stats/by-summoner/{summ_id}/ranked?season=SEASON{year}&api_key={api_key}'\\n .format(\\n region=self.region,\\n api_v=api_version['stats'],\\n summ_id=summoner_id,\\n year=dt.today().year,\\n api_key=API_KEY\\n )\\n )\\n try:\\n check_response(request)\\n champions = request.json()['champions']\\n if champions is not None:\\n for champion in champions:\\n if champion['id'] == champion_id:\\n total_won = champion['stats']['totalSessionsWon']\\n total = total_won + champion['stats']['totalSessionsLost']\\n\\n winrate = total_won / total\\n return [winrate, total]\\n return 0, 0\\n except ServiceException:\\n return 0, 0\",\n \"def winning_games_stats(self):\\n self.winning_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(winning_num_counts = 1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM3\\\":\\\"sum\\\",\\\"WFGA3\\\":\\\"sum\\\",\\\"WFTM\\\":\\\"sum\\\",\\\"WFTA\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"winning_num_counts\\\":\\\"sum\\\",\\n \\\"WOR\\\":\\\"sum\\\",\\\"WDR\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\n \\\"WAst\\\":\\\"sum\\\",\\\"WTO\\\":\\\"sum\\\",\\\"WStl\\\":\\\"sum\\\",\\\"WBlk\\\":\\\"sum\\\",\\\"WPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"LScore\\\":\\\"losing_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"LFGA\\\":\\\"LFGA_opp\\\",\\\"LFGM\\\":\\\"LFGM_opp\\\"})\\n )\",\n \"def _compute_winrates(synergy, counter, heroes_released):\\n for i in range(heroes_released):\\n for j in range(heroes_released):\\n if i != j and i != 23 and j != 23:\\n if synergy['games'][i, j] != 0:\\n synergy['winrate'][i, j] = synergy['wins'][i, j] / \\\\\\n float(synergy['games'][i, j])\\n\\n if counter['games'][i, j] != 0:\\n counter['winrate'][i, j] = counter['wins'][i, j] / \\\\\\n float(counter['games'][i, j])\",\n \"def calculateWinRate():\\n times = 10\\n winRate = 0.0\\n for i in range(times):\\n game = Game('user', 6, 6)\\n winRate += game.play(5, False, True, False, False)\\n winRate = winRate/times\\n print \\\"Winrate:\\\", winRate\",\n \"def get_final_coach_for_each_season(self):\\n self.final_coach_for_season = (\\n self.num_days_coach_for_season\\n .groupby(['Season','TeamID'])\\n .agg({\\\"CoachName\\\":\\\"count\\\"})\\n .reset_index()\\n .rename(columns={\\\"CoachName\\\":\\\"coach_counts\\\"})\\n .merge(self.num_days_coach_for_season,how='left',on=['Season','TeamID'])\\n .pipe(lambda x:x.assign(final_coach = np.where(x.num_season >= 0.5, x.CoachName, \\\"ignore\\\")))\\n [['Season','TeamID','final_coach']]\\n )\",\n \"def winrate(matches):\\n if not matches:\\n print('no matches')\\n return None\\n\\n win_loss = [match['result'] for match in matches]\\n return sum(win_loss)/len(win_loss)\",\n \"def marcels_players(goalie, date, df):\\n # 0 = that year, 1 is year b4 ....\\n marcel_weights = [.36, .29, .21, .14]\\n reg_const = 2000\\n reg_avg = 0 # Where to regress to\\n\\n # Use past 3 season to weight games played -> Just take weighted average\\n gp_weights = [8, 4, 2, 0]\\n\\n season = int(helpers.get_season(date))\\n\\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\\n weighted_gp_sum, weights_gp_sum = 0, 0\\n\\n # Past 4 Seasons\\n for i in range(0, 4):\\n if season - i > 2006:\\n # Subset from stats df\\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\\n\\n # Sanity Check\\n if df_goalie.shape[0] > 1:\\n print(\\\"Too many rows!!!!!!!\\\")\\n exit()\\n\\n # If he played that year\\n if not df_goalie.empty:\\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\\n\\n # -> To divide by at end...normalize everything\\n weights_marcel_sum += marcel_weights[i]\\n weights_gp_sum += gp_weights[i]\\n\\n # Normalize weighted sums\\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n\\n # Get Regressed fsv%\\n if weighted_fen_sum != 0:\\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\\n else:\\n weighted_adj_fsv = 0\\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\\n\\n # Get weighted gp\\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\\n\\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}\",\n \"def getSeasonStats(self):\\n df_season_agg = self.toSeasonAggFormat()\\n\\n # Calculate Possessions for each game\\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\\\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\\n\\n # Aggregate to Season Summary Level\\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\\n\\n season_stats = season_stats.rename(columns={'Win':'wins'})\\n\\n # Season Advanced Stats\\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\\n\\n season_stats.drop('DayNum', axis=1, inplace=True)\\n season_stats.drop('OppTeamID', axis=1, inplace=True)\\n season_stats.drop('rand', axis=1, inplace=True)\\n\\n return season_stats\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def cumulative_stats_for_teams_each_year(self):\\n self.cumulative_stats_for_team_each_year = (\\n self.combine_both_winning_losing_games_stats\\n .sort_values(['WTeamID','Season'])\\n .groupby(['WTeamID'])\\n .cumsum()\\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\\n .drop(['LTeamID','win_rate'],1)\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n # rebounds cumsum stats\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # assists, turnovers, steals, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win or lose by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def get_team_round_score(self, team: int, match_round: int) -> int:\\n assert self.rounds, \\\"There are no rounds or the requested round doesn't exist\\\"\\n\\n team_score = 0\\n\\n if team == 1:\\n for player in self.rounds[match_round - 1].team1.players:\\n team_score += player.scores\\n else:\\n for player in self.rounds[match_round - 1].team2.players:\\n team_score += player.scores\\n\\n return team_score\",\n \"def team_season_stats(team):\\n\\n # Get HTML Content\\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # Team's yearly stats are displayed in a table\\n season_stats = soup.find(id='stats').find('tbody')\\n\\n # Iterate through each year\\n for year in season_stats.find_all('tr', {'class': None}):\\n\\n season_year = year.find('th').text[0:4]\\n season_year = int(season_year) + 1\\n season = {'year': season_year}\\n\\n # Loop through each stat\\n for stat in year.find_all('td'):\\n season[stat['data-stat']] = stat.string\\n\\n # Rename relocated teams\\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\\n season['_id'] = season['team_id'] + '_' + str(season_year)\\n\\n # Remove unwanted stats\\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\\n for k in to_remove:\\n season.pop(k, None)\\n\\n # Add to MongoDB\\n m.insert('team_season', season)\",\n \"def get_winpercent(self, game: 'Game' = None, player: 'Player' = None):\\n if game and player:\\n pass\\n elif game:\\n play_count = self.play_set.filter(game=game).count()\\n win_count = self.play_set.filter(winner=self, game=game).count()\\n return win_count / play_count * 100\\n elif player:\\n pass\\n # play_count = self.play_set.filter(players__in=player).count()\\n # win_count = self.play_set.filter(\\n # winner=self, player=player).count()\\n # return win_count / play_count * 100\\n else:\\n return self.winpercent\",\n \"def glicko2_summarize(df, min_weeks=4, use_prior=False):\\n\\n # Loop through seasons and weeks to create full history of ratings by team\\n results = pd.DataFrame()\\n for season in df['Season'].sort_values().unique():\\n for week in df[df['Season']==season]['Week'].sort_values().unique():\\n if week > min_weeks:\\n if week == min_weeks + 1:\\n season_df = df[df['Season']==season].copy()\\n uniqueteamids = pd.concat([season_df['VisID'],\\n season_df['HomeID']]).unique()\\n if use_prior == True and season > df['Season'].min():\\n ratings = np.repeat(1500, len(uniqueteamids))\\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\\n sigma = np.repeat(0.06, len(uniqueteamids))\\n glicko_stats = pd.DataFrame({'ratings': ratings,\\n 'ratingsdeviance': ratingsdeviance,\\n 'sigma': sigma}, index=uniqueteamids)\\n prior = results[results['Season']==season-1]\\n prior_id_mask = [True if id in uniqueteamids else False for id in prior['TeamID']]\\n prior = prior[prior_id_mask]\\n prior = prior.sort_values('Week').groupby('TeamID').tail(1)\\n prior = prior.drop('Week',1)\\n prior = prior.set_index('TeamID')\\n glicko_stats.loc[prior.index, 'ratings'] = prior['Glicko_Rating'] - (prior['Glicko_Rating'] - 1500)/2\\n glicko_stats.loc[prior.index, 'ratingsdeviance'] = prior['Glicko_Rating_Deviance'] - (prior['Glicko_Rating_Deviance'] - 350)/2\\n glicko_stats.loc[prior.index, 'sigma'] = prior['Glicko_Sigma'] - (prior['Glicko_Sigma'] - 0.06)/2\\n else:\\n ratings = np.repeat(1500, len(uniqueteamids))\\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\\n sigma = np.repeat(0.06, len(uniqueteamids))\\n glicko_stats = pd.DataFrame({'ratings': ratings,\\n 'ratingsdeviance': ratingsdeviance,\\n 'sigma': sigma}, index=uniqueteamids)\\n\\n week_df = df[(df['Season']==season) & (df['Week']<week)].copy()\\n glicko_stats = glicko2(week_df, uniqueteamids, glicko_stats)\\n\\n\\n glicko_results = glicko_stats.reset_index()\\n print(glicko_results.head(), season)\\n glicko_results.columns = ['TeamID','Glicko_Rating',\\n 'Glicko_Rating_Deviance',\\n 'Glicko_Sigma']\\n glicko_results['Season'] = season\\n glicko_results['Week'] = week\\n results = pd.concat([results, glicko_results], axis=0,\\n ignore_index=True)\\n\\n # Join the ratings to the original schedule of games\\n df = df.merge(results, left_on=['Season','Week','HomeID'],\\n right_on=['Season','Week','TeamID'],\\n suffixes=('','_Home'))\\n df.drop('TeamID', 1, inplace=True)\\n\\n df = df.merge(results, left_on=['Season','Week','VisID'],\\n right_on=['Season','Week','TeamID'],\\n suffixes=('','_Away'))\\n df.drop('TeamID', 1, inplace=True)\\n\\n # Create key and set index to join with n_game summaries dataset.\\n df.set_index(['HomeID', 'VisID', 'Season', 'Week'], inplace=True)\\n df = df[['Glicko_Rating', 'Glicko_Rating_Deviance', 'Glicko_Sigma',\\n 'Glicko_Rating_Away', 'Glicko_Rating_Deviance_Away',\\n 'Glicko_Sigma_Away']]\\n df.columns = ['Glicko_Rating_Home', 'Glicko_Rating_Deviance_Home',\\n 'Glicko_Sigma_Home', 'Glicko_Rating_Away',\\n 'Glicko_Rating_Deviance_Away', 'Glicko_Sigma_Away']\\n\\n return df\",\n \"def calc_winner(self):\\n pass\",\n \"def rate_club(user, club):\\n if not user.is_authenticated():\\n return None\\n if not club.posel_set.exists():\\n return None\\n return sum(x[1] for x in rank_in_club(user, club)) / club.posel_set.count()\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"def get_new_ratings(players, teams):\\n nb_players_team0 = len(teams[0])\\n nb_players_team1 = len(teams[1])\\n winner = players[teams[0][0]]\\n loser = players[teams[1][0]]\\n if nb_players_team0 == 1 and nb_players_team1 == 1:\\n new_r1, new_r3 = rate_1vs1(winner,loser)\\n elif nb_players_team0 == 1 and nb_players_team1 > 1:\\n team_loser = [loser, players[teams[1][1]]]\\n (new_r1), (new_r3, new_r4) = rate([winner, team_loser], ranks=[0, 1]) \\n elif nb_players_team0 > 1 and nb_players_team1 == 1:\\n team_winner = [winner, players[teams[0][1]]]\\n (new_r1, new_r2), (new_r3) = rate([team_winner, loser], ranks=[0, 1]) \\n else:\\n team_loser = [loser, players[teams[1][1]]]\\n team_winner = [winner, players[teams[0][1]]]\\n (new_r1, new_r2), (new_r3, new_r4) = rate([team_winner, team_loser], ranks=[0, 1]) \\n player1 = {'name': teams[0][0], 'mu': new_r1.mu, 'sigma': new_r1.sigma}\\n player3 = {'name': teams[1][0], 'mu': new_r3.mu, 'sigma': new_r3.sigma}\\n if nb_players_team0 > 1:\\n player2 = {'name': teams[0][1], 'mu': new_r2.mu, 'sigma': new_r2.sigma}\\n if nb_players_team1 > 1:\\n player4 = {'name': teams[1][1], 'mu': new_r4.mu, 'sigma': new_r4.sigma}\\n if nb_players_team0 > 1:\\n return [player1, player2, player3, player4]\\n return [player1, player2, player4]\\n return [player1, player3]\",\n \"def combine_playoff_championship_for_each_coach(self):\\n self.final_coach_with_postseason_champion_each_year = (\\n self.final_coach_with_postseason_each_year\\n .merge(self.championship_team,how='left',on=['Season','TeamID'])\\n .fillna(0)\\n )\",\n \"def season_game_logs(team, year):\\n\\n # Check year value\\n if year > 2019 or year < 1950:\\n raise ValueError('Year Value Incorrect')\\n\\n # Rename teams that moved\\n team = scrape_utils.rename_team(team, year)\\n\\n # Get HTML content\\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n season_stats = soup.find(id='tgl_basic')\\n games = season_stats.find('tbody')\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # To find opponent statistics\\n opponent = re.compile('^opp_.*$')\\n\\n # Loop through every game in a team's season\\n for game in games.find_all('tr', {'class': None}):\\n\\n curr_team = {'team': team}\\n opp_team = {}\\n\\n # Loop through each stat\\n for stat in game.find_all('td'):\\n\\n stat_name = stat['data-stat']\\n\\n # These are opponent stats\\n if re.match(opponent, stat_name):\\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\\n else:\\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\\n\\n # Remove unnecessary information\\n del curr_team['game_season']\\n del curr_team['x']\\n\\n # Rename relocated teams\\n curr_team['team'] = scrape_utils.rename_team(team)\\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\\n\\n # Use the same ID as basketball reference\\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \\\"%Y-%m-%d\\\"),\\n 'season': year,\\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\\n '_id': game.find('a')['href'][-17:-5]}\\n\\n # Place the teams in the correct spot depending on who is the home team\\n if curr_team.pop('game_location') == 0:\\n result['home'] = curr_team\\n result['away'] = opp_team\\n else:\\n result['home'] = opp_team\\n result['away'] = curr_team\\n\\n # Insert into database\\n m.insert('game_log', result)\",\n \"def season_rounds(cls, season):\\r\\n\\t\\t\\r\\n\\t\\tfolder_name = cls.season_folder(season)\\r\\n\\t\\tround_list = os.listdir(f'Data/{folder_name}')\\r\\n\\r\\n\\t\\tall_rounds = []\\r\\n\\r\\n\\t\\tfor round_file in round_list:\\r\\n\\t\\t\\twith open(f'Data/{folder_name}/{round_file}', 'r', encoding='utf-8') as f:\\r\\n\\t\\t\\t\\tround_info = f.read().splitlines()\\r\\n\\r\\n\\t\\t\\tround_number = round_file[:-4]\\r\\n\\t\\t\\tfull_round_name = f\\\"{season} R{round_number}\\\"\\r\\n\\r\\n\\t\\t\\tround_date = int(round_info[0])\\r\\n\\t\\t\\tlookup_ind = DATES.month_diff(round_date, DATES.MIN_DATE)\\r\\n\\r\\n\\t\\t\\t# If the round isn't actually counted for TWOW Glicko\\r\\n\\t\\t\\tif full_round_name not in cls.ROUNDS[lookup_ind].keys():\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\r\\n\\t\\t\\tcontestant_count = len(round_info) - 1\\r\\n\\r\\n\\t\\t\\tstrength = cls.ROUNDS[lookup_ind][full_round_name][0]\\r\\n\\r\\n\\t\\t\\tall_rounds.append([\\r\\n\\t\\t\\t\\tround_number,\\r\\n\\t\\t\\t\\tround_date,\\r\\n\\t\\t\\t\\tcontestant_count,\\r\\n\\t\\t\\t\\tstrength\\r\\n\\t\\t\\t])\\r\\n\\t\\t\\r\\n\\t\\treturn all_rounds\",\n \"def get_tourney_rounds(self, conference, year):\\n ts_dict = self.get_tourney_slots()\\n seed_dict = self.get_tourney_seeds()\\n tr_dict = self.get_tourney_results()\\n \\n round_1 = list()\\n round_2 = list()\\n round_3 = list()\\n round_4 = list()\\n winner = list()\\n \\n round1_winners = list()\\n for seed, team in seed_dict[year].items():\\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n round1_winners.append(seed[1:])\\n #removes duplicates because I did this part weirdly... HEHEH\\n round1_winners = list(set(round1_winners))\\n\\n win_counter = defaultdict(int)\\n for seed, team in seed_dict[year].items(): \\n for winning, losing in tr_dict[year]:\\n if team == winning and conference in seed:\\n win_counter[winning] += 1\\n \\n for slot, matchup in ts_dict[year].items():\\n \\n if conference in slot and \\\"R1\\\" in slot: \\n round_1.append(\\\"{}-{}\\\".format(matchup[1:3], matchup[-2:]))\\n round_1 = sorted(round_1)\\n #for match in round_1:\\n for winner1 in round1_winners:\\n if winner1 in round_1[0]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-1]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[1]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-2]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[2]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-3]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_1[3]:\\n for winner2 in round1_winners:\\n if winner2 in round_1[-4]:\\n round_2.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_2 = sorted(round_2)\\n\\n round2_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 1:\\n round2_winners.append(seed[1:])\\n \\n for winner1 in round2_winners:\\n if winner1 in round_2[0]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-1]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n if winner1 in round_2[1]:\\n for winner2 in round2_winners:\\n if winner2 in round_2[-2]:\\n round_3.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_3 = sorted(round_3)\\n\\n round3_winners = list()\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 2:\\n round3_winners.append(seed[1:])\\n\\n for winner1 in round3_winners:\\n if winner1 in round_3[0]:\\n for winner2 in round3_winners:\\n if winner2 in round_3[-1]:\\n round_4.append(\\\"{}-{}\\\".format(winner1, winner2))\\n round_4 = sorted(round_4)\\n\\n for seed, team in seed_dict[year].items():\\n for team2, count in win_counter.items():\\n if team == team2 and count > 3:\\n winner.append(seed[1:])\\n\\n conferences = {\\\"W\\\": \\\"East\\\", \\\"X\\\": \\\"Midwest\\\", \\\"Y\\\": \\\"South\\\", \\\"Z\\\": \\\"West\\\"}\\n\\n #print(\\\"CONFERENCE: {}, YEAR: {}\\\".format(conferences[conference], year))\\n #print(\\\"ROUND1:\\\", round_1)\\n #print(\\\"ROUND2:\\\", round_2)\\n #print(\\\"ROUND3:\\\", round_3)\\n #print(\\\"ROUND4:\\\", round_4)\\n #print(\\\"WINNER:\\\", winner)\\n\\n #clearing out the tourney results dictionary\\n #tr_dict.clear()\\n\\n return round_1, round_2, round_3, round_4, winner\",\n \"def assign_win_points(game_dict):\\n def win_tuple(deck_dict):\\n \\\"\\\"\\\" Return tuple ordered by increasing final standing. \\\"\\\"\\\"\\n # negate turns so that max() behaves; points good, turns bad.\\n num_normal_turns = sum(not ( (POSSESSION in t and t[POSSESSION]) or \\\\\\n (OUTPOST in t and t[OUTPOST]))\\n for t in deck_dict[TURNS])\\n return (deck_dict[POINTS], -num_normal_turns)\\n\\n decks = game_dict[DECKS]\\n winner_tuple = max(win_tuple(p) for p in decks)\\n winners = [p for p in decks if win_tuple(p) == winner_tuple]\\n\\n win_points = float(len(decks)) / len(winners)\\n for player in decks:\\n player[WIN_POINTS] = win_points if player in winners else 0.0\",\n \"def compute_score(window, computer_piece):\\n score = 0\\n if window.count(computer_piece) == 4:\\n score += 100\\n elif window.count(computer_piece) == 3 and window.count(0) == 1:\\n score += 5\\n elif window.count(computer_piece) == 2 and window.count(0) == 2:\\n score += 2\\n if window.count(PLAYER_PIECE) == 2 and window.count(0) == 2:\\n score -= 1\\n if window.count(PLAYER_PIECE) == 3 and window.count(0) == 1:\\n score -= 100\\n return score\",\n \"def get_smmry_data(soup, game_dict):\\n\\n # Get date and time data.\\n try:\\n date_soup = soup.find(\\\"div\\\", {\\\"class\\\": \\\"spielbericht_tipp_status\\\"})\\n league = date_soup.div.span.text\\n date_string = date_soup.div.text\\n date = re.search(r'\\\\d{2}.\\\\d{2}.\\\\d{2}', date_string).group(0)\\n time = re.search(r'\\\\d{2}:\\\\d{2}', date_string).group(0)\\n matchday = re.search(r'[|]\\\\d+', date_string).group(0)[1:]\\n\\n game_dict[\\\"league\\\"] = league\\n game_dict[\\\"fb_date\\\"] = date\\n game_dict[\\\"fb_time\\\"] = time\\n game_dict[\\\"matchday\\\"] = matchday\\n except AttributeError:\\n pass\\n\\n # Get game result.\\n try:\\n result = soup.find(\\\"div\\\", {\\\"class\\\": \\\"stand\\\"}).text\\n game_dict[\\\"result\\\"] = result\\n except AttributeError:\\n pass\\n\\n # Try to get the referee name.\\n try:\\n referee = soup.find(\\\"span\\\", {\\\"class\\\": \\\"schiri_link\\\"}).text\\n game_dict[\\\"referee\\\"] = referee\\n except AttributeError:\\n pass\\n\\n # Get team, club name and repective url by team.\\n try:\\n smmry_soup = soup.find(\\n \\\"div\\\", {\\\"class\\\": \\\"spielbericht_ergebnis_wrapper\\\"})\\n club_title = smmry_soup.find_all(\\\"img\\\")\\n team_title = smmry_soup.findAll(\\\"div\\\", {\\\"class\\\": \\\"teaminfo\\\"})\\n\\n # Loop through teams.\\n for j, team in enumerate([\\\"home_\\\", \\\"away_\\\"]):\\n game_dict[team + \\\"team\\\"] = team_title[j].a[\\\"title\\\"]\\n game_dict[team + \\\"team_url\\\"] = team_title[j].a[\\\"href\\\"]\\n game_dict[team + \\\"club\\\"] = club_title[j][\\\"title\\\"]\\n except (AttributeError, TypeError):\\n pass\\n\\n return game_dict\",\n \"def winRate(DF):\\r\\n df = DF[\\\"return\\\"]\\r\\n pos = df[df>1]\\r\\n neg = df[df<1]\\r\\n return (len(pos) / len(pos + neg)) * 100\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def get_result(state, winrate_predictor):\\n teamA_picks = state[:, TEAM_A_PICK_INDICES]\\n teamB_picks = state[:, TEAM_B_PICK_INDICES]\\n team_comp = torch.cat((teamA_picks, teamB_picks), dim=1)\\n winrate = winrate_predictor(team_comp)[0, 0]\\n \\n if winrate >= 0.5:\\n return 0\\n return 1\",\n \"def win_ratio_avg(self):\\n win_ratio = 0\\n # Adds all the win ratios of team in this conference which will be\\n # used to compute the win ratio average.\\n for team_obj in self._conf_teams:\\n ### INVARIANT: team_obj is a Team class object and\\n ### self._conf_teams is a list of Team class objects.\\n win_ratio += team_obj._win_ratio\\n return win_ratio/len(self._conf_teams)\",\n \"def review(self):\\n # Compile standings\\n self.standings = COMPOSE_LEAGUE_STANDINGS(season=self)\\n # Name a champion\\n self.champion = self._name_champion()\\n self.league.history.champions_timeline[self.year] = self.champion\\n print \\\"THE {} HAVE WON THE {} {} CHAMPIONSHIP!\\\".format(\\n self.champion.team.name.upper(), self.year, self.league.name.upper()\\n )\\n # Compile league leaders\\n # self.league_leaders = COMPOSE_LEAGUE_LEADERS(season=self)\\n # Have each team review its season, as well\\n for team_season in self.teams:\\n team_season.review()\\n # Send the league into the offseason\\n self.league.season = None\",\n \"def opponents_score(self):\\n if self.opponent_wickets == 10:\\n var1 = \\\"All Out\\\"\\n return str('{0} {1}').format(self.opponent_runs, var1)\\n else:\\n var1 = self.opponent_wickets\\n return str('{0}-{1}').format(self.opponent_runs, var1)\",\n \"def getWinProbability(team1, team2, r, year=2019):\\n\\tR1_PROBS = R1_PROBS_2019 if year == 2019 else R1_PROBS_2020\\n\\tALPHA_VALS = ALPHA_VALS_2019 if year == 2019 else ALPHA_VALS_2020\\n\\n\\t# Currently using Power Model\\n\\ts1 = team1['seed']\\n\\ts2 = team2['seed']\\n\\n\\t# Use R1_PROBS for round 1\\n\\tif r == 1:\\n\\t\\tif not (s1 + s2 == 17):\\n\\t\\t\\texit('Invalid round 1 matchup: seeds {0} vs. {1}.'.format(s1, s2))\\n\\t\\treturn R1_PROBS[s1] if s1 < s2 else R1_PROBS[s2]\\n\\t\\n\\t# Use ALPHA_VALS for other rounds (unless seeds are same)\\n\\tif s1 == s2:\\n\\t\\treturn 0.5\\n\\n\\talpha = ALPHA_VALS[r]\\n\\ts1a = (s1 * 1.0) ** alpha\\n\\ts2a = (s2 * 1.0) ** alpha\\n\\treturn s2a / (s1a + s2a)\",\n \"def calc_match_points(self, match):\\n if match.winner == match.TIE:\\n match.home.tournament_score += 1\\n match.away.tournament_score += 1\\n else:\\n match.winner.tournament_score += 3\\n match.loser.tournament_score += 0\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def evaluate(game, player):\\n weights = [2, 200, 2000, 20000]\\n reward = 0\\n opponent = get_opponent(player)\\n for length in range(2, 6):\\n reward += weights[length - 2] * get_num_series(game, player, length)\\n reward -= weights[length - 2] * get_num_series(game, opponent, length)\\n return reward\",\n \"def RankSeason(Featuresfile: str, Placementsfile: str, week: int) -> None:\\r\\n Features = pd.read_csv(Featuresfile, encoding = \\\"ISO-8859-1\\\")\\r\\n Placements = pd.read_csv(Placementsfile, encoding = \\\"ISO-8859-1\\\")\\r\\n RF.PlacementPointsSeason(Placements, week)\\r\\n RF.FormulaTotalSeason(Features, Placements)\\r\\n Features['Rank'] = (Features['Points'] * -1).rank(method = 'min')\\r\\n Features.to_csv(Featuresfile, index = False, encoding = \\\"ISO-8859-1\\\")\",\n \"def get_player_career_reg_season_stats(player_id, player_data_url, headers):\\n\\tplayer_data_json=json.loads(requests.get(player_data_url+str(player_id), headers=headers).content)\\n\\tcareer_totals=player_data_json['resultSets'][1]\\n\\tdf_career_totals=pd.DataFrame.from_records(career_totals['rowSet'], columns=career_totals['headers'])\\n\\tdf_career_totals.PLAYER_ID=df_career_totals.PLAYER_ID.astype(int)\\n\\treturn df_career_totals\",\n \"def calculate_power_ranking(wins, losses, is_winning_streak,\\n streak_length, recent_wins, recent_losses):\\n pred1 = 0\\n pred2 = round( ( ((wins*1.0)/(wins+losses)) - 0.500 ) * 16 * 9 * 10/9 )\\n pred3 = recent_wins - recent_losses\\n streak_factor = 1 if is_winning_streak else -1\\n pred4 = streak_factor * round( ( streak_length - 1 ) / 2.0 )\\n print pred1, pred2, pred3, pred4\\n return pred1 + pred2 + pred3 + pred4\",\n \"def play_simulation(self, n_iter=1000):\\r\\n final_wins = final_wins = {t: 0 for t in self.teams.dteams.keys()}\\r\\n season_teams_ranked, _ = get_playoffs(self.season_calendar, self.teams_info)\\r\\n for i in tqdm(range(n_iter)):\\r\\n if not self.playoffs_only:\\r\\n self.season.play_regular_season(self.teams)\\r\\n season_teams_ranked = self.season.playoffs_teams_ranked\\r\\n\\r\\n playoffs_sim = Playoffs(season_teams_ranked, self.gsim)\\r\\n winner_playoff = playoffs_sim.get_winner(self.teams)\\r\\n\\r\\n final_wins[winner_playoff] += 1\\r\\n\\r\\n return final_wins\",\n \"def average_win_rate(strategy, baseline=always_roll(4)):\\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\\n\\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2\",\n \"def average_win_rate(strategy, baseline=always_roll(4)):\\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\\n\\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2\",\n \"def average_win_rate(strategy, baseline=always_roll(4)):\\n win_rate_as_player_0 = 1 - make_averaged(winner)(strategy, baseline)\\n win_rate_as_player_1 = make_averaged(winner)(baseline, strategy)\\n\\n return (win_rate_as_player_0 + win_rate_as_player_1) / 2\",\n \"def score(entry):\\n score = time_seconds(entry['Swim'])\\n score += time_seconds(entry['Run'])\\n score -= (int(entry['Push-ups']) * 2)\\n score -= int(entry['Sit-ups'])\\n score -= int(entry['Pull-ups']) * 6\\n return score\",\n \"def get_win_percentage(self) -> float:\\n if self.wins == 0:\\n return 0.0\\n else:\\n return round((self.wins / (self.wins + self.losses)) * 100, 2)\",\n \"def calc_win_lose_ratio(self):\\n total = len(self.train_y)\\n survived = 0\\n for i in self.train_y:\\n if i > 0:\\n survived += 1\\n\\n self.survival_sum = [survived, total-survived]\",\n \"def get_team_scores(self, team, include_home=True, include_away=True):\\n if include_away:\\n away_games = list(filter(lambda g: team == g.AwayTeam, self.games))\\n else:\\n away_games = []\\n\\n if include_home:\\n home_games = list(filter(lambda g: team == g.HomeTeam, self.games))\\n else:\\n home_games = []\\n\\n scored_h = [g.FTHG for g in home_games]\\n scored_a = [g.FTAG for g in away_games]\\n\\n conceded_h = [g.FTAG for g in home_games]\\n conceded_a = [g.FTHG for g in away_games]\\n\\n try:\\n mean_gd = mean(scored_h + scored_a) - mean(conceded_h + conceded_a)\\n home_gd = mean(scored_h) - mean(conceded_h)\\n home_adv = home_gd - mean_gd\\n except Exception:\\n home_adv = 0\\n\\n return {\\n \\\"scored_xg\\\": scored_h + scored_a,\\n \\\"conceded_xg\\\": conceded_h + conceded_a,\\n \\\"home_adv\\\": home_adv,\\n \\\"expected_points\\\": self.get_table(metric='points')[team] /\\n len(home_games + away_games)\\n }\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def winning_percentage(self):\\n return float(len(self.wins))/len((self.wins+self.losses))\",\n \"def compute_score(self, n_episodes=5):\\n\\n # Score is computed via aggregate over multiple episodes\\n score = 0\\n\\n for _ in range(n_episodes):\\n score += play_episode(self.model, self.env)\\n\\n return score / n_episodes\",\n \"def tally_results(self, verbose=False):\\n\\t\\tself.tally={\\n\\t\\t\\tself.player1.name: {\\n\\t\\t\\t\\t'wins': 0,\\n\\t\\t\\t\\t'draws': 0,\\n\\t\\t\\t\\t'losses': 0\\n\\t\\t\\t},\\n\\t\\t\\tself.player2.name: {\\n\\t\\t\\t\\t'wins': 0,\\n\\t\\t\\t\\t'draws': 0,\\n\\t\\t\\t\\t'losses': 0\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tfor key, value in self.winner_dict.items():\\n\\t\\t\\tself.tally[self.player1.name]['wins']+=1 if value == self.player1.name else 0\\n\\t\\t\\tself.tally[self.player1.name]['draws']+=1 if value is None else 0\\n\\t\\t\\tself.tally[self.player1.name]['losses']+=1 if value == self.player2.name else 0\\n\\t\\t\\tself.tally[self.player2.name]['wins']+=1 if value == self.player2.name else 0\\n\\t\\t\\tself.tally[self.player2.name]['draws']+=1 if value is None else 0\\n\\t\\t\\tself.tally[self.player2.name]['losses']+=1 if value == self.player1.name else 0\\n\\t\\tif verbose:\\n\\t\\t\\tprint('\\\\n--- FINAL RESULT ---\\\\n')\\n\\t\\t\\ttally_pretty=pd.DataFrame(self.tally).to_markdown()\\n\\t\\t\\tprint(tally_pretty)\\n\\t\\t\\tif self.tally[self.player1.name]['wins'] == self.tally[self.player2.name]['wins']:\\n\\t\\t\\t\\tprint('\\\\nIt\\\\'s a draw!\\\\n')\\n\\t\\t\\telse:\\n\\t\\t\\t\\twinner=self.player1.name if self.tally[self.player1.name]['wins'] > self.tally[self.player2.name]['wins'] else self.player2.name\\n\\t\\t\\t\\tprint('\\\\n{player} wins the game!\\\\n'.format(player=winner))\",\n \"def get_team_stats(players: list[Player]) -> dict[int]:\\n\\n team_stats = {}\\n\\n total_reaction = 0\\n total_mechanical_skill = 0\\n total_tactical_skill = 0\\n total_game_knowledge = 0\\n total_xp = 0\\n\\n for player in players:\\n total_reaction += player.reaction\\n total_mechanical_skill += player.mechanical_skill\\n total_tactical_skill += player.tactical_skill\\n total_game_knowledge += player.game_knowledge\\n total_xp += player.xp\\n\\n team_stats.update(\\n {\\\"reaction\\\": total_reaction,\\n \\\"mechanical_skill\\\": total_mechanical_skill,\\n \\\"tactical_skill\\\": total_tactical_skill,\\n \\\"game_knowledge\\\": total_game_knowledge,\\n \\\"xp\\\": total_xp})\\n\\n return team_stats\",\n \"def create_dreamteam_count_yearly(player, raw_list):\\n num_seasons = 0\\n yearly_dreamteam_count = 0\\n for i, raw in enumerate(raw_list):\\n if i == len(raw_list) - 1:\\n break\\n if player in raw['name'].values:\\n num_seasons += 1\\n yearly_dreamteam_count += raw[['dreamteam_count']][raw.name == player].values[0][0]\\n if num_seasons == 0:\\n return np.nan\\n return yearly_dreamteam_count / num_seasons\",\n \"def calculate_scores(self):\\n words = self.walk_board()\\n player_scores = {}\\n for word in words:\\n player = word.get_owning_player()\\n if player not in player_scores:\\n player_scores[player] = 0\\n player_scores[player] += word.get_score()\\n return player_scores\",\n \"def scrape():\\n league_year = Config.get_property(\\\"league_year\\\")\\n\\n # Create table\\n season_data = client.season_schedule(league_year)\\n season_data = br_enum_to_string(season_data)\\n return season_data\",\n \"def calc_marcel_weights(df):\\n cols = ['toi_on', 'gs60', 'toi/gp']\\n\\n # Only essential columns\\n df = df[[\\\"player\\\", \\\"player_id\\\", \\\"season\\\"] + cols]\\n\\n # Copy over ALL Data\\n predict_df = df.copy()\\n\\n # To get fours years in a row (3 predicting 1).. we run 'get_previous_year' 3 times\\n # Each time we get n-_ by using predict_col\\n # NOTE: I'm writing over df here!!!!