f', color)\\n s = struct.pack('>f', data[3])\\n i = struct.unpack('>l', s)[0]\\n pack = ctypes.c_uint32(i).value\\n\\n r = (pack & 0x00FF0000) >> 16\\n g = (pack & 0x0000FF00) >> 8\\n b = (pack & 0x000000FF)\\n\\n buffer.append(struct.pack('ffffBBBBIII', data[0], data[1], data[2], 1.0, b, g, r, 0, 0, 0, 0))\\n # print(len(bytes(struct.pack('ffffBBBBIII', data[0], data[1], data[2], 1.0, b, g, r, 0, 0, 0, 0))))\\n # buffer = buffer + (struct.pack('ffffBBBBIII', data[0], data[1], data[2], 1.0, b, g, r, 0, 0, 0, 0))\\n # print(len(bytes(struct.pack('ffffBBBBIII', data[0], data[1], data[2], 1.0, b, g, r, 0, 0, 0, 0))))\\n # ros_msg.data = bytes(buffer.encode('utf-8'))\\n ros_msg.data = int(bytes(buffer))\\n # print(str(buffer)[0])\\n\\n return ros_msg\",\n \"def reindex2d(self, index):\\n ds_out = xr.Dataset(attrs=self._obj.attrs)\\n for var in self.vars:\\n ds_out[var] = self._obj[var].raster.reindex2d(index=index)\\n return ds_out\",\n \"def __init__(self, narray, axes_names=None, axes_domains=None,\\n value_label=\\\"value\\\", meta_data=None):\\n logger.debug('xndarray.__init__ ...')\\n\\n narray = np.asarray(narray)\\n self.data = narray\\n self.value_label = value_label\\n self.meta_data = meta_data\\n self.has_deprecated_xml_header = True\\n\\n nbDims = self.data.ndim\\n\\n if axes_names is None:\\n self.axes_names = ['dim' + str(i) for i in xrange(nbDims)]\\n else:\\n assert type(axes_names) == list\\n if len(axes_names) != nbDims:\\n raise Exception(\\\"length of axes_names (%d) is different \\\"\\n \\\"from nb of dimensions (%d).\\\\n\\\"\\n \\\"Got axes names: %s\\\"\\n % (len(axes_names), nbDims, str(axes_names)))\\n\\n self.axes_names = axes_names[:]\\n\\n self.axes_ids = dict([(self.axes_names[i], i) for i in xrange(nbDims)])\\n\\n # By default: domain of axis = array of slice indexes\\n sh = self.data.shape\\n self.axes_domains = dict([(axis, np.arange(sh[i]))\\n for i, axis in enumerate(self.axes_names)])\\n\\n if axes_domains is not None:\\n assert isinstance(axes_domains, dict)\\n\\n for an, dom in axes_domains.iteritems():\\n if an not in self.axes_names:\\n raise Exception('Axis \\\"%s\\\" defined in domains not '\\n 'found in axes (%s)'\\n % (an, ','.join(self.axes_names)))\\n\\n ia = self.axes_names.index(an)\\n l = self.data.shape[ia]\\n if len(dom) != l:\\n raise Exception('Length of domain for axis \\\"%s\\\" (%d) '\\n 'does not match length of data '\\n 'axis %d (%d) ' % (an, len(dom), ia, l))\\n\\n if len(set(dom)) != len(dom):\\n raise Exception('Domain of axis \\\"%s\\\" does not contain '\\n 'unique values' % an)\\n\\n axes_domains[an] = np.asarray(dom)\\n\\n self.axes_domains.update(axes_domains)\\n\\n logger.debug('Axes names: %s', str(self.axes_names))\\n logger.debug('Axes domains: %s', str(self.axes_domains))\",\n \"def load_from_netcdf(filename):\\n filename = os.path.join(datadir, filename + '.nc')\\n return xr.open_dataarray(filename)\",\n \"def _from_xarray_Dataset(self, ds, ztsp, chem_names): \\n # Get the variable names in the Dataset\\n keys = []\\n for key in ds.data_vars:\\n keys += [key]\\n \\n # Get the correct set of variables chosen by the user\\n if 'all' in chem_names:\\n # We need to take all variables in the dataset\\n non_chems = ['time', 'lat', 'lon'] + ztsp\\n chem_names = [name for name in keys if name not in non_chems]\\n \\n # Make sure the current data are included as the user probably\\n # considers them much like the temperature and pressure\\n current_vars = ['ua', 'va', 'wa']\\n for var in current_vars:\\n if var not in chem_names and var in keys:\\n chem_names += [var]\\n \\n # Load the data from the netCDF dataset variables\\n ztsp_units, chem_units = get_xarray_data(ds, ztsp, chem_names)\\n \\n # Return the final set of data\\n return (ztsp_units, chem_names, chem_units)\",\n \"def _image_data(self):\\n if self.header['Image']['bytes per pixel'] == 2:\\n # 16-bit unsigned integers, short\\n dt = np.dtype(self._bo + 'H')\\n elif self.header['Image']['bytes per pixel'] == 4:\\n # 32-bit unsigned integers, int\\n dt = np.dtype(self._bo + 'I')\\n\\n shape = [self.header['Image']['planes'],\\n self.header['Image']['masses'],\\n self.header['Image']['height'],\\n self.header['Image']['width']]\\n\\n self.fh.seek(self.header['header size'])\\n\\n compressedfiles = (\\n gzip.GzipFile,\\n bz2.BZ2File,\\n tarfile.ExFileObject,\\n lzma.LZMAFile,\\n io.BytesIO\\n )\\n\\n # fromfile is about 2x faster than frombuffer(fh.read())\\n if isinstance(self.fh, compressedfiles):\\n data = np.frombuffer(self.fh.read(), dtype=dt).reshape(shape)\\n else:\\n data = np.fromfile(self.fh, dtype=dt).reshape(shape)\\n\\n # We want to have a cube of contiguous data (stacked images) for each\\n # mass. Swap axes 0 and 1. Returns a view, so make full copy to make\\n # data access faster.\\n data = data.swapaxes(0, 1).copy()\\n\\n self.data = xarray.DataArray(data,\\n dims=('species', 'frame', 'y', 'x'),\\n coords={'species': ('species', list(self.header['label list']))},\\n attrs={'unit': 'counts'})\",\n \"def serialize_numpy(self, buff, numpy):\\n try:\\n _x = self\\n buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\\n _x = self.header.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self\\n buff.write(_get_struct_2H().pack(_x.image_width, _x.image_height))\\n length = len(self.Hlines)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.Hlines:\\n _x = val1\\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\\n length = len(self.Vlines)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.Vlines:\\n _x = val1\\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\\n buff.write(_get_struct_H().pack(self.PFPS))\\n length = len(self.regions)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.regions:\\n _v5 = val1.color\\n _x = _v5\\n buff.write(_get_struct_4f().pack(_x.r, _x.g, _x.b, _x.a))\\n _v6 = val1.moment\\n _x = _v6\\n buff.write(_get_struct_10f().pack(_x.m00, _x.m10, _x.m01, _x.m11, _x.m20, _x.m02, _x.m21, _x.m12, _x.m30, _x.m03))\\n _x = self\\n buff.write(_get_struct_2H().pack(_x.box_width, _x.box_height))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"def _make_array(x):\\n try:\\n x = np.asfarray(x).squeeze()\\n except ValueError:\\n pass\\n return x\",\n \"def split_db_original(x, components):\\n cm = components[1]\\n ap = []\\n for itera in cm:\\n ap.append(x[:, itera].tolist())\\n ap_np = np.transpose(np.array(ap))\\n\\n return ap_np\",\n \"def coords_to_structure(self) -> None:\\n ...\",\n \"def from_chx(array):\\n return _backend._convert_arrays(array, _array_from_chainerx)\",\n \"def writeNetCDFData(out_nc, hrus, dr_time, hru_type, remapped_data, var_meta, var_attrs, var_encodings, remap_idx):\\n\\n dataset = xr.Dataset()\\n\\n for varname, meta in var_meta.items():\\n foo = xr.DataArray(remapped_data[varname][:, remap_idx],\\n dims=['time', 'basinID'],\\n name=varname)\\n\\n foo.encoding = var_encodings[varname]\\n foo.attrs = var_attrs[varname]\\n\\n dataset[varname] = foo\\n\\n # HRU ID variables\\n dataset['basinID'] = xr.DataArray(hrus[remap_idx], dims=['basinID'])\\n dataset['basinID'].encoding = {'dtype': hru_type, '_FillValue': None}\\n dataset['basinID'].attrs = {'long_name': 'Basin ID'}\\n\\n dataset[TIME_DIM_NAME] = dr_time\\n\\n dataset.to_netcdf(out_nc, unlimited_dims='time')\",\n \"def process_extended_xyz_file_to_array(extended_xyz_file_path, verbose=True):\\n\\n with open(extended_xyz_file_path, \\\"r\\\") as input_file:\\n\\n # Read all the lines at once\\n lines = input_file.readlines()\\n\\n # Get the number of atoms per block, which is always the first line of\\n # either an xyz or extended xyz file\\n n_atoms = int(lines[0].strip())\\n\\n # We can print some diagnostics to help us debug\\n if verbose:\\n print(\\n f\\\"Read {len(lines)} lines from {extended_xyz_file_path}, each \\\"\\n f\\\"block has {n_atoms} atoms\\\"\\n )\\n\\n # Each \\\"single\\\" xyz file has the following lines:\\n # A single line indicating how many atoms there are in the block\\n # A comment line\\n # n_atoms lines for the species type and coordinates\\n # With this information, we can \\\"chunk\\\" the list into some number of equal\\n # parts each containing 12+2 lines.\\n # Check out a way to do this here:\\n # https://www.delftstack.com/howto/python/\\n # python-split-list-into-chunks/\\n # #split-list-in-python-to-chunks-using-the-lambda-function\\n EXTRA_LINES = 2 # <- no magic numbers\\n offset = n_atoms + EXTRA_LINES\\n\\n # List comprehension is much faster than for loops. Try to avoid the latter\\n # when at all possible\\n chunked = [lines[ii:ii + offset] for ii in range(0, len(lines), offset)]\\n\\n if verbose:\\n print(f\\\"Got {len(chunked)} snapshots\\\")\\n\\n # Each entry of chunked contains the:\\n # - number of atoms (same for everything)\\n # - the energy (I think)\\n # - the atom types/coordinates\\n # Get the energies\\n comment_lines = np.array([\\n float(lines[ii + 1]) for ii in range(0, len(lines), offset)\\n ])\\n\\n # Get the atom list - only have to do this once!\\n atom_list = [line.split()[0] for line in chunked[0][EXTRA_LINES:]]\\n\\n # Finally, get the coordinates\\n chunked = np.array([\\n [line.split()[1:4] for line in chunk[EXTRA_LINES:]]\\n for chunk in chunked\\n ], dtype=float)\\n\\n return dict(energy=comment_lines, elements=atom_list, coordinates=chunked)\",\n \"def _finalize_data(self):\\n\\n if isinstance(self.node_data, np.ndarray): # SR workflow\\n self.node_data = da.from_array(self.node_data)\\n elif isinstance(self.node_data, list): # vr workflow\\n struct_data = np.empty(len(self.node_data), dtype=self.data.dtype)\\n datavals = np.array(self.node_data)\\n for cnt, varname in enumerate(self.data.dtype.names):\\n struct_data[varname] = datavals[:, cnt]\\n self.node_data = da.from_array(struct_data)\\n if isinstance(self.data, np.ndarray):\\n self.data = da.from_array(self.data)\",\n \"def __init__(self, data_array, cui2name):\\n\\t\\tassert isinstance(data_array, xr.DataArray), 'Constructor requires xr.DataArray.'\\n\\n\\t\\tself.data_array = data_array\\n\\t\\tself.data = self.data_array.values\\n\\t\\tself.sources = self.data_array.source.values\\n\\t\\tself.targets = self.data_array.target.values\\n\\t\\tself.metrics = self.data_array.metric.values\\n\\t\\tself.metapaths = self.data_array.metapath.values\\n\\n\\t\\t# Fetch the verbose names\\n\\t\\t#int_rf_df = pd.read_csv('data/impd_cogn_sources.tsv', delimiter='\\\\t')\\n\\t\\t#cui2name = {cui:name for cui, name in zip(int_rf_df.identifier, int_rf_df.name)} \\n\\t\\tself.source_names = np.array([cui2name[source] for source in self.sources])\",\n \"def make_test_dataArray():\\n x = np.zeros(shape=(3,31))\\n x[0,:] = np.nan\\n x[1,[1,2,3,4,5,6,15,23,24,25]] = [np.nan,np.nan,0.1,0.5,2.,2.,2.,2.,0.9,2.]\\n x[2,[3,4,5,6,15,23,24,25]] = [0.1,0.5,2.,2.,2.,2.,0.9,2.]\\n da = xr.DataArray(x, dims=['x','time'])\\n da.coords['time'] = pd.date_range('19790101', freq='D', periods=31)\\n return da\",\n \"def data_to_x(self, new_data):\\n pass\",\n \"def serialize_numpy(self, buff, numpy):\\n try:\\n _x = self\\n buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\\n _x = self.header.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(self.polygons)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.polygons:\\n _v3 = val1.stamp\\n _x = _v3\\n buff.write(_struct_2I.pack(_x.secs, _x.nsecs))\\n buff.write(_struct_I.pack(val1.ID))\\n buff.write(val1.parameter.tostring())\\n length = len(val1.score)\\n buff.write(_struct_I.pack(length))\\n for val2 in val1.score:\\n _x = val2\\n buff.write(_struct_If.pack(_x.ID, _x.prob))\\n length = len(val1.polyline)\\n buff.write(_struct_I.pack(length))\\n for val2 in val1.polyline:\\n _x = val2\\n buff.write(_struct_3f.pack(_x.x, _x.y, _x.edge_prob))\\n length = len(val1.features)\\n buff.write(_struct_I.pack(length))\\n for val2 in val1.features:\\n _x = val2\\n buff.write(_struct_3fI.pack(_x.x, _x.y, _x.z, _x.ID))\\n _x = val1.energy\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_f.pack(val1.weight))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"def prepare_X_data(ibs, gid_list, use_gps=True):\\n images = ibs.images(gid_list, caching=True)\\n gps_list_ = images.gps2\\n unixtime_list_ = images.unixtime2\\n gps_list_ = vt.ensure_shape(gps_list_, (None, 2))\\n has_gps = np.all(np.logical_not(np.isnan(gps_list_)), axis=1)\\n has_time = np.logical_not(np.isnan(unixtime_list_))\\n\\n if not use_gps:\\n has_gps[:] = False\\n\\n has_both = np.logical_and(has_time, has_gps)\\n has_either = np.logical_or(has_time, has_gps)\\n has_gps_only = np.logical_and(has_gps, np.logical_not(has_both))\\n has_time_only = np.logical_and(has_time, np.logical_not(has_both))\\n has_neither = np.logical_not(has_either)\\n\\n both = images.compress(has_both)\\n xgps = images.compress(has_gps_only)\\n xtime = images.compress(has_time_only)\\n neither = images.compress(has_neither)\\n\\n # Group imagse with different attributes separately\\n datas = {\\n 'both': (both.gids, both.unixtime2, both.gps2),\\n 'gps_only': (xgps.gids, None, xgps.gps2),\\n 'time_only': (xtime.gids, xtime.unixtime2, None),\\n 'neither': (neither.gids, None, None),\\n }\\n return datas\",\n \"def xr_dataset_to_array(ds, z_coord):\\n # Determine the size of the dataset\\n nvars = len(ds.keys())\\n nvals = len(ds.coords[z_coord].values)\\n \\n # Create an empty array to store the data\\n data = np.zeros((nvals, nvars+1))\\n units = []\\n \\n # Insert the depth coordinate\\n data[:,0] = ds.coords[z_coord].values\\n units.append(ds.coords[z_coord].attrs['units'])\\n \\n # Insert the rest of the data\\n variables = list(ds.keys())\\n for i in range(len(variables)):\\n data[:,i+1] = ds[variables[i]].values\\n units.append(ds[variables[i]].attrs['units'])\\n \\n # Create a list of variables names\\n names = [z_coord] + variables\\n \\n # Return the data\\n return (data, names, units)\",\n \"def get_x_data(\\n params, dimension, fold, subject_list,\\n):\\n X = []\\n input_file_path = os.path.join(\\n params[\\\"orig_path\\\"], \\\"ae_output_{}\\\".format(params[\\\"modality\\\"])\\n )\\n for i in range(len(subject_list)):\\n x_sub_data_path = os.path.join(\\n input_file_path,\\n str(dimension),\\n \\\"fold_{}\\\".format(fold),\\n \\\"X_{}.npy\\\".format(subject_list[i]),\\n )\\n if not os.path.exists(x_sub_data_path):\\n x_sub_data = build_x_data(\\n dimension, fold, subject_list, i, params, out_file=input_file_path,\\n )\\n else:\\n x_sub_data = np.load(x_sub_data_path)\\n X.append(x_sub_data)\\n X = np.array(X)\\n return X\\n # interTVA data has already been run on taskFMRI, on frioul\",\n \"def write_compressed(self, filename):\\n\\n # Define which molecules to use \\n # (counting indices of processed data set)\\n indices = np.arange(len(self))\\n # All charges and position arrays have the same size\\n # (the one of the biggest molecule)\\n size = np.max( self.num_atoms )\\n # Initialize arrays\\n num_atoms = np.zeros(len(indices))\\n labels = np.zeros(len(indices))\\n charges = np.zeros([len(indices),size])\\n positions = np.zeros([len(indices),size,3])\\n # For each molecule ...\\n for j,idx in enumerate(indices):\\n # load the data\\n sample = self[idx]\\n # assign per-molecule data\\n labels[j] = sample['data']\\n num_atoms[j] = sample['num_atoms']\\n # ... and for each atom:\\n for ia in range(sample['num_atoms']):\\n charges[j,ia] = sample['charges'][ia]\\n positions[j,ia,0] = sample['positions'][ia][0] \\n positions[j,ia,1] = sample['positions'][ia][1] \\n positions[j,ia,2] = sample['positions'][ia][2]\\n\\n # Merge pairs\\n print(labels.shape,charges.shape,positions.shape)\\n labels = labels[0::2]\\n charges = np.array([np.concatenate((charges[i],charges[i+1])) for i in indices[0::2]])\\n positions = np.array([np.concatenate((positions[i],positions[i+1])) for i in indices[0::2]])\\n print(labels.shape,charges.shape,positions.shape)\\n \\n # Create a dictionary with all the values to save\\n save_dict = {}\\n save_dict['label'] = labels\\n save_dict['charges'] = charges\\n save_dict['positions'] = positions\\n\\n # Save as a compressed array \\n np.savez_compressed(filename,**save_dict)\\n \\n return\",\n \"def transform(self, x: Array2D) -> Array2D:\",\n \"def save_ncds(vardict,coords,attrs={},filename=None):\\n \\n if 'time' in coords.keys():\\n newtime = [np.double((t-dt(1800,1,1)).total_seconds()/3600) for t in coords['time']['data']]\\n delta_t = np.gradient(newtime)[0]\\n \\n coords['time']['data'] = newtime\\n coords['time']['attrs'] = {'long_name':\\\"Time\\\",\\n 'delta_t':f\\\"0000-00-{int(delta_t/24):02} {int(delta_t%24):02}:00:00\\\",\\n 'standard_name':\\\"time\\\",\\n 'axis': \\\"T\\\",\\n 'units':\\\"hours since 1800-01-01 00:00:0.0\\\"}\\n \\n if 'climo' in vardict.keys():\\n long_name = vardict['climo']['attrs']['long_name']\\n vardict['climo']['attrs']['long_name'] = 'Climatology of '+long_name\\n \\n encoding = {k: {'dtype': 'double', '_FillValue': 1e30} for k in coords.keys()}\\n for k in vardict.keys():\\n encoding.update({k: {'dtype': 'single', '_FillValue': 1e30}})\\n \\n ds = xr.Dataset.from_dict({\\n 'coords':coords,\\n 'data_vars':vardict,\\n 'dims':[k for k,v in coords.items()],\\n 'attrs':attrs,\\n })\\n \\n if isinstance(filename,str):\\n ds.to_netcdf(filename,encoding=encoding,mode='w',engine='scipy')\\n ds.close()\\n else:\\n print('filename must be a string')\",\n \"def GPy_reformat_3D(array):\\r\\n n_timesteps = np.shape(array)[-1]\\r\\n if len(np.shape(array)) == 1:\\r\\n array = array.reshape(n_timesteps, 1)\\r\\n return [array, array, array]\\r\\n elif len(np.shape(array)) == 2:\\r\\n array = array.T\\r\\n array1 = array[:, 0, None]\\r\\n array2 = array[:, 1, None]\\r\\n array3 = array[:, 2, None]\\r\\n return [array1, array2, array3]\\r\\n else:\\r\\n return print(\\\"Error in GPy_reformat, input array is wrong shape.\\\")\",\n \"def data_for_grouping():\\n return RaggedArray(\\n [[1, 0], [1, 0], [], [], [0, 0], [0, 0], [1, 0], [2, 0]])\",\n \"def xarray_to_df(file_path):\\n df = xr.open_dataset(file_path).to_dataframe()\\n df = df.reset_index()\\n return df\",\n \"def canonicalize(data):\\n data = data.transpose(*[d for d in map(data.axes.find, 'TCIZYX') if d >= 0])\\n projection = []\\n\\n if 'T' in data.axes and data.shape[0] == 1:\\n projection.append(0) # remove trivial T dimension\\n\\n if 'C' not in data.axes:\\n projection.append(None) # add trivial C dimension\\n elif projection:\\n projection.append(slice(None))\\n\\n if projection:\\n projection += [slice(None) for d in 'ZYX']\\n data = data.lazyget(tuple(projection))\\n \\n return data\",\n \"def serialize_numpy(self, buff, numpy):\\n try:\\n _x = self.name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.type_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self\\n buff.write(_struct_4f.pack(_x.home.latitude, _x.home.longitude, _x.home.altitude, _x.home.heading))\\n length = len(self.movements)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.movements:\\n _x = val1.name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_b.pack(val1.type))\\n length = len(val1.pre_actions)\\n buff.write(_struct_I.pack(length))\\n for val2 in val1.pre_actions:\\n _x = val2.name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_b.pack(val2.type))\\n _x = val2.action_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(val2.parameters)\\n buff.write(_struct_I.pack(length))\\n for val3 in val2.parameters:\\n _x = val3.key\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val3.value\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val2.slot_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(val2.receivers_name)\\n buff.write(_struct_I.pack(length))\\n for val3 in val2.receivers_name:\\n length = len(val3)\\n if python3 or type(val3) == unicode:\\n val3 = val3.encode('utf-8')\\n length = len(val3)\\n buff.write(struct.pack('<I%ss'%length, length, val3))\\n length = len(val1.post_actions)\\n buff.write(_struct_I.pack(length))\\n for val2 in val1.post_actions:\\n _x = val2.name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_b.pack(val2.type))\\n _x = val2.action_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(val2.parameters)\\n buff.write(_struct_I.pack(length))\\n for val3 in val2.parameters:\\n _x = val3.key\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val3.value\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val2.slot_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(val2.receivers_name)\\n buff.write(_struct_I.pack(length))\\n for val3 in val2.receivers_name:\\n length = len(val3)\\n if python3 or type(val3) == unicode:\\n val3 = val3.encode('utf-8')\\n length = len(val3)\\n buff.write(struct.pack('<I%ss'%length, length, val3))\\n buff.write(_struct_f.pack(val1.altitude))\\n _v5 = val1.target_position\\n _x = _v5\\n buff.write(_struct_4f.pack(_x.latitude, _x.longitude, _x.altitude, _x.heading))\\n buff.write(_struct_b.pack(val1.strategy))\\n _v6 = val1.duration\\n _x = _v6\\n buff.write(_struct_2i.pack(_x.secs, _x.nsecs))\\n _x = val1\\n buff.write(_struct_2fBf.pack(_x.radius, _x.circle_altitude, _x.clockwise, _x.direction))\\n length = len(self.move_transitions)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.move_transitions:\\n buff.write(_struct_B.pack(val1.is_choice))\\n _x = val1.wait_for_slot_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val1.from_move_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val1.to_move_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_B.pack(val1.fluid))\\n _x = val1.condition_identifier\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val1.false_branch_move_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(self.slot_names)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.slot_names:\\n length = len(val1)\\n if python3 or type(val1) == unicode:\\n val1 = val1.encode('utf-8')\\n length = len(val1)\\n buff.write(struct.pack('<I%ss'%length, length, val1))\\n buff.write(_struct_b.pack(self.travel_mode))\\n except struct.error as se: self._check_types(se)\\n except TypeError as te: self._check_types(te)\",\n \"def to_dataset(self):\\n import xarray as xr\\n ds = xr.Dataset(coords={'x': (['x', ], self.center_grid.x_coord),\\n 'y': (['y', ], self.center_grid.y_coord)}\\n )\\n ds.attrs['pyproj_srs'] = self.proj.srs\\n return ds\",\n \"def export_array(in_array, output_path):\\n global proj, geotrans, row, col\\n proj = band.GetProjection()\\n geotrans = band.GetGeoTransform()\\n row = band.RasterYSize\\n col = band.RasterXSize\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n outdata = driver.Create(output_path, col, row, 1)\\n outband = outdata.GetRasterBand(1)\\n outband.SetNoDataValue(-9999)\\n outband.WriteArray(in_array)\\n # Georeference the image\\n outdata.SetGeoTransform(geotrans)\\n # Write projection information\\n outdata.SetProjection(proj)\\n outdata.FlushCache()\\n outdata = None\",\n \"def initialize_data(self , station = '', datasets = {} ):\\n \\n self.datasets = datasets\\n self.datasets_keys = datasets.keys()\\n self.station = station\\n \\n data = {} # container for the data of each dataset\\n source_configuration = {} # container for the source_configuration of each dataset\\n \\n\\n \\n \\\"\\\"\\\" Looping over the datasets \\\"\\\"\\\"\\n logging.info('*** Reading and Initializing the data from the netCDF files ')\\n \\n \\n for k,v in datasets.items() :\\n logging.info(' Initialising the dataset: *** %s ' , k )\\n data[k] = {} \\n data['cdm_tables'] = {} \\n \\n ### alternative with xarray \\n #ds = xr.load_dataset(v) \\n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n ### alternative with netCDF4\\n #ds = nc.Dataset(v) \\n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \\n \\n ###for h5py but cant extract date time units !!!\\n ds = h5py.File(v , driver=\\\"core\\\" ) \\n data[k]['df'] = ds # storing the entire file \\n try: \\n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\\n except:\\n data[k]['source_file'] = str(v) # temp fix \\n \\n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \\n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\\n #data[k]['recordindex'] = ds['recordindex'].value \\n #ds.close() \\n logging.debug('Reading the file with h5py ')\\n \\n \\n # add here appending datasets for the case of ncar_w and ncar_t \\n \\n \\n self.data = data\\n self.make_dataframe()\\n ds.close()\\n \\n \\\"\\\"\\\" Reading the header_table, station_configuration, source_configuration \\\"\\\"\\\"\\n for k,v in datasets.items() : \\n \\n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \\n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\\n #data[k]['recordindex'] = d['recordindex'].values \\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \\n data[k]['station_configuration'] = d.to_dataframe() \\n #data[k]['station_configuration'] = d ### USELESS ? \\n logging.debug('Done with %s station_configuration' , str(k) )\\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \\n logging.debug('Loading the header_table') \\n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['header_table'] = {}\\n for var in d.variables:\\n self.attributes['header_table'][var] = {}\\n self.attributes['header_table'][var]['description'] = d[var].description\\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \\n data[k]['header_table'] = d.to_dataframe() \\n logging.debug('Done with %s ' , k )\\n \\n logging.info(\\\"*** Loading the observations_table (might take time) %s\\\" , k ) \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['observations_table'] = {}\\n for var in d.variables:\\n self.attributes['observations_table'][var] = {}\\n self.attributes['observations_table'][var]['description'] = d[var].description\\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\\n \\n \\n logging.info(\\\"*** Loading the source configuration %s\\\" , k ) \\n try: \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\\n d = d.isel(hdrlen=[0])\\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \\n logging.debug('Done with %s source_configuration' , k )\\n except: \\n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \\n \\n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \\n data[k]['era5fb'] = d.to_dataframe() \\n logging.debug('Done with %s era5 feedback ', k )\\n \\n \\\"\\\"\\\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \\\"\\\"\\\" \\n if list(datasets.keys()).index(k) == 0 :\\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \\n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\\n data['cdm_tables'][t] = d \\n \\n d.close() \\n ds.close()\\n\\n \\\"\\\"\\\" Reading the name of the original source file \\\"\\\"\\\"\\n source_configuration[k] = {} \\n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\\n\\n \\n \\\"\\\"\\\" Storing the station configurations \\\"\\\"\\\" \\n self.source_configuration = source_configuration \\n \\n \\\"\\\"\\\" Making all date_times \\\"\\\"\\\" \\n self.make_all_datetime()\\n \\n \\n \\\"\\\"\\\" feedback columns \\\"\\\"\\\"\\n if 'era5_1' in list (self.data.keys() ):\\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \\n else:\\n self.fb_columns = ['empty']\",\n \"def _reindex2d(self, index, dst_nodata=np.nan):\\n # create new DataArray for output\\n dst_coords = {d: self._obj.coords[d] for d in self._obj.dims}\\n ys, xs = index.raster.ycoords, index.raster.xcoords\\n dst_coords.update({self.y_dim: ys, self.x_dim: xs})\\n da_reproject = full(\\n dst_coords,\\n nodata=dst_nodata,\\n dtype=self._obj.dtype,\\n name=self._obj.name,\\n attrs=self._obj.attrs,\\n crs=index.raster.crs,\\n shape=index.raster.shape\\n if self.dim0 is None\\n else (self._obj.shape[0], *index.raster.shape),\\n dims=self.dims if self.dim0 is None else (self.dim0, *self.dims),\\n )\\n # reproject by indexing\\n shape2d = (self._obj.shape[0] if self.dim0 else 1, self.size)\\n src_data = self._obj.load().data.reshape(shape2d)\\n idxs = index.values\\n valid = idxs >= 0\\n if self.dim0:\\n da_reproject.data[:, valid] = src_data[:, idxs[valid]]\\n else:\\n da_reproject.data[valid] = src_data[:, idxs[valid]].squeeze()\\n return da_reproject\",\n \"def test_netCDF_field_components(self):\\n # Geometries\\n f = cfdm.example_field(6)\\n\\n for component in (\\\"interior_ring\\\", \\\"node_count\\\", \\\"part_node_count\\\"):\\n f.nc_set_component_variable(component, \\\"ncvar\\\")\\n f.nc_set_component_variable_groups(component, [\\\"forecast\\\"])\\n\\n f.nc_clear_component_variable_groups(component)\\n f.nc_del_component_variable(component)\\n\\n f.nc_del_component_variable(component)\\n f.nc_clear_component_variable_groups(component)\\n\\n f.nc_set_component_variable(component, \\\"ncvar\\\")\\n f.nc_set_component_variable_groups(component, [\\\"forecast\\\"])\\n\\n for component in (\\\"interior_ring\\\", \\\"part_node_count\\\"):\\n f.nc_set_component_dimension(component, \\\"ncvar\\\")\\n f.nc_set_component_dimension_groups(component, [\\\"forecast\\\"])\\n\\n f.nc_clear_component_dimension_groups(component)\\n f.nc_del_component_dimension(component)\\n\\n f.nc_del_component_dimension(component)\\n f.nc_clear_component_dimension_groups(component)\\n\\n f.nc_set_component_dimension(component, \\\"ncvar\\\")\\n f.nc_set_component_dimension_groups(component, [\\\"forecast\\\"])\\n\\n # Compression: indexed and contiguous\\n f = cfdm.example_field(4)\\n f.compress(\\\"indexed_contiguous\\\", inplace=True)\\n\\n for component in (\\\"count\\\", \\\"index\\\"):\\n f.nc_set_component_variable(component, \\\"ncvar\\\")\\n f.nc_set_component_variable_groups(component, [\\\"forecast\\\"])\\n\\n f.nc_clear_component_variable_groups(component)\\n f.nc_del_component_variable(component)\\n\\n f.nc_del_component_variable(component)\\n f.nc_clear_component_variable_groups(component)\\n\\n f.nc_set_component_variable(component, \\\"ncvar\\\")\\n f.nc_set_component_variable_groups(component, [\\\"forecast\\\"])\\n\\n for component in (\\\"count\\\", \\\"index\\\"):\\n f.nc_set_component_dimension(component, \\\"ncvar\\\")\\n f.nc_set_component_dimension_groups(component, [\\\"forecast\\\"])\\n\\n f.nc_clear_component_dimension_groups(component)\\n f.nc_del_component_dimension(component)\\n\\n f.nc_del_component_dimension(component)\\n f.nc_clear_component_dimension_groups(component)\\n\\n f.nc_set_component_dimension(component, \\\"ncvar\\\")\\n f.nc_set_component_dimension_groups(component, [\\\"forecast\\\"])\\n\\n for component in (\\\"count\\\", \\\"index\\\"):\\n f.nc_set_component_sample_dimension(component, \\\"ncvar\\\")\\n f.nc_set_component_sample_dimension_groups(component, [\\\"forecast\\\"])\\n\\n f.nc_clear_component_sample_dimension_groups(component)\\n f.nc_del_component_sample_dimension(component)\\n\\n f.nc_del_component_sample_dimension(component)\\n f.nc_clear_component_sample_dimension_groups(component)\\n\\n f.nc_set_component_sample_dimension(component, \\\"ncvar\\\")\\n f.nc_set_component_sample_dimension_groups(component, [\\\"forecast\\\"])\\n\\n # Compression: gathered\\n component = \\\"list\\\"\\n\\n # Expected exceptions\\n for component in (\\\"list\\\", \\\"node_count\\\"):\\n with self.assertRaises(ValueError):\\n f.nc_set_component_dimension(component, \\\"ncvar\\\")\\n\\n with self.assertRaises(ValueError):\\n f.nc_del_component_dimension(component)\\n\\n with self.assertRaises(ValueError):\\n f.nc_set_component_dimension_groups(component, \\\"ncvar\\\")\\n\\n with self.assertRaises(ValueError):\\n f.nc_clear_component_dimension_groups(component)\\n\\n with self.assertRaises(ValueError):\\n f.nc_set_component_sample_dimension(component, \\\"ncvar\\\")\\n\\n with self.assertRaises(ValueError):\\n f.nc_del_component_sample_dimension(component)\\n\\n with self.assertRaises(ValueError):\\n f.nc_set_component_sample_dimension_groups(component, \\\"ncvar\\\")\\n\\n with self.assertRaises(ValueError):\\n f.nc_clear_component_sample_dimension_groups(component)\\n\\n # Expected exceptions\\n for component in (\\\"WRONG\\\",):\\n with self.assertRaises(ValueError):\\n f.nc_set_component_variable(component, \\\"ncvar\\\")\\n\\n with self.assertRaises(ValueError):\\n f.nc_del_component_variable(component)\\n\\n with self.assertRaises(ValueError):\\n f.nc_set_component_variable_groups(component, \\\"ncvar\\\")\\n\\n with self.assertRaises(ValueError):\\n f.nc_clear_component_variable_groups(component)\",\n \"def read_netcdf(self,filename):\",\n \"def reformat(x):\\n x = x.permute(0, 2, 3, 1)\\n N, D1, D2, Feat = x.size()\\n x = x.view(N, D1 * D2, Feat)\\n return x\",\n \"def reformat(x):\\n x = x.permute(0, 2, 3, 1)\\n N, D1, D2, Feat = x.size()\\n x = x.view(N, D1 * D2, Feat)\\n return x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57716465","0.5675053","0.5598693","0.5529777","0.5516983","0.54658604","0.54497313","0.54397637","0.53918636","0.5390577","0.5377511","0.5367062","0.5363264","0.5362354","0.53438973","0.5343177","0.5296879","0.5289519","0.5279383","0.5264651","0.5245457","0.519476","0.5188231","0.5187344","0.516218","0.51404256","0.5105498","0.5096492","0.50843686","0.50721246","0.50531244","0.50504994","0.503895","0.5036031","0.50353795","0.50307286","0.5027354","0.50060767","0.49995577","0.49982584","0.49945986","0.49909303","0.49906126","0.49836388","0.4979259","0.49782115","0.49749655","0.49742958","0.49669603","0.49637422","0.49558052","0.49518254","0.49512747","0.49506173","0.4938212","0.49194098","0.4908029","0.49068618","0.49026388","0.49025506","0.49025506","0.4902169","0.48991933","0.4893749","0.48906216","0.48900867","0.4887525","0.48725298","0.48708686","0.48699367","0.48684907","0.48550537","0.48543912","0.4853672","0.48485482","0.4848158","0.48472407","0.4845182","0.48343152","0.48328435","0.4830019","0.48219594","0.48212764","0.48195565","0.48169157","0.4811301","0.48063076","0.48019403","0.47994474","0.47989315","0.47966328","0.4794573","0.47937787","0.47911105","0.4789666","0.47787568","0.4778118","0.47771564","0.47760984","0.477604","0.477604"],"string":"[\n \"0.57716465\",\n \"0.5675053\",\n \"0.5598693\",\n \"0.5529777\",\n \"0.5516983\",\n \"0.54658604\",\n \"0.54497313\",\n \"0.54397637\",\n \"0.53918636\",\n \"0.5390577\",\n \"0.5377511\",\n \"0.5367062\",\n \"0.5363264\",\n \"0.5362354\",\n \"0.53438973\",\n \"0.5343177\",\n \"0.5296879\",\n \"0.5289519\",\n \"0.5279383\",\n \"0.5264651\",\n \"0.5245457\",\n \"0.519476\",\n \"0.5188231\",\n \"0.5187344\",\n \"0.516218\",\n \"0.51404256\",\n \"0.5105498\",\n \"0.5096492\",\n \"0.50843686\",\n \"0.50721246\",\n \"0.50531244\",\n \"0.50504994\",\n \"0.503895\",\n \"0.5036031\",\n \"0.50353795\",\n \"0.50307286\",\n \"0.5027354\",\n \"0.50060767\",\n \"0.49995577\",\n \"0.49982584\",\n \"0.49945986\",\n \"0.49909303\",\n \"0.49906126\",\n \"0.49836388\",\n \"0.4979259\",\n \"0.49782115\",\n \"0.49749655\",\n \"0.49742958\",\n \"0.49669603\",\n \"0.49637422\",\n \"0.49558052\",\n \"0.49518254\",\n \"0.49512747\",\n \"0.49506173\",\n \"0.4938212\",\n \"0.49194098\",\n \"0.4908029\",\n \"0.49068618\",\n \"0.49026388\",\n \"0.49025506\",\n \"0.49025506\",\n \"0.4902169\",\n \"0.48991933\",\n \"0.4893749\",\n \"0.48906216\",\n \"0.48900867\",\n \"0.4887525\",\n \"0.48725298\",\n \"0.48708686\",\n \"0.48699367\",\n \"0.48684907\",\n \"0.48550537\",\n \"0.48543912\",\n \"0.4853672\",\n \"0.48485482\",\n \"0.4848158\",\n \"0.48472407\",\n \"0.4845182\",\n \"0.48343152\",\n \"0.48328435\",\n \"0.4830019\",\n \"0.48219594\",\n \"0.48212764\",\n \"0.48195565\",\n \"0.48169157\",\n \"0.4811301\",\n \"0.48063076\",\n \"0.48019403\",\n \"0.47994474\",\n \"0.47989315\",\n \"0.47966328\",\n \"0.4794573\",\n \"0.47937787\",\n \"0.47911105\",\n \"0.4789666\",\n \"0.47787568\",\n \"0.4778118\",\n \"0.47771564\",\n \"0.47760984\",\n \"0.477604\",\n \"0.477604\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":676,"cells":{"query":{"kind":"string","value":"Taken a word, if it is an abbreviation it returns the meaning"},"document":{"kind":"string","value":"def get_abbr(self, word):\n assert (self.collection is not None)\n\n for conv in self.collection:\n ln = conv.split(\"*\")\n if word == ln[0]:\n return ln[1]\n\n return None"},"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 convert_abbrev(word):\r\n return abbreviations[word.lower()] if word.lower() in abbreviations.keys() else word","def aux_lemma(word):\n if re.match(r\"(does|did|doing)\", word):\n return (\"do\")\n elif re.match(r\"(had|has|'ve|having)\", word):\n return (\"have\")\n elif re.match(r\"(is|are|am|was|were|been|'s|being)\", word):\n return (\"be\")\n elif word == (\"'d\"):\n return (\"would\")\n else:\n return word.lower()","def get_casing(word):\n if len(word) == 0:\n return \"other\"\n elif word.isdigit(): # Is a digit\n return \"numeric\"\n elif word.islower(): # All lower case\n return \"allLower\"\n elif word.isupper(): # All upper case\n return \"allUpper\"\n # is a title, initial char upper, then all lower\n elif word[0].isupper():\n return \"initialUpper\"\n\n return \"other\"","def disambiguate(self, word):\n matches = re.match(r'^pen([cdjz])(.*)$', word)\n if matches:\n return matches.group(1) + matches.group(2)","def test_police_abbreviations(self):\n for word in self.report.get_words():\n for uword in self.rules.police_abbreviations:\n if uword[\"word\"] == word.text.lower():\n self.add_error(\n f\"{word.text} är en intern förkortning. \"\n f\"Använd {uword['means']} istället.\",\n word=word,\n )","def get_american_term_definition(word: str) -> str:\n word = process_word(word)\n return AMERICAN_ENGLISH_ONLY_TERMS[word]","def det_lemma(word):\n if word == (\"an\"):\n return (\"a\")\n else:\n return word.lower()","def main() -> None:\n word: str = input(\"Write some text with some uppercase letters: \")\n abbreviation_out: str = abbreviate(word)\n print(f\"The abbreviation is \\\"{abbreviation_out}\\\".\")\n return None","def match_name(sentence):\n if \"WIFE\" in sentence:\n return \"WIFE\"\n elif \"MAHAVIR\" in sentence or \"FATHER\" in sentence or \"SINGH\" in sentence: \n return \"MAHAVIR\"\n elif \"TEENAGER\" in sentence:\n return \"TEENAGER\"\n elif \"GIRL\" in sentence or \"WOMAN\" in sentence: \n return \"WOMAN\"\n elif \"GUY\" in sentence or \"MAN\" in sentence or \"BROTHER\" in sentence: \n return \"MAN\"\n elif \"COACH\" in sentence:\n return \"COACH\"\n elif \"COMMENT\" in sentence:\n return \"COMMENTATOR\"\n elif sentence[-2:] == \"ER\" or sentence[-3:] == \"IAN\" or sentence[-2:] == \"OR\" or sentence[-1:] == \"D\":\n return \"MISC\"\n \n return sentence","def verb_lemma(word):\n if word.endswith(\"ed\"):\n if word[:-2].endswith(\"v\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"at\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"it\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"et\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ut\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ac\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"i\"):\n return word[:-3].lower() + \"y\"\n elif word[:-2].endswith(\"ir\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ag\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"nc\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"nu\"):\n return word[:-2].lower() + \"e\"\n else:\n return word[:-2].lower() \n elif word.endswith(\"ing\"):\n if word[:-3].endswith(\"v\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"at\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"it\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"et\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ut\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ac\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"i\"):\n return word[:-4].lower() + \"y\"\n elif word[:-3].endswith(\"ir\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ag\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"nc\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"nu\"):\n return word[:-3].lower() + \"e\"\n else:\n return word[:-3].lower()\n elif re.match(r\"(does|did|done)\", word):\n return (\"do\")\n elif re.match(r\"(is|are|am|was|will|were|been)\", word):\n return (\"be\")\n elif word == (\"'s\"):\n return (\"be\")\n elif re.match(r\"(had|has|'ve)\", word):\n return (\"have\")\n else:\n return word.lower()","def is_abbr(word):\r\n\twith open(\"ABBR_DICT.txt\", \"r\", encoding = \"utf-8\") as f:\r\n\t\twordlist = [line.split(\"\\n\")[0] for line in f]\r\n\tif word.upper() in wordlist:\r\n\t\treturn 1\r\n\treturn 0","def find_abecedarian_words():\n pass","def is_abecedarian(word):\n pass","def part_lemma(word):\n if word == (\"n't\"):\n return (\"not\")\n else:\n return word.lower()","def verb_stem(s):\n \n #If the stem is have, its 3s form is has.\n if s == \"has\" :\n return \"have\"\n\n #If the stem ends in y preceded by a vowel, simply add s (pays, buys).\n elif re.match(r\"[A-z]+[aeiou][y]s\\b\", s):\n str = s[:-1]\n\n #If the stem ends in y preceded by a non-vowel and contains at least three letters, change the y to ies (flies, tries, unifies).\n elif re.match(r\"[A-z]+[^aeiou]ies\\b\", s):\n str = s[:-3] + 'y'\n\n #If the stem is of the form Xie where X is a single letter other than a vowel, simply add s (dies, lies, ties note that this doesnt account for unties).\n elif re.match(r\"[^aeiou]ies\\b\", s):\n str = s[:-1]\n\n #If the stem ends in o,x,ch,sh,ss or zz, add es (goes, boxes, attaches, washes, dresses, fizzes).\n elif re.match(r\"[A-z]+([ox]|[cs]h|[s]s|[z]z)es\\b\", s): \n str = s[:-2]\n\n #If the stem ends in se or ze but not in sse or zze, add s (loses, dazes, lapses, analyses).\n elif re.match(r\"[A-z]+([s][^s][e]|[z][^z][e])s\\b\", s):\n str = s[:-1]\n\n #If the stem ends in e not preceded by i,o,s,x,z,ch,sh, just add s (likes, hates, bathes).\n elif re.match(r\"[A-z]+([^iosxz]|[^ch]|[^sh])es\\b\", s):\n str = s[:-1]\n \n #If the stem ends in anything except s,x,y,z,ch,sh or a vowel, add s (eats, tells, shows)\n elif re.match(r\"[A-z]+([^sxyzaeiou]|[^cs]h)s\\b\", s):\n str = s[:-1]\n\n else: \n str = \"\"\n\n\n matches = [(w, t) for (w, t) in vb_list if (w == s or w == str)]\n\n tag_s = [(w, t) for (w, t) in matches if w == s and t == 'VBZ']\n\n if tag_s == True:\n return str\n else:\n tag_str = [t for (w, t) in matches if w == str and t == 'VB']\n\n if not (tag_s or tag_str):\n str = \"\"\n\n return str","def choose_word():\n pass","def acronym(name):\n return tuple(map(first, filter(capitalized, name.split())))","def adj_lemma(word):\n if word.endswith(\"er\"):\n return word[:-2].lower()\n elif word != (\"best\") and word.endswith(\"est\"):\n return word[:-3].lower()\n else:\n return word.lower()","def process_word(self, word: str) -> list[str]:\n d = self.d\n if not d:\n return None\n if d.check(word):\n return None\n # Speed doesn't matter here. The more we find, the more convenient.\n # Remove all digits.\n word = ''.join([i for i in word if not i.isdigit()])\n if d.check(word) or d.check(word.lower()):\n return None\n if word.find('_') > -1:\n # Snake case.\n words = word.split('_')\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n words = g.unCamel(word)\n if words:\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n return d.suggest(word)","def process_word(self, word: str) -> list[str]:\n d = self.d\n if not d:\n return None\n if d.check(word):\n return None\n # Speed doesn't matter here. The more we find, the more convenient.\n # Remove all digits.\n word = ''.join([i for i in word if not i.isdigit()])\n if d.check(word) or d.check(word.lower()):\n return None\n if word.find('_') > -1:\n # Snake case.\n words = word.split('_')\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n words = g.unCamel(word)\n if words:\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n return d.suggest(word)","def __isNoun__(self, word):\n self.nouns = ('door', 'bear', 'princess', 'cabinet')\n for noun in self.nouns:\n if noun == word:\n return ('noun', word), True\n return None, False","def get_british_term_definition(word: str) -> str:\n word = process_word(word)\n return BRITISH_ENGLISH_ONLY_TERMS[word]","def word_of_the_day():\n r = requests.get(\"http://www.urbandictionary.com\") # link is always homepage\n soup = BeautifulSoup(r.content, features=\"html.parser\") # sets up soup\n def_header = \"**\" + soup.find(\"div\", attrs={\"class\": \"def-header\"}).text.replace(\"unknown\",\n \"\") + \"**\" # header is the word we are defining\n # def_header = def_header[0:len(def_header) - 10] # header always ends in \"unknown\" this removes it\n meaning = soup.find(\"div\", attrs={\"class\": \"meaning\"}).text # gets the definition\n # formatting TODO move to controller\n for x in [1, 2, 3, 4, 5, 6, 7, 8, 9]:\n meaning = meaning.replace(str(x) + \". \", \"\\n\" + str(x) + \". \")\n for x in [\"v.\", \"n.\"]:\n meaning = meaning.replace(x, x.upper()[:-1])\n example = soup.find(\"div\", attrs={\"class\": \"example\"}).text # gets the example\n output = def_header + \": \" + \"```\" + meaning + \"\\nEx: \" + example + \"```\" # output string\n output = output.replace(\"&apos\", \"'\") # replaces weird formatting of ' from original\n return output # returns the word, defintion, and example","def getDisambiguatedByNextVerb(self, word):\n\t\treturn disambig_const.DISAMBIGUATATION_TABLE.get(word, {}).get('verb', {}).get('vocalized', word);","def indefinite(self):\n return \"an\" if self.short_desc[0] in 'aeiou' else \"a\"","def med_in_english(word):\r\n\treturn int(med(TextBlob(word).correct(), word))","def noun_lemma(word):\n if word.endswith(\"s\"):\n if word.endswith(\"ss\"):\n return word.lower()\n elif word.endswith(\"ies\"):\n return word[:-3].lower() + (\"y\")\n else:\n return word[:-1].lower()\n if word.endswith(\"men\"):\n return word[:-2].lower() + (\"an\")\n else:\n return word.lower()","def a_or_an(s):\n if s[0].lower() in 'aeiou':\n return 'an'\n return 'a'","def get_word(self) -> str: \n #return str(choice(word_list).upper())\n return \"ANONYMOUS\"","def good_word(self, word):\r\n return word.strip().lower()","def format_meaning(phrase, data):\n if 'tuc' not in data:\n return '%s: no meaning found' % phrase\n\n for t in data.get('tuc'):\n if 'meanings' in t:\n message = '; '.join([_clean_text(x['text'])\n for x in t['meanings']])\n return '%s: %s' % (phrase, message)\n\n # we didn't find a more detailed description, so check if there\n # is a simpler one so we don't return empty handed.