\\n for seasons in range(1, 4):\\n df = get_previous_yr(df, predict_df, seasons)\\n df = df[~df['toi_on_n-{}'.format(seasons)].isnull()]\\n\\n # Filter for minimum toi\\n # 400 for first 3 and 800 for last\\n df = df[(df['toi_on'] >= 800) & (df['toi_on_n-1'] >= 400) & (df['toi_on_n-2'] >= 400) & (df['toi_on_n-3'] >= 400)]\\n\\n print(\\\"\\\\nPlayers: {}\\\".format(df.shape[0]))\\n\\n for col in ['gs60', 'toi/gp']:\\n print(\\\"Getting the Weights for: \\\", col)\\n # Prepare shit\\n model_features = df[['{}_n-1'.format(col), '{}_n-2'.format(col), '{}_n-3'.format(col)]].values.tolist()\\n model_target = df[col].values.tolist()\\n model_features, model_target = np.array(model_features), np.array(model_target).ravel()\\n\\n lr = LinearRegression()\\n lr.fit(model_features, model_target)\\n\\n # Print all the Coefficient neatly\\n print(\\\"Coefficients:\\\")\\n for season, coef in zip(range(1, 4), lr.coef_):\\n print(\\\"Season n-{}:\\\".format(season), round(coef, 3))\\n\\n print(\\\"\\\")\",\n \"def update_scores(self, AI_win):\\n self.games_played += 1\\n if not AI_win:\\n self.games_won += 1\\n else:\\n self.games_lost += 1\",\n \"def tournament(tournament):\\n participants = TournamentParticipant.objects.filter(\\n tournament=tournament\\n ).order_by('ranking', 'revision__competitor__name').all()\\n\\n prev_ranking = None\\n rankings = []\\n for player in participants:\\n if prev_ranking is None or player.ranking != prev_ranking['ranking']:\\n prev_ranking = {\\n 'ranking': player.ranking,\\n 'span': 1\\n }\\n rankings.append(prev_ranking)\\n else:\\n prev_ranking['span'] += 1\\n rankings.append(None)\\n\\n return {\\n 'tournament': tournament,\\n 'good_progress_width': 100 * tournament.completed_duels / tournament.total_duels,\\n 'bad_progress_width': 100 * tournament.failed_duels / tournament.total_duels,\\n 'participants_and_rankings': list(zip(participants, rankings))\\n }\",\n \"def get_winner(game):\\n return game['winner']\",\n \"def getPlayerAdvStat(self, stat, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_call = stat.lower()\\r\\n stat_dict = {'touch':'Possessions', 'possession':'Possessions',\\r\\n 'speed':'SpeedDistance', 'distance':'SpeedDistance'}\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashptstats?College=&'\\\\\\r\\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\\\\r\\n 'DraftPick=&DraftYear=&GameScope=&Height=&LastNGames=0&'\\\\\\r\\n 'LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&'\\\\\\r\\n 'PORound=0&PerMode=PerGame&PlayerExperience=&PlayerOr'\\\\\\r\\n 'Team=Player&PlayerPosition=&PtMeasureType=' + \\\\\\r\\n stat_dict[stat_call] + '&Season=' + season + \\\\\\r\\n '&SeasonSegment=&SeasonType=Regular+Season&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision=&Weight='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n advStat_df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n return advStat_df\",\n \"def losing_games_stats(self):\\n self.losing_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(losing_num_counts=1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','LTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\\"LFGM3\\\":\\\"sum\\\",\\\"LFGA3\\\":\\\"sum\\\",\\\"LFTM\\\":\\\"sum\\\",\\\"LFTA\\\":\\\"sum\\\",\\\"losing_num_counts\\\":\\\"sum\\\",\\n \\\"LOR\\\":\\\"sum\\\",\\\"LDR\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\n \\\"LAst\\\":\\\"sum\\\",\\\"LTO\\\":\\\"sum\\\",\\\"LStl\\\":\\\"sum\\\",\\\"LBlk\\\":\\\"sum\\\",\\\"LPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"WScore\\\":\\\"winning_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"WFGA\\\":\\\"WFGA_opp\\\",\\\"WFGM\\\":\\\"WFGM_opp\\\"})\\n )\",\n \"def Winner(self, whichPlayer, tResult, score, gameCount):\\n\\n if whichPlayer == 0:\\n tResult[0] = tResult[0] + score[0]\\n else:\\n tResult[1] = tResult[1] + score[1]\\n print(data['tResult'],\\\"player1 \\\", tResult[0],\\\"player2 \\\",tResult[1])\\n if gameCount == 3:\\n if tResult[0] > tResult[1]:\\n print(data['mplayer1'],tResult[0] - tResult[1])\\n else:\\n print(data['mplayer2'],tResult[1] - tResult[0])\\n return whichPlayer, score, gameCount, tResult\",\n \"def _adv_counter(winrate_together, winrate_hero1, winrate_hero2):\\n return winrate_together\",\n \"def evaluateWinner(self):\\n\\t\\tif self.pots[-1] == 0:\\n\\t\\t\\tself.pots.pop()\\n\\t\\tlivePlayers = self.getLivePlayers()\\t\\n\\t\\tfor i in range(len(self.pots)):\\n\\t\\t\\tplayers = self.getPlayersInPot(i, livePlayers)\\n\\t\\t\\tevaluations = []\\n\\t\\t\\tfor x in players:\\n\\t\\t\\t\\tcombined = x.hand + self.communityCards\\n\\t\\t\\t\\tevaluations.append((x, self.evaluator.getRankOfSeven(\\tcombined[0], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[1], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[2], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[3], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[4], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[5], \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcombined[6] )))\\n\\t\\t\\twinners = self.getWinners(evaluations, i)\\n\\t\\t\\tself.handOutMoney(winners, i)\\n\\t\\t\\tself.potwinQ.append(winners[0].name)\",\n \"def score(self,player, board):\\r\\n numPlayer = 0\\r\\n numOpp = 0\\r\\n for i in self.squares():\\r\\n if board[i] == player:\\r\\n numPlayer+= SQUARE_WEIGHTS[i]\\r\\n else:\\r\\n numOpp+=SQUARE_WEIGHTS[i]\\r\\n return numPlayer-numOpp\",\n \"def stats_strategy_response(data: OTreeSessionData, ws=None):\\n\\n rounds = [data.get_round(i + 1) for i in range(data.num_rounds())]\\n rnd_stats = {}\\n for rnd, (pr, ne) in enumerate(zip(rounds[:-1], rounds[1:]), 2):\\n stats = {k1: {k2: 0 for k2 in product(('Node', 'Edge'), repeat=2)}\\n for k1 in product(list('CD'), repeat=2)}\\n print(f'\\\\rCalculating strategy response (round {rnd}) ... ', end='')\\n for pid in range(1, ne.num_players() + 1):\\n nep = ne.loc[pid]\\n prp = pr.loc[pid]\\n stats[(nep.player.choice_L, prp.player.choice_nei_L)][(nep.player.type, prp.player.type_L)] += 1\\n stats[(nep.player.choice_U, prp.player.choice_nei_U)][(nep.player.type, prp.player.type_U)] += 1\\n stats[(nep.player.choice_R, prp.player.choice_nei_R)][(nep.player.type, prp.player.type_R)] += 1\\n stats[(nep.player.choice_D, prp.player.choice_nei_D)][(nep.player.type, prp.player.type_D)] += 1\\n if ws:\\n ws.append((rnd, pid, nep.player.type,\\n nep.player.choice_L, nep.player.type_L, prp.player.choice_nei_L,\\n nep.player.choice_U, nep.player.type_U, prp.player.choice_nei_U,\\n nep.player.choice_R, nep.player.type_R, prp.player.choice_nei_R,\\n nep.player.choice_D, nep.player.type_D, prp.player.choice_nei_D))\\n rnd_stats[rnd] = stats\\n print('Done')\\n global_vars['rnd_stats'] = rnd_stats\\n return rnd_stats\",\n \"def get_winner(self):\\n winner: Player = Player('none')\\n points_winner = 0\\n for player in self.players:\\n for key, value in player.get_stats().items():\\n print('{}: {}'.format(key, value))\\n if key == 'points':\\n if value >= points_winner:\\n winner = player\\n print()\\n\\n print('The winner is: ' + winner.get_name())\\n return winner\",\n \"def determine_winner1(self): \\r\\n sorted_player_rank = self._rank()\\r\\n print(f\\\"sorted player rank: {sorted_player_rank}\\\")\\r\\n print(f\\\"winner is player {sorted_player_rank[0]}: with points {sorted_player_rank[0][1]}\\\")\",\n \"def tournament(submissions, num_rounds):\\n strategies = [Strategy(submission) for submission in submissions]\\n game_num = 1\\n games = []\\n for i in range(len(strategies)):\\n for j in range(i+1, len(strategies)):\\n #print(strategies[i].name, strategies[j].name)\\n g = Game(strategies[i], strategies[j], num_rounds, game_num)\\n score1, score2 = g.play()\\n strategies[i].update_score(score1)\\n strategies[j].update_score(score2)\\n game_num += 1\\n games.append(g)\\n \\n for strat in strategies:\\n print(\\\"Final score for {} submitted by {} is {}\\\".format(strat.name, strat.author, strat.score))\\n write_results(strategies, games)\",\n \"def getTotals(dataSet):\\n\\ttotalWins = 0\\n\\ttotalLosses = 0\\n\\ttotalGains = 0.0\\n\\n\\tfor row in dataSet:\\n\\t\\tbatchNum = row[0]\\n\\t\\tseasonCode = row[1]\\n\\t\\tresult = row[2]\\n\\t\\tfixtureDate = row[3]\\n\\t\\tworstOdds = row[4]\\n\\t\\tnumFixtures = row[5]\\n\\t\\tif result == 1:\\n\\t\\t\\ttotalWins += 1\\n\\t\\t\\ttotalGains += (worstOdds-1.0)\\n\\t\\telse:\\n\\t\\t\\ttotalLosses += 1\\n\\t\\t\\ttotalGains -= 1.0\\n\\treturn totalGains, totalWins, totalLosses\",\n \"def add_dreamteam_count(df, raw_seasons):\\n df17 = df.loc[df['season'] == 17].copy()\\n df18 = df.loc[df['season'] == 18].copy()\\n df19 = df.loc[df['season'] == 19].copy()\\n df20 = df.loc[df['season'] == 20].copy()\\n df21 = df.loc[df['season'] == 21].copy()\\n\\n dreamteam_count_yearly_average = []\\n\\n for _, row in df21.iterrows():\\n dreamteam_count_yearly_average.append(create_dreamteam_count_yearly(row['name'], raw_seasons))\\n df21['dreamteam_yearly_average'] = dreamteam_count_yearly_average\\n\\n complete_data = pd.concat([df17, df18, df19, df20, df21])\\n\\n return complete_data\",\n \"def evaulate_monster(generikmon):\\r\\n score = generikmon['headA']\\r\\n #score = generikmon['chinA']\\r\\n return score\",\n \"def get_sim_winners(teamNames,league,pokedex):\\n bData, wHtml = loadBattleData(league)\\n similarities = loadSims() \\n\\n results = []\\n\\n myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]\\n for d in bData:\\n winner, loser = determineWinner(d)\\n\\n score = 0\\n for pkm in myTeam:\\n score+= np.amax(teamToArray(winner,pokedex)*pkm)\\n\\n results.append((winner,score))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n \\n cutoff = min(len(results),5)\\n\\n htmls = []\\n for win,score in results[:cutoff]:\\n htmls.extend(wHtml[str(sorted(win))])\\n\\n return htmls\",\n \"def collect_stats(games: List[BaseGame], date_min = None, date_max = None):\\n if not games: games = self.games\\n\\n under2_5 = len(list(filter(lambda g: g.is_total_under(), games)))\\n under3_5 = len(list(filter(lambda g: g.is_total_under(3.5), games)))\\n under1_5 = len(list(filter(lambda g: g.is_total_under(1.5), games)))\\n\\n home_score = sum([g.FTHG for g in games])\\n away_score = sum([g.FTAG for g in games])\\n\\n home_wins = sum(1 for _ in filter(lambda g: g.is_home_win(), games))\\n away_wins = sum(1 for _ in filter(lambda g: g.is_away_win(), games))\\n draws = sum(1 for _ in filter(lambda g: g.is_draw(), games))\\n\\n return {\\n 'under2.5': float(under2_5) / len(games),\\n 'under3.5': float(under3_5) / len(games),\\n 'under1.5': float(under1_5) / len(games),\\n 'avgScoredHome': float(home_score) / len(games),\\n 'avgScoredAway': float(away_score) / len(games),\\n \\\"home_wins\\\": float(home_wins) / len(games),\\n \\\"away_wins\\\": float(away_wins) / len(games),\\n \\\"draws\\\": float(draws) / len(games),\\n }\",\n \"def _get_current_teams_score(self):\\n for game in self._get_live_games():\\n teams_playing = [x['abbreviation'] for index, x in game['teams'].items()]\\n if self.team in teams_playing:\\n # Our team is playing in this game, get the score \\n return int(game['scores'][self.team])\",\n \"def get_winners_of_game(self):\\n return self.game_winners\",\n \"def standings_by_season(season):\\n season = int(season) + 1\\n scoreboard = nba_py.Scoreboard(month=7,\\n day=1,\\n year=season)\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return render_template(\\\"standings.html\\\",\\n title=\\\"standings\\\",\\n east_standings=enumerate(east_standings, 1),\\n west_standings=enumerate(west_standings, 1),\\n team=CITY_TO_TEAM)\",\n \"def calculate(self, prev_scores):\\n self.set_scores(prev_scores)\\n for match in self.week.matches:\\n if match.played:\\n # Fetch old scores\\n winner_score = float(prev_scores[match.winner])\\n loser_score = float(prev_scores[match.loser])\\n\\n # Update this ranking's scores\\n score_delta = loser_score * 0.1\\n self.score[match.winner] = winner_score + score_delta\\n self.score[match.loser] = loser_score - score_delta\",\n \"def combine_both_winning_losing_games_stats(self):\\n self.combine_both_winning_losing_games_stats = (\\n self.winning_games_up_to_2013\\n .merge(self.losing_games_up_to_2013, how='left',left_on=['Season','WTeamID'],right_on=['Season','LTeamID'])\\n # on field goal percentage and winning counts\\n .pipe(lambda x:x.assign(total_score = x.WScore + x.LScore))\\n .pipe(lambda x:x.assign(total_opponent_score = x.winning_opponent_score + x.losing_opponent_score))\\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\\n .pipe(lambda x:x.assign(total_fga = x.WFGA + x.LFGA))\\n .pipe(lambda x:x.assign(total_fg3m = x.WFGM3 + x.LFGM3))\\n .pipe(lambda x:x.assign(total_fg3a = x.WFGA3 + x.LFGA3))\\n .pipe(lambda x:x.assign(total_ftm = x.WFTM + x.LFTM))\\n .pipe(lambda x:x.assign(total_fta = x.WFTA + x.LFTA))\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .sort_values(['WTeamID','Season'])\\n # on offensive and defensive rebounds\\n .pipe(lambda x:x.assign(total_rebounds = x.total_winning_rebounds + x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds = x.WOR + x.LOR))\\n .pipe(lambda x:x.assign(total_def_rebounds = x.WDR + x.LDR))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_team_missed_attempts = x.team_missed_attempts + x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_opp_team_missed_attempts = x.opp_team_missed_attempts + x.winning_opp_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # on steals, turnovers, assists, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_blocks = x.WBlk + x.LBlk))\\n .pipe(lambda x:x.assign(total_assists = x.WAst + x.LAst))\\n .pipe(lambda x:x.assign(total_steals = x.WStl + x.LStl))\\n .pipe(lambda x:x.assign(total_turnover = x.WTO + x.LTO))\\n .pipe(lambda x:x.assign(total_personalfoul = x.WPF + x.LPF))\\n .pipe(lambda x:x.assign(total_opp_fga = x.LFGA_opp + x.WFGA_opp))\\n .pipe(lambda x:x.assign(total_fgm = x.WFGM + x.LFGM))\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def custom_score_2(game, player):\\n \\n # get avaliable moves for each player\\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n \\n # shortcut to definite state:\\n # 1. my agent win -> return very high score\\n if opp_moves == 0:\\n return float(\\\"inf\\\")\\n # 2. opponenent's agent win -> return very low score\\n elif own_moves == 0:\\n return float(\\\"-inf\\\")\\n\\n # score: avaliable moves ratio\\n return float(own_moves/opp_moves)\",\n \"def get_wr_season_totals():\\n wr_totals = redis_cache('wr_games_key', NFL_WR_Game_2015.query.filter_by(is_season_totals=True).all)\\n return wr_totals\",\n \"async def player_ratio(self, ctx):\\r\\n player = ctx.message.content.split(' ')[1]\\r\\n if os.environ.get(\\\"WoW_Token\\\") is None:\\r\\n return\\r\\n else:\\r\\n async with aiohttp.ClientSession().get('https://us.api.battle.net/wow/character/zul\\\\'jin/' + player + '?fields=pvp&locale=en_US&apikey=' + os.environ.get(\\\"WoW_Token\\\")) as res:\\r\\n if res.status == 200:\\r\\n data = await res.json()\\r\\n player_pvp_stats = data['pvp']['brackets']['ARENA_BRACKET_3v3']\\r\\n await ctx.message.channel.send(u\\\"Player: {:s}\\\").format(player)\\r\\n await ctx.message.channel.send(\\\"Rating: {:d}\\\".format(player_pvp_stats['rating']))\\r\\n await ctx.message.channel.send(\\\"Season Wins: {:d}\\\".format(player_pvp_stats['seasonWon']))\\r\\n await ctx.message.channel.send(\\\"Season Losses: {:d}\\\".format(player_pvp_stats['seasonLost']))\\r\\n\\r\\n if player_pvp_stats['seasonWon'] == 0 or player_pvp_stats['seasonLost'] == 0:\\r\\n await ctx.message.channel.send(\\\"Ratio: 0\\\")\\r\\n else:\\r\\n await ctx.message.channel.send(\\\"Ratio: {:.4f}\\\".format(\\r\\n float(player_pvp_stats['seasonWon'])/\\r\\n float(player_pvp_stats['seasonLost']))\\r\\n )\",\n \"def count_wins(outcomes):\\n counter = {x[1]:0 for x in outcomes[0]}\\n numPlayers = len(outcomes[0])\\n \\n for game in outcomes:\\n \\n index = 0\\n toScore = numPlayers\\n counter[game[index][1]] += toScore\\n index += 1\\n while index < numPlayers:\\n if game[index][0] == game[index - 1][0]:\\n counter[game[index][1]] += toScore\\n else:\\n toScore -= 1\\n counter[game[index][1]] += toScore\\n index += 1\\n\\n return [(x, counter[x]) for x in sorted(counter,\\n reverse = True, \\n key = lambda y: counter[y])]\",\n \"def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\\n battleData, htmlData = loadBattleData(league)\\n similarities = loadSims() \\n \\n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\\n if len(oppTeam) == 0:\\n picks = set([])\\n while (len(picks) < NUMLOSINGTEAMS and (not len(picks) == len(battleData))):\\n picks.add(random.randint(0,len(battleData)-1))\\n\\n losers = []\\n loserDict = {}\\n for i in picks:\\n entry = battleData[i]\\n winner,loser = determineWinner(entry)\\n loserDict[str(loser)] = [winner]\\n losers.append( (loser,0) )\\n\\n #Given opponent team then find similar teams\\n else:\\n oppTeam = [getSimPokemon(opp,similarities) for opp in oppTeam]\\n\\n #create dictionary from losers team to the team that beat them.\\n loserDict = {}\\n sims = []\\n for d in battleData:\\n winner, loser = determineWinner(d)\\n\\n wTeam = teamToArray(winner,pokedex)\\n lTeam = np.array(teamToArray(loser, pokedex))\\n\\n score = 0\\n for oppNp in oppTeam:\\n score+= np.amax(lTeam*oppNp) \\n\\n if str(loser) in loserDict:\\n loserDict[str(loser)].append(winner)\\n else:\\n #new to dictonary\\n loserDict[str(loser)] = [winner]\\n\\n sims.append((loser, score))\\n\\n\\n sims = sorted(sims, key = lambda x : x[1], reverse = True)\\n\\n cutoff = min(len(sims),NUMLOSINGTEAMS)\\n losers = sims[:cutoff]\\n\\n #Gather winners to losing teams\\n winnersComp = []\\n for loser,_ in losers:\\n for winner in loserDict[str(loser)]:\\n winnersComp.append(teamToArray(winner,pokedex))\\n \\n topScore = len(winnersComp)*6 #pkmn team size\\n\\n results = []\\n inverted_idx = {}\\n\\n existsSet = []\\n\\n #Creates inverted index for teams, while simoultaneously weeding out any teams that are exactly similar.\\n for i in range(len(curTeams)):\\n team = curTeams[i]\\n results.append((team,0))\\n sTeam = set(team)\\n if not (sTeam in existsSet):\\n existsSet.append(sTeam)\\n for pkm in team:\\n if pkm != EMPTY:\\n if pkm in inverted_idx:\\n inverted_idx[pkm].append(i)\\n else:\\n inverted_idx[pkm] = [i]\\n \\n #Giving the similiarity scores to the winners based off of the inverted index.\\n for pkm in inverted_idx:\\n for winner in winnersComp:\\n wArr = np.array(winner)\\n #tArr = getSimPokemon(pkm,similarities)\\n tArr = similarities[pkm]\\n \\n vals = wArr * tArr\\n\\n score = np.amax(vals)\\n\\n for i in inverted_idx[pkm]:\\n results[i] = (results[i][0],results[i][1]+(score/topScore))\\n\\n results = sorted(results, key = lambda x : x[1], reverse = True)\\n\\n if len(results) < NUMTEAMSRETURN:\\n if len(results) == 0:\\n returnTeams = [[] for x in range(NUMTEAMSRETURN)]\\n teamScores = [0 for x in range(NUMTEAMSRETURN)]\\n\\n else:\\n returnTeams = [result[0] for result in results]\\n teamScores = [result[1] for result in results]\\n else:\\n firstResult, firstScore = results[0]\\n returnTeams = [firstResult]\\n teamScores = [round(firstScore*100,1)]\\n returnSets = [set(firstResult)]\\n \\n i = 1\\n\\n #Loops through results and adds teams with the proper edit distance away.\\n while(len(returnTeams) < NUMTEAMSRETURN and minDistWanted > 0):\\n teamToConsider,teamToConsiderScore = results[i]\\n \\n considerSet = set(teamToConsider)\\n add = True\\n ##checks the edit distance of teams is above wanted\\n for team in returnSets:\\n if len(team.union(considerSet)) < len(team)+minDistWanted:\\n add = False\\n\\n ##If indeed above wanted levels then add\\n if add:\\n returnTeams.append(teamToConsider)\\n returnSets.append(considerSet)\\n teamScores.append(round(teamToConsiderScore*100,1))\\n \\n i+=1\\n\\n if i >= len(results):\\n i = 1\\n minDistWanted -= 1 \\n \\n winHtmls = []\\n if htmlData != None:\\n for team,_ in losers:\\n for winner in loserDict[str(team)]:\\n winHtmls.extend(htmlData[str(sorted(winner))])\\n \\n\\n return returnTeams, teamScores, winHtmls\",\n \"def give_round_scores(list_of_players):\\n print(\\\"\\\\nThe round has ended !\\\\nWe shall now unveil the cards and the scores!\\\")\\n\\n for player in list_of_players:\\n cards = [card.name for card in player.cards]\\n cards_string = \\\" \\\"\\n for card in cards:\\n cards_string += card + \\\", \\\"\\n cards_string = cards_string[:-2]\\n print(\\\"\\\\n{} has these cards: \\\".format(player.name), cards_string)\\n print(\\\"{} has a score of {}\\\".format(player.name, player.score()))\\n final_scores = [player.score() for player in list_of_players]\\n min_score = min(final_scores)\\n winners_index = [i for i, x in enumerate(final_scores) if x == min_score]\\n if len(winners_index) == 1:\\n index_winner = winners_index[0]\\n winner = list_of_players[index_winner]\\n print(winner.name, \\\"won the round with a score of {}\\\".format(winner.score()))\\n if len(winners_index) > 1:\\n print(\\\"It's a tie!\\\")\\n winners_names = \\\"\\\"\\n winners = [list_of_players[i] for i in winners_index]\\n for winner in winners:\\n winners_names += winner.name\\n print(winners_names, \\\"won the round with a score of \\\", str(min_score))\",\n \"def custom_score(game, player):\\n \\n # get avaliable moves for each player\\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n \\n # return different between # of my agent's move and oppenent's\\n return float(own_moves - opp_moves)\",\n \"def getTeamStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\\\\r\\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\\\\r\\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n team_df = df[[\\\"TEAM_ID\\\",\\\"TEAM_NAME\\\",\\\"GP\\\",\\\"W\\\",\\\"L\\\",\\\"W_PCT\\\",\\\"MIN\\\",\\\"FGM\\\",\\r\\n \\\"FGA\\\",\\\"FG_PCT\\\",\\\"FG3M\\\",\\\"FG3A\\\",\\\"FG3_PCT\\\",\\\"FTM\\\",\\\"FTA\\\",\\\"FT_PCT\\\",\\r\\n \\\"OREB\\\",\\\"DREB\\\",\\\"REB\\\",\\\"AST\\\",\\\"TOV\\\",\\\"STL\\\",\\\"BLK\\\",\\\"BLKA\\\",\\\"PF\\\",\\r\\n \\\"PFD\\\",\\\"PTS\\\",\\\"PLUS_MINUS\\\"]]\\r\\n \\r\\n return team_df\",\n \"def _win(self, is_opponent: bool) -> pd.DataFrame:\\n records = []\\n for var in self.variable_vals:\\n # Add variable to parameters\\n self.parameters[self.variable] = var\\n # Add derived features\\n self._derived_features()\\n # Configure inputs\\n input_set = {\\n 'random_effect': 'team' if not is_opponent else 'opponent',\\n 'feature_set': self.feature_set,\\n 'inputs': {'RandomEffect': self.team if not is_opponent else self.opponent}\\n }\\n input_set['inputs'].update(self.parameters)\\n # Predict\\n output = self.predictor.predict(**input_set)[\\n ('team' if not is_opponent else 'opponent', self.feature_set, 'Win')\\n ]\\n # Wrangle output\\n record = {\\n 'RandomEffect': self.team if is_opponent else self.opponent,\\n self.variable: var,\\n 'WinLB_opp' if is_opponent else 'WinLB_team': expit(output['mu']['lb']),\\n 'Win_opp' if is_opponent else 'Win_team': expit(output['mu']['mean']),\\n 'WinUB_opp' if is_opponent else 'WinUB_team': expit(output['mu']['ub'])\\n }\\n records.append(record)\\n\\n return pd.DataFrame().from_records(records)\",\n \"def get_convergence_episode(self):\\n values = self.stats['return_stats']['episode_totals']\\n _, y, (y_lower, _) = self._moving_average(\\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\\n # The convergence is established as the first time the average return\\n # is above the lower bounds of the final return.\\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\\n return first_episode\",\n \"def wins(self):\\n return [g for g in self.games if g.winner is self.team]\",\n \"def first_round_history(self):\\n self.ts_dict = self.get_tourney_slots()\\n self.tsr_dict = self.match_seeds()\\n first_seed_win = 0\\n second_seed_win = 0\\n third_seed_win = 0\\n fourth_seed_win = 0\\n fifth_seed_win = 0\\n sixth_seed_win = 0\\n seventh_seed_win = 0\\n eighth_seed_win = 0\\n total_games = 128\\n\\n for year1 in self.ts_dict: \\n for slot, match_up in self.ts_dict[year1].items():\\n if slot[:2] == \\\"R1\\\":\\n for year2 in self.tsr_dict:\\n if year1 == year2:\\n for winning, losing in self.tsr_dict[year2].items():\\n if winning[5:] == match_up[:3]:\\n seed = winning[6:] \\n if seed == \\\"01\\\":\\n first_seed_win += 1\\n elif seed == \\\"02\\\":\\n second_seed_win += 1\\n elif seed == \\\"03\\\":\\n third_seed_win += 1\\n elif seed == \\\"04\\\":\\n fourth_seed_win += 1\\n elif seed == \\\"05\\\":\\n fifth_seed_win += 1\\n elif seed == \\\"06\\\":\\n sixth_seed_win += 1\\n elif seed == \\\"07\\\":\\n seventh_seed_win += 1\\n elif seed == \\\"08\\\":\\n eighth_seed_win += 1 \\n \\n #print(first_seed_win, second_seed_win, third_seed_win, fourth_seed_win, fifth_seed_win, sixth_seed_win, seventh_seed_win, eighth_seed_win, total_games)\\n\\n gauge = pygal.SolidGauge(inner_radius=0.70, title=\\\"NCAA First Round Results\\\")\\n ratio_first_seed = int(first_seed_win / total_games * 100)\\n ratio_second_seed = int(second_seed_win / total_games * 100)\\n ratio_third_seed = int(third_seed_win / total_games * 100)\\n ratio_fourth_seed = int(fourth_seed_win / total_games * 100)\\n ratio_fifth_seed = int(fifth_seed_win / total_games * 100)\\n ratio_sixth_seed = int(sixth_seed_win / total_games * 100)\\n ratio_seventh_seed = int(seventh_seed_win / total_games * 100)\\n ratio_eighth_seed = int(eighth_seed_win / total_games * 100) \\n\\n percent_formatter = lambda x: '{:.10g}%'.format(x)\\n gauge.value_formatter = percent_formatter\\n gauge.add('1 vs. 16', [{'value': ratio_first_seed, 'max_value': 100}])\\n gauge.add('2 vs. 15', [{'value': ratio_second_seed, 'max_value': 100}])\\n gauge.add('3 vs. 14', [{'value': ratio_third_seed, 'max_value': 100}])\\n gauge.add('4 vs. 13', [{'value': ratio_fourth_seed, 'max_value': 100}])\\n gauge.add('5 vs. 12', [{'value': ratio_fifth_seed, 'max_value': 100}])\\n gauge.add('6 vs. 11', [{'value': ratio_sixth_seed, 'max_value': 100}])\\n gauge.add('7 vs. 10', [{'value': ratio_seventh_seed, 'max_value': 100}])\\n gauge.add('8 vs. 9', [{'value': ratio_eighth_seed, 'max_value': 100}])\\n \\n gauge.render_to_file('chart.svg')\",\n \"def snowfall_average_for_date(resort_name, date):\\n resort_table = resort_table_dict[resort_name]\\n\\n date_month = int(date[4:6])\\n date_day = int(date[6:8])\\n query = \\\"SELECT snowfall FROM %s WHERE CAST(EXTRACT(MONTH FROM status_date) AS INTEGER) = %d AND CAST(EXTRACT(DAY FROM status_date) AS INTEGER) = %d\\\" %(resort_table, date_month, date_day)\\n connection = get_connection()\\n total = 0\\n counter = 0\\n for row in get_select_query_results(connection, query):\\n counter += 1\\n total += int(row[0])\\n if (counter != 0):\\n snowfall_to_return = int(total/counter)\\n else:\\n snowfall_to_return = 0\\n return json.dumps(snowfall_to_return)\",\n \"def host_result_value(winner: Winner) -> float:\\n if winner == Winner.HOME:\\n return 1\\n if winner == Winner.AWAY:\\n return 0\\n return 0.5\",\n \"def win_rate(returns_pred, returns_real, symbol=None, horizon=None):\\n # Input processing\\n if not horizon:\\n horizons = [1, 3, 5, 10, 20, 40, 60, 90, 120]\\n elif type(symbol) == str:\\n return SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon)\\n else:\\n horizons = [horizon]\\n\\n # Data frame skeleton\\n win_rate_all = pd.DataFrame(index=horizons)\\n\\n # Compute win rate for each symbol\\n for symbol in returns_pred.columns:\\n win_rate = []\\n for horizon in horizons:\\n win_rate.append(SingleStockEWM.win_rate_symbol_horizon(returns_pred, returns_real, symbol, horizon))\\n win_rate_all[symbol] = win_rate\\n\\n # Compute statistics across all symbols\\n win_rate_all = win_rate_all.agg(['mean', 'std'], axis=1).merge(win_rate_all, left_index=True, right_index=True)\\n\\n # Formatting\\n cm = sns.light_palette(\\\"green\\\", as_cmap=True)\\n return win_rate_all.style.background_gradient(cmap=cm).format(\\\"{:.1%}\\\")\",\n \"def get_winner_df(row):\\n\\tteam1 = row['team1']\\n\\tteam2 = row['team2']\\n\\tprob = row['prob']\\n\\trand_num = float(randint(0,1000)/1000)\\n\\tprint('prob: %s rand %s' % (str(prob), str(rand_num)) )\\n\\tif rand_num <= prob:\\n\\t\\twinner = team1\\n\\telse:\\n\\t\\twinner = team2\\n\\n\\treturn winner\",\n \"def find_winner(players_dict: dict) -> list:\\n\\n podium = []\\n for player, points in players_dict.items():\\n podium.append((player, sum_points(points)))\\n\\n podium.sort(key=lambda x: x[1], reverse=True)\\n\\n return podium\",\n \"def run_tournament(agents, randomize_round_num=True):\\r\\n\\r\\n for a in agents:\\r\\n try:\\r\\n a.load_payoff_conditions(payoffs)\\r\\n except Exception as e:\\r\\n pass\\r\\n\\r\\n score_dict = {}\\r\\n\\r\\n for a in agents:\\r\\n score_dict[a.name] = 0\\r\\n\\r\\n for (a1, a2) in itertools.product(agents, agents):\\r\\n\\r\\n if randomize_round_num:\\r\\n match_rounds = random.randint(min_rounds, max_rounds)\\r\\n else:\\r\\n match_rounds = rounds\\r\\n\\r\\n (a1_score, a2_score) = run_match(a1, a2, match_rounds, payoffs)\\r\\n\\r\\n score_dict[a1.name] += a1_score\\r\\n score_dict[a2.name] += a2_score\\r\\n\\r\\n return score_dict\",\n \"def play(self):\\n if self.stats['round'] == 0:\\n if self.data['personalities'] and self.data['events']:\\n self.choose_opponent()\\n self.resolve_conflict()\\n else:\\n self.stats['round'] += 1\\n elif self.stats['round'] == 1:\\n if self.data['locations']:\\n self.choose_location()\\n self.resolve_conflict()\\n else:\\n self.stats['round'] += 1\\n else:\\n print(\\\"You've won\\\")\\n self.game_over = True\\n return self.stats\",\n \"def get_team_rating(pdo, tsotr, tsr):\\n normalised_tsr = (tsr - 0.5) * 2.0\\n rating = (0.82 * normalised_tsr) + (0.45 * tsotr) + (0.4 * pdo)\\n return rating\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7711851","0.6832915","0.6480246","0.63182116","0.6245573","0.61112136","0.611068","0.602987","0.59854066","0.5965334","0.5964837","0.57526016","0.57137907","0.5671769","0.5644969","0.5635259","0.56290424","0.5599348","0.5596914","0.5574907","0.5569233","0.5504836","0.5483212","0.54613936","0.54592854","0.54412836","0.5427208","0.54221195","0.54116994","0.5408788","0.5406029","0.53984565","0.5393134","0.5390793","0.537681","0.5368688","0.53422546","0.5341387","0.52986073","0.5291416","0.5288147","0.52723396","0.52723396","0.52723396","0.5269976","0.5265671","0.52642035","0.52608436","0.52494204","0.524162","0.5241125","0.52399343","0.5238332","0.5227926","0.52189046","0.5217363","0.52138716","0.5193794","0.5187578","0.51830274","0.5171177","0.516883","0.51660454","0.5160851","0.5153851","0.5146645","0.514525","0.5137601","0.5135859","0.5132302","0.51320684","0.512863","0.51245564","0.5119873","0.5108357","0.51079327","0.51058334","0.51003516","0.50974405","0.5090853","0.50812644","0.5077588","0.5075021","0.50730073","0.50689995","0.5064157","0.5063445","0.50613374","0.50592536","0.5053748","0.5051943","0.50503546","0.5042423","0.50409466","0.5026926","0.5025063","0.5023072","0.5008386","0.49997488","0.49975947"],"string":"[\n \"0.7711851\",\n \"0.6832915\",\n \"0.6480246\",\n \"0.63182116\",\n \"0.6245573\",\n \"0.61112136\",\n \"0.611068\",\n \"0.602987\",\n \"0.59854066\",\n \"0.5965334\",\n \"0.5964837\",\n \"0.57526016\",\n \"0.57137907\",\n \"0.5671769\",\n \"0.5644969\",\n \"0.5635259\",\n \"0.56290424\",\n \"0.5599348\",\n \"0.5596914\",\n \"0.5574907\",\n \"0.5569233\",\n \"0.5504836\",\n \"0.5483212\",\n \"0.54613936\",\n \"0.54592854\",\n \"0.54412836\",\n \"0.5427208\",\n \"0.54221195\",\n \"0.54116994\",\n \"0.5408788\",\n \"0.5406029\",\n \"0.53984565\",\n \"0.5393134\",\n \"0.5390793\",\n \"0.537681\",\n \"0.5368688\",\n \"0.53422546\",\n \"0.5341387\",\n \"0.52986073\",\n \"0.5291416\",\n \"0.5288147\",\n \"0.52723396\",\n \"0.52723396\",\n \"0.52723396\",\n \"0.5269976\",\n \"0.5265671\",\n \"0.52642035\",\n \"0.52608436\",\n \"0.52494204\",\n \"0.524162\",\n \"0.5241125\",\n \"0.52399343\",\n \"0.5238332\",\n \"0.5227926\",\n \"0.52189046\",\n \"0.5217363\",\n \"0.52138716\",\n \"0.5193794\",\n \"0.5187578\",\n \"0.51830274\",\n \"0.5171177\",\n \"0.516883\",\n \"0.51660454\",\n \"0.5160851\",\n \"0.5153851\",\n \"0.5146645\",\n \"0.514525\",\n \"0.5137601\",\n \"0.5135859\",\n \"0.5132302\",\n \"0.51320684\",\n \"0.512863\",\n \"0.51245564\",\n \"0.5119873\",\n \"0.5108357\",\n \"0.51079327\",\n \"0.51058334\",\n \"0.51003516\",\n \"0.50974405\",\n \"0.5090853\",\n \"0.50812644\",\n \"0.5077588\",\n \"0.5075021\",\n \"0.50730073\",\n \"0.50689995\",\n \"0.5064157\",\n \"0.5063445\",\n \"0.50613374\",\n \"0.50592536\",\n \"0.5053748\",\n \"0.5051943\",\n \"0.50503546\",\n \"0.5042423\",\n \"0.50409466\",\n \"0.5026926\",\n \"0.5025063\",\n \"0.5023072\",\n \"0.5008386\",\n \"0.49997488\",\n \"0.49975947\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8241553"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14999,"cells":{"query":{"kind":"string","value":"Test that upload Logger Type file without microsite_id will not be inserted to database"},"document":{"kind":"string","value":"def test_logger_type_upload_MicrositeId_None(self):\n test_filename = 'server/tests/test_data_files/Test/Test_New_Logger_Type_MicrositeId_None.csv'\n with self.app.test_client() as client:\n with client.session_transaction() as sess:\n sess['logged_in'] = True\n response = client.post('/upload', \n data={\n 'loggerTypeFile': (open(test_filename, 'rb'), 'Test_New_Logger_Type_MicrositeId_None.csv')\n }, follow_redirects=True)\n query = (\"SELECT * from cnx_logger_biomimic_type where biomimic_type='DummyBiomimicTypeNone'\")\n cursor = self.db.connection.cursor()\n cursor.execute(query)\n results = cursor.fetchall()\n results = list(results)\n self.assertEqual(len(results), 0)"},"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 test_upload_wrong_file_type(self):\n file = dict(\n file=(BytesIO(b'my file contents'), \"foto.doc\"),\n )\n response = self.client.post('/upload',\n content_type='multipart/form-data',\n data=file)\n self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code)\n if os.path.exists(PHOTOS_SAVE_PATH):\n self.assertNotIn('foto.jpg', os.listdir(PHOTOS_SAVE_PATH))","def test_upload_file(self):\n pass","def test_upload_file1(self):\n pass","def test_file_upload_file_with_the_same_name_already_exists(\n staff_api_client, media_root, site_settings\n):\n # given\n image_file1, image_name1 = create_image()\n path = default_storage.save(image_file1._name, image_file1)\n\n image_file, image_name = create_image()\n assert image_file1 != image_file\n assert image_name == image_name1\n assert image_file._name == image_file1._name\n\n variables = {\"image\": image_name}\n body = get_multipart_request_body(\n FILE_UPLOAD_MUTATION, variables, image_file, image_name\n )\n\n # when\n response = staff_api_client.post_multipart(body)\n\n # then\n content = get_graphql_content(response)\n data = content[\"data\"][\"fileUpload\"]\n errors = data[\"errors\"]\n\n domain = site_settings.site.domain\n assert not errors\n assert data[\"uploadedFile\"][\"contentType\"] == \"image/png\"\n file_url = data[\"uploadedFile\"][\"url\"]\n assert file_url != f\"http://{domain}/media/{image_file._name}\"\n assert file_url != f\"http://{domain}/media/{path}\"\n assert default_storage.exists(file_url.replace(f\"http://{domain}/media/\", \"\"))","def test_local_uploader_upload_wrong_file(self, mock):\r\n mock.save.return_value = None\r\n u = LocalUploader()\r\n file = FileStorage(filename='test.txt')\r\n res = u.upload_file(file, container='user_3')\r\n err_msg = (\"Upload file should return False, \\\r\n as this extension is not allowed\")\r\n assert res is False, err_msg","def test_upload_bad_file(self):\n url = image_upload_url(self.reteta.id)\n res = self.client.post(url, {'image': 'notimage'}, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","def test_empty_upload(self):\r\n self._login_admin()\r\n\r\n res = self.app.post(\r\n '/admin/import',\r\n params={'api_key': self.api_key},\r\n upload_files=[],\r\n )\r\n self.assertTrue(\r\n 'Please provide a file to import' in res.body,\r\n \"Error message should be present\")","def test_file_field():","def test_local_uploader_upload_correct_file(self, mock):\r\n mock.save.return_value = None\r\n u = LocalUploader()\r\n file = FileStorage(filename='test.jpg')\r\n res = u.upload_file(file, container='user_3')\r\n err_msg = (\"Upload file should return True, \\\r\n as this extension is allowed\")\r\n assert res is True, err_msg","def test_upload(self):\n with self.client:\n file = dict(\n file=(BytesIO(b'my file contents'), \"foto.jpg\"),\n )\n response = self.client.post('/upload',\n content_type='multipart/form-data',\n data=file)\n self.assertEqual(status.HTTP_200_OK, response.status_code)\n self.assertIn('foto.jpg', os.listdir(PHOTOS_SAVE_PATH))\n self.assertIn('foto.jpg', [photo.filename for photo in Photo.query.all()])","def test_missing_extension(client: FlaskClient):\n file = get_example_file(ExampleFileType.Txt)\n file.filename = \"test\"\n response = util.upload_file(client, DEFAULT_USER, file)\n assert response.status == \"400 BAD REQUEST\"","def test_logger_temperature_upload_duplicate(self):\n test_type_filename = 'server/tests/test_data_files/Test/Test_New_Logger_Type_Positive.csv'\n test_temp_filename = 'server/tests/test_data_files/Test/temp_files/DUMMYID_2000_pgsql_Duplicate.txt'\n with self.app.test_client() as client:\n with client.session_transaction() as sess:\n sess['logged_in'] = True\n response = client.post('/upload', \n data={\n 'loggerTypeFile': (open(test_type_filename, 'rb'), 'Test_New_Logger_Type_Positive.csv')\n }, follow_redirects=True)\n response = client.post('/upload', \n data={\n 'loggerTempFile': (open(test_temp_filename, 'rb'), 'DUMMYID_2000_pgsql_Duplicate.txt')\n }, follow_redirects=True)\n record_type = {\n \"microsite_id\" : \"DUMMYID\",\n \"site\" : \"DUMMYSITE\",\n \"biomimic_type\" : \"Dummybiomimictype\",\n \"country\" : \"Dummycountry\",\n \"state_province\" : \"Dummystate\",\n \"location\" : \"Dummylocation\",\n \"field_lat\" : \"36.621933330000\",\n \"field_lon\" : \"-121.905316700000\",\n \"zone\" : \"DummyZone\",\n \"sub_zone\" : \"DummySubZone\",\n \"wave_exp\" : \"DummyWave\",\n \"start_date\": str(datetime.strptime(\"7/1/2000\",'%m/%d/%Y').date()),\n \"end_date\": str(datetime.strptime(\"7/2/2000\",'%m/%d/%Y').date())} \n where_condition = self.db.build_where_condition(record_type)\n query = (\"SELECT temp.Time_GMT, temp.Temp_C \"\n \"FROM `cnx_logger` logger \"\n \"INNER JOIN `cnx_logger_biomimic_type` biotype ON biotype.