\n for t in data.get('tuc'):\n if 'phrase' in t and 'text' in t.get('phrase', {}):\n return '%s: %s' % (phrase, _clean_text(t['phrase']['text']))\n\n return '%s: no meaning found' % phrase","def lookup_pronunciations_for_word(word: Text) -> Sequence[Word]:\n return EnglishUtils.all_possible_forms_for(word)","def pluralized(word):\n defined_plurals = {\n \"person\": \"people\"\n }\n if word in defined_plurals:\n return defined_plurals[word]\n\n es_endings = [\"s\", \"sh\", \"ch\", \"x\", \"z\"]\n if any([word.endswith(ending) for ending in es_endings]):\n return f\"{word}es\"\n else:\n return f\"{word}s\"","def noun_stem (s):\n\n \"\"\"codes from statements.py (PART A)\"\"\"\n def match (p):\n return re.match(p + '$', s, re.IGNORECASE)\n\n if (s in unchanging_plurals_list):\n return s\n elif (s[-3:] == 'men'):\n return s[0:-3] + 'man'\n elif match('.*(?<!.[aeiousxyz]|sh|ch)s'):\n return s[:-1]\n elif match('.*[aeiou]ys'):\n return s[:-1]\n elif match('.*.[^aeiou]ies'):\n return s[:-3] + 'y'\n elif match('[^aeiou]ies'):\n return s[:-1]\n elif match('.*(o|x|ch|ss|zz|sh)es'):\n return s[:-2]\n elif match('.*([^s]se|[^z]ze)s'):\n return s[:-1]\n elif match('.*(?<!.[iosxz]|sh|ch)es'):\n return s[:-1]\n else:\n return ''","def fry(word):\n\n # looks for a Y or y which will be (captured) followed and ended by an 'ou'\n match_you = re.match('([Yy])ou$', word)\n\n # First group will be the (captured) group so either 'Y' or 'y'\n if match_you:\n return match_you.group(1) + \"'all\"\n\n # looks for anyword ending in 'ing'\n match_ing = re.search('(.+)ing$', word)\n\n # checks if vowel exists before the 'ing'\n if match_ing:\n vowel_check = re.search('[aeiouy]', match_ing.group(1))\n # First group will be the (captured) group so everything before the 'ing'\n if vowel_check:\n return match_ing.group(1) + \"in'\"\n\n return word","def abbreviation(self):\n return self._abbreviation","def stem(self, word):\n word = word.lower()\n\n if word in self.__special_words:\n return self.__special_words[word]\n\n # Map the different apostrophe characters to a single consistent one\n word = (word.replace(u(\"\\u2019\"), u(\"\\x27\"))\n .replace(u(\"\\u2018\"), u(\"\\x27\"))\n .replace(u(\"\\u201B\"), u(\"\\x27\")))\n\n if word.startswith(u(\"\\x27\")):\n word = word[1:]\n\n if word.startswith(\"y\"):\n word = \"\".join((\"Y\", word[1:]))\n\n for i in range(1, len(word)):\n if word[i - 1] in self.__vowels and word[i] == \"y\":\n word = \"\".join((word[:i], \"Y\", word[i + 1:]))\n\n step1a_vowel_found = False\n step1b_vowel_found = False\n\n r1 = \"\"\n r2 = \"\"\n\n if word.startswith((\"gener\", \"commun\", \"arsen\")):\n if word.startswith((\"gener\", \"arsen\")):\n r1 = word[5:]\n else:\n r1 = word[6:]\n\n for i in range(1, len(r1)):\n if r1[i] not in self.__vowels and r1[i - 1] in self.__vowels:\n r2 = r1[i + 1:]\n break\n else:\n r1, r2 = self._r1r2_standard(word, self.__vowels)\n\n # STEP 0\n for suffix in self.__step0_suffixes:\n if word.endswith(suffix):\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n break\n\n # STEP 1a\n for suffix in self.__step1a_suffixes:\n if word.endswith(suffix):\n\n if suffix == \"sses\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix in (\"ied\", \"ies\"):\n if len(word[:-len(suffix)]) > 1:\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n else:\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n\n elif suffix == \"s\":\n for letter in word[:-2]:\n if letter in self.__vowels:\n step1a_vowel_found = True\n break\n\n if step1a_vowel_found:\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n break\n\n # STEP 1b\n for suffix in self.__step1b_suffixes:\n if word.endswith(suffix):\n if suffix in (\"eed\", \"eedly\"):\n\n if r1.endswith(suffix):\n word = \"\".join((word[:-len(suffix)], \"ee\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ee\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ee\"))\n else:\n r2 = \"\"\n else:\n for letter in word[:-len(suffix)]:\n if letter in self.__vowels:\n step1b_vowel_found = True\n break\n\n if step1b_vowel_found:\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n\n if word.endswith((\"at\", \"bl\", \"iz\")):\n word = \"\".join((word, \"e\"))\n r1 = \"\".join((r1, \"e\"))\n\n if len(word) > 5 or len(r1) >= 3:\n r2 = \"\".join((r2, \"e\"))\n\n elif word.endswith(self.__double_consonants):\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n\n elif ((r1 == \"\" and len(word) >= 3 and\n word[-1] not in self.__vowels and\n word[-1] not in \"wxY\" and\n word[-2] in self.__vowels and\n word[-3] not in self.__vowels)\n or\n (r1 == \"\" and len(word) == 2 and\n word[0] in self.__vowels and\n word[1] not in self.__vowels)):\n\n word = \"\".join((word, \"e\"))\n\n if len(r1) > 0:\n r1 = \"\".join((r1, \"e\"))\n\n if len(r2) > 0:\n r2 = \"\".join((r2, \"e\"))\n break\n\n # STEP 1c\n if (len(word) > 2\n and word[-1] in \"yY\"\n and word[-2] not in self.__vowels):\n word = \"\".join((word[:-1], \"i\"))\n if len(r1) >= 1:\n r1 = \"\".join((r1[:-1], \"i\"))\n else:\n r1 = \"\"\n\n if len(r2) >= 1:\n r2 = \"\".join((r2[:-1], \"i\"))\n else:\n r2 = \"\"\n\n # STEP 2\n for suffix in self.__step2_suffixes:\n if word.endswith(suffix):\n if r1.endswith(suffix):\n if suffix == \"tional\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix in (\"enci\", \"anci\", \"abli\"):\n word = \"\".join((word[:-1], \"e\"))\n\n if len(r1) >= 1:\n r1 = \"\".join((r1[:-1], \"e\"))\n else:\n r1 = \"\"\n\n if len(r2) >= 1:\n r2 = \"\".join((r2[:-1], \"e\"))\n else:\n r2 = \"\"\n\n elif suffix == \"entli\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix in (\"izer\", \"ization\"):\n word = \"\".join((word[:-len(suffix)], \"ize\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ize\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ize\"))\n else:\n r2 = \"\"\n\n elif suffix in (\"ational\", \"ation\", \"ator\"):\n word = \"\".join((word[:-len(suffix)], \"ate\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ate\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ate\"))\n else:\n r2 = \"e\"\n\n elif suffix in (\"alism\", \"aliti\", \"alli\"):\n word = \"\".join((word[:-len(suffix)], \"al\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"al\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"al\"))\n else:\n r2 = \"\"\n\n elif suffix == \"fulness\":\n word = word[:-4]\n r1 = r1[:-4]\n r2 = r2[:-4]\n\n elif suffix in (\"ousli\", \"ousness\"):\n word = \"\".join((word[:-len(suffix)], \"ous\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ous\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ous\"))\n else:\n r2 = \"\"\n\n elif suffix in (\"iveness\", \"iviti\"):\n word = \"\".join((word[:-len(suffix)], \"ive\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ive\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ive\"))\n else:\n r2 = \"e\"\n\n elif suffix in (\"biliti\", \"bli\"):\n word = \"\".join((word[:-len(suffix)], \"ble\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ble\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ble\"))\n else:\n r2 = \"\"\n\n elif suffix == \"ogi\" and word[-4] == \"l\":\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n\n elif suffix in (\"fulli\", \"lessli\"):\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix == \"li\" and word[-3] in self.__li_ending:\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n break\n\n # STEP 3\n for suffix in self.__step3_suffixes:\n if word.endswith(suffix):\n if r1.endswith(suffix):\n if suffix == \"tional\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix == \"ational\":\n word = \"\".join((word[:-len(suffix)], \"ate\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ate\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ate\"))\n else:\n r2 = \"\"\n\n elif suffix == \"alize\":\n word = word[:-3]\n r1 = r1[:-3]\n r2 = r2[:-3]\n\n elif suffix in (\"icate\", \"iciti\", \"ical\"):\n word = \"\".join((word[:-len(suffix)], \"ic\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ic\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ic\"))\n else:\n r2 = \"\"\n\n elif suffix in (\"ful\", \"ness\"):\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n\n elif suffix == \"ative\" and r2.endswith(suffix):\n word = word[:-5]\n r1 = r1[:-5]\n r2 = r2[:-5]\n break\n\n # STEP 4\n for suffix in self.__step4_suffixes:\n if word.endswith(suffix):\n if r2.endswith(suffix):\n if suffix == \"ion\":\n if word[-4] in \"st\":\n word = word[:-3]\n r1 = r1[:-3]\n r2 = r2[:-3]\n else:\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n break\n\n # STEP 5\n if r2.endswith(\"l\") and word[-2] == \"l\":\n word = word[:-1]\n elif r2.endswith(\"e\"):\n word = word[:-1]\n elif r1.endswith(\"e\"):\n if len(word) >= 4 and (word[-2] in self.__vowels or\n word[-2] in \"wxY\" or\n word[-3] not in self.__vowels or\n word[-4] in self.__vowels):\n word = word[:-1]\n\n word = word.replace(\"Y\", \"y\")\n return word","def meaning_of(word, app_id, app_key):\n \n url = 'https://od-api.oxforddictionaries.com:443/api/v1/entries/' + language + '/' + word.lower()\n r = requests.get(url, headers={\"app_id\":app_id, \"app_key\":app_key})\n\n data = r.json()\n useful_data = {}\n\n for i in data['results'][0]['lexicalEntries'][0]['entries']:\n for j in i:\n for k in i[j][0]:\n try:\n subdata = i[j][0][k]\n if k == 'subsenses':\n useful_data.update({\"meanings\":subdata[0]['definitions']})\n elif k == 'examples':\n useful_data.update({\"examples\":subdata[0]['text']})\n else:\n pass\n except:\n pass\n return useful_data","def main():\n word = input(\"Give me a word! \\n\\n\")\n vowels = ['a', 'e', 'i', 'o', 'u']\n if word[0].lower() in vowels:\n print(f\"\\n\\nPig latin: {word}way\")\n else:\n print(f\"\\n\\nPig latin: {word[1:]}{word[0]}ay\")","def acronym_gen(name):\n return tuple(w[0] for w in name.split() if capitalized(w))","def findPOS(word):\r\n\t\r\n lisPOS = list(wordtags[word])\r\n if \"ADJ\" in lisPOS:\r\n return \"ADJECTIVE\"\r\n if \"ADV\" in lisPOS:\r\n return \"ADVERB\"\r\n if \"NOUN\" in lisPOS:\r\n return \"NOUN\"","def getCasing(word):\n casing = 'other'\n \n numDigits = 0\n for char in word:\n if char.isdigit():\n numDigits += 1\n \n digitFraction = numDigits / float(len(word))\n \n if word.isdigit(): #Is a digit\n casing = 'numeric'\n elif digitFraction > 0.5:\n casing = 'mainly_numeric'\n elif word.islower(): #All lower case\n casing = 'allLower'\n elif word.isupper(): #All upper case\n casing = 'allUpper'\n elif word[0].isupper(): #is a title, initial char upper, then all lower\n casing = 'initialUpper'\n elif numDigits > 0:\n casing = 'contains_digit'\n \n return casing","def my_spell(word):\r\n from autocorrect import spell\r\n \r\n corrected_word = ''\r\n rescued_typo = 0\r\n \r\n if len(word)>1: # one letter word are not considered \r\n \r\n # try to correct typo\r\n if is_typo(word): \r\n print('typo: ' + word)\r\n word = spell(word)\r\n print('autocorrected typo: ' + word)\r\n\r\n if not is_typo(word): \r\n rescued_typo = 1\r\n corrected_word = word\r\n else:\r\n corrected_word = word\r\n\r\n return corrected_word, rescued_typo","def _match_word_vocab(word, vocab):\n if word not in vocab:\n if word.lower() in vocab:\n return word.lower()\n elif word.upper() in vocab:\n return word.upper()\n elif word.capitalize() in vocab:\n return word.capitalize()\n return word","def estimate(word):\n parts = re.split(r'[^aeiouy]+', word)\n valid_parts = []\n\n for part in parts:\n if part != '':\n valid_parts.append(part)\n\n syllables = 0\n\n for p in re_subsyllables:\n if p.match(word):\n syllables -= 1\n\n for p in re_addsyllables:\n if p.match(word):\n syllables += 1\n\n syllables += len(valid_parts)\n\n if syllables <= 0:\n syllables = 1\n\n return syllables","def getDisambiguatedByNextNoun(self, word):\n\t\treturn disambig_const.DISAMBIGUATATION_TABLE.get(word, {}).get('noun', {}).get('vocalized', word);","def getWord(wordType):\n if (wordType == ADJECTIVE) or (wordType == ADJECTIVE):\n newWord = input('Enter an ' + wordType.lower() + \":\\n\")\n return newWord\n else:\n newWord = input('Enter a ' + wordType.lower() + \":\\n\")\n return newWord","def stem(s):\n special = {'appall', 'kill', 'stroll', 'kiss', 'thrill', 'chugg', 'dress', 'err', 'express', 'fall', 'free', 'gall', 'add','cross', 'impress', 'inn', 'call', 'ball', 'bill', 'buzz'} \n ie_words = {'vying', 'lying', 'dying', 'tying'}\n short_ing = {'bring','sling','sping', 'bring', 'sing', 'ring', 'king', 'cling' ,'fling', 'wing', 'ding', 'ping', 'ting'}\n c_k_words = {'kick', 'muck', 'lock','pick', 'back', 'mock', 'peck', 'lock', 'nick'}\n\n if len(s) <= 3:\n return s\n if s[-3:] == 'ing' or s[-4:] == 'ings': \n if s in short_ing:\n return s\n elif s in special:\n return s[:-3]\n elif s[:-3] not in special and s[-4] == s[-5]:\n return s[:-4]\n elif s[:-3] not in c_k_words and s[-4] == 'k':\n return s[:-4]\n elif s == 'everything' or s == 'anything' or s == 'something':\n return s[:-5]\n elif s in ie_words:\n return s[0] + 'ie'\n else:\n return s[:-3]\n elif s[-3:] == 'ers':\n return s[:-3]\n elif s[-2:] == 'es':\n return s[:-2]\n elif s[-2:] == 'en':\n return s[:-2]\n elif s[-2:] == 'er':\n if s[-3] == s[-4]:\n return s[:-3]\n else:\n return s[:-2] \n elif s[-2:] == 'ed':\n if s[-3] == s[-4]:\n return s[:-3]\n else:\n return s[:-2]\n elif s[-3:] == 'ies':\n return s[:-2]\n elif s[-1:] == 's':\n return s[:-1]\n elif s[-1:] == 'e' and s not in ie_words:\n return s[:-1]\n elif s[-3:] == 'ful':\n return s[:-3]\n elif s[:2] == 'de':\n return s[2:]\n elif len(s) > 4 and s[-4:] == 'able' or s[-4] == 'ible':\n return s[:-4]\n elif s[:2] == 'in' or s[:2] == 'il' or s[:2] == 'ir':\n return s[2:]\n elif s[-1:] == 'y':\n return s[:-1] + 'i'\n else:\n return s","def filter(self, word):\n \n word = word.lower()\n try:\n self.engine.fetch(word)\n except socket.error:\n raise LemmaAPIError\n part_of_speeches = self.engine.part_of_speeches\n\n \n self.basic_form = word\n for part in part_of_speeches:\n if part == 'verb':\n if self.engine.is_verb_conjugated():\n if not self.conEngine.is_verb_regular(word, self.engine.get_basic_verb()):\n self.basic_form = self.engine.get_basic_verb()\n return word\n else:\n self.basic_form = self.engine.get_basic_verb()\n\n elif part == 'noun':\n if self.engine.is_noun_plural():\n if not self.conEngine.is_noun_regular(word, self.engine.get_singular_noun()):\n self.basic_form = self.engine.get_singular_noun() \n return word\n else:\n self.basic_form = self.engine.get_singular_noun()\n\n return self.basic_form","def stem(self, word):\n word = word.lower()\n\n step1_success = False\n\n # All acute accents are replaced by grave accents.\n word = (word.replace(u(\"\\xE1\"), u(\"\\xE0\"))\n .replace(u(\"\\xE9\"), u(\"\\xE8\"))\n .replace(u(\"\\xED\"), u(\"\\xEC\"))\n .replace(u(\"\\xF3\"), u(\"\\xF2\"))\n .replace(u(\"\\xFA\"), u(\"\\xF9\")))\n\n # Every occurrence of 'u' after 'q'\n # is put into upper case.\n for i in range(1, len(word)):\n if word[i - 1] == \"q\" and word[i] == \"u\":\n word = \"\".join((word[:i], \"U\", word[i + 1:]))\n\n # Every occurrence of 'u' and 'i'\n # between vowels is put into upper case.\n for i in range(1, len(word) - 1):\n if word[i - 1] in self.__vowels and word[i + 1] in self.__vowels:\n if word[i] == \"u\":\n word = \"\".join((word[:i], \"U\", word[i + 1:]))\n elif word[i] == \"i\":\n word = \"\".join((word[:i], \"I\", word[i + 1:]))\n\n r1, r2 = self._r1r2_standard(word, self.__vowels)\n rv = self._rv_standard(word, self.__vowels)\n\n # STEP 0: Attached pronoun\n for suffix in self.__step0_suffixes:\n if rv.endswith(suffix):\n if rv[-len(suffix) - 4:-len(suffix)] in (\"ando\", \"endo\"):\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n rv = rv[:-len(suffix)]\n\n elif (rv[-len(suffix) - 2:-len(suffix)] in\n (\"ar\", \"er\", \"ir\")):\n word = \"\".join((word[:-len(suffix)], \"e\"))\n r1 = \"\".join((r1[:-len(suffix)], \"e\"))\n r2 = \"\".join((r2[:-len(suffix)], \"e\"))\n rv = \"\".join((rv[:-len(suffix)], \"e\"))\n break\n\n # STEP 1: Standard suffix removal\n for suffix in self.__step1_suffixes:\n if word.endswith(suffix):\n if suffix == \"amente\" and r1.endswith(suffix):\n step1_success = True\n word = word[:-6]\n r2 = r2[:-6]\n rv = rv[:-6]\n\n if r2.endswith(\"iv\"):\n word = word[:-2]\n r2 = r2[:-2]\n rv = rv[:-2]\n\n if r2.endswith(\"at\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith((\"os\", \"ic\")):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2 .endswith(\"abil\"):\n word = word[:-4]\n rv = rv[:-4]\n\n elif (suffix in (\"amento\", \"amenti\",\n \"imento\", \"imenti\") and\n rv.endswith(suffix)):\n step1_success = True\n word = word[:-6]\n rv = rv[:-6]\n\n elif r2.endswith(suffix):\n step1_success = True\n if suffix in (\"azione\", \"azioni\", \"atore\", \"atori\"):\n word = word[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n rv = rv[:-len(suffix)]\n\n if r2.endswith(\"ic\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif suffix in (\"logia\", \"logie\"):\n word = word[:-2]\n rv = word[:-2]\n\n elif suffix in (\"uzione\", \"uzioni\",\n \"usione\", \"usioni\"):\n word = word[:-5]\n rv = rv[:-5]\n\n elif suffix in (\"enza\", \"enze\"):\n word = \"\".join((word[:-2], \"te\"))\n rv = \"\".join((rv[:-2], \"te\"))\n\n elif suffix == u(\"it\\xE0\"):\n word = word[:-3]\n r2 = r2[:-3]\n rv = rv[:-3]\n\n if r2.endswith((\"ic\", \"iv\")):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith(\"abil\"):\n word = word[:-4]\n rv = rv[:-4]\n\n elif suffix in (\"ivo\", \"ivi\", \"iva\", \"ive\"):\n word = word[:-3]\n r2 = r2[:-3]\n rv = rv[:-3]\n\n if r2.endswith(\"at\"):\n word = word[:-2]\n r2 = r2[:-2]\n rv = rv[:-2]\n\n if r2.endswith(\"ic\"):\n word = word[:-2]\n rv = rv[:-2]\n else:\n word = word[:-len(suffix)]\n rv = rv[:-len(suffix)]\n break\n\n # STEP 2: Verb suffixes\n if not step1_success:\n for suffix in self.__step2_suffixes:\n if rv.endswith(suffix):\n word = word[:-len(suffix)]\n rv = rv[:-len(suffix)]\n break\n\n # STEP 3a\n if rv.endswith((\"a\", \"e\", \"i\", \"o\", u(\"\\xE0\"), u(\"\\xE8\"),\n u(\"\\xEC\"), u(\"\\xF2\"))):\n word = word[:-1]\n rv = rv[:-1]\n\n if rv.endswith(\"i\"):\n word = word[:-1]\n rv = rv[:-1]\n\n # STEP 3b\n if rv.endswith((\"ch\", \"gh\")):\n word = word[:-1]\n\n word = word.replace(\"I\", \"i\").replace(\"U\", \"u\")\n return word","def define(word):\n\treturn lexicon.get(word.upper(), \"I couldn't find the definition of {}\\n\".format(word))","async def get_demon(self, ctx, game: str, name: str):\n\n name = await self.nearest_spelling(ctx, name.lower(), self.names[game])\n if name is not None:\n name = \" \".join([i.capitalize() for i in name.split()])\n return name","def review_word(word):\n\n print \"===== NEXT WORD ======\"\n print \"Is this word \"","def normalize(word):\n word = word.lower()\n # removing plural, it facilitates the matching\n if len(word)>0 and word[-1] == 's':\n return word[0:-1]\n return word","def detectCapitalUse(self, word: str) -> bool:\n if not word:\n return True\n\n head_upper = word[0].isupper()\n\n # except for the head\n has_lower = False\n has_upper = False\n for w in word[1:]:\n if w.isupper():\n has_upper = True\n if has_lower or not head_upper:\n return False\n else:\n has_lower = True\n if has_upper:\n return False\n return True","def __process_word(self, word):\n output = ''\n capitals = self.__capital_positions(word)\n c_index = 0\n\n for c in word:\n if c_index in capitals:\n output += c.upper()\n else:\n output += c.lower()\n\n c_index += 1\n\n return output","def detectCapitalUse(self, word):\n\n # Check for no upper or all upper\n if all(l.isupper() for l in word) or all(l.islower() for l in word):\n return True\n elif word[0].isupper() and word[1:].islower():\n return True\n else:\n return False","def findMeaning(data: str, shorten: bool) -> str:\n # # remove all spaces\n # data.replace(' ', '')\n\n # two cases\n # if 》string exists, then meaning is string after this string\n if data.find('》') != -1:\n # find the line after 》\n sym = '》'\n symbol_lines_index = symbol_line_location(data, sym, move=0, addLast=True)\n symbol = find(data, '》')\n\n # now find all places with period\n period = find(data, '。')\n\n # initialize meaning\n meaning = ''\n # add to the meaning\n for i in range(len(symbol)):\n indStart = symbol[i]\n indEnd = max([period[j] for j in range(len(period)) if (period[j] >= symbol_lines_index[i][1]\n and period[j] < symbol_lines_index[i + 1][0]) or\n period[j] < symbol_lines_index[i][1]])\n\n single_meaning = data[int(indStart + 1):indEnd + 1]\n meaning += single_meaning\n\n # if 》string does not exist, then meaning is string after 】\n else:\n # find the line after 】\n sym = '】'\n symbol_lines_index = symbol_line_location(data, sym, move=1, addLast=True)\n\n symbol = find(data, '】')\n\n # now find all places with period\n period = find(data, '。')\n\n # initialize meaning\n meaning = ''\n # add to the meaning\n for i in range(len(symbol)):\n indStart = symbol_lines_index[i][0]\n indEnd = max([period[j] for j in range(len(period)) if (period[j] >= symbol_lines_index[i][1]\n and period[j] < symbol_lines_index[i+1][0]) or period[j] < symbol_lines_index[i][1]])\n\n\n single_meaning = data[int(indStart + 1):indEnd+1]\n meaning += single_meaning\n\n # showMessage(symbol)\n # showMessage(line)\n # showMessage(lines_index)\n # showMessage(symbol_lines_index)\n # showMessage(period)\n\n # remove new lines\n meaning = meaning.replace('\\n', '')\n\n # lastly, get rid of spaces\n meaning = meaning.replace(\" \", \"\")\n\n # if we want to shorten, we will only take the first sentence\n if shorten:\n # find period\n period = find(meaning, '。')\n # take the first period\n meaning = meaning[0:period[0]]\n return meaning","def test_abbreviation(self):\n self.assertEqual(self.compound.abbreviation, \"Cool\")","def stem(s):\n short_words = {'is': 'is', 'the': 'the','he': 'he', 'she': 'she', \\\n 'my': 'my', }\n if s in short_words:\n return s\n if s[-1] == 's':\n s = s[:-1]\n special_cases = {'children': 'child', 'doing': 'do', 'did': 'do', \\\n 'string': 'string', 'spring': 'spring'}\n if s in special_cases:\n return special_cases[s]\n if s[-1] == 'e':\n s = s[:-1]\n if s[-3:] == 'ing' and len(s) > 5:\n if s[-5:-3] == 'mm' or s[-5:-3] == 'tt':\n s = s[-4]\n else:\n s = s[:-3]\n if s[-1] == 'y':\n s = s[:-1] + 'i'\n elif s[-2:] == 'er' and len(s) > 4:\n if s[-4:-2] == 'mm' or s[-4:-2] == 'tt':\n s = s[:-3]\n else:\n s = s[:-2]\n elif s[-2:] == 'ed' and len(s) > 4:\n if s[-4:-2] == 'mm' or s[-4:-2] == 'tt':\n s = s[:-3]\n else:\n s = s[:-2]\n return s","def profanity_word_handler(word):\n return word[0] + ''.join([settings.CENSOR_PROFANITY_REPLACEMENT_CHARACTER for I in range(len(word)-2)]) + word [-1]","def stationabbreviation(station):\n stations = {'Utrecht': 'Ut',\n 'Amsterdam Centraal': 'asd'}\n if station in stations:\n return stations[station]","def test_find_word(self):\n mic = mi.MicrophoneToText()\n\n teststring = 'x transcript\": ort lautet testort }x'\n\n word = mic.find_word(teststring)\n\n self.assertEqual(word, ' ort lautet testort ')","def stem(self, word):\n word = word.lower()\n\n if word in self.stopwords:\n return word\n\n step1_success = False\n\n r1, r2 = self._r1r2_standard(word, self.__vowels)\n rv = self._rv_standard(word, self.__vowels)\n\n # STEP 0: Attached pronoun\n for suffix in self.__step0_suffixes:\n if not (word.endswith(suffix) and rv.endswith(suffix)):\n continue\n\n if (\n rv[: -len(suffix)].endswith(\n (\n \"ando\",\n \"ar\",\n \"er\",\n \"iendo\",\n \"ir\",\n )\n )\n ) or (\n rv[: -len(suffix)].endswith(\"yendo\")\n and word[: -len(suffix)].endswith(\"uyendo\")\n ):\n\n word = self.__replace_accented(word[: -len(suffix)])\n r1 = self.__replace_accented(r1[: -len(suffix)])\n r2 = self.__replace_accented(r2[: -len(suffix)])\n rv = self.__replace_accented(rv[: -len(suffix)])\n break\n\n # STEP 1: Standard suffix removal\n for suffix in self.__step1_suffixes:\n if not word.endswith(suffix):\n continue\n\n if suffix == \"amente\" and r1.endswith(suffix):\n step1_success = True\n word = word[:-6]\n r2 = r2[:-6]\n rv = rv[:-6]\n\n if r2.endswith(\"iv\"):\n word = word[:-2]\n r2 = r2[:-2]\n rv = rv[:-2]\n\n if r2.endswith(\"at\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith((\"os\", \"ic\", \"ad\")):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith(suffix):\n step1_success = True\n if suffix in (\n \"adora\",\n \"ador\",\n \"acion\",\n \"adoras\",\n \"adores\",\n \"aciones\",\n \"ante\",\n \"antes\",\n \"ancia\",\n \"ancias\",\n ):\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n\n if r2.endswith(\"ic\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif suffix in (\"logia\", \"logias\"):\n word = suffix_replace(word, suffix, \"log\")\n rv = suffix_replace(rv, suffix, \"log\")\n\n elif suffix in (\"ucion\", \"uciones\"):\n word = suffix_replace(word, suffix, \"u\")\n rv = suffix_replace(rv, suffix, \"u\")\n\n elif suffix in (\"encia\", \"encias\"):\n word = suffix_replace(word, suffix, \"ente\")\n rv = suffix_replace(rv, suffix, \"ente\")\n\n elif suffix == \"mente\":\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n\n if r2.endswith((\"ante\", \"able\", \"ible\")):\n word = word[:-4]\n rv = rv[:-4]\n\n elif suffix in (\"idad\", \"idades\"):\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n\n for pre_suff in (\"abil\", \"ic\", \"iv\"):\n if r2.endswith(pre_suff):\n word = word[: -len(pre_suff)]\n rv = rv[: -len(pre_suff)]\n\n elif suffix in (\"ivo\", \"iva\", \"ivos\", \"ivas\"):\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n if r2.endswith(\"at\"):\n word = word[:-2]\n rv = rv[:-2]\n else:\n word = word[: -len(suffix)]\n rv = rv[: -len(suffix)]\n break\n\n # STEP 2a: Verb suffixes beginning 'y'\n if not step1_success:\n for suffix in self.__step2a_suffixes:\n if rv.endswith(suffix) and word[-len(suffix) - 1 : -len(suffix)] == \"u\":\n word = word[: -len(suffix)]\n rv = rv[: -len(suffix)]\n break\n\n # STEP 2b: Other verb suffixes\n for suffix in self.__step2b_suffixes:\n if rv.endswith(suffix):\n word = word[: -len(suffix)]\n rv = rv[: -len(suffix)]\n if suffix in (\"en\", \"es\", \"eis\", \"emos\"):\n if word.endswith(\"gu\"):\n word = word[:-1]\n\n if rv.endswith(\"gu\"):\n rv = rv[:-1]\n break\n\n # STEP 3: Residual suffix\n for suffix in self.__step3_suffixes:\n if rv.endswith(suffix):\n word = word[: -len(suffix)]\n if suffix in (\"e\", \"\\xE9\"):\n rv = rv[: -len(suffix)]\n\n if word[-2:] == \"gu\" and rv.endswith(\"u\"):\n word = word[:-1]\n break\n\n word = self.__replace_accented(word)\n\n return word","def get_lemma(word):\n return stemmer.stem(word)","def __isVerb__(self, word):\n self.verbs = ('go', 'stop', 'kill', 'eat')\n for verb in self.verbs:\n if verb == word:\n return ('verb', word), True\n return None, False","def hey(self, sentence=\"\"):\n if sentence == \"\" or sentence.replace(\" \", \"\") == \"\":\n return \"Fine. Be that way!\"\n if sentence.isupper():\n return \"Woah, chill out!\"\n if sentence[-1] == \"?\":\n return \"Sure.\"\n return \"Whatever.\"","def abbreviation(self):\n\n return self._abbreviation","def usedWord(afz, word, output=True):\n count = 0\n for msg in msgs:\n if msg.afz == afz:\n if word.lower() in msg.msg.lower():\n count = count + 1\n print afz, 'heeft', count, 'keer het woord', word, 'gebruikt.'","def normalize_word(self, word, treebank_tag):\n wordnet_pos, part_of_speech = self.get_wordnet_pos(treebank_tag)\n\n if wordnet_pos == wordnet.NOUN and part_of_speech == 'proper':\n return word, 'proper_noun'\n\n lemword = self.wordnetlemmatize.lemmatize(word, wordnet_pos)\n return self.stemmer.stem(lemword), part_of_speech","def _parse_abbreviation(self, cell_content):\n span = cell_content.find(\"span\")\n full = span.attrs[\"title\"].strip()\n abbrv = span.text.strip()\n return abbrv, full","def a_or_an(value):\n # TODO: handle confusing things like \"an hour\" or \"a unicycle\"\n vowel_sounds = [\"a\", \"e\", \"i\", \"o\", \"u\"]\n if value[0].lower() in vowel_sounds:\n return \"an\"\n else:\n return \"a\"","def form_present_verb(word, number, person):\n assert word\n assert isinstance(word, basestring)\n if _is_first_person(person) or _is_second_person(person):\n return word\n elif _is_third_person(person):\n if _is_singular(number):\n return pluralize(word)\n if _is_dual(number) or _is_plural(number): \n return word\n return None","def deabbreviate(self, st):\n\t\tabbrs = {'gws': 'greater western sydney giants',\n\t\t\t\t 'gwsg': 'greater western sydney giants',\n\t\t\t\t 'afl': 'australian football league',\n\t\t\t\t 'nrc': 'national rugby championship',\n\t\t\t\t 'nrl': 'national rugby league',\n\t\t\t\t 'syd': 'sydney',\n\t\t\t\t 'mel': 'melbourne',\n\t\t\t\t 'melb': 'melbourne',\n\t\t\t\t 'bris': 'brisbane',\n\t\t\t\t 'brisb': 'brisbane',\n\t\t\t\t 'gc': 'gold coast',\n\t\t\t\t 'adel': 'adelaide',\n\t\t\t\t 'canb': 'canberra',\n\t\t\t\t 'mt': 'mount',\n\t\t\t\t 'utd': 'united',\n\t\t\t\t 'cty': 'city',\n\t\t\t\t 'football club': 'fc',\n\t\t\t\t 'snr': 'senior',\n\t\t\t\t 'jr': 'junion',\n\t\t\t\t 'nsw': 'new south wales' ,\n\t\t\t\t 'vic': 'victoria',\n\t\t\t\t 'tas' : 'tasmania',\n\t\t\t\t 'sa': 'south australia',\n\t\t\t\t 'wa': 'western australia',\n\t\t\t\t 'act': 'australian capital territory',\n\t\t\t\t 'nt': 'northern territory',\n\t\t\t\t 'qld': 'queensland',\n\t\t\t\t 'champs': 'championships', \n\t\t\t\t 'champ': 'championship', \n\t\t\t\t 'soc': 'society',\n\t\t\t\t 'ent': 'entertainment',\n\t\t\t\t 'intl': 'international', \n\t\t\t\t 'int': 'international', \n\t\t\t\t 'aust': 'australian'}\n\n\t\t# first replace full state names by abbreviations;\n\t\tfor ab in abbrs:\n\t\t\tst = re.sub(r'\\b' + ab + r'\\b', abbrs[ab], st)\n\n\t\treturn st","def replace_word_candidate(self, word):\n capital_flag = word[0].isupper()\n word = word.lower()\n if capital_flag and word in self.teencode_dict:\n return self.replace_teencode(word).capitalize()\n elif word in self.teencode_dict:\n return self.replace_teencode(word)\n\n for couple in self.word_couples:\n for i in range(2):\n if couple[i] == word:\n if i == 0:\n if capital_flag:\n return couple[1].capitalize()\n else:\n return couple[1]\n else:\n if capital_flag:\n return couple[0].capitalize()\n else:\n return couple[0]","def guess_article(item_name):\n return 'an' if item_name[0].lower() in 'aeiouy' else 'a'","def just_do_it(text):\n from string import capwords\n return capwords(text)","def convert_single_word_into_plural_form(word):\n\n if word.endswith('y'):\n return word[:-1] + 'ies'\n elif word[-1] in 'sx' or word[-2:] in ['sh', 'ch']:\n return word + 'es'\n elif word.endswith('an'):\n return word[:-2] + 'en'\n else:\n return word + 's'","def word_tag(self, word):\n if word[1] in (\"NN\", \"NNS\", \"NNP\", \"NNPS\"):\n return _wordnet.NOUN\n if word[1] in (\"JJ\", \"JJR\", \"JJS\"):\n return _wordnet.ADJ\n if word[1] in (\"VB\", \"VBD\", \"VBG\", \"VBN\", \"VBP\", \"VBZ\"):\n return _wordnet.VERB\n if word[1] in (\"RB\", \"RBR\", \"RBS\"):\n return _wordnet.ADV\n\n return None","def get_token(word, flag):\n if flag == 1:\n return \"_RARE_\"\n elif flag == 2:\n if bool(re.search(r'\\d', word)):\n return \"AlphaNum\"\n else:\n return \"oThEr\"\n elif flag == 3:\n if word[-3:] == \"ing\":\n return \"enDiNg\"\n else:\n return \"oThEr\"","def getType(word):\n rules = {\n 'direction': ['north', 'south', 'east', 'west', 'down', 'up', 'left', 'right', 'back'],\n 'verb': ['go', 'stop', 'kill', 'eat'],\n 'stop': ['the', 'in', 'of', 'from', 'at', 'it'],\n 'noun': ['door', 'bear', 'princess', 'cabinet']\n }\n if isNumber(word):\n return 'number'\n\n for rule, rule_list in rules.items():\n if word in rule_list:\n return rule\n\n return 'error'","def check_word_capitalization(word):\n return_value = False\n if (len(word) > 1):\n return_value = True if (word[0].isupper() and word[1].islower()) else False\n return return_value","def isDisambiguatedByNextVerb(self, word):\n\t\treturn 'verb' in disambig_const.DISAMBIGUATATION_TABLE.get(word, {});","def replace_word(word, is_first, vocab, minimal_fequency):\n ### YOUR CODE HERE\n if word.isdigit():\n if len(word) == 2:\n return 'twoDigitNum'\n elif len(word) == 4:\n return 'fourDigitNum'\n else:\n return 'othernum'\n elif contains_digit(word):\n if contains_alpha(word):\n return 'containsDigitAndAlpha'\n elif '-' in word:\n return 'containsDigitAndDash'\n elif '/' in word or '\\\\' in word:\n return 'containsDigitAndSlash'\n elif '.' in word:\n return 'containsDigitAndPeriod'\n if word.isalpha() and word.isupper():\n return 'allCaps'\n elif CAP_PERIOD_PATTERN.match(word):\n return 'capPeriod'\n if is_first and vocab.get(word.lower(), 0) >= minimal_fequency:\n return word.lower()\n if not is_first and word[0].isupper():\n return 'initCap'\n if word.isalpha():\n for suffix in SUFFIXES:\n if word.endswith(suffix):\n return 'wordSuffix' + suffix\n if word.isalpha():\n for prefix in PREFIXES:\n if word.startswith(prefix):\n return prefix + 'WordPrefix'\n if '-' in word:\n return 'withDash'\n elif word.isalpha() and word.lower() == word:\n return 'lowercase'\n ### END YOUR CODE\n return UNKNOWN_WORD","def isUnique(self, word):\n if len(word) < 3:\n abbrev = word\n else:\n abbrev = word[0] + str(len(word) - 2) + word[-1]\n if not abbrev in self.abbrev_dict:\n return True\n elif word in self.abbrev_dict[abbrev] and len(self.abbrev_dict[abbrev]) == 1:\n return True\n else:\n return False","def guess_word(self, prefix):\n prefix = prefix.lower()\n current = self.search_prefix(prefix)\n if current:\n print(\"You typed: \" + '\"' + prefix + '\"')\n if current.get_end():\n print('\"' + prefix + '\" is a word, but you could have also been typing out:')\n else:\n print('\"' + prefix + '\" is not a word, perhaps you were typing out:')\n library = [prefix + word for word in self.get_library(current, library = [])]\n for word in library:\n print(word)\n return library # list includes empty string if its already a word\n print(\"I'm not quite sure what you meant by \" + '\"' + prefix + '\"...')\n return []","def _step1b(self, word):\n # this NLTK-only block extends the original algorithm, so that\n # 'spied'->'spi' but 'died'->'die' etc\n if self.mode == self.NLTK_EXTENSIONS:\n if word.endswith(\"ied\"):\n if len(word) == 4:\n return self._replace_suffix(word, \"ied\", \"ie\")\n else:\n return self._replace_suffix(word, \"ied\", \"i\")\n\n # (m>0) EED -> EE\n if word.endswith(\"eed\"):\n stem = self._replace_suffix(word, \"eed\", \"\")\n if self._measure(stem) > 0:\n return stem + \"ee\"\n else:\n return word\n\n rule_2_or_3_succeeded = False\n\n for suffix in [\"ed\", \"ing\"]:\n if word.endswith(suffix):\n intermediate_stem = self._replace_suffix(word, suffix, \"\")\n if self._contains_vowel(intermediate_stem):\n rule_2_or_3_succeeded = True\n break\n\n if not rule_2_or_3_succeeded:\n return word\n\n return self._apply_rule_list(\n intermediate_stem,\n [\n (\"at\", \"ate\", None), # AT -> ATE\n (\"bl\", \"ble\", None), # BL -> BLE\n (\"iz\", \"ize\", None), # IZ -> IZE\n # (*d and not (*L or *S or *Z))\n # -> single letter\n (\n \"*d\",\n intermediate_stem[-1],\n lambda stem: intermediate_stem[-1] not in (\"l\", \"s\", \"z\"),\n ),\n # (m=1 and *o) -> E\n (\n \"\",\n \"e\",\n lambda stem: (self._measure(stem) == 1 and self._ends_cvc(stem)),\n ),\n ],\n )","def keyword_from_meaning(name):\n # Try to adhere to keyword scheme in DICOM (CP850)\n\n # singular/plural alternative forms are made plural\n # e.g., “Physician(s) of Record” becomes “PhysiciansOfRecord”\n name = name.replace(\"(s)\", \"s\")\n\n # “Patient’s Name” -> “PatientName”\n # “Operators’ Name” -> “OperatorsName”\n name = name.replace(\"’s \", \" \")\n name = name.replace(\"'s \", \" \")\n name = name.replace(\"s’ \", \"s \")\n name = name.replace(\"s' \", \"s \")\n\n # Mathematical symbols\n name = name.replace(\"%\", \" Percent \")\n name = name.replace(\">\", \" Greater Than \")\n name = name.replace(\"=\", \" Equals \")\n name = name.replace(\"<\", \" Lesser Than \")\n\n name = re.sub(r\"([0-9]+)\\.([0-9]+)\", \"\\\\1 Point \\\\2\", name)\n name = re.sub(r\"\\s([0-9.]+)-([0-9.]+)\\s\", \" \\\\1 To \\\\2 \", name)\n\n name = re.sub(r\"([0-9]+)day\", \"\\\\1 Day\", name)\n name = re.sub(r\"([0-9]+)y\", \"\\\\1 Years\", name)\n\n # Remove category modifiers, such as \"(specimen)\", \"(procedure)\",\n # \"(body structure)\", etc.\n name = re.sub(r\"^(.+) \\([a-z ]+\\)$\", \"\\\\1\", name)\n\n name = camel_case(name.strip())\n\n # Python variables must not begin with a number.\n if re.match(r\"[0-9]\", name):\n name = \"_\" + name\n\n return name","def getWord(self,):\n\t\treturn self.word;","def guessed_word(self): # helper function to display_correct_guess()\n if self.correct_guess():\n if self.display_word.replace(' ', '') == self.chosen_word:\n self.display_word = self.chosen_word # process will continue until it reaches ***\n return True, self.display_word\n else:\n return False","def __check_word__(self, word):\n self.directionValue, self.isDirection = self.__isDirection__(word.lower())\n self.verbValue, self.isVerb = self.__isVerb__(word.lower())\n self.stopValue, self.isStop = self.__isStopWord__(word.lower())\n self.nounValue, self.isNoun = self.__isNoun__(word.lower())\n self.numberValue, self.isNumber = self.__isNumber__(word.lower())\n\n if self.isDirection:\n return self.directionValue\n elif self.isVerb:\n return self.verbValue\n elif self.isStop:\n return self.stopValue\n elif self.isNoun:\n return self.nounValue\n elif self.isNumber:\n return self.numberValue\n else:\n return ('error', word)","def is_cap_word(self, word):\n try:\n return word[0].isupper()\n except:\n return False","def get_word_probability(self, label, term):\n\n if 'sod' in label:\n return self.cond_prob_sod[term]\n elif 'pop' in label:\n return self.cond_prob_pop[term]\n else:\n print(\"Just run the doctest Dev\")\n \n pass","def normalize_word (word):\n return st.stem(word.strip ().lower ())","def test_word_info(self):\n word = \"vitality\"\n rv = self.wordInfo(input_word=word)\n expected_output = {\n word: {\n \"frequency\": \"975\",\n \"defination\": \"{'Noun': ['an energetic style', 'a healthy capacity for vigorous activity', '(biology', 'not physical or chemical', 'the property of being able to survive and grow']}\",\n \"antonyms\": \"['enervation', 'inactivity', 'lethargy', 'weakness', 'lack']\",\n \"examples\": \"{1: 'And finally, both Lord Robertson and Secretary of State Powell pointed to what they called the vitality and the relevance of NATO, and said any damage done to the reputation of NATO over the last couple weeks can quite, in their words, be easily overcome.', 2: \\\"Professor Huxley himself has told us that he lived in 'the hope and the faith that in course of time we shall see our way from the constituents of the protoplasm to its properties,' _i. e._ from carbonic acid, water, and ammonia to that mysterious thing which we call vitality or life -- from the molecular motion of the brain to Socratic wisdom,\\\", 3: 'The strongest, the most amply endowed with what we call vitality or power to live, win.', 4: 'But the thought that it is mechanics and chemistry applied by something of which they as such, form no part, some agent or principle which we call vitality, is welcome to us.', 5: '\\\"The Indian savages,\\\" said Margrave, sullenly, \\\"have not a health as perfect as mine, and in what you call vitality -- the blissful consciousness of life -- they are as sticks and stones compared to me.\\\"'}\",\n \"pronounciation\": \"V AY0 T AE1 L AH0 T IY0\",\n \"synonyms\": \"['vigor', 'continuity', 'spunk', 'strength', 'verve']\"\n }\n }\n response_data = json.loads(rv.get_data(as_text=True))\n\n self.assertEquals(rv.status_code, 200)\n self.assertEquals(response_data[word][\"defination\"], expected_output[word][\"defination\"])\n self.assertEquals(response_data[word][\"antonyms\"], expected_output[word][\"antonyms\"])\n self.assertEquals(response_data[word][\"examples\"], expected_output[word][\"examples\"])\n self.assertEquals(response_data[word][\"frequency\"], expected_output[word][\"frequency\"])\n self.assertEquals(response_data[word][\"pronounciation\"], expected_output[word][\"pronounciation\"])\n self.assertEquals(response_data[word][\"synonyms\"], expected_output[word][\"synonyms\"])","def morph_noun(word, number, a_an, feature):\n word = morph_number(word, number)\n if not (number in ['first', 'second']) and word[-1] == 's':\n return mark_noun_as_plural(word)\n else:\n return word\n\n if a_an == 'an':\n return mark_noun_as_an(word)\n else:\n return word","def med_in_hindi(word):\r\n\treturn int(med(correction(word),word))","def get_word(w):\n return ''.join(c for c in w if c.isalpha()).lower()","def abbreviate(x: str) -> str:\n i = 0\n abbreviation: str = \"\"\n while i < len(x):\n if x[i].isupper():\n abbreviation += x[i]\n i += 1\n return abbreviation","def map_word(self, word):\n for invariance in self.invariances:\n word = invariance.map_word(word)\n return word"],"string":"[\n \"def convert_abbrev(word):\\r\\n return abbreviations[word.lower()] if word.lower() in abbreviations.keys() else word\",\n \"def aux_lemma(word):\\n if re.match(r\\\"(does|did|doing)\\\", word):\\n return (\\\"do\\\")\\n elif re.match(r\\\"(had|has|'ve|having)\\\", word):\\n return (\\\"have\\\")\\n elif re.match(r\\\"(is|are|am|was|were|been|'s|being)\\\", word):\\n return (\\\"be\\\")\\n elif word == (\\\"'d\\\"):\\n return (\\\"would\\\")\\n else:\\n return word.lower()\",\n \"def get_casing(word):\\n if len(word) == 0:\\n return \\\"other\\\"\\n elif word.isdigit(): # Is a digit\\n return \\\"numeric\\\"\\n elif word.islower(): # All lower case\\n return \\\"allLower\\\"\\n elif word.isupper(): # All upper case\\n return \\\"allUpper\\\"\\n # is a title, initial char upper, then all lower\\n elif word[0].isupper():\\n return \\\"initialUpper\\\"\\n\\n return \\\"other\\\"\",\n \"def disambiguate(self, word):\\n matches = re.match(r'^pen([cdjz])(.*)$', word)\\n if matches:\\n return matches.group(1) + matches.group(2)\",\n \"def test_police_abbreviations(self):\\n for word in self.report.get_words():\\n for uword in self.rules.police_abbreviations:\\n if uword[\\\"word\\\"] == word.text.lower():\\n self.add_error(\\n f\\\"{word.text} är en intern förkortning. \\\"\\n f\\\"Använd {uword['means']} istället.\\\",\\n word=word,\\n )\",\n \"def get_american_term_definition(word: str) -> str:\\n word = process_word(word)\\n return AMERICAN_ENGLISH_ONLY_TERMS[word]\",\n \"def det_lemma(word):\\n if word == (\\\"an\\\"):\\n return (\\\"a\\\")\\n else:\\n return word.lower()\",\n \"def main() -> None:\\n word: str = input(\\\"Write some text with some uppercase letters: \\\")\\n abbreviation_out: str = abbreviate(word)\\n print(f\\\"The abbreviation is \\\\\\\"{abbreviation_out}\\\\\\\".\\\")\\n return None\",\n \"def match_name(sentence):\\n if \\\"WIFE\\\" in sentence:\\n return \\\"WIFE\\\"\\n elif \\\"MAHAVIR\\\" in sentence or \\\"FATHER\\\" in sentence or \\\"SINGH\\\" in sentence: \\n return \\\"MAHAVIR\\\"\\n elif \\\"TEENAGER\\\" in sentence:\\n return \\\"TEENAGER\\\"\\n elif \\\"GIRL\\\" in sentence or \\\"WOMAN\\\" in sentence: \\n return \\\"WOMAN\\\"\\n elif \\\"GUY\\\" in sentence or \\\"MAN\\\" in sentence or \\\"BROTHER\\\" in sentence: \\n return \\\"MAN\\\"\\n elif \\\"COACH\\\" in sentence:\\n return \\\"COACH\\\"\\n elif \\\"COMMENT\\\" in sentence:\\n return \\\"COMMENTATOR\\\"\\n elif sentence[-2:] == \\\"ER\\\" or sentence[-3:] == \\\"IAN\\\" or sentence[-2:] == \\\"OR\\\" or sentence[-1:] == \\\"D\\\":\\n return \\\"MISC\\\"\\n \\n return sentence\",\n \"def verb_lemma(word):\\n if word.endswith(\\\"ed\\\"):\\n if word[:-2].endswith(\\\"v\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"at\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"it\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"et\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ut\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ac\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"i\\\"):\\n return word[:-3].lower() + \\\"y\\\"\\n elif word[:-2].endswith(\\\"ir\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ag\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"nc\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"nu\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n else:\\n return word[:-2].lower() \\n elif word.endswith(\\\"ing\\\"):\\n if word[:-3].endswith(\\\"v\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"at\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"it\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"et\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ut\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ac\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"i\\\"):\\n return word[:-4].lower() + \\\"y\\\"\\n elif word[:-3].endswith(\\\"ir\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ag\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"nc\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"nu\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n else:\\n return word[:-3].lower()\\n elif re.match(r\\\"(does|did|done)\\\", word):\\n return (\\\"do\\\")\\n elif re.match(r\\\"(is|are|am|was|will|were|been)\\\", word):\\n return (\\\"be\\\")\\n elif word == (\\\"'s\\\"):\\n return (\\\"be\\\")\\n elif re.match(r\\\"(had|has|'ve)\\\", word):\\n return (\\\"have\\\")\\n else:\\n return word.lower()\",\n \"def is_abbr(word):\\r\\n\\twith open(\\\"ABBR_DICT.txt\\\", \\\"r\\\", encoding = \\\"utf-8\\\") as f:\\r\\n\\t\\twordlist = [line.split(\\\"\\\\n\\\")[0] for line in f]\\r\\n\\tif word.upper() in wordlist:\\r\\n\\t\\treturn 1\\r\\n\\treturn 0\",\n \"def find_abecedarian_words():\\n pass\",\n \"def is_abecedarian(word):\\n pass\",\n \"def part_lemma(word):\\n if word == (\\\"n't\\\"):\\n return (\\\"not\\\")\\n else:\\n return word.lower()\",\n \"def verb_stem(s):\\n \\n #If the stem is have, its 3s form is has.\\n if s == \\\"has\\\" :\\n return \\\"have\\\"\\n\\n #If the stem ends in y preceded by a vowel, simply add s (pays, buys).\\n elif re.match(r\\\"[A-z]+[aeiou][y]s\\\\b\\\", s):\\n str = s[:-1]\\n\\n #If the stem ends in y preceded by a non-vowel and contains at least three letters, change the y to ies (flies, tries, unifies).\\n elif re.match(r\\\"[A-z]+[^aeiou]ies\\\\b\\\", s):\\n str = s[:-3] + 'y'\\n\\n #If the stem is of the form Xie where X is a single letter other than a vowel, simply add s (dies, lies, ties note that this doesnt account for unties).\\n elif re.match(r\\\"[^aeiou]ies\\\\b\\\", s):\\n str = s[:-1]\\n\\n #If the stem ends in o,x,ch,sh,ss or zz, add es (goes, boxes, attaches, washes, dresses, fizzes).\\n elif re.match(r\\\"[A-z]+([ox]|[cs]h|[s]s|[z]z)es\\\\b\\\", s): \\n str = s[:-2]\\n\\n #If the stem ends in se or ze but not in sse or zze, add s (loses, dazes, lapses, analyses).\\n elif re.match(r\\\"[A-z]+([s][^s][e]|[z][^z][e])s\\\\b\\\", s):\\n str = s[:-1]\\n\\n #If the stem ends in e not preceded by i,o,s,x,z,ch,sh, just add s (likes, hates, bathes).\\n elif re.match(r\\\"[A-z]+([^iosxz]|[^ch]|[^sh])es\\\\b\\\", s):\\n str = s[:-1]\\n \\n #If the stem ends in anything except s,x,y,z,ch,sh or a vowel, add s (eats, tells, shows)\\n elif re.match(r\\\"[A-z]+([^sxyzaeiou]|[^cs]h)s\\\\b\\\", s):\\n str = s[:-1]\\n\\n else: \\n str = \\\"\\\"\\n\\n\\n matches = [(w, t) for (w, t) in vb_list if (w == s or w == str)]\\n\\n tag_s = [(w, t) for (w, t) in matches if w == s and t == 'VBZ']\\n\\n if tag_s == True:\\n return str\\n else:\\n tag_str = [t for (w, t) in matches if w == str and t == 'VB']\\n\\n if not (tag_s or tag_str):\\n str = \\\"\\\"\\n\\n return str\",\n \"def choose_word():\\n pass\",\n \"def acronym(name):\\n return tuple(map(first, filter(capitalized, name.split())))\",\n \"def adj_lemma(word):\\n if word.endswith(\\\"er\\\"):\\n return word[:-2].lower()\\n elif word != (\\\"best\\\") and word.endswith(\\\"est\\\"):\\n return word[:-3].lower()\\n else:\\n return word.lower()\",\n \"def process_word(self, word: str) -> list[str]:\\n d = self.d\\n if not d:\\n return None\\n if d.check(word):\\n return None\\n # Speed doesn't matter here. The more we find, the more convenient.\\n # Remove all digits.\\n word = ''.join([i for i in word if not i.isdigit()])\\n if d.check(word) or d.check(word.lower()):\\n return None\\n if word.find('_') > -1:\\n # Snake case.\\n words = word.split('_')\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n words = g.unCamel(word)\\n if words:\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n return d.suggest(word)\",\n \"def process_word(self, word: str) -> list[str]:\\n d = self.d\\n if not d:\\n return None\\n if d.check(word):\\n return None\\n # Speed doesn't matter here. The more we find, the more convenient.\\n # Remove all digits.\\n word = ''.join([i for i in word if not i.isdigit()])\\n if d.check(word) or d.check(word.lower()):\\n return None\\n if word.find('_') > -1:\\n # Snake case.\\n words = word.split('_')\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n words = g.unCamel(word)\\n if words:\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n return d.suggest(word)\",\n \"def __isNoun__(self, word):\\n self.nouns = ('door', 'bear', 'princess', 'cabinet')\\n for noun in self.nouns:\\n if noun == word:\\n return ('noun', word), True\\n return None, False\",\n \"def get_british_term_definition(word: str) -> str:\\n word = process_word(word)\\n return BRITISH_ENGLISH_ONLY_TERMS[word]\",\n \"def word_of_the_day():\\n r = requests.get(\\\"http://www.urbandictionary.com\\\") # link is always homepage\\n soup = BeautifulSoup(r.content, features=\\\"html.parser\\\") # sets up soup\\n def_header = \\\"**\\\" + soup.find(\\\"div\\\", attrs={\\\"class\\\": \\\"def-header\\\"}).text.replace(\\\"unknown\\\",\\n \\\"\\\") + \\\"**\\\" # header is the word we are defining\\n # def_header = def_header[0:len(def_header) - 10] # header always ends in \\\"unknown\\\" this removes it\\n meaning = soup.find(\\\"div\\\", attrs={\\\"class\\\": \\\"meaning\\\"}).text # gets the definition\\n # formatting TODO move to controller\\n for x in [1, 2, 3, 4, 5, 6, 7, 8, 9]:\\n meaning = meaning.replace(str(x) + \\\". \\\", \\\"\\\\n\\\" + str(x) + \\\". \\\")\\n for x in [\\\"v.\\\", \\\"n.\\\"]:\\n meaning = meaning.replace(x, x.upper()[:-1])\\n example = soup.find(\\\"div\\\", attrs={\\\"class\\\": \\\"example\\\"}).text # gets the example\\n output = def_header + \\\": \\\" + \\\"```\\\" + meaning + \\\"\\\\nEx: \\\" + example + \\\"```\\\" # output string\\n output = output.replace(\\\"&apos\\\", \\\"'\\\") # replaces weird formatting of ' from original\\n return output # returns the word, defintion, and example\",\n \"def getDisambiguatedByNextVerb(self, word):\\n\\t\\treturn disambig_const.DISAMBIGUATATION_TABLE.get(word, {}).get('verb', {}).get('vocalized', word);\",\n \"def indefinite(self):\\n return \\\"an\\\" if self.short_desc[0] in 'aeiou' else \\\"a\\\"\",\n \"def med_in_english(word):\\r\\n\\treturn int(med(TextBlob(word).correct(), word))\",\n \"def noun_lemma(word):\\n if word.endswith(\\\"s\\\"):\\n if word.endswith(\\\"ss\\\"):\\n return word.lower()\\n elif word.endswith(\\\"ies\\\"):\\n return word[:-3].lower() + (\\\"y\\\")\\n else:\\n return word[:-1].lower()\\n if word.endswith(\\\"men\\\"):\\n return word[:-2].lower() + (\\\"an\\\")\\n else:\\n return word.lower()\",\n \"def a_or_an(s):\\n if s[0].lower() in 'aeiou':\\n return 'an'\\n return 'a'\",\n \"def get_word(self) -> str: \\n #return str(choice(word_list).upper())\\n return \\\"ANONYMOUS\\\"\",\n \"def good_word(self, word):\\r\\n return word.strip().lower()\",\n \"def format_meaning(phrase, data):\\n if 'tuc' not in data:\\n return '%s: no meaning found' % phrase\\n\\n for t in data.get('tuc'):\\n if 'meanings' in t:\\n message = '; '.join([_clean_text(x['text'])\\n for x in t['meanings']])\\n return '%s: %s' % (phrase, message)\\n\\n # we didn't find a more detailed description, so check if there\\n # is a simpler one so we don't return empty handed.\\n for t in data.get('tuc'):\\n if 'phrase' in t and 'text' in t.get('phrase', {}):\\n return '%s: %s' % (phrase, _clean_text(t['phrase']['text']))\\n\\n return '%s: no meaning found' % phrase\",\n \"def lookup_pronunciations_for_word(word: Text) -> Sequence[Word]:\\n return EnglishUtils.all_possible_forms_for(word)\",\n \"def pluralized(word):\\n defined_plurals = {\\n \\\"person\\\": \\\"people\\\"\\n }\\n if word in defined_plurals:\\n return defined_plurals[word]\\n\\n es_endings = [\\\"s\\\", \\\"sh\\\", \\\"ch\\\", \\\"x\\\", \\\"z\\\"]\\n if any([word.endswith(ending) for ending in es_endings]):\\n return f\\\"{word}es\\\"\\n else:\\n return f\\\"{word}s\\\"\",\n \"def noun_stem (s):\\n\\n \\\"\\\"\\\"codes from statements.py (PART A)\\\"\\\"\\\"\\n def match (p):\\n return re.match(p + '$', s, re.IGNORECASE)\\n\\n if (s in unchanging_plurals_list):\\n return s\\n elif (s[-3:] == 'men'):\\n return s[0:-3] + 'man'\\n elif match('.*(?<!.[aeiousxyz]|sh|ch)s'):\\n return s[:-1]\\n elif match('.*[aeiou]ys'):\\n return s[:-1]\\n elif match('.*.[^aeiou]ies'):\\n return s[:-3] + 'y'\\n elif match('[^aeiou]ies'):\\n return s[:-1]\\n elif match('.*(o|x|ch|ss|zz|sh)es'):\\n return s[:-2]\\n elif match('.*([^s]se|[^z]ze)s'):\\n return s[:-1]\\n elif match('.*(?<!.[iosxz]|sh|ch)es'):\\n return s[:-1]\\n else:\\n return ''\",\n \"def fry(word):\\n\\n # looks for a Y or y which will be (captured) followed and ended by an 'ou'\\n match_you = re.match('([Yy])ou$', word)\\n\\n # First group will be the (captured) group so either 'Y' or 'y'\\n if match_you:\\n return match_you.group(1) + \\\"'all\\\"\\n\\n # looks for anyword ending in 'ing'\\n match_ing = re.search('(.+)ing$', word)\\n\\n # checks if vowel exists before the 'ing'\\n if match_ing:\\n vowel_check = re.search('[aeiouy]', match_ing.group(1))\\n # First group will be the (captured) group so everything before the 'ing'\\n if vowel_check:\\n return match_ing.group(1) + \\\"in'\\\"\\n\\n return word\",\n \"def abbreviation(self):\\n return self._abbreviation\",\n \"def stem(self, word):\\n word = word.lower()\\n\\n if word in self.__special_words:\\n return self.__special_words[word]\\n\\n # Map the different apostrophe characters to a single consistent one\\n word = (word.replace(u(\\\"\\\\u2019\\\"), u(\\\"\\\\x27\\\"))\\n .replace(u(\\\"\\\\u2018\\\"), u(\\\"\\\\x27\\\"))\\n .replace(u(\\\"\\\\u201B\\\"), u(\\\"\\\\x27\\\")))\\n\\n if word.startswith(u(\\\"\\\\x27\\\")):\\n word = word[1:]\\n\\n if word.startswith(\\\"y\\\"):\\n word = \\\"\\\".join((\\\"Y\\\", word[1:]))\\n\\n for i in range(1, len(word)):\\n if word[i - 1] in self.__vowels and word[i] == \\\"y\\\":\\n word = \\\"\\\".join((word[:i], \\\"Y\\\", word[i + 1:]))\\n\\n step1a_vowel_found = False\\n step1b_vowel_found = False\\n\\n r1 = \\\"\\\"\\n r2 = \\\"\\\"\\n\\n if word.startswith((\\\"gener\\\", \\\"commun\\\", \\\"arsen\\\")):\\n if word.startswith((\\\"gener\\\", \\\"arsen\\\")):\\n r1 = word[5:]\\n else:\\n r1 = word[6:]\\n\\n for i in range(1, len(r1)):\\n if r1[i] not in self.__vowels and r1[i - 1] in self.__vowels:\\n r2 = r1[i + 1:]\\n break\\n else:\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\n\\n # STEP 0\\n for suffix in self.__step0_suffixes:\\n if word.endswith(suffix):\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n break\\n\\n # STEP 1a\\n for suffix in self.__step1a_suffixes:\\n if word.endswith(suffix):\\n\\n if suffix == \\\"sses\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix in (\\\"ied\\\", \\\"ies\\\"):\\n if len(word[:-len(suffix)]) > 1:\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n else:\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n\\n elif suffix == \\\"s\\\":\\n for letter in word[:-2]:\\n if letter in self.__vowels:\\n step1a_vowel_found = True\\n break\\n\\n if step1a_vowel_found:\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n break\\n\\n # STEP 1b\\n for suffix in self.__step1b_suffixes:\\n if word.endswith(suffix):\\n if suffix in (\\\"eed\\\", \\\"eedly\\\"):\\n\\n if r1.endswith(suffix):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ee\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ee\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ee\\\"))\\n else:\\n r2 = \\\"\\\"\\n else:\\n for letter in word[:-len(suffix)]:\\n if letter in self.__vowels:\\n step1b_vowel_found = True\\n break\\n\\n if step1b_vowel_found:\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n\\n if word.endswith((\\\"at\\\", \\\"bl\\\", \\\"iz\\\")):\\n word = \\\"\\\".join((word, \\\"e\\\"))\\n r1 = \\\"\\\".join((r1, \\\"e\\\"))\\n\\n if len(word) > 5 or len(r1) >= 3:\\n r2 = \\\"\\\".join((r2, \\\"e\\\"))\\n\\n elif word.endswith(self.__double_consonants):\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n\\n elif ((r1 == \\\"\\\" and len(word) >= 3 and\\n word[-1] not in self.__vowels and\\n word[-1] not in \\\"wxY\\\" and\\n word[-2] in self.__vowels and\\n word[-3] not in self.__vowels)\\n or\\n (r1 == \\\"\\\" and len(word) == 2 and\\n word[0] in self.__vowels and\\n word[1] not in self.__vowels)):\\n\\n word = \\\"\\\".join((word, \\\"e\\\"))\\n\\n if len(r1) > 0:\\n r1 = \\\"\\\".join((r1, \\\"e\\\"))\\n\\n if len(r2) > 0:\\n r2 = \\\"\\\".join((r2, \\\"e\\\"))\\n break\\n\\n # STEP 1c\\n if (len(word) > 2\\n and word[-1] in \\\"yY\\\"\\n and word[-2] not in self.__vowels):\\n word = \\\"\\\".join((word[:-1], \\\"i\\\"))\\n if len(r1) >= 1:\\n r1 = \\\"\\\".join((r1[:-1], \\\"i\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= 1:\\n r2 = \\\"\\\".join((r2[:-1], \\\"i\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n # STEP 2\\n for suffix in self.__step2_suffixes:\\n if word.endswith(suffix):\\n if r1.endswith(suffix):\\n if suffix == \\\"tional\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix in (\\\"enci\\\", \\\"anci\\\", \\\"abli\\\"):\\n word = \\\"\\\".join((word[:-1], \\\"e\\\"))\\n\\n if len(r1) >= 1:\\n r1 = \\\"\\\".join((r1[:-1], \\\"e\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= 1:\\n r2 = \\\"\\\".join((r2[:-1], \\\"e\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"entli\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix in (\\\"izer\\\", \\\"ization\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ize\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ize\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ize\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix in (\\\"ational\\\", \\\"ation\\\", \\\"ator\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ate\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r2 = \\\"e\\\"\\n\\n elif suffix in (\\\"alism\\\", \\\"aliti\\\", \\\"alli\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"al\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"al\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"al\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"fulness\\\":\\n word = word[:-4]\\n r1 = r1[:-4]\\n r2 = r2[:-4]\\n\\n elif suffix in (\\\"ousli\\\", \\\"ousness\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ous\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ous\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ous\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix in (\\\"iveness\\\", \\\"iviti\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ive\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ive\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ive\\\"))\\n else:\\n r2 = \\\"e\\\"\\n\\n elif suffix in (\\\"biliti\\\", \\\"bli\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ble\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ble\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ble\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"ogi\\\" and word[-4] == \\\"l\\\":\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n\\n elif suffix in (\\\"fulli\\\", \\\"lessli\\\"):\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix == \\\"li\\\" and word[-3] in self.__li_ending:\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n break\\n\\n # STEP 3\\n for suffix in self.__step3_suffixes:\\n if word.endswith(suffix):\\n if r1.endswith(suffix):\\n if suffix == \\\"tional\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix == \\\"ational\\\":\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ate\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"alize\\\":\\n word = word[:-3]\\n r1 = r1[:-3]\\n r2 = r2[:-3]\\n\\n elif suffix in (\\\"icate\\\", \\\"iciti\\\", \\\"ical\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ic\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ic\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ic\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix in (\\\"ful\\\", \\\"ness\\\"):\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n\\n elif suffix == \\\"ative\\\" and r2.endswith(suffix):\\n word = word[:-5]\\n r1 = r1[:-5]\\n r2 = r2[:-5]\\n break\\n\\n # STEP 4\\n for suffix in self.__step4_suffixes:\\n if word.endswith(suffix):\\n if r2.endswith(suffix):\\n if suffix == \\\"ion\\\":\\n if word[-4] in \\\"st\\\":\\n word = word[:-3]\\n r1 = r1[:-3]\\n r2 = r2[:-3]\\n else:\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n break\\n\\n # STEP 5\\n if r2.endswith(\\\"l\\\") and word[-2] == \\\"l\\\":\\n word = word[:-1]\\n elif r2.endswith(\\\"e\\\"):\\n word = word[:-1]\\n elif r1.endswith(\\\"e\\\"):\\n if len(word) >= 4 and (word[-2] in self.__vowels or\\n word[-2] in \\\"wxY\\\" or\\n word[-3] not in self.__vowels or\\n word[-4] in self.__vowels):\\n word = word[:-1]\\n\\n word = word.replace(\\\"Y\\\", \\\"y\\\")\\n return word\",\n \"def meaning_of(word, app_id, app_key):\\n \\n url = 'https://od-api.oxforddictionaries.com:443/api/v1/entries/' + language + '/' + word.lower()\\n r = requests.get(url, headers={\\\"app_id\\\":app_id, \\\"app_key\\\":app_key})\\n\\n data = r.json()\\n useful_data = {}\\n\\n for i in data['results'][0]['lexicalEntries'][0]['entries']:\\n for j in i:\\n for k in i[j][0]:\\n try:\\n subdata = i[j][0][k]\\n if k == 'subsenses':\\n useful_data.update({\\\"meanings\\\":subdata[0]['definitions']})\\n elif k == 'examples':\\n useful_data.update({\\\"examples\\\":subdata[0]['text']})\\n else:\\n pass\\n except:\\n pass\\n return useful_data\",\n \"def main():\\n word = input(\\\"Give me a word! \\\\n\\\\n\\\")\\n vowels = ['a', 'e', 'i', 'o', 'u']\\n if word[0].lower() in vowels:\\n print(f\\\"\\\\n\\\\nPig latin: {word}way\\\")\\n else:\\n print(f\\\"\\\\n\\\\nPig latin: {word[1:]}{word[0]}ay\\\")\",\n \"def acronym_gen(name):\\n return tuple(w[0] for w in name.split() if capitalized(w))\",\n \"def findPOS(word):\\r\\n\\t\\r\\n lisPOS = list(wordtags[word])\\r\\n if \\\"ADJ\\\" in lisPOS:\\r\\n return \\\"ADJECTIVE\\\"\\r\\n if \\\"ADV\\\" in lisPOS:\\r\\n return \\\"ADVERB\\\"\\r\\n if \\\"NOUN\\\" in lisPOS:\\r\\n return \\\"NOUN\\\"\",\n \"def getCasing(word):\\n casing = 'other'\\n \\n numDigits = 0\\n for char in word:\\n if char.isdigit():\\n numDigits += 1\\n \\n digitFraction = numDigits / float(len(word))\\n \\n if word.isdigit(): #Is a digit\\n casing = 'numeric'\\n elif digitFraction > 0.5:\\n casing = 'mainly_numeric'\\n elif word.islower(): #All lower case\\n casing = 'allLower'\\n elif word.isupper(): #All upper case\\n casing = 'allUpper'\\n elif word[0].isupper(): #is a title, initial char upper, then all lower\\n casing = 'initialUpper'\\n elif numDigits > 0:\\n casing = 'contains_digit'\\n \\n return casing\",\n \"def my_spell(word):\\r\\n from autocorrect import spell\\r\\n \\r\\n corrected_word = ''\\r\\n rescued_typo = 0\\r\\n \\r\\n if len(word)>1: # one letter word are not considered \\r\\n \\r\\n # try to correct typo\\r\\n if is_typo(word): \\r\\n print('typo: ' + word)\\r\\n word = spell(word)\\r\\n print('autocorrected typo: ' + word)\\r\\n\\r\\n if not is_typo(word): \\r\\n rescued_typo = 1\\r\\n corrected_word = word\\r\\n else:\\r\\n corrected_word = word\\r\\n\\r\\n return corrected_word, rescued_typo\",\n \"def _match_word_vocab(word, vocab):\\n if word not in vocab:\\n if word.lower() in vocab:\\n return word.lower()\\n elif word.upper() in vocab:\\n return word.upper()\\n elif word.capitalize() in vocab:\\n return word.capitalize()\\n return word\",\n \"def estimate(word):\\n parts = re.split(r'[^aeiouy]+', word)\\n valid_parts = []\\n\\n for part in parts:\\n if part != '':\\n valid_parts.append(part)\\n\\n syllables = 0\\n\\n for p in re_subsyllables:\\n if p.match(word):\\n syllables -= 1\\n\\n for p in re_addsyllables:\\n if p.match(word):\\n syllables += 1\\n\\n syllables += len(valid_parts)\\n\\n if syllables <= 0:\\n syllables = 1\\n\\n return syllables\",\n \"def getDisambiguatedByNextNoun(self, word):\\n\\t\\treturn disambig_const.DISAMBIGUATATION_TABLE.get(word, {}).get('noun', {}).get('vocalized', word);\",\n \"def getWord(wordType):\\n if (wordType == ADJECTIVE) or (wordType == ADJECTIVE):\\n newWord = input('Enter an ' + wordType.lower() + \\\":\\\\n\\\")\\n return newWord\\n else:\\n newWord = input('Enter a ' + wordType.lower() + \\\":\\\\n\\\")\\n return newWord\",\n \"def stem(s):\\n special = {'appall', 'kill', 'stroll', 'kiss', 'thrill', 'chugg', 'dress', 'err', 'express', 'fall', 'free', 'gall', 'add','cross', 'impress', 'inn', 'call', 'ball', 'bill', 'buzz'} \\n ie_words = {'vying', 'lying', 'dying', 'tying'}\\n short_ing = {'bring','sling','sping', 'bring', 'sing', 'ring', 'king', 'cling' ,'fling', 'wing', 'ding', 'ping', 'ting'}\\n c_k_words = {'kick', 'muck', 'lock','pick', 'back', 'mock', 'peck', 'lock', 'nick'}\\n\\n if len(s) <= 3:\\n return s\\n if s[-3:] == 'ing' or s[-4:] == 'ings': \\n if s in short_ing:\\n return s\\n elif s in special:\\n return s[:-3]\\n elif s[:-3] not in special and s[-4] == s[-5]:\\n return s[:-4]\\n elif s[:-3] not in c_k_words and s[-4] == 'k':\\n return s[:-4]\\n elif s == 'everything' or s == 'anything' or s == 'something':\\n return s[:-5]\\n elif s in ie_words:\\n return s[0] + 'ie'\\n else:\\n return s[:-3]\\n elif s[-3:] == 'ers':\\n return s[:-3]\\n elif s[-2:] == 'es':\\n return s[:-2]\\n elif s[-2:] == 'en':\\n return s[:-2]\\n elif s[-2:] == 'er':\\n if s[-3] == s[-4]:\\n return s[:-3]\\n else:\\n return s[:-2] \\n elif s[-2:] == 'ed':\\n if s[-3] == s[-4]:\\n return s[:-3]\\n else:\\n return s[:-2]\\n elif s[-3:] == 'ies':\\n return s[:-2]\\n elif s[-1:] == 's':\\n return s[:-1]\\n elif s[-1:] == 'e' and s not in ie_words:\\n return s[:-1]\\n elif s[-3:] == 'ful':\\n return s[:-3]\\n elif s[:2] == 'de':\\n return s[2:]\\n elif len(s) > 4 and s[-4:] == 'able' or s[-4] == 'ible':\\n return s[:-4]\\n elif s[:2] == 'in' or s[:2] == 'il' or s[:2] == 'ir':\\n return s[2:]\\n elif s[-1:] == 'y':\\n return s[:-1] + 'i'\\n else:\\n return s\",\n \"def filter(self, word):\\n \\n word = word.lower()\\n try:\\n self.engine.fetch(word)\\n except socket.error:\\n raise LemmaAPIError\\n part_of_speeches = self.engine.part_of_speeches\\n\\n \\n self.basic_form = word\\n for part in part_of_speeches:\\n if part == 'verb':\\n if self.engine.is_verb_conjugated():\\n if not self.conEngine.is_verb_regular(word, self.engine.get_basic_verb()):\\n self.basic_form = self.engine.get_basic_verb()\\n return word\\n else:\\n self.basic_form = self.engine.get_basic_verb()\\n\\n elif part == 'noun':\\n if self.engine.is_noun_plural():\\n if not self.conEngine.is_noun_regular(word, self.engine.get_singular_noun()):\\n self.basic_form = self.engine.get_singular_noun() \\n return word\\n else:\\n self.basic_form = self.engine.get_singular_noun()\\n\\n return self.basic_form\",\n \"def stem(self, word):\\n word = word.lower()\\n\\n step1_success = False\\n\\n # All acute accents are replaced by grave accents.\\n word = (word.replace(u(\\\"\\\\xE1\\\"), u(\\\"\\\\xE0\\\"))\\n .replace(u(\\\"\\\\xE9\\\"), u(\\\"\\\\xE8\\\"))\\n .replace(u(\\\"\\\\xED\\\"), u(\\\"\\\\xEC\\\"))\\n .replace(u(\\\"\\\\xF3\\\"), u(\\\"\\\\xF2\\\"))\\n .replace(u(\\\"\\\\xFA\\\"), u(\\\"\\\\xF9\\\")))\\n\\n # Every occurrence of 'u' after 'q'\\n # is put into upper case.\\n for i in range(1, len(word)):\\n if word[i - 1] == \\\"q\\\" and word[i] == \\\"u\\\":\\n word = \\\"\\\".join((word[:i], \\\"U\\\", word[i + 1:]))\\n\\n # Every occurrence of 'u' and 'i'\\n # between vowels is put into upper case.\\n for i in range(1, len(word) - 1):\\n if word[i - 1] in self.__vowels and word[i + 1] in self.__vowels:\\n if word[i] == \\\"u\\\":\\n word = \\\"\\\".join((word[:i], \\\"U\\\", word[i + 1:]))\\n elif word[i] == \\\"i\\\":\\n word = \\\"\\\".join((word[:i], \\\"I\\\", word[i + 1:]))\\n\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\n rv = self._rv_standard(word, self.__vowels)\\n\\n # STEP 0: Attached pronoun\\n for suffix in self.__step0_suffixes:\\n if rv.endswith(suffix):\\n if rv[-len(suffix) - 4:-len(suffix)] in (\\\"ando\\\", \\\"endo\\\"):\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n\\n elif (rv[-len(suffix) - 2:-len(suffix)] in\\n (\\\"ar\\\", \\\"er\\\", \\\"ir\\\")):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"e\\\"))\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"e\\\"))\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"e\\\"))\\n rv = \\\"\\\".join((rv[:-len(suffix)], \\\"e\\\"))\\n break\\n\\n # STEP 1: Standard suffix removal\\n for suffix in self.__step1_suffixes:\\n if word.endswith(suffix):\\n if suffix == \\\"amente\\\" and r1.endswith(suffix):\\n step1_success = True\\n word = word[:-6]\\n r2 = r2[:-6]\\n rv = rv[:-6]\\n\\n if r2.endswith(\\\"iv\\\"):\\n word = word[:-2]\\n r2 = r2[:-2]\\n rv = rv[:-2]\\n\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith((\\\"os\\\", \\\"ic\\\")):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2 .endswith(\\\"abil\\\"):\\n word = word[:-4]\\n rv = rv[:-4]\\n\\n elif (suffix in (\\\"amento\\\", \\\"amenti\\\",\\n \\\"imento\\\", \\\"imenti\\\") and\\n rv.endswith(suffix)):\\n step1_success = True\\n word = word[:-6]\\n rv = rv[:-6]\\n\\n elif r2.endswith(suffix):\\n step1_success = True\\n if suffix in (\\\"azione\\\", \\\"azioni\\\", \\\"atore\\\", \\\"atori\\\"):\\n word = word[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n\\n if r2.endswith(\\\"ic\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif suffix in (\\\"logia\\\", \\\"logie\\\"):\\n word = word[:-2]\\n rv = word[:-2]\\n\\n elif suffix in (\\\"uzione\\\", \\\"uzioni\\\",\\n \\\"usione\\\", \\\"usioni\\\"):\\n word = word[:-5]\\n rv = rv[:-5]\\n\\n elif suffix in (\\\"enza\\\", \\\"enze\\\"):\\n word = \\\"\\\".join((word[:-2], \\\"te\\\"))\\n rv = \\\"\\\".join((rv[:-2], \\\"te\\\"))\\n\\n elif suffix == u(\\\"it\\\\xE0\\\"):\\n word = word[:-3]\\n r2 = r2[:-3]\\n rv = rv[:-3]\\n\\n if r2.endswith((\\\"ic\\\", \\\"iv\\\")):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith(\\\"abil\\\"):\\n word = word[:-4]\\n rv = rv[:-4]\\n\\n elif suffix in (\\\"ivo\\\", \\\"ivi\\\", \\\"iva\\\", \\\"ive\\\"):\\n word = word[:-3]\\n r2 = r2[:-3]\\n rv = rv[:-3]\\n\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n r2 = r2[:-2]\\n rv = rv[:-2]\\n\\n if r2.endswith(\\\"ic\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n else:\\n word = word[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n break\\n\\n # STEP 2: Verb suffixes\\n if not step1_success:\\n for suffix in self.__step2_suffixes:\\n if rv.endswith(suffix):\\n word = word[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n break\\n\\n # STEP 3a\\n if rv.endswith((\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", u(\\\"\\\\xE0\\\"), u(\\\"\\\\xE8\\\"),\\n u(\\\"\\\\xEC\\\"), u(\\\"\\\\xF2\\\"))):\\n word = word[:-1]\\n rv = rv[:-1]\\n\\n if rv.endswith(\\\"i\\\"):\\n word = word[:-1]\\n rv = rv[:-1]\\n\\n # STEP 3b\\n if rv.endswith((\\\"ch\\\", \\\"gh\\\")):\\n word = word[:-1]\\n\\n word = word.replace(\\\"I\\\", \\\"i\\\").replace(\\\"U\\\", \\\"u\\\")\\n return word\",\n \"def define(word):\\n\\treturn lexicon.get(word.upper(), \\\"I couldn't find the definition of {}\\\\n\\\".format(word))\",\n \"async def get_demon(self, ctx, game: str, name: str):\\n\\n name = await self.nearest_spelling(ctx, name.lower(), self.names[game])\\n if name is not None:\\n name = \\\" \\\".join([i.capitalize() for i in name.split()])\\n return name\",\n \"def review_word(word):\\n\\n print \\\"===== NEXT WORD ======\\\"\\n print \\\"Is this word \\\"\",\n \"def normalize(word):\\n word = word.lower()\\n # removing plural, it facilitates the matching\\n if len(word)>0 and word[-1] == 's':\\n return word[0:-1]\\n return word\",\n \"def detectCapitalUse(self, word: str) -> bool:\\n if not word:\\n return True\\n\\n head_upper = word[0].isupper()\\n\\n # except for the head\\n has_lower = False\\n has_upper = False\\n for w in word[1:]:\\n if w.isupper():\\n has_upper = True\\n if has_lower or not head_upper:\\n return False\\n else:\\n has_lower = True\\n if has_upper:\\n return False\\n return True\",\n \"def __process_word(self, word):\\n output = ''\\n capitals = self.__capital_positions(word)\\n c_index = 0\\n\\n for c in word:\\n if c_index in capitals:\\n output += c.upper()\\n else:\\n output += c.lower()\\n\\n c_index += 1\\n\\n return output\",\n \"def detectCapitalUse(self, word):\\n\\n # Check for no upper or all upper\\n if all(l.isupper() for l in word) or all(l.islower() for l in word):\\n return True\\n elif word[0].isupper() and word[1:].islower():\\n return True\\n else:\\n return False\",\n \"def findMeaning(data: str, shorten: bool) -> str:\\n # # remove all spaces\\n # data.replace(' ', '')\\n\\n # two cases\\n # if 》string exists, then meaning is string after this string\\n if data.find('》') != -1:\\n # find the line after 》\\n sym = '》'\\n symbol_lines_index = symbol_line_location(data, sym, move=0, addLast=True)\\n symbol = find(data, '》')\\n\\n # now find all places with period\\n period = find(data, '。')\\n\\n # initialize meaning\\n meaning = ''\\n # add to the meaning\\n for i in range(len(symbol)):\\n indStart = symbol[i]\\n indEnd = max([period[j] for j in range(len(period)) if (period[j] >= symbol_lines_index[i][1]\\n and period[j] < symbol_lines_index[i + 1][0]) or\\n period[j] < symbol_lines_index[i][1]])\\n\\n single_meaning = data[int(indStart + 1):indEnd + 1]\\n meaning += single_meaning\\n\\n # if 》string does not exist, then meaning is string after 】\\n else:\\n # find the line after 】\\n sym = '】'\\n symbol_lines_index = symbol_line_location(data, sym, move=1, addLast=True)\\n\\n symbol = find(data, '】')\\n\\n # now find all places with period\\n period = find(data, '。')\\n\\n # initialize meaning\\n meaning = ''\\n # add to the meaning\\n for i in range(len(symbol)):\\n indStart = symbol_lines_index[i][0]\\n indEnd = max([period[j] for j in range(len(period)) if (period[j] >= symbol_lines_index[i][1]\\n and period[j] < symbol_lines_index[i+1][0]) or period[j] < symbol_lines_index[i][1]])\\n\\n\\n single_meaning = data[int(indStart + 1):indEnd+1]\\n meaning += single_meaning\\n\\n # showMessage(symbol)\\n # showMessage(line)\\n # showMessage(lines_index)\\n # showMessage(symbol_lines_index)\\n # showMessage(period)\\n\\n # remove new lines\\n meaning = meaning.replace('\\\\n', '')\\n\\n # lastly, get rid of spaces\\n meaning = meaning.replace(\\\" \\\", \\\"\\\")\\n\\n # if we want to shorten, we will only take the first sentence\\n if shorten:\\n # find period\\n period = find(meaning, '。')\\n # take the first period\\n meaning = meaning[0:period[0]]\\n return meaning\",\n \"def test_abbreviation(self):\\n self.assertEqual(self.compound.abbreviation, \\\"Cool\\\")\",\n \"def stem(s):\\n short_words = {'is': 'is', 'the': 'the','he': 'he', 'she': 'she', \\\\\\n 'my': 'my', }\\n if s in short_words:\\n return s\\n if s[-1] == 's':\\n s = s[:-1]\\n special_cases = {'children': 'child', 'doing': 'do', 'did': 'do', \\\\\\n 'string': 'string', 'spring': 'spring'}\\n if s in special_cases:\\n return special_cases[s]\\n if s[-1] == 'e':\\n s = s[:-1]\\n if s[-3:] == 'ing' and len(s) > 5:\\n if s[-5:-3] == 'mm' or s[-5:-3] == 'tt':\\n s = s[-4]\\n else:\\n s = s[:-3]\\n if s[-1] == 'y':\\n s = s[:-1] + 'i'\\n elif s[-2:] == 'er' and len(s) > 4:\\n if s[-4:-2] == 'mm' or s[-4:-2] == 'tt':\\n s = s[:-3]\\n else:\\n s = s[:-2]\\n elif s[-2:] == 'ed' and len(s) > 4:\\n if s[-4:-2] == 'mm' or s[-4:-2] == 'tt':\\n s = s[:-3]\\n else:\\n s = s[:-2]\\n return s\",\n \"def profanity_word_handler(word):\\n return word[0] + ''.join([settings.CENSOR_PROFANITY_REPLACEMENT_CHARACTER for I in range(len(word)-2)]) + word [-1]\",\n \"def stationabbreviation(station):\\n stations = {'Utrecht': 'Ut',\\n 'Amsterdam Centraal': 'asd'}\\n if station in stations:\\n return stations[station]\",\n \"def test_find_word(self):\\n mic = mi.MicrophoneToText()\\n\\n teststring = 'x transcript\\\": ort lautet testort }x'\\n\\n word = mic.find_word(teststring)\\n\\n self.assertEqual(word, ' ort lautet testort ')\",\n \"def stem(self, word):\\n word = word.lower()\\n\\n if word in self.stopwords:\\n return word\\n\\n step1_success = False\\n\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\n rv = self._rv_standard(word, self.__vowels)\\n\\n # STEP 0: Attached pronoun\\n for suffix in self.__step0_suffixes:\\n if not (word.endswith(suffix) and rv.endswith(suffix)):\\n continue\\n\\n if (\\n rv[: -len(suffix)].endswith(\\n (\\n \\\"ando\\\",\\n \\\"ar\\\",\\n \\\"er\\\",\\n \\\"iendo\\\",\\n \\\"ir\\\",\\n )\\n )\\n ) or (\\n rv[: -len(suffix)].endswith(\\\"yendo\\\")\\n and word[: -len(suffix)].endswith(\\\"uyendo\\\")\\n ):\\n\\n word = self.__replace_accented(word[: -len(suffix)])\\n r1 = self.__replace_accented(r1[: -len(suffix)])\\n r2 = self.__replace_accented(r2[: -len(suffix)])\\n rv = self.__replace_accented(rv[: -len(suffix)])\\n break\\n\\n # STEP 1: Standard suffix removal\\n for suffix in self.__step1_suffixes:\\n if not word.endswith(suffix):\\n continue\\n\\n if suffix == \\\"amente\\\" and r1.endswith(suffix):\\n step1_success = True\\n word = word[:-6]\\n r2 = r2[:-6]\\n rv = rv[:-6]\\n\\n if r2.endswith(\\\"iv\\\"):\\n word = word[:-2]\\n r2 = r2[:-2]\\n rv = rv[:-2]\\n\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith((\\\"os\\\", \\\"ic\\\", \\\"ad\\\")):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith(suffix):\\n step1_success = True\\n if suffix in (\\n \\\"adora\\\",\\n \\\"ador\\\",\\n \\\"acion\\\",\\n \\\"adoras\\\",\\n \\\"adores\\\",\\n \\\"aciones\\\",\\n \\\"ante\\\",\\n \\\"antes\\\",\\n \\\"ancia\\\",\\n \\\"ancias\\\",\\n ):\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n\\n if r2.endswith(\\\"ic\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif suffix in (\\\"logia\\\", \\\"logias\\\"):\\n word = suffix_replace(word, suffix, \\\"log\\\")\\n rv = suffix_replace(rv, suffix, \\\"log\\\")\\n\\n elif suffix in (\\\"ucion\\\", \\\"uciones\\\"):\\n word = suffix_replace(word, suffix, \\\"u\\\")\\n rv = suffix_replace(rv, suffix, \\\"u\\\")\\n\\n elif suffix in (\\\"encia\\\", \\\"encias\\\"):\\n word = suffix_replace(word, suffix, \\\"ente\\\")\\n rv = suffix_replace(rv, suffix, \\\"ente\\\")\\n\\n elif suffix == \\\"mente\\\":\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n\\n if r2.endswith((\\\"ante\\\", \\\"able\\\", \\\"ible\\\")):\\n word = word[:-4]\\n rv = rv[:-4]\\n\\n elif suffix in (\\\"idad\\\", \\\"idades\\\"):\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n\\n for pre_suff in (\\\"abil\\\", \\\"ic\\\", \\\"iv\\\"):\\n if r2.endswith(pre_suff):\\n word = word[: -len(pre_suff)]\\n rv = rv[: -len(pre_suff)]\\n\\n elif suffix in (\\\"ivo\\\", \\\"iva\\\", \\\"ivos\\\", \\\"ivas\\\"):\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n else:\\n word = word[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n break\\n\\n # STEP 2a: Verb suffixes beginning 'y'\\n if not step1_success:\\n for suffix in self.__step2a_suffixes:\\n if rv.endswith(suffix) and word[-len(suffix) - 1 : -len(suffix)] == \\\"u\\\":\\n word = word[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n break\\n\\n # STEP 2b: Other verb suffixes\\n for suffix in self.__step2b_suffixes:\\n if rv.endswith(suffix):\\n word = word[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n if suffix in (\\\"en\\\", \\\"es\\\", \\\"eis\\\", \\\"emos\\\"):\\n if word.endswith(\\\"gu\\\"):\\n word = word[:-1]\\n\\n if rv.endswith(\\\"gu\\\"):\\n rv = rv[:-1]\\n break\\n\\n # STEP 3: Residual suffix\\n for suffix in self.__step3_suffixes:\\n if rv.endswith(suffix):\\n word = word[: -len(suffix)]\\n if suffix in (\\\"e\\\", \\\"\\\\xE9\\\"):\\n rv = rv[: -len(suffix)]\\n\\n if word[-2:] == \\\"gu\\\" and rv.endswith(\\\"u\\\"):\\n word = word[:-1]\\n break\\n\\n word = self.__replace_accented(word)\\n\\n return word\",\n \"def get_lemma(word):\\n return stemmer.stem(word)\",\n \"def __isVerb__(self, word):\\n self.verbs = ('go', 'stop', 'kill', 'eat')\\n for verb in self.verbs:\\n if verb == word:\\n return ('verb', word), True\\n return None, False\",\n \"def hey(self, sentence=\\\"\\\"):\\n if sentence == \\\"\\\" or sentence.replace(\\\" \\\", \\\"\\\") == \\\"\\\":\\n return \\\"Fine. Be that way!\\\"\\n if sentence.isupper():\\n return \\\"Woah, chill out!\\\"\\n if sentence[-1] == \\\"?\\\":\\n return \\\"Sure.\\\"\\n return \\\"Whatever.\\\"\",\n \"def abbreviation(self):\\n\\n return self._abbreviation\",\n \"def usedWord(afz, word, output=True):\\n count = 0\\n for msg in msgs:\\n if msg.afz == afz:\\n if word.lower() in msg.msg.lower():\\n count = count + 1\\n print afz, 'heeft', count, 'keer het woord', word, 'gebruikt.'\",\n \"def normalize_word(self, word, treebank_tag):\\n wordnet_pos, part_of_speech = self.get_wordnet_pos(treebank_tag)\\n\\n if wordnet_pos == wordnet.NOUN and part_of_speech == 'proper':\\n return word, 'proper_noun'\\n\\n lemword = self.wordnetlemmatize.lemmatize(word, wordnet_pos)\\n return self.stemmer.stem(lemword), part_of_speech\",\n \"def _parse_abbreviation(self, cell_content):\\n span = cell_content.find(\\\"span\\\")\\n full = span.attrs[\\\"title\\\"].strip()\\n abbrv = span.text.strip()\\n return abbrv, full\",\n \"def a_or_an(value):\\n # TODO: handle confusing things like \\\"an hour\\\" or \\\"a unicycle\\\"\\n vowel_sounds = [\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\"]\\n if value[0].lower() in vowel_sounds:\\n return \\\"an\\\"\\n else:\\n return \\\"a\\\"\",\n \"def form_present_verb(word, number, person):\\n assert word\\n assert isinstance(word, basestring)\\n if _is_first_person(person) or _is_second_person(person):\\n return word\\n elif _is_third_person(person):\\n if _is_singular(number):\\n return pluralize(word)\\n if _is_dual(number) or _is_plural(number): \\n return word\\n return None\",\n \"def deabbreviate(self, st):\\n\\t\\tabbrs = {'gws': 'greater western sydney giants',\\n\\t\\t\\t\\t 'gwsg': 'greater western sydney giants',\\n\\t\\t\\t\\t 'afl': 'australian football league',\\n\\t\\t\\t\\t 'nrc': 'national rugby championship',\\n\\t\\t\\t\\t 'nrl': 'national rugby league',\\n\\t\\t\\t\\t 'syd': 'sydney',\\n\\t\\t\\t\\t 'mel': 'melbourne',\\n\\t\\t\\t\\t 'melb': 'melbourne',\\n\\t\\t\\t\\t 'bris': 'brisbane',\\n\\t\\t\\t\\t 'brisb': 'brisbane',\\n\\t\\t\\t\\t 'gc': 'gold coast',\\n\\t\\t\\t\\t 'adel': 'adelaide',\\n\\t\\t\\t\\t 'canb': 'canberra',\\n\\t\\t\\t\\t 'mt': 'mount',\\n\\t\\t\\t\\t 'utd': 'united',\\n\\t\\t\\t\\t 'cty': 'city',\\n\\t\\t\\t\\t 'football club': 'fc',\\n\\t\\t\\t\\t 'snr': 'senior',\\n\\t\\t\\t\\t 'jr': 'junion',\\n\\t\\t\\t\\t 'nsw': 'new south wales' ,\\n\\t\\t\\t\\t 'vic': 'victoria',\\n\\t\\t\\t\\t 'tas' : 'tasmania',\\n\\t\\t\\t\\t 'sa': 'south australia',\\n\\t\\t\\t\\t 'wa': 'western australia',\\n\\t\\t\\t\\t 'act': 'australian capital territory',\\n\\t\\t\\t\\t 'nt': 'northern territory',\\n\\t\\t\\t\\t 'qld': 'queensland',\\n\\t\\t\\t\\t 'champs': 'championships', \\n\\t\\t\\t\\t 'champ': 'championship', \\n\\t\\t\\t\\t 'soc': 'society',\\n\\t\\t\\t\\t 'ent': 'entertainment',\\n\\t\\t\\t\\t 'intl': 'international', \\n\\t\\t\\t\\t 'int': 'international', \\n\\t\\t\\t\\t 'aust': 'australian'}\\n\\n\\t\\t# first replace full state names by abbreviations;\\n\\t\\tfor ab in abbrs:\\n\\t\\t\\tst = re.sub(r'\\\\b' + ab + r'\\\\b', abbrs[ab], st)\\n\\n\\t\\treturn st\",\n \"def replace_word_candidate(self, word):\\n capital_flag = word[0].isupper()\\n word = word.lower()\\n if capital_flag and word in self.teencode_dict:\\n return self.replace_teencode(word).capitalize()\\n elif word in self.teencode_dict:\\n return self.replace_teencode(word)\\n\\n for couple in self.word_couples:\\n for i in range(2):\\n if couple[i] == word:\\n if i == 0:\\n if capital_flag:\\n return couple[1].capitalize()\\n else:\\n return couple[1]\\n else:\\n if capital_flag:\\n return couple[0].capitalize()\\n else:\\n return couple[0]\",\n \"def guess_article(item_name):\\n return 'an' if item_name[0].lower() in 'aeiouy' else 'a'\",\n \"def just_do_it(text):\\n from string import capwords\\n return capwords(text)\",\n \"def convert_single_word_into_plural_form(word):\\n\\n if word.endswith('y'):\\n return word[:-1] + 'ies'\\n elif word[-1] in 'sx' or word[-2:] in ['sh', 'ch']:\\n return word + 'es'\\n elif word.endswith('an'):\\n return word[:-2] + 'en'\\n else:\\n return word + 's'\",\n \"def word_tag(self, word):\\n if word[1] in (\\\"NN\\\", \\\"NNS\\\", \\\"NNP\\\", \\\"NNPS\\\"):\\n return _wordnet.NOUN\\n if word[1] in (\\\"JJ\\\", \\\"JJR\\\", \\\"JJS\\\"):\\n return _wordnet.ADJ\\n if word[1] in (\\\"VB\\\", \\\"VBD\\\", \\\"VBG\\\", \\\"VBN\\\", \\\"VBP\\\", \\\"VBZ\\\"):\\n return _wordnet.VERB\\n if word[1] in (\\\"RB\\\", \\\"RBR\\\", \\\"RBS\\\"):\\n return _wordnet.ADV\\n\\n return None\",\n \"def get_token(word, flag):\\n if flag == 1:\\n return \\\"_RARE_\\\"\\n elif flag == 2:\\n if bool(re.search(r'\\\\d', word)):\\n return \\\"AlphaNum\\\"\\n else:\\n return \\\"oThEr\\\"\\n elif flag == 3:\\n if word[-3:] == \\\"ing\\\":\\n return \\\"enDiNg\\\"\\n else:\\n return \\\"oThEr\\\"\",\n \"def getType(word):\\n rules = {\\n 'direction': ['north', 'south', 'east', 'west', 'down', 'up', 'left', 'right', 'back'],\\n 'verb': ['go', 'stop', 'kill', 'eat'],\\n 'stop': ['the', 'in', 'of', 'from', 'at', 'it'],\\n 'noun': ['door', 'bear', 'princess', 'cabinet']\\n }\\n if isNumber(word):\\n return 'number'\\n\\n for rule, rule_list in rules.items():\\n if word in rule_list:\\n return rule\\n\\n return 'error'\",\n \"def check_word_capitalization(word):\\n return_value = False\\n if (len(word) > 1):\\n return_value = True if (word[0].isupper() and word[1].islower()) else False\\n return return_value\",\n \"def isDisambiguatedByNextVerb(self, word):\\n\\t\\treturn 'verb' in disambig_const.DISAMBIGUATATION_TABLE.get(word, {});\",\n \"def replace_word(word, is_first, vocab, minimal_fequency):\\n ### YOUR CODE HERE\\n if word.isdigit():\\n if len(word) == 2:\\n return 'twoDigitNum'\\n elif len(word) == 4:\\n return 'fourDigitNum'\\n else:\\n return 'othernum'\\n elif contains_digit(word):\\n if contains_alpha(word):\\n return 'containsDigitAndAlpha'\\n elif '-' in word:\\n return 'containsDigitAndDash'\\n elif '/' in word or '\\\\\\\\' in word:\\n return 'containsDigitAndSlash'\\n elif '.' in word:\\n return 'containsDigitAndPeriod'\\n if word.isalpha() and word.isupper():\\n return 'allCaps'\\n elif CAP_PERIOD_PATTERN.match(word):\\n return 'capPeriod'\\n if is_first and vocab.get(word.lower(), 0) >= minimal_fequency:\\n return word.lower()\\n if not is_first and word[0].isupper():\\n return 'initCap'\\n if word.isalpha():\\n for suffix in SUFFIXES:\\n if word.endswith(suffix):\\n return 'wordSuffix' + suffix\\n if word.isalpha():\\n for prefix in PREFIXES:\\n if word.startswith(prefix):\\n return prefix + 'WordPrefix'\\n if '-' in word:\\n return 'withDash'\\n elif word.isalpha() and word.lower() == word:\\n return 'lowercase'\\n ### END YOUR CODE\\n return UNKNOWN_WORD\",\n \"def isUnique(self, word):\\n if len(word) < 3:\\n abbrev = word\\n else:\\n abbrev = word[0] + str(len(word) - 2) + word[-1]\\n if not abbrev in self.abbrev_dict:\\n return True\\n elif word in self.abbrev_dict[abbrev] and len(self.abbrev_dict[abbrev]) == 1:\\n return True\\n else:\\n return False\",\n \"def guess_word(self, prefix):\\n prefix = prefix.lower()\\n current = self.search_prefix(prefix)\\n if current:\\n print(\\\"You typed: \\\" + '\\\"' + prefix + '\\\"')\\n if current.get_end():\\n print('\\\"' + prefix + '\\\" is a word, but you could have also been typing out:')\\n else:\\n print('\\\"' + prefix + '\\\" is not a word, perhaps you were typing out:')\\n library = [prefix + word for word in self.get_library(current, library = [])]\\n for word in library:\\n print(word)\\n return library # list includes empty string if its already a word\\n print(\\\"I'm not quite sure what you meant by \\\" + '\\\"' + prefix + '\\\"...')\\n return []\",\n \"def _step1b(self, word):\\n # this NLTK-only block extends the original algorithm, so that\\n # 'spied'->'spi' but 'died'->'die' etc\\n if self.mode == self.NLTK_EXTENSIONS:\\n if word.endswith(\\\"ied\\\"):\\n if len(word) == 4:\\n return self._replace_suffix(word, \\\"ied\\\", \\\"ie\\\")\\n else:\\n return self._replace_suffix(word, \\\"ied\\\", \\\"i\\\")\\n\\n # (m>0) EED -> EE\\n if word.endswith(\\\"eed\\\"):\\n stem = self._replace_suffix(word, \\\"eed\\\", \\\"\\\")\\n if self._measure(stem) > 0:\\n return stem + \\\"ee\\\"\\n else:\\n return word\\n\\n rule_2_or_3_succeeded = False\\n\\n for suffix in [\\\"ed\\\", \\\"ing\\\"]:\\n if word.endswith(suffix):\\n intermediate_stem = self._replace_suffix(word, suffix, \\\"\\\")\\n if self._contains_vowel(intermediate_stem):\\n rule_2_or_3_succeeded = True\\n break\\n\\n if not rule_2_or_3_succeeded:\\n return word\\n\\n return self._apply_rule_list(\\n intermediate_stem,\\n [\\n (\\\"at\\\", \\\"ate\\\", None), # AT -> ATE\\n (\\\"bl\\\", \\\"ble\\\", None), # BL -> BLE\\n (\\\"iz\\\", \\\"ize\\\", None), # IZ -> IZE\\n # (*d and not (*L or *S or *Z))\\n # -> single letter\\n (\\n \\\"*d\\\",\\n intermediate_stem[-1],\\n lambda stem: intermediate_stem[-1] not in (\\\"l\\\", \\\"s\\\", \\\"z\\\"),\\n ),\\n # (m=1 and *o) -> E\\n (\\n \\\"\\\",\\n \\\"e\\\",\\n lambda stem: (self._measure(stem) == 1 and self._ends_cvc(stem)),\\n ),\\n ],\\n )\",\n \"def keyword_from_meaning(name):\\n # Try to adhere to keyword scheme in DICOM (CP850)\\n\\n # singular/plural alternative forms are made plural\\n # e.g., “Physician(s) of Record” becomes “PhysiciansOfRecord”\\n name = name.replace(\\\"(s)\\\", \\\"s\\\")\\n\\n # “Patient’s Name” -> “PatientName”\\n # “Operators’ Name” -> “OperatorsName”\\n name = name.replace(\\\"’s \\\", \\\" \\\")\\n name = name.replace(\\\"'s \\\", \\\" \\\")\\n name = name.replace(\\\"s’ \\\", \\\"s \\\")\\n name = name.replace(\\\"s' \\\", \\\"s \\\")\\n\\n # Mathematical symbols\\n name = name.replace(\\\"%\\\", \\\" Percent \\\")\\n name = name.replace(\\\">\\\", \\\" Greater Than \\\")\\n name = name.replace(\\\"=\\\", \\\" Equals \\\")\\n name = name.replace(\\\"<\\\", \\\" Lesser Than \\\")\\n\\n name = re.sub(r\\\"([0-9]+)\\\\.([0-9]+)\\\", \\\"\\\\\\\\1 Point \\\\\\\\2\\\", name)\\n name = re.sub(r\\\"\\\\s([0-9.]+)-([0-9.]+)\\\\s\\\", \\\" \\\\\\\\1 To \\\\\\\\2 \\\", name)\\n\\n name = re.sub(r\\\"([0-9]+)day\\\", \\\"\\\\\\\\1 Day\\\", name)\\n name = re.sub(r\\\"([0-9]+)y\\\", \\\"\\\\\\\\1 Years\\\", name)\\n\\n # Remove category modifiers, such as \\\"(specimen)\\\", \\\"(procedure)\\\",\\n # \\\"(body structure)\\\", etc.\\n name = re.sub(r\\\"^(.+) \\\\([a-z ]+\\\\)$\\\", \\\"\\\\\\\\1\\\", name)\\n\\n name = camel_case(name.strip())\\n\\n # Python variables must not begin with a number.\\n if re.match(r\\\"[0-9]\\\", name):\\n name = \\\"_\\\" + name\\n\\n return name\",\n \"def getWord(self,):\\n\\t\\treturn self.word;\",\n \"def guessed_word(self): # helper function to display_correct_guess()\\n if self.correct_guess():\\n if self.display_word.replace(' ', '') == self.chosen_word:\\n self.display_word = self.chosen_word # process will continue until it reaches ***\\n return True, self.display_word\\n else:\\n return False\",\n \"def __check_word__(self, word):\\n self.directionValue, self.isDirection = self.