`biomimic_id` = logger.`biomimic_id` \"\n \"INNER JOIN `cnx_logger_geographics` geo ON geo.`geo_id` = logger.`geo_id` \"\n \"INNER JOIN `cnx_logger_properties` prop ON prop.`prop_id` = logger.`prop_id` \"\n \"INNER JOIN `cnx_logger_temperature` temp ON temp.`logger_id` = logger.`logger_id` \")\n cursor = self.db.connection.cursor()\n cursor.execute(query + where_condition)\n results = cursor.fetchall()\n results = list(results)\n self.cleanUpLoggerTemp(cursor)\n self.cleanUpLoggerType(cursor, record_type) \n cursor.close()\n self.assertEqual(len(results), 1)","def test_should_file_field(self):\n self.assertIn(\"image\", self.fields)","def test_upload_file(self):\n\n uploadFile = os.path.join(testdatadir, \"upload.data\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(link).read()\n )\n\n # Re-upload slightly different file.\n uploadFile2 = os.path.join(testdatadir, \"upload2.data\")\n r = gracedb.writeFile(\n eventId,\n filename=\"upload.data\",\n filecontents=open(uploadFile2, 'r'))\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link2 = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(link2).read()\n )\n\n self.assertNotEqual(link, link2)","def upload_build(self, name, directory):\n logging.info('Not uploading build because no Filestore.')","def test_no_mimetype(self):\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n del file.content_type\n with pytest.raises(forms.ValidationError):\n field.clean(file)","def test_upload_microbe_directory(self, auth_headers, *_):\n sample = Sample(name='SMPL_Microbe_Directory_01').save()\n sample_uuid = str(sample.uuid)\n with self.client:\n response = self.client.post(\n f'/api/v1/samples/{sample_uuid}/microbe_directory_annotate',\n headers=auth_headers,\n data=json.dumps(TEST_DIRECTORY),\n content_type='application/json',\n )\n data = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 201)\n self.assertIn('success', data['status'])\n for field in TEST_DIRECTORY:\n self.assertIn(field, data['data'])\n\n # Reload object to ensure microbe directory result was stored properly\n sample = Sample.objects.get(uuid=sample_uuid)\n self.assertTrue(sample.microbe_directory_annotate)","def test_create_model_without_file(test_client, dataset):\n response = test_client.post('/create?target=Species',\n data={\"file\": \"No file\"},\n content_type=\"multipart/form-data\")\n assert response.status_code == 400","def test_local_uploader_upload_fails(self, mock):\r\n u = LocalUploader()\r\n file = FileStorage(filename='test.jpg')\r\n res = u.upload_file(file, container='user_3')\r\n err_msg = (\"Upload file should return False, \\\r\n as there is an exception\")\r\n assert res is False, err_msg","def upload_coverage(self, name, directory):\n logging.info('Not uploading coverage because no Filestore.')","def test_upload_job_description_file_post(self):\n pass","def test_api_create_unknown_file(api):\n api.create_article(\n {\"title\": \"A Title\"},\n {\"key1\": \"value1\"},\n {'test.zip': 'FFFDASFAFADADFA'},\n )\n req_call = requests.request\n req_kw = req_call.call_args[1]\n data_lines = req_kw['data'].split(b'\\r\\n')\n # Unknown file type is skipped\n assert len(data_lines) == 11","def test_upload_manifest_non_existing_trial_id(\n cidc_api, some_file, clean_db, monkeypatch\n):\n user_id = setup_trial_and_user(cidc_api, monkeypatch)\n make_admin(user_id, cidc_api)\n\n mocks = UploadMocks(monkeypatch, prismify_trial_id=\"test-non-existing-trial-id\")\n\n client = cidc_api.test_client()\n\n res = client.post(MANIFEST_UPLOAD, data=form_data(\"pbmc.xlsx\", some_file, \"pbmc\"))\n assert res.status_code == 400\n assert \"test-non-existing-trial-id\" in str(res.json[\"_error\"][\"message\"])\n\n # Check that we tried to upload the excel file\n mocks.upload_xlsx.assert_not_called()\n mocks.iter_errors.assert_called_once()\n mocks.prismify.assert_called_once()","def test_upload_empty(self):\n rv = self.post(url='/queue/', content=None, token=self.user_token)\n self.assertJSONError(rv, 'TagalleryMissingFile')\n return","def test_no_mimetype_magic(self, mock_get_content_type):\n mock_get_content_type.side_effect = ValueError\n\n field = TypedFileField(required=False, type_whitelist=self.good_types)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n with pytest.raises(forms.ValidationError):\n field.clean(file)","def test_invalid_file_type(self):\n file = SimpleUploadedFile(\"test.csv\", b\"\\xe8\")\n form = MomConnectImportForm(\n data={\"source\": \"MomConnect Import\"}, files={\"file\": file}\n )\n self.assertTrue(form.is_valid())\n instance = form.save()\n self.assertEqual(instance.status, MomConnectImport.Status.ERROR)\n [error] = instance.errors.all()\n self.assertEqual(error.error, \"File is not a CSV\")","def test_file_upload(self):\n\n with tempfile.NamedTemporaryFile() as test_file:\n test_file.write(\n u'date,category,employee name,employee address,expense description,pre-tax amount,tax name,tax amount\\n')\n test_file.write(\n u'12/1/2013,Travel,Don Draper,\"783 Park Ave, New York, NY 10021\",Taxi ride, 350.00 ,NY Sales tax, 31.06\\n')\n test_file.flush()\n response = self.send_file_upload_request(view_name='csv_import_view', filename=test_file.name)\n\n actual_import_logs = ImportLog.objects.all()\n self.assertEqual(1, len(actual_import_logs))\n\n actual_import_log = actual_import_logs[0]\n expected_file_name = os.path.basename(test_file.name)\n self.assertEqual(expected_file_name, actual_import_log.file_name)\n\n expense_items = ExpenseItem.objects.all()\n self.assertEqual(1, len(expense_items))\n self.assertEqual('Don Draper', expense_items[0].employee.name)\n\n self.assertEqual('{\"upload_id\": 1}', response.content)","def upload_crashes(self, name, directory):\n logging.info('Not uploading crashes because no Filestore.')","def test_upload_duplicate(client: FlaskClient):\n file = get_example_file(ExampleFileType.Png)\n response1 = util.upload_file(client, DEFAULT_USER, file)\n response2 = util.upload_file(client, DEFAULT_USER, file)\n\n assert response1.status == \"201 CREATED\"\n assert response2.status == \"200 OK\"\n assert response1.json == response2.json","def test_NoFileUploaded_NoRedirectionAfterSubmint(self):\n\n self.open(config.url)\n self.click(config.submit_file)\n self.assert_element_present('#send-title')","def test_upload_file(self):\n data = dict(additional_metadata='additional_metadata_example',\n file='file_example')\n response = self.client.open(\n '/pet/{petId}/uploadImage'.format(pet_id=789),\n method='POST',\n data=data,\n content_type='multipart/form-data')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def webhook_upload(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log):\n hook_log = WebhookLog.objects.get(id=hook_log)\n hook_log.file_upload_event = True\n hook_log.file_upload_datetime = timezone.now()\n hook_log.save()\n process_files(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log=hook_log)\n info_debug_log(event='Webhook upload',status='success')","def test_metadata_schema_json_valid_file_upload(mock_irods):\n\n schema_file_path = 'pytest/assets/mi_schema.json'\n file_size = os.stat(schema_file_path).st_size\n assert file_size > 0\n file_to_upload = UploadedFile(file=open(schema_file_path, 'rb'),\n name=os.path.basename(schema_file_path), size=file_size)\n\n form_data = {\"mp_program_type\": \"Test Model Program\"}\n files = {\"mi_json_schema_file\": file_to_upload}\n metadata_validation_form = ModelProgramMetadataValidationForm(data=form_data, files=files)\n assert metadata_validation_form.is_valid()\n assert len(metadata_validation_form.cleaned_data['mi_json_schema_file']) > 0","def test_upload_step__invalid_file(self):\n # Set Up\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\n\n # Test\n with open(f\"{ETL_TEST_DATA_DIR}invalid_extension.txt\", \"r\") as fp:\n response = self.client.post(\n self.WIZARD_URL,\n {\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\n self.SELECTED_ITEM: self.ITEM_UPLOAD_FILE,\n \"upload_file\": fp,\n \"submit\": \"submit\",\n },\n )\n\n # Assert\n self.assertEqual(response.status_code, 200)\n error_data = response.context_data[\"wizard\"][\"form\"].errors.get_json_data()\n self.assertEqual(\n response.context[\"wizard\"][\"steps\"].current, FeedUpdateWizard.UPLOAD_STEP\n )\n self.assertEqual(\n error_data,\n {\n \"upload_file\": [\n {\n \"code\": \"invalid\",\n \"message\": \"The file is not in a correct format\",\n }\n ]\n },\n )","def test_upload_step__no_change(self):\n # Set Up\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\n\n # Test\n response = self.client.post(\n self.WIZARD_URL,\n {\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\n \"submit\": \"submit\",\n },\n )\n\n # Assert\n self.assertEqual(response.status_code, 200)\n error_data = response.context_data[\"wizard\"][\"form\"].errors.get_json_data()\n self.assertEqual(\n response.context[\"wizard\"][\"steps\"].current, FeedUpdateWizard.UPLOAD_STEP\n )\n self.assertEqual(\n error_data,\n {\"url_link\": [{\"message\": \"Please provide a file or url\", \"code\": \"all\"}]},\n )","def test_import_upload(self):\r\n self._login_admin()\r\n\r\n # verify we get the form\r\n res = self.app.get('/admin/import')\r\n self.assertTrue(\r\n '<form' in res.body,\r\n 'Should have a form in the body for submitting the upload')\r\n\r\n res = self._upload()\r\n\r\n self.assertEqual(\r\n res.status,\r\n \"302 Found\",\r\n msg='Import status is 302 redirect by home, ' + res.status)\r\n\r\n # now verify that we've got our record\r\n imp = ImportQueueMgr.get_ready()\r\n imp = imp[0]\r\n self.assertTrue(imp, 'We should have a record')\r\n self.assertTrue(imp.file_path.endswith('admin.delicious.html'))\r\n self.assertEqual(imp.status, 0, 'start out as default status of 0')","def test_upload_step__missing_file(self):\n # Set Up\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\n\n # Test\n response = self.client.post(\n self.WIZARD_URL,\n {\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\n self.SELECTED_ITEM: self.ITEM_UPLOAD_FILE,\n \"submit\": \"submit\",\n },\n )\n\n # Assert\n self.assertEqual(response.status_code, 200)\n error_data = response.context_data[\"wizard\"][\"form\"].errors.get_json_data()\n self.assertEqual(\n response.context[\"wizard\"][\"steps\"].current, FeedUpdateWizard.UPLOAD_STEP\n )\n self.assertEqual(\n error_data,\n {\"upload_file\": [{\"message\": \"Please provide a file\", \"code\": \"required\"}]},\n )","def testMediaUpload(self):\n self._testUpload(DefaultStorage(), 'media')\n self._testUpload(StaticStorage(), 'static')","def test_upload_voice_dataset(self):\n pass","def test_unsupported_extension(client: FlaskClient):\n file = get_example_file(ExampleFileType.Txt)\n file.filename = \"test.py\"\n response = util.upload_file(client, DEFAULT_USER, file)\n assert response.status == \"400 BAD REQUEST\"","def test_file_upload_fail(self):\r\n module = self.get_module_from_location(self.problem_location)\r\n\r\n # Simulate a student saving an answer\r\n response = module.handle_ajax(\"save_answer\", {\"student_answer\": self.answer_text})\r\n response = json.loads(response)\r\n self.assertFalse(response['success'])\r\n self.assertIn('error', response)","def test_create_document(self):\n self.client.force_login(user=Customer.objects.get(id=self.dummy.customer[0]).user)\n url = reverse('document-list')\n\n data = {\n 'file':open('Murphy.txt'),\n 'type':2,\n 'res':self.res_id,\n 'obsolete': True,\n 'description':'Sth',\n }\n response = self.client.post(url,data,format='multipart')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n document = Document.objects.get(id=json.loads(response.content)['id'])\n self.assertEqual(document.res_id,self.res_id)\n self.client.logout()","def test_track_validation(self):\n self.go200('minus_upload')\n self.formfile('minus_upload', 'file', AUDIO_FILE)\n self.fv('minus_upload', 'title', 'мінус тест')\n self.submit200()\n MinusRecord.objects.get(title = u'Тест')\n\n self.go200('minus_upload')\n self.formfile('minus_upload', 'file', AUDIO_FILE)\n self.fv('minus_upload', 'title', 'порно тест')\n self.submit200()\n self.url('minus_upload')\n self.assert_equal(MinusRecord.objects.count(), 1) # no new record","def test_create_model_without_target(test_client, dataset):\n response = test_client.post('/create',\n data={\"file\": dataset},\n content_type=\"multipart/form-data\")\n assert response.status_code == 400","def testFileField(self):\n filename = 'test_file_%s.txt' % str(time.time()).split('.')[0]\n\n from s3_folder_storage.tests.testapp.models import TestModel\n my_model = TestModel()\n my_model.upload = SimpleUploadedFile(filename, 'blahblah')\n my_model.save()\n\n # make sure it's saved on the model correctly\n self.assertRegexpMatches(my_model.upload.url, \".*/media/%s\" % filename)\n\n # test the file is reachable\n response = requests.get(my_model.upload.url)\n self.assertEqual(response.status_code, 200)\n\n # clean up\n f = my_model.upload.storage.open(filename, 'r')\n f.obj.delete()","def test_upload(self):\n fake_file_name = 'fake_file_name'\n\n backend = self.test_init_valid()\n backend.upload(fake_file_name)\n\n backend.vault.concurrent_create_archive_from_file.assert_called_once_with(filename=fake_file_name, description='')","def test_adding_invalid_file(self):\n (added, status) = self.app.add_album(__file__)\n self.assertEqual(added, False)\n self.assertIn('Unable to load', status)\n self.assertEqual(self.get_album_count(), 0)","def test_upload_invalid_image_field(self):\n url = create_upload_image_url(self.recipe.id)\n\n res = self.client.post(url, {\"image\": \"not_image\"}, format=\"multipart\")\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","def test_metadata_schema_json_invalid_file_upload(invalid_schema_file, mock_irods):\n\n schema_file_path = 'pytest/assets/{}'.format(invalid_schema_file)\n file_size = os.stat(schema_file_path).st_size\n assert file_size > 0\n file_to_upload = UploadedFile(file=open(schema_file_path, 'rb'),\n name=os.path.basename(schema_file_path), size=file_size)\n\n form_data = {\"mp_program_type\": \"Test Model Program\"}\n files = {\"mi_json_schema_file\": file_to_upload}\n metadata_validation_form = ModelProgramMetadataValidationForm(data=form_data, files=files)\n assert not metadata_validation_form.is_valid()","def test_create_model_with_wrong_target(test_client, dataset):\n response = test_client.post('/create?target=Test',\n data={\"file\": dataset},\n content_type=\"multipart/form-data\")\n assert response.status_code == 500","def test_file_upload_creates_db_entry_properly(self):\n\n video_file = SimpleUploadedFile(\n \"jfk_evidence.mp4\", b\"...\", content_type=\"video/mp4\"\n )\n\n self.client.force_login(self.test_user)\n response = self.client.post(\n reverse(\"upload-form\"), {\"upload_type\": \"file_upload\", \"file\": video_file}\n )\n upload_obj = response.context[\"upload\"]\n self.assertEqual(response.status_code, 200)\n self.assertContains(\n response, \"Password\"\n ) # This implicitly checks for HTTP 200, BTW...\n\n # Ensure rendered form contains the generated password.\n self.assertContains(response, upload_obj.password)","def test_upload_binary(self):\n uploadFile = os.path.join(testdatadir, \"upload.data.gz\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED","def test_mimetypes(self):\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n assert field.clean(file) is file\n\n for t in self.bad_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n with pytest.raises(forms.ValidationError):\n field.clean(file)","def upload_single_file(request):\n message, success, title = \"\", 0, \"error\"\n is_data_ok = False\n\n if request.method == 'POST':\n data_in_post = [\"id_campaign\", \"field_name\"]\n # defined in utils.py\n is_data_in_post = check_all_data_available_in_post(\n data_in_post, request.POST)\n\n if is_data_in_post['success']:\n is_data_ok = True\n else:\n message = is_data_in_post['message']\n\n if is_data_ok:\n for filename, file in request.FILES.items():\n name = request.FILES[filename].name\n print(\"filename : \", name)\n\n # myfile = request.FILES['abm_company_list_file']\n myfile = request.FILES[filename]\n fs = FileSystemStorage()\n filename = fs.save(\"campaign/\" + myfile.name, myfile)\n print(filename)\n\n # get campaign id\n id_campaign = request.POST.get(\"id_campaign\")\n\n # django get campaign object from model\n campaign = Campaign.objects.filter(id=id_campaign).first()\n\n if campaign:\n # get specification record\n specification = Specification.objects.filter(campaign=campaign).first()\n if specification:\n # get field name to save\n field_name = request.POST.get(\"field_name\")\n\n # check object has property with field name\n if hasattr(specification, field_name):\n # nested_setattr(object, 'pet.name', 'Sparky')\n model_field_name = str(field_name) + \".name\"\n model_field_name = model_field_name.replace(\" \", \"\")\n print(model_field_name)\n\n # set nested attribute\n # ex. form.name\n nested_setattr(specification, model_field_name, filename)\n\n specification.save()\n print(nested_getattr(specification, model_field_name, 'default')) # will print string similar to filename\n\n success = 1\n title = 'success'\n message = \"specification updated successfully\"\n else:\n message += \"Error... Specification table has no field '\" + field_name + \"'\"\n\n else:\n message += \"Specification not exists with campaign: '\", str(campaign), \"'\"\n else:\n message += \"Campaign not exist with id : '\", id_campaign, \"'\"\n\n # uploaded_file_url = fs.url(filename)\n success = 1\n else:\n message = \"Please post data using post method\"\n\n jsonresponse = {\n \"success\": 1,\n \"title\": request.POST,\n \"message\": message,\n }\n return JsonResponse(jsonresponse, safe=False)","def test_upload(self):\n package = make_package()\n datastr = 'foobar'\n data = StringIO(datastr)\n self.storage.upload(package, data)\n filename = self.storage.get_path(package)\n self.assertTrue(os.path.exists(filename))\n with open(filename, 'r') as ifile:\n self.assertEqual(ifile.read(), 'foobar')","def test_upload_dir_contents_one_file(self):\n self._test_upload_dir_contents(filenames=['file1'])","def test_invalid_filetype(self):\n rv = self.post('/queue/',\n content={'image': (StringIO('This is not an image'),\n 'text.txt')},\n token=self.user_token)\n self.assertJSONError(rv, 'TagalleryInvalidFileExtension')\n return","def test_upload_image_bad(self):\n url = image_upload_url(self.recipe.id)\n res = self.client.post(url, {'image': 'notimage', format: 'multipart'})\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","def test_moderator_uploads_plusrecord(self):\n self.go200('minus_plus_upload_user',[self.user.id])\n self.formfile('minus_plus_upload', 'file', AUDIO_FILE)\n self.submit200()\n self.logout('auth_logout')\n self.login('u', 'p', url='auth_login', formid='id_login')\n self.go200('minus_upload')\n self.showforms()\n self.formfile('minus_upload', 'file', AUDIO_FILE)\n self.submit200()\n plus = MinusPlusRecord.objects.all()[0]\n minus = MinusRecord.objects.all()[0]\n self.assert_equal(plus.user, self.user)\n self.assert_equal(minus.user, self.user)\n self.assert_equal(plus.minus, minus)","def test_upload_existing_empty_file(self):\n ps = PersistenceStore(s3_client=S3ExistingEmptyUpload())\n\n with pytest.raises(Exception) as e:\n ps.update({}, 'filename.json')\n\n assert str(e.value) == 'Unable to get the json data path: ' \\\n 'developer-analytics-audit-report/filename.json'","def test_successfull_upload(self):\n self.go200('minus_upload')\n self.assert_equal(MinusRecord.objects.count(),0)\n self.assert_equal(MinusAuthor.objects.count(),0)\n self.find('Завантажити мінусовку')\n self.find('Файл')\n self.formfile('minus_upload', 'file', AUDIO_FILE)\n self.submit200()\n self.assert_equal(MinusAuthor.objects.count(),1)\n self.assert_equal(MinusRecord.objects.count(),1)\n minus = MinusRecord.objects.all()[0]\n self.url('minus_detail', [minus.author, minus.id])\n self.assert_equal(minus.author.name,\"Richard Cheese\")\n self.assert_equal(minus.title,\"Jingle Bells\")\n self.assert_equal(minus.length,datetime.time(0,1,10))\n self.assert_equal(minus.bitrate, 195)\n self.assert_equal(minus.user, self.superuser)","def test_create_file_without_buckets(db, location):\n record = CernSearchRecord.create({\"title\": \"test\"}) # type: CernSearchRecord\n db.session.commit()\n\n assert record.bucket_id == \"\"\n assert record.bucket_content_id == \"\"\n\n assert \"_bucket\" not in record\n assert \"_bucket_content\" not in record","def test_upload_step__valid_file(self):\n # Set Up\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\n\n # Test\n # Re-uploading the same file, this should be irrelevant\n with open(f\"{ETL_TEST_DATA_DIR}ea_20-1A-A-y08-1.xml\", \"r\") as fp:\n response = self.client.post(\n self.WIZARD_URL,\n {\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\n self.SELECTED_ITEM: self.ITEM_UPLOAD_FILE,\n \"upload_file\": fp,\n \"submit\": \"submit\",\n },\n )\n\n # Assert\n self.assertEqual(response.status_code, 200)\n self.assertEqual(\n response.context[\"wizard\"][\"steps\"].current, FeedUpdateWizard.COMMENT_STEP\n )","def test_unicode_param(self):\n uploadFile = os.path.join(testdatadir, \"upload.data.gz\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED","def test_invalid_image_upload(self):\n res = self.client.post(\n reverse('articles:add-image',\n kwargs={\n \"slug\": Article.objects.get().slug\n }),\n data={\n \"file\": self.temporary_unsupported_image\n },\n format='multipart'\n )\n self.assertEquals(res.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertRaises(Exception)","def test_upload_duplicate_file_create(self):\n url = reverse('content-list')\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file.\\n\")\n content_file.seek(0)\n data = {\n 'name': 'Content File',\n 'description': 'File 1',\n 'content_file': content_file,\n 'updated_time': date.today(),\n 'creators': [],\n 'coverage': '',\n 'subjects': [],\n 'keywords': [],\n 'workareas': [],\n 'language': '',\n 'cataloger': ''\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n self.assertEqual(Content.objects.count(), 1)\n first_content = Content.objects.first()\n\n # Duplicate File.\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file.\\n\")\n content_file.seek(0)\n data = {\n 'name': 'Content File',\n 'description': 'File 1',\n 'content_file': content_file,\n 'updated_time': date.today(),\n 'creators': [],\n 'coverage': '',\n 'subjects': [],\n 'keywords': [],\n 'workareas': [],\n 'language': '',\n 'cataloger': ''\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_409_CONFLICT)\n response_payload = json.loads(response.content.decode(\"utf-8\"))\n self.assertEqual(response_payload['result'], 'error')\n self.assertEqual(response_payload['error'], 'DUPLICATE_FILE_UPLOADED')\n self.assertRegex(\n response_payload['existing_content']['content_url'],\n '%s$' % reverse('content-detail', args=[first_content.pk])\n )\n self.assertRegex(\n response_payload['existing_content']['file_url'],\n '%s$' % first_content.content_file.url\n )","def test_upload_image_bad_request(self):\n url = image_upload_url(self.recipe.id)\n res = self.client.post(url,{'image':'notimage'},format='multipart')\n self.assertEqual(res.status_code,status.HTTP_400_BAD_REQUEST)","def test_api_create_no_assets(api):\n api.create_article(\n {\"title\": \"A Title\"},\n {\"key1\": \"value1\"},\n )\n req_call = requests.request\n req_kw = req_call.call_args[1]\n data_lines = req_kw['data'].split(b'\\r\\n')\n # Unknown file type is skipped\n assert len(data_lines) == 11","def test_log_file_created(self, mock_parsing_handler, mock_api_handler, mock_progress):\n\n directory = path.join(path_to_module, \"fake_ngs_data\")\n directory_status = DirectoryStatus(directory)\n log_file = path.join(directory, \"irida-uploader.log\")\n # Check that log file does not exist before starting\n self.assertFalse(path.exists(log_file))\n\n cli_entry._validate_and_upload(directory_status, False)\n\n # Make sure log file is created\n self.assertTrue(path.exists(log_file))","def test_exif_broken(self):\n user = UserFactory.create()\n file_path = os.path.join(os.path.dirname(__file__), \"broken_exif.jpg\")\n self._upload_photo(user, file_path)","def test_one_bad_file_classes(self):\n url = reverse('classes-list')\n act_data = {'activity': 'Bad_test',\n 'videoclass': None,\n 'trainer': 'Ex',\n 'workarea': 'T'}\n response = self.client.post(url, act_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_415_UNSUPPORTED_MEDIA_TYPE)\n self.assertEqual(Class.objects.count(), 0)","def test_InvalideFileExtension_NoRedirectionAfterSubmint(self):\n\n self.open(config.url)\n self.choose_file(config.file_upload, config.wrong_file_format_filepath)\n self.click(config.submit_file)\n self.assert_element_present('#file_response')","def test_cannot_write_file(self):\n self.api.write_data('/some-fake/path/to-create-file/', 'some-string')","def test_upload_file_extensions(db_conn, cards_table):\n\n card, errors = UploadCard.insert(db_conn, {\n 'unit_id': 'RUF531',\n 'name': 'What is?',\n 'body': 'Testing 1234',\n 'rubric': True, # TODO\n })\n assert len(errors) == 1\n card, errors = card.update(db_conn, {'file_extensions': ['jpg']})\n assert len(errors) == 0","def test_video_image_upload_disabled(self):\n video_image_upload_url = self.get_url_for_course_key(self.course.id, {'edx_video_id': 'test_vid_id'})\n response = self.client.post(video_image_upload_url, {'file': 'dummy_file'}, format='multipart')\n self.assertEqual(response.status_code, 404)","def test_existing_file_name(self):\n\t\ttp = self.sess.query(sql.Post).filter(sql.Post.reddit_id == 't3_ahal9v').first()\n\t\tfile = ng.choose_file_name(tp.urls[0], tp, sql.session(), album_size=1)\n\t\tself.assertTrue(file.endswith(' - 2'), msg='Failed to increment duplicate post!')","def test_upload_duplicate(self):\n # Attempt to upload a duplicate\n with self.assertRaises(trovebox.TroveboxDuplicateError):\n self.client.photo.upload(\"tests/data/test_photo1.jpg\",\n title=self.TEST_TITLE)\n\n # Check there are still three photos\n self.photos = self.client.photos.list()\n self.assertEqual(len(self.photos), 3)","def test_upload_image_bad_request(self):\n url = image_upload_url(self.recipe.id)\n res = self.client.post(url, {'image':'notimage'}, format='multipart')\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","def test_upload_no_overwrite(self):\n request = DummyRequest()\n request.access = DummyAccess(request)\n cache = DummyCache(request)\n request.access.allow_overwrite = []\n name, version, filename = \"a\", \"1\", \"a-1.tar.gz\"\n cache.upload(filename, BytesIO(b\"test1234\"), name, version)\n with self.assertRaises(ValueError):\n cache.upload(filename, BytesIO(b\"test1234\"), name, version)","def test_add_model_file_empty(self):\n with DataArchive(self.user, DATA_DOWNLOADS_WORKING_DIR) as archive:\n file_path = archive.add_model_file(self.user.avatar_tmp)\n\n self.assertIsNone(file_path)\n self.assertFalse(os.listdir(archive.data_dir_path))","def test_fs_instance(self):\n b1 = BaseModel()\n models.storage.save()\n self.assertEqual(os.path.exists('file.json'), True)","def upload_submission(request, learner, trigger, no_thumbnail=True):\n base_dir_for_file_uploads = settings.MEDIA_ROOT\n thumbnail_file_name_django = ''\n entry_point = trigger.entry_point\n\n files = request.FILES.getlist('file_upload', None)\n if files is None:\n return None\n\n # Is the storage space reachable?\n deepest_dir = base_dir_for_file_uploads + 'uploads/{0}/tmp/'.format(\n entry_point.id)\n\n try:\n os.makedirs(deepest_dir)\n except OSError:\n if not os.path.isdir(deepest_dir):\n logger.error('Cannot create directory for upload: {0}'.format(\n deepest_dir))\n raise\n\n if len(files) == 1:\n filename = files[0].name\n extension = filename.split('.')[-1].lower()\n submitted_file_name_django = 'uploads/{0}/{1}'.format(entry_point.id,\n generate_random_token(token_length=16) + '.' + extension)\n full_path = base_dir_for_file_uploads + submitted_file_name_django\n with open(full_path, 'wb+') as dst:\n for chunk in files[0].chunks():\n dst.write(chunk)\n\n\n f_size = os.path.getsize(full_path)\n if f_size > trigger.max_file_upload_size_MB * 1024 * 1024:\n logger.warning('File too large {0}'.format(\n submitted_file_name_django))\n return None, ('File too large ({0} MB); it must be less than '\n '{1} MB.'.format(round(float(f_size/1024.0/1024.0), 1),\n trigger.max_file_upload_size_MB))\n\n\n else: #if trigger.allow_multiple_files: this is removed for now\n filename = ''\n extension = ''\n submitted_file_name_django = ''\n full_path = ''\n\n\n # Check that the file format is PDF, if that is required.