__isDirection__(word.lower())\\n self.verbValue, self.isVerb = self.__isVerb__(word.lower())\\n self.stopValue, self.isStop = self.__isStopWord__(word.lower())\\n self.nounValue, self.isNoun = self.__isNoun__(word.lower())\\n self.numberValue, self.isNumber = self.__isNumber__(word.lower())\\n\\n if self.isDirection:\\n return self.directionValue\\n elif self.isVerb:\\n return self.verbValue\\n elif self.isStop:\\n return self.stopValue\\n elif self.isNoun:\\n return self.nounValue\\n elif self.isNumber:\\n return self.numberValue\\n else:\\n return ('error', word)\",\n \"def is_cap_word(self, word):\\n try:\\n return word[0].isupper()\\n except:\\n return False\",\n \"def get_word_probability(self, label, term):\\n\\n if 'sod' in label:\\n return self.cond_prob_sod[term]\\n elif 'pop' in label:\\n return self.cond_prob_pop[term]\\n else:\\n print(\\\"Just run the doctest Dev\\\")\\n \\n pass\",\n \"def normalize_word (word):\\n return st.stem(word.strip ().lower ())\",\n \"def test_word_info(self):\\n word = \\\"vitality\\\"\\n rv = self.wordInfo(input_word=word)\\n expected_output = {\\n word: {\\n \\\"frequency\\\": \\\"975\\\",\\n \\\"defination\\\": \\\"{'Noun': ['an energetic style', 'a healthy capacity for vigorous activity', '(biology', 'not physical or chemical', 'the property of being able to survive and grow']}\\\",\\n \\\"antonyms\\\": \\\"['enervation', 'inactivity', 'lethargy', 'weakness', 'lack']\\\",\\n \\\"examples\\\": \\\"{1: 'And finally, both Lord Robertson and Secretary of State Powell pointed to what they called the vitality and the relevance of NATO, and said any damage done to the reputation of NATO over the last couple weeks can quite, in their words, be easily overcome.', 2: \\\\\\\"Professor Huxley himself has told us that he lived in 'the hope and the faith that in course of time we shall see our way from the constituents of the protoplasm to its properties,' _i. e._ from carbonic acid, water, and ammonia to that mysterious thing which we call vitality or life -- from the molecular motion of the brain to Socratic wisdom,\\\\\\\", 3: 'The strongest, the most amply endowed with what we call vitality or power to live, win.', 4: 'But the thought that it is mechanics and chemistry applied by something of which they as such, form no part, some agent or principle which we call vitality, is welcome to us.', 5: '\\\\\\\"The Indian savages,\\\\\\\" said Margrave, sullenly, \\\\\\\"have not a health as perfect as mine, and in what you call vitality -- the blissful consciousness of life -- they are as sticks and stones compared to me.\\\\\\\"'}\\\",\\n \\\"pronounciation\\\": \\\"V AY0 T AE1 L AH0 T IY0\\\",\\n \\\"synonyms\\\": \\\"['vigor', 'continuity', 'spunk', 'strength', 'verve']\\\"\\n }\\n }\\n response_data = json.loads(rv.get_data(as_text=True))\\n\\n self.assertEquals(rv.status_code, 200)\\n self.assertEquals(response_data[word][\\\"defination\\\"], expected_output[word][\\\"defination\\\"])\\n self.assertEquals(response_data[word][\\\"antonyms\\\"], expected_output[word][\\\"antonyms\\\"])\\n self.assertEquals(response_data[word][\\\"examples\\\"], expected_output[word][\\\"examples\\\"])\\n self.assertEquals(response_data[word][\\\"frequency\\\"], expected_output[word][\\\"frequency\\\"])\\n self.assertEquals(response_data[word][\\\"pronounciation\\\"], expected_output[word][\\\"pronounciation\\\"])\\n self.assertEquals(response_data[word][\\\"synonyms\\\"], expected_output[word][\\\"synonyms\\\"])\",\n \"def morph_noun(word, number, a_an, feature):\\n word = morph_number(word, number)\\n if not (number in ['first', 'second']) and word[-1] == 's':\\n return mark_noun_as_plural(word)\\n else:\\n return word\\n\\n if a_an == 'an':\\n return mark_noun_as_an(word)\\n else:\\n return word\",\n \"def med_in_hindi(word):\\r\\n\\treturn int(med(correction(word),word))\",\n \"def get_word(w):\\n return ''.join(c for c in w if c.isalpha()).lower()\",\n \"def abbreviate(x: str) -> str:\\n i = 0\\n abbreviation: str = \\\"\\\"\\n while i < len(x):\\n if x[i].isupper():\\n abbreviation += x[i]\\n i += 1\\n return abbreviation\",\n \"def map_word(self, word):\\n for invariance in self.invariances:\\n word = invariance.map_word(word)\\n return word\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6904979","0.68378323","0.6644737","0.6541667","0.6537515","0.6440209","0.6420517","0.6390089","0.63880235","0.6369538","0.63481516","0.63338417","0.6322253","0.6277778","0.62644935","0.6260187","0.62546563","0.62457216","0.6238056","0.6238056","0.6198607","0.61820537","0.61625916","0.6147041","0.6146263","0.6145105","0.61258423","0.6106864","0.6077692","0.6065827","0.60480535","0.60479647","0.60433114","0.60301346","0.60254735","0.60036254","0.6000636","0.59983194","0.59896314","0.5987526","0.59861356","0.59831095","0.5970029","0.5967068","0.5963","0.5943353","0.5908138","0.5885942","0.58848673","0.5877995","0.58575135","0.585694","0.5856682","0.58510935","0.5843788","0.58410114","0.584004","0.5832579","0.5831531","0.5831312","0.5831232","0.5825457","0.58101565","0.580916","0.5799324","0.5791568","0.57841176","0.5762731","0.5753913","0.5748402","0.57481796","0.5739254","0.5735073","0.57318044","0.57286024","0.57279074","0.57087654","0.5702749","0.5694456","0.5693287","0.5692739","0.5689398","0.5687838","0.5685031","0.5675526","0.5672941","0.56652874","0.5664375","0.56580323","0.56563973","0.5655768","0.5653358","0.5652246","0.5651475","0.565108","0.564712","0.56465846","0.5639597","0.56356245","0.5631102"],"string":"[\n \"0.6904979\",\n \"0.68378323\",\n \"0.6644737\",\n \"0.6541667\",\n \"0.6537515\",\n \"0.6440209\",\n \"0.6420517\",\n \"0.6390089\",\n \"0.63880235\",\n \"0.6369538\",\n \"0.63481516\",\n \"0.63338417\",\n \"0.6322253\",\n \"0.6277778\",\n \"0.62644935\",\n \"0.6260187\",\n \"0.62546563\",\n \"0.62457216\",\n \"0.6238056\",\n \"0.6238056\",\n \"0.6198607\",\n \"0.61820537\",\n \"0.61625916\",\n \"0.6147041\",\n \"0.6146263\",\n \"0.6145105\",\n \"0.61258423\",\n \"0.6106864\",\n \"0.6077692\",\n \"0.6065827\",\n \"0.60480535\",\n \"0.60479647\",\n \"0.60433114\",\n \"0.60301346\",\n \"0.60254735\",\n \"0.60036254\",\n \"0.6000636\",\n \"0.59983194\",\n \"0.59896314\",\n \"0.5987526\",\n \"0.59861356\",\n \"0.59831095\",\n \"0.5970029\",\n \"0.5967068\",\n \"0.5963\",\n \"0.5943353\",\n \"0.5908138\",\n \"0.5885942\",\n \"0.58848673\",\n \"0.5877995\",\n \"0.58575135\",\n \"0.585694\",\n \"0.5856682\",\n \"0.58510935\",\n \"0.5843788\",\n \"0.58410114\",\n \"0.584004\",\n \"0.5832579\",\n \"0.5831531\",\n \"0.5831312\",\n \"0.5831232\",\n \"0.5825457\",\n \"0.58101565\",\n \"0.580916\",\n \"0.5799324\",\n \"0.5791568\",\n \"0.57841176\",\n \"0.5762731\",\n \"0.5753913\",\n \"0.5748402\",\n \"0.57481796\",\n \"0.5739254\",\n \"0.5735073\",\n \"0.57318044\",\n \"0.57286024\",\n \"0.57279074\",\n \"0.57087654\",\n \"0.5702749\",\n \"0.5694456\",\n \"0.5693287\",\n \"0.5692739\",\n \"0.5689398\",\n \"0.5687838\",\n \"0.5685031\",\n \"0.5675526\",\n \"0.5672941\",\n \"0.56652874\",\n \"0.5664375\",\n \"0.56580323\",\n \"0.56563973\",\n \"0.5655768\",\n \"0.5653358\",\n \"0.5652246\",\n \"0.5651475\",\n \"0.565108\",\n \"0.564712\",\n \"0.56465846\",\n \"0.5639597\",\n \"0.56356245\",\n \"0.5631102\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66452616"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":677,"cells":{"query":{"kind":"string","value":"Create an instance of all possible analysis. If you're not able to create it... you're not able to use it."},"document":{"kind":"string","value":"def build_all_analysis(self, matrix_handler, trajectory_handler):\n distance_matrix = matrix_handler.distance_matrix\n\n self.all_possible_analysis = {}\n\n # Pure queries\n self.all_possible_analysis[\"Details\"] = Analysis(\"Details\", self.analysis_function_details)\n self.all_possible_analysis[\"NumClusters\"] = Analysis(\"Number of clusters\", self.analysis_function_num_clusters)\n self.all_possible_analysis[\"NumClusteredElems\"] = Analysis(\"Number of clustered elements\", self.analysis_function_total_elements)\n self.all_possible_analysis[\"MeanClusterSize\"] = Analysis(\"Mean cluster size\", self.analysis_function_mean_cluster_size)\n self.all_possible_analysis[\"PercentInTop4\"] = Analysis(\"Percent in top 4 clusters\", self.analysis_function_top_4)\n self.all_possible_analysis[\"PercentInTop\"] = Analysis(\"Percent in top cluster\", self.analysis_function_top_percent)\n self.all_possible_analysis[\"ClustersTo90\"] = Analysis(\"Clusters to 90\", self.analysis_function_num_clusters_to_percent, 90)\n self.all_possible_analysis[\"NoiseLevel\"] = Analysis(\"Noise level\", self.analysis_function_noise_level, distance_matrix.row_length)\n\n # Evaluators\n self.all_possible_analysis[\"MirrorCohesion\"] = Analysis(\"MirrorCohesion\", self.evaluate_with_calculator,\n {\"class\":MirrorCohesionCalculator,\"matrix\":distance_matrix})\n\n self.all_possible_analysis[\"Cohesion\"] = Analysis(\"Cohesion\", self.evaluate_with_calculator,\n {\"class\":CohesionCalculator,\"matrix\":distance_matrix})\n\n self.all_possible_analysis[\"Separation\"] = Analysis(\"Separation\", self.evaluate_with_calculator,\n {\"class\":SeparationCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"MinimumMeanSeparation\"] = Analysis(\"MinimumMeanSeparation\", self.evaluate_with_calculator,\n {\"class\":MeanMinimumDistanceCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"Silhouette\"] = Analysis(\"Silhouette\", self.evaluate_with_calculator,\n {\"class\":SilhouetteCoefficientCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"Calinski-Harabasz\"] = Analysis(\"Calinski-Harabasz\", self.evaluate_with_calculator,\n {\"class\":CalinskiHarabaszCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"Dunn\"] = Analysis(\"Dunn\", self.evaluate_with_calculator,\n {\"class\":DunnCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"Davies-Bouldin\"] = Analysis(\"Davies-Bouldin\", self.evaluate_with_calculator,\n {\"class\":DaviesBouldinCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"GaussianSeparation\"] = Analysis(\"GaussianSeparation\", self.evaluate_with_calculator,\n {\"class\":GaussianSeparationCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"Compactness\"] = Analysis(\"Compactness\", self.evaluate_with_calculator,\n {\"class\":CompactnessCalculator,\"matrix\":distance_matrix})\n\n # Cython\n self.all_possible_analysis[\"CythonMirrorCohesion\"] = Analysis(\"CythonMirrorCohesion\", self.evaluate_with_calculator,\n {\"class\":CythonMirrorCohesionCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"CythonMinimumMeanSeparation\"] = Analysis(\"CythonMinimumMeanSeparation\", self.evaluate_with_calculator,\n {\"class\":CythonMeanMinimumDistanceCalculator,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"CythonSilhouette\"] = Analysis(\"CythonSilhouette\", self.evaluate_with_calculator,\n {\"class\":CythonSilhouetteCoefficientCalculator,\"matrix\":distance_matrix})\n\n # Graph\n self.all_possible_analysis[\"RatioCut\"] = Analysis(\"RatioCut\", self.evaluate_with_calculator,\n {\"class\":RatioCut,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"NCut\"] = Analysis(\"NCut\", self.evaluate_with_calculator,\n {\"class\":NCut,\"matrix\":distance_matrix})\n self.all_possible_analysis[\"NormNCut\"] = Analysis(\"NormNCut\", self.analysis_function_norm_n_cut,distance_matrix)\n self.all_possible_analysis[\"MinMaxCut\"] = Analysis(\"MinMaxCut\", self.evaluate_with_calculator,\n {\"class\":MinMaxCut,\"matrix\":distance_matrix})\n\n # Cython & Graph\n self.all_possible_analysis[\"CythonNormNCut\"] = Analysis(\"CythonNormNCut\", self.analysis_function_cython_norm_n_cut,distance_matrix)\n\n # PCA\n self.all_possible_analysis[\"PCAanalysis\"] = Analysis(\"PCAanalysis\", self.analysis_function_pca, trajectory_handler)"},"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 create_analysis_tools(self):\r\n raise NotImplementedError()","def analysis_setup(self):\n pass","def init():\n # analyzer es utilizado para interactuar con el modelo\n analyzer = model.newAnalyzer()\n return analyzer","def analysis(self, checkid):\r\n return analysis.Analysis(self, checkid)","def __init__(self, samples, analysis):\r\n self.samples = samples\r\n self.analysis = analysis","def newAnalyzer():\n analyzer = {'crimes': None,\n 'dateIndex': None,\n 'autors': None,\n 'instrumentalness': None,\n 'tempo':None,\n 'liveness':None,\n 'speechiness':None,\n 'danceability':None,\n 'valence':None,\n 'loudness':None,\n 'acousticness':None,\n 'energy':None,\n 'generos':None\n }\n\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n\n analyzer['autors'] = om.newMap(omaptype='RBT',\n comparefunction=compareAUTOR)\n\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['tempo'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['liveness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['danceability'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n analyzer['valence'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['loudness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['energy'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n\n analyzer['generos']= m.newMap(11,\n maptype='CHAINING',\n loadfactor=4.0)\n \n return analyzer","def __init__(self, autosub=False):\n self.G = nx.DiGraph()\n self.autosub = autosub\n \"\"\"\n Graph object of this analyzer.\n It is actually a networkx directed graph object(DiGraph), so you can apply all operations available to DiGraph object using networkx.\n \"\"\"\n self.entityList = [dict() for x in range(len(NEList))]\n \"\"\"\n List of entities appeared during this analysis round.\n \"\"\"\n self.proList = list()\n \"\"\"\n List of pronouns appeared during this analysis round.\n \"\"\"\n self.pos = 0\n \"\"\"\n Current position of the analyzer.\n \"\"\"\n self.proc = Subprocess('cabocha -f1')\n \"\"\"\n Communicator to backend for KnowledgeAnalyzer.\n \"\"\"","def newAnalyzer():\n analyzer = {'tracks': None,\n 'songs': None,\n 'artists': None,\n 'char': None,\n 'char2': None,\n 'char3': None,\n }\n\n analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)\n analyzer['songs'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['artists'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char2'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char3'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char4'] = mp.newMap(numelements=50,\n maptype='PROBING',\n comparefunction=compareValue)\n \n return analyzer","def __init__(self, input=None):\r\n BaseAnalyzer.__init__(self, input)","def __init__(self, input=None):\r\n BaseAnalyzer.__init__(self, input)","def __init__(self, analyzers: Iterable[Type[Analyzer]], model_repository: ModelRepository,\n data_service: DataService):\n self._model_repository = model_repository\n analyzers = [(a.__name__, a) for a in analyzers]\n analyzers.sort()\n self._analyzers = [a[1] for a in analyzers]\n self._data_service = data_service","def analyse(self):\n pass","def initAnalyzer():\n return controller.initAnalyzer()","def analysis(self):\r\n return analysis.Analysis(self.parent, self.object_id)","def __init__(self):\n super(TestAnalyzer, self).__init__()\n self._results = []","def __init__(self, out_dir, analysis_out_dir, evaluation_run_name,\n evaluation_name):\n if out_dir is None:\n out_dir = os.getcwd()\n self.out_dir = out_dir\n\n # Copy the analysis results to the report output directory, so that the HTML\n # report can be correctly rendered even if we move the csv files, plots,\n # etc.\n if out_dir != analysis_out_dir:\n analysis_file_dirs = evaluator.load_directory_tree(\n out_dir=analysis_out_dir,\n run_name=evaluation_run_name,\n evaluation_name=evaluation_name)\n shutil.copytree(\n analysis_file_dirs[evaluator.KEY_RUN_DIR],\n os.path.join(out_dir, evaluation_run_name))\n\n self.analysis_results = analyzer.get_analysis_results(\n out_dir, evaluation_run_name, evaluation_name)\n\n self.analysis_results[KEY_NUM_ESTIMABLE_SETS_STATS_DF] = (\n ReportGenerator.add_parsed_sketch_estimator_name_cols(\n self.analysis_results[KEY_NUM_ESTIMABLE_SETS_STATS_DF],\n analyzer.SKETCH_ESTIMATOR_NAME))\n\n self.analysis_type = None","def analyze(self):\n dataset = self.config.dataset\n class_config = dataset.class_config\n\n scene_id_to_cfg = {s.id: s for s in dataset.all_scenes}\n\n @lru_cache(maxsize=len(dataset.all_scenes))\n def build_scene(scene_id: str) -> Scene:\n cfg = scene_id_to_cfg[scene_id]\n scene = cfg.build(\n class_config, self.tmp_dir, use_transformers=False)\n return scene\n\n # build and run each AnalyzerConfig for each scene group\n for a in self.config.analyzers:\n for group_name, group_ids in dataset.scene_groups.items():\n if len(group_ids) == 0:\n log.info(f'Skipping scene group \"{group_name}\". '\n 'Empty scene group.')\n continue\n group_scenes = (build_scene(id) for id in group_ids)\n analyzer = a.build(scene_group=(group_name, group_scenes))\n\n log.info(f'Running {type(analyzer).__name__} on '\n f'scene group \"{group_name}\"...')\n analyzer.process(group_scenes, self.tmp_dir)","def _build_analyzer():\n analyzer = {}\n for e in LOSSLESS:\n analyzer[e] = lambda d, _ext, name: d.lossless.append(name)\n for e in COMPRESSED:\n analyzer[e] = lambda d, _ext, name: d.compressed.append(name)\n for e in IMAGES:\n analyzer[e] = lambda d, _ext, name: d.images.append(name)\n for e in VIDEOS:\n analyzer[e] = lambda d, _ext, name: d.videos.append(name)\n\n def _increment_ignored(d, _ext, _name):\n d.ignored += 1 # Can't use assignment in lambda\n\n for e in IGNORE:\n analyzer[e] = _increment_ignored\n analyzer['cue'] = lambda d, _, name: d.cue.append(name)\n\n return analyzer","def createAnalysisSampleVisitor(config, cuts):\n\n # TODO: warn user if this function is called but 'cutbased' is false? (or no jobs yet booked - must do this first if a cutbased analysis is desired)\n\n # read the channel definitions\n channels = config.getTagVString(\"channels\")\n\n CLI = config.getFolder(\"CLI+\")\n # flag indicating to run a robust analysis\n robust = CLI.getTagBoolDefault(\"robust\",False)\n # flag indicating to run a dummy analysis\n dummy = CLI.getTagBoolDefault(\"dummy\",False)\n\n if not config.getTagBoolDefault(\"useMultiChannelVisitor\",False) or robust or dummy:\n # using regular analysis sample visitor (default)\n visitor = QFramework.TQAnalysisSampleVisitor()\n visitor.setVerbose(True)\n visitor.setBaseCut(cuts)\n visitor.setPrettyPrint(config.getTagBoolDefault(\"prettyPrint\",True))\n visitor.setLineUpdates(config.getTagBoolDefault(\"lineUpdates\",True))\n visitor.setTagDouble(\"progressInterval\",config.getTagDoubleDefault(\"progressInterval\",5.))\n else:\n # using fast MultiChannel analysis sample visitor\n visitor = QFramework.TQMultiChannelAnalysisSampleVisitor()\n visitor.setVerbose(True)\n visitor.setPrettyPrint(config.getTagBoolDefault(\"prettyPrint\",True))\n visitor.setLineUpdates(config.getTagBoolDefault(\"lineUpdates\",True))\n visitor.setTagDouble(\"progressInterval\",config.getTagDoubleDefault(\"progressInterval\",5.))\n\n runtime = config.getFolder(\"runtime+\")\n # TODO: add some protection against not finding the mcasvchannels in the runtime config for whatever reason\n mcasvchannels = runtime.getTagVStandardString(\"mcasvchannels\")\n\n # TODO: cutlist was defined in runAnalysis.py, but aparently not used\n #cutlist = []\n for channel in mcasvchannels:\n cut = cuts.getClone()\n #cutlist.append(cut)\n visitor.addChannel(channel,cut)\n # TODO: used previously just as a list for cloneObservablesSmart\n # mcvchannels.append(channel)\n if config.getTagBoolDefault(\"showChannels\",False):\n visitor.printChannels()\n\n # TODO: SmartObservableCloning not yet migrated to CAFExample (or fully implemented?) - Initial author = Carsten\n # Safe for this to go here? i.e. only for MCASV and before analysis algorithms are attached\n cloneObservablesSmart = False\n if config.getTagBoolDefault(\"reduceMCVObservables\",False):\n try:\n from CAFExample.SmartObservableCloning import cloneSetSmart\n cloneObservablesSmart = True\n except ImportError:\n cloneObservablesSmart = False\n QFramework.ERROR(\"smart observable cloning unavailable, skipping\")\n if cloneObservablesSmart:\n for channel in mcasvchannels:\n QFramework.TQObservable.getManager().cloneActiveSet(channel)\n\n return visitor","def __init__(self):\n Sampler.__init__(self)\n self._registeredIdentifiers = set() # tracks job identifiers used for this adaptive sampler and its inheritors\n self._prefixToIdentifiers = {} # tracks the mapping of run prefixes to particular identifiers\n self._inputIdentifiers = {} # identifiers for a single realization\n self._targetEvaluation = None # data object with feedback from sample realizations\n self._solutionExport = None # data object for solution printing\n self._requireSolnExport = False # if this object requires a solution export\n # NOTE TargetEvaluations consider all the Step <Output> DataObjects as candidates, so requiring\n # exactly one TargetEvaluation forces only having one <Output> DataObject in AdaptiveSampling\n # MultiRun Steps. For now, we leave it as \"n\".\n self.addAssemblerObject('TargetEvaluation', InputData.Quantity.one_to_infinity) # Place where realization evaluations go","def newAnalyzer():\n analyzer = {'accidentes': None,\n 'dateIndex': None\n }\n\n analyzer['accidentes'] = lt.newList('SINGLE_LINKED', compareseverity)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n return analyzer","def newAnalyzer():\n analyzer = {'accidents': None,\n 'dateIndex': None,\n 'timeIndex': None\n }\n\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['timeIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareTimes)\n \n return analyzer","def __init__(self):\n IContainsAnimals.__init__(self, 15)\n IContainsPlants.__init__(self, 3)\n Identifiable.__init__(self)\n Biome.__init__(self, \"Coastline\")","def __init__(self):\n self.analysis=''\n self.terms=''\n self.path=''","def __init__(self):\n self._tool_data = {}\n self.feedback = []\n self.ignored_feedback = []\n self.suppressions = {}\n self.suppressed_labels = {}\n self.hiddens = set()\n self.groups = []\n self.group = None\n self.group_names = {}\n self.hooks = {}\n self.class_hooks = {}\n self.submission = None\n self.format = Formatter()\n self.result = None\n self.resolves = []\n self.overridden_feedbacks = set()\n log.debug(\"New Pedal Report created.\")","def __init__(self):\n self.train(positivity_files, 0)\n self.train(subjectivity_files, 1)","def __init__(self):\n self.model_description: Dict[str, Any] = get_model_description()\n self.model_name: str = self.model_description['name']\n self.model_version: str = self.model_description['version']\n\n # Make sure we do not have a trailing slash to muck up processing later.\n self.event_dir: Optional[str] = None\n self.zone_name: Optional[str] = None\n self.fault_time: Optional[str] = None\n\n self.example: Example = None\n self.validator: ExampleValidator = ExampleValidator()\n self.common_features_df: pd.DataFrame = None\n\n self.cavity_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\n 'model_files',\n 'cavity_model.onnx'))\n self.fault_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\n 'model_files',\n 'fault_model.onnx'))","def __init__(self, store_directory):\n # Read in data\n analysis_script_path = os.path.join(store_directory, 'analysis.yaml')\n if not os.path.isfile(analysis_script_path):\n err_msg = 'Cannot find analysis.yaml script in {}'.format(store_directory)\n raise RuntimeError(err_msg)\n with open(analysis_script_path, 'r') as f:\n analysis = yaml.load(f)\n phases = []\n signs = {}\n ncfiles = {}\n for phase, sign in analysis:\n phases.append(phase)\n signs[phase] = sign\n ncfile_path = os.path.join(store_directory, phase + '.nc')\n ncfiles[phase] = nc.Dataset(ncfile_path, 'r')\n self.phases = phases\n self.signs = signs\n self.ncfiles = ncfiles\n self.nphases = len(phases)\n # Assign flags for other sections along with their global variables\n # General Data\n self._general_run = False\n self.iterations = {}\n # Equilibration\n self._equilibration_run = False\n self.u_ns = {}\n self.nequils = {}\n self.g_ts = {}\n self.Neff_maxs = {}\n # Decorrelation break-down\n self._decorrelation_run = False\n # Mixing Run (state)\n self._mixing_run = False\n # Replica mixing\n self._replica_mixing_run = False\n self._free_energy_run = False","def __init__(self):\n self.libpath = os.sep.join(os.path.abspath(__file__).split(os.sep)[:-1])\n sys.path.append(self.libpath)\n libpath2 = os.sep.join(self.libpath.split(os.sep)[:-1])\n sys.path.append(libpath2)\n # Initialize TCMetaSchema with correct libpath\n TCMetaSchema(self.libpath)\n self.args, self.unknown = IceteaManager._parse_arguments()\n # If called with --clean, clean up logs.\n if self.args.clean:\n _cleanlogs(silent=self.args.silent, log_location=self.args.log)\n\n LogManager.init_base_logging(self.args.log, verbose=self.args.verbose,\n silent=self.args.silent, color=self.args.color,\n no_file=(self.args.list or self.args.listsuites),\n truncate=not self.args.disable_log_truncate)\n\n self.logger = LogManager.get_logger(\"icetea\")\n self.pluginmanager = None\n self.resourceprovider = ResourceProvider(self.args)\n self._init_pluginmanager()\n self.resourceprovider.set_pluginmanager(self.pluginmanager)","def _generate_trading_instances(self):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\n self.strategy = self.strategy_class(self.data_handler, self.events)\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler","def __init__(self, test):\n self.all_grams = Ngram(self.START_OF_SENTENCE_TOKEN)\n for label in self.label_type_map:\n self.words_labels_counts[label] = {}\n self.words_labels_counts[label][self.UNKNOWN_TOKEN] = 0\n if test:\n self.train(\"train.txt\")\n self.test(\"test.txt\")\n else:\n self.train(\"train_partial.txt\")\n self.validate(\"validation_partial.txt\")","def __init__(self, **kwargs):\n\n # call base class constructor registering that this tool performs everything.\n Algorithm.__init__(\n self,\n performs_projection = True,\n use_projected_features_for_enrollment = True,\n requires_enroller_training = True\n )","def __init__(self):\r\n self.filter_p_number = 3 # First one with enough data for statistics\r\n self.prfs_d = extract_settings_elvis()\r\n\r\n ccds = True\r\n filtered = False\r\n scamp = False\r\n\r\n input_df = read_csv('cats/cat_clean_ssos.csv', index_col=0)\r\n filt_cat = self.gets_filtered_catalog() # Gets data from filtered\r\n\r\n if ccds:\r\n cats_d = self.extract_cats()\r\n self.extract_stats_ccds(cats_d, input_df, filt_cat)\r\n elif filtered:\r\n self.extract_stats_filt(filt_cat, input_df)\r\n elif scamp:\r\n pass\r\n # self.extract_stats_scamp(input_df)\r\n else:\r\n pass","def __init__(self, analysis: Analysis) -> None:\n self.analysis = analysis\n self.output_basename = os.path.join(\"REPORTS\", self.analysis.basename)\n self.only_human = True\n\n self.max_records_str = utils.get_picard_max_records_string(\n self.analysis.parameters[\"picard_max_records\"]\n )\n self.sort_tempdir = os.path.join(\n self.analysis.get_bam_dir(), \"%s_sort_tmp\" % self.analysis.sample\n )","def __init__(self):\n\n self.read_input_file()\n self.read_simulation_files()","def __init__(self):\n self.data = None\n self.decisionTree = {}\n self.enClass = 0\n self.nlClass = 0\n self.listAttributes = [\"Contains-het\", \"Contains-de\", \"Contains-een\", \"Contains-en/aan\", \"Contains-ij\", \"wordLength14\",\n \"Contains-a/an\", \"Contains-are/were\", \"Contains-and\", \"Contains-on/to\", \"Contains-the\"]\n self.infoGain = []\n self.entropy = 0","def __init__(self):\n self._predefined_cluster_topics()\n self._gatherSEs()","def analysis(self, game_info):\n pass","def __init__(self, x=None, y=None, analysis=None, error=None):\n logger.info(\"Creating Diva 2D Result object\")\n if isinstance(x, np.ndarray):\n self.x = x\n elif isinstance(x, list):\n self.x = np.array(x)\n else:\n logger.debug(\"X vector not defined. Should be np.ndarray or list\")\n self.x = None\n\n if isinstance(y, np.ndarray):\n self.y = y\n elif isinstance(x, list):\n self.y = np.array(y)\n else:\n logger.debug(\"Y vector not defined. Should be np.ndarray or list\")\n self.y = None\n\n if isinstance(analysis, np.ndarray):\n if analysis.shape[0] == len(x) and analysis.shape[1] == len(y):\n logger.debug('Consistent dimensions for the analysed field')\n self.analysis = analysis\n if isinstance(error, np.ndarray):\n if analysis.shape == error.shape:\n logger.debug('Consistent dimensions for the error field')\n self.error = error\n else:\n logger.error(\"Dimension mismatch\")\n raise Exception(\"Dimension mismatch\")\n else:\n logger.debug(\"Error field not defined\")\n self.error = None\n else:\n logger.error(\"Dimension mismatch\")\n raise Exception(\"Dimension mismatch\")\n else:\n logger.debug(\"Analysed field not defined\")\n self.analysis = None\n self.error = None","def __init__(self):\n for item in grammar:\n item['matches_compiled'] = {}\n for name,pattern in item['matches'].items():\n item['matches_compiled'][name] = \\\n re.compile(pattern, re.IGNORECASE)\n\n item['semantics_compiled'] = {}\n for name,pattern in item['semantics'].items():\n item['semantics_compiled'][name] = \\\n re.compile(pattern)\n\n if constants.SPELLCHECK:\n self.didyoumean = DidYouMean('en-us', constants.DICT_DIR)","def __init__(\n self, api,\n ):\n self._api = api\n self._api_response = self._api.find_cases(range=\"all\", sort=[])\n self._all30_dict = {}\n self._all60_dict = {}\n self._all90_dict = {}\n\n self._data_frame_30days = None\n self._data_frame_60days = None\n self._data_frame_90days = None\n self._data_frame_counts = None\n self._dataset = None","def run_analysis(wf):\n if wf.analysis[\"type\"] == \"one_sample_tests\":\n start_one_sample_tests(wf)\n\n elif wf.analysis[\"type\"] == \"two_sample_tests\":\n start_two_sample_tests(wf)\n\n elif wf.analysis[\"type\"] == \"factorial_tests\":\n start_factorial_tests(wf)\n\n elif wf.analysis[\"type\"] == \"n_sample_tests\":\n start_n_sample_tests(wf)\n\n info(\"> Finished analysis\")","def initiateAnalysis(self,):\n\n #\n # Imports\n #\n import os\n import sys\n\n #\n # get optional arguments from commandline\n #\n self.getComandLineOptions()\n \n #\n # for logmessages\n #\n tmpLogMessages = ['----------------\\n']\n tmpLogMessage = self.createLogHeader()\n tmpLogMessages.append(tmpLogMessage)\n #print tmpLogMessage\n \n #\n # check analysis path\n #\n if os.path.isdir(self.analysisPath):\n tmpLogMessage = 'WARNING: the analysis path already exists.\\n'\n print tmpLogMessage\n tmpLogMessages.append(tmpLogMessage)\n else:\n tmpLogMessage = 'Creating directory \"'+self.analysisPath+'\".\\n'\n #print tmpLogMessage\n tmpLogMessages.append(tmpLogMessage)\n os.makedirs(self.analysisPath)\n \n #\n # create the logfile\n #\n tmpLogMessages += self.openLogfileConnection()\n \n #\n # write tmpLogMessages to logfile\n #\n SEAseqPipeLine.logfile.write(''.join(tmpLogMessages))\n \n #\n # create the database\n #\n self.database.create()\n \n #\n # add run to runs table\n #\n self.database.addToRunsTable(self.startTimeStr, self.command, self.commandLine, True, MASTER)\n \n return 0","def __init__(self, *args):\n # The first Analysis object with all dependencies.\n # Any item from the first executable cannot be removed.\n self._main = None\n\n self._dependencies = {}\n\n self._id_to_path = {}\n for _, i, p in args:\n self._id_to_path[os.path.normcase(i)] = p\n\n # Get the longest common path\n common_prefix = os.path.commonprefix([os.path.normcase(os.path.abspath(a.scripts[-1][1])) for a, _, _ in args])\n self._common_prefix = os.path.dirname(common_prefix)\n if self._common_prefix[-1] != os.sep:\n self._common_prefix += os.sep\n logger.info(\"Common prefix: %s\", self._common_prefix)\n\n self._merge_dependencies(args)","def __init__(self, langName):\n self.langName = langName\n self.readDataSets(langName)\n # self.getVMWEReport()\n self.analyzeSents()\n self.orderParentVMWEs()\n self.getTrainAndTest()\n self.cleanSents()\n self.extractDictionaries()\n self.deleteNonRecognizableMWE()\n printStats(self.trainingSents, 'Train', mweDic=self.mweDictionary, langName=langName, test=False)\n printStats(self.testingSents, 'Test', mweDic=self.mweDictionary, test=True)","def __init__(self):\n\n self.name = None\n self.summary = None\n self.cases = []","def __init__(self,classes=['normalizeText','tagger','stem','stopWord','spellChecker']):\n self._support = prebotSupport()\n self._classes = classes\n if(\"tagger\" in self._classes):\n self._tagger = tagger()\n if(\"normalizeText\" in self._classes):\n self._normalize = normalizeText()\n if(\"spellChecker\" in self._classes):\n self._spellChecker = spellChecker()\n if(\"stopWord\" in self._classes):\n self._stopWord = stopWord()\n if(\"stem\" in self._classes):\n self._stem = stemming()","def do_analysis(ckpt, queries_type, entities_type, request):\n global currently_analyzing, results, d, analysis_user\n try:\n print(\"starting analysis!\")\n if entities_type == \"all\":\n print(\"using all entities detected!\")\n elif entities_type == \"uploaded\":\n print(\"using only entities specified in csv file!\")\n \n currently_analyzing = True\n analysis_user = request.user.username\n results = []\n proj_path = os.path.abspath(os.path.dirname(__file__)).split(\"FYP_Web_App\")[0]\n ckpt = proj_path + \"FewRel/checkpoint/\" + ckpt\n if d is None or d.ckpt_path != ckpt:\n d = DetectionFramework(ckpt_path=ckpt)\n if cancel_flag[0]:\n return\n d.clear_support_queries()\n if len([i for i in os.listdir(\"temp/relation_support_datasets\") if 'csv' in i and request.user.username in i]) == 0:\n raise ValueError(\"Please upload relation support dataset!\")\n \n d.load_support_files(\"temp/relation_support_datasets\", request.user.username)\n if queries_type == \"csv_option\":\n if not os.path.exists(\"temp/queries.csv\"):\n raise ValueError(\"Please upload query CSV dataset!\")\n d.load_queries_csv(\"temp/queries.csv\")\n \n elif queries_type == \"url_option\":\n if not os.path.exists(\"temp/url.txt\"):\n raise ValueError(\"Please specify news article url!\")\n with open(\"temp/url.txt\") as f:\n url = f.read()\n d.load_url(url)\n \n elif queries_type == \"txt_option\":\n d.load_text_files(os.path.abspath(\"temp/text_files\"))\n \n elif queries_type == \"ind_sentence_option\":\n ind_sentence = request.POST.get('ind_sent')\n d.load_ind_sentence(ind_sentence)\n \n elif queries_type == \"html_option\":\n d.load_html_file_queries(os.path.abspath(\"temp/html_files\"))\n \n if entities_type == \"uploaded\":\n d.trim_queries_based_on_entities_file(os.path.abspath(\"temp/entities_csv_file.csv\"))\n\n if cancel_flag[0]:\n return\n d.detect(rt_results=results, cancel_flag=cancel_flag)\n if cancel_flag[0]:\n return\n src=None\n if queries_type == \"csv_option\":\n src = \"queries_csv\"\n elif queries_type == \"txt_option\":\n src = \"queries_text_file\"\n elif queries_type == \"ind_sentence_option\":\n src = \"ind_sentence\"\n elif queries_type == \"url_option\":\n with open(\"temp/url.txt\") as f:\n src = f.read()\n elif queries_type == \"html_option\":\n src = \"html_files\"\n \n s = Source(source=src, user=request.user)\n s.save()\n for r in results:\n er = ExtractedRelation(sentence=r['sentence'],head=r['head'],tail=r['tail'],pred_relation=r['pred_relation'],sentiment=r['sent'],conf=r['conf'],ckpt=ckpt, source=s)\n er.save()\n except Exception as e:\n print(len(str(e)))\n print(str(e))\n errors.append(str(e))\n tb = traceback.format_exc()\n print(tb)\n finally:\n currently_analyzing = False\n analysis_user = None","def analyse(self, data=None):\n pass","def __init__(self, database):\n self.max_axiom_arity = max([p.arity() for p in database.non_entails_axioms.itervalues()]) + 1 # one more than the max\n\n self.database = database\n\n self.max_unconstrained_arity = 10000000\n self.searcher = SearchProblem(database, max_unconstrained_arity = self.max_unconstrained_arity)\n\n self.tautologies = set()\n for p in self.database.propositions.itervalues():\n e_hyps = [h for h in p.hyps if h.type == 'e']\n if p.vclass=='|-' and len(e_hyps) == 0:\n self.tautologies.add(p.label)\n print 'tautologies:', len(self.tautologies)\n \n # the propositions with trivial unconstrained arity. That is, the ones\n # that are really easy to apply.\n self.constrained_propositions = set(\n p.label for p in self.database.propositions.itervalues()\n if p.vclass == '|-' and p.unconstrained_arity() == 0\n )\n \n # figure out the names of the read variables\n # self.real_wff_names = set()\n # self.real_set_names = set()\n # self.real_class_names = set()\n # real_name_dict = {'wff': self.real_wff_names, 'set': self.real_set_names, 'class': self.real_class_names}\n #\n # for p in self.database.propositions.itervalues():\n # for label in p.f:\n # vclass = p.f[label].vclass\n # real_name_dict[vclass].add(label)\n # print real_name_dict\n\n self.constructor_dictionary = [{} for _ in range(self.max_axiom_arity)]\n\n # we need to define some extra variables, which we'll randomly assign when we read in a statement\n # this is a reasonable amount of data augmentation.\n self.extra_wffs = language_model_extra_variables_of_each_type+max(len([f for f in p.f.itervalues() if f.vclass=='wff']) for p in database.propositions.itervalues() )\n self.extra_classes = language_model_extra_variables_of_each_type+max(len([f for f in p.f.itervalues() if f.vclass=='class']) for p in database.propositions.itervalues() )\n self.extra_sets = language_model_extra_variables_of_each_type+max(len([f for f in p.f.itervalues() if f.vclass=='set']) for p in database.propositions.itervalues() )\n\n # hand code these in.\n self.extra_sets = 20\n self.extra_wffs = 18\n self.extra_classes = 27\n\n self.wff_names = ['WFFVar'+str(i) for i in range(self.extra_wffs)]\n self.set_names = ['SetVar'+str(i) for i in range(self.extra_sets)]\n self.class_names = ['ClassVar'+str(i) for i in range(self.extra_classes)]\n\n self.num_extra_variable_names = len(self.wff_names)+len(self.set_names)+len(self.class_names)\n self.extra_variable_dict = {}\n\n # the names for the unconstrained variables\n #self.ua_names = ['UA'+str(i) for i in range(self.max_unconstrained_arity)]\n\n # add them to the dictionary\n arityzerodict = self.constructor_dictionary[0]\n for i in range(self.extra_wffs):\n arityzerodict['WFFVar'+str(i)]=len(arityzerodict)\n self.extra_variable_dict['WFFVar'+str(i)]=len(self.extra_variable_dict)\n for i in range(self.extra_classes):\n arityzerodict['ClassVar'+str(i)]=len(arityzerodict)\n self.extra_variable_dict['ClassVar'+str(i)]=len(self.extra_variable_dict)\n for i in range(self.extra_sets):\n arityzerodict['SetVar'+str(i)]=len(arityzerodict)\n self.extra_variable_dict['SetVar'+str(i)]=len(self.extra_variable_dict)\n # for i in range(len(self.ua_names)):\n # arityzerodict['UA'+str(i)]=len(arityzerodict)\n # self.extra_variable_dict['UA'+str(i)]=len(self.extra_variable_dict)\n\n # a block to create a dictionary that takes a symbol to its vclass\n self.symbol_to_vclass = {label:database.propositions[label].vclass for label in database.non_entails_axioms}\n for symbol in self.wff_names:\n self.symbol_to_vclass[symbol] = 'wff'\n for symbol in self.set_names:\n self.symbol_to_vclass[symbol] = 'set'\n for symbol in self.class_names:\n self.symbol_to_vclass[symbol] = 'class'\n\n # a list of all of the extra variables, for use later\n self.new_names = self.wff_names+self.set_names+self.class_names\n\n # describe the number of variables we've used\n print 'wff variables:',self.extra_wffs\n print 'class variables:',self.extra_classes\n print 'set variables:',self.extra_sets\n #print 'ua variables:', self.ua_names\n\n # now add the actual constructor axioms to our dictionary\n for p in database.non_entails_axioms.itervalues():\n c_dict = self.constructor_dictionary[p.arity()]\n c_dict[p.label] = len(c_dict)\n\n for i in range(self.max_axiom_arity):\n print len(self.constructor_dictionary[i]),'constructor axioms with arity',i\n\n # build a pair of dictionaries that convert (arity,num) to total_num\n # and vice versa. This is ugly. Whatever\n self.arity_num_to_global_index = {}\n self.global_index_to_arity_num=[]\n global_index = 0\n for arity in range(self.max_axiom_arity):\n for num in range(len(self.constructor_dictionary[arity])):\n self.global_index_to_arity_num.append((arity,num))\n self.arity_num_to_global_index[(arity,num)]=global_index\n global_index+=1\n\n \"\"\"sets up the data sets. We divide the propositions into training/validation/test and\n then compile the corresponding list of statements\"\"\"\n list_of_propositions = self.database.propositions_list[:] # database.propositions.values()\n np.random.seed(seed=121451345)\n list_of_propositions = np.random.permutation(list_of_propositions)\n\n num_validation = len(list_of_propositions)/10\n num_test = num_validation\n num_training = len(list_of_propositions)-num_test-num_validation\n self.training_propositions = list_of_propositions[:num_training]\n self.training_propositions = [_ for _ in self.training_propositions if _.type=='p']\n self.validation_propositions = list_of_propositions[num_training:num_training+num_validation]\n self.validation_propositions = [_ for _ in self.validation_propositions if _.type=='p']\n self.test_propositions = list_of_propositions[num_training+num_validation:]\n self.test_propositions = [_ for _ in self.test_propositions if _.type=='p']\n\n if self.database.remember_proof_steps:\n self.all_proof_steps = [] # except those that refer to e or f-type hypotheses\n for p in self.database.propositions.itervalues():\n self.all_proof_steps += [step for step in p.entails_proof_steps if not (step.prop.type=='f' or step.prop.type == 'e')]\n\n\n self.training_proof_steps = []\n for p in self.training_propositions:\n self.training_proof_steps += [step for step in p.entails_proof_steps\n if not (step.prop.type=='f' or step.prop.type == 'e')]\n\n self.validation_proof_steps = []\n for p in self.validation_propositions:\n self.validation_proof_steps += [step for step in p.entails_proof_steps\n if not (step.prop.type=='f' or step.prop.type == 'e')]\n\n self.test_proof_steps = []\n for p in self.test_propositions:\n self.test_proof_steps += [step for step in p.entails_proof_steps\n if not (step.prop.type=='f' or step.prop.type == 'e')]\n\n print\n print 'training steps:', len(self.training_proof_steps)\n print 'validation steps:', len(self.validation_proof_steps)\n print 'test steps:', len(self.test_proof_steps)\n\n\n # figure out how frequenly each proposition is used\n self.prop_usage = [0 for p in self.database.propositions]\n for s in self.all_proof_steps:\n self.prop_usage[s.prop.number]+=1\n\n # figure out what the most difficult proof step is\n self.max_depth = max([s.height for s in self.all_proof_steps]) + 1\n print 'max proof step depth:', self.max_depth-1\n\n\n # figure out the number of times each proposition is used.\n # self.prop_uses = [0.1] * len(self.database.propositions) # for numberical stability\n # for step in self.all_proof_steps:\n # self.prop_uses[step.prop.number] += 1\n # self.initial_b = np.log(1.0*np.array(self.prop_uses)/sum(self.prop_uses))\n\n\n # build up a database of propositions by unconstrained arity\n # that is, total_unconstrained_arity is the total\n # of all of the unconstrained arities of all of the propositions.\n # and unconstrained_arity_indices is a list of p.unconstrained_arity()\n # unique indices for each proposition p.\n self.total_unconstrained_arity = 0\n self.unconstrained_arity_indices = {}\n self.unconstrained_label_to_number = {}\n for p in self.database.propositions_list: # in order of proposition number\n u_arity = p.unconstrained_arity()\n self.unconstrained_arity_indices[p.label]=range(self.total_unconstrained_arity, self.total_unconstrained_arity + u_arity)\n self.total_unconstrained_arity += u_arity\n self.unconstrained_label_to_number[p.label]=len(self.unconstrained_label_to_number)\n #self.max_unconstrained_arity = max([p.unconstrained_arity() for p in self.database.propositions.itervalues()])\n\n self.total_constructor_arity = 0\n self.constructor_arity_indices = {}\n self.constructor_label_to_number = {}\n self.constructor_labels = []\n for p in database.non_entails_axioms.itervalues():\n u_arity = p.arity()\n self.constructor_arity_indices[p.label]=range(self.total_constructor_arity, self.total_constructor_arity + u_arity)\n self.total_constructor_arity += u_arity\n self.constructor_label_to_number[p.label]=len(self.constructor_label_to_number)\n self.constructor_labels.append(p.label)\n for name in self.wff_names+self.set_names+self.class_names: #+self.ua_names:\n self.constructor_arity_indices[name] = [] # the extra arity 0 constructors\n self.constructor_label_to_number[name]=len(self.constructor_label_to_number)\n self.constructor_labels.append(name)\n\n # a lookup table for the index into all the propositions of the label\n self.label_to_number = {x.label:x.number for x in self.database.propositions.itervalues()}\n for x in self.new_names:\n self.label_to_number[x] = -1 # all variables should always be included","def __init__(self):\n\n # Dictionary of types seen so far. Builtin types always available.\n # Values : list of constructors which the type defines\n # This is a smartdict, so keys can be retrieved.\n self.knownTypes = smartdict.Smartdict()\n for typecon in ast.builtin_types_map.values():\n self.knownTypes[typecon()] = None\n\n # Dictionary of constructors encountered so far.\n # Value: Type which the constructor produces.\n # This is a smartdict, so keys can be retrieved.\n self.knownConstructors = smartdict.Smartdict()","def _init(self, *args, **kwargs):\n f = Framer()\n election = Election.objects.filter(electionid=self.this_election).first()\n for measure in self.list_of_measures:\n requested_url = \"%s%s\" % (self.api_url, measure)\n response = requests.get(requested_url, headers=self.request_headers)\n measure_data = response.json()[\"measure\"]\n identifying_information = measure_data[\"official_identifier\"].split(\" \")\n measure_data[\"official_identifier\"] = \"Proposition %s\" % (identifying_information[1])\n measure_data[\"official_identifier_slug\"] = f._slug(measure_data[\"official_identifier\"])\n measure_data[\"election_id\"] = election.id\n measure_data = f._massage_measure_title(measure_data)\n saver = Saver()\n saver.make_measure(measure_data)\n saver.make_measure_contributor(measure_data)\n saver.make_measure_total(measure_data)","def main():\n\t#ps = PackageScanner()\n\t#packages = ps.getInstalledPackages()\n\t#print(packages)\n\t#ps.saveScanResults()\n\n\tan = Analyzer()\n\tan.loadFromFile(config.PKG_SCAN_DIR / config.PKG_SCAN_FILE)\n\t#an.loadFromPackageCont(packages)\n\tan.analyze()\n\tan.saveAnalysisResults()","def test_init_analysis(network):\n bf_init_analysis(\"test_analysis\", _stable_question_dir)","def testGetAnalyzerInstances(self):\n analyzer_names = manager.AnalyzersManager.GetAnalyzerNames()\n analyzers = manager.AnalyzersManager.GetAnalyzerInstances(analyzer_names)\n self.assertEqual(len(analyzer_names), len(analyzers))\n for analyzer in analyzers:\n self.assertIsInstance(analyzer, interface.BaseAnalyzer)","def make_all(self):\n # General matrices #\n self.tsv_seq_to_concepts()\n self.tsv_seq_to_names()\n self.list_sequence_concept()\n # Only in the with 'samples' case #\n if self.a.abundances: self.tsv_samples_to_names()\n if self.a.abundances: self.biom_output()\n # Graphical outputs #\n self.per_seq_dot_files()\n if self.a.abundances: self.per_sample_dot_files()","def __init__(self):\n self.cause_texts = set()\n self.effect_texts = set()\n self.evidence_texts = set()\n self.cause_polarity = None\n self.effect_polarity = None\n self.cause_type = None\n self.effect_type = None","def __init__(self, description):\n super(AimaProver, self).__init__()\n description = DistinctAndNotMover.run(description)\n self.knowledgeBase = KnowledgeBase(Sets.newHashSet(description))","def __init__(self):\n self.name = \"Schaffer\"\n objectives = [o_sh_1, o_sh_2]\n decisions = [Decision(-10 ** 5, 10 ** 5)]\n Model.__init__(self, objectives, None, decisions)","def newAnalyzer():\n analyzer = {'accidents': None,\n 'dateIndex': None\n }\n\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='BST',\n comparefunction=compareDates)\n return analyzer","def __init__(self):\n self.name = \"Kursawe\"\n objectives = [o_ku_1, o_ku_2]\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\n Model.__init__(self, objectives, None, decisions)","def analyze_run():\n file_datas_dict = load_datas(Args.data_files)\n plotables_dict = dict()\n for file_name, datas in file_datas_dict.viewitems():\n analized_datas = analyze_datas(datas,Args.analysis_attributes)\n plotables = ana_results_to_plotables(\n analized_datas,\n Args.analysis_attributes\n )\n if Args.dm_file_out:\n analysis_save_dm(\n analized_datas,\n plotables,\n Args.analysis_attributes,\n Args.dm_file_out\n )\n if Args.mat_file_out:\n analysis_save(\n plotables,\n Args.analysis_attributes,\n Args.mat_file_out\n )\n if Args.verbose:\n plotables_dict[file_name] = plotables\n if Args.verbose:\n ana_plot_figures(plotables_dict,Args.analysis_attributes)","def __init__(self):\n\n #: Dict[str, Any]: Experiment metadata\n self.__experiment_metadata = None\n\n #: List[CurveData]: Processed experiment data set.\n self.__processed_data_set = list()\n\n #: Backend: backend object used for experimentation\n self.__backend = None\n\n # Add expected options to instance variable so that every method can access to.\n for key in self._default_options().__dict__:\n setattr(self, f\"__{key}\", None)\n\n # Add fixed parameters to instance variable so that every method can access to.\n for key in self.__fixed_parameters__:\n setattr(self, f\"__{key}\", None)","def __init__(self):\n self.dataset_path = input('Enter the path to the root directory of your dataset:\\n')\n self.classes = [c.lower() for c in os.listdir(self.dataset_path)]\n self.year = str(datetime.datetime.now().year)\n self.kit_path = input(\"Enter the path ot your VOCdevkit directory:\\n\")\n self.annotation_path = self.kit_path + '/VOC' + self.year + '/Annotations'\n self.renamer = data_renamer.DataRenamer(self.dataset_path, self.year)\n self.data_splitter = data_splitter.DataSplitter(self.dataset_path, self.classes, self.year, self.kit_path)\n self.annotation_maker = annotation_maker.AnnotationMaker(self.dataset_path, self.kit_path, self.year,\n self.annotation_path)","def get_analysis(analysis_info):\n return _get_analysis(_LOCAL_API_ENDPOINT,\n analysis_info[\"ecosystem\"],\n analysis_info[\"package\"],\n analysis_info[\"version\"])","def __init__(self):\n\n self.current_path = os.getcwd()\n self.data_path = self.current_path + \"/data\"\n\n self.original_files = {}\n self.imitation_files = {}\n self.original_test_files = {}\n self.imitation_test_files = {}\n\n self.training_set = None\n self.original_test_set = None\n self.imitation_test_set = None\n\n self.accuracy = 0.\n self.threshold = 0.\n\n self.get_files()","def __init__(self):\n\t\tself.parsed = False\n\t\tdir_path = os.path.dirname(os.path.realpath(__file__))\n\t\tself.xsdfilename = os.path.join(dir_path, 'xml', 'schema.xsd')\n\t\tself.schema = 'schema.xsd'\n\t\tself.predictors = []\n\t\tself.predictors_types = []\n\t\tself.preprocessing_methods = []","def newAnalyzer():\n analyzer = {'events': None,\n \"musical_genre\":None,\n 'artist_ID': None,\n \"track_ID\": None,\n \"instrumentalness\": None,\n \"acousticness\": None,\n \"liveness\": None,\n \"speechiness\": None,\n \"energy\":None,\n \"danceability\": None,\n \"valence\": None\n }\n # Listas\n analyzer['events'] = lt.newList('ARRAY_LIST', compareIds)\n\n # RBT \n analyzer[\"instrumentalness\"] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['acousticness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['liveness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['speechiness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['energy'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['danceability'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['valence'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['tempo'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['created_at'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['dates_u'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n \n\n # Tablas de Hash \n analyzer['artist_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer['track_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer['ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer[\"musical_genre\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer[\"sen\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer['track_ID_S'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n\n return analyzer","def single_analysis(config, name):\n # graphviz = GephiOutput()\n graphviz = GraphvizOutput()\n graphviz.output_file = name\n\n print \"Preparing test case...\"\n radio, lines = _prepare_test_case()\n\n print \"Running test case...\"\n with PyCallGraph(output=graphviz, config=config):\n _run_test_case(radio, lines)","def __init__(self):\n self.site = pywikibot.Site(u'commons', u'commons')\n self.generator = self.getGenerator()","def _generate_trading_instances(self):\n print(\n \"Initizalization...\"\n )\n\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\n self.end_date)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\n self.strategy_id, self.spread, self.commission,self.csv_dir)\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission)\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)","def __init__(self):\n self._results = {}\n self._logs = {}","def get_sentiment_analysis(sender, instance, **kwargs):\n text_analysis = TextAnalysis(instance.text)\n\n # Prevent sentiment_analysis API call every time the document is saved\n if instance.sentiment_analysis is None:\n instance.get_sentiment_analysis()","async def _analyze(self) -> Report:\n forecasts = []\n daily = []\n hourly = []\n for data in self._series:\n model = await self._build_model(data=data)\n future = await self._forecast_single(model=model)\n h, d = await self._process_trends_single(future=future)\n forecasts.append(Timeseries.from_df(\n name=data.get_name() + '_forecast',\n df=future,\n time_col='ds',\n val_col='yhat',\n ))\n daily.append(d)\n hourly.append(h)\n daily_agg = pd.concat(daily).groupby(level=0).mean()\n hourly_agg = pd.concat(hourly).groupby(level=0).mean()\n return Report(\n forecasts=forecasts,\n daily_trend=Trend(trend_vals=daily_agg.to_dict()),\n hourly_trend=Trend(trend_vals=hourly_agg.to_dict()),\n )","def __init__(self):\n self.machines = {}\n self.configs = {}\n self.systems = {}\n self.jobs = {}\n self.benchmarks = {}\n self.projects = {}","def __init__(self, objectives, constraints, decisions):\n self.objectives = objectives\n self.constraints = constraints\n self.decisions = decisions","def __init__(self, **kwargs):\n super(BaseAG, self).__init__()\n self.domains = None\n self._info = {}\n\n mutation_name = kwargs.get(\"mutation\", self._args.mutation)\n representation_name = kwargs.get(\"representation\",\n self._args.representation)\n crossover_name = kwargs.get(\"crossover\", self._args.crossover)\n selection_name = kwargs.get(\"selection\", self._args.selection)\n\n self._population = kwargs.get(\"population\", self._args.population)\n self._selection_crossover = kwargs.get(\"selection_crossover\",\n self._args.selection_crossover)\n self._selection_mutation = kwargs.get(\"selection_mutation\",\n self._args.selection_mutation)\n self._generations = kwargs.get(\"generations\",\n self._args.generations)\n dimension = kwargs.get(\"dimension\", self._args.dimensions)\n precision = kwargs.get(\"precision\", self._args.precision)\n\n crossovers = cros_factory.crossover_factory()\n mutations = mut_factory.mutation_factory()\n representations = repr_factory.representations_factory()\n selections = selection_factory.selection_factory()\n\n for item in crossovers:\n if crossover_name == item.name():\n self._crossover = item()\n for item in representations:\n if representation_name == item.name():\n # NOTE(mmicu): the dimension is know when we get the function\n # eliminate this requirement\n self._representation = item(dimension,\n precision)\n\n for item in mutations:\n if mutation_name == item.name():\n self._mutation = item()\n\n for item in selections:\n if selection_name == item.name():\n self._selection = item(self._representation)","def my_main() -> None: # pragma: no cover\n universe = AutomataUniverse(SQUARE_GRID_NEIGHBORS, [2,3], [3])\n instance = AutomataTransforms(universe)\n assert isinstance(instance, AutomataTransforms)\n # # _is_rot_mat_test(instance)\n # # _rotations_check(instance)\n # # _prime_cells_check(instance)\n # _check_transform_test(instance)\n # # _hashable_transform_test(instance)\n # _duplicate_test(instance)\n # _collision_test(instance)\n # _end_cycle_test(instance)\n # _add_transform_test(instance)\n # instance.generate_combination_transforms()\n\n # # _matrix_rotate_test(instance)\n # # _duplicate_test(instance) # test again after transform(s) added\n # # _collision_test(instance) # test again after transform(s) added «also refactoring»\n # instance.dbg_report_instance() # DEBUG","def async_analysis(kwargs):\n # we can't pickle our objects for remote works so we pickle the raw request\n # and then load it here.\n data = analysis_input_schema.load(kwargs).data\n return analysis(**data)","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 setup_class(self):\n self.iqcalc = iqcalc_astropy.IQCalc(logger=self.logger)\n self.fwhm_funcs = (self.iqcalc.calc_fwhm_gaussian,\n self.iqcalc.calc_fwhm_moffat,\n self.iqcalc.calc_fwhm_lorentz)\n self.answers = ((2.8551, 2.7732), # Gaussian\n (2.77949, 2.6735), # Moffat\n (1.9570, 1.8113) # Lorentz\n )","def setUp(self):\n self.m = m = random.randint(1, 100)\n self.n = n = random.randint(1, 100)\n self.sig = sig = Signature(\"name\", Dim(\"m\"), Dim(\"n\"),\n sData(\"A\", \"ldA * n\"), Ld(\"ldA\", \"m\"),\n dData(\"B\", \"ldB * m\"), Ld(\"ldB\", \"m\"),\n cData(\"C\", \"ldC * n\"), Ld(\"ldC\", \"n\"))\n self.ex = ex = Experiment()\n ex.calls = [sig(m, n, \"X\", None, \"Y\", None, \"Z\", None)]\n ex.infer_lds()\n self.i = Symbol(\"i\")\n self.j = Symbol(\"j\")","def create_analysis():\n \n date_now = datetime.now()\n for analysis in Analysis.objects.filter(activated=True):\n\t\n\tif analysis.last_report == None or analysis.last_report <= date_now - timedelta( seconds=PERIOD_CHOICES[analysis.interval]):\n\t \n\t if analysis.last_report != None and analysis.interval == 'n':\n\t\tcontinue\n\t \n\t results = []\n\t for report in analysis.queries.filter(activated=True):\n\t\t\n\t\tif analysis.date_from != None and analysis.date_to != None:\n\t\t report_results = ReportResult.objects.filter(report=report, run_date__lte=analysis.date_to, run_date__gte=analyses.date_from).order_by('run_date') \n\t\telif analysis.date_from == None and analysis.date_to != None:\n\t\t report_results = ReportResult.objects.filter(report=report, run_date__lte=analysis.date_to).order_by('run_date')\n\t\telif analysis.date_from != None and analysis.date_to == None:\n\t\t report_results = ReportResult.objects.filter(report=report, run_date__gte=analyses.date_from).order_by('run_date')\n\t\telse:\n\t\t report_results = ReportResult.objects.filter(report=report).order_by('run_date')\n\t\t\n\t\t# create output from mongo output\n\t\toutput_result = OutputResult(report=report.title)\n\t\toutput_result.date_array = []\n\t\toutput_result.output_array = []\n\t\tprint \"\\n KOLIK: \"+ str(output_result.output_array)\n\t\tfor result in report_results:\n\t\t output_result.date_array.append(result.run_date)\n\t\t #print result.output\n\t\t #print \"\\nouttest: \"+str(output_result.output_array)\n\t\t mongo_output = OutputMongo(result.output)\n\t\t output_result.output_array.append(mongo_output.getoutput())\n\n\t\tprint \"out: \",output_result.output_array\n\t\tresults.append(output_result) \n\n\n\t #print results[0].output_array\n\t #print \"\\n\\n\"\n\t #print results[1].output_array\n\t # process outputs\n\t if not process_output_reports(results, analysis, date_now):\n\t\tprint \"Error in execute analysis: %s\" % (analysis.title)\n\t\tcontinue\n\t \n\t if analysis.interval != 'n':\n\t\tif analysis.date_to != None:\n\t\t analysis.date_to = analysis.date_to + timedelta( seconds=PERIOD_CHOICES[analysis.interval])\n\t\tif analysis.date_from != None:\n\t\t analysis.date_from = analysis.date_from + timedelta( seconds=PERIOD_CHOICES[analysis.interval])\n\t\t \n return True","def __init__(self):\n self.RRTFamilySolver = RRTFamilyPathPlanner()\n self.PRMSolver = PRMPathPlanner()","def __init__(self) -> None:\n self.metrics = {}\n self.current = None\n self.run = None","def __init__(self):\r\n\t\twith open(\"eqs.json\") as qData:\r\n\t\t\tself.questions = json.load(qData)\r\n\t\twith open(\"eqsave.json\") as uData:\r\n\t\t\tself.records = json.load(uData)\r\n\t\tself.types = {\"1\": \"Reformer\", \"2\": \"Helper\", \"3\": \"Achiever\", \"4\": \"Individualist\", \"5\": \"Investigator\", \"6\": \"Loyalist\", \"7\": \"Enthusiast\", \"8\": \"Challenger\", \"9\": \"Peacemaker\"}","def __init__(self, num_detectors):\n self.dataset = []\n self.num_detectors = num_detectors\n self.add_detectors()","def __init__(self):\n\n self.result = None # To store the result\n self.predictor = None # To store the fit predictor","def init_analysis(session, args):\r\n path = os.path.join(session.abs_path,'{}_{}_{}_{}'.format(args[12],args[13],args[14],args[15]))\r\n session.case = Case(path, session.method)\r\n case = session.case\r\n if args[0].split('#')[0]=='R':\r\n args[7]= -args[7]\r\n\r\n case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[6], args[7]))\r\n\r\n case.file_U.set_field('boundaryField',\r\n {'inlet': {'type': 'freestream',\r\n 'freestreamValue': 'uniform ({} {} 0)'.format(args[6], args[7])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'fixedValue', 'value':'uniform (0 0 0)'},\r\n 'extrados': {'type': 'fixedValue', 'value':'uniform (0 0 0)'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n case.file_p.set_field('internalField', 'uniform {}'.format(args[10]))\r\n case.file_p.set_field('boundaryField',\r\n {'inlet': {'type': 'freestreamPressure'},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_T.set_field('internalField', 'uniform {}'.format(args[11]))\r\n session.case.file_T.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[11])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'slip'},\r\n 'extrados': {'type': 'slip'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n \"\"\"\r\n\r\n session.case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[6], args[7]))\r\n session.case.file_U.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue',\r\n 'value': 'uniform ({} {} 0)'.format(args[6], args[7])},\r\n 'outlet': {'type': 'inletOutlet','inletValue':'uniform ({} {} 0)'.format(args[6], args[7]),\r\n 'value':'uniform ({} {} 0)'.format(args[6], args[7])},\r\n 'intrados': {'type': 'noSlip'},\r\n 'extrados': {'type': 'noSlip'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_p.set_field('internalField', 'uniform {}'.format(args[10]))\r\n session.case.file_p.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[10])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_T.set_field('internalField', 'uniform {}'.format(args[11]))\r\n session.case.file_T.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue','value':'uniform {}'.format(args[11])},\r\n 'outlet': {'type': 'inletOutlet','inletValue':'uniform {}'.format(args[11]),'value':'uniform {}'.format(args[11])},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n \"\"\"\r\n \"\"\"\r\n session.case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[7], args[6]))\r\n session.case.file_U.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue',\r\n 'value': 'uniform ({} {} 0)'.format(args[7], args[6])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'noSlip'},\r\n 'extrados': {'type': 'noSlip'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_p.set_field('internalField', 'uniform {}'.format(args[10]))\r\n session.case.file_p.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[10])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_T.set_field('internalField', 'uniform {}'.format(args[11]))\r\n session.case.file_T.set_field('boundaryField',\r\n {'inlet': {'type': 'zeroGradient'},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n \"\"\"\r\n \"\"\"\r\n session.case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[5], args[6]))\r\n session.case.file_U.set_field('boundaryField',\r\n {'inlet': {'type': 'fixedValue', 'value': 'uniform ({} {} 0)'.format(args[5], args[6])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'noSlip'},\r\n 'extrados': {'type': 'noSlip'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_p.set_field('boundaryField', {'inlet': {'type': 'fixedValue','value':'uniform {}'.format(args[9])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n session.case.file_p.set_field('internalField', 'uniform {}'.format(args[9]))\r\n\r\n session.case.file_T.set_field('boundaryField', {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[10])},\r\n 'outlet': {'type': 'zeroGradient'},\r\n 'intrados': {'type': 'zeroGradient'},\r\n 'extrados': {'type': 'zeroGradient'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n session.case.file_T.set_field('internalField','uniform 300')\r\n\r\n session.case.file_nut.set_field('boundaryField', {'inlet':{'type':'calculated', 'value':'uniform 0'},\r\n 'outlet':{'type':'calculated', 'value':'uniform 0'},\r\n 'intrados': {'type': 'nutkWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 0'},\r\n 'extrados': {'type': 'nutkWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 0'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_k.set_field('internalField', 'uniform 1')\r\n session.case.file_k.set_field('boundaryField', {\r\n 'inlet': {'type': 'turbulentIntensityKineticEnergyInlet', 'intensity': '0.05', 'value': 'uniform 1'},\r\n 'outlet': {'type': 'inletOutlet', 'inletValue': 'uniform 1', 'value': 'uniform 1'},\r\n 'intrados': {'type': 'kqRWallFunction','value':'uniform 1'},\r\n 'extrados': {'type': 'kqRWallFunction','value':'uniform 1'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n\r\n session.case.file_epsilon.set_field('boundaryField', {'inlet': {'type': 'turbulentMixingLengthDissipationRateInlet', 'mixingLength': '0.005', 'value': 'uniform 200'},\r\n 'outlet': {'type': 'inletOutlet', 'inletValue': 'uniform 200', 'value': 'uniform 200'},\r\n 'intrados': {'type': 'epsilonWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 200'},\r\n 'extrados': {'type': 'epsilonWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 200'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n session.case.file_epsilon.set_field('internalField', 'uniform 200')\r\n\r\n session.case.file_alphat.set_field('boundaryField', {'inlet':{'type':'calculated', 'value':'uniform 0'},\r\n 'outlet':{'type':'calculated', 'value':'uniform 0'},\r\n 'intrados': {'type': 'compressible::alphatWallFunction', 'Prt':'0.85', 'value':'uniform 0'},\r\n 'extrados': {'type': 'compressible::alphatWallFunction', 'Prt':'0.85', 'value':'uniform 0'},\r\n 'top_down': {'type': 'empty'},\r\n 'cyclic_in_1': {'type': 'cyclic'},\r\n 'cyclic_in_2': {'type': 'cyclic'},\r\n 'cyclic_out_1': {'type': 'cyclic'},\r\n 'cyclic_out_2': {'type': 'cyclic'}})\r\n \"\"\"\r\n session.case.file_controlDict.set_field('endTime', '10000')\r\n session.case.file_controlDict.set_field('startFrom', 'latestTime')\r\n session.case.file_controlDict.set_field('functions', {\"#includeFunc\":\"MachNo\"})\r\n session.case.file_turbulenceProperties.set_field('simulationType', 'laminar')\r\n session.case.interacting(100)\r\n sim = session.case.simulation(\"open40\") # Build files\r\n sim.limit_write = 50\r\n sim.block_mesh(string=write_block_mesh(args[1], args[2], args[3], args[4], args[5], session.mesh))\r\n sim.check_mesh()\r\n\r\n result_dict={\"T\": 0, \"p\":0, \"Theta\":0, \"z\":0, \"profile\":args[14]}\r\n\r\n def _function(container, args):\r\n current_time = container['current_time']\r\n if float(current_time)>=0.000015:\r\n print('Parsing results')\r\n sim.foamToVTK()\r\n results = sim.get_last_results('outlet')\r\n result_U = results.GetCellData('U')\r\n result_p = results.GetCellData('p')\r\n result_T = results.GetCellData('T')\r\n theta = 0.0\r\n z = 0.0\r\n p=0.0\r\n t=0.0\r\n U_length = len(result_U)\r\n p_length = len(result_p)\r\n t_length = len(result_T)\r\n for i,j,k in zip(result_p, result_T, result_U):\r\n p+= float(i[0])/p_length\r\n t+= float(j[0])/t_length\r\n theta += float(k[1])/U_length\r\n z += float(k[0])/U_length\r\n\r\n args[\"T\"] = t\r\n args[\"p\"] = p\r\n args[\"Theta\"] = theta\r\n args[\"z\"] = z\r\n return True\r\n return False\r\n \r\n #sim.run(_function, result_dict)\r\n #result_dict = {'T': 195.38959999999997, 'z': 429.3120571428572, 'p': 74001.90285714286, 'Theta': -207.19442857142855, 'profile': 0}\r\n\r\n print('Sending results')\r\n if args[0].split('#')[0]=='R':\r\n result_dict['Theta']= -result_dict['Theta']\r\n\r\n session.socket_design.send({'new_data':result_dict})","def __init__(self, logging=True):\n self.matrix_creator = MatrixCreator()\n self.matrix_computer = MatrixComputer()\n self.equation_parser = EquationParser()\n self.balancing_validator = BalancingValidator(logging=logging)\n self.logger = Logger(active=logging)","def __init__(self):\n self._names = []\n self._forwardFactories = []\n self._inputs = []\n self._inputFilters = {}\n self._outputFilters = []\n self._inputCheckers = []\n pass","def __init__(self, model = None, cso = None, fast_classification = True, paper = None):\n self.cso = cso #Stores the CSO Ontology\n self.paper = paper #Paper to analyse\n self.model = model #contains the cached model\n self.min_similarity = 0.90 #Initialises the min_similarity\n self.fast_classification = fast_classification # if will use the full model or not\n self.explanation = dict()","def __init__(self, language, dataset_name):\n self._language = language\n self._dataset_name = dataset_name\n\n # TODO: Maybe the paths should be passed as parameters or read from a configuration file.\n self._trainset_path = \"data/raw/{}/{}_Train.tsv\".format(language.lower(), dataset_name)\n self._devset_path = \"data/raw/{}/{}_Dev.tsv\".format(language.lower(), dataset_name)\n self._testset_path = \"data/raw/{}/{}_Test.tsv\".format(language.lower(), dataset_name)\n\n self._trainset = None\n self._devset = None\n self._testset = None\n\n \"\"\"spaCy object handling\"\"\"\n if self._language == \"english\":\n self.nlp = spacy.load('en_core_web_lg')\n elif self._language == \"spanish\":\n self.nlp = spacy.load(\"es_core_news_md\")\n elif self._language == \"german\":\n self.nlp = spacy.load('de_core_news_sm')\n elif self._language == \"french\":\n self.nlp = spacy.load('fr_core_news_md')\n\n self._trainset_spacy_path = \"data/interim/{}/{}_Train-spacy-objs.pkl\".format(\n language.lower(), dataset_name)\n self._devset_spacy_path = \"data/interim/{}/{}_Dev-spacy-objs.pkl\".format(\n language.lower(), dataset_name)\n self._testset_spacy_path = \"data/interim/{}/{}_Test-spacy-objs.pkl\".format(\n language.lower(), dataset_name)","def __init__(self):\n super(Modules, self).__init__()\n \n global superclasses\n superclasses['universe'] = []\n superclasses['actions'] = ['universe']\n superclasses['booleans'] = ['universe']\n\n global instances\n instances['universe'] = set()\n instances['actions'] = set()\n instances['booleans'] = set()","def __init__(self):\n \n #\n # imports\n #\n import time\n from socket import gethostname\n import commands\n \n #\n # Declare variables and set standard default values\n #\n self.database = None\n self.analysisPath = None\n self.command = None\n self.commandLine = None\n self.commandLineList = None\n SEAseqPipeLine.settings = Settings()\n SEAseqPipeLine.logfile = None\n SEAseqPipeLine.startTime = time.time()\n SEAseqPipeLine.startTimeStr = time.strftime(\"%A, %d %b %Y %H:%M:%S\",time.localtime())\n self.availableCommands = {\n 'initiateAnalysis':self.initiateAnalysis,\n 'addData':self.addData,\n 'changeSettings':self.changeSettings,\n 'startAnalysis':self.startAnalysis,\n 'commandLog':self.commandLog,\n 'help':self.printHelp,\n }\n if gethostname().split('.')[1] == 'uppmax': self.onUppmax = True\n else: self.onUppmax = False\n tempFolderName = 'SEAseq2temporaryFiles'\n if self.onUppmax: self.tempFileFolder = os.path.abspath(commands.getoutput('echo $SNIC_TMP'))+'/'+tempFolderName\n else: self.tempFileFolder = self.analysisPath+'/'+tempFolderName\n if not os.path.isdir(self.tempFileFolder): os.makedirs(self.tempFileFolder)\n \n #\n # Get information from commandline\n #\n self.getComandAndPath()\n self.doCurrentTask()","def analyses(self, analysis_id=None, count=None, offset=None, format_group_name=None):\n if analysis_id:\n logger.debug(\"Get analysis\")\n return self._raw_api.analyses.get(analysis_id)\n\n logger.debug(\"Get analysis list\")\n data = filter_data(\n count=count,\n offset=offset,\n format_group_name=format_group_name\n )\n return self._raw_api.analyses.get(json=data)","def __init__(self):\n this = _libsbml.new_RDFAnnotationParser()\n try: self.this.append(this)\n except: self.this = this","def test_creation(self):\n config = config_factory()\n db = config.arango_db\n\n # Create analyzer\n analyzer = ArangoAnalyzer(\"analyzer_sample\")\n analyzer.set_stopwords(\n language=\"english\",\n custom_stopwords=[\"stop\", \"word\"],\n include_default=False,\n )\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\n\n analyzer.create(db)\n assert analyzer.__dict__ == {\n \"name\": \"analyzer_sample\",\n \"type\": \"text\",\n \"features\": [\"frequency\", \"norm\", \"position\"],\n \"locale\": \"en\",\n \"case\": \"lower\",\n \"stopwords\": [\"stop\", \"word\"],\n \"accent\": False,\n \"stemming\": True,\n \"edge_ngram\": None,\n \"delimiter\": \",\",\n \"min\": 2,\n \"max\": 5,\n \"preserve_original\": False,\n \"start_marker\": \"\",\n \"end_marker\": \"\",\n \"stem_type\": \"binary\",\n }\n assert isinstance(db.analyzer(\"analyzer_sample\"), dict)","def __init__(self, datapath, fileType='TargetLynx', sop='Generic', **kwargs):\n\n super().__init__(sop=sop, **kwargs)\n self.filePath, fileName = os.path.split(datapath)\n self.fileName, fileExtension = os.path.splitext(fileName)\n\n self.name = self.fileName\n\n # Load files and match data, calibration report and SOP, then Apply the limits of quantification\n if fileType == 'TargetLynx':\n # Read files, filter calibration samples, filter IS, applyLLOQ, clean object\n self._loadTargetLynxDataset(datapath, **kwargs)\n # Finalise object\n self.VariableType = VariableType.Discrete\n self.AnalyticalPlatform = AnalyticalPlatform.MS\n self.initialiseMasks()\n elif fileType == 'Bruker Quantification':\n # Read files, clean object\n self._loadBrukerXMLDataset(datapath, **kwargs)\n # Finalise object\n self.VariableType = VariableType.Discrete\n self.AnalyticalPlatform = AnalyticalPlatform.NMR\n self.initialiseMasks()\n elif fileType == 'empty':\n # Build empty object for testing\n pass\n else:\n raise NotImplementedError\n\n # Check the final object is valid and log\n if fileType != 'empty':\n validDataset = self.validateObject(verbose=False, raiseError=False, raiseWarning=False)\n if not validDataset['BasicTargetedDataset']:\n raise ValueError('Import Error: The imported dataset does not satisfy to the Basic TargetedDataset definition')\n self.Attributes['Log'].append([datetime.now(),\n '%s instance initiated, with %d samples, %d features, from %s'\n % (self.__class__.__name__, self.noSamples, self.noFeatures, datapath)])\n # Check later\n if 'Metadata Available' not in self.sampleMetadata:\n self.sampleMetadata['Metadata Available'] = False","def __init__(self):\n self.parser_model_dir = None\n self.parser_options = {}\n self.reranker_model = None\n self.unified_model_dir = None"],"string":"[\n \"def create_analysis_tools(self):\\r\\n raise NotImplementedError()\",\n \"def analysis_setup(self):\\n pass\",\n \"def init():\\n # analyzer es utilizado para interactuar con el modelo\\n analyzer = model.newAnalyzer()\\n return analyzer\",\n \"def analysis(self, checkid):\\r\\n return analysis.Analysis(self, checkid)\",\n \"def __init__(self, samples, analysis):\\r\\n self.samples = samples\\r\\n self.analysis = analysis\",\n \"def newAnalyzer():\\n analyzer = {'crimes': None,\\n 'dateIndex': None,\\n 'autors': None,\\n 'instrumentalness': None,\\n 'tempo':None,\\n 'liveness':None,\\n 'speechiness':None,\\n 'danceability':None,\\n 'valence':None,\\n 'loudness':None,\\n 'acousticness':None,\\n 'energy':None,\\n 'generos':None\\n }\\n\\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n\\n analyzer['autors'] = om.newMap(omaptype='RBT',\\n comparefunction=compareAUTOR)\\n\\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['tempo'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['liveness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['danceability'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n analyzer['valence'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['loudness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['energy'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n\\n analyzer['generos']= m.newMap(11,\\n maptype='CHAINING',\\n loadfactor=4.0)\\n \\n return analyzer\",\n \"def __init__(self, autosub=False):\\n self.G = nx.DiGraph()\\n self.autosub = autosub\\n \\\"\\\"\\\"\\n Graph object of this analyzer.\\n It is actually a networkx directed graph object(DiGraph), so you can apply all operations available to DiGraph object using networkx.\\n \\\"\\\"\\\"\\n self.entityList = [dict() for x in range(len(NEList))]\\n \\\"\\\"\\\"\\n List of entities appeared during this analysis round.\\n \\\"\\\"\\\"\\n self.proList = list()\\n \\\"\\\"\\\"\\n List of pronouns appeared during this analysis round.\\n \\\"\\\"\\\"\\n self.pos = 0\\n \\\"\\\"\\\"\\n Current position of the analyzer.\\n \\\"\\\"\\\"\\n self.proc = Subprocess('cabocha -f1')\\n \\\"\\\"\\\"\\n Communicator to backend for KnowledgeAnalyzer.\\n \\\"\\\"\\\"\",\n \"def newAnalyzer():\\n analyzer = {'tracks': None,\\n 'songs': None,\\n 'artists': None,\\n 'char': None,\\n 'char2': None,\\n 'char3': None,\\n }\\n\\n analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)\\n analyzer['songs'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['artists'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char2'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char3'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char4'] = mp.newMap(numelements=50,\\n maptype='PROBING',\\n comparefunction=compareValue)\\n \\n return analyzer\",\n \"def __init__(self, input=None):\\r\\n BaseAnalyzer.__init__(self, input)\",\n \"def __init__(self, input=None):\\r\\n BaseAnalyzer.__init__(self, input)\",\n \"def __init__(self, analyzers: Iterable[Type[Analyzer]], model_repository: ModelRepository,\\n data_service: DataService):\\n self._model_repository = model_repository\\n analyzers = [(a.__name__, a) for a in analyzers]\\n analyzers.sort()\\n self._analyzers = [a[1] for a in analyzers]\\n self._data_service = data_service\",\n \"def analyse(self):\\n pass\",\n \"def initAnalyzer():\\n return controller.initAnalyzer()\",\n \"def analysis(self):\\r\\n return analysis.Analysis(self.parent, self.object_id)\",\n \"def __init__(self):\\n super(TestAnalyzer, self).__init__()\\n self._results = []\",\n \"def __init__(self, out_dir, analysis_out_dir, evaluation_run_name,\\n evaluation_name):\\n if out_dir is None:\\n out_dir = os.getcwd()\\n self.out_dir = out_dir\\n\\n # Copy the analysis results to the report output directory, so that the HTML\\n # report can be correctly rendered even if we move the csv files, plots,\\n # etc.\\n if out_dir != analysis_out_dir:\\n analysis_file_dirs = evaluator.load_directory_tree(\\n out_dir=analysis_out_dir,\\n run_name=evaluation_run_name,\\n evaluation_name=evaluation_name)\\n shutil.copytree(\\n analysis_file_dirs[evaluator.KEY_RUN_DIR],\\n os.path.join(out_dir, evaluation_run_name))\\n\\n self.analysis_results = analyzer.get_analysis_results(\\n out_dir, evaluation_run_name, evaluation_name)\\n\\n self.analysis_results[KEY_NUM_ESTIMABLE_SETS_STATS_DF] = (\\n ReportGenerator.add_parsed_sketch_estimator_name_cols(\\n self.analysis_results[KEY_NUM_ESTIMABLE_SETS_STATS_DF],\\n analyzer.SKETCH_ESTIMATOR_NAME))\\n\\n self.analysis_type = None\",\n \"def analyze(self):\\n dataset = self.config.dataset\\n class_config = dataset.class_config\\n\\n scene_id_to_cfg = {s.id: s for s in dataset.all_scenes}\\n\\n @lru_cache(maxsize=len(dataset.all_scenes))\\n def build_scene(scene_id: str) -> Scene:\\n cfg = scene_id_to_cfg[scene_id]\\n scene = cfg.build(\\n class_config, self.tmp_dir, use_transformers=False)\\n return scene\\n\\n # build and run each AnalyzerConfig for each scene group\\n for a in self.config.analyzers:\\n for group_name, group_ids in dataset.scene_groups.items():\\n if len(group_ids) == 0:\\n log.info(f'Skipping scene group \\\"{group_name}\\\". '\\n 'Empty scene group.')\\n continue\\n group_scenes = (build_scene(id) for id in group_ids)\\n analyzer = a.build(scene_group=(group_name, group_scenes))\\n\\n log.info(f'Running {type(analyzer).__name__} on '\\n f'scene group \\\"{group_name}\\\"...')\\n analyzer.process(group_scenes, self.tmp_dir)\",\n \"def _build_analyzer():\\n analyzer = {}\\n for e in LOSSLESS:\\n analyzer[e] = lambda d, _ext, name: d.lossless.append(name)\\n for e in COMPRESSED:\\n analyzer[e] = lambda d, _ext, name: d.compressed.append(name)\\n for e in IMAGES:\\n analyzer[e] = lambda d, _ext, name: d.images.append(name)\\n for e in VIDEOS:\\n analyzer[e] = lambda d, _ext, name: d.videos.append(name)\\n\\n def _increment_ignored(d, _ext, _name):\\n d.ignored += 1 # Can't use assignment in lambda\\n\\n for e in IGNORE:\\n analyzer[e] = _increment_ignored\\n analyzer['cue'] = lambda d, _, name: d.cue.append(name)\\n\\n return analyzer\",\n \"def createAnalysisSampleVisitor(config, cuts):\\n\\n # TODO: warn user if this function is called but 'cutbased' is false? (or no jobs yet booked - must do this first if a cutbased analysis is desired)\\n\\n # read the channel definitions\\n channels = config.getTagVString(\\\"channels\\\")\\n\\n CLI = config.getFolder(\\\"CLI+\\\")\\n # flag indicating to run a robust analysis\\n robust = CLI.getTagBoolDefault(\\\"robust\\\",False)\\n # flag indicating to run a dummy analysis\\n dummy = CLI.getTagBoolDefault(\\\"dummy\\\",False)\\n\\n if not config.getTagBoolDefault(\\\"useMultiChannelVisitor\\\",False) or robust or dummy:\\n # using regular analysis sample visitor (default)\\n visitor = QFramework.TQAnalysisSampleVisitor()\\n visitor.setVerbose(True)\\n visitor.setBaseCut(cuts)\\n visitor.setPrettyPrint(config.getTagBoolDefault(\\\"prettyPrint\\\",True))\\n visitor.setLineUpdates(config.getTagBoolDefault(\\\"lineUpdates\\\",True))\\n visitor.setTagDouble(\\\"progressInterval\\\",config.getTagDoubleDefault(\\\"progressInterval\\\",5.))\\n else:\\n # using fast MultiChannel analysis sample visitor\\n visitor = QFramework.TQMultiChannelAnalysisSampleVisitor()\\n visitor.setVerbose(True)\\n visitor.setPrettyPrint(config.getTagBoolDefault(\\\"prettyPrint\\\",True))\\n visitor.setLineUpdates(config.getTagBoolDefault(\\\"lineUpdates\\\",True))\\n visitor.setTagDouble(\\\"progressInterval\\\",config.getTagDoubleDefault(\\\"progressInterval\\\",5.))\\n\\n runtime = config.getFolder(\\\"runtime+\\\")\\n # TODO: add some protection against not finding the mcasvchannels in the runtime config for whatever reason\\n mcasvchannels = runtime.getTagVStandardString(\\\"mcasvchannels\\\")\\n\\n # TODO: cutlist was defined in runAnalysis.py, but aparently not used\\n #cutlist = []\\n for channel in mcasvchannels:\\n cut = cuts.getClone()\\n #cutlist.append(cut)\\n visitor.addChannel(channel,cut)\\n # TODO: used previously just as a list for cloneObservablesSmart\\n # mcvchannels.append(channel)\\n if config.getTagBoolDefault(\\\"showChannels\\\",False):\\n visitor.printChannels()\\n\\n # TODO: SmartObservableCloning not yet migrated to CAFExample (or fully implemented?) - Initial author = Carsten\\n # Safe for this to go here? i.e. only for MCASV and before analysis algorithms are attached\\n cloneObservablesSmart = False\\n if config.getTagBoolDefault(\\\"reduceMCVObservables\\\",False):\\n try:\\n from CAFExample.SmartObservableCloning import cloneSetSmart\\n cloneObservablesSmart = True\\n except ImportError:\\n cloneObservablesSmart = False\\n QFramework.ERROR(\\\"smart observable cloning unavailable, skipping\\\")\\n if cloneObservablesSmart:\\n for channel in mcasvchannels:\\n QFramework.TQObservable.getManager().cloneActiveSet(channel)\\n\\n return visitor\",\n \"def __init__(self):\\n Sampler.__init__(self)\\n self._registeredIdentifiers = set() # tracks job identifiers used for this adaptive sampler and its inheritors\\n self._prefixToIdentifiers = {} # tracks the mapping of run prefixes to particular identifiers\\n self._inputIdentifiers = {} # identifiers for a single realization\\n self._targetEvaluation = None # data object with feedback from sample realizations\\n self._solutionExport = None # data object for solution printing\\n self._requireSolnExport = False # if this object requires a solution export\\n # NOTE TargetEvaluations consider all the Step <Output> DataObjects as candidates, so requiring\\n # exactly one TargetEvaluation forces only having one <Output> DataObject in AdaptiveSampling\\n # MultiRun Steps. For now, we leave it as \\\"n\\\".\\n self.addAssemblerObject('TargetEvaluation', InputData.Quantity.one_to_infinity) # Place where realization evaluations go\",\n \"def newAnalyzer():\\n analyzer = {'accidentes': None,\\n 'dateIndex': None\\n }\\n\\n analyzer['accidentes'] = lt.newList('SINGLE_LINKED', compareseverity)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n return analyzer\",\n \"def newAnalyzer():\\n analyzer = {'accidents': None,\\n 'dateIndex': None,\\n 'timeIndex': None\\n }\\n\\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['timeIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareTimes)\\n \\n return analyzer\",\n \"def __init__(self):\\n IContainsAnimals.__init__(self, 15)\\n IContainsPlants.__init__(self, 3)\\n Identifiable.__init__(self)\\n Biome.__init__(self, \\\"Coastline\\\")\",\n \"def __init__(self):\\n self.analysis=''\\n self.terms=''\\n self.path=''\",\n \"def __init__(self):\\n self._tool_data = {}\\n self.feedback = []\\n self.ignored_feedback = []\\n self.suppressions = {}\\n self.suppressed_labels = {}\\n self.hiddens = set()\\n self.groups = []\\n self.group = None\\n self.group_names = {}\\n self.hooks = {}\\n self.class_hooks = {}\\n self.submission = None\\n self.format = Formatter()\\n self.result = None\\n self.resolves = []\\n self.overridden_feedbacks = set()\\n log.debug(\\\"New Pedal Report created.\\\")\",\n \"def __init__(self):\\n self.train(positivity_files, 0)\\n self.train(subjectivity_files, 1)\",\n \"def __init__(self):\\n self.model_description: Dict[str, Any] = get_model_description()\\n self.model_name: str = self.model_description['name']\\n self.model_version: str = self.model_description['version']\\n\\n # Make sure we do not have a trailing slash to muck up processing later.\\n self.event_dir: Optional[str] = None\\n self.zone_name: Optional[str] = None\\n self.fault_time: Optional[str] = None\\n\\n self.example: Example = None\\n self.validator: ExampleValidator = ExampleValidator()\\n self.common_features_df: pd.DataFrame = None\\n\\n self.cavity_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\\n 'model_files',\\n 'cavity_model.onnx'))\\n self.fault_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\\n 'model_files',\\n 'fault_model.onnx'))\",\n \"def __init__(self, store_directory):\\n # Read in data\\n analysis_script_path = os.path.join(store_directory, 'analysis.yaml')\\n if not os.path.isfile(analysis_script_path):\\n err_msg = 'Cannot find analysis.yaml script in {}'.format(store_directory)\\n raise RuntimeError(err_msg)\\n with open(analysis_script_path, 'r') as f:\\n analysis = yaml.load(f)\\n phases = []\\n signs = {}\\n ncfiles = {}\\n for phase, sign in analysis:\\n phases.append(phase)\\n signs[phase] = sign\\n ncfile_path = os.path.join(store_directory, phase + '.nc')\\n ncfiles[phase] = nc.Dataset(ncfile_path, 'r')\\n self.phases = phases\\n self.signs = signs\\n self.ncfiles = ncfiles\\n self.nphases = len(phases)\\n # Assign flags for other sections along with their global variables\\n # General Data\\n self._general_run = False\\n self.iterations = {}\\n # Equilibration\\n self._equilibration_run = False\\n self.u_ns = {}\\n self.nequils = {}\\n self.g_ts = {}\\n self.Neff_maxs = {}\\n # Decorrelation break-down\\n self._decorrelation_run = False\\n # Mixing Run (state)\\n self._mixing_run = False\\n # Replica mixing\\n self._replica_mixing_run = False\\n self._free_energy_run = False\",\n \"def __init__(self):\\n self.libpath = os.sep.join(os.path.abspath(__file__).split(os.sep)[:-1])\\n sys.path.append(self.libpath)\\n libpath2 = os.sep.join(self.libpath.split(os.sep)[:-1])\\n sys.path.append(libpath2)\\n # Initialize TCMetaSchema with correct libpath\\n TCMetaSchema(self.libpath)\\n self.args, self.unknown = IceteaManager._parse_arguments()\\n # If called with --clean, clean up logs.\\n if self.args.clean:\\n _cleanlogs(silent=self.args.silent, log_location=self.args.log)\\n\\n LogManager.init_base_logging(self.args.log, verbose=self.args.verbose,\\n silent=self.args.silent, color=self.args.color,\\n no_file=(self.args.list or self.args.listsuites),\\n truncate=not self.args.disable_log_truncate)\\n\\n self.logger = LogManager.get_logger(\\\"icetea\\\")\\n self.pluginmanager = None\\n self.resourceprovider = ResourceProvider(self.args)\\n self._init_pluginmanager()\\n self.resourceprovider.set_pluginmanager(self.pluginmanager)\",\n \"def _generate_trading_instances(self):\\n print(\\\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\\\")\\n\\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\\n self.strategy = self.strategy_class(self.data_handler, self.events)\\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler\",\n \"def __init__(self, test):\\n self.all_grams = Ngram(self.START_OF_SENTENCE_TOKEN)\\n for label in self.label_type_map:\\n self.words_labels_counts[label] = {}\\n self.words_labels_counts[label][self.UNKNOWN_TOKEN] = 0\\n if test:\\n self.train(\\\"train.txt\\\")\\n self.test(\\\"test.txt\\\")\\n else:\\n self.train(\\\"train_partial.txt\\\")\\n self.validate(\\\"validation_partial.txt\\\")\",\n \"def __init__(self, **kwargs):\\n\\n # call base class constructor registering that this tool performs everything.\\n Algorithm.