\n strike1 = False\n if 'pdf' in trigger.accepted_file_types_comma_separated.lower() and \\\n extension in ('pdf',):\n try:\n mime = magic.from_file(full_path, mime=True)\n if not(isinstance(mime, str)):\n mime = mime.decode('utf-8')\n except Exception as exp:\n logger.error('Could not determine MIME type: ' + str(exp))\n mime = ''\n strike1 = True\n\n if 'application/pdf' not in mime.lower():\n strike1 = True\n\n if strike1:\n logger.debug('Invalid PDF upload: {0} [{1}]'.format(mime,\n full_path))\n #return None, 'Invalid file uploaded. Uploaded file must be a PDF.'\n\n doc = PdfFileReader(full_path)\n if doc.isEncrypted:\n logger.debug('Encrypted PDF upload: {0}'.format(full_path))\n return None, ('An encrypted PDF cannot be uploaded. Please remove '\n 'the encryption and try again.')\n\n\n strike1 = False\n if (('jpeg' in trigger.accepted_file_types_comma_separated.lower()) or \\\n ('jpg' in trigger.accepted_file_types_comma_separated.lower())) and \\\n extension in ('jpg', 'jpeg'):\n\n try:\n mime = magic.from_file(full_path, mime=True)\n if not(isinstance(mime, str)):\n mime = mime.decode('utf-8')\n except Exception as exp:\n logger.error('Could not determine MIME type: ' + str(exp))\n mime = ''\n strike1 = True\n\n if 'image/jpeg' not in mime.lower():\n strike1 = True\n\n if strike1:\n logger.debug('Invalid JPG upload: {0} [{1}]'.format(mime,\n full_path))\n return None, ('Invalid file. Uploaded image should be a valid '\n 'and readable JPEG file.')\n\n\n strike1 = False\n if ('png' in trigger.accepted_file_types_comma_separated.lower()) and \\\n extension in ('png',):\n\n try:\n mime = magic.from_file(full_path, mime=True)\n if not(isinstance(mime, str)):\n mime = mime.decode('utf-8')\n except Exception as exp:\n logger.error('Could not determine MIME type: ' + str(exp))\n mime = ''\n strike1 = True\n\n if 'image/png' not in mime.lower():\n strike1 = True\n\n if strike1:\n logger.debug('Invalid PNG upload: {0} [{1}]'.format(mime,\n full_path))\n return None, ('Invalid file. Uploaded image should be a valid '\n 'and readable PNG file.')\n\n\n strike2 = False\n if extension.lower() not in \\\n trigger.accepted_file_types_comma_separated.lower():\n logger.debug('Invalid file type upload: received \".{0}\"; [{1}]'.format(\\\n extension, full_path))\n return None, ('Invalid file uploaded. Uploaded file must be: {}'.format(\\\n trigger.accepted_file_types_comma_separated))\n\n\n if trigger == entry_point:\n # In some instances we don't use triggers, just entry_points\n prior = Submission.objects.filter(status='S',\n submitted_by=learner,\n entry_point=entry_point,\n is_valid=True\n )\n else:\n prior_indiv = Q(status='S', submitted_by=learner, entry_point=entry_point,\n trigger=trigger, is_valid=True)\n\n # We need this here, but also for the code later in the next\n # if (trigger==entry_point) part\n\n # Default returned by this function is ``None`` if the user is not\n # enrolled in a group, or if this course simply does not use groups.\n group_submitted = is_group_submission(learner, entry_point)\n if is_group_submission(learner, entry_point):\n group_submitted = group_submitted.group\n\n prior_group = Q(status='S', group_submitted=group_submitted,\n entry_point=entry_point, trigger=trigger,\n is_valid=True)\n else:\n prior_group = Q()\n\n prior = Submission.objects.filter(prior_indiv | prior_group)\n\n\n for item in prior:\n logger.debug(('Setting prior submission to False: {0} and name '\n '\"{1}\"'.format(str(item), item.submitted_file_name)))\n item.is_valid = False\n item.save()\n\n\n if trigger == entry_point:\n # In some instances we don't use triggers, just entry_points\n sub = Submission(submitted_by=learner,\n group_submitted=None,\n status='S',\n entry_point=entry_point,\n is_valid=True,\n file_upload=submitted_file_name_django,\n thumbnail=thumbnail_file_name_django,\n submitted_file_name=filename,\n ip_address=get_IP_address(request),\n )\n sub.save()\n else:\n\n sub = Submission(submitted_by=learner,\n group_submitted=group_submitted,\n status='S',\n entry_point=entry_point,\n trigger=trigger,\n is_valid=True,\n file_upload=submitted_file_name_django,\n thumbnail=thumbnail_file_name_django,\n submitted_file_name=filename,\n ip_address=get_IP_address(request),\n )\n sub.save()\n\n if 'pdf' in trigger.accepted_file_types_comma_separated.lower() and \\\n extension in ('pdf',):\n clean_PDF(sub)\n\n return sub","def test_mutation_file_upload(client):\n query = '''mutation M {\n updatePerson(id: 1,avatar: \"cat1.jpg\") {\n person {\n id name age avatar\n }\n }\n }\n '''\n data = {\n 'query': query,\n 'file': (open('files/cat1.jpg'), 'cat1.jpg'),\n }\n response = client.post(\n '/graphql', data=data,\n content_type='multipart/form-data',\n )\n\n expected_response = '{\"data\":{\"updatePerson\":{\"person\":{\"id\":1,\"name\":null,\"age\":34.0,\"avatar\":\"/files/cat1.jpg\"}}}}'\n assert response.data == expected_response","def test_get_file_fail(self):\n from django.contrib.messages import get_messages\n path = reverse(\"setting-csv\")\n user = mixer.blend(User, is_staff=True, is_superuser=True)\n client = Client()\n client.force_login(user)\n r = client.post(path, {\"title\": \"hotel\", \"url\": \"http://rachel.wrongurltofetchdata.nl/djangocase/hotel.csv\",\n \"username\": \"py\", \"password\": \"30_bumps\", \"save\": \"on\"})\n messages = list(get_messages(r.wsgi_request))\n assert r.status_code == 200\n assert len(messages) == 1\n assert \"Received an error\" in str(messages[0])","def test_add_media_type(self):\n\n # check if documentalist has access to create new media-types\n self.login_documentalist()\n response = self.client.get('/multimedia/media-type/new' )\n\n # 403 = unauthorized\n self.assertEqual(response.status_code, 403)\n\n self.client.logout()\n self.login_admin()\n\n form_data = {\n 'status': '0',\n 'acronym': 'foto',\n 'name': 'Foto',\n 'language' : 'pt-br',\n 'mediatypelocal_set-TOTAL_FORMS': '0',\n 'mediatypelocal_set-INITIAL_FORMS': '0',\n }\n\n response = self.client.post('/multimedia/media-type/new', form_data, follow=True )\n\n self.assertRedirects(response, '/multimedia/media-types')\n self.assertContains(response, \"Foto\")","def test_upload(api):\n # upload the file to file.io servers\n uploaded_file = api.upload(\n tag='test_upload',\n expiry='1w',\n path='tests/test_file.txt'\n )\n\n assert uploaded_file.link\n assert uploaded_file.key\n assert uploaded_file.tag\n assert uploaded_file.path\n\n with pytest.raises(APIConnectionError):\n while True:\n api.upload(\n tag='test_upload',\n expiry='1w',\n path='tests/test_file.txt'\n )","def test_file_creation(data, logging_file_name):\n create_instance(data, logging_file_name)\n log_file_name = create_file_path(logging_file_name)\n print(log_file_name)\n if data is None or len(data) == 0:\n assert not os.path.exists(log_file_name)\n else:\n assert os.path.exists(log_file_name)","def tests_ti_file_add_tag(self, request: FixtureRequest):\n super().indicator_add_tag(request)","def test_both(self):\n field = TypedFileField(required=False,\n ext_whitelist=self.good_extensions,\n type_whitelist=self.good_types,\n use_magic=False)\n\n for ext in self.good_extensions:\n name = 'somefooname.%s' % ext\n\n for t in self.good_types:\n file = UploadedFile(name=name, size=1, content_type=t)\n assert field.clean(file) is file\n\n for t in self.bad_types:\n file = UploadedFile(name=name, size=1, content_type=t)\n with pytest.raises(forms.ValidationError):\n field.clean(file)\n\n for ext in self.bad_extensions:\n name = 'somefooname.%s' % ext\n\n for t in self.good_types:\n file = UploadedFile(name=name, size=1, content_type=t)\n with pytest.raises(forms.ValidationError):\n field.clean(file)\n\n for t in self.bad_types:\n file = UploadedFile(name=name, size=1, content_type=t)\n with pytest.raises(forms.ValidationError):\n field.clean(file)","def test_create_with_bad_backend(self):\n # Count the number of records before the save\n post_data = {\n 'source_type': 'test',\n 'source_id': '4bCOAuhvjsxbVBM5MM8oik',\n }\n resp = self.api_client.post('/api/metadata/tracks/', data=post_data)\n data = json.loads(resp.content)\n\n # Ensure the request filed with a 404, and an error message is returned\n self.assertEqual(resp.status_code, 404)\n self.assertEqual(data['detail'], u'The record could not be found.')","def test_upload_video(self):\n with self.client:\n path = '../data/example.mp4'\n path = os.path.join(os.path.dirname(__file__), path)\n with open(os.path.abspath(path), 'rb') as file:\n data = dict(file=(file, 'example.mp4'))\n response = self.client.post('/upload',\n content_type='multipart/form-data',\n data=data)\n file.close()\n self.assertEqual(status.HTTP_200_OK, response.status_code)\n self.assertIn('example.mp4', os.listdir(VIDEOS_SAVE_PATH))\n self.assertIn('example.mp4', [video.filename for video in Video.query.all()])","def test_extensions(self):\n field = TypedFileField(required=False, ext_whitelist=self.good_extensions)\n\n for ext in self.good_extensions:\n name = 'somefooname.%s' % ext\n file = UploadedFile(name=name, size=1)\n assert field.clean(file) is file\n\n for ext in self.bad_extensions:\n name = 'somefooname.%s' % ext\n file = UploadedFile(name=name, size=1)\n with pytest.raises(forms.ValidationError):\n field.clean(file)","def test_multipart_upload(self):\n cwl_local_path = os.path.abspath('testdata/md5sum.cwl')\n workflow_attachment_path = os.path.abspath('testdata/dockstore-tool-md5sum.cwl')\n out_file_path, run_id = run_cwl_md5sum(cwl_input='file://' + cwl_local_path,\n workflow_attachment='file://' + workflow_attachment_path)\n\n get_response = get_log_request(run_id)[\"request\"]\n\n self.assertTrue(check_for_file(out_file_path), 'Output file was not found: '\n + get_response[\"workflow_attachment\"])\n self.assertTrue(check_for_file(get_response[\"workflow_url\"][7:]), 'Output file was not found: '\n + get_response[\"workflow_url\"][:7])","def test_upload_image_bad_request(self):\n url = image_upload_url(self.movie.id)\n res = self.client.post(url, {'image': 'notimage'}, format='multipart')\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","def test_csv_import_hotel_fail(self):\n from django.contrib.messages import get_messages\n path = reverse(\"import-csv\")\n user = mixer.blend(User, is_staff=True, is_superuser=True)\n client = Client()\n client.force_login(user)\n file = open(\"hotel.csv\")\n r = client.post(path, {\"title\": \"hotel\", \"csv_file\": file})\n messages = list(get_messages(r.wsgi_request))\n assert r.status_code == 200\n assert len(messages) >= 1\n for message in messages:\n assert \"can not import\" in str(message)","def test_file_can_be_uploaded_and_viewed_by_correct_users(self):\n \n project = self.testproject \n \n name1 = self.giverandomfilename(self.root)\n name2 = self.giverandomfilename(self.projectadmin)\n name3 = self.giverandomfilename(self.participant)\n name4 = self.giverandomfilename(self.participant2)\n \n resp1 = self._upload_test_file(self.root,self.testproject,name1)\n resp2 = self._upload_test_file(self.projectadmin,self.testproject,name2)\n resp3 = self._upload_test_file(self.participant,self.testproject,name3)\n resp4 = self._upload_test_file(self.participant2,self.testproject,name4)\n \n # root and projectadmin should see all files\n self.uploaded_files_are_all_shown_on_uploadpage([name1,name2,name3,name4],self.root)\n self.uploaded_files_are_all_shown_on_uploadpage([name1,name2,name3,name4],self.projectadmin)\n \n # participant1 sees only his or her own file\n self.uploaded_files_are_all_shown_on_uploadpage([name3],self.participant)\n self.uploaded_files_are_not_shown_on_uploadpage([name1,name2,name4],self.participant)\n \n # participant2 also sees only his or her own file\n self.uploaded_files_are_all_shown_on_uploadpage([name4],self.participant2)\n self.uploaded_files_are_not_shown_on_uploadpage([name1,name2,name3],self.participant2)","def test_upload_text(client: FlaskClient):\n file = get_example_file(ExampleFileType.Txt)\n response = util.upload_file(client, DEFAULT_USER, file)\n assert response.status == \"201 CREATED\"\n assert response.json[\"filetype\"] == \"DOCUMENT\"\n assert response.json[\"upload_name\"] == file.filename\n assert response.json[\"uploaded_by\"][\"id\"] == 1\n assert response.json[\"size\"] == len(file.contents)\n assert response.json[\"hash\"] == hashlib.md5(file.contents).hexdigest()\n\n # Access the file via URL\n response_get = client.get(response.json[\"url\"])\n assert response_get.status == \"200 OK\"\n assert list(response_get.response)[0] == file.contents","def test_02_not_exist(self):\n with self.assertLogs(\"borg\", \"WARNING\") as logger:\n self.api.extract(self.archive, self.file_3)\n message = logger.records[0].getMessage()\n self.assertRegex(\n message,\n r\".*?file_3.*never\",\n \"Warning not logged for bad path\",\n )","def test_fetch_or_create_requires_file_type():\n pytest.raises(ValueError, media.fetch_or_create_media_item, b'spam')","def test_upload_duplicate_file_upload(self):\n url = reverse('content-list')\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file.\\n\")\n content_file.seek(0)\n data = {\n 'name': 'Content File',\n 'description': 'File 1',\n 'content_file': content_file,\n 'updated_time': date.today(),\n 'creators': [],\n 'coverage': '',\n 'subjects': [],\n 'keywords': [],\n 'workareas': [],\n 'language': '',\n 'cataloger': ''\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n self.assertEqual(Content.objects.count(), 1)\n first_content = Content.objects.first()\n\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file 2.\\n\")\n content_file.seek(0)\n data = {\n 'name': 'Content File 2',\n 'description': 'File 2',\n 'content_file': content_file,\n 'updated_time': date.today(),\n 'creators': [],\n 'coverage': '',\n 'subjects': [],\n 'keywords': [],\n 'workareas': [],\n 'language': '',\n 'cataloger': ''\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n self.assertEqual(Content.objects.count(), 2)\n second_content = Content.objects.filter(name='Content File 2').first()\n\n # Duplicate File.\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file.\\n\")\n content_file.seek(0)\n data = {\n 'content_file': content_file,\n }\n\n url = reverse('content-detail', args=[second_content.pk])\n response = self.client.patch(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_409_CONFLICT)\n response_payload = json.loads(response.content.decode(\"utf-8\"))\n self.assertEqual(response_payload['result'], 'error')\n self.assertEqual(response_payload['error'], 'DUPLICATE_FILE_UPLOADED')\n self.assertRegex(\n response_payload['existing_content']['content_url'],\n '%s$' % reverse('content-detail', args=[first_content.pk])\n )\n self.assertRegex(\n response_payload['existing_content']['file_url'],\n '%s$' % first_content.content_file.url\n )"],"string":"[\n \"def test_upload_wrong_file_type(self):\\n file = dict(\\n file=(BytesIO(b'my file contents'), \\\"foto.doc\\\"),\\n )\\n response = self.client.post('/upload',\\n content_type='multipart/form-data',\\n data=file)\\n self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code)\\n if os.path.exists(PHOTOS_SAVE_PATH):\\n self.assertNotIn('foto.jpg', os.listdir(PHOTOS_SAVE_PATH))\",\n \"def test_upload_file(self):\\n pass\",\n \"def test_upload_file1(self):\\n pass\",\n \"def test_file_upload_file_with_the_same_name_already_exists(\\n staff_api_client, media_root, site_settings\\n):\\n # given\\n image_file1, image_name1 = create_image()\\n path = default_storage.save(image_file1._name, image_file1)\\n\\n image_file, image_name = create_image()\\n assert image_file1 != image_file\\n assert image_name == image_name1\\n assert image_file._name == image_file1._name\\n\\n variables = {\\\"image\\\": image_name}\\n body = get_multipart_request_body(\\n FILE_UPLOAD_MUTATION, variables, image_file, image_name\\n )\\n\\n # when\\n response = staff_api_client.post_multipart(body)\\n\\n # then\\n content = get_graphql_content(response)\\n data = content[\\\"data\\\"][\\\"fileUpload\\\"]\\n errors = data[\\\"errors\\\"]\\n\\n domain = site_settings.site.domain\\n assert not errors\\n assert data[\\\"uploadedFile\\\"][\\\"contentType\\\"] == \\\"image/png\\\"\\n file_url = data[\\\"uploadedFile\\\"][\\\"url\\\"]\\n assert file_url != f\\\"http://{domain}/media/{image_file._name}\\\"\\n assert file_url != f\\\"http://{domain}/media/{path}\\\"\\n assert default_storage.exists(file_url.replace(f\\\"http://{domain}/media/\\\", \\\"\\\"))\",\n \"def test_local_uploader_upload_wrong_file(self, mock):\\r\\n mock.save.return_value = None\\r\\n u = LocalUploader()\\r\\n file = FileStorage(filename='test.txt')\\r\\n res = u.upload_file(file, container='user_3')\\r\\n err_msg = (\\\"Upload file should return False, \\\\\\r\\n as this extension is not allowed\\\")\\r\\n assert res is False, err_msg\",\n \"def test_upload_bad_file(self):\\n url = image_upload_url(self.reteta.id)\\n res = self.client.post(url, {'image': 'notimage'}, format='multipart')\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def test_empty_upload(self):\\r\\n self._login_admin()\\r\\n\\r\\n res = self.app.post(\\r\\n '/admin/import',\\r\\n params={'api_key': self.api_key},\\r\\n upload_files=[],\\r\\n )\\r\\n self.assertTrue(\\r\\n 'Please provide a file to import' in res.body,\\r\\n \\\"Error message should be present\\\")\",\n \"def test_file_field():\",\n \"def test_local_uploader_upload_correct_file(self, mock):\\r\\n mock.save.return_value = None\\r\\n u = LocalUploader()\\r\\n file = FileStorage(filename='test.jpg')\\r\\n res = u.upload_file(file, container='user_3')\\r\\n err_msg = (\\\"Upload file should return True, \\\\\\r\\n as this extension is allowed\\\")\\r\\n assert res is True, err_msg\",\n \"def test_upload(self):\\n with self.client:\\n file = dict(\\n file=(BytesIO(b'my file contents'), \\\"foto.jpg\\\"),\\n )\\n response = self.client.post('/upload',\\n content_type='multipart/form-data',\\n data=file)\\n self.assertEqual(status.HTTP_200_OK, response.status_code)\\n self.assertIn('foto.jpg', os.listdir(PHOTOS_SAVE_PATH))\\n self.assertIn('foto.jpg', [photo.filename for photo in Photo.query.all()])\",\n \"def test_missing_extension(client: FlaskClient):\\n file = get_example_file(ExampleFileType.Txt)\\n file.filename = \\\"test\\\"\\n response = util.upload_file(client, DEFAULT_USER, file)\\n assert response.status == \\\"400 BAD REQUEST\\\"\",\n \"def test_logger_temperature_upload_duplicate(self):\\n test_type_filename = 'server/tests/test_data_files/Test/Test_New_Logger_Type_Positive.csv'\\n test_temp_filename = 'server/tests/test_data_files/Test/temp_files/DUMMYID_2000_pgsql_Duplicate.txt'\\n with self.app.test_client() as client:\\n with client.session_transaction() as sess:\\n sess['logged_in'] = True\\n response = client.post('/upload', \\n data={\\n 'loggerTypeFile': (open(test_type_filename, 'rb'), 'Test_New_Logger_Type_Positive.csv')\\n }, follow_redirects=True)\\n response = client.post('/upload', \\n data={\\n 'loggerTempFile': (open(test_temp_filename, 'rb'), 'DUMMYID_2000_pgsql_Duplicate.txt')\\n }, follow_redirects=True)\\n record_type = {\\n \\\"microsite_id\\\" : \\\"DUMMYID\\\",\\n \\\"site\\\" : \\\"DUMMYSITE\\\",\\n \\\"biomimic_type\\\" : \\\"Dummybiomimictype\\\",\\n \\\"country\\\" : \\\"Dummycountry\\\",\\n \\\"state_province\\\" : \\\"Dummystate\\\",\\n \\\"location\\\" : \\\"Dummylocation\\\",\\n \\\"field_lat\\\" : \\\"36.621933330000\\\",\\n \\\"field_lon\\\" : \\\"-121.905316700000\\\",\\n \\\"zone\\\" : \\\"DummyZone\\\",\\n \\\"sub_zone\\\" : \\\"DummySubZone\\\",\\n \\\"wave_exp\\\" : \\\"DummyWave\\\",\\n \\\"start_date\\\": str(datetime.strptime(\\\"7/1/2000\\\",'%m/%d/%Y').date()),\\n \\\"end_date\\\": str(datetime.strptime(\\\"7/2/2000\\\",'%m/%d/%Y').date())} \\n where_condition = self.db.build_where_condition(record_type)\\n query = (\\\"SELECT temp.Time_GMT, temp.Temp_C \\\"\\n \\\"FROM `cnx_logger` logger \\\"\\n \\\"INNER JOIN `cnx_logger_biomimic_type` biotype ON biotype.`biomimic_id` = logger.`biomimic_id` \\\"\\n \\\"INNER JOIN `cnx_logger_geographics` geo ON geo.`geo_id` = logger.`geo_id` \\\"\\n \\\"INNER JOIN `cnx_logger_properties` prop ON prop.`prop_id` = logger.`prop_id` \\\"\\n \\\"INNER JOIN `cnx_logger_temperature` temp ON temp.`logger_id` = logger.`logger_id` \\\")\\n cursor = self.db.connection.cursor()\\n cursor.execute(query + where_condition)\\n results = cursor.fetchall()\\n results = list(results)\\n self.cleanUpLoggerTemp(cursor)\\n self.cleanUpLoggerType(cursor, record_type) \\n cursor.close()\\n self.assertEqual(len(results), 1)\",\n \"def test_should_file_field(self):\\n self.assertIn(\\\"image\\\", self.fields)\",\n \"def test_upload_file(self):\\n\\n uploadFile = os.path.join(testdatadir, \\\"upload.data\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(link).read()\\n )\\n\\n # Re-upload slightly different file.\\n uploadFile2 = os.path.join(testdatadir, \\\"upload2.data\\\")\\n r = gracedb.writeFile(\\n eventId,\\n filename=\\\"upload.data\\\",\\n filecontents=open(uploadFile2, 'r'))\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link2 = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(link2).read()\\n )\\n\\n self.assertNotEqual(link, link2)\",\n \"def upload_build(self, name, directory):\\n logging.info('Not uploading build because no Filestore.')\",\n \"def test_no_mimetype(self):\\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\\n\\n for t in self.good_types:\\n name = 'somefooname'\\n file = UploadedFile(name=name, size=1, content_type=t)\\n del file.content_type\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\",\n \"def test_upload_microbe_directory(self, auth_headers, *_):\\n sample = Sample(name='SMPL_Microbe_Directory_01').save()\\n sample_uuid = str(sample.uuid)\\n with self.client:\\n response = self.client.post(\\n f'/api/v1/samples/{sample_uuid}/microbe_directory_annotate',\\n headers=auth_headers,\\n data=json.dumps(TEST_DIRECTORY),\\n content_type='application/json',\\n )\\n data = json.loads(response.data.decode())\\n self.assertEqual(response.status_code, 201)\\n self.assertIn('success', data['status'])\\n for field in TEST_DIRECTORY:\\n self.assertIn(field, data['data'])\\n\\n # Reload object to ensure microbe directory result was stored properly\\n sample = Sample.objects.get(uuid=sample_uuid)\\n self.assertTrue(sample.microbe_directory_annotate)\",\n \"def test_create_model_without_file(test_client, dataset):\\n response = test_client.post('/create?target=Species',\\n data={\\\"file\\\": \\\"No file\\\"},\\n content_type=\\\"multipart/form-data\\\")\\n assert response.status_code == 400\",\n \"def test_local_uploader_upload_fails(self, mock):\\r\\n u = LocalUploader()\\r\\n file = FileStorage(filename='test.jpg')\\r\\n res = u.upload_file(file, container='user_3')\\r\\n err_msg = (\\\"Upload file should return False, \\\\\\r\\n as there is an exception\\\")\\r\\n assert res is False, err_msg\",\n \"def upload_coverage(self, name, directory):\\n logging.info('Not uploading coverage because no Filestore.')\",\n \"def test_upload_job_description_file_post(self):\\n pass\",\n \"def test_api_create_unknown_file(api):\\n api.create_article(\\n {\\\"title\\\": \\\"A Title\\\"},\\n {\\\"key1\\\": \\\"value1\\\"},\\n {'test.zip': 'FFFDASFAFADADFA'},\\n )\\n req_call = requests.request\\n req_kw = req_call.call_args[1]\\n data_lines = req_kw['data'].split(b'\\\\r\\\\n')\\n # Unknown file type is skipped\\n assert len(data_lines) == 11\",\n \"def test_upload_manifest_non_existing_trial_id(\\n cidc_api, some_file, clean_db, monkeypatch\\n):\\n user_id = setup_trial_and_user(cidc_api, monkeypatch)\\n make_admin(user_id, cidc_api)\\n\\n mocks = UploadMocks(monkeypatch, prismify_trial_id=\\\"test-non-existing-trial-id\\\")\\n\\n client = cidc_api.test_client()\\n\\n res = client.post(MANIFEST_UPLOAD, data=form_data(\\\"pbmc.xlsx\\\", some_file, \\\"pbmc\\\"))\\n assert res.status_code == 400\\n assert \\\"test-non-existing-trial-id\\\" in str(res.json[\\\"_error\\\"][\\\"message\\\"])\\n\\n # Check that we tried to upload the excel file\\n mocks.upload_xlsx.assert_not_called()\\n mocks.iter_errors.assert_called_once()\\n mocks.prismify.assert_called_once()\",\n \"def test_upload_empty(self):\\n rv = self.post(url='/queue/', content=None, token=self.user_token)\\n self.assertJSONError(rv, 'TagalleryMissingFile')\\n return\",\n \"def test_no_mimetype_magic(self, mock_get_content_type):\\n mock_get_content_type.side_effect = ValueError\\n\\n field = TypedFileField(required=False, type_whitelist=self.good_types)\\n\\n for t in self.good_types:\\n name = 'somefooname'\\n file = UploadedFile(name=name, size=1, content_type=t)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\",\n \"def test_invalid_file_type(self):\\n file = SimpleUploadedFile(\\\"test.csv\\\", b\\\"\\\\xe8\\\")\\n form = MomConnectImportForm(\\n data={\\\"source\\\": \\\"MomConnect Import\\\"}, files={\\\"file\\\": file}\\n )\\n self.assertTrue(form.is_valid())\\n instance = form.save()\\n self.assertEqual(instance.status, MomConnectImport.Status.ERROR)\\n [error] = instance.errors.all()\\n self.assertEqual(error.error, \\\"File is not a CSV\\\")\",\n \"def test_file_upload(self):\\n\\n with tempfile.NamedTemporaryFile() as test_file:\\n test_file.write(\\n u'date,category,employee name,employee address,expense description,pre-tax amount,tax name,tax amount\\\\n')\\n test_file.write(\\n u'12/1/2013,Travel,Don Draper,\\\"783 Park Ave, New York, NY 10021\\\",Taxi ride, 350.00 ,NY Sales tax, 31.06\\\\n')\\n test_file.flush()\\n response = self.send_file_upload_request(view_name='csv_import_view', filename=test_file.name)\\n\\n actual_import_logs = ImportLog.objects.all()\\n self.assertEqual(1, len(actual_import_logs))\\n\\n actual_import_log = actual_import_logs[0]\\n expected_file_name = os.path.basename(test_file.name)\\n self.assertEqual(expected_file_name, actual_import_log.file_name)\\n\\n expense_items = ExpenseItem.objects.all()\\n self.assertEqual(1, len(expense_items))\\n self.assertEqual('Don Draper', expense_items[0].employee.name)\\n\\n self.assertEqual('{\\\"upload_id\\\": 1}', response.content)\",\n \"def upload_crashes(self, name, directory):\\n logging.info('Not uploading crashes because no Filestore.')\",\n \"def test_upload_duplicate(client: FlaskClient):\\n file = get_example_file(ExampleFileType.Png)\\n response1 = util.upload_file(client, DEFAULT_USER, file)\\n response2 = util.upload_file(client, DEFAULT_USER, file)\\n\\n assert response1.status == \\\"201 CREATED\\\"\\n assert response2.status == \\\"200 OK\\\"\\n assert response1.json == response2.json\",\n \"def test_NoFileUploaded_NoRedirectionAfterSubmint(self):\\n\\n self.open(config.url)\\n self.click(config.submit_file)\\n self.assert_element_present('#send-title')\",\n \"def test_upload_file(self):\\n data = dict(additional_metadata='additional_metadata_example',\\n file='file_example')\\n response = self.client.open(\\n '/pet/{petId}/uploadImage'.format(pet_id=789),\\n method='POST',\\n data=data,\\n content_type='multipart/form-data')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def webhook_upload(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log):\\n hook_log = WebhookLog.objects.get(id=hook_log)\\n hook_log.file_upload_event = True\\n hook_log.file_upload_datetime = timezone.now()\\n hook_log.save()\\n process_files(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log=hook_log)\\n info_debug_log(event='Webhook upload',status='success')\",\n \"def test_metadata_schema_json_valid_file_upload(mock_irods):\\n\\n schema_file_path = 'pytest/assets/mi_schema.json'\\n file_size = os.stat(schema_file_path).st_size\\n assert file_size > 0\\n file_to_upload = UploadedFile(file=open(schema_file_path, 'rb'),\\n name=os.path.basename(schema_file_path), size=file_size)\\n\\n form_data = {\\\"mp_program_type\\\": \\\"Test Model Program\\\"}\\n files = {\\\"mi_json_schema_file\\\": file_to_upload}\\n metadata_validation_form = ModelProgramMetadataValidationForm(data=form_data, files=files)\\n assert metadata_validation_form.is_valid()\\n assert len(metadata_validation_form.cleaned_data['mi_json_schema_file']) > 0\",\n \"def test_upload_step__invalid_file(self):\\n # Set Up\\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\\n\\n # Test\\n with open(f\\\"{ETL_TEST_DATA_DIR}invalid_extension.txt\\\", \\\"r\\\") as fp:\\n response = self.client.post(\\n self.WIZARD_URL,\\n {\\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\\n self.SELECTED_ITEM: self.ITEM_UPLOAD_FILE,\\n \\\"upload_file\\\": fp,\\n \\\"submit\\\": \\\"submit\\\",\\n },\\n )\\n\\n # Assert\\n self.assertEqual(response.status_code, 200)\\n error_data = response.context_data[\\\"wizard\\\"][\\\"form\\\"].errors.get_json_data()\\n self.assertEqual(\\n response.context[\\\"wizard\\\"][\\\"steps\\\"].current, FeedUpdateWizard.UPLOAD_STEP\\n )\\n self.assertEqual(\\n error_data,\\n {\\n \\\"upload_file\\\": [\\n {\\n \\\"code\\\": \\\"invalid\\\",\\n \\\"message\\\": \\\"The file is not in a correct format\\\",\\n }\\n ]\\n },\\n )\",\n \"def test_upload_step__no_change(self):\\n # Set Up\\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\\n\\n # Test\\n response = self.client.post(\\n self.WIZARD_URL,\\n {\\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\\n \\\"submit\\\": \\\"submit\\\",\\n },\\n )\\n\\n # Assert\\n self.assertEqual(response.status_code, 200)\\n error_data = response.context_data[\\\"wizard\\\"][\\\"form\\\"].errors.get_json_data()\\n self.assertEqual(\\n response.context[\\\"wizard\\\"][\\\"steps\\\"].current, FeedUpdateWizard.UPLOAD_STEP\\n )\\n self.assertEqual(\\n error_data,\\n {\\\"url_link\\\": [{\\\"message\\\": \\\"Please provide a file or url\\\", \\\"code\\\": \\\"all\\\"}]},\\n )\",\n \"def test_import_upload(self):\\r\\n self._login_admin()\\r\\n\\r\\n # verify we get the form\\r\\n res = self.app.get('/admin/import')\\r\\n self.assertTrue(\\r\\n '<form' in res.body,\\r\\n 'Should have a form in the body for submitting the upload')\\r\\n\\r\\n res = self._upload()\\r\\n\\r\\n self.assertEqual(\\r\\n res.status,\\r\\n \\\"302 Found\\\",\\r\\n msg='Import status is 302 redirect by home, ' + res.status)\\r\\n\\r\\n # now verify that we've got our record\\r\\n imp = ImportQueueMgr.get_ready()\\r\\n imp = imp[0]\\r\\n self.assertTrue(imp, 'We should have a record')\\r\\n self.assertTrue(imp.file_path.endswith('admin.delicious.html'))\\r\\n self.assertEqual(imp.status, 0, 'start out as default status of 0')\",\n \"def test_upload_step__missing_file(self):\\n # Set Up\\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\\n\\n # Test\\n response = self.client.post(\\n self.WIZARD_URL,\\n {\\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\\n self.SELECTED_ITEM: self.ITEM_UPLOAD_FILE,\\n \\\"submit\\\": \\\"submit\\\",\\n },\\n )\\n\\n # Assert\\n self.assertEqual(response.status_code, 200)\\n error_data = response.context_data[\\\"wizard\\\"][\\\"form\\\"].errors.get_json_data()\\n self.assertEqual(\\n response.context[\\\"wizard\\\"][\\\"steps\\\"].current, FeedUpdateWizard.UPLOAD_STEP\\n )\\n self.assertEqual(\\n error_data,\\n {\\\"upload_file\\\": [{\\\"message\\\": \\\"Please provide a file\\\", \\\"code\\\": \\\"required\\\"}]},\\n )\",\n \"def testMediaUpload(self):\\n self._testUpload(DefaultStorage(), 'media')\\n self._testUpload(StaticStorage(), 'static')\",\n \"def test_upload_voice_dataset(self):\\n pass\",\n \"def test_unsupported_extension(client: FlaskClient):\\n file = get_example_file(ExampleFileType.Txt)\\n file.filename = \\\"test.py\\\"\\n response = util.upload_file(client, DEFAULT_USER, file)\\n assert response.status == \\\"400 BAD REQUEST\\\"\",\n \"def test_file_upload_fail(self):\\r\\n module = self.get_module_from_location(self.problem_location)\\r\\n\\r\\n # Simulate a student saving an answer\\r\\n response = module.handle_ajax(\\\"save_answer\\\", {\\\"student_answer\\\": self.answer_text})\\r\\n response = json.loads(response)\\r\\n self.assertFalse(response['success'])\\r\\n self.assertIn('error', response)\",\n \"def test_create_document(self):\\n self.client.force_login(user=Customer.objects.get(id=self.dummy.customer[0]).user)\\n url = reverse('document-list')\\n\\n data = {\\n 'file':open('Murphy.txt'),\\n 'type':2,\\n 'res':self.res_id,\\n 'obsolete': True,\\n 'description':'Sth',\\n }\\n response = self.client.post(url,data,format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n document = Document.objects.get(id=json.loads(response.content)['id'])\\n self.assertEqual(document.res_id,self.res_id)\\n self.client.logout()\",\n \"def test_track_validation(self):\\n self.go200('minus_upload')\\n self.formfile('minus_upload', 'file', AUDIO_FILE)\\n self.fv('minus_upload', 'title', 'мінус тест')\\n self.submit200()\\n MinusRecord.objects.get(title = u'Тест')\\n\\n self.go200('minus_upload')\\n self.formfile('minus_upload', 'file', AUDIO_FILE)\\n self.fv('minus_upload', 'title', 'порно тест')\\n self.submit200()\\n self.url('minus_upload')\\n self.assert_equal(MinusRecord.objects.count(), 1) # no new record\",\n \"def test_create_model_without_target(test_client, dataset):\\n response = test_client.post('/create',\\n data={\\\"file\\\": dataset},\\n content_type=\\\"multipart/form-data\\\")\\n assert response.status_code == 400\",\n \"def testFileField(self):\\n filename = 'test_file_%s.txt' % str(time.time()).split('.')[0]\\n\\n from s3_folder_storage.tests.testapp.models import TestModel\\n my_model = TestModel()\\n my_model.upload = SimpleUploadedFile(filename, 'blahblah')\\n my_model.save()\\n\\n # make sure it's saved on the model correctly\\n self.assertRegexpMatches(my_model.upload.url, \\\".*/media/%s\\\" % filename)\\n\\n # test the file is reachable\\n response = requests.get(my_model.upload.url)\\n self.assertEqual(response.status_code, 200)\\n\\n # clean up\\n f = my_model.upload.storage.open(filename, 'r')\\n f.obj.delete()\",\n \"def test_upload(self):\\n fake_file_name = 'fake_file_name'\\n\\n backend = self.test_init_valid()\\n backend.upload(fake_file_name)\\n\\n backend.vault.concurrent_create_archive_from_file.assert_called_once_with(filename=fake_file_name, description='')\",\n \"def test_adding_invalid_file(self):\\n (added, status) = self.app.add_album(__file__)\\n self.assertEqual(added, False)\\n self.assertIn('Unable to load', status)\\n self.assertEqual(self.get_album_count(), 0)\",\n \"def test_upload_invalid_image_field(self):\\n url = create_upload_image_url(self.recipe.id)\\n\\n res = self.client.post(url, {\\\"image\\\": \\\"not_image\\\"}, format=\\\"multipart\\\")\\n\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def test_metadata_schema_json_invalid_file_upload(invalid_schema_file, mock_irods):\\n\\n schema_file_path = 'pytest/assets/{}'.format(invalid_schema_file)\\n file_size = os.stat(schema_file_path).st_size\\n assert file_size > 0\\n file_to_upload = UploadedFile(file=open(schema_file_path, 'rb'),\\n name=os.path.basename(schema_file_path), size=file_size)\\n\\n form_data = {\\\"mp_program_type\\\": \\\"Test Model Program\\\"}\\n files = {\\\"mi_json_schema_file\\\": file_to_upload}\\n metadata_validation_form = ModelProgramMetadataValidationForm(data=form_data, files=files)\\n assert not metadata_validation_form.is_valid()\",\n \"def test_create_model_with_wrong_target(test_client, dataset):\\n response = test_client.post('/create?target=Test',\\n data={\\\"file\\\": dataset},\\n content_type=\\\"multipart/form-data\\\")\\n assert response.status_code == 500\",\n \"def test_file_upload_creates_db_entry_properly(self):\\n\\n video_file = SimpleUploadedFile(\\n \\\"jfk_evidence.mp4\\\", b\\\"...\\\", content_type=\\\"video/mp4\\\"\\n )\\n\\n self.client.force_login(self.test_user)\\n response = self.client.post(\\n reverse(\\\"upload-form\\\"), {\\\"upload_type\\\": \\\"file_upload\\\", \\\"file\\\": video_file}\\n )\\n upload_obj = response.context[\\\"upload\\\"]\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(\\n response, \\\"Password\\\"\\n ) # This implicitly checks for HTTP 200, BTW...\\n\\n # Ensure rendered form contains the generated password.\\n self.assertContains(response, upload_obj.password)\",\n \"def test_upload_binary(self):\\n uploadFile = os.path.join(testdatadir, \\\"upload.data.gz\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\",\n \"def test_mimetypes(self):\\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\\n\\n for t in self.good_types:\\n name = 'somefooname'\\n file = UploadedFile(name=name, size=1, content_type=t)\\n assert field.clean(file) is file\\n\\n for t in self.bad_types:\\n name = 'somefooname'\\n file = UploadedFile(name=name, size=1, content_type=t)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\",\n \"def upload_single_file(request):\\n message, success, title = \\\"\\\", 0, \\\"error\\\"\\n is_data_ok = False\\n\\n if request.method == 'POST':\\n data_in_post = [\\\"id_campaign\\\", \\\"field_name\\\"]\\n # defined in utils.py\\n is_data_in_post = check_all_data_available_in_post(\\n data_in_post, request.POST)\\n\\n if is_data_in_post['success']:\\n is_data_ok = True\\n else:\\n message = is_data_in_post['message']\\n\\n if is_data_ok:\\n for filename, file in request.FILES.items():\\n name = request.FILES[filename].name\\n print(\\\"filename : \\\", name)\\n\\n # myfile = request.FILES['abm_company_list_file']\\n myfile = request.FILES[filename]\\n fs = FileSystemStorage()\\n filename = fs.save(\\\"campaign/\\\" + myfile.name, myfile)\\n print(filename)\\n\\n # get campaign id\\n id_campaign = request.POST.get(\\\"id_campaign\\\")\\n\\n # django get campaign object from model\\n campaign = Campaign.objects.filter(id=id_campaign).first()\\n\\n if campaign:\\n # get specification record\\n specification = Specification.objects.filter(campaign=campaign).first()\\n if specification:\\n # get field name to save\\n field_name = request.POST.get(\\\"field_name\\\")\\n\\n # check object has property with field name\\n if hasattr(specification, field_name):\\n # nested_setattr(object, 'pet.name', 'Sparky')\\n model_field_name = str(field_name) + \\\".name\\\"\\n model_field_name = model_field_name.replace(\\\" \\\", \\\"\\\")\\n print(model_field_name)\\n\\n # set nested attribute\\n # ex. form.name\\n nested_setattr(specification, model_field_name, filename)\\n\\n specification.save()\\n print(nested_getattr(specification, model_field_name, 'default')) # will print string similar to filename\\n\\n success = 1\\n title = 'success'\\n message = \\\"specification updated successfully\\\"\\n else:\\n message += \\\"Error... Specification table has no field '\\\" + field_name + \\\"'\\\"\\n\\n else:\\n message += \\\"Specification not exists with campaign: '\\\", str(campaign), \\\"'\\\"\\n else:\\n message += \\\"Campaign not exist with id : '\\\", id_campaign, \\\"'\\\"\\n\\n # uploaded_file_url = fs.url(filename)\\n success = 1\\n else:\\n message = \\\"Please post data using post method\\\"\\n\\n jsonresponse = {\\n \\\"success\\\": 1,\\n \\\"title\\\": request.POST,\\n \\\"message\\\": message,\\n }\\n return JsonResponse(jsonresponse, safe=False)\",\n \"def test_upload(self):\\n package = make_package()\\n datastr = 'foobar'\\n data = StringIO(datastr)\\n self.storage.upload(package, data)\\n filename = self.storage.get_path(package)\\n self.assertTrue(os.path.exists(filename))\\n with open(filename, 'r') as ifile:\\n self.assertEqual(ifile.read(), 'foobar')\",\n \"def test_upload_dir_contents_one_file(self):\\n self._test_upload_dir_contents(filenames=['file1'])\",\n \"def test_invalid_filetype(self):\\n rv = self.post('/queue/',\\n content={'image': (StringIO('This is not an image'),\\n 'text.txt')},\\n token=self.user_token)\\n self.assertJSONError(rv, 'TagalleryInvalidFileExtension')\\n return\",\n \"def test_upload_image_bad(self):\\n url = image_upload_url(self.recipe.id)\\n res = self.client.post(url, {'image': 'notimage', format: 'multipart'})\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def test_moderator_uploads_plusrecord(self):\\n self.go200('minus_plus_upload_user',[self.user.id])\\n self.formfile('minus_plus_upload', 'file', AUDIO_FILE)\\n self.submit200()\\n self.logout('auth_logout')\\n self.login('u', 'p', url='auth_login', formid='id_login')\\n self.go200('minus_upload')\\n self.showforms()\\n self.formfile('minus_upload', 'file', AUDIO_FILE)\\n self.submit200()\\n plus = MinusPlusRecord.objects.all()[0]\\n minus = MinusRecord.objects.all()[0]\\n self.assert_equal(plus.user, self.user)\\n self.assert_equal(minus.user, self.user)\\n self.assert_equal(plus.minus, minus)\",\n \"def test_upload_existing_empty_file(self):\\n ps = PersistenceStore(s3_client=S3ExistingEmptyUpload())\\n\\n with pytest.raises(Exception) as e:\\n ps.update({}, 'filename.json')\\n\\n assert str(e.value) == 'Unable to get the json data path: ' \\\\\\n 'developer-analytics-audit-report/filename.json'\",\n \"def test_successfull_upload(self):\\n self.go200('minus_upload')\\n self.assert_equal(MinusRecord.objects.count(),0)\\n self.assert_equal(MinusAuthor.objects.count(),0)\\n self.find('Завантажити мінусовку')\\n self.find('Файл')\\n self.formfile('minus_upload', 'file', AUDIO_FILE)\\n self.submit200()\\n self.assert_equal(MinusAuthor.objects.count(),1)\\n self.assert_equal(MinusRecord.objects.count(),1)\\n minus = MinusRecord.objects.all()[0]\\n self.url('minus_detail', [minus.author, minus.id])\\n self.assert_equal(minus.author.name,\\\"Richard Cheese\\\")\\n self.assert_equal(minus.title,\\\"Jingle Bells\\\")\\n self.assert_equal(minus.length,datetime.time(0,1,10))\\n self.assert_equal(minus.bitrate, 195)\\n self.assert_equal(minus.user, self.superuser)\",\n \"def test_create_file_without_buckets(db, location):\\n record = CernSearchRecord.create({\\\"title\\\": \\\"test\\\"}) # type: CernSearchRecord\\n db.session.commit()\\n\\n assert record.bucket_id == \\\"\\\"\\n assert record.bucket_content_id == \\\"\\\"\\n\\n assert \\\"_bucket\\\" not in record\\n assert \\\"_bucket_content\\\" not in record\",\n \"def test_upload_step__valid_file(self):\\n # Set Up\\n self.go_to_step(FeedUpdateWizard.UPLOAD_STEP)\\n\\n # Test\\n # Re-uploading the same file, this should be irrelevant\\n with open(f\\\"{ETL_TEST_DATA_DIR}ea_20-1A-A-y08-1.xml\\\", \\\"r\\\") as fp:\\n response = self.client.post(\\n self.WIZARD_URL,\\n {\\n self.WIZARD_CURRENT_STEP: FeedUpdateWizard.UPLOAD_STEP,\\n self.SELECTED_ITEM: self.ITEM_UPLOAD_FILE,\\n \\\"upload_file\\\": fp,\\n \\\"submit\\\": \\\"submit\\\",\\n },\\n )\\n\\n # Assert\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(\\n response.context[\\\"wizard\\\"][\\\"steps\\\"].current, FeedUpdateWizard.COMMENT_STEP\\n )\",\n \"def test_unicode_param(self):\\n uploadFile = os.path.join(testdatadir, \\\"upload.data.gz\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\",\n \"def test_invalid_image_upload(self):\\n res = self.client.post(\\n reverse('articles:add-image',\\n kwargs={\\n \\\"slug\\\": Article.objects.get().slug\\n }),\\n data={\\n \\\"file\\\": self.temporary_unsupported_image\\n },\\n format='multipart'\\n )\\n self.assertEquals(res.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertRaises(Exception)\",\n \"def test_upload_duplicate_file_create(self):\\n url = reverse('content-list')\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'name': 'Content File',\\n 'description': 'File 1',\\n 'content_file': content_file,\\n 'updated_time': date.today(),\\n 'creators': [],\\n 'coverage': '',\\n 'subjects': [],\\n 'keywords': [],\\n 'workareas': [],\\n 'language': '',\\n 'cataloger': ''\\n }\\n response = self.client.post(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n self.assertEqual(Content.objects.count(), 1)\\n first_content = Content.objects.first()\\n\\n # Duplicate File.\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'name': 'Content File',\\n 'description': 'File 1',\\n 'content_file': content_file,\\n 'updated_time': date.today(),\\n 'creators': [],\\n 'coverage': '',\\n 'subjects': [],\\n 'keywords': [],\\n 'workareas': [],\\n 'language': '',\\n 'cataloger': ''\\n }\\n response = self.client.post(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_409_CONFLICT)\\n response_payload = json.loads(response.content.decode(\\\"utf-8\\\"))\\n self.assertEqual(response_payload['result'], 'error')\\n self.assertEqual(response_payload['error'], 'DUPLICATE_FILE_UPLOADED')\\n self.assertRegex(\\n response_payload['existing_content']['content_url'],\\n '%s$' % reverse('content-detail', args=[first_content.pk])\\n )\\n self.assertRegex(\\n response_payload['existing_content']['file_url'],\\n '%s$' % first_content.content_file.url\\n )\",\n \"def test_upload_image_bad_request(self):\\n url = image_upload_url(self.recipe.id)\\n res = self.client.post(url,{'image':'notimage'},format='multipart')\\n self.assertEqual(res.status_code,status.HTTP_400_BAD_REQUEST)\",\n \"def test_api_create_no_assets(api):\\n api.create_article(\\n {\\\"title\\\": \\\"A Title\\\"},\\n {\\\"key1\\\": \\\"value1\\\"},\\n )\\n req_call = requests.request\\n req_kw = req_call.call_args[1]\\n data_lines = req_kw['data'].split(b'\\\\r\\\\n')\\n # Unknown file type is skipped\\n assert len(data_lines) == 11\",\n \"def test_log_file_created(self, mock_parsing_handler, mock_api_handler, mock_progress):\\n\\n directory = path.join(path_to_module, \\\"fake_ngs_data\\\")\\n directory_status = DirectoryStatus(directory)\\n log_file = path.join(directory, \\\"irida-uploader.log\\\")\\n # Check that log file does not exist before starting\\n self.assertFalse(path.exists(log_file))\\n\\n cli_entry._validate_and_upload(directory_status, False)\\n\\n # Make sure log file is created\\n self.assertTrue(path.exists(log_file))\",\n \"def test_exif_broken(self):\\n user = UserFactory.create()\\n file_path = os.path.join(os.path.dirname(__file__), \\\"broken_exif.jpg\\\")\\n self._upload_photo(user, file_path)\",\n \"def test_one_bad_file_classes(self):\\n url = reverse('classes-list')\\n act_data = {'activity': 'Bad_test',\\n 'videoclass': None,\\n 'trainer': 'Ex',\\n 'workarea': 'T'}\\n response = self.client.post(url, act_data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_415_UNSUPPORTED_MEDIA_TYPE)\\n self.assertEqual(Class.objects.count(), 0)\",\n \"def test_InvalideFileExtension_NoRedirectionAfterSubmint(self):\\n\\n self.open(config.url)\\n self.choose_file(config.file_upload, config.wrong_file_format_filepath)\\n self.click(config.submit_file)\\n self.assert_element_present('#file_response')\",\n \"def test_cannot_write_file(self):\\n self.api.write_data('/some-fake/path/to-create-file/', 'some-string')\",\n \"def test_upload_file_extensions(db_conn, cards_table):\\n\\n card, errors = UploadCard.insert(db_conn, {\\n 'unit_id': 'RUF531',\\n 'name': 'What is?',\\n 'body': 'Testing 1234',\\n 'rubric': True, # TODO\\n })\\n assert len(errors) == 1\\n card, errors = card.update(db_conn, {'file_extensions': ['jpg']})\\n assert len(errors) == 0\",\n \"def test_video_image_upload_disabled(self):\\n video_image_upload_url = self.get_url_for_course_key(self.course.id, {'edx_video_id': 'test_vid_id'})\\n response = self.client.post(video_image_upload_url, {'file': 'dummy_file'}, format='multipart')\\n self.assertEqual(response.status_code, 404)\",\n \"def test_existing_file_name(self):\\n\\t\\ttp = self.sess.query(sql.Post).filter(sql.Post.reddit_id == 't3_ahal9v').first()\\n\\t\\tfile = ng.choose_file_name(tp.urls[0], tp, sql.session(), album_size=1)\\n\\t\\tself.assertTrue(file.endswith(' - 2'), msg='Failed to increment duplicate post!')\",\n \"def test_upload_duplicate(self):\\n # Attempt to upload a duplicate\\n with self.assertRaises(trovebox.TroveboxDuplicateError):\\n self.client.photo.upload(\\\"tests/data/test_photo1.jpg\\\",\\n title=self.TEST_TITLE)\\n\\n # Check there are still three photos\\n self.photos = self.client.photos.list()\\n self.assertEqual(len(self.photos), 3)\",\n \"def test_upload_image_bad_request(self):\\n url = image_upload_url(self.recipe.id)\\n res = self.client.post(url, {'image':'notimage'}, format='multipart')\\n\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def test_upload_no_overwrite(self):\\n request = DummyRequest()\\n request.access = DummyAccess(request)\\n cache = DummyCache(request)\\n request.access.allow_overwrite = []\\n name, version, filename = \\\"a\\\", \\\"1\\\", \\\"a-1.tar.gz\\\"\\n cache.upload(filename, BytesIO(b\\\"test1234\\\"), name, version)\\n with self.assertRaises(ValueError):\\n cache.upload(filename, BytesIO(b\\\"test1234\\\"), name, version)\",\n \"def test_add_model_file_empty(self):\\n with DataArchive(self.user, DATA_DOWNLOADS_WORKING_DIR) as archive:\\n file_path = archive.add_model_file(self.user.avatar_tmp)\\n\\n self.assertIsNone(file_path)\\n self.assertFalse(os.listdir(archive.data_dir_path))\",\n \"def test_fs_instance(self):\\n b1 = BaseModel()\\n models.storage.save()\\n self.assertEqual(os.path.exists('file.json'), True)\",\n \"def upload_submission(request, learner, trigger, no_thumbnail=True):\\n base_dir_for_file_uploads = settings.MEDIA_ROOT\\n thumbnail_file_name_django = ''\\n entry_point = trigger.entry_point\\n\\n files = request.FILES.getlist('file_upload', None)\\n if files is None:\\n return None\\n\\n # Is the storage space reachable?\\n deepest_dir = base_dir_for_file_uploads + 'uploads/{0}/tmp/'.format(\\n entry_point.id)\\n\\n try:\\n os.makedirs(deepest_dir)\\n except OSError:\\n if not os.path.isdir(deepest_dir):\\n logger.error('Cannot create directory for upload: {0}'.format(\\n deepest_dir))\\n raise\\n\\n if len(files) == 1:\\n filename = files[0].name\\n extension = filename.split('.')[-1].lower()\\n submitted_file_name_django = 'uploads/{0}/{1}'.format(entry_point.id,\\n generate_random_token(token_length=16) + '.' + extension)\\n full_path = base_dir_for_file_uploads + submitted_file_name_django\\n with open(full_path, 'wb+') as dst:\\n for chunk in files[0].chunks():\\n dst.write(chunk)\\n\\n\\n f_size = os.path.getsize(full_path)\\n if f_size > trigger.max_file_upload_size_MB * 1024 * 1024:\\n logger.warning('File too large {0}'.format(\\n submitted_file_name_django))\\n return None, ('File too large ({0} MB); it must be less than '\\n '{1} MB.'.format(round(float(f_size/1024.0/1024.0), 1),\\n trigger.max_file_upload_size_MB))\\n\\n\\n else: #if trigger.allow_multiple_files: this is removed for now\\n filename = ''\\n extension = ''\\n submitted_file_name_django = ''\\n full_path = ''\\n\\n\\n # Check that the file format is PDF, if that is required.\\n strike1 = False\\n if 'pdf' in trigger.accepted_file_types_comma_separated.lower() and \\\\\\n extension in ('pdf',):\\n try:\\n mime = magic.from_file(full_path, mime=True)\\n if not(isinstance(mime, str)):\\n mime = mime.decode('utf-8')\\n except Exception as exp:\\n logger.error('Could not determine MIME type: ' + str(exp))\\n mime = ''\\n strike1 = True\\n\\n if 'application/pdf' not in mime.lower():\\n strike1 = True\\n\\n if strike1:\\n logger.debug('Invalid PDF upload: {0} [{1}]'.format(mime,\\n full_path))\\n #return None, 'Invalid file uploaded. Uploaded file must be a PDF.'\\n\\n doc = PdfFileReader(full_path)\\n if doc.isEncrypted:\\n logger.debug('Encrypted PDF upload: {0}'.format(full_path))\\n return None, ('An encrypted PDF cannot be uploaded. Please remove '\\n 'the encryption and try again.')\\n\\n\\n strike1 = False\\n if (('jpeg' in trigger.accepted_file_types_comma_separated.lower()) or \\\\\\n ('jpg' in trigger.accepted_file_types_comma_separated.lower())) and \\\\\\n extension in ('jpg', 'jpeg'):\\n\\n try:\\n mime = magic.from_file(full_path, mime=True)\\n if not(isinstance(mime, str)):\\n mime = mime.decode('utf-8')\\n except Exception as exp:\\n logger.error('Could not determine MIME type: ' + str(exp))\\n mime = ''\\n strike1 = True\\n\\n if 'image/jpeg' not in mime.lower():\\n strike1 = True\\n\\n if strike1:\\n logger.debug('Invalid JPG upload: {0} [{1}]'.format(mime,\\n full_path))\\n return None, ('Invalid file. Uploaded image should be a valid '\\n 'and readable JPEG file.')\\n\\n\\n strike1 = False\\n if ('png' in trigger.accepted_file_types_comma_separated.lower()) and \\\\\\n extension in ('png',):\\n\\n try:\\n mime = magic.from_file(full_path, mime=True)\\n if not(isinstance(mime, str)):\\n mime = mime.decode('utf-8')\\n except Exception as exp:\\n logger.error('Could not determine MIME type: ' + str(exp))\\n mime = ''\\n strike1 = True\\n\\n if 'image/png' not in mime.lower():\\n strike1 = True\\n\\n if strike1:\\n logger.debug('Invalid PNG upload: {0} [{1}]'.format(mime,\\n full_path))\\n return None, ('Invalid file. Uploaded image should be a valid '\\n 'and readable PNG file.')\\n\\n\\n strike2 = False\\n if extension.lower() not in \\\\\\n trigger.accepted_file_types_comma_separated.lower():\\n logger.debug('Invalid file type upload: received \\\".{0}\\\"; [{1}]'.format(\\\\\\n extension, full_path))\\n return None, ('Invalid file uploaded. Uploaded file must be: {}'.format(\\\\\\n trigger.accepted_file_types_comma_separated))\\n\\n\\n if trigger == entry_point:\\n # In some instances we don't use triggers, just entry_points\\n prior = Submission.objects.filter(status='S',\\n submitted_by=learner,\\n entry_point=entry_point,\\n is_valid=True\\n )\\n else:\\n prior_indiv = Q(status='S', submitted_by=learner, entry_point=entry_point,\\n trigger=trigger, is_valid=True)\\n\\n # We need this here, but also for the code later in the next\\n # if (trigger==entry_point) part\\n\\n # Default returned by this function is ``None`` if the user is not\\n # enrolled in a group, or if this course simply does not use groups.\\n group_submitted = is_group_submission(learner, entry_point)\\n if is_group_submission(learner, entry_point):\\n group_submitted = group_submitted.group\\n\\n prior_group = Q(status='S', group_submitted=group_submitted,\\n entry_point=entry_point, trigger=trigger,\\n is_valid=True)\\n else:\\n prior_group = Q()\\n\\n prior = Submission.objects.filter(prior_indiv | prior_group)\\n\\n\\n for item in prior:\\n logger.debug(('Setting prior submission to False: {0} and name '\\n '\\\"{1}\\\"'.format(str(item), item.submitted_file_name)))\\n item.is_valid = False\\n item.save()\\n\\n\\n if trigger == entry_point:\\n # In some instances we don't use triggers, just entry_points\\n sub = Submission(submitted_by=learner,\\n group_submitted=None,\\n status='S',\\n entry_point=entry_point,\\n is_valid=True,\\n file_upload=submitted_file_name_django,\\n thumbnail=thumbnail_file_name_django,\\n submitted_file_name=filename,\\n ip_address=get_IP_address(request),\\n )\\n sub.save()\\n else:\\n\\n sub = Submission(submitted_by=learner,\\n group_submitted=group_submitted,\\n status='S',\\n entry_point=entry_point,\\n trigger=trigger,\\n is_valid=True,\\n file_upload=submitted_file_name_django,\\n thumbnail=thumbnail_file_name_django,\\n submitted_file_name=filename,\\n ip_address=get_IP_address(request),\\n )\\n sub.save()\\n\\n if 'pdf' in trigger.accepted_file_types_comma_separated.lower() and \\\\\\n extension in ('pdf',):\\n clean_PDF(sub)\\n\\n return sub\",\n \"def test_mutation_file_upload(client):\\n query = '''mutation M {\\n updatePerson(id: 1,avatar: \\\"cat1.jpg\\\") {\\n person {\\n id name age avatar\\n }\\n }\\n }\\n '''\\n data = {\\n 'query': query,\\n 'file': (open('files/cat1.jpg'), 'cat1.jpg'),\\n }\\n response = client.post(\\n '/graphql', data=data,\\n content_type='multipart/form-data',\\n )\\n\\n expected_response = '{\\\"data\\\":{\\\"updatePerson\\\":{\\\"person\\\":{\\\"id\\\":1,\\\"name\\\":null,\\\"age\\\":34.0,\\\"avatar\\\":\\\"/files/cat1.jpg\\\"}}}}'\\n assert response.data == expected_response\",\n \"def test_get_file_fail(self):\\n from django.contrib.messages import get_messages\\n path = reverse(\\\"setting-csv\\\")\\n user = mixer.blend(User, is_staff=True, is_superuser=True)\\n client = Client()\\n client.force_login(user)\\n r = client.post(path, {\\\"title\\\": \\\"hotel\\\", \\\"url\\\": \\\"http://rachel.wrongurltofetchdata.nl/djangocase/hotel.csv\\\",\\n \\\"username\\\": \\\"py\\\", \\\"password\\\": \\\"30_bumps\\\", \\\"save\\\": \\\"on\\\"})\\n messages = list(get_messages(r.wsgi_request))\\n assert r.status_code == 200\\n assert len(messages) == 1\\n assert \\\"Received an error\\\" in str(messages[0])\",\n \"def test_add_media_type(self):\\n\\n # check if documentalist has access to create new media-types\\n self.login_documentalist()\\n response = self.client.get('/multimedia/media-type/new' )\\n\\n # 403 = unauthorized\\n self.assertEqual(response.status_code, 403)\\n\\n self.client.logout()\\n self.login_admin()\\n\\n form_data = {\\n 'status': '0',\\n 'acronym': 'foto',\\n 'name': 'Foto',\\n 'language' : 'pt-br',\\n 'mediatypelocal_set-TOTAL_FORMS': '0',\\n 'mediatypelocal_set-INITIAL_FORMS': '0',\\n }\\n\\n response = self.client.post('/multimedia/media-type/new', form_data, follow=True )\\n\\n self.assertRedirects(response, '/multimedia/media-types')\\n self.assertContains(response, \\\"Foto\\\")\",\n \"def test_upload(api):\\n # upload the file to file.io servers\\n uploaded_file = api.upload(\\n tag='test_upload',\\n expiry='1w',\\n path='tests/test_file.txt'\\n )\\n\\n assert uploaded_file.link\\n assert uploaded_file.key\\n assert uploaded_file.tag\\n assert uploaded_file.path\\n\\n with pytest.raises(APIConnectionError):\\n while True:\\n api.upload(\\n tag='test_upload',\\n expiry='1w',\\n path='tests/test_file.txt'\\n )\",\n \"def test_file_creation(data, logging_file_name):\\n create_instance(data, logging_file_name)\\n log_file_name = create_file_path(logging_file_name)\\n print(log_file_name)\\n if data is None or len(data) == 0:\\n assert not os.path.exists(log_file_name)\\n else:\\n assert os.path.exists(log_file_name)\",\n \"def tests_ti_file_add_tag(self, request: FixtureRequest):\\n super().indicator_add_tag(request)\",\n \"def test_both(self):\\n field = TypedFileField(required=False,\\n ext_whitelist=self.good_extensions,\\n type_whitelist=self.good_types,\\n use_magic=False)\\n\\n for ext in self.good_extensions:\\n name = 'somefooname.%s' % ext\\n\\n for t in self.good_types:\\n file = UploadedFile(name=name, size=1, content_type=t)\\n assert field.clean(file) is file\\n\\n for t in self.bad_types:\\n file = UploadedFile(name=name, size=1, content_type=t)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\\n\\n for ext in self.bad_extensions:\\n name = 'somefooname.%s' % ext\\n\\n for t in self.good_types:\\n file = UploadedFile(name=name, size=1, content_type=t)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\\n\\n for t in self.bad_types:\\n file = UploadedFile(name=name, size=1, content_type=t)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\",\n \"def test_create_with_bad_backend(self):\\n # Count the number of records before the save\\n post_data = {\\n 'source_type': 'test',\\n 'source_id': '4bCOAuhvjsxbVBM5MM8oik',\\n }\\n resp = self.api_client.post('/api/metadata/tracks/', data=post_data)\\n data = json.loads(resp.content)\\n\\n # Ensure the request filed with a 404, and an error message is returned\\n self.assertEqual(resp.status_code, 404)\\n self.assertEqual(data['detail'], u'The record could not be found.')\",\n \"def test_upload_video(self):\\n with self.client:\\n path = '../data/example.mp4'\\n path = os.path.join(os.path.dirname(__file__), path)\\n with open(os.path.abspath(path), 'rb') as file:\\n data = dict(file=(file, 'example.mp4'))\\n response = self.client.post('/upload',\\n content_type='multipart/form-data',\\n data=data)\\n file.close()\\n self.assertEqual(status.HTTP_200_OK, response.status_code)\\n self.assertIn('example.mp4', os.listdir(VIDEOS_SAVE_PATH))\\n self.assertIn('example.mp4', [video.filename for video in Video.query.all()])\",\n \"def test_extensions(self):\\n field = TypedFileField(required=False, ext_whitelist=self.good_extensions)\\n\\n for ext in self.good_extensions:\\n name = 'somefooname.%s' % ext\\n file = UploadedFile(name=name, size=1)\\n assert field.clean(file) is file\\n\\n for ext in self.bad_extensions:\\n name = 'somefooname.%s' % ext\\n file = UploadedFile(name=name, size=1)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\",\n \"def test_multipart_upload(self):\\n cwl_local_path = os.path.abspath('testdata/md5sum.cwl')\\n workflow_attachment_path = os.path.abspath('testdata/dockstore-tool-md5sum.cwl')\\n out_file_path, run_id = run_cwl_md5sum(cwl_input='file://' + cwl_local_path,\\n workflow_attachment='file://' + workflow_attachment_path)\\n\\n get_response = get_log_request(run_id)[\\\"request\\\"]\\n\\n self.assertTrue(check_for_file(out_file_path), 'Output file was not found: '\\n + get_response[\\\"workflow_attachment\\\"])\\n self.assertTrue(check_for_file(get_response[\\\"workflow_url\\\"][7:]), 'Output file was not found: '\\n + get_response[\\\"workflow_url\\\"][:7])\",\n \"def test_upload_image_bad_request(self):\\n url = image_upload_url(self.movie.id)\\n res = self.client.post(url, {'image': 'notimage'}, format='multipart')\\n\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def test_csv_import_hotel_fail(self):\\n from django.contrib.messages import get_messages\\n path = reverse(\\\"import-csv\\\")\\n user = mixer.blend(User, is_staff=True, is_superuser=True)\\n client = Client()\\n client.force_login(user)\\n file = open(\\\"hotel.csv\\\")\\n r = client.post(path, {\\\"title\\\": \\\"hotel\\\", \\\"csv_file\\\": file})\\n messages = list(get_messages(r.wsgi_request))\\n assert r.status_code == 200\\n assert len(messages) >= 1\\n for message in messages:\\n assert \\\"can not import\\\" in str(message)\",\n \"def test_file_can_be_uploaded_and_viewed_by_correct_users(self):\\n \\n project = self.testproject \\n \\n name1 = self.giverandomfilename(self.root)\\n name2 = self.giverandomfilename(self.projectadmin)\\n name3 = self.giverandomfilename(self.participant)\\n name4 = self.giverandomfilename(self.participant2)\\n \\n resp1 = self._upload_test_file(self.root,self.testproject,name1)\\n resp2 = self._upload_test_file(self.projectadmin,self.testproject,name2)\\n resp3 = self._upload_test_file(self.participant,self.testproject,name3)\\n resp4 = self._upload_test_file(self.participant2,self.testproject,name4)\\n \\n # root and projectadmin should see all files\\n self.uploaded_files_are_all_shown_on_uploadpage([name1,name2,name3,name4],self.root)\\n self.uploaded_files_are_all_shown_on_uploadpage([name1,name2,name3,name4],self.projectadmin)\\n \\n # participant1 sees only his or her own file\\n self.uploaded_files_are_all_shown_on_uploadpage([name3],self.participant)\\n self.uploaded_files_are_not_shown_on_uploadpage([name1,name2,name4],self.participant)\\n \\n # participant2 also sees only his or her own file\\n self.uploaded_files_are_all_shown_on_uploadpage([name4],self.participant2)\\n self.uploaded_files_are_not_shown_on_uploadpage([name1,name2,name3],self.participant2)\",\n \"def test_upload_text(client: FlaskClient):\\n file = get_example_file(ExampleFileType.Txt)\\n response = util.upload_file(client, DEFAULT_USER, file)\\n assert response.status == \\\"201 CREATED\\\"\\n assert response.json[\\\"filetype\\\"] == \\\"DOCUMENT\\\"\\n assert response.json[\\\"upload_name\\\"] == file.filename\\n assert response.json[\\\"uploaded_by\\\"][\\\"id\\\"] == 1\\n assert response.json[\\\"size\\\"] == len(file.contents)\\n assert response.json[\\\"hash\\\"] == hashlib.md5(file.contents).hexdigest()\\n\\n # Access the file via URL\\n response_get = client.get(response.json[\\\"url\\\"])\\n assert response_get.status == \\\"200 OK\\\"\\n assert list(response_get.response)[0] == file.contents\",\n \"def test_02_not_exist(self):\\n with self.assertLogs(\\\"borg\\\", \\\"WARNING\\\") as logger:\\n self.api.extract(self.archive, self.file_3)\\n message = logger.records[0].getMessage()\\n self.assertRegex(\\n message,\\n r\\\".*?file_3.*never\\\",\\n \\\"Warning not logged for bad path\\\",\\n )\",\n \"def test_fetch_or_create_requires_file_type():\\n pytest.raises(ValueError, media.fetch_or_create_media_item, b'spam')\",\n \"def test_upload_duplicate_file_upload(self):\\n url = reverse('content-list')\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'name': 'Content File',\\n 'description': 'File 1',\\n 'content_file': content_file,\\n 'updated_time': date.today(),\\n 'creators': [],\\n 'coverage': '',\\n 'subjects': [],\\n 'keywords': [],\\n 'workareas': [],\\n 'language': '',\\n 'cataloger': ''\\n }\\n response = self.client.post(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n self.assertEqual(Content.objects.count(), 1)\\n first_content = Content.objects.first()\\n\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file 2.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'name': 'Content File 2',\\n 'description': 'File 2',\\n 'content_file': content_file,\\n 'updated_time': date.today(),\\n 'creators': [],\\n 'coverage': '',\\n 'subjects': [],\\n 'keywords': [],\\n 'workareas': [],\\n 'language': '',\\n 'cataloger': ''\\n }\\n response = self.client.post(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n self.assertEqual(Content.objects.count(), 2)\\n second_content = Content.objects.filter(name='Content File 2').first()\\n\\n # Duplicate File.\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'content_file': content_file,\\n }\\n\\n url = reverse('content-detail', args=[second_content.pk])\\n response = self.client.patch(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_409_CONFLICT)\\n response_payload = json.loads(response.content.decode(\\\"utf-8\\\"))\\n self.assertEqual(response_payload['result'], 'error')\\n self.assertEqual(response_payload['error'], 'DUPLICATE_FILE_UPLOADED')\\n self.assertRegex(\\n response_payload['existing_content']['content_url'],\\n '%s$' % reverse('content-detail', args=[first_content.pk])\\n )\\n self.assertRegex(\\n response_payload['existing_content']['file_url'],\\n '%s$' % first_content.content_file.url\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.676866","0.66889274","0.65706724","0.64305943","0.640982","0.639273","0.6387645","0.63190717","0.62991345","0.6276941","0.62692577","0.62305576","0.6219944","0.6209555","0.6150867","0.61412674","0.6103418","0.6050772","0.602671","0.6003001","0.5965992","0.5957162","0.5927887","0.59203917","0.5907153","0.5901281","0.58993","0.5894678","0.5893892","0.58770597","0.5861593","0.58523667","0.5843627","0.58299345","0.5806146","0.5802926","0.5792778","0.57883286","0.5777032","0.5757101","0.5753552","0.57466847","0.57324946","0.5732421","0.57222736","0.5718956","0.57118666","0.56997687","0.56993586","0.5687501","0.5674837","0.56715345","0.56701803","0.56585056","0.5657988","0.56442446","0.56333023","0.56258965","0.5625434","0.5622874","0.56117594","0.56110394","0.5610739","0.5608589","0.5598509","0.5591321","0.5590039","0.55722636","0.5569664","0.5567644","0.5565744","0.55556333","0.553414","0.55290955","0.5519022","0.55059695","0.5505539","0.5501068","0.54968435","0.54907215","0.54862374","0.5478412","0.5476316","0.5472193","0.5431098","0.5427","0.54129606","0.5409981","0.54095435","0.5400257","0.5397705","0.5397581","0.53942615","0.5393204","0.53929067","0.5392774","0.53880394","0.5387326","0.5386179","0.53848416"],"string":"[\n \"0.676866\",\n \"0.66889274\",\n \"0.65706724\",\n \"0.64305943\",\n \"0.640982\",\n \"0.639273\",\n \"0.6387645\",\n \"0.63190717\",\n \"0.62991345\",\n \"0.6276941\",\n \"0.62692577\",\n \"0.62305576\",\n \"0.6219944\",\n \"0.6209555\",\n \"0.6150867\",\n \"0.61412674\",\n \"0.6103418\",\n \"0.6050772\",\n \"0.602671\",\n \"0.6003001\",\n \"0.5965992\",\n \"0.5957162\",\n \"0.5927887\",\n \"0.59203917\",\n \"0.5907153\",\n \"0.5901281\",\n \"0.58993\",\n \"0.5894678\",\n \"0.5893892\",\n \"0.58770597\",\n \"0.5861593\",\n \"0.58523667\",\n \"0.5843627\",\n \"0.58299345\",\n \"0.5806146\",\n \"0.5802926\",\n \"0.5792778\",\n \"0.57883286\",\n \"0.5777032\",\n \"0.5757101\",\n \"0.5753552\",\n \"0.57466847\",\n \"0.57324946\",\n \"0.5732421\",\n \"0.57222736\",\n \"0.5718956\",\n \"0.57118666\",\n \"0.56997687\",\n \"0.56993586\",\n \"0.5687501\",\n \"0.5674837\",\n \"0.56715345\",\n \"0.56701803\",\n \"0.56585056\",\n \"0.5657988\",\n \"0.56442446\",\n \"0.56333023\",\n \"0.56258965\",\n \"0.5625434\",\n \"0.5622874\",\n \"0.56117594\",\n \"0.56110394\",\n \"0.5610739\",\n \"0.5608589\",\n \"0.5598509\",\n \"0.5591321\",\n \"0.5590039\",\n \"0.55722636\",\n \"0.5569664\",\n \"0.5567644\",\n \"0.5565744\",\n \"0.55556333\",\n \"0.553414\",\n \"0.55290955\",\n \"0.5519022\",\n \"0.55059695\",\n \"0.5505539\",\n \"0.5501068\",\n \"0.54968435\",\n \"0.54907215\",\n \"0.54862374\",\n \"0.5478412\",\n \"0.5476316\",\n \"0.5472193\",\n \"0.5431098\",\n \"0.5427\",\n \"0.54129606\",\n \"0.5409981\",\n \"0.54095435\",\n \"0.5400257\",\n \"0.5397705\",\n \"0.5397581\",\n \"0.53942615\",\n \"0.5393204\",\n \"0.53929067\",\n \"0.5392774\",\n \"0.53880394\",\n \"0.5387326\",\n \"0.5386179\",\n \"0.53848416\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81712246"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":149,"numItemsPerPage":100,"numTotalItems":95772,"offset":14900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjA3NzE4NCwianRpIjoiZDExNmIzMDUtYTAxMy00YzMxLTgyOWUtMjM4OWY5YzY3MDFmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwODA3ODQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.sSNeZpo2vxQMIHCtB-5fj_I7bptOWomI53YMIQiIBqQsh6xdnruobUh5Ue80ykUZgju8_xlaOxM_2qfXlAktBg","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 |