__init__(\\n self,\\n performs_projection = True,\\n use_projected_features_for_enrollment = True,\\n requires_enroller_training = True\\n )\",\n \"def __init__(self):\\r\\n self.filter_p_number = 3 # First one with enough data for statistics\\r\\n self.prfs_d = extract_settings_elvis()\\r\\n\\r\\n ccds = True\\r\\n filtered = False\\r\\n scamp = False\\r\\n\\r\\n input_df = read_csv('cats/cat_clean_ssos.csv', index_col=0)\\r\\n filt_cat = self.gets_filtered_catalog() # Gets data from filtered\\r\\n\\r\\n if ccds:\\r\\n cats_d = self.extract_cats()\\r\\n self.extract_stats_ccds(cats_d, input_df, filt_cat)\\r\\n elif filtered:\\r\\n self.extract_stats_filt(filt_cat, input_df)\\r\\n elif scamp:\\r\\n pass\\r\\n # self.extract_stats_scamp(input_df)\\r\\n else:\\r\\n pass\",\n \"def __init__(self, analysis: Analysis) -> None:\\n self.analysis = analysis\\n self.output_basename = os.path.join(\\\"REPORTS\\\", self.analysis.basename)\\n self.only_human = True\\n\\n self.max_records_str = utils.get_picard_max_records_string(\\n self.analysis.parameters[\\\"picard_max_records\\\"]\\n )\\n self.sort_tempdir = os.path.join(\\n self.analysis.get_bam_dir(), \\\"%s_sort_tmp\\\" % self.analysis.sample\\n )\",\n \"def __init__(self):\\n\\n self.read_input_file()\\n self.read_simulation_files()\",\n \"def __init__(self):\\n self.data = None\\n self.decisionTree = {}\\n self.enClass = 0\\n self.nlClass = 0\\n self.listAttributes = [\\\"Contains-het\\\", \\\"Contains-de\\\", \\\"Contains-een\\\", \\\"Contains-en/aan\\\", \\\"Contains-ij\\\", \\\"wordLength14\\\",\\n \\\"Contains-a/an\\\", \\\"Contains-are/were\\\", \\\"Contains-and\\\", \\\"Contains-on/to\\\", \\\"Contains-the\\\"]\\n self.infoGain = []\\n self.entropy = 0\",\n \"def __init__(self):\\n self._predefined_cluster_topics()\\n self._gatherSEs()\",\n \"def analysis(self, game_info):\\n pass\",\n \"def __init__(self, x=None, y=None, analysis=None, error=None):\\n logger.info(\\\"Creating Diva 2D Result object\\\")\\n if isinstance(x, np.ndarray):\\n self.x = x\\n elif isinstance(x, list):\\n self.x = np.array(x)\\n else:\\n logger.debug(\\\"X vector not defined. Should be np.ndarray or list\\\")\\n self.x = None\\n\\n if isinstance(y, np.ndarray):\\n self.y = y\\n elif isinstance(x, list):\\n self.y = np.array(y)\\n else:\\n logger.debug(\\\"Y vector not defined. Should be np.ndarray or list\\\")\\n self.y = None\\n\\n if isinstance(analysis, np.ndarray):\\n if analysis.shape[0] == len(x) and analysis.shape[1] == len(y):\\n logger.debug('Consistent dimensions for the analysed field')\\n self.analysis = analysis\\n if isinstance(error, np.ndarray):\\n if analysis.shape == error.shape:\\n logger.debug('Consistent dimensions for the error field')\\n self.error = error\\n else:\\n logger.error(\\\"Dimension mismatch\\\")\\n raise Exception(\\\"Dimension mismatch\\\")\\n else:\\n logger.debug(\\\"Error field not defined\\\")\\n self.error = None\\n else:\\n logger.error(\\\"Dimension mismatch\\\")\\n raise Exception(\\\"Dimension mismatch\\\")\\n else:\\n logger.debug(\\\"Analysed field not defined\\\")\\n self.analysis = None\\n self.error = None\",\n \"def __init__(self):\\n for item in grammar:\\n item['matches_compiled'] = {}\\n for name,pattern in item['matches'].items():\\n item['matches_compiled'][name] = \\\\\\n re.compile(pattern, re.IGNORECASE)\\n\\n item['semantics_compiled'] = {}\\n for name,pattern in item['semantics'].items():\\n item['semantics_compiled'][name] = \\\\\\n re.compile(pattern)\\n\\n if constants.SPELLCHECK:\\n self.didyoumean = DidYouMean('en-us', constants.DICT_DIR)\",\n \"def __init__(\\n self, api,\\n ):\\n self._api = api\\n self._api_response = self._api.find_cases(range=\\\"all\\\", sort=[])\\n self._all30_dict = {}\\n self._all60_dict = {}\\n self._all90_dict = {}\\n\\n self._data_frame_30days = None\\n self._data_frame_60days = None\\n self._data_frame_90days = None\\n self._data_frame_counts = None\\n self._dataset = None\",\n \"def run_analysis(wf):\\n if wf.analysis[\\\"type\\\"] == \\\"one_sample_tests\\\":\\n start_one_sample_tests(wf)\\n\\n elif wf.analysis[\\\"type\\\"] == \\\"two_sample_tests\\\":\\n start_two_sample_tests(wf)\\n\\n elif wf.analysis[\\\"type\\\"] == \\\"factorial_tests\\\":\\n start_factorial_tests(wf)\\n\\n elif wf.analysis[\\\"type\\\"] == \\\"n_sample_tests\\\":\\n start_n_sample_tests(wf)\\n\\n info(\\\"> Finished analysis\\\")\",\n \"def initiateAnalysis(self,):\\n\\n #\\n # Imports\\n #\\n import os\\n import sys\\n\\n #\\n # get optional arguments from commandline\\n #\\n self.getComandLineOptions()\\n \\n #\\n # for logmessages\\n #\\n tmpLogMessages = ['----------------\\\\n']\\n tmpLogMessage = self.createLogHeader()\\n tmpLogMessages.append(tmpLogMessage)\\n #print tmpLogMessage\\n \\n #\\n # check analysis path\\n #\\n if os.path.isdir(self.analysisPath):\\n tmpLogMessage = 'WARNING: the analysis path already exists.\\\\n'\\n print tmpLogMessage\\n tmpLogMessages.append(tmpLogMessage)\\n else:\\n tmpLogMessage = 'Creating directory \\\"'+self.analysisPath+'\\\".\\\\n'\\n #print tmpLogMessage\\n tmpLogMessages.append(tmpLogMessage)\\n os.makedirs(self.analysisPath)\\n \\n #\\n # create the logfile\\n #\\n tmpLogMessages += self.openLogfileConnection()\\n \\n #\\n # write tmpLogMessages to logfile\\n #\\n SEAseqPipeLine.logfile.write(''.join(tmpLogMessages))\\n \\n #\\n # create the database\\n #\\n self.database.create()\\n \\n #\\n # add run to runs table\\n #\\n self.database.addToRunsTable(self.startTimeStr, self.command, self.commandLine, True, MASTER)\\n \\n return 0\",\n \"def __init__(self, *args):\\n # The first Analysis object with all dependencies.\\n # Any item from the first executable cannot be removed.\\n self._main = None\\n\\n self._dependencies = {}\\n\\n self._id_to_path = {}\\n for _, i, p in args:\\n self._id_to_path[os.path.normcase(i)] = p\\n\\n # Get the longest common path\\n common_prefix = os.path.commonprefix([os.path.normcase(os.path.abspath(a.scripts[-1][1])) for a, _, _ in args])\\n self._common_prefix = os.path.dirname(common_prefix)\\n if self._common_prefix[-1] != os.sep:\\n self._common_prefix += os.sep\\n logger.info(\\\"Common prefix: %s\\\", self._common_prefix)\\n\\n self._merge_dependencies(args)\",\n \"def __init__(self, langName):\\n self.langName = langName\\n self.readDataSets(langName)\\n # self.getVMWEReport()\\n self.analyzeSents()\\n self.orderParentVMWEs()\\n self.getTrainAndTest()\\n self.cleanSents()\\n self.extractDictionaries()\\n self.deleteNonRecognizableMWE()\\n printStats(self.trainingSents, 'Train', mweDic=self.mweDictionary, langName=langName, test=False)\\n printStats(self.testingSents, 'Test', mweDic=self.mweDictionary, test=True)\",\n \"def __init__(self):\\n\\n self.name = None\\n self.summary = None\\n self.cases = []\",\n \"def __init__(self,classes=['normalizeText','tagger','stem','stopWord','spellChecker']):\\n self._support = prebotSupport()\\n self._classes = classes\\n if(\\\"tagger\\\" in self._classes):\\n self._tagger = tagger()\\n if(\\\"normalizeText\\\" in self._classes):\\n self._normalize = normalizeText()\\n if(\\\"spellChecker\\\" in self._classes):\\n self._spellChecker = spellChecker()\\n if(\\\"stopWord\\\" in self._classes):\\n self._stopWord = stopWord()\\n if(\\\"stem\\\" in self._classes):\\n self._stem = stemming()\",\n \"def do_analysis(ckpt, queries_type, entities_type, request):\\n global currently_analyzing, results, d, analysis_user\\n try:\\n print(\\\"starting analysis!\\\")\\n if entities_type == \\\"all\\\":\\n print(\\\"using all entities detected!\\\")\\n elif entities_type == \\\"uploaded\\\":\\n print(\\\"using only entities specified in csv file!\\\")\\n \\n currently_analyzing = True\\n analysis_user = request.user.username\\n results = []\\n proj_path = os.path.abspath(os.path.dirname(__file__)).split(\\\"FYP_Web_App\\\")[0]\\n ckpt = proj_path + \\\"FewRel/checkpoint/\\\" + ckpt\\n if d is None or d.ckpt_path != ckpt:\\n d = DetectionFramework(ckpt_path=ckpt)\\n if cancel_flag[0]:\\n return\\n d.clear_support_queries()\\n if len([i for i in os.listdir(\\\"temp/relation_support_datasets\\\") if 'csv' in i and request.user.username in i]) == 0:\\n raise ValueError(\\\"Please upload relation support dataset!\\\")\\n \\n d.load_support_files(\\\"temp/relation_support_datasets\\\", request.user.username)\\n if queries_type == \\\"csv_option\\\":\\n if not os.path.exists(\\\"temp/queries.csv\\\"):\\n raise ValueError(\\\"Please upload query CSV dataset!\\\")\\n d.load_queries_csv(\\\"temp/queries.csv\\\")\\n \\n elif queries_type == \\\"url_option\\\":\\n if not os.path.exists(\\\"temp/url.txt\\\"):\\n raise ValueError(\\\"Please specify news article url!\\\")\\n with open(\\\"temp/url.txt\\\") as f:\\n url = f.read()\\n d.load_url(url)\\n \\n elif queries_type == \\\"txt_option\\\":\\n d.load_text_files(os.path.abspath(\\\"temp/text_files\\\"))\\n \\n elif queries_type == \\\"ind_sentence_option\\\":\\n ind_sentence = request.POST.get('ind_sent')\\n d.load_ind_sentence(ind_sentence)\\n \\n elif queries_type == \\\"html_option\\\":\\n d.load_html_file_queries(os.path.abspath(\\\"temp/html_files\\\"))\\n \\n if entities_type == \\\"uploaded\\\":\\n d.trim_queries_based_on_entities_file(os.path.abspath(\\\"temp/entities_csv_file.csv\\\"))\\n\\n if cancel_flag[0]:\\n return\\n d.detect(rt_results=results, cancel_flag=cancel_flag)\\n if cancel_flag[0]:\\n return\\n src=None\\n if queries_type == \\\"csv_option\\\":\\n src = \\\"queries_csv\\\"\\n elif queries_type == \\\"txt_option\\\":\\n src = \\\"queries_text_file\\\"\\n elif queries_type == \\\"ind_sentence_option\\\":\\n src = \\\"ind_sentence\\\"\\n elif queries_type == \\\"url_option\\\":\\n with open(\\\"temp/url.txt\\\") as f:\\n src = f.read()\\n elif queries_type == \\\"html_option\\\":\\n src = \\\"html_files\\\"\\n \\n s = Source(source=src, user=request.user)\\n s.save()\\n for r in results:\\n er = ExtractedRelation(sentence=r['sentence'],head=r['head'],tail=r['tail'],pred_relation=r['pred_relation'],sentiment=r['sent'],conf=r['conf'],ckpt=ckpt, source=s)\\n er.save()\\n except Exception as e:\\n print(len(str(e)))\\n print(str(e))\\n errors.append(str(e))\\n tb = traceback.format_exc()\\n print(tb)\\n finally:\\n currently_analyzing = False\\n analysis_user = None\",\n \"def analyse(self, data=None):\\n pass\",\n \"def __init__(self, database):\\n self.max_axiom_arity = max([p.arity() for p in database.non_entails_axioms.itervalues()]) + 1 # one more than the max\\n\\n self.database = database\\n\\n self.max_unconstrained_arity = 10000000\\n self.searcher = SearchProblem(database, max_unconstrained_arity = self.max_unconstrained_arity)\\n\\n self.tautologies = set()\\n for p in self.database.propositions.itervalues():\\n e_hyps = [h for h in p.hyps if h.type == 'e']\\n if p.vclass=='|-' and len(e_hyps) == 0:\\n self.tautologies.add(p.label)\\n print 'tautologies:', len(self.tautologies)\\n \\n # the propositions with trivial unconstrained arity. That is, the ones\\n # that are really easy to apply.\\n self.constrained_propositions = set(\\n p.label for p in self.database.propositions.itervalues()\\n if p.vclass == '|-' and p.unconstrained_arity() == 0\\n )\\n \\n # figure out the names of the read variables\\n # self.real_wff_names = set()\\n # self.real_set_names = set()\\n # self.real_class_names = set()\\n # real_name_dict = {'wff': self.real_wff_names, 'set': self.real_set_names, 'class': self.real_class_names}\\n #\\n # for p in self.database.propositions.itervalues():\\n # for label in p.f:\\n # vclass = p.f[label].vclass\\n # real_name_dict[vclass].add(label)\\n # print real_name_dict\\n\\n self.constructor_dictionary = [{} for _ in range(self.max_axiom_arity)]\\n\\n # we need to define some extra variables, which we'll randomly assign when we read in a statement\\n # this is a reasonable amount of data augmentation.\\n self.extra_wffs = language_model_extra_variables_of_each_type+max(len([f for f in p.f.itervalues() if f.vclass=='wff']) for p in database.propositions.itervalues() )\\n self.extra_classes = language_model_extra_variables_of_each_type+max(len([f for f in p.f.itervalues() if f.vclass=='class']) for p in database.propositions.itervalues() )\\n self.extra_sets = language_model_extra_variables_of_each_type+max(len([f for f in p.f.itervalues() if f.vclass=='set']) for p in database.propositions.itervalues() )\\n\\n # hand code these in.\\n self.extra_sets = 20\\n self.extra_wffs = 18\\n self.extra_classes = 27\\n\\n self.wff_names = ['WFFVar'+str(i) for i in range(self.extra_wffs)]\\n self.set_names = ['SetVar'+str(i) for i in range(self.extra_sets)]\\n self.class_names = ['ClassVar'+str(i) for i in range(self.extra_classes)]\\n\\n self.num_extra_variable_names = len(self.wff_names)+len(self.set_names)+len(self.class_names)\\n self.extra_variable_dict = {}\\n\\n # the names for the unconstrained variables\\n #self.ua_names = ['UA'+str(i) for i in range(self.max_unconstrained_arity)]\\n\\n # add them to the dictionary\\n arityzerodict = self.constructor_dictionary[0]\\n for i in range(self.extra_wffs):\\n arityzerodict['WFFVar'+str(i)]=len(arityzerodict)\\n self.extra_variable_dict['WFFVar'+str(i)]=len(self.extra_variable_dict)\\n for i in range(self.extra_classes):\\n arityzerodict['ClassVar'+str(i)]=len(arityzerodict)\\n self.extra_variable_dict['ClassVar'+str(i)]=len(self.extra_variable_dict)\\n for i in range(self.extra_sets):\\n arityzerodict['SetVar'+str(i)]=len(arityzerodict)\\n self.extra_variable_dict['SetVar'+str(i)]=len(self.extra_variable_dict)\\n # for i in range(len(self.ua_names)):\\n # arityzerodict['UA'+str(i)]=len(arityzerodict)\\n # self.extra_variable_dict['UA'+str(i)]=len(self.extra_variable_dict)\\n\\n # a block to create a dictionary that takes a symbol to its vclass\\n self.symbol_to_vclass = {label:database.propositions[label].vclass for label in database.non_entails_axioms}\\n for symbol in self.wff_names:\\n self.symbol_to_vclass[symbol] = 'wff'\\n for symbol in self.set_names:\\n self.symbol_to_vclass[symbol] = 'set'\\n for symbol in self.class_names:\\n self.symbol_to_vclass[symbol] = 'class'\\n\\n # a list of all of the extra variables, for use later\\n self.new_names = self.wff_names+self.set_names+self.class_names\\n\\n # describe the number of variables we've used\\n print 'wff variables:',self.extra_wffs\\n print 'class variables:',self.extra_classes\\n print 'set variables:',self.extra_sets\\n #print 'ua variables:', self.ua_names\\n\\n # now add the actual constructor axioms to our dictionary\\n for p in database.non_entails_axioms.itervalues():\\n c_dict = self.constructor_dictionary[p.arity()]\\n c_dict[p.label] = len(c_dict)\\n\\n for i in range(self.max_axiom_arity):\\n print len(self.constructor_dictionary[i]),'constructor axioms with arity',i\\n\\n # build a pair of dictionaries that convert (arity,num) to total_num\\n # and vice versa. This is ugly. Whatever\\n self.arity_num_to_global_index = {}\\n self.global_index_to_arity_num=[]\\n global_index = 0\\n for arity in range(self.max_axiom_arity):\\n for num in range(len(self.constructor_dictionary[arity])):\\n self.global_index_to_arity_num.append((arity,num))\\n self.arity_num_to_global_index[(arity,num)]=global_index\\n global_index+=1\\n\\n \\\"\\\"\\\"sets up the data sets. We divide the propositions into training/validation/test and\\n then compile the corresponding list of statements\\\"\\\"\\\"\\n list_of_propositions = self.database.propositions_list[:] # database.propositions.values()\\n np.random.seed(seed=121451345)\\n list_of_propositions = np.random.permutation(list_of_propositions)\\n\\n num_validation = len(list_of_propositions)/10\\n num_test = num_validation\\n num_training = len(list_of_propositions)-num_test-num_validation\\n self.training_propositions = list_of_propositions[:num_training]\\n self.training_propositions = [_ for _ in self.training_propositions if _.type=='p']\\n self.validation_propositions = list_of_propositions[num_training:num_training+num_validation]\\n self.validation_propositions = [_ for _ in self.validation_propositions if _.type=='p']\\n self.test_propositions = list_of_propositions[num_training+num_validation:]\\n self.test_propositions = [_ for _ in self.test_propositions if _.type=='p']\\n\\n if self.database.remember_proof_steps:\\n self.all_proof_steps = [] # except those that refer to e or f-type hypotheses\\n for p in self.database.propositions.itervalues():\\n self.all_proof_steps += [step for step in p.entails_proof_steps if not (step.prop.type=='f' or step.prop.type == 'e')]\\n\\n\\n self.training_proof_steps = []\\n for p in self.training_propositions:\\n self.training_proof_steps += [step for step in p.entails_proof_steps\\n if not (step.prop.type=='f' or step.prop.type == 'e')]\\n\\n self.validation_proof_steps = []\\n for p in self.validation_propositions:\\n self.validation_proof_steps += [step for step in p.entails_proof_steps\\n if not (step.prop.type=='f' or step.prop.type == 'e')]\\n\\n self.test_proof_steps = []\\n for p in self.test_propositions:\\n self.test_proof_steps += [step for step in p.entails_proof_steps\\n if not (step.prop.type=='f' or step.prop.type == 'e')]\\n\\n print\\n print 'training steps:', len(self.training_proof_steps)\\n print 'validation steps:', len(self.validation_proof_steps)\\n print 'test steps:', len(self.test_proof_steps)\\n\\n\\n # figure out how frequenly each proposition is used\\n self.prop_usage = [0 for p in self.database.propositions]\\n for s in self.all_proof_steps:\\n self.prop_usage[s.prop.number]+=1\\n\\n # figure out what the most difficult proof step is\\n self.max_depth = max([s.height for s in self.all_proof_steps]) + 1\\n print 'max proof step depth:', self.max_depth-1\\n\\n\\n # figure out the number of times each proposition is used.\\n # self.prop_uses = [0.1] * len(self.database.propositions) # for numberical stability\\n # for step in self.all_proof_steps:\\n # self.prop_uses[step.prop.number] += 1\\n # self.initial_b = np.log(1.0*np.array(self.prop_uses)/sum(self.prop_uses))\\n\\n\\n # build up a database of propositions by unconstrained arity\\n # that is, total_unconstrained_arity is the total\\n # of all of the unconstrained arities of all of the propositions.\\n # and unconstrained_arity_indices is a list of p.unconstrained_arity()\\n # unique indices for each proposition p.\\n self.total_unconstrained_arity = 0\\n self.unconstrained_arity_indices = {}\\n self.unconstrained_label_to_number = {}\\n for p in self.database.propositions_list: # in order of proposition number\\n u_arity = p.unconstrained_arity()\\n self.unconstrained_arity_indices[p.label]=range(self.total_unconstrained_arity, self.total_unconstrained_arity + u_arity)\\n self.total_unconstrained_arity += u_arity\\n self.unconstrained_label_to_number[p.label]=len(self.unconstrained_label_to_number)\\n #self.max_unconstrained_arity = max([p.unconstrained_arity() for p in self.database.propositions.itervalues()])\\n\\n self.total_constructor_arity = 0\\n self.constructor_arity_indices = {}\\n self.constructor_label_to_number = {}\\n self.constructor_labels = []\\n for p in database.non_entails_axioms.itervalues():\\n u_arity = p.arity()\\n self.constructor_arity_indices[p.label]=range(self.total_constructor_arity, self.total_constructor_arity + u_arity)\\n self.total_constructor_arity += u_arity\\n self.constructor_label_to_number[p.label]=len(self.constructor_label_to_number)\\n self.constructor_labels.append(p.label)\\n for name in self.wff_names+self.set_names+self.class_names: #+self.ua_names:\\n self.constructor_arity_indices[name] = [] # the extra arity 0 constructors\\n self.constructor_label_to_number[name]=len(self.constructor_label_to_number)\\n self.constructor_labels.append(name)\\n\\n # a lookup table for the index into all the propositions of the label\\n self.label_to_number = {x.label:x.number for x in self.database.propositions.itervalues()}\\n for x in self.new_names:\\n self.label_to_number[x] = -1 # all variables should always be included\",\n \"def __init__(self):\\n\\n # Dictionary of types seen so far. Builtin types always available.\\n # Values : list of constructors which the type defines\\n # This is a smartdict, so keys can be retrieved.\\n self.knownTypes = smartdict.Smartdict()\\n for typecon in ast.builtin_types_map.values():\\n self.knownTypes[typecon()] = None\\n\\n # Dictionary of constructors encountered so far.\\n # Value: Type which the constructor produces.\\n # This is a smartdict, so keys can be retrieved.\\n self.knownConstructors = smartdict.Smartdict()\",\n \"def _init(self, *args, **kwargs):\\n f = Framer()\\n election = Election.objects.filter(electionid=self.this_election).first()\\n for measure in self.list_of_measures:\\n requested_url = \\\"%s%s\\\" % (self.api_url, measure)\\n response = requests.get(requested_url, headers=self.request_headers)\\n measure_data = response.json()[\\\"measure\\\"]\\n identifying_information = measure_data[\\\"official_identifier\\\"].split(\\\" \\\")\\n measure_data[\\\"official_identifier\\\"] = \\\"Proposition %s\\\" % (identifying_information[1])\\n measure_data[\\\"official_identifier_slug\\\"] = f._slug(measure_data[\\\"official_identifier\\\"])\\n measure_data[\\\"election_id\\\"] = election.id\\n measure_data = f._massage_measure_title(measure_data)\\n saver = Saver()\\n saver.make_measure(measure_data)\\n saver.make_measure_contributor(measure_data)\\n saver.make_measure_total(measure_data)\",\n \"def main():\\n\\t#ps = PackageScanner()\\n\\t#packages = ps.getInstalledPackages()\\n\\t#print(packages)\\n\\t#ps.saveScanResults()\\n\\n\\tan = Analyzer()\\n\\tan.loadFromFile(config.PKG_SCAN_DIR / config.PKG_SCAN_FILE)\\n\\t#an.loadFromPackageCont(packages)\\n\\tan.analyze()\\n\\tan.saveAnalysisResults()\",\n \"def test_init_analysis(network):\\n bf_init_analysis(\\\"test_analysis\\\", _stable_question_dir)\",\n \"def testGetAnalyzerInstances(self):\\n analyzer_names = manager.AnalyzersManager.GetAnalyzerNames()\\n analyzers = manager.AnalyzersManager.GetAnalyzerInstances(analyzer_names)\\n self.assertEqual(len(analyzer_names), len(analyzers))\\n for analyzer in analyzers:\\n self.assertIsInstance(analyzer, interface.BaseAnalyzer)\",\n \"def make_all(self):\\n # General matrices #\\n self.tsv_seq_to_concepts()\\n self.tsv_seq_to_names()\\n self.list_sequence_concept()\\n # Only in the with 'samples' case #\\n if self.a.abundances: self.tsv_samples_to_names()\\n if self.a.abundances: self.biom_output()\\n # Graphical outputs #\\n self.per_seq_dot_files()\\n if self.a.abundances: self.per_sample_dot_files()\",\n \"def __init__(self):\\n self.cause_texts = set()\\n self.effect_texts = set()\\n self.evidence_texts = set()\\n self.cause_polarity = None\\n self.effect_polarity = None\\n self.cause_type = None\\n self.effect_type = None\",\n \"def __init__(self, description):\\n super(AimaProver, self).__init__()\\n description = DistinctAndNotMover.run(description)\\n self.knowledgeBase = KnowledgeBase(Sets.newHashSet(description))\",\n \"def __init__(self):\\n self.name = \\\"Schaffer\\\"\\n objectives = [o_sh_1, o_sh_2]\\n decisions = [Decision(-10 ** 5, 10 ** 5)]\\n Model.__init__(self, objectives, None, decisions)\",\n \"def newAnalyzer():\\n analyzer = {'accidents': None,\\n 'dateIndex': None\\n }\\n\\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='BST',\\n comparefunction=compareDates)\\n return analyzer\",\n \"def __init__(self):\\n self.name = \\\"Kursawe\\\"\\n objectives = [o_ku_1, o_ku_2]\\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\\n Model.__init__(self, objectives, None, decisions)\",\n \"def analyze_run():\\n file_datas_dict = load_datas(Args.data_files)\\n plotables_dict = dict()\\n for file_name, datas in file_datas_dict.viewitems():\\n analized_datas = analyze_datas(datas,Args.analysis_attributes)\\n plotables = ana_results_to_plotables(\\n analized_datas,\\n Args.analysis_attributes\\n )\\n if Args.dm_file_out:\\n analysis_save_dm(\\n analized_datas,\\n plotables,\\n Args.analysis_attributes,\\n Args.dm_file_out\\n )\\n if Args.mat_file_out:\\n analysis_save(\\n plotables,\\n Args.analysis_attributes,\\n Args.mat_file_out\\n )\\n if Args.verbose:\\n plotables_dict[file_name] = plotables\\n if Args.verbose:\\n ana_plot_figures(plotables_dict,Args.analysis_attributes)\",\n \"def __init__(self):\\n\\n #: Dict[str, Any]: Experiment metadata\\n self.__experiment_metadata = None\\n\\n #: List[CurveData]: Processed experiment data set.\\n self.__processed_data_set = list()\\n\\n #: Backend: backend object used for experimentation\\n self.__backend = None\\n\\n # Add expected options to instance variable so that every method can access to.\\n for key in self._default_options().__dict__:\\n setattr(self, f\\\"__{key}\\\", None)\\n\\n # Add fixed parameters to instance variable so that every method can access to.\\n for key in self.__fixed_parameters__:\\n setattr(self, f\\\"__{key}\\\", None)\",\n \"def __init__(self):\\n self.dataset_path = input('Enter the path to the root directory of your dataset:\\\\n')\\n self.classes = [c.lower() for c in os.listdir(self.dataset_path)]\\n self.year = str(datetime.datetime.now().year)\\n self.kit_path = input(\\\"Enter the path ot your VOCdevkit directory:\\\\n\\\")\\n self.annotation_path = self.kit_path + '/VOC' + self.year + '/Annotations'\\n self.renamer = data_renamer.DataRenamer(self.dataset_path, self.year)\\n self.data_splitter = data_splitter.DataSplitter(self.dataset_path, self.classes, self.year, self.kit_path)\\n self.annotation_maker = annotation_maker.AnnotationMaker(self.dataset_path, self.kit_path, self.year,\\n self.annotation_path)\",\n \"def get_analysis(analysis_info):\\n return _get_analysis(_LOCAL_API_ENDPOINT,\\n analysis_info[\\\"ecosystem\\\"],\\n analysis_info[\\\"package\\\"],\\n analysis_info[\\\"version\\\"])\",\n \"def __init__(self):\\n\\n self.current_path = os.getcwd()\\n self.data_path = self.current_path + \\\"/data\\\"\\n\\n self.original_files = {}\\n self.imitation_files = {}\\n self.original_test_files = {}\\n self.imitation_test_files = {}\\n\\n self.training_set = None\\n self.original_test_set = None\\n self.imitation_test_set = None\\n\\n self.accuracy = 0.\\n self.threshold = 0.\\n\\n self.get_files()\",\n \"def __init__(self):\\n\\t\\tself.parsed = False\\n\\t\\tdir_path = os.path.dirname(os.path.realpath(__file__))\\n\\t\\tself.xsdfilename = os.path.join(dir_path, 'xml', 'schema.xsd')\\n\\t\\tself.schema = 'schema.xsd'\\n\\t\\tself.predictors = []\\n\\t\\tself.predictors_types = []\\n\\t\\tself.preprocessing_methods = []\",\n \"def newAnalyzer():\\n analyzer = {'events': None,\\n \\\"musical_genre\\\":None,\\n 'artist_ID': None,\\n \\\"track_ID\\\": None,\\n \\\"instrumentalness\\\": None,\\n \\\"acousticness\\\": None,\\n \\\"liveness\\\": None,\\n \\\"speechiness\\\": None,\\n \\\"energy\\\":None,\\n \\\"danceability\\\": None,\\n \\\"valence\\\": None\\n }\\n # Listas\\n analyzer['events'] = lt.newList('ARRAY_LIST', compareIds)\\n\\n # RBT \\n analyzer[\\\"instrumentalness\\\"] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['acousticness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['liveness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['speechiness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['energy'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['danceability'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['valence'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['tempo'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['created_at'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['dates_u'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n \\n\\n # Tablas de Hash \\n analyzer['artist_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer['track_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer['ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer[\\\"musical_genre\\\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer[\\\"sen\\\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer['track_ID_S'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n\\n return analyzer\",\n \"def single_analysis(config, name):\\n # graphviz = GephiOutput()\\n graphviz = GraphvizOutput()\\n graphviz.output_file = name\\n\\n print \\\"Preparing test case...\\\"\\n radio, lines = _prepare_test_case()\\n\\n print \\\"Running test case...\\\"\\n with PyCallGraph(output=graphviz, config=config):\\n _run_test_case(radio, lines)\",\n \"def __init__(self):\\n self.site = pywikibot.Site(u'commons', u'commons')\\n self.generator = self.getGenerator()\",\n \"def _generate_trading_instances(self):\\n print(\\n \\\"Initizalization...\\\"\\n )\\n\\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\\n self.end_date)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\\n self.strategy_id, self.spread, self.commission,self.csv_dir)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission)\\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)\",\n \"def __init__(self):\\n self._results = {}\\n self._logs = {}\",\n \"def get_sentiment_analysis(sender, instance, **kwargs):\\n text_analysis = TextAnalysis(instance.text)\\n\\n # Prevent sentiment_analysis API call every time the document is saved\\n if instance.sentiment_analysis is None:\\n instance.get_sentiment_analysis()\",\n \"async def _analyze(self) -> Report:\\n forecasts = []\\n daily = []\\n hourly = []\\n for data in self._series:\\n model = await self._build_model(data=data)\\n future = await self._forecast_single(model=model)\\n h, d = await self._process_trends_single(future=future)\\n forecasts.append(Timeseries.from_df(\\n name=data.get_name() + '_forecast',\\n df=future,\\n time_col='ds',\\n val_col='yhat',\\n ))\\n daily.append(d)\\n hourly.append(h)\\n daily_agg = pd.concat(daily).groupby(level=0).mean()\\n hourly_agg = pd.concat(hourly).groupby(level=0).mean()\\n return Report(\\n forecasts=forecasts,\\n daily_trend=Trend(trend_vals=daily_agg.to_dict()),\\n hourly_trend=Trend(trend_vals=hourly_agg.to_dict()),\\n )\",\n \"def __init__(self):\\n self.machines = {}\\n self.configs = {}\\n self.systems = {}\\n self.jobs = {}\\n self.benchmarks = {}\\n self.projects = {}\",\n \"def __init__(self, objectives, constraints, decisions):\\n self.objectives = objectives\\n self.constraints = constraints\\n self.decisions = decisions\",\n \"def __init__(self, **kwargs):\\n super(BaseAG, self).__init__()\\n self.domains = None\\n self._info = {}\\n\\n mutation_name = kwargs.get(\\\"mutation\\\", self._args.mutation)\\n representation_name = kwargs.get(\\\"representation\\\",\\n self._args.representation)\\n crossover_name = kwargs.get(\\\"crossover\\\", self._args.crossover)\\n selection_name = kwargs.get(\\\"selection\\\", self._args.selection)\\n\\n self._population = kwargs.get(\\\"population\\\", self._args.population)\\n self._selection_crossover = kwargs.get(\\\"selection_crossover\\\",\\n self._args.selection_crossover)\\n self._selection_mutation = kwargs.get(\\\"selection_mutation\\\",\\n self._args.selection_mutation)\\n self._generations = kwargs.get(\\\"generations\\\",\\n self._args.generations)\\n dimension = kwargs.get(\\\"dimension\\\", self._args.dimensions)\\n precision = kwargs.get(\\\"precision\\\", self._args.precision)\\n\\n crossovers = cros_factory.crossover_factory()\\n mutations = mut_factory.mutation_factory()\\n representations = repr_factory.representations_factory()\\n selections = selection_factory.selection_factory()\\n\\n for item in crossovers:\\n if crossover_name == item.name():\\n self._crossover = item()\\n for item in representations:\\n if representation_name == item.name():\\n # NOTE(mmicu): the dimension is know when we get the function\\n # eliminate this requirement\\n self._representation = item(dimension,\\n precision)\\n\\n for item in mutations:\\n if mutation_name == item.name():\\n self._mutation = item()\\n\\n for item in selections:\\n if selection_name == item.name():\\n self._selection = item(self._representation)\",\n \"def my_main() -> None: # pragma: no cover\\n universe = AutomataUniverse(SQUARE_GRID_NEIGHBORS, [2,3], [3])\\n instance = AutomataTransforms(universe)\\n assert isinstance(instance, AutomataTransforms)\\n # # _is_rot_mat_test(instance)\\n # # _rotations_check(instance)\\n # # _prime_cells_check(instance)\\n # _check_transform_test(instance)\\n # # _hashable_transform_test(instance)\\n # _duplicate_test(instance)\\n # _collision_test(instance)\\n # _end_cycle_test(instance)\\n # _add_transform_test(instance)\\n # instance.generate_combination_transforms()\\n\\n # # _matrix_rotate_test(instance)\\n # # _duplicate_test(instance) # test again after transform(s) added\\n # # _collision_test(instance) # test again after transform(s) added «also refactoring»\\n # instance.dbg_report_instance() # DEBUG\",\n \"def async_analysis(kwargs):\\n # we can't pickle our objects for remote works so we pickle the raw request\\n # and then load it here.\\n data = analysis_input_schema.load(kwargs).data\\n return analysis(**data)\",\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 setup_class(self):\\n self.iqcalc = iqcalc_astropy.IQCalc(logger=self.logger)\\n self.fwhm_funcs = (self.iqcalc.calc_fwhm_gaussian,\\n self.iqcalc.calc_fwhm_moffat,\\n self.iqcalc.calc_fwhm_lorentz)\\n self.answers = ((2.8551, 2.7732), # Gaussian\\n (2.77949, 2.6735), # Moffat\\n (1.9570, 1.8113) # Lorentz\\n )\",\n \"def setUp(self):\\n self.m = m = random.randint(1, 100)\\n self.n = n = random.randint(1, 100)\\n self.sig = sig = Signature(\\\"name\\\", Dim(\\\"m\\\"), Dim(\\\"n\\\"),\\n sData(\\\"A\\\", \\\"ldA * n\\\"), Ld(\\\"ldA\\\", \\\"m\\\"),\\n dData(\\\"B\\\", \\\"ldB * m\\\"), Ld(\\\"ldB\\\", \\\"m\\\"),\\n cData(\\\"C\\\", \\\"ldC * n\\\"), Ld(\\\"ldC\\\", \\\"n\\\"))\\n self.ex = ex = Experiment()\\n ex.calls = [sig(m, n, \\\"X\\\", None, \\\"Y\\\", None, \\\"Z\\\", None)]\\n ex.infer_lds()\\n self.i = Symbol(\\\"i\\\")\\n self.j = Symbol(\\\"j\\\")\",\n \"def create_analysis():\\n \\n date_now = datetime.now()\\n for analysis in Analysis.objects.filter(activated=True):\\n\\t\\n\\tif analysis.last_report == None or analysis.last_report <= date_now - timedelta( seconds=PERIOD_CHOICES[analysis.interval]):\\n\\t \\n\\t if analysis.last_report != None and analysis.interval == 'n':\\n\\t\\tcontinue\\n\\t \\n\\t results = []\\n\\t for report in analysis.queries.filter(activated=True):\\n\\t\\t\\n\\t\\tif analysis.date_from != None and analysis.date_to != None:\\n\\t\\t report_results = ReportResult.objects.filter(report=report, run_date__lte=analysis.date_to, run_date__gte=analyses.date_from).order_by('run_date') \\n\\t\\telif analysis.date_from == None and analysis.date_to != None:\\n\\t\\t report_results = ReportResult.objects.filter(report=report, run_date__lte=analysis.date_to).order_by('run_date')\\n\\t\\telif analysis.date_from != None and analysis.date_to == None:\\n\\t\\t report_results = ReportResult.objects.filter(report=report, run_date__gte=analyses.date_from).order_by('run_date')\\n\\t\\telse:\\n\\t\\t report_results = ReportResult.objects.filter(report=report).order_by('run_date')\\n\\t\\t\\n\\t\\t# create output from mongo output\\n\\t\\toutput_result = OutputResult(report=report.title)\\n\\t\\toutput_result.date_array = []\\n\\t\\toutput_result.output_array = []\\n\\t\\tprint \\\"\\\\n KOLIK: \\\"+ str(output_result.output_array)\\n\\t\\tfor result in report_results:\\n\\t\\t output_result.date_array.append(result.run_date)\\n\\t\\t #print result.output\\n\\t\\t #print \\\"\\\\nouttest: \\\"+str(output_result.output_array)\\n\\t\\t mongo_output = OutputMongo(result.output)\\n\\t\\t output_result.output_array.append(mongo_output.getoutput())\\n\\n\\t\\tprint \\\"out: \\\",output_result.output_array\\n\\t\\tresults.append(output_result) \\n\\n\\n\\t #print results[0].output_array\\n\\t #print \\\"\\\\n\\\\n\\\"\\n\\t #print results[1].output_array\\n\\t # process outputs\\n\\t if not process_output_reports(results, analysis, date_now):\\n\\t\\tprint \\\"Error in execute analysis: %s\\\" % (analysis.title)\\n\\t\\tcontinue\\n\\t \\n\\t if analysis.interval != 'n':\\n\\t\\tif analysis.date_to != None:\\n\\t\\t analysis.date_to = analysis.date_to + timedelta( seconds=PERIOD_CHOICES[analysis.interval])\\n\\t\\tif analysis.date_from != None:\\n\\t\\t analysis.date_from = analysis.date_from + timedelta( seconds=PERIOD_CHOICES[analysis.interval])\\n\\t\\t \\n return True\",\n \"def __init__(self):\\n self.RRTFamilySolver = RRTFamilyPathPlanner()\\n self.PRMSolver = PRMPathPlanner()\",\n \"def __init__(self) -> None:\\n self.metrics = {}\\n self.current = None\\n self.run = None\",\n \"def __init__(self):\\r\\n\\t\\twith open(\\\"eqs.json\\\") as qData:\\r\\n\\t\\t\\tself.questions = json.load(qData)\\r\\n\\t\\twith open(\\\"eqsave.json\\\") as uData:\\r\\n\\t\\t\\tself.records = json.load(uData)\\r\\n\\t\\tself.types = {\\\"1\\\": \\\"Reformer\\\", \\\"2\\\": \\\"Helper\\\", \\\"3\\\": \\\"Achiever\\\", \\\"4\\\": \\\"Individualist\\\", \\\"5\\\": \\\"Investigator\\\", \\\"6\\\": \\\"Loyalist\\\", \\\"7\\\": \\\"Enthusiast\\\", \\\"8\\\": \\\"Challenger\\\", \\\"9\\\": \\\"Peacemaker\\\"}\",\n \"def __init__(self, num_detectors):\\n self.dataset = []\\n self.num_detectors = num_detectors\\n self.add_detectors()\",\n \"def __init__(self):\\n\\n self.result = None # To store the result\\n self.predictor = None # To store the fit predictor\",\n \"def init_analysis(session, args):\\r\\n path = os.path.join(session.abs_path,'{}_{}_{}_{}'.format(args[12],args[13],args[14],args[15]))\\r\\n session.case = Case(path, session.method)\\r\\n case = session.case\\r\\n if args[0].split('#')[0]=='R':\\r\\n args[7]= -args[7]\\r\\n\\r\\n case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[6], args[7]))\\r\\n\\r\\n case.file_U.set_field('boundaryField',\\r\\n {'inlet': {'type': 'freestream',\\r\\n 'freestreamValue': 'uniform ({} {} 0)'.format(args[6], args[7])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'fixedValue', 'value':'uniform (0 0 0)'},\\r\\n 'extrados': {'type': 'fixedValue', 'value':'uniform (0 0 0)'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n case.file_p.set_field('internalField', 'uniform {}'.format(args[10]))\\r\\n case.file_p.set_field('boundaryField',\\r\\n {'inlet': {'type': 'freestreamPressure'},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_T.set_field('internalField', 'uniform {}'.format(args[11]))\\r\\n session.case.file_T.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[11])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'slip'},\\r\\n 'extrados': {'type': 'slip'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n session.case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[6], args[7]))\\r\\n session.case.file_U.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue',\\r\\n 'value': 'uniform ({} {} 0)'.format(args[6], args[7])},\\r\\n 'outlet': {'type': 'inletOutlet','inletValue':'uniform ({} {} 0)'.format(args[6], args[7]),\\r\\n 'value':'uniform ({} {} 0)'.format(args[6], args[7])},\\r\\n 'intrados': {'type': 'noSlip'},\\r\\n 'extrados': {'type': 'noSlip'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_p.set_field('internalField', 'uniform {}'.format(args[10]))\\r\\n session.case.file_p.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[10])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_T.set_field('internalField', 'uniform {}'.format(args[11]))\\r\\n session.case.file_T.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue','value':'uniform {}'.format(args[11])},\\r\\n 'outlet': {'type': 'inletOutlet','inletValue':'uniform {}'.format(args[11]),'value':'uniform {}'.format(args[11])},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n \\\"\\\"\\\"\\r\\n \\\"\\\"\\\"\\r\\n session.case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[7], args[6]))\\r\\n session.case.file_U.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue',\\r\\n 'value': 'uniform ({} {} 0)'.format(args[7], args[6])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'noSlip'},\\r\\n 'extrados': {'type': 'noSlip'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_p.set_field('internalField', 'uniform {}'.format(args[10]))\\r\\n session.case.file_p.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[10])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_T.set_field('internalField', 'uniform {}'.format(args[11]))\\r\\n session.case.file_T.set_field('boundaryField',\\r\\n {'inlet': {'type': 'zeroGradient'},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n \\\"\\\"\\\"\\r\\n \\\"\\\"\\\"\\r\\n session.case.file_U.set_field('internalField', 'uniform ({} {} 0)'.format(args[5], args[6]))\\r\\n session.case.file_U.set_field('boundaryField',\\r\\n {'inlet': {'type': 'fixedValue', 'value': 'uniform ({} {} 0)'.format(args[5], args[6])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'noSlip'},\\r\\n 'extrados': {'type': 'noSlip'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_p.set_field('boundaryField', {'inlet': {'type': 'fixedValue','value':'uniform {}'.format(args[9])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n session.case.file_p.set_field('internalField', 'uniform {}'.format(args[9]))\\r\\n\\r\\n session.case.file_T.set_field('boundaryField', {'inlet': {'type': 'fixedValue', 'value': 'uniform {}'.format(args[10])},\\r\\n 'outlet': {'type': 'zeroGradient'},\\r\\n 'intrados': {'type': 'zeroGradient'},\\r\\n 'extrados': {'type': 'zeroGradient'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n session.case.file_T.set_field('internalField','uniform 300')\\r\\n\\r\\n session.case.file_nut.set_field('boundaryField', {'inlet':{'type':'calculated', 'value':'uniform 0'},\\r\\n 'outlet':{'type':'calculated', 'value':'uniform 0'},\\r\\n 'intrados': {'type': 'nutkWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 0'},\\r\\n 'extrados': {'type': 'nutkWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 0'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_k.set_field('internalField', 'uniform 1')\\r\\n session.case.file_k.set_field('boundaryField', {\\r\\n 'inlet': {'type': 'turbulentIntensityKineticEnergyInlet', 'intensity': '0.05', 'value': 'uniform 1'},\\r\\n 'outlet': {'type': 'inletOutlet', 'inletValue': 'uniform 1', 'value': 'uniform 1'},\\r\\n 'intrados': {'type': 'kqRWallFunction','value':'uniform 1'},\\r\\n 'extrados': {'type': 'kqRWallFunction','value':'uniform 1'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n\\r\\n session.case.file_epsilon.set_field('boundaryField', {'inlet': {'type': 'turbulentMixingLengthDissipationRateInlet', 'mixingLength': '0.005', 'value': 'uniform 200'},\\r\\n 'outlet': {'type': 'inletOutlet', 'inletValue': 'uniform 200', 'value': 'uniform 200'},\\r\\n 'intrados': {'type': 'epsilonWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 200'},\\r\\n 'extrados': {'type': 'epsilonWallFunction', 'Cmu':'0.09', 'kappa':'0.41', 'E':'9.8', 'value':'uniform 200'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n session.case.file_epsilon.set_field('internalField', 'uniform 200')\\r\\n\\r\\n session.case.file_alphat.set_field('boundaryField', {'inlet':{'type':'calculated', 'value':'uniform 0'},\\r\\n 'outlet':{'type':'calculated', 'value':'uniform 0'},\\r\\n 'intrados': {'type': 'compressible::alphatWallFunction', 'Prt':'0.85', 'value':'uniform 0'},\\r\\n 'extrados': {'type': 'compressible::alphatWallFunction', 'Prt':'0.85', 'value':'uniform 0'},\\r\\n 'top_down': {'type': 'empty'},\\r\\n 'cyclic_in_1': {'type': 'cyclic'},\\r\\n 'cyclic_in_2': {'type': 'cyclic'},\\r\\n 'cyclic_out_1': {'type': 'cyclic'},\\r\\n 'cyclic_out_2': {'type': 'cyclic'}})\\r\\n \\\"\\\"\\\"\\r\\n session.case.file_controlDict.set_field('endTime', '10000')\\r\\n session.case.file_controlDict.set_field('startFrom', 'latestTime')\\r\\n session.case.file_controlDict.set_field('functions', {\\\"#includeFunc\\\":\\\"MachNo\\\"})\\r\\n session.case.file_turbulenceProperties.set_field('simulationType', 'laminar')\\r\\n session.case.interacting(100)\\r\\n sim = session.case.simulation(\\\"open40\\\") # Build files\\r\\n sim.limit_write = 50\\r\\n sim.block_mesh(string=write_block_mesh(args[1], args[2], args[3], args[4], args[5], session.mesh))\\r\\n sim.check_mesh()\\r\\n\\r\\n result_dict={\\\"T\\\": 0, \\\"p\\\":0, \\\"Theta\\\":0, \\\"z\\\":0, \\\"profile\\\":args[14]}\\r\\n\\r\\n def _function(container, args):\\r\\n current_time = container['current_time']\\r\\n if float(current_time)>=0.000015:\\r\\n print('Parsing results')\\r\\n sim.foamToVTK()\\r\\n results = sim.get_last_results('outlet')\\r\\n result_U = results.GetCellData('U')\\r\\n result_p = results.GetCellData('p')\\r\\n result_T = results.GetCellData('T')\\r\\n theta = 0.0\\r\\n z = 0.0\\r\\n p=0.0\\r\\n t=0.0\\r\\n U_length = len(result_U)\\r\\n p_length = len(result_p)\\r\\n t_length = len(result_T)\\r\\n for i,j,k in zip(result_p, result_T, result_U):\\r\\n p+= float(i[0])/p_length\\r\\n t+= float(j[0])/t_length\\r\\n theta += float(k[1])/U_length\\r\\n z += float(k[0])/U_length\\r\\n\\r\\n args[\\\"T\\\"] = t\\r\\n args[\\\"p\\\"] = p\\r\\n args[\\\"Theta\\\"] = theta\\r\\n args[\\\"z\\\"] = z\\r\\n return True\\r\\n return False\\r\\n \\r\\n #sim.run(_function, result_dict)\\r\\n #result_dict = {'T': 195.38959999999997, 'z': 429.3120571428572, 'p': 74001.90285714286, 'Theta': -207.19442857142855, 'profile': 0}\\r\\n\\r\\n print('Sending results')\\r\\n if args[0].split('#')[0]=='R':\\r\\n result_dict['Theta']= -result_dict['Theta']\\r\\n\\r\\n session.socket_design.send({'new_data':result_dict})\",\n \"def __init__(self, logging=True):\\n self.matrix_creator = MatrixCreator()\\n self.matrix_computer = MatrixComputer()\\n self.equation_parser = EquationParser()\\n self.balancing_validator = BalancingValidator(logging=logging)\\n self.logger = Logger(active=logging)\",\n \"def __init__(self):\\n self._names = []\\n self._forwardFactories = []\\n self._inputs = []\\n self._inputFilters = {}\\n self._outputFilters = []\\n self._inputCheckers = []\\n pass\",\n \"def __init__(self, model = None, cso = None, fast_classification = True, paper = None):\\n self.cso = cso #Stores the CSO Ontology\\n self.paper = paper #Paper to analyse\\n self.model = model #contains the cached model\\n self.min_similarity = 0.90 #Initialises the min_similarity\\n self.fast_classification = fast_classification # if will use the full model or not\\n self.explanation = dict()\",\n \"def __init__(self, language, dataset_name):\\n self._language = language\\n self._dataset_name = dataset_name\\n\\n # TODO: Maybe the paths should be passed as parameters or read from a configuration file.