|---|---|---|---|---|---|---|
Return True if privacy mode is active. | def is_in_privacy_mode(self) -> bool:
return self.data[Attribute.CAMERA_PRIVACY] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_aprentice(self):\n return self.user_profile_status == self.APPRENTICE",
"def private(self) -> bool:\n return pulumi.get(self, \"private\")",
"def authorized(self) -> bool:\n\n return (\n self.activated\n or self.on_screen\n or self.on_file\n ... | [
"0.6490553",
"0.6430658",
"0.64281636",
"0.638807",
"0.63529915",
"0.63529915",
"0.63529915",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
"0.6319265",
... | 0.86465675 | 0 |
Return True if camera is online. | def is_online(self) -> bool:
return self.data[Attribute.ONLINE] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def online(self):\n return False",
"def online(self) -> bool:\n\t\treturn self._raw_result['data']['online']",
"def is_connected(self):\n if self.server: return True\n return False",
"def verify_network_stream(self, link):\n\n cap = cv2.VideoCapture(link)\n if not cap.isOpe... | [
"0.7281513",
"0.71982807",
"0.70609236",
"0.702339",
"0.68787086",
"0.6874467",
"0.68721676",
"0.6796776",
"0.6791388",
"0.6686526",
"0.6682214",
"0.66420615",
"0.6639014",
"0.66362894",
"0.6620995",
"0.6608166",
"0.6605227",
"0.65999436",
"0.65874165",
"0.6573971",
"0.657156... | 0.7001187 | 4 |
Camera's wireless signal strength. | def wireless_signal_strength(self) -> int:
return self.data[Attribute.WIRELESS_SIGNAL_STRENGTH] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetCurrentSignalStrength(self, iwconfig=None):\n try:\n strength = int(self.wifi.GetSignalStrength(iwconfig))\n except:\n strength = 0\n return strength",
"def strength(self) -> float:\n ...",
"def sensorStrength(self):\n # TODO: also return type of sens... | [
"0.7031714",
"0.6994032",
"0.67061955",
"0.6649575",
"0.64029896",
"0.63176775",
"0.6286045",
"0.61830634",
"0.61751175",
"0.6073168",
"0.6055971",
"0.6050908",
"0.5939323",
"0.59383196",
"0.59170634",
"0.5894795",
"0.5874123",
"0.5873845",
"0.58524925",
"0.5838355",
"0.58368... | 0.8146503 | 0 |
Request a new thumbnail for the camera. | async def request_thumbnail(self) -> None:
await self.vivintskyapi.request_camera_thumbnail(
self.alarm_panel.id, self.alarm_panel.partition_id, self.id
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def thumbnail(self, thumbnail):\n self._thumbnail = thumbnail",
"def set_thumbnail(self, **kwargs):\n self.thumbnail_url = kwargs.get('url')",
"def GetThumbnail(self, type, maxsize): # real signature unknown; restored from __doc__\n pass",
"def thumbnail(self, width, height, path, **kwargs):... | [
"0.6508987",
"0.6476307",
"0.6436708",
"0.6407975",
"0.6400892",
"0.63398254",
"0.6334903",
"0.62879765",
"0.6212906",
"0.62107",
"0.6181639",
"0.60656625",
"0.6060553",
"0.60002947",
"0.5988563",
"0.59305435",
"0.59282035",
"0.5918891",
"0.5912826",
"0.59014267",
"0.58705056... | 0.821199 | 0 |
Return the latest camera thumbnail URL. | async def get_thumbnail_url(self) -> str:
# Sometimes this date field comes back with a "Z" at the end
# and sometimes it doesn't, so let's just safely remove it.
camera_thumbnail_date = datetime.strptime(
self.data[Attribute.CAMERA_THUMBNAIL_DATE].replace("Z", ""),
"%Y-%... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_camera_image_url(self) -> str:\n return self.camera_info[\"cover_path\"]",
"def get_thumbnail_url(self):\n if not self.id_video or not self.original_url or not self.xml_response:\n return ''\n return self.xml_response.find('framegrab_url').text",
"def get_thumbnail_url(... | [
"0.7910915",
"0.7836688",
"0.7768984",
"0.7726674",
"0.7721377",
"0.7670391",
"0.7665452",
"0.76559013",
"0.76526964",
"0.75800186",
"0.75523674",
"0.74557847",
"0.7306187",
"0.7166377",
"0.70493877",
"0.70226187",
"0.7014065",
"0.6992731",
"0.6937823",
"0.6921818",
"0.691271... | 0.8384189 | 0 |
Return the rtsp URL for the camera. | async def get_rtsp_url(self, internal: bool = False, hd: bool = False) -> str:
credentials = await self.alarm_panel.get_panel_credentials()
url = self.data[f"c{'i' if internal else 'e'}u{'' if hd else 's'}"][0]
return f"{url[:7]}{credentials[PanelCredentialAttribute.NAME]}:{credentials[PanelCred... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rtsp_stream_url(self) -> str:\n return self.properties.get(MessageField.RTSP_STREAM_URL.value)",
"async def get_direct_rtsp_url(self, hd: bool = False) -> str:\n return (\n f\"rtsp://{self.data[Attribute.USERNAME]}:{self.data[Attribute.PASSWORD]}@{self.ip_address}:{self.data[Attribut... | [
"0.79006195",
"0.689613",
"0.6728681",
"0.63702255",
"0.6178544",
"0.61279833",
"0.6083949",
"0.6080557",
"0.5965044",
"0.575143",
"0.5714059",
"0.56403685",
"0.5609133",
"0.55965275",
"0.5580069",
"0.55688417",
"0.5547634",
"0.55053115",
"0.55026656",
"0.5496402",
"0.5487867... | 0.6958374 | 1 |
Return the direct rtsp url for this camera, in HD if requested, if any. | async def get_direct_rtsp_url(self, hd: bool = False) -> str:
return (
f"rtsp://{self.data[Attribute.USERNAME]}:{self.data[Attribute.PASSWORD]}@{self.ip_address}:{self.data[Attribute.CAMERA_IP_PORT]}/{self.data[Attribute.CAMERA_DIRECT_STREAM_PATH if hd else Attribute.CAMERA_DIRECT_STREAM_PATH_STANDA... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rtsp_stream_url(self) -> str:\n return self.properties.get(MessageField.RTSP_STREAM_URL.value)",
"async def get_rtsp_url(self, internal: bool = False, hd: bool = False) -> str:\n credentials = await self.alarm_panel.get_panel_credentials()\n url = self.data[f\"c{'i' if internal else 'e'}... | [
"0.72032493",
"0.68680316",
"0.61045647",
"0.60359323",
"0.6011566",
"0.5905714",
"0.5760486",
"0.56779563",
"0.5652585",
"0.56033957",
"0.55442023",
"0.5505961",
"0.5492464",
"0.5490018",
"0.5473845",
"0.5441808",
"0.5429904",
"0.54259044",
"0.54249716",
"0.53899205",
"0.534... | 0.76972985 | 0 |
Handle a pubnub message addressed to this camera. | def handle_pubnub_message(self, message: dict) -> None:
super().handle_pubnub_message(message)
event = None
if message.get(Attribute.CAMERA_THUMBNAIL_DATE):
event = THUMBNAIL_READY
elif message.get(Attribute.DING_DONG):
event = DOORBELL_DING
elif message... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _handle_message(self, msg):\n self.event('message', msg)",
"def handle(self, message):\n print(\"You received a message:\")\n print(message)\n # Overwrite this function to do something with the message!",
"def received_message(self, m):\n self.receiver.handle_message(m)",... | [
"0.7261088",
"0.6893499",
"0.689115",
"0.67964315",
"0.6787809",
"0.6784308",
"0.67636156",
"0.6758844",
"0.67415947",
"0.6718704",
"0.67065203",
"0.66710377",
"0.66152173",
"0.6600205",
"0.65741396",
"0.65554744",
"0.6550199",
"0.6538151",
"0.6529823",
"0.65268636",
"0.65175... | 0.7852151 | 0 |
Return all .js files in the project root folder The project file is not included. | def get_all_js_files(self, root):
res = []
for fname in os.listdir(root):
mo = re.match(r'(\w+)\.js$', fname)
if mo:
res.append({
'name': mo.group(1),
'src': file_contents(os.path.join(root, mo.group()))
})
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect_project_source_files():\n source_files = glob.glob(PROJECT_SOURCE_FILES_FOLDER + '/**/*.py', recursive=True)\n # Insert root main.py at the beginning.\n source_files.insert(0, os.path.join(PROJECT_ROOT_FOLDER, 'main.py'))\n return list(map(lambda path: posixpath.join(*path.split('\\\\')), s... | [
"0.7026188",
"0.663383",
"0.66265917",
"0.6534242",
"0.6475579",
"0.6318005",
"0.62322176",
"0.62103647",
"0.62050897",
"0.61848503",
"0.618283",
"0.6143342",
"0.6108959",
"0.60054374",
"0.59731764",
"0.59707844",
"0.5969685",
"0.5961811",
"0.59470624",
"0.59345275",
"0.59284... | 0.7880714 | 0 |
Load data from csv. | def load_data():
# Load data
X_train = pd.read_csv(
"data/trimmed_merged_no_track_id_or_session_id.csv"
) # load data from csv
drop_columns = ["mode"] # drop unimportant columns
X_train.drop(labels=drop_columns, axis=1, inplace=True)
y_train = X_train["skip"]
X_train.drop(labels=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_csv():",
"def loadCSV(input_file):",
"def read_csv(self, csv_input):\n # https://stackoverflow.com/a/45063514\n dtypes = {\n 'lat': 'U',\n 'long': 'U'\n }\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''], dtype=dtypes)\n\n ... | [
"0.78015375",
"0.7773901",
"0.75440395",
"0.7447164",
"0.7401897",
"0.7377548",
"0.7352664",
"0.7304863",
"0.7292403",
"0.7289765",
"0.72523564",
"0.7250736",
"0.7196531",
"0.7195724",
"0.7157978",
"0.7074943",
"0.7060418",
"0.70362383",
"0.7036197",
"0.7026979",
"0.70115405"... | 0.0 | -1 |
Returns accuracy on test set. | def get_test_accuracy(model, X_test, y_test):
# Make predictions - test accuracy
test_pred = model.predict(X_test)
score = accuracy_score(test_pred, y_test)
print("Test Accuracy:", score)
return test_pred | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAccuracy(self):\n\t\tcorrect = (self.testing[self.classLabel]==self.bestLabel).sum()\n\t\tself.accuracy = (correct/float(len(self.testing))) * 100.0",
"def get_accuracy(self) -> float:\n self.network.load_data()\n self.network.train()\n\n n = len(self.network.y_test)\n correct ... | [
"0.8180938",
"0.81681293",
"0.8084099",
"0.8014265",
"0.7994126",
"0.79616797",
"0.7932465",
"0.7924577",
"0.7861055",
"0.7811287",
"0.7755529",
"0.77538913",
"0.77185225",
"0.77163464",
"0.7673537",
"0.7650585",
"0.76219594",
"0.75354797",
"0.7525053",
"0.75063103",
"0.74756... | 0.7460319 | 21 |
Displays normalized confusion matrix. | def create_confusion_matrix(model, X_test, y_test, test_pred, title):
# Confusion matrix
class_names = ["Skipped", "Not Skipped"]
print("Confusion Matrix: %s" % title)
print(confusion_matrix(y_test, test_pred))
disp = plot_confusion_matrix(
model,
X_test,
y_test,
dis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def showConfusionMatrix(self): \r\n sn.heatmap(self.conf_matrix, annot=True)\r\n plt.plot( label=\"Accuracy\")\r\n plt.plot( label=\"Error\")\r\n plt.figtext(0,0,'Accuracy: {}\\nError: {}\\nRecall: {}\\nPrecision: {}'.format(self.accuracy,\r\n ... | [
"0.769349",
"0.76869637",
"0.751314",
"0.74637914",
"0.7293348",
"0.7269612",
"0.72648704",
"0.72433674",
"0.72433656",
"0.7228648",
"0.7225077",
"0.72247624",
"0.7224549",
"0.7219793",
"0.7218805",
"0.72116745",
"0.71959615",
"0.71941775",
"0.71938807",
"0.71917826",
"0.7187... | 0.0 | -1 |
if we've got a cropping annotation for the given fieldname and scale, set self._rescale to False, to prevent plone.app.imaging traverser to overwrite our cropped scale since the self.modified() method does not know about the currently requested scale name, we need to use the _rescale property | def _need_rescale(self, fieldname, scale):
cropped = IAnnotations(self.context).get(PAI_STORAGE_KEY)
if cropped and '%s_%s' % (fieldname, scale) in cropped:
self._allow_rescale = False
else:
self._allow_rescale = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _crop(self, fieldname, scale, box):\n croputils = IImageCroppingUtils(self.context)\n data = croputils.get_image_data(fieldname)\n\n original_file = StringIO(data)\n image = PIL.Image.open(original_file)\n image_format = image.format or self.DEFAULT_FORMAT\n\n cropped_... | [
"0.7229034",
"0.6486539",
"0.63547015",
"0.6094969",
"0.5937726",
"0.5919167",
"0.5832441",
"0.5809148",
"0.57294387",
"0.56831604",
"0.5682105",
"0.55893314",
"0.557489",
"0.55414176",
"0.55127174",
"0.5507968",
"0.5507968",
"0.5496308",
"0.54725057",
"0.54692656",
"0.546926... | 0.87613535 | 0 |
we overwrite the default method that would return the modification time of the context, to return a way back modification time in case the currently requested scale is a cropped scale. (so plone.scale does not create a new scale w/o cropping information | def modified(self):
if self._allow_rescale:
return super(ImageScaling, self).modified()
else:
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modified(self):\n if self._allow_rescale:\n return super(NamedfileImageScaling, self).modified()\n else:\n return 1",
"def get_modified_time(self, name):\n raise NotImplementedError(\n \"subclasses of Storage must provide a get_modified_ti... | [
"0.6460381",
"0.56831473",
"0.5679432",
"0.5437756",
"0.53410375",
"0.5333899",
"0.5274236",
"0.5244045",
"0.51951635",
"0.51917946",
"0.516668",
"0.51533455",
"0.51174325",
"0.5113334",
"0.509966",
"0.5089414",
"0.5071341",
"0.5068429",
"0.5064854",
"0.50535154",
"0.50339556... | 0.6318901 | 1 |
we overwrite the default method that would return the modification time of the context, to return a way back modification time in case the currently requested scale is a cropped scale. (so plone.scale does not create a new scale w/o cropping information | def modified(self):
if self._allow_rescale:
return super(NamedfileImageScaling, self).modified()
else:
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modified(self):\n if self._allow_rescale:\n return super(ImageScaling, self).modified()\n else:\n return 1",
"def get_modified_time(self, name):\n raise NotImplementedError(\n \"subclasses of Storage must provide a get_modified_time() method\"\n )"... | [
"0.63181275",
"0.5682004",
"0.56780356",
"0.5436276",
"0.53405464",
"0.53323674",
"0.52736956",
"0.5243496",
"0.5193847",
"0.5192097",
"0.51653427",
"0.51530874",
"0.51168525",
"0.5112648",
"0.50986654",
"0.5087256",
"0.5069669",
"0.50668234",
"0.5063405",
"0.50531495",
"0.50... | 0.6459762 | 0 |
Do NOT initialize directly, use ChartManager.get_chart method | def __init__(
self,
parent: Widget,
chart_manager,
pair_color: LinkColor,
gran_color: LinkColor,
quote_kind_color: LinkColor,
flags: bool,
width: int,
height: int,
):
Initializer.initialize(parent.winfo_toplevel())
Frame.__init_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _charts(self):\n # lazy instantiation here to avoid creating the charts object unless needed.\n if self.__charts is None:\n self.__charts = Charts(self)\n self.AddObserver(\"StartEvent\", partial(try_callback, self._before_render_event))\n return self.__charts",
"de... | [
"0.73053795",
"0.71199065",
"0.6492899",
"0.6401118",
"0.6342331",
"0.6342331",
"0.6255669",
"0.62405497",
"0.6101077",
"0.60051626",
"0.59999484",
"0.598143",
"0.59550667",
"0.5876069",
"0.58671856",
"0.58526546",
"0.58114016",
"0.5807358",
"0.5770516",
"0.57400477",
"0.5738... | 0.0 | -1 |
Set the pair and reload data if its new. | def set_pair(self, pair: Pair):
if pair != self.pair:
self.pair = pair
self.load_candles() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_pair(self, pair: StudentPair) -> None:\n self._edit_pair = pair\n self.line_edit_title.setText(str(self._edit_pair[\"title\"]))\n self.line_edit_lecturer.setText(str(self._edit_pair[\"lecturer\"]))\n self.combo_box_type.setCurrentText(str(self._edit_pair[\"type\"]))\n sel... | [
"0.6273834",
"0.62562305",
"0.62263435",
"0.6111727",
"0.5962303",
"0.59485316",
"0.59118456",
"0.5875051",
"0.58309335",
"0.58295953",
"0.58218694",
"0.58065355",
"0.5751621",
"0.5708703",
"0.5699012",
"0.56972504",
"0.5696664",
"0.5620271",
"0.55983835",
"0.5595588",
"0.559... | 0.76077706 | 0 |
Set the granularity and reload data if its new. | def set_gran(self, gran: Gran):
if gran != self.gran:
self.gran = gran
self.load_candles() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _resolution_changed(self):\n self.reinitialiseData()",
"def reload(self):\n self._populate(self.hierarchy[-1])",
"def refresh(self) -> None:\n self.data = {}\n self.load_settings_file(self.default_settings_path / \"settings.yaml\", file_key=\"internal\")\n self.load_syste... | [
"0.5652916",
"0.5497101",
"0.54564565",
"0.5395625",
"0.53053933",
"0.5266499",
"0.51898026",
"0.51679134",
"0.5159813",
"0.5147838",
"0.51331675",
"0.5116666",
"0.5112451",
"0.50447494",
"0.5042116",
"0.5025085",
"0.50143266",
"0.50115645",
"0.49833155",
"0.4946549",
"0.4946... | 0.61495394 | 0 |
Set the quote kind and reload data if its new. | def set_quote_kind(self, quote_kind: QuoteKind):
if quote_kind != self.quote_kind:
self.quote_kind = quote_kind
if self.geo is None:
self.load_candles()
else:
self.geo.update(quote_kind=quote_kind)
self.chart.redraw(self.geo) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quotes(self, quotes):\n\n self._quotes = quotes",
"def quote_type(self, quote_type):\n allowed_values = [None,\"Price\", \"Spread\", \"Rate\", \"LogNormalVol\", \"NormalVol\", \"ParSpread\", \"IsdaSpread\", \"Upfront\", \"Index\", \"Ratio\", \"Delta\", \"PoolFactor\"] # noqa: E501\n if ... | [
"0.61072516",
"0.60456073",
"0.5505138",
"0.5404351",
"0.5160823",
"0.51101786",
"0.50316006",
"0.5006308",
"0.49826854",
"0.4947969",
"0.4940617",
"0.49285766",
"0.492396",
"0.49115217",
"0.49072197",
"0.49046135",
"0.48957124",
"0.48728767",
"0.48653087",
"0.48302495",
"0.4... | 0.8109521 | 0 |
Get start time of a request | def before_request():
request._prometheus_metrics_request_start_time = time.time() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_time(self):\n return self._get(\"start_time\")",
"def start_time(self) -> str:\n return pulumi.get(self, \"start_time\")",
"def start_time(self) -> str:\n return pulumi.get(self, \"start_time\")",
"def start_time(self):\n return self._meta['start_time']",
"def start_ti... | [
"0.7987992",
"0.77852345",
"0.77852345",
"0.7713794",
"0.76825255",
"0.7674555",
"0.76548463",
"0.76548463",
"0.76548463",
"0.76548463",
"0.76548463",
"0.76548463",
"0.76548463",
"0.76548463",
"0.7601079",
"0.7601079",
"0.75667506",
"0.7550781",
"0.7533342",
"0.75080675",
"0.... | 0.0 | -1 |
Register Prometheus metrics after each request | def after_request(response):
request_latency = time.time() - request._prometheus_metrics_request_start_time
METRICS_REQUEST_LATENCY.labels(request.method, request.path).observe(
request_latency
)
METRICS_REQUEST_COUNT.labels(
request.method, request.path, response.status_code
).inc()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def before_request():\n request._prometheus_metrics_request_start_time = default_timer()",
"def before_request():\n request._prometheus_metrics_request_start_time = time.time()",
"def register_prometheus(app: Flask, registry=REGISTRY) -> None:\n\n def after(response: Response) -> Response:\n en... | [
"0.7261535",
"0.7187321",
"0.71751827",
"0.69744253",
"0.67358434",
"0.6703973",
"0.65655565",
"0.61613506",
"0.60699874",
"0.59922606",
"0.5975581",
"0.5957732",
"0.59373957",
"0.59337866",
"0.59261703",
"0.5708112",
"0.56908506",
"0.56708866",
"0.5616908",
"0.5616374",
"0.5... | 0.65932864 | 6 |
Envia un mensaje de registro al servicio de registro usando una performativa Request y una accion Register del servicio de directorio | def register_message():
global mss_cnt
gmess = Graph()
# Construimos el mensaje de registro
gmess.bind('foaf', FOAF)
gmess.bind('dso', DSO)
reg_obj = agn[InfoAgent.name+'-Register']
gmess.add((reg_obj, RDF.type, DSO.Register))
gmess.add((reg_obj, DSO.Uri, InfoAgent.uri))
gmess.add(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_message():\n\n logger.info('Nos registramos')\n\n gr = register_agent(AgGestordeTransporte, DirectoryAgent, AgGestordeTransporte.uri, get_count())\n return gr",
"def register_message():\n global mss_cnt\n\n gmess = Graph()\n\n # Construimos el mensaje de registro\n gmess.bind('f... | [
"0.6691251",
"0.6527108",
"0.6477159",
"0.6445688",
"0.59789985",
"0.5869981",
"0.58134043",
"0.57443154",
"0.5697601",
"0.56877166",
"0.5673448",
"0.5609995",
"0.56073344",
"0.5529587",
"0.5469366",
"0.54542726",
"0.5444462",
"0.54406416",
"0.54077536",
"0.5405717",
"0.53978... | 0.6426234 | 4 |
Permite la comunicacion con el agente via un navegador via un formulario | def browser_iface():
return 'Nothing to see here' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def registrarAgenteJogador(self):\r\n return",
"def registrarAgenteJogador(self):\r\n return 1",
"def comunicacion():\n global dsgraph\n global mss_cnt\n\n #Extraemos el mensaje y creamos un grafo con el\n message= request.args['content']\n gm = Graph()\n gm.parse(data=message)\... | [
"0.6522337",
"0.64792037",
"0.6301096",
"0.6174628",
"0.60004866",
"0.59829485",
"0.58120006",
"0.58120006",
"0.57704735",
"0.5689164",
"0.56864554",
"0.56029874",
"0.5602099",
"0.5598478",
"0.5560209",
"0.55412084",
"0.5537694",
"0.5468752",
"0.54232013",
"0.54169464",
"0.54... | 0.0 | -1 |
Entrypoint que para el agente | def stop():
tidyup()
shutdown_server()
return "Parando Servidor" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(self):\r\n pass",
"def entry_point():",
"def entry_point():",
"def entry_point():",
"def main(self) -> None:\n pass",
"def launch(self):",
"def main(self):",
"def run(self):\n \n pass",
"def run(self):\n pass",
"def run(self):\n pass",
"def run(sel... | [
"0.7666677",
"0.73571783",
"0.73571783",
"0.73571783",
"0.7303973",
"0.7302566",
"0.72822535",
"0.7276224",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",
"0.7247397",... | 0.0 | -1 |
Entrypoint de comunicacion del agente Simplementet retorna un objeto fijo que representa una respuesta a una busqueda de hotel Asumimos que se reciben siempre acciones que se refieren a lo que puede hacer el agente (buscar con ciertas restricciones, reservar) Las acciones se mandan siempre con un Request Prodriamos res... | def comunicacion():
global dsgraph
global mss_cnt
#Extraemos el mensaje y creamos un grafo con el
message= request.args['content']
gm = Graph()
gm.parse(data=message)
msgdic = get_message_properties(gm)
# Comprobamos que sea un mensaje FIPA ACL
if msgdic is None:
# Si no e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n params = demisto.params()\n service_principal = params.get('credentials', {}).get('identifier')\n secret = params.get('credentials', {}).get('password')\n\n # Remove trailing slash to prevent wrong URL path to service\n server_url = params['url'][:-1] if (params['url'] and params['url'... | [
"0.55482316",
"0.55241877",
"0.54317117",
"0.5350504",
"0.5336382",
"0.5291278",
"0.5161638",
"0.51271015",
"0.50222015",
"0.49934793",
"0.49824604",
"0.4941026",
"0.4907765",
"0.48800912",
"0.48760772",
"0.48596963",
"0.48327702",
"0.48133352",
"0.48021013",
"0.47898054",
"0... | 0.5444783 | 2 |
Acciones previas a parar el agente | def tidyup():
global cola1
cola1.put(0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onActionTaken(self, agent):\n\n pass",
"def transact(self):",
"def transact(self):",
"def step_forward(self):",
"def after_turn(self):\n pass",
"def prev_action(self):\n raise NotImplementedError",
"def onTrainStepTaken(self, agent):\n pass",
"def take_one_step(self):\n\t\... | [
"0.61127394",
"0.5894486",
"0.5894486",
"0.5766817",
"0.5702878",
"0.5650589",
"0.56006277",
"0.55813426",
"0.5556894",
"0.5546448",
"0.5490769",
"0.5456815",
"0.54158676",
"0.54030174",
"0.539905",
"0.53859925",
"0.53773886",
"0.5372",
"0.5372",
"0.5356669",
"0.53446186",
... | 0.0 | -1 |
Un comportamiento del agente | def agentbehavior1(cola):
# Registramos el agente
gr = register_message()
# Escuchando la cola hasta que llegue un 0
fin = False
while not fin:
while cola.empty():
pass
v = cola.get()
if v == 0:
fin = True
else:
print v
# Self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def registrarAgenteJogador(self):\r\n return 1",
"def _compute_aero_torque(self):\n pass",
"def get_effective_agent(self):\n raise Unimplemented()",
"def registrarAgenteJogador(self):\r\n return",
"def crier_ordres(self, personnage):\n adverse = self.adverse\n msg ... | [
"0.5817508",
"0.5582098",
"0.5572627",
"0.54882556",
"0.5440284",
"0.5395733",
"0.5393866",
"0.5352673",
"0.5327155",
"0.52700335",
"0.52678186",
"0.5248248",
"0.520054",
"0.5200132",
"0.5191209",
"0.51896375",
"0.51809883",
"0.518049",
"0.5152505",
"0.51451963",
"0.51447564"... | 0.5321401 | 10 |
Return a list of completion strings Simple completion based on pythonlike identifiers and whitespace | def get_completions(self, info):
items = []
if (info.line.strip().startswith(('import ', 'from ')) and
info.is_python_like):
items += module_completion(info.line, [info.filename])
elif info.obj:
base = info.obj
tokens = set(info.split_wo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def completenames(self, text, line, begidx, endidx):\n command = text\n if self.case_insensitive:\n command = text.lower()\n\n # Call super class method. Need to do it this way for Python 2 and 3 compatibility\n cmd_completion = cmd.Cmd.completenames(self, command)\n\n ... | [
"0.6619886",
"0.66005045",
"0.6515913",
"0.63659024",
"0.6294996",
"0.6283689",
"0.62707794",
"0.6202358",
"0.6130983",
"0.61280894",
"0.6114059",
"0.6094237",
"0.6084692",
"0.6065162",
"0.6063104",
"0.6040103",
"0.6009062",
"0.5986601",
"0.59493244",
"0.59490955",
"0.5941500... | 0.6904953 | 0 |
Find the definition for an object within a set of source code This is used to find the path of pythonlike modules (e.g. cython and enaml) for a goto definition | def get_definition(self, info):
token = info.obj
lines = info.lines
source_code = info.source_code
filename = info.filename
line_nr = None
if '.' in token:
token = token.split('.')[-1]
line_nr = get_definition_with_regex(source_code, token,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findsource(object):\r\n file = getsourcefile(object)\r\n if not file:\r\n raise IOError('source code not available')\r\n module = getmodule(object, file)\r\n if module:\r\n lines = linecache.getlines(file, module.__dict__)\r\n else:\r\n lines = linecache.getlines(file)\r\n ... | [
"0.6904011",
"0.6001746",
"0.59132284",
"0.58806264",
"0.5847208",
"0.58444947",
"0.5813577",
"0.5789314",
"0.567056",
"0.5662456",
"0.5642679",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56179917",
"0.56... | 0.639387 | 1 |
Locate a module path based on an import line in an pythonlike file import_line is the line of source code containing the import alt_path specifies an alternate base path for the module stop_token specifies the desired name to stop on This is used to a find the path to pythonlike modules (e.g. cython and enaml) for a go... | def python_like_mod_finder(import_line, alt_path=None,
stop_token=None):
if stop_token and '.' in stop_token:
stop_token = stop_token.split('.')[-1]
tokens = re.split(r'\W', import_line)
if tokens[0] in ['from', 'import']:
# find the base location
tr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moduleCompletion(line):\n def tryImport(mod, only_modules=False):\n def isImportable(module, attr):\n if only_modules:\n return inspect.ismodule(getattr(module, attr))\n else:\n return not(attr[:2] == '__' and attr[-2:] == '__')\n try:\n ... | [
"0.64819837",
"0.6139575",
"0.6079234",
"0.5968648",
"0.5918364",
"0.5828221",
"0.55816394",
"0.5489672",
"0.544243",
"0.5438753",
"0.5432876",
"0.5428505",
"0.5426799",
"0.542341",
"0.53607404",
"0.5345444",
"0.5345008",
"0.53337634",
"0.5321171",
"0.5308447",
"0.5307642",
... | 0.811714 | 0 |
Find the definition of an object within a source closest to a given line | def get_definition_with_regex(source, token, start_line=-1):
if not token:
return None
if DEBUG_EDITOR:
t0 = time.time()
patterns = [ # python / cython keyword definitions
'^c?import.*\W{0}{1}',
'from.*\W{0}\W.*c?import ',
'from .* c?i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_relevant_line(self):\n # () -> (Phi.Line)\n line_name = self._get_line_name()\n print(\"looking for \"+str(line_name))\n return Phi.findLine(line_name)",
"def findsource(object):\r\n file = getsourcefile(object)\r\n if not file:\r\n raise IOError('source code not... | [
"0.6721103",
"0.6425865",
"0.594324",
"0.5941179",
"0.59397006",
"0.59163904",
"0.5842844",
"0.58050025",
"0.5782345",
"0.5758031",
"0.5743304",
"0.5533894",
"0.5482378",
"0.54344255",
"0.5429776",
"0.54285425",
"0.5424473",
"0.54120266",
"0.5374897",
"0.5373373",
"0.5371944"... | 0.66474533 | 1 |
Return a list of all pythonlike extensions | def python_like_exts():
exts = []
for lang in sourcecode.PYTHON_LIKE_LANGUAGES:
exts.extend(list(sourcecode.ALL_LANGUAGES[lang]))
return ['.' + ext for ext in exts] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extensions():\n\n pass",
"def all_editable_exts():\r\n exts = []\r\n for (language, extensions) in sourcecode.ALL_LANGUAGES.items():\r\n exts.extend(list(extensions))\r\n return ['.' + ext for ext in exts]",
"def get_loaded_extensions():\n raise NotImplementedError()",
"def get_supp... | [
"0.7843823",
"0.77088356",
"0.7419539",
"0.7320552",
"0.714457",
"0.70847607",
"0.70469284",
"0.7008349",
"0.6991765",
"0.6977103",
"0.69406205",
"0.69406205",
"0.6892772",
"0.68143374",
"0.6790699",
"0.67160887",
"0.6696911",
"0.6656253",
"0.665139",
"0.6639324",
"0.66269875... | 0.86687607 | 0 |
Return a list of all editable extensions | def all_editable_exts():
exts = []
for (language, extensions) in sourcecode.ALL_LANGUAGES.items():
exts.extend(list(extensions))
return ['.' + ext for ext in exts] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_extensions(self, **_params):\r\n return self.get(self.extensions_path, params=_params)",
"def extensions(self):\n return list(self._list(extension.Extension, paginated=False))",
"def extensions(self):\n return self.properties.get('extensions',\n E... | [
"0.6947238",
"0.6891938",
"0.6843709",
"0.6733824",
"0.66740257",
"0.66740257",
"0.6525709",
"0.63423103",
"0.6303439",
"0.62761074",
"0.6270803",
"0.624791",
"0.62398034",
"0.61868507",
"0.6155842",
"0.604202",
"0.6034626",
"0.6003988",
"0.5982591",
"0.5959434",
"0.59400076"... | 0.82879215 | 0 |
Perform completion of filesystem path. | def _complete_path(path=None):
if not path:
return _listdir('.')
dirname, rest = os.path.split(path)
tmp = dirname if dirname else '.'
res = [p for p in _listdir(tmp) if p.startswith(rest)]
# more than one match, or single match which does not exist (typo)
if len(res) > 1 or not o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def completion() -> None:",
"def finish_path(self, last_val=0):\n if self.adv_fn == \"simple\":\n self.rtg_finish_path()\n elif self.adv_fn == \"adv\":\n self.adv_path_finish()\n else:\n self.gae_path_finish(last_val)\n self.path_start_idx = self.ptr",... | [
"0.6521788",
"0.64863706",
"0.6081697",
"0.58194625",
"0.56301624",
"0.56139445",
"0.55415404",
"0.5516951",
"0.55002517",
"0.54814917",
"0.54412085",
"0.5404463",
"0.53575504",
"0.5347605",
"0.5322104",
"0.53052616",
"0.5289109",
"0.5282877",
"0.52677727",
"0.52631444",
"0.5... | 0.4867119 | 88 |
Tests the recipe for AWS>GCP disk copy. | def testRunRecipe(self):
warnings.filterwarnings(
action="ignore", message="unclosed", category=ResourceWarning)
# Load the recipe, set the arguments, and run
self.test_state.LoadRecipe(RECIPE, TEST_MODULES)
self.test_state.command_line_options = {
'aws_region': self.aws_region,
'gc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testFetchGs(self):\n # pylint: disable=unused-argument\n def _Fetch(_ctx, cmd, capture_output):\n # Touch file we tried to copy too.\n osutils.Touch(cmd[-1])\n\n self.gs_mock.AddCmdResult(\n ['cp', '-v', '--', partial_mock.Ignore(), partial_mock.Ignore()],\n side_effect=_Fetch)... | [
"0.64040744",
"0.6082685",
"0.57742435",
"0.5700392",
"0.5675572",
"0.56447816",
"0.56269157",
"0.5600139",
"0.55963385",
"0.55918896",
"0.55627227",
"0.5555737",
"0.55307716",
"0.5498585",
"0.54770666",
"0.5464479",
"0.54632187",
"0.5451441",
"0.54501855",
"0.54433",
"0.5442... | 0.70389867 | 0 |
Clean up after the test. | def tearDown(self):
log.warning("Cleaning up after test...")
# All of the following artefacts are created: AWSSnapshot, AWSS3Object,
# GCSObject, GCEImage, GCEDisk
for c in self.test_state.GetContainers(containers.AWSSnapshot):
self._removeAWSSnapshot(c.id)
for c in self.test_state.GetContaine... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n self.tmp.cleanup()",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",... | [
"0.8629318",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.8455004",
"0.84468895",
"0.8403054",
"0.8403054",
"0.8394459",
"0.8363534",
"0.8363534",
"0.8363534",
"0.8357604",
"0.8357604",
... | 0.0 | -1 |
Deletes an AWS EBS Snapshot with ID `id`. | def _removeAWSSnapshot(self, snap_id: str):
log.warning(f'Deleting AWS EBS Snapshot {snap_id}')
ec2_client = boto3.client('ec2', region_name=self.aws_region)
try:
ec2_client.delete_snapshot(SnapshotId=snap_id)
except Exception as error: # pylint: disable=broad-except
log.error(f'Failed to d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_snapshot(self, snapshot_id):\r\n\r\n self.iscsi_svc.deleteObject(id=snapshot_id)",
"def cli(env, snapshot_id):\n block_manager = SoftLayer.BlockStorageManager(env.client)\n deleted = block_manager.delete_snapshot(snapshot_id)\n\n if deleted:\n click.echo('Snapshot %s deleted' % ... | [
"0.78719264",
"0.7736575",
"0.7644279",
"0.75770277",
"0.736929",
"0.71240944",
"0.69008183",
"0.68352735",
"0.67959744",
"0.6736099",
"0.6706821",
"0.66655517",
"0.6488515",
"0.63967794",
"0.6360391",
"0.61587834",
"0.6127205",
"0.60837644",
"0.6061843",
"0.60463864",
"0.604... | 0.78926736 | 0 |
Deletes an S3 object at `path`. | def _removeAWSS3Object(self, path: str):
log.warning(f'Deleting AWS S3 object {path}')
bucket, key = storage_utils.SplitStoragePath(path)
s3_client = boto3.client('s3')
try:
s3_client.delete_object(Bucket=bucket, Key=key)
except Exception as error: # pylint: disable=broad-except
log.err... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_object(self, s3_path):\n logging.info(\"Deleting \\\"{}\\\" file from S3\".format(s3_path))\n bucket_name, key = S3Util.get_bucket_and_key(s3_path)\n self.s3_resource.ObjectSummary(bucket_name, key).delete()",
"def delete_object(self, object_path):\n bucket_name, save_path ... | [
"0.8589754",
"0.81548566",
"0.7849945",
"0.7838405",
"0.7520646",
"0.7260223",
"0.7242306",
"0.7200459",
"0.709181",
"0.7063145",
"0.7008641",
"0.6976428",
"0.6947444",
"0.6919413",
"0.69042146",
"0.6903383",
"0.69029564",
"0.6900946",
"0.6897261",
"0.68503684",
"0.685027",
... | 0.85541904 | 1 |
Delete a GCS object at `path`. | def _removeGCSObject(self, path: str):
log.warning(f'Deleting GCS object {path}')
try:
storage.GoogleCloudStorage(self.gcp_project_id).DeleteObject(path)
except Exception as error: # pylint: disable=broad-except
log.error(f'Failed to delete GCS Object {path}: {str(error)}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def rm(path: str):\n _ = path.strip('/').split('/')\n bucket = _[0]\n key = '/'.join(_[1:])\n if path.endswith('/'):\n key += '/'\n async with _create_client() as client:\n try:\n await client.delete_object(Bucket=bucket, Key=key)\n logger.info(f'Delete file... | [
"0.78189796",
"0.7468063",
"0.7263031",
"0.7235776",
"0.7158689",
"0.7100991",
"0.7084128",
"0.7063686",
"0.7039927",
"0.70041424",
"0.69738555",
"0.6940997",
"0.69260657",
"0.68535155",
"0.6739612",
"0.6701492",
"0.66956896",
"0.6692519",
"0.66546154",
"0.66297257",
"0.65918... | 0.8232839 | 0 |
Remove GCE Image with name `name`. | def _removeGCEImage(self, name: str):
log.warning(f'Deleting GCE Image {name}')
try:
compute.GoogleComputeImage(
self.gcp_project_id, self.gcp_zone, name
).Delete()
except Exception as error: # pylint: disable=broad-except
log.error(f'Failed to delete GCE Image {name}: {str(erro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_image(Name=None):\n pass",
"def del_image(self, name):\r\n if self.images is None or name not in self.images:\r\n return\r\n l = self.images\r\n self.images = None\r\n l.setdefault('/empties/', [])\r\n # push the number on the empties list\r\n l[... | [
"0.77334917",
"0.75917494",
"0.7531193",
"0.722961",
"0.7067009",
"0.69292396",
"0.68562293",
"0.6789977",
"0.67663544",
"0.6758139",
"0.6702444",
"0.66960543",
"0.66144276",
"0.6586997",
"0.6548269",
"0.6530188",
"0.64906603",
"0.6479747",
"0.6467619",
"0.6458344",
"0.645698... | 0.8452742 | 0 |
Remove the disk with name `name`. | def _removeGCEDisk(self, name: str):
log.warning(f'Deleting GCE Disk {name}')
try:
gce_disk_client = common.GoogleCloudComputeClient(
project_id=self.gcp_project_id).GceApi().disks()
gce_disk_client.delete(
project=self.gcp_project_id,
zone=self.gcp_zone,
disk... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rm(self, name: str) -> None:\n path = self.get_path(name)\n if os.path.exists(path):\n os.remove(path)",
"def vm_diskdelete(args):\n name = args.name\n diskname = args.diskname\n pool = args.pool\n config = Kconfig(client=args.client, debug=args.debug, region=args.region,... | [
"0.75362515",
"0.71105665",
"0.70604837",
"0.69789946",
"0.69264275",
"0.69136286",
"0.6910295",
"0.68948543",
"0.6887401",
"0.684326",
"0.67783725",
"0.6729041",
"0.67081046",
"0.66445243",
"0.6617731",
"0.65383613",
"0.6531545",
"0.65064126",
"0.64904535",
"0.6488289",
"0.6... | 0.76121897 | 0 |
Read project information to run e2e test. | def ReadProjectInfo() -> Dict[str, str]:
project_info_file = os.environ.get('PROJECT_INFO')
if project_info_file is None:
raise OSError('Error: please make sure that you defined the '
'"PROJECT_INFO" environment variable pointing '
'to your project settings.')
try:
json... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_read_project(self):\n pass",
"def test_read_project(self):\n pass",
"def test_get_project(self):\n pass",
"def test_project_detail(self):\n rv = self.app.get(\"/Assignment0\")\n self.assertIn(\"Assignment0\", rv.data)\n self.assertIn(\"2015-02-04 21:57:12.15... | [
"0.76227367",
"0.76227367",
"0.6705366",
"0.6565403",
"0.6225712",
"0.6197374",
"0.6003806",
"0.59884864",
"0.5940054",
"0.5864681",
"0.58614016",
"0.58614016",
"0.58614016",
"0.585416",
"0.583981",
"0.5817701",
"0.5744278",
"0.5716439",
"0.5716439",
"0.57119983",
"0.5707653"... | 0.6311821 | 4 |
Add blogpost to manuscript | def add_blogpost(manuscript, subject, url):
line_number = 0
with open(manuscript, "r") as file:
lines = file.readlines()
for line in lines:
if ("## ブロマガ全集" in line):
lines.insert(line_number + 2, f"- [{subject}]({url})\n")
with open(manuscript, "w") as file:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(blog, date):\n template = front_matter({\n \"title\": blog,\n \"date\": get_date(\"%Y-%m-%d %H:%M:%S %z\"),\n })\n new_blog(date + '-' + blog + '.markdown', template)",
"def add_blog(self, text):\n self.blog.add_blog(text)\n self.refre... | [
"0.6208906",
"0.6076283",
"0.59839743",
"0.59753376",
"0.5914017",
"0.5913528",
"0.58720726",
"0.58409494",
"0.5834559",
"0.582747",
"0.57706946",
"0.57343227",
"0.56686044",
"0.5657509",
"0.56546366",
"0.5640166",
"0.5577207",
"0.557562",
"0.557433",
"0.556912",
"0.55233467"... | 0.7508624 | 0 |
Update paleo channel blogposts. | def main():
# Pick files
token_file = os.path.abspath(TOKEN_FILE)
creds_file = os.path.abspath(CREDS_FILE)
manuscript_file = os.path.abspath(MANUSCRIPT_FILE)
# Authorize Gmail API
creds = gmail.authorize(SCOPES, token_file, creds_file)
# Build Gmail API
service = gmail.build_service(c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():\n update_all_posts()\n push_updates()",
"def updatePost(updatePostEvent):\n postId = updatePostEvent[\"data\"][\"post_id\"]\n with conn.cursor() as cur:\n for key, value in updatePostEvent[\"data\"].items():\n if key not in unableToUpdate:\n query = 'UPDATE... | [
"0.5982719",
"0.5916697",
"0.58925104",
"0.5890833",
"0.5881056",
"0.5785574",
"0.5755134",
"0.5648949",
"0.5598788",
"0.5589578",
"0.5584814",
"0.5547204",
"0.5526978",
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"0.5494415",
"0.5486405",
"0.54856944",
"0.5431346",
"0.5415686",
"0.5410492",
... | 0.5808636 | 5 |
Evalutes whether the passed dict matches the complex logic of the LogicNode | def eval_logic(self, checkDict):
result = True
#gets individual evaluations from children
passList = []
for child in self.children:
myVal = child.eval_comparison(checkDict)
passList.append(child.eval_comparison(checkDict))
#if only one child returns the o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_RestrictingNodeTransformer__visit_In_Dict():\n assert restricted_eval('2 in {1: 1, 2: 2, 3: 3}') is True",
"def eval_act_logic(self, act_logic_str):\n if self.ignore_act_logic:\n return True\n # return true for empty string\n if len(act_logic_str) == 0:\n re... | [
"0.61918426",
"0.6141805",
"0.6042099",
"0.5969799",
"0.5877197",
"0.586158",
"0.58614135",
"0.58304363",
"0.581069",
"0.5801263",
"0.5798829",
"0.57972205",
"0.57903963",
"0.57780564",
"0.57741153",
"0.57624376",
"0.57561654",
"0.57198983",
"0.5661767",
"0.56597126",
"0.5608... | 0.6642864 | 0 |
Embeds a batch of states and returns a batch of embeddings. | def forward(self, states):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(self, images):\n\n i = 0\n N = len(images)\n embs = None\n\n while True:\n end = min(N, i + self.batch_size)\n batch = images[i: end]\n\n size = end - i\n if size < self.batch_size:\n batch += self._input_padding[:sel... | [
"0.6478759",
"0.64233774",
"0.63584566",
"0.6301887",
"0.60907364",
"0.5999774",
"0.5967661",
"0.59666103",
"0.5826861",
"0.5792818",
"0.5779593",
"0.5755447",
"0.5737904",
"0.5717163",
"0.5715993",
"0.56992686",
"0.56904775",
"0.5687835",
"0.56503177",
"0.5648871",
"0.563578... | 0.0 | -1 |
add the keeper to the player's field | def play(self, game, playerNumber):
# needs check for keeper limit?
super(Keeper, self).play(game, playerNumber)
p = game.players[playerNumber]
p.field.add(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_player(self, player):\n\t\tself.players.append(player)",
"def add_player(self, players):\n try:\n players[self.ward]\n except:\n players[self.ward] = self",
"def add_played_disk(self, x, y, player):\n self.played_disks.append((x, y, player))",
"def add_playe... | [
"0.6364632",
"0.6120998",
"0.601679",
"0.5704426",
"0.5608644",
"0.55661637",
"0.5504809",
"0.54787284",
"0.54737884",
"0.54316384",
"0.5415775",
"0.5406429",
"0.5401013",
"0.53916353",
"0.5390193",
"0.53633857",
"0.53564775",
"0.5346505",
"0.5302579",
"0.5288975",
"0.5288402... | 0.7416112 | 0 |
Read NWA data and plot results | def read_data_test_MLOG(self):
self.na.set_query_timeout(10e3)
self.na.set_format('mlog')
fpts, mags = self.na.read_data()
plt.figure()
plt.plot(fpts, mags)
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Plot_RCWA_Ssim(Path) :\n #/Users/simonvassant/Documents/20090707_TestsPython/Result_Champ/SSim_O21Pr_2000H_res1000F1.res\n x,y=[],[]\n fs = open(Path, 'r') \n#index_array = 0\n while 1: \n txt = fs.readline()\n if txt =='': \n break\n x.append(float(txt[0:12]))\n ... | [
"0.60809547",
"0.59711033",
"0.59694964",
"0.58526397",
"0.5809397",
"0.5761579",
"0.5761087",
"0.5738626",
"0.57299054",
"0.5708017",
"0.5697068",
"0.5625891",
"0.56195134",
"0.5602345",
"0.5590794",
"0.5582271",
"0.5557602",
"0.555228",
"0.5509402",
"0.54959875",
"0.5489333... | 0.5621301 | 12 |
Read NWA data and plot results | def read_data_test_Polar(self):
self.na.set_query_timeout(10e3)
self.na.set_format('polar')
fpts, mags, phases = self.na.read_data()
plt.figure()
plt.plot(fpts, mags)
plt.plot(fpts, phases)
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Plot_RCWA_Ssim(Path) :\n #/Users/simonvassant/Documents/20090707_TestsPython/Result_Champ/SSim_O21Pr_2000H_res1000F1.res\n x,y=[],[]\n fs = open(Path, 'r') \n#index_array = 0\n while 1: \n txt = fs.readline()\n if txt =='': \n break\n x.append(float(txt[0:12]))\n ... | [
"0.60809547",
"0.59711033",
"0.59694964",
"0.58526397",
"0.5809397",
"0.5761579",
"0.5761087",
"0.5738626",
"0.57299054",
"0.5708017",
"0.5697068",
"0.5625891",
"0.5621301",
"0.56195134",
"0.5602345",
"0.5590794",
"0.5582271",
"0.5557602",
"0.555228",
"0.5509402",
"0.54959875... | 0.0 | -1 |
should be able to clear average and take averaged trace | def take_one_averaged(self):
self.na.set_center_frequency(6.160574e9)
self.na.set_span(10e6)
self.na.set_power(-5, 1)
self.na.set_ifbw(1e3)
self.na.set_query_timeout(40e3)
set_format = self.na.set_format('polar')
print "set_format returned: ", set_format
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_average(self):\n self._total_time = 0\n self._average_time = 0\n self._calls = 0",
"def clear_averaging(self):\n self.write('SENS:AVER:CLE')",
"def ResetAvgFilter(self):\n self.k = 1\n self.prevAvg = 0",
"def reset(self):\n self.avg = 0\n self... | [
"0.74404645",
"0.7266164",
"0.6948134",
"0.65590173",
"0.6538304",
"0.6257147",
"0.6257147",
"0.62516356",
"0.6093857",
"0.60403436",
"0.60174435",
"0.5992177",
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"0.59871846",
"0.5923661",
"0.5902001",
"0.59007776",
"0.58243155",
"0.5820704",
"0.5818743",
"0.58016... | 0.64930433 | 5 |
From BERT repository Get the vocab file and casing info from the Hub module. | def create_tokenizer_from_hub_module(self):
with tf.Graph().as_default():
bert_module = hub.Module(self.params["BERT_MODEL_HUB"])
tokenization_info = bert_module(
signature="tokenization_info", as_dict=True)
with tf.Session() as sess:
vocab_fil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vocab(self):\n\n\t\tself.parse_transcript() \n\t\tself.purge_words()\n\t\tself.analyze_words()\n\t\tself.sort_word_analysis()",
"def get_vocab():\n if data_dir is not None and vocab_filename is not None:\n vocab_filepath = os.path.join(data_dir, vocab_filename)\n if tf.gfile.Exists(vocab_fil... | [
"0.6224301",
"0.617789",
"0.6144918",
"0.5987793",
"0.5987313",
"0.59658396",
"0.59385157",
"0.5870894",
"0.58384573",
"0.5824791",
"0.5618645",
"0.5575307",
"0.5560295",
"0.5522708",
"0.55189157",
"0.5506514",
"0.5476414",
"0.5438602",
"0.5422487",
"0.5402238",
"0.5400183",
... | 0.5293677 | 28 |
Transform the string into BERT features | def sentences_to_features(self, sentences, labels):
input_examples = [run_classifier.InputExample(guid="", text_a=s, text_b=None, label=l) for s, l in
zip(sentences, labels)] # here, "" is just a dummy label
input_features = run_classifier.convert_examples_to_features(input_e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_bert_features(raw_text, tokenizer, model):\n # Load pre-trained model tokenizer (vocabulary)\n text_preprocessed = bert_preprocess(raw_text)\n \n # tokenize\n #tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n tokenized_text = tokenizer.tokenize(text_preprocessed)[:512]\... | [
"0.6911272",
"0.6759593",
"0.6526839",
"0.64172846",
"0.63434416",
"0.62007135",
"0.6190314",
"0.6180227",
"0.6179751",
"0.6128173",
"0.6090893",
"0.60580146",
"0.6048897",
"0.60008186",
"0.5982143",
"0.5967461",
"0.59670514",
"0.5959061",
"0.59244144",
"0.5888641",
"0.588864... | 0.5555488 | 41 |
Creates a classification model. | def create_model(is_predicting, input_ids, input_mask, segment_ids, labels,
num_labels):
bert_module = hub.Module(
ClassifierModel.BERT_MODEL_HUB,
trainable=True)
bert_inputs = dict(
input_ids=input_ids,
input_mask=input_mask,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_model(self):\n self.classifier = DecisionTreeClassifier(max_depth=1)",
"def create_model(self, C : float =1):\n self.classifier = RandomForestClassifier(max_depth=5)",
"def create_model():\n\n # Create a sequential model (a simple NN is created) adding a softmax activation at the en... | [
"0.7451048",
"0.716728",
"0.70753753",
"0.69427526",
"0.69337225",
"0.69306064",
"0.6929345",
"0.68631464",
"0.68004555",
"0.6769279",
"0.6737084",
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"0.660953",
"0.65912193",
"0.6564333",
"0.6534873",
"0.6526978",
"0.6525293",
"0.6521778",
"0.651739... | 0.0 | -1 |
Returns `model_fn` closure for TPUEstimator. model_fn_builder actually creates the model function using the passed parameters for num_labels, learning_rate, etc. | def model_fn_builder(num_labels, learning_rate, num_train_steps,
num_warmup_steps):
def model_fn(features, labels, mode, params):
"""The `model_fn` for TPUEstimator."""
input_ids = features["input_ids"]
input_mask = features["input_mask"]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def model_fn_builder(model_config,\n train_params):\n def model_fn(features, labels, mode, params):\n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n logging.info(\" name = %s, shape = %s\" % (name, featu... | [
"0.7927153",
"0.79216784",
"0.78488064",
"0.7728147",
"0.7653819",
"0.7596977",
"0.7499681",
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"0.73120433",
"0.72905254",
"0.72691625",
"0.72314054",
"0.71903694",
"0.7071... | 0.81900585 | 0 |
The `model_fn` for TPUEstimator. | def model_fn(features, labels, mode, params):
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _augment_model_fn(self, model_fn, batch_axis):\n\n def _model_fn(features, labels, mode, config, params):\n \"\"\"A Estimator `model_fn` for TPUEstimator.\"\"\"\n with self._ctx.with_mode(mode) as ctx:\n model_fn_wrapper = _ModelFnWrapper(model_fn, config, params, ctx)\n\n if mode !=... | [
"0.7227465",
"0.7181237",
"0.70457387",
"0.6964359",
"0.68955207",
"0.6842497",
"0.68148303",
"0.67315537",
"0.6647428",
"0.66089046",
"0.65367836",
"0.6524187",
"0.6450448",
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"0.6296868",
"0.6282705",
"0.6272923",
"0.62583596",
"0.6243498",
"0.6196114",
"0.61711... | 0.57536846 | 47 |
Split X, y as train and test sets. Then convert them to features, classify and score. Shows train and test score to infer whether model is too simple or overfitted. | def train(self, X, y):
tf.logging.set_verbosity(
tf.logging.INFO) # comment if you don't want to display the information during training/evaluation
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=self.params["TEST_SIZE"], random_state=42, stratify=y)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def TrainTestSplit(self,X, y):\n X_train, X_test, y_train, y_test = train_test_split(X, y, \n test_size=0.33,\n random_state=42)\n return X_train, X_test, y_train, y_test",
"def tra... | [
"0.7304593",
"0.72026986",
"0.7181663",
"0.7158527",
"0.7110067",
"0.7026293",
"0.70036715",
"0.6956012",
"0.69486713",
"0.6906381",
"0.6901432",
"0.6900246",
"0.6840159",
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"0.6827838",
"0.6819217",
"0.6759291",
"0.67436206",
"0.6722954",
"0.6708005",
"0.67071015... | 0.647704 | 48 |
Predicts output based on training done during former step | def predict_estimator(self, X, y=None, labels=[0, 1]):
# throw an exception if classifier is not trained
if not self.classifier_trained:
raise Exception("Train estimator first")
if len(X) == 1: # predict doesn't work if only one element
X.append("")
labels = labe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict():\n\n predict_cfg = get_predict_args()\n device = get_device()\n print(device)\n\n # load checkpoint\n ckpt_path = find_ckpt_in_directory(predict_cfg.ckpt)\n ckpt = torch.load(ckpt_path, map_location=device)\n best_iter = ckpt[\"best_iter\"]\n cfg = ckpt[\"cfg\"]\n aspect = ... | [
"0.7502397",
"0.7433937",
"0.73870665",
"0.7331327",
"0.7303667",
"0.72871506",
"0.72688246",
"0.7240414",
"0.7220206",
"0.7153669",
"0.71483386",
"0.70746833",
"0.7046412",
"0.7046357",
"0.70393884",
"0.7022155",
"0.7013938",
"0.7006865",
"0.6990209",
"0.6966313",
"0.6959013... | 0.0 | -1 |
Necessary to export the model | def serving_input_fn(self):
label_ids = tf.placeholder(tf.int32, [None], name='label_ids')
input_ids = tf.placeholder(
tf.int32, [None, self.params["MAX_SEQ_LENGTH"]], name='input_ids')
input_mask = tf.placeholder(
tf.int32, [None, self.params["MAX_SEQ_LENGTH"]], name='in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump_model(self):",
"def export_model(self, save_path: str, save_format: Optional[str] = None) -> None:",
"def export_model(self):\n\n model_pkg = dict()\n\n for k, v in self.__dict__.items():\n if k not in ['datas'] and not k.startswith('_'):\n model_pkg[k] = v\n\n ... | [
"0.7629469",
"0.7263386",
"0.6981971",
"0.69330186",
"0.69330186",
"0.69330186",
"0.69330186",
"0.69330186",
"0.68604165",
"0.6842855",
"0.6696678",
"0.66769975",
"0.66073906",
"0.66003066",
"0.6590629",
"0.65098584",
"0.6466474",
"0.64420897",
"0.6336027",
"0.6336027",
"0.62... | 0.0 | -1 |
Defines input column according to model (raw text or clean text) | def define_input_output(self):
if self.classifier_model.text_preprocessing == ClassifierModel.TEXT_CLEAN:
self.clean_range()
self.input_col = self.df["clean_text"]
elif self.classifier_model.text_preprocessing == ClassifierModel.TEXT_RAW:
self.input_col = self.df[se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reconstruct_input_ext(self, model_in):",
"def on_columnvalue_modified( self, *data ):\n\t\tif (len(data) == 4):\t( cell, path, model, user_data ) = data\n\t\telse:\t\t\t( cell, path, new_text, model, user_data ) = data\n\t\t(datatype,) = user_data\n\t\tcolid = self.window2.type2colid[datatype]\n\t\tif \t(dat... | [
"0.5679452",
"0.56548154",
"0.5609584",
"0.5550475",
"0.5462915",
"0.5444646",
"0.53719866",
"0.52677375",
"0.5246684",
"0.52359515",
"0.52223074",
"0.51953554",
"0.5169166",
"0.5150608",
"0.51421463",
"0.5139192",
"0.5078079",
"0.507589",
"0.50427675",
"0.5032137",
"0.499628... | 0.6507603 | 0 |
Call the Classifiermodel method | def train(self):
if self.input_col is None:
raise Exception("Preprocessing not specified")
self.classifier_model.train(self.input_col, self.output_col) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trainModel( self, featureTrain, classTrain):",
"def apply_classifier(self):\n for detected_object in self.detected_objects:\n detected_object.predict_class(self.original_image)",
"def classify(self, data):\n \"*** YOUR CODE HERE ***\"\n return self.sklearn_classifier.predict... | [
"0.68371236",
"0.67320436",
"0.65875846",
"0.65784526",
"0.64575154",
"0.64436054",
"0.64287776",
"0.63473463",
"0.63165176",
"0.63009554",
"0.62688",
"0.6236218",
"0.6235001",
"0.6223766",
"0.62237513",
"0.6220831",
"0.62114865",
"0.619955",
"0.61918926",
"0.61892086",
"0.61... | 0.62022305 | 17 |
Make predictions for the given dataset Warning, the train methods already split the dataset into train and test and evaluate the model on the test set, this method should be used only to test the good generalization of the model, on a data set it has never seen | def predict_estimator_test(self, X, y=None):
if self.classifier_model.classifier_trained:
results = self.classifier_model.predict_estimator(
X, y, self.classifier_model.label_list)
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self, dataset): \n predictions = np.zeros(len(dataset), int)\n \n accuracy = self.random_forest.score(dataset[:,:-1], dataset[:,-1]) # Predict and compute accuracy.\n predictions = self.predict(dataset[:,:-1]) # Predict and return list of predictions.\n \n return... | [
"0.7167993",
"0.7110903",
"0.71050954",
"0.69779205",
"0.6971445",
"0.69079924",
"0.6887662",
"0.6883227",
"0.6852751",
"0.6805507",
"0.6804817",
"0.675774",
"0.67502844",
"0.6743817",
"0.6736981",
"0.6736298",
"0.67184407",
"0.6714148",
"0.6714112",
"0.6701825",
"0.66946995"... | 0.0 | -1 |
Adds an item to the stack | def push(self, item):
super().add_item_to_front(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def push(self, item):\n self.stack.append(item)",
"def push(self, item):\n self.stack.append(item)",
"def push(self, item):\r\n self.stack.insert(0, item)",
"def push(self, item):\n\n self._stack.append(item)",
"def append(self, item):\r\n self.stack.append(item)",
"def... | [
"0.8976076",
"0.8976076",
"0.8965512",
"0.87010676",
"0.8623993",
"0.8406798",
"0.8379469",
"0.8236489",
"0.81122154",
"0.81077427",
"0.81018645",
"0.8097757",
"0.80937",
"0.80937",
"0.80937",
"0.80937",
"0.80937",
"0.80827004",
"0.8045621",
"0.80422616",
"0.798588",
"0.795... | 0.79412425 | 22 |
Removes and returns an item for the stack on a LIFO basis | def pop(self):
return super().remove_item_from_front() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(self):\n return self.stack_list.pop()",
"def pop(self): # 06:30 Lecture Week 2 \"Stacks\" (16:24)\n if self.isEmpty():\n raise Exception(\"Stack underflow\")\n item = self.first.Item # save item to return\n self.first = self.first.Next # delete last Node added\n self.N -... | [
"0.81453216",
"0.79197675",
"0.7856534",
"0.77557087",
"0.76387227",
"0.75160515",
"0.74301594",
"0.7404803",
"0.74040264",
"0.7393654",
"0.73591006",
"0.73380005",
"0.73172677",
"0.7300573",
"0.729812",
"0.72950584",
"0.72745514",
"0.72589195",
"0.72188485",
"0.7211036",
"0.... | 0.6968351 | 41 |
Returns the element at the top of the stack | def top(self):
return super().peek() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top(self):\n\t\tif self.is_empty():\n\t\t\traise Empty('Stack is empty')\n\t\treturn self._head._element",
"def top(self):\n if self.stack == []:\n return None\n return self.stack[-1]",
"def top(self):\n if self.is_empty():\n raise Empty('Stack is empty')\n ... | [
"0.8426997",
"0.83845615",
"0.8306367",
"0.8226858",
"0.807717",
"0.80328375",
"0.80252784",
"0.8019078",
"0.797039",
"0.79673326",
"0.7955196",
"0.79414254",
"0.7932987",
"0.7927457",
"0.7792212",
"0.77724975",
"0.7744826",
"0.7740041",
"0.76800865",
"0.76103806",
"0.7591366... | 0.71395046 | 61 |
Initialize state data once. | def __call__(self, **kwargs):
for item, value in kwargs.items():
if item not in self._state["data"]:
self._state["data"][item] = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_initial_state(self):\n pass",
"def initialize(self):\n\n for i, item in enumerate(self.v.items()):\n state, value = item\n if value == None:\n raise ValueError, \"state '%s' has no value\" % state\n self.S[i]=value\n self.storage=Stora... | [
"0.7450729",
"0.73820895",
"0.72283614",
"0.72150916",
"0.71687126",
"0.7122427",
"0.7085805",
"0.7071513",
"0.7037798",
"0.7025954",
"0.7009983",
"0.6964747",
"0.69580865",
"0.6846593",
"0.6830056",
"0.6822155",
"0.68070364",
"0.67243207",
"0.67123103",
"0.67005974",
"0.6700... | 0.0 | -1 |
Return a saved state value, None if item is undefined. | def __getitem__(self, item):
return self._state["data"].get(item, None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, item):\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\n return self._state[\"data\"].get(item, None)",
"def __getattr__(self, item):\r\n return self... | [
"0.63567495",
"0.63567495",
"0.63567495",
"0.6147721",
"0.6147721",
"0.6143838",
"0.61314815",
"0.60985714",
"0.60985714",
"0.60985714",
"0.5938411",
"0.5815865",
"0.5792894",
"0.57494944",
"0.57324725",
"0.57256",
"0.5721121",
"0.5721121",
"0.5684823",
"0.5684823",
"0.568482... | 0.6427416 | 0 |
Return a saved state value, None if item is undefined. | def __getattr__(self, item):
return self._state["data"].get(item, None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\n return se... | [
"0.64281183",
"0.64281183",
"0.6357475",
"0.6357475",
"0.6357475",
"0.61450446",
"0.6130774",
"0.6099511",
"0.6099511",
"0.6099511",
"0.59375536",
"0.58148605",
"0.5792937",
"0.57490504",
"0.5732397",
"0.5725432",
"0.572095",
"0.572095",
"0.56847996",
"0.56847996",
"0.5684799... | 0.6148605 | 5 |
Clear session state and request a rerun. | def clear(self):
self._state["data"].clear()
self._state["session"].request_rerun() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset():\n if app.config['SESSION_KEY'] in session:\n session.pop(app.config['SESSION_KEY'])\n return redirect(url_for('home'))",
"def trigger_rerun():\n session_infos = Server.get_current()._session_info_by_id.values()\n for session_info in session_infos:\n this_session = session_i... | [
"0.7043216",
"0.69138795",
"0.67887557",
"0.67887557",
"0.67887557",
"0.6764239",
"0.6704957",
"0.65826434",
"0.64558333",
"0.644461",
"0.6416975",
"0.6393411",
"0.6388334",
"0.62632084",
"0.62208897",
"0.6181936",
"0.60033035",
"0.5980395",
"0.5871535",
"0.5871535",
"0.58715... | 0.6886666 | 2 |
Rerun the app with all state values up to date from the beginning to fix rollbacks. | def sync(self):