\\n self._trainset_path = \\\"data/raw/{}/{}_Train.tsv\\\".format(language.lower(), dataset_name)\\n self._devset_path = \\\"data/raw/{}/{}_Dev.tsv\\\".format(language.lower(), dataset_name)\\n self._testset_path = \\\"data/raw/{}/{}_Test.tsv\\\".format(language.lower(), dataset_name)\\n\\n self._trainset = None\\n self._devset = None\\n self._testset = None\\n\\n \\\"\\\"\\\"spaCy object handling\\\"\\\"\\\"\\n if self._language == \\\"english\\\":\\n self.nlp = spacy.load('en_core_web_lg')\\n elif self._language == \\\"spanish\\\":\\n self.nlp = spacy.load(\\\"es_core_news_md\\\")\\n elif self._language == \\\"german\\\":\\n self.nlp = spacy.load('de_core_news_sm')\\n elif self._language == \\\"french\\\":\\n self.nlp = spacy.load('fr_core_news_md')\\n\\n self._trainset_spacy_path = \\\"data/interim/{}/{}_Train-spacy-objs.pkl\\\".format(\\n language.lower(), dataset_name)\\n self._devset_spacy_path = \\\"data/interim/{}/{}_Dev-spacy-objs.pkl\\\".format(\\n language.lower(), dataset_name)\\n self._testset_spacy_path = \\\"data/interim/{}/{}_Test-spacy-objs.pkl\\\".format(\\n language.lower(), dataset_name)\",\n \"def __init__(self):\\n super(Modules, self).__init__()\\n \\n global superclasses\\n superclasses['universe'] = []\\n superclasses['actions'] = ['universe']\\n superclasses['booleans'] = ['universe']\\n\\n global instances\\n instances['universe'] = set()\\n instances['actions'] = set()\\n instances['booleans'] = set()\",\n \"def __init__(self):\\n \\n #\\n # imports\\n #\\n import time\\n from socket import gethostname\\n import commands\\n \\n #\\n # Declare variables and set standard default values\\n #\\n self.database = None\\n self.analysisPath = None\\n self.command = None\\n self.commandLine = None\\n self.commandLineList = None\\n SEAseqPipeLine.settings = Settings()\\n SEAseqPipeLine.logfile = None\\n SEAseqPipeLine.startTime = time.time()\\n SEAseqPipeLine.startTimeStr = time.strftime(\\\"%A, %d %b %Y %H:%M:%S\\\",time.localtime())\\n self.availableCommands = {\\n 'initiateAnalysis':self.initiateAnalysis,\\n 'addData':self.addData,\\n 'changeSettings':self.changeSettings,\\n 'startAnalysis':self.startAnalysis,\\n 'commandLog':self.commandLog,\\n 'help':self.printHelp,\\n }\\n if gethostname().split('.')[1] == 'uppmax': self.onUppmax = True\\n else: self.onUppmax = False\\n tempFolderName = 'SEAseq2temporaryFiles'\\n if self.onUppmax: self.tempFileFolder = os.path.abspath(commands.getoutput('echo $SNIC_TMP'))+'/'+tempFolderName\\n else: self.tempFileFolder = self.analysisPath+'/'+tempFolderName\\n if not os.path.isdir(self.tempFileFolder): os.makedirs(self.tempFileFolder)\\n \\n #\\n # Get information from commandline\\n #\\n self.getComandAndPath()\\n self.doCurrentTask()\",\n \"def analyses(self, analysis_id=None, count=None, offset=None, format_group_name=None):\\n if analysis_id:\\n logger.debug(\\\"Get analysis\\\")\\n return self._raw_api.analyses.get(analysis_id)\\n\\n logger.debug(\\\"Get analysis list\\\")\\n data = filter_data(\\n count=count,\\n offset=offset,\\n format_group_name=format_group_name\\n )\\n return self._raw_api.analyses.get(json=data)\",\n \"def __init__(self):\\n this = _libsbml.new_RDFAnnotationParser()\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def test_creation(self):\\n config = config_factory()\\n db = config.arango_db\\n\\n # Create analyzer\\n analyzer = ArangoAnalyzer(\\\"analyzer_sample\\\")\\n analyzer.set_stopwords(\\n language=\\\"english\\\",\\n custom_stopwords=[\\\"stop\\\", \\\"word\\\"],\\n include_default=False,\\n )\\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\\n\\n analyzer.create(db)\\n assert analyzer.__dict__ == {\\n \\\"name\\\": \\\"analyzer_sample\\\",\\n \\\"type\\\": \\\"text\\\",\\n \\\"features\\\": [\\\"frequency\\\", \\\"norm\\\", \\\"position\\\"],\\n \\\"locale\\\": \\\"en\\\",\\n \\\"case\\\": \\\"lower\\\",\\n \\\"stopwords\\\": [\\\"stop\\\", \\\"word\\\"],\\n \\\"accent\\\": False,\\n \\\"stemming\\\": True,\\n \\\"edge_ngram\\\": None,\\n \\\"delimiter\\\": \\\",\\\",\\n \\\"min\\\": 2,\\n \\\"max\\\": 5,\\n \\\"preserve_original\\\": False,\\n \\\"start_marker\\\": \\\"\\\",\\n \\\"end_marker\\\": \\\"\\\",\\n \\\"stem_type\\\": \\\"binary\\\",\\n }\\n assert isinstance(db.analyzer(\\\"analyzer_sample\\\"), dict)\",\n \"def __init__(self, datapath, fileType='TargetLynx', sop='Generic', **kwargs):\\n\\n super().__init__(sop=sop, **kwargs)\\n self.filePath, fileName = os.path.split(datapath)\\n self.fileName, fileExtension = os.path.splitext(fileName)\\n\\n self.name = self.fileName\\n\\n # Load files and match data, calibration report and SOP, then Apply the limits of quantification\\n if fileType == 'TargetLynx':\\n # Read files, filter calibration samples, filter IS, applyLLOQ, clean object\\n self._loadTargetLynxDataset(datapath, **kwargs)\\n # Finalise object\\n self.VariableType = VariableType.Discrete\\n self.AnalyticalPlatform = AnalyticalPlatform.MS\\n self.initialiseMasks()\\n elif fileType == 'Bruker Quantification':\\n # Read files, clean object\\n self._loadBrukerXMLDataset(datapath, **kwargs)\\n # Finalise object\\n self.VariableType = VariableType.Discrete\\n self.AnalyticalPlatform = AnalyticalPlatform.NMR\\n self.initialiseMasks()\\n elif fileType == 'empty':\\n # Build empty object for testing\\n pass\\n else:\\n raise NotImplementedError\\n\\n # Check the final object is valid and log\\n if fileType != 'empty':\\n validDataset = self.validateObject(verbose=False, raiseError=False, raiseWarning=False)\\n if not validDataset['BasicTargetedDataset']:\\n raise ValueError('Import Error: The imported dataset does not satisfy to the Basic TargetedDataset definition')\\n self.Attributes['Log'].append([datetime.now(),\\n '%s instance initiated, with %d samples, %d features, from %s'\\n % (self.__class__.__name__, self.noSamples, self.noFeatures, datapath)])\\n # Check later\\n if 'Metadata Available' not in self.sampleMetadata:\\n self.sampleMetadata['Metadata Available'] = False\",\n \"def __init__(self):\\n self.parser_model_dir = None\\n self.parser_options = {}\\n self.reranker_model = None\\n self.unified_model_dir = None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6476997","0.64702034","0.63428855","0.62444305","0.623065","0.5992507","0.59902406","0.59565115","0.5947113","0.5947113","0.59458697","0.5939853","0.59148765","0.58706826","0.5858152","0.58355874","0.58180684","0.5758296","0.5750707","0.57501674","0.57413834","0.5738538","0.5715878","0.56486434","0.5614197","0.5611831","0.5589196","0.55858403","0.55789053","0.5572369","0.55642426","0.5562384","0.5529692","0.55241185","0.5520147","0.5517567","0.55152875","0.5515282","0.5508454","0.5488664","0.5477658","0.5477041","0.5473829","0.5470642","0.5453264","0.5451085","0.5435981","0.5417105","0.54141426","0.5412175","0.54075485","0.5393213","0.53888947","0.53853875","0.5383172","0.53604674","0.53519857","0.5351758","0.5338383","0.5327076","0.5317966","0.5309994","0.53022146","0.52984214","0.5295567","0.52852947","0.52825624","0.5281911","0.5277759","0.5266383","0.5265841","0.52640754","0.5257535","0.5256249","0.52534366","0.5253423","0.52512443","0.5248757","0.5248619","0.5241286","0.52372897","0.52367264","0.5232069","0.5229489","0.52280605","0.52177083","0.5217616","0.52146685","0.5211114","0.52062017","0.5203665","0.5203595","0.51946443","0.51946187","0.5191996","0.5185701","0.51832896","0.51790726","0.51783323","0.51773155"],"string":"[\n \"0.6476997\",\n \"0.64702034\",\n \"0.63428855\",\n \"0.62444305\",\n \"0.623065\",\n \"0.5992507\",\n \"0.59902406\",\n \"0.59565115\",\n \"0.5947113\",\n \"0.5947113\",\n \"0.59458697\",\n \"0.5939853\",\n \"0.59148765\",\n \"0.58706826\",\n \"0.5858152\",\n \"0.58355874\",\n \"0.58180684\",\n \"0.5758296\",\n \"0.5750707\",\n \"0.57501674\",\n \"0.57413834\",\n \"0.5738538\",\n \"0.5715878\",\n \"0.56486434\",\n \"0.5614197\",\n \"0.5611831\",\n \"0.5589196\",\n \"0.55858403\",\n \"0.55789053\",\n \"0.5572369\",\n \"0.55642426\",\n \"0.5562384\",\n \"0.5529692\",\n \"0.55241185\",\n \"0.5520147\",\n \"0.5517567\",\n \"0.55152875\",\n \"0.5515282\",\n \"0.5508454\",\n \"0.5488664\",\n \"0.5477658\",\n \"0.5477041\",\n \"0.5473829\",\n \"0.5470642\",\n \"0.5453264\",\n \"0.5451085\",\n \"0.5435981\",\n \"0.5417105\",\n \"0.54141426\",\n \"0.5412175\",\n \"0.54075485\",\n \"0.5393213\",\n \"0.53888947\",\n \"0.53853875\",\n \"0.5383172\",\n \"0.53604674\",\n \"0.53519857\",\n \"0.5351758\",\n \"0.5338383\",\n \"0.5327076\",\n \"0.5317966\",\n \"0.5309994\",\n \"0.53022146\",\n \"0.52984214\",\n \"0.5295567\",\n \"0.52852947\",\n \"0.52825624\",\n \"0.5281911\",\n \"0.5277759\",\n \"0.5266383\",\n \"0.5265841\",\n \"0.52640754\",\n \"0.5257535\",\n \"0.5256249\",\n \"0.52534366\",\n \"0.5253423\",\n \"0.52512443\",\n \"0.5248757\",\n \"0.5248619\",\n \"0.5241286\",\n \"0.52372897\",\n \"0.52367264\",\n \"0.5232069\",\n \"0.5229489\",\n \"0.52280605\",\n \"0.52177083\",\n \"0.5217616\",\n \"0.52146685\",\n \"0.5211114\",\n \"0.52062017\",\n \"0.5203665\",\n \"0.5203595\",\n \"0.51946443\",\n \"0.51946187\",\n \"0.5191996\",\n \"0.5185701\",\n \"0.51832896\",\n \"0.51790726\",\n \"0.51783323\",\n \"0.51773155\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60086685"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":678,"cells":{"query":{"kind":"string","value":"Generates the list of required analyzers."},"document":{"kind":"string","value":"def get_analysis_list(self):\n analysys_list = []\n\n analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)\n\n for analysis_type in analysis_types:\n if analysis_type in self.all_possible_analysis:\n analysys_list.append(self.all_possible_analysis[analysis_type])\n else:\n print \"[WARNING]\", analysis_type, \"is not an allowed analysis type\"\n\n return analysys_list"},"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 list_analyzers(args: argparse.Namespace):\n first = True\n queue = [tuple(c) + (\"lookout.\",) for c in pkgutil.iter_modules(lookout.__path__)]\n while queue:\n importer, name, ispkg, prefix = queue.pop(0)\n\n if not ispkg or name == \"core\":\n continue\n\n m = importer.find_module(name).load_module(name)\n if getattr(m, \"__meta__\", False):\n queue.extend(tuple(c) + (prefix + name + \".\",)\n for c in pkgutil.iter_modules(m.__path__))\n continue\n\n try:\n cls = m.analyzer_class\n except AttributeError:\n continue\n if first:\n first = False\n else:\n print()\n print(prefix + name)\n print(\"\\t%s\" % cls.version)\n print(\"\\t\" + cls.description)","def get_analyzers(cls):\n for analyzer_name, analyzer_class in iter(cls._class_registry.items()):\n yield analyzer_name, analyzer_class","def main(args):\n\n # If the given output format is not 'table', redirect logger's output to\n # the stderr.\n logger.setup_logger(args.verbose if 'verbose' in args else None,\n None if args.output_format == 'table' else 'stderr')\n\n context = analyzer_context.get_context()\n working_analyzers, errored = analyzer_types.check_supported_analyzers(\n args.analyzers,\n context)\n analyzer_types.check_available_analyzers(working_analyzers, errored)\n\n analyzer_environment = env.extend(context.path_env_extra,\n context.ld_lib_path_extra)\n\n analyzer_config_map = analyzer_types.build_config_handlers(\n args, context, working_analyzers)\n\n def uglify(text):\n \"\"\"\n csv and json format output contain this non human readable header\n string: no CamelCase and no space.\n \"\"\"\n return text.lower().replace(' ', '_')\n\n def match_guideline(checker_name, selected_guidelines):\n \"\"\"\n Returns True if checker_name gives reports related to any of the\n selected guideline rule.\n checker_name -- A full checker name.\n selected_guidelines -- A list of guideline names or guideline rule IDs.\n \"\"\"\n guideline = context.guideline_map.get(checker_name, {})\n guideline_set = set(guideline)\n for value in guideline.values():\n guideline_set |= set(value)\n\n return any(g in guideline_set for g in selected_guidelines)\n\n def format_guideline(guideline):\n \"\"\"\n Convert guideline rules to human-readable format.\n guideline -- Dictionary in the following format:\n {\"guideline_1\": [\"rule_1\", \"rule_2\"]}\n \"\"\"\n return ' '.join('Related {} rules: {}'.format(g, ', '.join(r))\n for g, r in guideline.items())\n\n # List available checker profiles.\n if 'profile' in args and args.profile == 'list':\n if 'details' in args:\n header = ['Profile name', 'Description']\n rows = context.profile_map.available_profiles().items()\n else:\n header = ['Profile name']\n rows = [(key, \"\") for key in\n context.profile_map.available_profiles()]\n\n if args.output_format in ['csv', 'json']:\n header = list(map(uglify, header))\n\n print(twodim.to_str(args.output_format, header, rows))\n return\n\n # List checker config options.\n if 'checker_config' in args:\n if 'details' in args:\n header = ['Option', 'Description']\n else:\n header = ['Option']\n\n if args.output_format in ['csv', 'json']:\n header = list(map(uglify, header))\n\n rows = []\n analyzer_failures = []\n for analyzer in working_analyzers:\n config_handler = analyzer_config_map.get(analyzer)\n analyzer_class = analyzer_types.supported_analyzers[analyzer]\n\n configs = analyzer_class.get_checker_config(config_handler,\n analyzer_environment)\n if not configs:\n analyzer_failures.append(analyzer)\n continue\n\n rows.extend((':'.join((analyzer, c[0])), c[1]) if 'details' in args\n else (':'.join((analyzer, c[0])),) for c in configs)\n\n if rows:\n print(twodim.to_str(args.output_format, header, rows))\n\n if analyzer_failures:\n LOG.error(\"Failed to get checker configuration options for '%s' \"\n \"analyzer(s)! Please try to upgrade your analyzer \"\n \"version to use this feature.\",\n ', '.join(analyzer_failures))\n sys.exit(1)\n\n return\n\n if args.guideline is not None and len(args.guideline) == 0:\n result = defaultdict(set)\n\n for _, guidelines in context.guideline_map.items():\n for guideline, rules in guidelines.items():\n result[guideline] |= set(rules)\n\n header = ['Guideline', 'Rules']\n if args.output_format in ['csv', 'json']:\n header = list(map(uglify, header))\n\n if args.output_format == 'json':\n rows = [(g, sorted(list(r))) for g, r in result.items()]\n else:\n rows = [(g, ', '.join(sorted(r))) for g, r in result.items()]\n\n if args.output_format == 'rows':\n for row in rows:\n print('Guideline: {}'.format(row[0]))\n print('Rules: {}'.format(row[1]))\n else:\n print(twodim.to_str(args.output_format, header, rows))\n return\n\n # List available checkers.\n if 'details' in args:\n header = ['Enabled', 'Name', 'Analyzer', 'Severity', 'Guideline',\n 'Description']\n else:\n header = ['Name']\n\n if args.output_format in ['csv', 'json']:\n header = list(map(uglify, header))\n\n rows = []\n for analyzer in working_analyzers:\n config_handler = analyzer_config_map.get(analyzer)\n analyzer_class = analyzer_types.supported_analyzers[analyzer]\n\n checkers = analyzer_class.get_analyzer_checkers(config_handler,\n analyzer_environment)\n\n profile_checkers = []\n if 'profile' in args:\n if args.profile not in context.profile_map.available_profiles():\n LOG.error(\"Checker profile '%s' does not exist!\",\n args.profile)\n LOG.error(\"To list available profiles, use '--profile list'.\")\n sys.exit(1)\n\n profile_checkers = [('profile:' + args.profile, True)]\n\n config_handler.initialize_checkers(context,\n checkers,\n profile_checkers)\n\n for checker_name, value in config_handler.checks().items():\n state, description = value\n\n if state != CheckerState.enabled and 'profile' in args:\n continue\n\n if state == CheckerState.enabled and 'only_disabled' in args:\n continue\n elif state != CheckerState.enabled and 'only_enabled' in args:\n continue\n\n if args.output_format == 'json':\n state = state == CheckerState.enabled\n else:\n state = '+' if state == CheckerState.enabled else '-'\n\n if args.guideline is not None:\n if not match_guideline(checker_name, args.guideline):\n continue\n\n if 'details' in args:\n severity = context.severity_map.get(checker_name)\n guideline = context.guideline_map.get(checker_name, {})\n if args.output_format != 'json':\n guideline = format_guideline(guideline)\n rows.append([state, checker_name, analyzer,\n severity, guideline, description])\n else:\n rows.append([checker_name])\n\n if 'show_warnings' in args:\n severity = context.severity_map.get('clang-diagnostic-')\n for warning in get_warnings(analyzer_environment):\n warning = 'clang-diagnostic-' + warning\n\n if args.guideline is not None:\n if not match_guideline(warning, args.guideline):\n continue\n\n guideline = context.guideline_map.get(warning, {})\n if args.output_format != 'json':\n guideline = format_guideline(guideline)\n\n if 'details' in args:\n rows.append(['', warning, '-', severity, guideline, '-'])\n else:\n rows.append([warning])\n\n if rows:\n print(twodim.to_str(args.output_format, header, rows))\n\n analyzer_types.print_unsupported_analyzers(errored)","def __init__(self, analyzers: Iterable[Type[Analyzer]], model_repository: ModelRepository,\n data_service: DataService):\n self._model_repository = model_repository\n analyzers = [(a.__name__, a) for a in analyzers]\n analyzers.sort()\n self._analyzers = [a[1] for a in analyzers]\n self._data_service = data_service","def test_get_analyzers(self):\n analyzers = manager.AnalysisManager.get_analyzers()\n analyzer_list = [x for x in analyzers]\n first_analyzer_tuple = analyzer_list[0]\n analyzer_name, analyzer_class = first_analyzer_tuple\n self.assertIsInstance(analyzer_list, list)\n self.assertIsInstance(first_analyzer_tuple, tuple)\n self.assertEqual(analyzer_class, MockAnalyzer)\n self.assertEqual(analyzer_name, 'mockanalyzer')","def initAnalyzer():\n return controller.initAnalyzer()","def check_for_analyzers(self):\n executed = self.analyzer_state.executed_analyzers()\n for analyzer in executed:\n # check for wish\n if self.analyzer_state.check_wish(analyzer, 'cancel'):\n print(\"validator: cancelled {} upon request\".format(analyzer['_id']))\n continue\n\n print(\"validating and committing {}\".format(analyzer['_id']))\n\n self.analyzer_state.transition(analyzer['_id'], 'executed', 'validating')\n\n exe_res = analyzer['execution_result']\n temporary_coll = self.cc.temporary_db[exe_res['temporary_coll']]\n\n if exe_res['timespans'] is not None and exe_res['upload_ids'] is not None:\n self.analyzer_state.transition_to_error(analyzer['_id'],\n 'internal error: either timespans or upload_ids can have a '\n 'value but not both. I cannot decide if direct or normal analyzer')\n continue\n\n if exe_res['timespans'] is None and exe_res['upload_ids'] is None:\n self.analyzer_state.transition_to_error(analyzer['_id'],\n 'internal error: it\\'s not allowed to have both timespans and upload_ids to be None. '\n 'I cannot decide if direct or normal analyzer')\n continue\n\n if exe_res['upload_ids'] is not None:\n print(\"using direct commit\")\n valid_count, errors, action_id = commit_direct(\n analyzer['_id'], analyzer['working_dir'], self._action_id_creator,\n exe_res['upload_ids'], exe_res['max_action_id'], temporary_coll,\n self.cc.observations_coll, analyzer['output_types'],\n self.cc.action_log)\n else:\n print(\"using normal commit\")\n valid_count, errors, action_id = commit_normal(\n analyzer['_id'], analyzer['working_dir'], self._action_id_creator,\n exe_res['timespans'], exe_res['max_action_id'], temporary_coll,\n self.cc.observations_coll, analyzer['output_types'],\n self.cc.action_log)\n\n if len(errors) > 0:\n print(\"analyzer {} with action id {} has at least {} valid records but {} have problems:\".format(analyzer['_id'], action_id, valid_count, len(errors)))\n for idx, error in enumerate(errors):\n print(\"{}: {}\".format(idx, error))\n\n self.analyzer_state.transition_to_error(analyzer['_id'], 'error when executing validator:\\n' + '\\n'.join((str(error) for error in errors)))\n else:\n print(\"successfully commited analyzer {} run with action id {}. {} records inserted\".format(analyzer['_id'], action_id, valid_count))\n self.analyzer_state.transition(analyzer['_id'], 'validating', 'sensing', {'action_id': action_id})","def newAnalyzer():\n analyzer = {'crimes': None,\n 'dateIndex': None,\n 'autors': None,\n 'instrumentalness': None,\n 'tempo':None,\n 'liveness':None,\n 'speechiness':None,\n 'danceability':None,\n 'valence':None,\n 'loudness':None,\n 'acousticness':None,\n 'energy':None,\n 'generos':None\n }\n\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n\n analyzer['autors'] = om.newMap(omaptype='RBT',\n comparefunction=compareAUTOR)\n\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['tempo'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['liveness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['danceability'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n analyzer['valence'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['loudness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['energy'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n\n analyzer['generos']= m.newMap(11,\n maptype='CHAINING',\n loadfactor=4.0)\n \n return analyzer","def create_analysis_tools(self):\r\n raise NotImplementedError()","def create_phases(inputs):\n feed_tensors = inputs.values()\n\n remaining_analyzers = tf.get_collection(analyzers.ANALYZER_COLLECTION)\n analyzer_output_ready = {}\n for analyzer in remaining_analyzers:\n for tensor in analyzer.outputs:\n analyzer_output_ready[tensor] = False\n\n # Construct `AnalyzerInfo`s, removing any tensors that are analyzer outputs\n # from the ASSET_FILEPATHS collection. These tensors will be replaced and\n # the replacements will be added to the ASSET_FILEPATHS. Setting\n # AnalyzerOutputInfo.is_asset instructs the implementation to do this.\n asset_filepaths_collection = tf.get_collection_ref(\n tf.GraphKeys.ASSET_FILEPATHS)\n asset_filepaths = collections.OrderedDict(\n (tensor, True)\n for tensor in tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS))\n\n phases = []\n while remaining_analyzers:\n analyzer_inputs = []\n for analyzer in remaining_analyzers:\n analyzer_inputs.extend(analyzer.inputs)\n ready_init_ops, ready_analyzer_inputs = (\n graph_tools.determine_ready_tensors_and_table_initializers(\n tf.get_default_graph(), analyzer_inputs, feed_tensors,\n analyzer_output_ready))\n ready_analyzer_inputs = set(ready_analyzer_inputs)\n\n new_remaining_analyzers = []\n analyzer_infos = []\n for analyzer in remaining_analyzers:\n if all(tensor in ready_analyzer_inputs for tensor in analyzer.inputs):\n input_tensor_names = [tensor.name for tensor in analyzer.inputs]\n output_infos = [\n AnalyzerOutputInfo(tensor.name, asset_filepaths.pop(tensor, False))\n for tensor in analyzer.outputs]\n analyzer_infos.append(AnalyzerInfo(\n input_tensor_names, analyzer.attributes, output_infos))\n\n for tensor in analyzer.outputs:\n analyzer_output_ready[tensor] = True\n else:\n new_remaining_analyzers.append(analyzer)\n phases.append(Phase(analyzer_infos, ready_init_ops))\n\n assert len(new_remaining_analyzers) < len(remaining_analyzers)\n remaining_analyzers = new_remaining_analyzers\n\n del asset_filepaths_collection[:]\n asset_filepaths_collection.extend(six.iterkeys(asset_filepaths))\n\n return phases","def newAnalyzer():\n analyzer = {'accidents': None,\n 'dateIndex': None,\n 'timeIndex': None\n }\n\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['timeIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareTimes)\n \n return analyzer","def testGetAnalyzerInstances(self):\n analyzer_names = manager.AnalyzersManager.GetAnalyzerNames()\n analyzers = manager.AnalyzersManager.GetAnalyzerInstances(analyzer_names)\n self.assertEqual(len(analyzer_names), len(analyzers))\n for analyzer in analyzers:\n self.assertIsInstance(analyzer, interface.BaseAnalyzer)","def newAnalyzer():\n analyzer = {'accidents': None,\n 'dateIndex': None\n }\n\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='BST',\n comparefunction=compareDates)\n return analyzer","def add_analyzer_arg(parser):\n parser.add(\"analyzer\", nargs=\"+\",\n help=\"Fully qualified package name with an analyzer. Current directory is \"\n \"included into PYTHONPATH.\")","def _build_analyzer():\n analyzer = {}\n for e in LOSSLESS:\n analyzer[e] = lambda d, _ext, name: d.lossless.append(name)\n for e in COMPRESSED:\n analyzer[e] = lambda d, _ext, name: d.compressed.append(name)\n for e in IMAGES:\n analyzer[e] = lambda d, _ext, name: d.images.append(name)\n for e in VIDEOS:\n analyzer[e] = lambda d, _ext, name: d.videos.append(name)\n\n def _increment_ignored(d, _ext, _name):\n d.ignored += 1 # Can't use assignment in lambda\n\n for e in IGNORE:\n analyzer[e] = _increment_ignored\n analyzer['cue'] = lambda d, _, name: d.cue.append(name)\n\n return analyzer","def newAnalyzer():\n analyzer = {'accidentes': None,\n 'dateIndex': None\n }\n\n analyzer['accidentes'] = lt.newList('SINGLE_LINKED', compareseverity)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n return analyzer","def _generate_index_analysis(self, query_analysis, indexes):\n needs_recommendation = True\n full_indexes = []\n partial_indexes = []\n coverage = \"unknown\"\n\n if indexes is not None:\n for index_key in indexes.keys():\n index = indexes[index_key]\n index_report = self._generate_index_report(index,\n query_analysis)\n if index_report['supported'] is True:\n if index_report['coverage'] == 'full':\n full_indexes.append(index_report)\n if index_report['idealOrder']:\n needs_recommendation = False\n elif index_report['coverage'] == 'partial':\n partial_indexes.append(index_report)\n\n if len(full_indexes) > 0:\n coverage = \"full\"\n elif (len(partial_indexes)) > 0:\n coverage = \"partial\"\n elif query_analysis['supported']:\n coverage = \"none\"\n\n # INDEX ANALYSIS\n return OrderedDict([('indexStatus', coverage),\n ('fullIndexes', full_indexes),\n ('partialIndexes', partial_indexes)])","def run_analyzers(args: argparse.Namespace):\n log = logging.getLogger(\"run\")\n model_repository = create_model_repo_from_args(args)\n log.info(\"Created %s\", model_repository)\n if args.request_server == \"auto\":\n data_request_address = \"%s:10301\" % args.server.split(\":\")[0]\n else:\n data_request_address = args.request_server\n data_service = DataService(data_request_address)\n log.info(\"Created %s\", data_service)\n sys.path.append(os.getcwd())\n manager = AnalyzerManager(\n analyzers=[importlib.import_module(a).analyzer_class for a in args.analyzer],\n model_repository=model_repository,\n data_service=data_service,\n )\n sys.path = sys.path[:-1]\n log.info(\"Created %s\", manager)\n listener = EventListener(address=args.server, handlers=manager, n_workers=args.workers)\n log.info(\"Created %s\", listener)\n listener.start()\n log.info(\"Listening %s\", args.server)\n listener.block()\n model_repository.shutdown()\n data_service.shutdown()","def _generate_index_analysis(self, query_analysis, indexes):\r\n needs_recommendation = True\r\n full_indexes = []\r\n partial_indexes = []\r\n coverage = \"unknown\"\r\n\r\n if indexes is not None:\r\n for index_key in indexes.keys():\r\n index = indexes[index_key]\r\n index_report = self._generate_index_report(index,\r\n query_analysis)\r\n if index_report['supported'] is True:\r\n if index_report['coverage'] == 'full':\r\n full_indexes.append(index_report)\r\n if index_report['idealOrder']:\r\n needs_recommendation = False\r\n elif index_report['coverage'] == 'partial':\r\n partial_indexes.append(index_report)\r\n\r\n if len(full_indexes) > 0:\r\n coverage = \"full\"\r\n elif (len(partial_indexes)) > 0:\r\n coverage = \"partial\"\r\n elif query_analysis['supported']:\r\n coverage = \"none\"\r\n\r\n # INDEX ANALYSIS\r\n return OrderedDict([('indexStatus', coverage),\r\n ('fullIndexes', full_indexes),\r\n ('partialIndexes', partial_indexes)])","def build_analyzer(self):\n\t\tanalyser = super(TfidfVectorizer, self).build_analyzer()\n\t\treturn lambda doc: (lemmatizer.lemmatize(w) for w in analyser(doc))","def get_learners(beam_width, min_covered_examples, max_rule_length):\n ordered_entropy = Orange.classification.rules.CN2Learner()\n\n unordered = Orange.classification.rules.CN2UnorderedLearner()\n\n laplace = Orange.classification.rules.CN2Learner()\n laplace.rule_finder.quality_evaluator = Orange.classification.rules.LaplaceAccuracyEvaluator()\n\n learners = [ordered_entropy, unordered, laplace]\n labels = [\"ordered\", \"unordered\", \"laplace\"]\n\n for learner in learners:\n \"\"\"\n Parametrize the learners\n \"\"\"\n learner.rule_finder.search_algorithm.beam_width = beam_width\n learner.rule_finder.general_validator.min_covered_examples = min_covered_examples\n learner.rule_finder.general_validator.max_rule_length = max_rule_length\n\n return learners, labels","def newAnalyzer():\n analyzer = {'tracks': None,\n 'songs': None,\n 'artists': None,\n 'char': None,\n 'char2': None,\n 'char3': None,\n }\n\n analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)\n analyzer['songs'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['artists'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char2'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char3'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char4'] = mp.newMap(numelements=50,\n maptype='PROBING',\n comparefunction=compareValue)\n \n return analyzer","def required_tool_results():\n return [MethylResultModule]","def generate(env, daos_prefix, comp_prefix, args):\n analyzer = Analyzer(env, daos_prefix, comp_prefix, args)\n analyzer.analyze_on_exit()","def list_resolver_rules(MaxResults=None, NextToken=None, Filters=None):\n pass","def handle(self, *args, **options):\n chains = sorted(plugin.BY_REQUIREMENTS.keys())\n for chain in chains:\n print(chain, \"chain:\")\n reqs = sorted(\"{} -> {}\".format(\", \".join(sorted(k)) or 'START',\n \", \".join(sorted(v))) for k, v in\n plugin.BY_REQUIREMENTS[chain].iteritems())\n for req in reqs:\n print(\"-\", req)","def build_suggesters(DomainName=None):\n pass","def do_generate(self, args):\n\t\t[lang.hierarchyLengths for lang in self.languages]","def requires(self):\n bamcls = self.parent()[0]\n indexcls = ratatosk.lib.tools.samtools.Index\n return [bamcls(target=self.source()[0])] + [CombineVariants(target=os.path.join(self.outdir, \"CombinedVariants.vcf\"))] + [indexcls(target=rreplace(self.source()[0], bamcls().sfx(), indexcls().sfx(), 1), parent_task=fullclassname(bamcls))]","def main(aligner):\n\n # load config file\n config = file_utils.load_json(\n os.path.join(\n os.path.dirname(os.path.realpath(__file__)),\n 'config',\n 'normal_config.json',\n ))\n\n # map of type of analyses required before particular analysis can run\n # note: keep this order to avoid checking requirements more than once\n required_analyses_map = {\n 'annotation': [\n 'hmmcopy',\n 'align',\n ],\n 'hmmcopy': ['align'],\n 'align': [],\n }\n\n # get colossus analysis information objects with status not complete\n analyses = colossus_api.list(\n \"analysis_information\",\n analysis_run__run_status_ne=\"complete\",\n aligner=aligner if aligner else config[\"default_aligner\"],\n )\n\n for analysis in analyses:\n # get library id\n library_id = analysis[\"library\"][\"pool_id\"]\n log.info(f\"{library_id}\")\n\n # skip analysis if marked as complete\n status = analysis[\"analysis_run\"][\"run_status\"]\n\n # skip analyses older than this year\n # parse off ending time range\n last_updated_date = parser.parse(analysis[\"analysis_run\"][\"last_updated\"][:-6])\n if last_updated_date < datetime(2020, 1, 1):\n continue\n\n jira_ticket = analysis[\"analysis_jira_ticket\"]\n log.info(f\"checking ticket {jira_ticket} library {library_id}\")\n for analysis_type in required_analyses_map:\n log.info(f\"checking requirements for {analysis_type}\")\n # check if analysis exists on tantalus\n try:\n tantalus_analysis = tantalus_api.get(\n 'analysis',\n jira_ticket=jira_ticket,\n analysis_type__name=analysis_type,\n )\n except:\n tantalus_analysis = None\n\n if tantalus_analysis is not None:\n # check if running or complete\n status = tantalus_analysis[\"status\"]\n if status in ('running', 'complete'):\n log.info(f\"skipping {analysis_type} for {jira_ticket} since status is {status}\")\n\n # update run status on colossus\n if analysis_type == \"annotation\" and status == \"complete\":\n analysis_run_id = analysis[\"analysis_run\"][\"id\"]\n analysis_run = colossus_api.get(\"analysis_run\", id=analysis_run_id)\n colossus_api.update(\"analysis_run\", id=analysis_run_id, run_status=\"complete\")\n\n continue\n\n log.info(f\"running {analysis_type} in library {library_id} with ticket {jira_ticket}\")\n # otherwise run analysis\n saltant_utils.run_analysis(\n tantalus_analysis['id'],\n analysis_type,\n jira_ticket,\n config[\"scp_version\"],\n library_id,\n aligner if aligner else config[\"default_aligner\"],\n config,\n )\n else:\n # set boolean determining trigger of run\n is_ready_to_create = True\n # check if required completed analyses exist\n for required_analysis_type in required_analyses_map[analysis_type]:\n try:\n required_analysis = tantalus_api.get(\n 'analysis',\n jira_ticket=jira_ticket,\n analysis_type__name=required_analysis_type,\n status=\"complete\",\n )\n except:\n log.error(\n f\"a completed {required_analysis_type} analysis is required to run before {analysis_type} runs for {jira_ticket}\"\n )\n # set boolean as false since analysis cannot be created yet\n is_ready_to_create = False\n break\n\n # create analysis and trigger on saltant if analysis creation has met requirements\n if is_ready_to_create:\n log.info(f\"creating {analysis_type} analysis for ticket {jira_ticket}\")\n\n try:\n tantalus_utils.create_qc_analyses_from_library(\n library_id,\n jira_ticket,\n config[\"scp_version\"],\n analysis_type,\n )\n except Exception as e:\n log.error(f\"failed to create {analysis_type} analysis for ticket {jira_ticket}\")\n continue\n tantalus_analysis = tantalus_api.get(\n 'analysis',\n jira_ticket=jira_ticket,\n analysis_type__name=analysis_type,\n )\n\n log.info(f\"running {analysis_type} in library {library_id} with ticket {jira_ticket}\")\n saltant_utils.run_analysis(\n tantalus_analysis['id'],\n analysis_type,\n jira_ticket,\n config[\"scp_version\"],\n library_id,\n aligner if aligner else config[\"default_aligner\"],\n config,\n )\n\n # get completed analyses that need montage loading\n analyses = colossus_api.list(\n \"analysis_information\",\n montage_status=\"Pending\",\n analysis_run__run_status=\"complete\",\n )\n\n for analysis in analyses:\n # get library id\n library_id = analysis[\"library\"][\"pool_id\"]\n\n # skip analyses older than this year\n # parse off ending time range\n last_updated_date = parser.parse(analysis[\"analysis_run\"][\"last_updated\"][:-6])\n if last_updated_date < datetime(2020, 1, 1):\n continue\n\n jira_ticket = analysis[\"analysis_jira_ticket\"]\n update_jira_dlp(jira_ticket, \"M\")\n # upload qc report to jira ticket\n attach_qc_report(jira_ticket, library_id, config[\"storages\"])\n\n # load analysis into montage\n load_ticket(jira_ticket)","def help_analyze(self):\n print(ANALYZE)","def workflowLessTypes(self):\n\n tools = [c.getName() for c in\n self.atgenerator.getGeneratedTools(self.package)\n if not\n utils.isTGVFalse(c.getTaggedValue('autoinstall'))]\n tools.sort()\n return tools","def build_action_randomizers(cls, constants) -> List[ActionRandomizer]:\n return []","def existing_analysis_sweeps(self):\n setup_list = self.existing_analysis_setups\n sweep_list = []\n s_type = self.solution_type\n for el in setup_list:\n sweep_list.append(el + \" : \" + s_type)\n return sweep_list","def list_rulesets(command):\n namespace = app.main(command)\n assert namespace.command == 'lr' or namespace.command == \"listrulesets\"","def generate_sla_metrics(self):\n for module in self.account_definitions:\n\n metric_spec = Definition.return_spec(\n type_set='metric_set',\n module=module\n )\n \n metric_module = importlib.util.module_from_spec(metric_spec)\n metric_spec.loader.exec_module(metric_module)\n try:\n self.metric_sets.append(metric_module.metric_set)\n except AttributeError as _ex:\n print(\"Module has no attribute metric_set\")\n sla_spec = Definition.return_spec(\n type_set='sla_set',\n module=module\n )\n \n sla_module = importlib.util.module_from_spec(sla_spec)\n sla_spec.loader.exec_module(sla_module)\n try:\n self.sla_sets.append(sla_module.sla_set)\n except AttributeError as _ex:\n print(\"Module has no attribute sla_set\")","def list_algorithms(CreationTimeAfter=None, CreationTimeBefore=None, MaxResults=None, NameContains=None, NextToken=None, SortBy=None, SortOrder=None):\n pass","def generateAssociationRule(freqSet):","def analysis_setup(self):\n pass","def _make_suggestions(self):\n\n #build concordance based on current approved\n concordance = dict()\n for term in self.tree.get_children('approved'):\n words = [word.strip(',.:;*').lower() \\\n for word in str(self.tree.item(term)['values'][0]).split(' ')]\n for word in words:\n# if word == 'ad':\n# messagebox.showwarning(\"word == 'ad'\",\"concordance={}\".format(concordance))\n# pass\n if word not in ['and', 'the', 'a', 'to', 'of'] \\\n and not word.isdigit():\n if word not in concordance:\n concordance[word] = set([term, ])\n else:\n concordance[word].add(term)\n# if word == 'ad':\n# messagebox.showwarning(\"word 'ad' added?\",\"concordance={}\".format(concordance))\n# pass\n \n \n #so concordance now holds a list of words in approved terms along with\\\n #list of index of terms() they occur in\n \n for term in self.tree.get_children('suggestions'):\n self._look_in_concordance(term, concordance)\n\n for term in self.tree.get_children('unknown'):\n self._look_in_concordance(term, concordance)\n\n self._collapse_all()","def main(argv):\n argparser = ArgumentParser(\n description=\"Measure agreement of the sentiment corpus.\")\n argparser.add_argument(\"dir1\",\n help=\"directort containing annotations of the\"\n \" first expert\")\n argparser.add_argument(\"dir2\",\n help=\"directort containing annotations of the\"\n \" second expert\")\n args = argparser.parse_args(argv)\n\n def check_arg(arg):\n if not os.path.isdir(args.dir1) or not os.access(args.dir1, os.R_OK):\n print(\"Invalid argument {!r} should be a readable\"\n \" directory\".format(arg), file=sys.stderr)\n\n check_arg(args.dir1)\n check_arg(args.dir2)\n annotations = []\n skip_ids = set()\n for fpath1 in glob.iglob(os.path.join(args.dir1, \"*.tsv\")):\n if not os.access(fpath1, os.R_OK):\n print(\"WARNING: cannot read file {:s}\".format(\n fpath1\n ), file=sys.stderr)\n continue\n base_fpath1 = os.path.basename(fpath1)\n fpath2 = os.path.join(args.dir2, base_fpath1)\n if not os.path.exists(fpath2):\n print(\"WARNING: file {:s} not found\".format(\n fpath1\n ), file=sys.stderr)\n continue\n elif not os.access(fpath2, os.R_OK):\n print(\"WARNING: cannot read file {:s}\".format(\n fpath1\n ), file=sys.stderr)\n continue\n read_annotations(annotations, skip_ids, fpath1, 1)\n read_annotations(annotations, skip_ids, fpath2, 2)\n annotations = [(coder, item, label)\n for (coder, item, label) in annotations\n if item not in skip_ids]\n at = AnnotationTask(annotations, distance=interval_distance)\n # print(at.avg_Ao())\n # print(at.Ae_kappa())\n print(\"Cohen's Kappa: {:.4f}\\nKrippendorff's Alpha: {:4f}\".format(\n at.kappa(), at.alpha()\n ))","def build_frequency_list(name_list):\n analyzer = build_analyzer()\n char_list = []\n for name in name_list:\n char_list += analyzer(name)\n return char_list","def testAnalyzerRegistration(self):\n number_of_analyzers = len(manager.AnalyzersManager._analyzer_classes)\n manager.AnalyzersManager.RegisterAnalyzer(TestAnalyzer)\n self.assertEqual(\n number_of_analyzers + 1, len(\n manager.AnalyzersManager._analyzer_classes))\n\n with self.assertRaises(KeyError):\n manager.AnalyzersManager.RegisterAnalyzer(TestAnalyzer)\n\n manager.AnalyzersManager.DeregisterAnalyzer(TestAnalyzer)\n\n self.assertEqual(\n number_of_analyzers, len(manager.AnalyzersManager._analyzer_classes))","def newAnalyzer():\n analyzer = {'events': None,\n \"musical_genre\":None,\n 'artist_ID': None,\n \"track_ID\": None,\n \"instrumentalness\": None,\n \"acousticness\": None,\n \"liveness\": None,\n \"speechiness\": None,\n \"energy\":None,\n \"danceability\": None,\n \"valence\": None\n }\n # Listas\n analyzer['events'] = lt.newList('ARRAY_LIST', compareIds)\n\n # RBT \n analyzer[\"instrumentalness\"] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['acousticness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['liveness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['speechiness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['energy'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['danceability'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['valence'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['tempo'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['created_at'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n analyzer['dates_u'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\n \n\n # Tablas de Hash \n analyzer['artist_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer['track_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer['ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer[\"musical_genre\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer[\"sen\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n analyzer['track_ID_S'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\n\n return analyzer","def create_rules(app_names, error):\n # If no applications is given on the command line, generate the rules\n # for all the registered applications\n if not app_names:\n app_names = [entry.name for entry in pkg_resources.iter_entry_points('nagare.applications')]\n\n package = pkg_resources.Requirement.parse('nagare')\n static = pkg_resources.resource_filename(package, 'static')\n\n apps = [('nagare', static)] # Initialize the result tuple with the static contents of the framework\n\n for app_name in app_names:\n (cfgfile, app, dist, aconf) = util.read_application(app_name, error)\n\n static = aconf['application'].get('static', os.path.join(dist.location, 'static') if dist else None)\n\n if static and os.path.isdir(static):\n apps.append((aconf['application']['name'], static))\n\n return sorted(apps, key=lambda x: len(x[0]))","def get_agency_terms(self):\n return # osid.authentication.AgencyQueryInspector","def compile_tuning(self, cf):\n logger = self._logger\n for s in cf.sections():\n logger.debug('Collecting definitions for %s', s)\n try:\n rexp = cf.get(s, 'regex')\n except ConfigParser.NoOptionError:\n self._logger.error('Section \"%s\" missing \"regex\" option', s)\n continue\n try:\n r = re.compile(rexp)\n except:\n self._logger.error('Section \"%s\" invalid \"regex\" option', s)\n continue\n try:\n transfer = cf.getint(s, 'transfer')\n except ConfigParser.NoOptionError:\n self._logger.info('Section \"%s\" missing \"transfer\" option', s)\n transfer = 512\n except ValueError:\n self._logger.error('Section \"%s\", \"transfer\" option must be integer', s)\n continue\n try:\n readahead = cf.getint(s, 'readahead')\n except ConfigParser.NoOptionError:\n self._logger.info('Section \"%s\" missing \"readahead\" option', s)\n readahead = 2*transfer\n except ValueError:\n self._logger.error('Section \"%s\", \"readahead\" option must be integer', s)\n continue\n try:\n scheduler = cf.get(s, 'scheduler')\n except ConfigParser.NoOptionError:\n self._logger.info('Section \"%s\" missing \"scheduler\" option', s)\n scheduler = None\n opts = []\n if scheduler == 'deadline':\n opts = []\n for deadOpt in ('fifo_batch', 'read_expire', 'write_expire', 'writes_starved', 'front_merges'):\n try:\n deadVal = cf.getint(s, deadOpt)\n except ConfigParser.NoOptionError:\n self._logger.info('Section \"%s\" missing \"%s\" option', s, deadOpt)\n deadVal = None\n except ValueError:\n self.logger.error('Section \"%s\", \"%s\" option must be integer', s, deadOpt)\n continue\n opts += [ deadVal ]\n self._lunMatch.append( ( r, transfer, readahead, scheduler, opts ) )","def test_creation(self):\n config = config_factory()\n db = config.arango_db\n\n # Create analyzer\n analyzer = ArangoAnalyzer(\"analyzer_sample\")\n analyzer.set_stopwords(\n language=\"english\",\n custom_stopwords=[\"stop\", \"word\"],\n include_default=False,\n )\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\n\n analyzer.create(db)\n assert analyzer.__dict__ == {\n \"name\": \"analyzer_sample\",\n \"type\": \"text\",\n \"features\": [\"frequency\", \"norm\", \"position\"],\n \"locale\": \"en\",\n \"case\": \"lower\",\n \"stopwords\": [\"stop\", \"word\"],\n \"accent\": False,\n \"stemming\": True,\n \"edge_ngram\": None,\n \"delimiter\": \",\",\n \"min\": 2,\n \"max\": 5,\n \"preserve_original\": False,\n \"start_marker\": \"\",\n \"end_marker\": \"\",\n \"stem_type\": \"binary\",\n }\n assert isinstance(db.analyzer(\"analyzer_sample\"), dict)","def get_all_parsers():\n return [OptimizerFactory.get_parser(optimizer) for optimizer in OptimizerFactory.optimizers]","def list_modules(lookup_paths: list = None):\n result = []\n\n if lookup_paths is None:\n lookup_paths = analyzer_paths()\n\n for path in lookup_paths:\n analyzer_module_root = resource_filename(path, \"modules\")\n # analyzer_root = os.path.join(anchore_module_root, \"modules\")\n for f in os.listdir(analyzer_module_root):\n thecmd = os.path.join(analyzer_module_root, f)\n if re.match(r\".*\\.py$\", thecmd):\n result.append(thecmd)\n\n result.sort(key=lambda x: analyzer_name_from_path(x))\n return result","def add_arguments_to_parser(parser):\n\n parser.add_argument('--analyzers',\n nargs='+',\n dest='analyzers',\n metavar='ANALYZER',\n required=False,\n choices=analyzer_types.supported_analyzers,\n default=list(analyzer_types.supported_analyzers.\n keys()),\n help=\"Show checkers only from the analyzers \"\n \"specified.\")\n\n if get_diagtool_bin():\n parser.add_argument('-w', '--warnings',\n dest='show_warnings',\n default=argparse.SUPPRESS,\n action='store_true',\n required=False,\n help=\"Show available warning flags.\")\n\n parser.add_argument('--details',\n dest='details',\n default=argparse.SUPPRESS,\n action='store_true',\n required=False,\n help=\"Show details about the checker, such as \"\n \"description, if available.