# Ensure to rerun only once to avoid infinite loops
# caused by a constantly changing state value at each run.
#
# Example: state.value += 1
if self._state["is_rerun"]:
self._state["is_rerun"] = False
elif self._state["hash"] is not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rerun():\n widget_states = _get_widget_states()\n raise RerunException(RerunData(widget_states))",
"def rollback(self, apps):\n while len(apps) > 0:\n app = apps.pop()\n print(\"Rolling back app %s ...\" % app.name)\n app.rollback_job()",
"def restart(self):\n ... | [
"0.6341983",
"0.6264009",
"0.6008757",
"0.6008757",
"0.6008757",
"0.59590656",
"0.59546226",
"0.5923881",
"0.5918356",
"0.5912256",
"0.58985865",
"0.5896702",
"0.58921117",
"0.58627576",
"0.5841164",
"0.5841025",
"0.5810446",
"0.5789196",
"0.5786671",
"0.57622343",
"0.5748626... | 0.0 | -1 |
Create a dataset from a list of Examples and Fields. | def __init__(self, examples, filter_pred=None, **kwargs):
if filter_pred is not None:
make_list = isinstance(examples, list)
examples = filter(filter_pred, examples)
if make_list:
examples = list(examples)
self.examples = examples | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CreateDataset(all_arrays):\n dataset = Dataset()\n\n dataset._addData(all_arrays[0])\n dataset._addData(all_arrays[1])\n dataset._addData(all_arrays[3])\n dataset._addData(all_arrays[5])\n dataset._addData(all_arrays[6])\n dataset._addData(all_arrays[9])\n dataset._addData(all_arrays[8]... | [
"0.66175133",
"0.65675855",
"0.6489426",
"0.64110506",
"0.6397567",
"0.63752276",
"0.6319362",
"0.6216499",
"0.6209005",
"0.6183818",
"0.61597437",
"0.6138031",
"0.61348975",
"0.6127125",
"0.61253273",
"0.61253273",
"0.61253273",
"0.6124141",
"0.6122481",
"0.60865337",
"0.606... | 0.0 | -1 |
Create Dataset objects for multiple splits of a dataset. | def splits(cls, root='.data', train=None, validation=None, test=None, **kwargs):
path = cls.download(root)
train_data = None if train is None else cls(os.path.join(path, train), **kwargs)
val_data = None if validation is None else cls(os.path.join(path, validation), **kwargs)
test_data =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_dataset(self, split, shuffle_files=None):\n return self._as_dataset(split=split, shuffle_files=shuffle_files)",
"def __split_dataset(self):\n self.train, self.valid, _, _ = train_test_split(self.data, self.data, test_size=0.2)\n self.valid, self.test, _, _ = train_test_split(self.valid, s... | [
"0.714736",
"0.71266127",
"0.7124513",
"0.6978415",
"0.69318837",
"0.6857103",
"0.6852228",
"0.6806051",
"0.6730736",
"0.67166394",
"0.66391075",
"0.66319656",
"0.6599639",
"0.65770274",
"0.6532192",
"0.6507811",
"0.65007865",
"0.64922684",
"0.6482739",
"0.64560986",
"0.63962... | 0.0 | -1 |
Download and unzip an online archive (.zip, .gz, or .tgz). | def download(cls, root, check=None):
path = os.path.join(root, cls.name)
check = path if check is None else check
if not os.path.isdir(check):
for url in cls.urls:
if isinstance(url, tuple):
url, filename = url
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_and_unzip(url, zip_path, csv_path, data_folder):\n\n download_from_url(url, zip_path)\n\n unzip(zip_path, csv_path, data_folder)\n\n print('Done.')",
"def _download(url, outpath=None, dirname=None, branch='master', release=None):\n six.print_('downloading...')\n outfolder = outpath or os.ge... | [
"0.766572",
"0.75655353",
"0.7496965",
"0.73008174",
"0.72016865",
"0.7129517",
"0.71224624",
"0.70703506",
"0.70628434",
"0.7030601",
"0.6981109",
"0.6945264",
"0.6934098",
"0.68902963",
"0.6858346",
"0.683862",
"0.68155247",
"0.6783569",
"0.6730443",
"0.6667935",
"0.6647641... | 0.0 | -1 |
Interleave bits from two sort keys to form a joint sort key. Examples that are similar in both of the provided keys will have similar values for the key defined by this function. Useful for tasks with two text fields like machine translation or natural language inference. | def interleave_keys(a, b):
def interleave(args):
return ''.join([x for t in zip(*args) for x in t])
return int(''.join(interleave(format(x, '016b') for x in (a, b))), base=2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addKey(s1, s2): \r\n return [i ^ j for i, j in zip(s1, s2)]",
"def concatKey(str1,str2):\n return concat(concat(str1, '_'), str2)",
"def inner_join(sorted1, sorted2, key1, key2):\n p1 = 0\n p2 = 0\n result = []\n\n while (p1 < len(sorted1) and p2 < len(sorted2)):\n # if entries\n ... | [
"0.61941266",
"0.61463404",
"0.5867205",
"0.5854237",
"0.57316273",
"0.57308954",
"0.56912524",
"0.56198055",
"0.5488593",
"0.53060853",
"0.53048044",
"0.52917767",
"0.5274578",
"0.5135413",
"0.5129714",
"0.5127249",
"0.5121423",
"0.51084983",
"0.5099582",
"0.50963557",
"0.50... | 0.7362512 | 0 |
Download file, with logic (from tensor2tensor) for Google Drive | def download_from_url(url, path):
if 'drive.google.com' not in url:
try:
return urllib.request.urlretrieve(url, path)
except Exception:
res = requests.get(url)
with open(path, 'wb') as out:
out.write(res.content)
print('downloading from Google ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maybe_download(filename):\n\n if not tf.gfile.Exists(WORK_DIRECTORY):\n tf.gfile.MakeDirs(WORK_DIRECTORY)\n filepath = os.path.join(WORK_DIRECTORY, filename)\n if not tf.gfile.Exists(filepath):\n filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)\n with tf.gfile.GFile(filepat... | [
"0.6828218",
"0.67926407",
"0.6701384",
"0.6701384",
"0.6690748",
"0.6690537",
"0.66409",
"0.6583277",
"0.65828484",
"0.65616095",
"0.65463537",
"0.646932",
"0.646782",
"0.6464549",
"0.6458871",
"0.6456325",
"0.64409536",
"0.6418602",
"0.64159805",
"0.6411759",
"0.6410923",
... | 0.63422894 | 23 |
Used to keep track of scope via white space | def get_indent(self, s):
return len(s) - len(s.lstrip()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scope(self): # noqa: ANN201",
"def enterScope(self, name):",
"def scope(self, name):\r\n raise NotImplementedError",
"def beginScope():",
"def name_scope(self):\n pass",
"def _getScopeName(self):\r\n return self.name # + \"@b%d\" % self.blscope_ids[-1]\r",
"def scope_line(self, text):... | [
"0.6925732",
"0.67302036",
"0.64443195",
"0.63425654",
"0.6285401",
"0.6097456",
"0.5997095",
"0.5971598",
"0.59524256",
"0.5929008",
"0.5902731",
"0.5902731",
"0.58621186",
"0.5850101",
"0.58111405",
"0.58057463",
"0.57699937",
"0.5768783",
"0.57478744",
"0.57442987",
"0.566... | 0.0 | -1 |
Send HTTP get requests to a URL with a delay following Poisson distribution. | async def send_requests(url: str, count: int = 100, lamda: float = 5.0) -> None:
successful = failed = 0
current = 0
while count - current > 0:
request = urllib.request.Request(url)
try:
response = urllib.request.urlopen(request)
except Exception:
failed += 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, url: str) -> None:\n\n if self.number_of_requests_made % self.change_ip_after == 0:\n self.renew_ip()\n\n else:\n self.wait()\n\n self.last_call_timestamp = round(time.time(), 2)\n super().get(url)\n\n self.number_of_requests_made += 1",
"def... | [
"0.69498026",
"0.64508325",
"0.63998294",
"0.62229043",
"0.62177193",
"0.619114",
"0.6111653",
"0.6070869",
"0.6041272",
"0.59756577",
"0.592942",
"0.5914116",
"0.5910035",
"0.586417",
"0.58601296",
"0.5842746",
"0.5841606",
"0.5837707",
"0.5828307",
"0.58107495",
"0.5783712"... | 0.5814416 | 19 |
GET / retorna status code 302 | def test_get(self):
animal = Animal.objects.create(propriedade=self.propriedade1,
sexo=1,
brinco = 'A123452',
)
response = self.client.get(reverse('... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_redirect(self):\r\n sess = FuturesSession()\r\n future = sess.get(httpbin('redirect-to?url=get'))\r\n self.assertIsInstance(future, Future)\r\n resp = future.result()\r\n self.assertIsInstance(resp, Response)\r\n self.assertEqual(200, resp.status_code)\r\n\r\n ... | [
"0.763196",
"0.73813707",
"0.72912335",
"0.728111",
"0.72572887",
"0.713811",
"0.70875144",
"0.6918813",
"0.69020045",
"0.6795535",
"0.6774023",
"0.6738423",
"0.6714167",
"0.66844994",
"0.6627861",
"0.65451616",
"0.6530949",
"0.6511822",
"0.64325136",
"0.64173377",
"0.6387123... | 0.0 | -1 |
GET / retorna status code 404 | def test_get_autenticado(self):
login = self.client.login(username='user1', password='12345')
animal = Animal.objects.create(propriedade=self.propriedade1,
sexo=1,
brinco = 'A113456',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def not_found():\n return HttpError(404)",
"def not_found(self, error):\n return jsonify({'error': 'NOT FOUND'}), 404",
"def normal404(e):\n return jsonify({\"error\": \"Not found\"}), 404",
"def handleStatus_404(self):\n log.err('HTTP Error 404')",
"def not_found():\n raise cherrypy.HTT... | [
"0.8387215",
"0.7864697",
"0.7690975",
"0.76671004",
"0.7622888",
"0.7616264",
"0.7606746",
"0.76056755",
"0.7594924",
"0.7594645",
"0.75129735",
"0.74642843",
"0.74385124",
"0.74385124",
"0.7401526",
"0.7398156",
"0.7396078",
"0.7374972",
"0.7358382",
"0.7358309",
"0.735549"... | 0.0 | -1 |
verifica se tem o link para voltar para a listagem de animais | def test_animais_list_link(self):
PropriedadeUser.objects.create(propriedade=self.propriedade1,
user=self.user1,
owner=True)
login = self.client.login(username='user1', password='12345')
response = self.client.get(reve... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getLink(self):",
"def add_link():\n return True",
"def requestShowLink(self, *args, **kwargs): # real signature unknown\n pass",
"def is_valid_listings(link):\n if link.has_attr(\"href\") and link.attrs[\"href\"].startswith(LISTING_PREFIX):\n return True\n return False",
"def rem... | [
"0.5543123",
"0.5494667",
"0.53836703",
"0.5344362",
"0.52966595",
"0.5271885",
"0.52616286",
"0.5257237",
"0.5244915",
"0.5165959",
"0.51506627",
"0.5111753",
"0.50975",
"0.5086898",
"0.5062339",
"0.5051371",
"0.50483936",
"0.5039112",
"0.50307196",
"0.49970207",
"0.49806777... | 0.5964893 | 0 |
verifica os textos no html | def test_textos_no_html(self):
PropriedadeUser.objects.create(propriedade=self.propriedade1,
user=self.user1,
owner=True)
login = self.client.login(username='user1', password='12345')
response = self.client.get(reverse... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getText():",
"def getText():",
"def getText():",
"def getText():",
"def getText():",
"def is_text( self ):\n return self.get_main_type() == 'text'",
"def obtain_text():\n pass",
"def hasRawText(self, text):\n r = re.compile(r'<(p|blockquote|div|form|table|ul|ol|dl|pre|h\\d)[^>]*?... | [
"0.675838",
"0.675838",
"0.675838",
"0.675838",
"0.675838",
"0.66166764",
"0.6557563",
"0.65164775",
"0.6472427",
"0.64069116",
"0.6390485",
"0.6258951",
"0.6230542",
"0.6212179",
"0.6210339",
"0.6207658",
"0.6206313",
"0.6205104",
"0.62009335",
"0.61976314",
"0.6178452",
"... | 0.6883659 | 0 |
For update de Wallet balance. | def save(self, *args, **kwargs):
wallet = self.wallet.withdraw(self.value)
super(Payment, self).save(*args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_balance(self):\n if self.calculated_balance < 0:\n raise AccountBalanceError('calculated_balance on account {} is below 0'.format(self))\n self.balance = self.calculated_balance\n self.save()",
"async def update_account_balance():\n\n try:\n balance = App.clie... | [
"0.72338796",
"0.70078224",
"0.69661176",
"0.68936557",
"0.6885542",
"0.68758094",
"0.68758094",
"0.6799369",
"0.67223907",
"0.66990334",
"0.66625327",
"0.66507715",
"0.66184336",
"0.66184336",
"0.66184336",
"0.6594879",
"0.6594879",
"0.6594879",
"0.6592823",
"0.6580972",
"0.... | 0.6641524 | 12 |
Computes the rowwise cosine similarity matrix of a rating matrix. | def similarity_matrix(feat_mat):
sim_mat = cosine_similarity(feat_mat)
np.fill_diagonal(sim_mat, 0)
return sim_mat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_sim_cf(matrix):\n if type(matrix) is not lil_matrix:\n matrix = lil_matrix(matrix)\n\n n = matrix.shape[0]\n rows, cols, data = [], [], []\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\n\n for i in range(n):\n i_ratings, ... | [
"0.77840763",
"0.7654144",
"0.7522678",
"0.7134965",
"0.71157086",
"0.6820214",
"0.67638975",
"0.66730607",
"0.6665848",
"0.6656157",
"0.6655661",
"0.6623994",
"0.66095823",
"0.6570017",
"0.6487632",
"0.64274234",
"0.6385559",
"0.63674223",
"0.63396907",
"0.6329559",
"0.63160... | 0.72332346 | 3 |
Computes the elementwise average (mean) of some matrices. | def sim_avg(sim_mats):
return np.array(sim_mats).mean(axis=0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrix_mean(matrix):\n return sum(map(mean,matrix))",
"def mean_matrix(datasets, axes = None, label = None):\n fn = lambda fd, axis: fd[axis].mean()\n \n return fn_matrix(datasets, fn, axes, label)",
"def amean (inarray,dimension=None,keepdims=0):\r\n if inarray.dtype in [N.int_, N.short,N.u... | [
"0.7331233",
"0.69245744",
"0.67887264",
"0.67166",
"0.66820306",
"0.66804636",
"0.66455996",
"0.66441685",
"0.66332096",
"0.66087085",
"0.6591833",
"0.6468679",
"0.64240265",
"0.64155746",
"0.640505",
"0.640505",
"0.63970006",
"0.63970006",
"0.63970006",
"0.6351404",
"0.6338... | 0.68238235 | 2 |
Computes the elementwise minimum of some matrices. | def sim_min(sim_mats):
return np.array(sim_mats).min(axis=0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrix_min(data):\n if is_SparseDataFrame(data):\n data = [np.min(data[col]) for col in data.columns]\n elif is_sparse_dataframe(data):\n data = [sparse_series_min(data[col]) for col in data.columns]\n elif isinstance(data, pd.DataFrame):\n data = np.min(data)\n elif isinstance... | [
"0.6745464",
"0.6651574",
"0.6647899",
"0.64386827",
"0.6284703",
"0.6036429",
"0.6033137",
"0.5970848",
"0.5958427",
"0.5948367",
"0.5947503",
"0.59440386",
"0.59381294",
"0.5921218",
"0.59118783",
"0.5897402",
"0.5858605",
"0.5849506",
"0.58406216",
"0.5815177",
"0.5813334"... | 0.7239349 | 0 |
Computes the elementwise maximum of some matrices. | def sim_max(sim_mats):
return np.array(sim_mats).max(axis=0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def three_array_max(array_list: List[np.ndarray]) -> np.ndarray:\n temp = np.maximum(array_list[0], array_list[1])\n all_maxs = np.maximum(temp, array_list[2])\n\n return all_maxs",
"def d_max(x, y):\n axis = np.argmax(x.shape)\n return np.max(np.array([x, y]), axis=axis)",
"def max(x, reduce_in... | [
"0.6566944",
"0.6431522",
"0.63984",
"0.6369676",
"0.6349694",
"0.6326043",
"0.6279134",
"0.6229197",
"0.6208424",
"0.61817044",
"0.61411893",
"0.60957426",
"0.60940033",
"0.6092088",
"0.60329014",
"0.6031537",
"0.60274714",
"0.6022276",
"0.60205376",
"0.6020099",
"0.5998723"... | 0.69111717 | 0 |
Supports an additional argument ``jsonp_callback``. If specified, the JSON serialized data string will be wrapped in parenthesis and prefixed by the value of ``jsonp_callback``. | def __init__(self, *args, **kwargs):
self.jsonp_callback = kwargs.pop('jsonp_callback', None)
super(JsonResponse, self).__init__(*args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeJSONP(callback, data):\n response_data = callback\n response_data += '('\n response_data += json_dumps(data)\n response_data += ');'\n return response_data",
"def to_jsonp(self, data, options=None):\r\n options = options or {}\r\n json = self.to_json(data, options)\r\n ... | [
"0.77660763",
"0.77004063",
"0.71376044",
"0.7129176",
"0.7104028",
"0.7059136",
"0.69853944",
"0.69456315",
"0.6932959",
"0.68902475",
"0.68627375",
"0.678367",
"0.62437904",
"0.6236117",
"0.6163715",
"0.57675",
"0.5750369",
"0.5558858",
"0.5427558",
"0.5421836",
"0.5384548"... | 0.6793149 | 11 |
Takes in an Image message and identifies the locations of the three dumbbells | def identify_dbs(image):
locations = {"red": Point(), "green": Point(), "blue": Point()}
masks = {"red": [], "green": [], "blue": []}
bridge = cv_bridge.CvBridge()
image = bridge.imgmsg_to_cv2(image, "bgr8")
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# upper and lower bounds for red
# us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def belt(image):\n\n # Belt Detector\n x, y = circular_detector(image, 70, 80)\n\n return x, y",
"def describe(image):\n needle = cv2.imread(image, 0)\n orb = cv2.ORB()\n keypoints, description = orb.detectAndCompute(needle, None)\n print(keypoints)\n print(description)\n return keypoints, descrip... | [
"0.5904444",
"0.58341223",
"0.5803662",
"0.5694714",
"0.564857",
"0.5638822",
"0.5634845",
"0.5602222",
"0.55758303",
"0.5494642",
"0.5491051",
"0.548308",
"0.5441922",
"0.5413018",
"0.54116",
"0.5403581",
"0.5379999",
"0.53695244",
"0.53540814",
"0.5338005",
"0.53375745",
... | 0.6506251 | 0 |
Takes in a list of Image messages and identifies the block number in each image | def identify_blocks(images):
locations = {1: Point(), 2: Point(), 3: Point()}
blocks = {"left": 0, "middle": 0, "right": 0}
pipeline = keras_ocr.pipeline.Pipeline()
cv2_images = []
for image in images:
bridge = cv_bridge.CvBridge()
cv2_images.append(bridge.imgmsg_to_cv2(image, "bgr8... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getBlocks(self):\n blocks = self.getBlocksMsg(b'\\x00')\n last_locator = self.largeMessageControl(blocks, 'inv', 0)\n\n while last_locator[1] < TARGET_BLOCK:\n blocks = self.getBlocksMsg(bytearray.fromhex(convertLittleBig(last_locator[0])))\n last_locator = self.large... | [
"0.5884302",
"0.5805182",
"0.5722689",
"0.5625986",
"0.5602621",
"0.55785567",
"0.5520678",
"0.5473761",
"0.5423533",
"0.53958166",
"0.5387366",
"0.53733194",
"0.537314",
"0.5340807",
"0.5339228",
"0.5338712",
"0.533123",
"0.53037155",
"0.5302177",
"0.52923036",
"0.5285601",
... | 0.6633512 | 0 |
Initialize attributes for simulated use. | def __init__(self,gameboard,events,mouse_pos):
super().__init__(gameboard)
self.events = events
self.mouse_pos = mouse_pos | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_attrs(self):\n raise NotImplementedError",
"def _init_attributes(self):\n self.attr = {\n 'name': None,\n 'tags': [],\n 'openHours': None,\n 'type': None,\n 'parent': None,\n 'locationId': None,\n 'bannerAbbreviat... | [
"0.7777734",
"0.76090693",
"0.74320334",
"0.72761893",
"0.7228054",
"0.7202334",
"0.71504873",
"0.7127317",
"0.7108561",
"0.69425267",
"0.6913489",
"0.6910913",
"0.6900658",
"0.68599164",
"0.6859132",
"0.6831615",
"0.6831615",
"0.67674065",
"0.67426705",
"0.6740823",
"0.67338... | 0.0 | -1 |
Return inputted mouse position. | def get_mouse_pos(self):
return self.mouse_pos | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mouse_position(self):\n raise NotImplementedError",
"def mousePos():\n data = display.Display().screen().root.query_pointer()._data\n return data[\"root_x\"], data[\"root_y\"]",
"def read_current_mouse_position():\n import pyautogui\n pyautogui.FAILSAFE = False\n return pyautogui.... | [
"0.80970645",
"0.79738057",
"0.7952894",
"0.7875623",
"0.7716459",
"0.7669065",
"0.7658517",
"0.7630226",
"0.7630226",
"0.74635327",
"0.7459193",
"0.73358524",
"0.7272576",
"0.7244995",
"0.71246344",
"0.70662653",
"0.70661056",
"0.7031796",
"0.69420946",
"0.6930538",
"0.69280... | 0.8032351 | 1 |
Initialize type and button. | def __init__(self, event_type, button):
self.type = event_type
self.button = button | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.handlers = {}\n self.start_state = None\n self.end_states = []\n self.btn = Button()",
"def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._... | [
"0.6788451",
"0.6683954",
"0.65892327",
"0.6497996",
"0.64862543",
"0.6442674",
"0.6437974",
"0.64198774",
"0.64093477",
"0.63769287",
"0.63739365",
"0.63739365",
"0.6325634",
"0.6292619",
"0.6286793",
"0.62425756",
"0.62415385",
"0.6241002",
"0.6228872",
"0.619486",
"0.61938... | 0.7603971 | 0 |
calls the appropriate funciton to resample an image based on the interpolation method | def resize(self, image, fx, fy, interpolation):
if interpolation == 'bilinear':
return self.bilinear_interpolation(image, fx, fy)
elif interpolation == 'nearest_neighbor':
return self.nearest_neighbor(image, fx, fy) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resampled_to_img(self, target_image, interpolation=None):\n # IMPORTANT: Polymorphism can be implemented by walking the \n # MRO and finding a method that does not raise\n # NotImplementedError. \n raise NotImplementedError",
"def Resampler(name):\n\n def resample_average(path, ds... | [
"0.777782",
"0.72679806",
"0.69222504",
"0.6829409",
"0.67728364",
"0.6588891",
"0.6578838",
"0.65778905",
"0.65753907",
"0.6556866",
"0.6504161",
"0.64685297",
"0.6434631",
"0.6428031",
"0.64210665",
"0.6419218",
"0.6414669",
"0.6380477",
"0.6313253",
"0.6260271",
"0.6226804... | 0.63449955 | 18 |
resizes an image using bilinear interpolation approximation for resampling | def nearest_neighbor(self, image, fx, fy):
#Write your code for nearest neighbor interpolation here
w = image.shape[1]
h = image.shape[0]
newW = int(w*float(fx))
newH = int(h*float(fy))
ratioW = w/newW
ratioH = h/newH
import numpy as np
newImg ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale_image(image, scale=1, interpolation='linear'):\n if interpolation == 'linear':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n if interpolation == 'nearest':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)",
... | [
"0.6840502",
"0.68318045",
"0.68003356",
"0.6773299",
"0.6769946",
"0.6753389",
"0.67483824",
"0.670136",
"0.6658069",
"0.65596086",
"0.65593255",
"0.6544679",
"0.65280706",
"0.6521803",
"0.6505017",
"0.649654",
"0.64671725",
"0.6443924",
"0.64256537",
"0.64148885",
"0.640677... | 0.0 | -1 |
resizes an image using bilinear interpolation approximation for resampling | def bilinear_interpolation(self, image, fx, fy):
# Write your code for bilinear interpolation here
w = image.shape[1]
h = image.shape[0]
newW = int(w*float(fx))
newH = int(h*float(fy))
ratioW = w / newW
ratioH = h / newH
import numpy as np
impo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale_image(image, scale=1, interpolation='linear'):\n if interpolation == 'linear':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n if interpolation == 'nearest':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)",
... | [
"0.6840322",
"0.6831431",
"0.68000495",
"0.67738384",
"0.6769506",
"0.67526364",
"0.674664",
"0.6701318",
"0.6659351",
"0.65608776",
"0.6559787",
"0.65459263",
"0.6527563",
"0.6523229",
"0.65071964",
"0.6495799",
"0.6469559",
"0.64450675",
"0.64279085",
"0.64148057",
"0.64094... | 0.60840994 | 71 |
get final coach for each session coach with more than half the season will be the credited coach for eventual playoff and championship won | def get_final_coach_for_each_season(self):
self.final_coach_for_season = (
self.num_days_coach_for_season
.groupby(['Season','TeamID'])
.agg({"CoachName":"count"})
.reset_index()
.rename(columns={"CoachName":"coach_counts"})
.merge(self.num... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"c... | [
"0.69078004",
"0.66235226",
"0.65739715",
"0.64645517",
"0.54283714",
"0.53823304",
"0.5381159",
"0.5307479",
"0.5282068",
"0.52351725",
"0.51759636",
"0.51322395",
"0.5129283",
"0.50992453",
"0.5090998",
"0.5084592",
"0.5073128",
"0.5072545",
"0.5070027",
"0.50515336",
"0.50... | 0.757074 | 0 |
Get teams who won the championship for each year | def get_championship_won_for_each_coach(self):
self.championship_team = (
self.raw_data_postseason
.merge(self.season_max_days,how='left',on=['Season'])
.query("DayNum == season_max_days")
.groupby(['Season','WTeamID'])
.agg({"NumOT":"count"})
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_player_games(self, year, use_local=True):",
"def collect_teams(year):\n\n team_list = Team.objects.filter(year=year).order_by('location')\n teams = []\n for t in team_list:\n team = {\n 'id': t.abbreviation,\n 'team': t,\n }\n teams.append(team)\n re... | [
"0.70646495",
"0.67921704",
"0.6662633",
"0.6608853",
"0.6473593",
"0.6421649",
"0.6351773",
"0.6312248",
"0.6311487",
"0.63097686",
"0.6242666",
"0.6128153",
"0.6068329",
"0.6029508",
"0.6004093",
"0.59811854",
"0.5938113",
"0.593734",
"0.5913684",
"0.5901109",
"0.58794063",... | 0.6963645 | 1 |
Combine teams who made to playoff and won championship to one dataframe | def combine_playoff_championship_for_each_coach(self):
self.final_coach_with_postseason_champion_each_year = (
self.final_coach_with_postseason_each_year
.merge(self.championship_team,how='left',on=['Season','TeamID'])
.fillna(0)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_championship_won_for_each_coach(self):\n self.championship_team = (\n self.raw_data_postseason\n .merge(self.season_max_days,how='left',on=['Season'])\n .query(\"DayNum == season_max_days\")\n .groupby(['Season','WTeamID'])\n .agg({\"NumOT\":\"c... | [
"0.72284454",
"0.7174533",
"0.6835919",
"0.65570146",
"0.65117526",
"0.64460844",
"0.6409053",
"0.62457347",
"0.6203352",
"0.6171146",
"0.6097149",
"0.60657734",
"0.6041406",
"0.6035894",
"0.60347044",
"0.6033802",
"0.60308903",
"0.60294217",
"0.59119236",
"0.59107935",
"0.58... | 0.6916625 | 2 |
Get win rate for regular season for each coach | def get_win_rate_regular_season_for_each_coach(self):
self.games_won_for_coaches = (
self.raw_data_regularseason
[['Season','DayNum','WTeamID']]
# merge for winning team
.merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Seas... | [
"0.7728886",
"0.6802758",
"0.66279066",
"0.6522059",
"0.64248955",
"0.6424082",
"0.6392534",
"0.62645185",
"0.6220719",
"0.6031486",
"0.6006317",
"0.5837831",
"0.5770262",
"0.57343024",
"0.57129455",
"0.5707701",
"0.56960624",
"0.5694389",
"0.56909186",
"0.56840575",
"0.56737... | 0.8015784 | 0 |
Get win rate for post season for each coach | def get_win_rate_post_season_for_each_coach(self):
# get winning games for coaches
self.post_games_won_for_coaches = (
self.raw_data_postseason
[['Season','DayNum','WTeamID']]
# merge for winning team
.merge(self.num_days_coach_for_season[['Season','TeamID... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum',... | [
"0.7711851",
"0.6832915",
"0.6480246",
"0.63182116",
"0.6245573",
"0.61112136",
"0.611068",
"0.602987",
"0.59854066",
"0.5965334",
"0.5964837",
"0.57526016",
"0.57137907",
"0.5671769",
"0.5644969",
"0.5635259",
"0.56290424",
"0.5599348",
"0.5596914",
"0.5574907",
"0.5569233",... | 0.8241553 | 0 |
Test that upload Logger Type file without microsite_id will not be inserted to database | def test_logger_type_upload_MicrositeId_None(self):
test_filename = 'server/tests/test_data_files/Test/Test_New_Logger_Type_MicrositeId_None.csv'
with self.app.test_client() as client:
with client.session_transaction() as sess:
sess['logged_in'] = True
response = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_upload_wrong_file_type(self):\n file = dict(\n file=(BytesIO(b'my file contents'), \"foto.doc\"),\n )\n response = self.client.post('/upload',\n content_type='multipart/form-data',\n data=file)\n s... | [
"0.676866",
"0.66889274",
"0.65706724",
"0.64305943",
"0.640982",
"0.639273",
"0.6387645",
"0.63190717",
"0.62991345",
"0.6276941",
"0.62692577",
"0.62305576",
"0.6219944",
"0.6209555",
"0.6150867",
"0.61412674",
"0.6103418",
"0.6050772",
"0.602671",
"0.6003001",
"0.5965992",... | 0.81712246 | 0 |
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