\")\n\n parser.add_argument('--profile',\n dest='profile',\n metavar='PROFILE/list',\n required=False,\n default=argparse.SUPPRESS,\n help=\"List checkers enabled by the selected profile. \"\n \"'list' is a special option showing details \"\n \"about profiles collectively.\")\n\n parser.add_argument('--guideline',\n dest='guideline',\n nargs='*',\n required=False,\n default=None,\n help=\"List checkers that report on a specific \"\n \"guideline rule. Here you can add the guideline \"\n \"name or the ID of a rule. Without additional \"\n \"parameter, the available guidelines and their \"\n \"corresponding rules will be listed.\")\n\n parser.add_argument('--checker-config',\n dest='checker_config',\n default=argparse.SUPPRESS,\n action='store_true',\n required=False,\n help=\"Show checker configuration options for all \"\n \"existing checkers supported by the analyzer. \"\n \"These can be given to 'CodeChecker analyze \"\n \"--checker-config'.\")\n\n filters = parser.add_mutually_exclusive_group(required=False)\n\n filters.add_argument('--only-enabled',\n dest='only_enabled',\n default=argparse.SUPPRESS,\n action='store_true',\n help=\"Show only the enabled checkers.\")\n\n filters.add_argument('--only-disabled',\n dest='only_disabled',\n default=argparse.SUPPRESS,\n action='store_true',\n help=\"Show only the disabled checkers.\")\n\n parser.add_argument('-o', '--output',\n dest='output_format',\n required=False,\n default='rows',\n choices=USER_FORMATS,\n help=\"The format to list the applicable checkers as.\")\n\n logger.add_verbose_arguments(parser)\n parser.set_defaults(func=main)","def collect_rules():\n all_rules = []\n keys = ['mobileconfig',\n 'macOS',\n 'severity',\n 'title',\n 'check',\n 'fix',\n 'odv',\n 'tags',\n 'id',\n 'references',\n 'result',\n 'discussion']\n references = ['disa_stig',\n 'cci',\n 'cce',\n '800-53r4',\n 'srg']\n\n\n for rule in glob.glob('../rules/**/*.yaml',recursive=True) + glob.glob('../custom/rules/**/*.yaml',recursive=True):\n if \"supplemental\" in rule:\n continue\n rule_yaml = get_rule_yaml(rule, custom=False)\n for key in keys:\n try:\n rule_yaml[key]\n except:\n rule_yaml.update({key: \"missing\"})\n if key == \"references\":\n for reference in references:\n try:\n rule_yaml[key][reference]\n except:\n rule_yaml[key].update({reference: [\"None\"]})\n\n if \"n_a\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"n_a\")\n if \"inherent\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"inherent\")\n if \"manual\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"manual\")\n if \"none\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"none\")\n if \"permanent\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"permanent\")\n if \"supplemental\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"supplemental\")\n if \"i386\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"i386\")\n if \"arm64\" in rule_yaml['tags']:\n rule_yaml['tags'].remove(\"arm64\")\n\n all_rules.append(MacSecurityRule(rule_yaml['title'].replace('|', '\\|'),\n rule_yaml['id'].replace('|', '\\|'),\n rule_yaml['severity'].replace('|', '\\|'),\n rule_yaml['discussion'].replace('|', '\\|'),\n rule_yaml['check'].replace('|', '\\|'),\n rule_yaml['fix'].replace('|', '\\|'),\n rule_yaml['references']['cci'],\n rule_yaml['references']['cce'],\n rule_yaml['references']['800-53r4'],\n rule_yaml['references']['disa_stig'],\n rule_yaml['references']['srg'],\n rule_yaml['odv'],\n rule_yaml['tags'],\n rule_yaml['result'],\n rule_yaml['mobileconfig'],\n rule_yaml['mobileconfig_info']\n ))\n return all_rules","def scan(self):\n exclude_patterns_config = self.doxygen_config.get(\"EXCLUDE_PATTERNS\", [])\n exclude_patterns = [\n pattern.replace(\"*/\", \"**/\") for pattern in exclude_patterns_config\n ]\n file_patterns = self.doxygen_config.get(\"FILE_PATTERNS\", [\"*.c\", \"*.h\"])\n if self.doxygen_config.get(\"RECURSIVE\", [\"YES\"]) == [\"YES\"]:\n file_patterns = [f\"**/{pattern}\" for pattern in file_patterns]\n nodes = []\n names = []\n for node in self.doxygen_input:\n if os.path.isdir(node.abspath()):\n for i in node.ant_glob(incl=file_patterns, excl=exclude_patterns):\n nodes.append(i)\n else:\n nodes.append(node)\n return (nodes, names)","def check(self, manager):\n for all_json in self.api_dir.rglob(ALL_JSON):\n stem = all_json.relative_to(self.api_dir)\n yield dict(\n name=f\"validate:translation:{stem}\",\n doc=f\"Validate {stem} with the JupyterLab Translation API\",\n file_dep=[all_json],\n actions=[(self.validate_one_json_file, [None, all_json])],\n )","def extract_rule_names(self):\n if self.scanner == YARA:\n return sorted({result['rule'] for result in self.results})\n if self.scanner == CUSTOMS and 'matchedRules' in self.results:\n return self.results['matchedRules']\n # We do not have support for the remaining scanners (yet).\n return []","def _generate_recommendation(self,\n query_analysis,\n db_name,\n collection_name):\n index_rec = '{'\n for query_field in query_analysis['analyzedFields']:\n if query_field['fieldType'] is EQUIV_TYPE:\n if len(index_rec) is not 1:\n index_rec += ', '\n index_rec += '\"' + query_field['fieldName'] + '\": 1'\n for query_field in query_analysis['analyzedFields']:\n if query_field['fieldType'] is SORT_TYPE:\n if len(index_rec) is not 1:\n index_rec += ', '\n index_rec += '\"' + query_field['fieldName'] + '\": 1'\n for query_field in query_analysis['analyzedFields']:\n if query_field['fieldType'] is RANGE_TYPE:\n if len(index_rec) is not 1:\n index_rec += ', '\n index_rec += '\"' + query_field['fieldName'] + '\": 1'\n index_rec += '}'\n\n # RECOMMENDATION\n return OrderedDict([('index',index_rec),\n ('shellCommand', self.generate_shell_command(collection_name, index_rec))])","def version(self) -> str:\n return \" \".join(self._model_id(a) for a in self._analyzers)","def gen_docs(jsons: [{}], char_wb: bool = False, add_taint: bool = False) -> [Learner.LabelledDocs]:\n docs = []\n taint_counts = 0\n for flow in jsons:\n line = Analyzer.filter_url_words(flow['url'])\n if '_' in flow['taint']:\n taint_counts += 1\n if add_taint:\n line = line + ' ' + 't_' + flow['taint']\n label = 1 if flow['label'] == '1' else 0\n real_label = 1 if flow['real_label'] == '1' else 0\n if real_label != label:\n logger.info(\"Flow's real label does not match the training label for %s, real_label = %d label = %d\",\n flow['url'], real_label, label)\n numeric = [flow[name] for name in Analyzer.numeric_features]\n docs.append(Learner.LabelledDocs(line, label, numeric, real_label, char_wb=char_wb))\n logger.info('The number of flows who have more than 1 taints: %d', taint_counts)\n return docs","def GenerateACLString(self):\n target_string = ''\n app_id = 100 # variable in ACL sentences.\n\n for terms in self.silverpeak_terms:\n for term in terms:\n for unit in term.GenerateUnitList():\n if term.term.precedence:\n for precedence in term.term.precedence:\n target_string += self._GenerateACLLine(app_id, term,\n unit, precedence)\n app_id += 100\n else:\n target_string += self._GenerateACLLine(app_id, term, unit)\n app_id += 100\n # finalize the target string\n target_string = self._FinalizeString(target_string, self.pre_string,\n self.fixed_content)\n return target_string","def init():\n # analyzer es utilizado para interactuar con el modelo\n analyzer = model.newAnalyzer()\n return analyzer","def get_rules(cls) -> list:\n return [factory() for factory in cls._rules_factories]","def _generate_recommendation(self,\r\n query_analysis,\r\n db_name,\r\n collection_name):\r\n index_rec = '{'\r\n for query_field in query_analysis['analyzedFields']:\r\n if query_field['fieldType'] is EQUIV_TYPE:\r\n if len(index_rec) is not 1:\r\n index_rec += ', '\r\n index_rec += '\"' + query_field['fieldName'] + '\": 1'\r\n for query_field in query_analysis['analyzedFields']:\r\n if query_field['fieldType'] is SORT_TYPE:\r\n if len(index_rec) is not 1:\r\n index_rec += ', '\r\n index_rec += '\"' + query_field['fieldName'] + '\": 1'\r\n for query_field in query_analysis['analyzedFields']:\r\n if query_field['fieldType'] is RANGE_TYPE:\r\n if len(index_rec) is not 1:\r\n index_rec += ', '\r\n index_rec += '\"' + query_field['fieldName'] + '\": 1'\r\n index_rec += '}'\r\n\r\n # RECOMMENDATION\r\n return OrderedDict([('index',index_rec),\r\n ('shellCommand', self.generate_shell_command(collection_name, index_rec))])","def get_rules():\n rules = []\n\n for app_module in get_config('tipfy', 'apps_installed'):\n try:\n # Load the urls module from the app and extend our rules.\n app_rules = import_string('%s.urls' % app_module)\n rules.extend(app_rules.get_rules())\n except ImportError:\n pass\n\n return rules","def build_corpus(self):\n # #############################\n\n doc = metapy.index.Document()\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\n tok = metapy.analyzers.LowercaseFilter(tok)\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\n tok = metapy.analyzers.Porter2Filter(tok)\n tok = metapy.analyzers.ListFilter(tok, \"lemur-stopwords.txt\", metapy.analyzers.ListFilter.Type.Reject)\n collection = -1\n\n with open(self.documents_path) as file:\n for num, line in enumerate(file):\n l = line.strip()\n c = int(l[0])\n l = l[2:]\n doc.content(l)\n tok.set_content(doc.content())\n if c != collection:\n self.documents.append([])\n collection = c\n self.documents[c].append([token for token in tok])\n self.number_of_collections = len(self.documents)\n self.number_of_documents = len(self.documents[0])\n #print(self.number_of_collections)\n #print(self.number_of_documents)\n #print(self.documents[0])","def hrules(self):\n ...","def list_all_agencies():\n return JsonResponse.create(StatusCode.OK, get_all_agencies())","def get_all_rules(self):\n\n rules = set()\n for a_dict in self.get_dicts():\n rules = keywords.union(a_dict['rules'])\n return sorted(keywords)","def __all_Algs_ ( self ) :\n _algs = self.algorithms()\n\n algs = []\n for _a in _algs :\n algs += [ self.algorithm ( _a ) ]\n return algs","def __all_Algs_ ( self ) :\n _algs = self.algorithms()\n\n algs = []\n for _a in _algs :\n algs += [ self.algorithm ( _a ) ]\n return algs","def bookAlgorithms(config, visitor):\n import imp\n import os\n\n CLI = config.getFolder(\"CLI+\")\n # flag indicating to run a robust analysis\n robust = CLI.getTagBoolDefault(\"robust\",False)\n # flag indicating to run a dummy analysis\n dummy = CLI.getTagBoolDefault(\"dummy\",False)\n\n # book any algorithms\n for algorithmscript_TString in config.getTagVString(\"algorithms.snippets\"):\n QFramework.TQStringUtils.removeLeadingBlanks(algorithmscript_TString)\n QFramework.TQStringUtils.removeTrailingBlanks(algorithmscript_TString)\n QFramework.TQStringUtils.removeTrailingText(algorithmscript_TString, \".py\")\n algorithmscript = algorithmscript_TString.Data()\n found_modules = []\n algorithmsDirs = config.getTagVStandardString(\"algorithms.directories\")\n # search through the directories provided in the config\n for algorithmsPath in algorithmsDirs:\n module = QFramework.TQFolder.concatPaths(algorithmsPath, algorithmscript) + \".py\"\n module = common.findConfigPath(module, False)\n # findConfigPath returns \"\" if no module was found\n if len(module) > 0:\n # snippet was found in this directory -\n # add its absolute path and the directory it was found in\n # to a list in tuple form\n found_modules.append((module, algorithmsPath))\n if len(found_modules) == 0:\n # check CommonAnalysisHelpers for an algorithm snippet as fall-back\n CAHAlgorithmsDir = \"CommonAnalysisHelpers/share/algorithms\"\n algorithmsDirs.push_back(CAHAlgorithmsDir)\n module = QFramework.TQFolder.concatPaths(CAHAlgorithmsDir, algorithmscript) + \".py\"\n module = QFramework.TQPathManager.findFileFromEnvVarWithoutExecDir(module, \"CAFCOREDIR\")\n if len(module) > 0:\n found_modules.append((module, CAHAlgorithmsDir))\n print(len(found_modules))\n # continue only if there was one match found\n if len(found_modules) == 0:\n QFramework.BREAK(\"No module found for '{:s}' in the custom algorithm directories provided:\\n{:s}\\n\".format(algorithmscript,', '.join(algorithmsDirs))+\n \"Please make sure that there exists a snippet by the name of '{:s}.py' available in one of them.\\n\".format(algorithmscript))\n elif len(found_modules) > 1:\n QFramework.BREAK(\"Ambiguity detected while resolving custom algorithm snippet location. Multiple modules found for {:s} in the custom algorithm directories provided:\\n{:s}\\n\".format(algorithmscript,', '.join(algorithmsDirs))+\n \"Consider placing the {:s}.py snippet only in a common directory if it's used by more than one (sub)analysis.\".format(algorithmscript))\n abs_path = found_modules[0][0]\n module_name = os.path.basename(abs_path).rstrip(\".py\")\n relative_path = QFramework.TQFolder.concatPaths(found_modules[0][1], algorithmscript)+\".py\"\n QFramework.START(\"l.\",\"loading algorithms from '{:s}'\".format(str(relative_path)))\n try:\n addalgorithms = imp.load_source(module_name, abs_path)\n added = addalgorithms.addAlgorithms(visitor,config)\n if added:\n QFramework.END(QFramework.TQMessageStream.OK)\n else:\n QFramework.END(QFramework.TQMessageStream.FAIL)\n QFramework.BREAK(\"unable to properly setup custom algorithms\")\n except IOError as error:\n QFramework.END(QFramework.TQMessageStream.FAIL)\n QFramework.BREAK(\"unable to open file '{:s}' - please double-check!\\n\".format(abs_path)+\"Message from python:\\n\"+str(error))\n except NameError as error:\n QFramework.END(QFramework.TQMessageStream.FAIL)\n if not robust and not dummy:\n QFramework.BREAK(\"syntax error in algorithm snippet '{:s}' - please double-check!\\n\".format(abs_path)+\"Message from python:\\n\"+str(error))\n except AttributeError as error:\n QFramework.END(QFramework.TQMessageStream.FAIL)\n if not robust and not dummy:\n QFramework.BREAK(\"attribute error in algorithm snippet '{:s}' - please double-check!\\n\".format(abs_path)+\n \"If the message from python below is\\n'module' object has no attribute 'addAlgorithms'\\nplease make sure that the snippet has the function addAlgorithms() defined.\\n\"\n \"Message from python:\\n\"+str(error))\n\n # only try and do the xAOD skimming configuration below if we are running with the MCASV\n # since all channels should be considered at the same time (e.g. systematic variations)\n if isinstance(visitor,QFramework.TQMultiChannelAnalysisSampleVisitor):\n # TODO: these two lines are also done in bookAnalysisJobs\n xAODdumpingConfig = QFramework.TQTaggable()\n dumpXAODs = (xAODdumpingConfig.importTagsWithoutPrefix(config,\"xAODdumping.\") > 0)\n\n jobID = CLI.getTagStringDefault(\"jobID\",\"analyze\")\n\n #add xAODskimmingAlgorithm if requested (only for MCASV as we'd have event duplications otherwise!)\n #note: if we ever implement an option to limit the number of channels executed at the same time we must ensure this does not run in such a configuration!!!!\n if dumpXAODs:\n print(\"Setting up xAOD skimming Algorithm...\")\n xAODskimmingAlg = ROOT.TQxAODskimmingAlgorithm()\n xAODskimmingAlg.SetName(\"xAODdumper\")\n if xAODdumpingConfig.hasTag(\"flagName\"): xAODskimmingAlg.setFlagName(xAODdumpingConfig.getTagStringDefault(\"flagName\",\"\"))\n xAODskimmingAlg.setOutputDir( xAODdumpingConfig.getTagStringDefault(\"outputDir\",\"CAFxAODs\") )\n xAODskimmingAlg.setFilePrefix(jobID+\"_\")\n if config.hasTag(\"nameTagName\") : xAODskimmingAlg.setPrefix( config.getTagStringDefault( ROOT.TString(\"aliases.\")+config.getTagStringDefault(\"nameTagName\",\"\"), \"\" ) )\n visitor.addAlgorithm( xAODskimmingAlg )","def genlangs(self):\r\n raise NotImplementedError","def requires(self):\n return []","def _compile_codecoolers(cls):\n cls.codecoolers = Student.get_students() + Mentor.get_mentors() + Admin.get_admins()","def antonyms_pipeline(config: SettingConfig) -> None:\n raw_antonym_pairs = generate_antonym_pairs(config)\n for pos in config.pos.keys():\n processed_synonym_pairs = postprocess_pairs(raw_antonym_pairs[pos], config)\n write_pairs(processed_synonym_pairs, config.constraints_root_path, pos, \"antonyms\")","def required(cls):\n return []","def get_authorizers(self, ApiId: str, MaxResults: str = None, NextToken: str = None) -> Dict:\n pass","def make_control_knowledge(self, horizon):\n\n \"\"\" *** YOUR CODE HERE *** \"\"\"\n\n # ADD_RULE1_COUNT = 0\n # ADD_RULE2_COUNT = 0\n # ADD_RULE3_COUNT = 0\n\n close = list()\n far = list()\n\n for g in self.problem.goal:\n for p in self.problem.propositions:\n if re.match(r'at\\spackage\\d+\\scity\\d+-\\d+', str(p)):\n p_split = str(p).split()\n g_split = str(g).split()\n\n # if \"at\" and \"package[oo]\" match\n if p_split[0] == g_split[0] and p_split[1] == g_split[1]:\n # also \"city[oo]-[xx]\" match\n if p_split[2][:-2] == g_split[2][:-2]:\n close.append(p)\n else:\n far.append(p)\n\n # Rule 1:\n # ===============================\n # If a package is at its goal location, then it must remain there.\n # p@t and goal@t) -> p@t+1), where p is at(package, location)\n # cnf: not p@t or not goal@t or p@t+1\n\n for g in self.problem.goal:\n for t in range(0, horizon):\n clause = list()\n clause.append(-self.proposition_fluent_codes[(g, t)])\n clause.append(self.proposition_fluent_codes[(g, t + 1)])\n self.add_clause(clause, \"control\")\n # ADD_RULE1_COUNT += 1\n\n for t in range(0, horizon):\n for a in self.problem.actions:\n\n # Rule 2\n # ===============================\n\n # RULE\n # close -> do not load airplane\n # p1: close@t\n # p2: at the location of an airport @t\n # p3: airplane at this location @t\n # p4: plane is not loaded\n # a: load this airplane\n #\n # p1@t and p2@t and p3@t and p4@t => a@t\n # not p1@t or not p2@t or not p3@t or not p4@t or a@t\n # cnf: not p@t or not a@t\n if str(a).startswith('load-airplane'):\n for i in close:\n package = str(i).split()[1]\n if str(a).split()[1] == package:\n clause = list()\n clause.append(\n -self.proposition_fluent_codes[(i, t)])\n clause.append(-self.action_fluent_codes[(a, t)])\n self.add_clause(clause, \"control\")\n # ADD_RULE2_COUNT += 1\n\n # Rule 3\n # ===============================\n\n # RULE\n # far -> do not unload airplane\n # p@t -> not a@t, where p is far, a is unload-airplane\n # cnf: not p@t or not a@t\n if str(a).startswith('unload-airplane'):\n for j in far:\n package = str(j).split()[1]\n if str(a).split()[1] == package:\n clause = list()\n clause.append(\n -self.proposition_fluent_codes[(j, t)])\n clause.append(-self.action_fluent_codes[(a, t)])\n self.add_clause(clause, \"control\")\n # ADD_RULE3_COUNT += 1\n\n # # RULE\n # # if an airplane has a package on it and the package's\n # # destination is close do not fly this airplane.\n # # in fact, if the destination of package is far,\n # # fly this plane to it.\n # #\n # # p1: package on airplane @ t\n # # p2: package at a place @ t\n # # p3: the place and the goal are in the same city\n # # rule: p1@t and p2@t and p@3 => not fly plane@t\n # # and unload the plane@t\n #\n # # not p1@t or not p2@t or not fly@t\n # # not p1@t or not p2@t or unload\n #\n # # rule: p1@t and p2@t and not p3@t => fly plane@t and not\n # # unload the plane@t\n #\n # if str(a).startswith('fly-airplane'):\n # plane = str(a).split()[1]\n # # loc_from = str(a).split()[2]\n # for p1 in self.problem.propositions:\n # if str(p1).startswith('in package') and str(p1).split()[2] == plane: # in package plane\n # package = str(p1).split()[1]\n # for p2 in self.problem.propositions:\n # if p2 in close and str(p2).split()[1] == package: # at package location\n # clause = list()\n # clause.append(-self.proposition_fluent_codes[p1, t])\n # clause.append(-self.proposition_fluent_codes[p2, t])\n # clause.append(-self.action_fluent_codes[a, t])\n # self.add_clause(clause, 'control')\n # ADD_RULE2_COUNT += 1\n #\n #\n # for g in self.problem.goal:\n # if str(g).split()[1] == package:\n # destination = str(g).split()[2]\n # for do in self.problem.actions:\n # # unload-airplane package00 plane00 city00-00\n # if str(do).startswith('unload') and str(do).split()[1] == package and str(do).split()[2] == plane and str(do).split()[3] == destination:\n # clause2 = list()\n # clause2.append(-\n # self.proposition_fluent_codes[\n # p1, t])\n # clause2.append(-\n # self.proposition_fluent_codes[\n # p2, t])\n # clause2.append(\n # self.action_fluent_codes[\n # do, t])\n # self.add_clause(clause2,\n # 'control')\n #\n # ADD_RULE3_COUNT += 1\n\n # RULE\n # if there is no package needs to be transferred at a location,\n # and the location has a truck\n # drive the truck to its airport\n\n # p1: (at package__ city__-__ /\\ (it is a goal)@t\n # p2: (at truck__ city__-__)@t\n # p3: (city__-__ is not airport)\n # not p1/\\p2/\\p3 => drive_truck_to_its_airport@t\n #\n #\n # CNF: p1 V not p2 V not p3 V drive_truck_to_its_airport@t\n # if str(a).startswith('DRIVE-TRUCK'):\n # for p1 in self.problem.goal:\n # city = str(p1).split()[2]\n # for p2 in self.problem.propositions:\n # if str(p2).startswith('at truck') and str(p2).split()[2] == city:\n # for p3 in self.problem.propositions:\n # if str(p3).startswith('airport') and str(p3).split()[1] == city:\n # clause = list()\n # clause.append(self.proposition_fluent_codes[(p1, t)])\n # clause.append(-self.proposition_fluent_codes[(p2, t)])\n # clause.append(-self.proposition_fluent_codes[(p3, t)])\n # clause.append(self.action_fluent_codes[(a, t)])\n # self.add_clause(clause, \"control\")\n\n # RULE\n # if there is an airplane is loaded with a package need\n # transfer (to another city), fly airplane to the corresponding\n # city.\n\n # p1: (at airplane__ city__-__)@t\n # p2: (in package__ airplane__)@t\n # p3: ( p2 is in far)\n # p1/\\p2/\\p3 => fly_airplane_to_its_airport@t\n #\n #\n # CNF: not p1@t V not p2@t V not p3@t V fly_plane_to_airport@t\n\n # print(\"ADDED RULE 1:\")\n # print(ADD_RULE1_COUNT)\n #\n # print(\"ADDED RULE 2:\")\n # print(ADD_RULE2_COUNT)\n #\n # print(\"ADDED RULE 3:\")\n # print(ADD_RULE3_COUNT)","def generate(self):\n\n\t\tfor datapoint in self.dataSet[:]:\n\t\t\trule, degree = self.makeRule(datapoint)\n\t\t\tself.generatedRules.append((rule, degree))","def get_argparser_ctor_args():\n\n data_files_dir_path = analyzer_context.get_context().data_files_dir_path\n config_dir_path = os.path.join(data_files_dir_path, 'config')\n return {\n 'prog': 'CodeChecker checkers',\n 'formatter_class': arg.RawDescriptionDefaultHelpFormatter,\n\n # Description is shown when the command's help is queried directly\n 'description': \"Get the list of checkers available and their enabled \"\n \"status in the supported analyzers.\",\n\n # Epilogue is shown after the arguments when the help is queried\n # directly.\n 'epilog': \"\"\"\nThe list of checkers that are enabled or disabled by default can be edited by\nediting the file '{}'.\n\nEnvironment variables\n------------------------------------------------\n CC_SEVERITY_MAP_FILE Path of the checker-severity mapping config file.\n Default: '{}'\n CC_GUIDELINE_MAP_FILE Path of the checker-guideline mapping config file.\n Default: '{}'\n CC_PROFILE_MAP_FILE Path of the checker-profile mapping config file.\n Default: '{}'\n\"\"\".format(os.path.join(config_dir_path, 'checker_profile_map.json'),\n os.path.join(config_dir_path, 'checker_severity_map.json'),\n os.path.join(config_dir_path, 'checker_guideline_map.json'),\n os.path.join(config_dir_path, 'checker_profile_map.json')),\n\n # Help is shown when the \"parent\" CodeChecker command lists the\n # individual subcommands.\n 'help': \"List the checkers available for code analysis.\"\n }","def _get_installed_targets(target_types):\r\n lines = [TargetsHelp.INSTALLED_TARGETS_HEADER]\r\n for target_type in sorted(target_types.keys()):\r\n if target_types[target_type].__doc__ is None:\r\n desc = 'Description unavailable.'\r\n else:\r\n desc = target_types[target_type].__doc__.split('\\n')[0]\r\n lines.append(' %s: %s' % (\r\n TargetsHelp.TARGET_TO_ALIAS[target_type].rjust(TargetsHelp.MAX_ALIAS_LEN), desc))\r\n return lines","def setup(self):\n declared = []\n for obj in Rt.objective:\n var_list = split(\"[+*/-]\", obj)\n for v in var_list:\n if v not in declared:\n self.add_input(v)\n declared.append(v)\n self.add_output(\"Objective function \" + obj)","def get_rules(cls):\n raise NotImplementedError()","def main():\n parser = argparse.ArgumentParser(description='investigate code health and random statistics')\n sub_parsers = parser.add_subparsers(dest='command_name', title='Commands', help='', metavar='<command>')\n\n sub = sub_parsers.add_parser('line-count', help='list line counts')\n sub.add_argument('files', nargs='+', help='files or folders to look in')\n sub.add_argument('--each', type=int, default=1)\n sub.add_argument('--show', action='store_true')\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\n sub.set_defaults(func=handle_line_count)\n\n sub = sub_parsers.add_parser('include-list', help='list headers from files')\n cc.add_argument(sub)\n sub.add_argument('files', nargs='+')\n sub.add_argument('--print', dest='print_files', action='store_true')\n sub.add_argument('--print-stats', dest='print_stats', action='store_true')\n sub.add_argument('--print-max', dest='print_max', action='store_true')\n sub.add_argument('--no-list', dest='print_list', action='store_false')\n sub.add_argument('--count', default=2, type=int, help=\"only print includes that are more or equal to <count>\")\n sub.add_argument('--limit', nargs='+', help=\"limit search to theese files and folders\")\n sub.set_defaults(func=handle_list)\n\n sub = sub_parsers.add_parser('include-gv', help='generate a graphviz of the includes')\n cc.add_argument(sub)\n sub.add_argument('files', nargs='+')\n sub.add_argument('--limit', nargs='+', help=\"limit search to theese files and folders\")\n sub.add_argument('--group', action='store_true', help=\"group output\")\n sub.add_argument('--cluster', action='store_true', help=\"group output into clusters\")\n sub.set_defaults(func=handle_gv)\n\n sub = sub_parsers.add_parser('list-indents', help='list the files with the maximum indents')\n sub.add_argument('files', nargs='+')\n sub.add_argument('--each', type=int, default=1, help='group counts')\n sub.add_argument('--show', action='store_true', help='include files in list')\n sub.add_argument('--hist', action='store_true', help='show simple histogram')\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\n sub.set_defaults(func=handle_list_indents)\n\n sub = sub_parsers.add_parser('missing-pragma-once', help='find headers with missing include guards')\n sub.add_argument('files', nargs='+')\n sub.set_defaults(func=handle_missing_include_guards)\n\n sub = sub_parsers.add_parser('missing-in-cmake', help='find files that existis on disk but missing in cmake')\n sub.add_argument('files', nargs='+')\n cc.add_argument(sub)\n sub.set_defaults(func=handle_missing_in_cmake)\n\n sub = sub_parsers.add_parser('list-no-project-folders', help='find projects that have not set the solution folder')\n sub.add_argument('files', nargs='+')\n cc.add_argument(sub)\n sub.set_defaults(func=handle_list_no_project_folder)\n\n sub = sub_parsers.add_parser('check-files', help=\"find files that doesn't match the name style\")\n sub.add_argument('files', nargs='+')\n cc.add_argument(sub)\n sub.set_defaults(func=handle_check_files)\n\n args = parser.parse_args()\n if args.command_name is not None:\n args.func(args)\n else:\n parser.print_help()","def test_get_analyzer(self):\n analyzer_class = manager.AnalysisManager.get_analyzer('mockanalyzer')\n self.assertEqual(analyzer_class, MockAnalyzer)","def get_rules(self):\n rules = []\n for item in self.name:\n rules.append(item)\n return rules","def list_resolver_rule_associations(MaxResults=None, NextToken=None, Filters=None):\n pass","def main():\n\t#ps = PackageScanner()\n\t#packages = ps.getInstalledPackages()\n\t#print(packages)\n\t#ps.saveScanResults()\n\n\tan = Analyzer()\n\tan.loadFromFile(config.PKG_SCAN_DIR / config.PKG_SCAN_FILE)\n\t#an.loadFromPackageCont(packages)\n\tan.analyze()\n\tan.saveAnalysisResults()","def test_analyzer():\n import analyzer\n\n analyzer # Fake usage.","def doc_analyzer(self, doc):\n\n if self.lowercase is None or self.lowercase == 'none':\n lowercase = set()\n elif self.lowercase in {'both', 'all'}:\n lowercase = {'char', 'word'}\n else: lowercase = {self.lowercase}\n\n # character n-grams\n if 'char' in lowercase:\n docfeat = self.get_ngrams(list(doc.lower()),\n self.c_ngmin, self.c_ngmax)\n else:\n docfeat = self.get_ngrams(list(doc),\n self.c_ngmin, self.c_ngmax)\n # word n-grams\n if 'word' in lowercase:\n docfeat.extend(self.get_ngrams(self.tokenizer(doc.lower()),\n self.w_ngmin, self.w_ngmax,\n suffix=\"⅏\", separator=\" \"))\n else:\n docfeat.extend(self.get_ngrams(self.tokenizer(doc),\n self.w_ngmin, self.w_ngmax,\n suffix=\"⅏\", separator=\" \"))\n return docfeat","def get_checker_configs(self):\n LOG.debug(\"Tidy extra args: %s\", self.analyzer_extra_arguments)\n\n res = []\n\n # Get config from the extra arguments if there is any.\n try:\n tidy_config_parser = argparse.ArgumentParser()\n tidy_config_parser.add_argument('-config',\n dest='tidy_config',\n default='',\n type=str)\n\n args, _ = tidy_config_parser.parse_known_args(\n shlex.split(self.analyzer_extra_arguments))\n\n except Exception as ex:\n LOG.debug('No config found in the tidy extra args.')\n LOG.debug(ex)\n return res\n\n try:\n # Match for clang tidy analyzer names and attributes.\n clang_tidy_checker_rex = r'^(?P<checker_name>([^.]+))' \\\n r'\\.(?P<checker_attribute>([^.]+))$'\n\n tidy_pattern = re.compile(clang_tidy_checker_rex)\n tidy_config = json.loads(args.tidy_config)\n for checker_option in tidy_config.get('CheckOptions', []):\n value = checker_option['value']\n # We only store configs related to tidy checks. We run static\n # analyzer separately, so it does not affect the SA invocation.\n key_values_tidy = re.match(tidy_pattern, checker_option['key'])\n if key_values_tidy:\n checker_name = key_values_tidy.group('checker_name')\n checker_attr = key_values_tidy.group('checker_attribute')\n res.append((checker_name, checker_attr, value))\n else:\n LOG.debug('no match')\n except ValueError as verr:\n LOG.debug('Failed to parse config.')\n LOG.debug(verr)\n except Exception as ex:\n LOG.debug('Failed to process config.')\n LOG.debug(ex)\n\n return res","def generate_schema_list():\n src = os.path.join(os.path.dirname(__file__), '../schemas')\n for root, dirs, files in os.walk(src):\n for fname in files:\n if not fname.endswith('.yaml'):\n continue\n if os.path.splitext(fname)[0] in (\n 'draft-01', 'asdf-schema-1.0.0'):\n continue\n yield os.path.join(root, fname)","def getAnalyzerIndex(self, name):\n\n self.ensureNotCreated()\n\n if not name in self.analyzers:\n raise Exception('Analyzer %r is not present in the framework configuration' % name)\n\n return self.analyzers.index(name)","def affixes(etymologies):\n for affix in AFFIXES:\n name = FIRST(affix.groupindex.keys())\n print('building {} dictionary...'.format(name))\n dictionary = defaultdict(list)\n with ProgressBar(maxval=len(etymologies)) as progress:\n for i, key in enumerate(etymologies):\n for definition in etymologies[key]:\n if definition.has_key(name):\n dictionary[key].append(definition)\n progress.update(i)\n yield name, dictionary","def __init__(self):\n \n self.label = \"ArcSDM Tools\"\n self.alias = \"ArcSDM\" \n\n # List of tool classes associated with this toolbox\n self.tools = [PartitionNNInputFiles, CombineNNOutputFiles, NeuralNetworkOutputFiles, NeuralNetworkInputFiles, \n CalculateWeightsTool,SiteReductionTool,CategoricalMembershipToool,\n CategoricalAndReclassTool, TOCFuzzificationTool, CalculateResponse, LogisticRegressionTool, Symbolize, \n ROCTool, AgterbergChengCITest, AreaFrequencyTable, GetSDMValues, GrandWofe]","def root_lex_analysis():\n\n return {\"cat\": \"VerbLex\",\n \"vform\": \"bare\",\n \"orthoForm\": [\"*RootForm\"]}","def generateRules(L, support_data, min_confidence=0.5):\r\n rules = []\r\n for i in range(1, len(L)):\r\n for freqSet in L[i]:\r\n H1 = [frozenset([item]) for item in freqSet]\r\n if (i > 1):\r\n rules_from_conseq(freqSet, H1, support_data, rules, min_confidence)\r\n else:\r\n calc_confidence(freqSet, H1, support_data, rules, min_confidence)\r\n return rules","def main():\n if len(sys.argv) == 1:\n print(\"No dependencies file to validate!\")\n return\n dependencies_file = sys.argv[1]\n try:\n dependencies = json.loads(open(dependencies_file, 'r').read())\n except json.decoder.JSONDecodeError:\n print(\"Invalid dependency file syntax! Make sure you don't have any commas at the end of your last dependency.\")\n return\n for dependency in dependencies:\n if 'target_path' in dependency and 'repository' in dependency:\n print(\"Validated {}\".format(dependency['target_path']))\n suggest_edits(dependency)\n elif 'target_path' not in dependency and 'repository' in dependency:\n print(\"Define target_path for dependency {}\".format(dependency['repository']))\n elif 'repository' not in dependency and 'target_path' in dependency:\n print(\"Define repository for dependency {}\".format(dependency['target_path']))\n else:\n print(\"Invalid format, missing repository and target_path for dependency {}\".format(dependencies.index(dependency)))","def populate_lexical():\n in_dir = os.path.join(buildconfig.FORM_INDEX_DIR, 'refined')\n frequency_cutoff = buildconfig.FREQUENCY_CUTOFF\n\n taxonomy = LanguageTaxonomy()\n lemma_counter = 0\n definition_counter = 0\n\n for letter in string.ascii_lowercase:\n stdout.write('Inserting data for %s...\\n' % letter)\n blocks = []\n in_file = os.path.join(in_dir, letter + '.json')\n with open(in_file, 'r') as filehandle:\n for line in filehandle:\n data = json.loads(line.strip())\n blocks.append(BlockData(*data))\n\n lemmas = []\n wordforms = []\n definitions = []\n for i, block in enumerate(blocks):\n lang_node = taxonomy.node(language=block.language)\n if lang_node is None:\n language_id = None\n else:\n language_id = lang_node.id\n\n if block.definition and block.f2000 < frequency_cutoff:\n definition_counter += 1\n definitions.append(Definition(id=definition_counter,\n text=block.definition[:100]))\n definition_id = definition_counter\n else:\n definition_id = None\n\n lemma_counter += 1\n lemmas.append(Lemma(id=lemma_counter,\n lemma=block.lemma,\n sort=block.sort,\n wordclass=block.wordclass,\n firstyear=block.start,\n lastyear=block.end,\n refentry=block.refentry,\n refid=block.refid,\n thesaurus_id=block.htlink,\n language_id=language_id,\n definition_id=definition_id,\n f2000=_rounder(block.f2000),\n f1950=_rounder(block.f1950),\n f1900=_rounder(block.f1900),\n f1850=_rounder(block.f1850),\n f1800=_rounder(block.f1800),\n f1750=_rounder(block.f1750),))\n\n for typelist in (block.standard_types,\n block.variant_types,\n block.alien_types):\n for typeunit in typelist:\n wordforms.append(Wordform(sort=typeunit[0],\n wordform=typeunit[1],\n wordclass=typeunit[2],\n lemma_id=lemma_counter,\n f2000=_rounder(typeunit[4]),\n f1900=_rounder(typeunit[5]),\n f1800=_rounder(typeunit[6]),))\n\n if i % 1000 == 0:\n Definition.objects.bulk_create(definitions)\n Lemma.objects.bulk_create(lemmas)\n Wordform.objects.bulk_create(wordforms)\n definitions = []\n lemmas = []\n wordforms = []\n\n Definition.objects.bulk_create(definitions)\n Lemma.objects.bulk_create(lemmas)\n Wordform.objects.bulk_create(wordforms)","def getChecks(self):\r\n raise AbstractError\r\n return []","def get_all_relaxed_candidates_after_generation(self, gen):\n q = 'relaxed=1,extinct=0,generation<={0}'\n entries = self.c.select(q.format(gen))\n\n trajs = []\n for v in entries:\n t = self.get_atoms(id=v.id)\n t.info['confid'] = v.gaid\n t.info['relax_id'] = v.id\n trajs.append(t)\n trajs.sort(key=lambda x: get_raw_score(x),\n reverse=True)\n return trajs"],"string":"[\n \"def list_analyzers(args: argparse.Namespace):\\n first = True\\n queue = [tuple(c) + (\\\"lookout.\\\",) for c in pkgutil.iter_modules(lookout.__path__)]\\n while queue:\\n importer, name, ispkg, prefix = queue.pop(0)\\n\\n if not ispkg or name == \\\"core\\\":\\n continue\\n\\n m = importer.find_module(name).load_module(name)\\n if getattr(m, \\\"__meta__\\\", False):\\n queue.extend(tuple(c) + (prefix + name + \\\".\\\",)\\n for c in pkgutil.iter_modules(m.__path__))\\n continue\\n\\n try:\\n cls = m.analyzer_class\\n except AttributeError:\\n continue\\n if first:\\n first = False\\n else:\\n print()\\n print(prefix + name)\\n print(\\\"\\\\t%s\\\" % cls.version)\\n print(\\\"\\\\t\\\" + cls.description)\",\n \"def get_analyzers(cls):\\n for analyzer_name, analyzer_class in iter(cls._class_registry.items()):\\n yield analyzer_name, analyzer_class\",\n \"def main(args):\\n\\n # If the given output format is not 'table', redirect logger's output to\\n # the stderr.\\n logger.setup_logger(args.verbose if 'verbose' in args else None,\\n None if args.output_format == 'table' else 'stderr')\\n\\n context = analyzer_context.get_context()\\n working_analyzers, errored = analyzer_types.check_supported_analyzers(\\n args.analyzers,\\n context)\\n analyzer_types.check_available_analyzers(working_analyzers, errored)\\n\\n analyzer_environment = env.extend(context.path_env_extra,\\n context.ld_lib_path_extra)\\n\\n analyzer_config_map = analyzer_types.build_config_handlers(\\n args, context, working_analyzers)\\n\\n def uglify(text):\\n \\\"\\\"\\\"\\n csv and json format output contain this non human readable header\\n string: no CamelCase and no space.\\n \\\"\\\"\\\"\\n return text.lower().replace(' ', '_')\\n\\n def match_guideline(checker_name, selected_guidelines):\\n \\\"\\\"\\\"\\n Returns True if checker_name gives reports related to any of the\\n selected guideline rule.\\n checker_name -- A full checker name.\\n selected_guidelines -- A list of guideline names or guideline rule IDs.\\n \\\"\\\"\\\"\\n guideline = context.guideline_map.get(checker_name, {})\\n guideline_set = set(guideline)\\n for value in guideline.values():\\n guideline_set |= set(value)\\n\\n return any(g in guideline_set for g in selected_guidelines)\\n\\n def format_guideline(guideline):\\n \\\"\\\"\\\"\\n Convert guideline rules to human-readable format.\\n guideline -- Dictionary in the following format:\\n {\\\"guideline_1\\\": [\\\"rule_1\\\", \\\"rule_2\\\"]}\\n \\\"\\\"\\\"\\n return ' '.join('Related {} rules: {}'.format(g, ', '.join(r))\\n for g, r in guideline.items())\\n\\n # List available checker profiles.\\n if 'profile' in args and args.profile == 'list':\\n if 'details' in args:\\n header = ['Profile name', 'Description']\\n rows = context.profile_map.available_profiles().items()\\n else:\\n header = ['Profile name']\\n rows = [(key, \\\"\\\") for key in\\n context.profile_map.available_profiles()]\\n\\n if args.output_format in ['csv', 'json']:\\n header = list(map(uglify, header))\\n\\n print(twodim.to_str(args.output_format, header, rows))\\n return\\n\\n # List checker config options.\\n if 'checker_config' in args:\\n if 'details' in args:\\n header = ['Option', 'Description']\\n else:\\n header = ['Option']\\n\\n if args.output_format in ['csv', 'json']:\\n header = list(map(uglify, header))\\n\\n rows = []\\n analyzer_failures = []\\n for analyzer in working_analyzers:\\n config_handler = analyzer_config_map.get(analyzer)\\n analyzer_class = analyzer_types.supported_analyzers[analyzer]\\n\\n configs = analyzer_class.get_checker_config(config_handler,\\n analyzer_environment)\\n if not configs:\\n analyzer_failures.append(analyzer)\\n continue\\n\\n rows.extend((':'.join((analyzer, c[0])), c[1]) if 'details' in args\\n else (':'.join((analyzer, c[0])),) for c in configs)\\n\\n if rows:\\n print(twodim.to_str(args.output_format, header, rows))\\n\\n if analyzer_failures:\\n LOG.error(\\\"Failed to get checker configuration options for '%s' \\\"\\n \\\"analyzer(s)! Please try to upgrade your analyzer \\\"\\n \\\"version to use this feature.\\\",\\n ', '.join(analyzer_failures))\\n sys.exit(1)\\n\\n return\\n\\n if args.guideline is not None and len(args.guideline) == 0:\\n result = defaultdict(set)\\n\\n for _, guidelines in context.guideline_map.items():\\n for guideline, rules in guidelines.items():\\n result[guideline] |= set(rules)\\n\\n header = ['Guideline', 'Rules']\\n if args.output_format in ['csv', 'json']:\\n header = list(map(uglify, header))\\n\\n if args.output_format == 'json':\\n rows = [(g, sorted(list(r))) for g, r in result.items()]\\n else:\\n rows = [(g, ', '.join(sorted(r))) for g, r in result.items()]\\n\\n if args.output_format == 'rows':\\n for row in rows:\\n print('Guideline: {}'.format(row[0]))\\n print('Rules: {}'.format(row[1]))\\n else:\\n print(twodim.to_str(args.output_format, header, rows))\\n return\\n\\n # List available checkers.\\n if 'details' in args:\\n header = ['Enabled', 'Name', 'Analyzer', 'Severity', 'Guideline',\\n 'Description']\\n else:\\n header = ['Name']\\n\\n if args.output_format in ['csv', 'json']:\\n header = list(map(uglify, header))\\n\\n rows = []\\n for analyzer in working_analyzers:\\n config_handler = analyzer_config_map.get(analyzer)\\n analyzer_class = analyzer_types.supported_analyzers[analyzer]\\n\\n checkers = analyzer_class.get_analyzer_checkers(config_handler,\\n analyzer_environment)\\n\\n profile_checkers = []\\n if 'profile' in args:\\n if args.profile not in context.profile_map.available_profiles():\\n LOG.error(\\\"Checker profile '%s' does not exist!\\\",\\n args.profile)\\n LOG.error(\\\"To list available profiles, use '--profile list'.\\\")\\n sys.exit(1)\\n\\n profile_checkers = [('profile:' + args.profile, True)]\\n\\n config_handler.initialize_checkers(context,\\n checkers,\\n profile_checkers)\\n\\n for checker_name, value in config_handler.checks().items():\\n state, description = value\\n\\n if state != CheckerState.enabled and 'profile' in args:\\n continue\\n\\n if state == CheckerState.enabled and 'only_disabled' in args:\\n continue\\n elif state != CheckerState.enabled and 'only_enabled' in args:\\n continue\\n\\n if args.output_format == 'json':\\n state = state == CheckerState.enabled\\n else:\\n state = '+' if state == CheckerState.enabled else '-'\\n\\n if args.guideline is not None:\\n if not match_guideline(checker_name, args.guideline):\\n continue\\n\\n if 'details' in args:\\n severity = context.severity_map.get(checker_name)\\n guideline = context.guideline_map.get(checker_name, {})\\n if args.output_format != 'json':\\n guideline = format_guideline(guideline)\\n rows.append([state, checker_name, analyzer,\\n severity, guideline, description])\\n else:\\n rows.append([checker_name])\\n\\n if 'show_warnings' in args:\\n severity = context.severity_map.get('clang-diagnostic-')\\n for warning in get_warnings(analyzer_environment):\\n warning = 'clang-diagnostic-' + warning\\n\\n if args.guideline is not None:\\n if not match_guideline(warning, args.guideline):\\n continue\\n\\n guideline = context.guideline_map.get(warning, {})\\n if args.output_format != 'json':\\n guideline = format_guideline(guideline)\\n\\n if 'details' in args:\\n rows.append(['', warning, '-', severity, guideline, '-'])\\n else:\\n rows.append([warning])\\n\\n if rows:\\n print(twodim.to_str(args.output_format, header, rows))\\n\\n analyzer_types.print_unsupported_analyzers(errored)\",\n \"def __init__(self, analyzers: Iterable[Type[Analyzer]], model_repository: ModelRepository,\\n data_service: DataService):\\n self._model_repository = model_repository\\n analyzers = [(a.__name__, a) for a in analyzers]\\n analyzers.sort()\\n self._analyzers = [a[1] for a in analyzers]\\n self._data_service = data_service\",\n \"def test_get_analyzers(self):\\n analyzers = manager.AnalysisManager.get_analyzers()\\n analyzer_list = [x for x in analyzers]\\n first_analyzer_tuple = analyzer_list[0]\\n analyzer_name, analyzer_class = first_analyzer_tuple\\n self.assertIsInstance(analyzer_list, list)\\n self.assertIsInstance(first_analyzer_tuple, tuple)\\n self.assertEqual(analyzer_class, MockAnalyzer)\\n self.assertEqual(analyzer_name, 'mockanalyzer')\",\n \"def initAnalyzer():\\n return controller.initAnalyzer()\",\n \"def check_for_analyzers(self):\\n executed = self.analyzer_state.executed_analyzers()\\n for analyzer in executed:\\n # check for wish\\n if self.analyzer_state.check_wish(analyzer, 'cancel'):\\n print(\\\"validator: cancelled {} upon request\\\".format(analyzer['_id']))\\n continue\\n\\n print(\\\"validating and committing {}\\\".format(analyzer['_id']))\\n\\n self.analyzer_state.transition(analyzer['_id'], 'executed', 'validating')\\n\\n exe_res = analyzer['execution_result']\\n temporary_coll = self.cc.temporary_db[exe_res['temporary_coll']]\\n\\n if exe_res['timespans'] is not None and exe_res['upload_ids'] is not None:\\n self.analyzer_state.transition_to_error(analyzer['_id'],\\n 'internal error: either timespans or upload_ids can have a '\\n 'value but not both. I cannot decide if direct or normal analyzer')\\n continue\\n\\n if exe_res['timespans'] is None and exe_res['upload_ids'] is None:\\n self.analyzer_state.transition_to_error(analyzer['_id'],\\n 'internal error: it\\\\'s not allowed to have both timespans and upload_ids to be None. '\\n 'I cannot decide if direct or normal analyzer')\\n continue\\n\\n if exe_res['upload_ids'] is not None:\\n print(\\\"using direct commit\\\")\\n valid_count, errors, action_id = commit_direct(\\n analyzer['_id'], analyzer['working_dir'], self._action_id_creator,\\n exe_res['upload_ids'], exe_res['max_action_id'], temporary_coll,\\n self.cc.observations_coll, analyzer['output_types'],\\n self.cc.action_log)\\n else:\\n print(\\\"using normal commit\\\")\\n valid_count, errors, action_id = commit_normal(\\n analyzer['_id'], analyzer['working_dir'], self._action_id_creator,\\n exe_res['timespans'], exe_res['max_action_id'], temporary_coll,\\n self.cc.observations_coll, analyzer['output_types'],\\n self.cc.action_log)\\n\\n if len(errors) > 0:\\n print(\\\"analyzer {} with action id {} has at least {} valid records but {} have problems:\\\".format(analyzer['_id'], action_id, valid_count, len(errors)))\\n for idx, error in enumerate(errors):\\n print(\\\"{}: {}\\\".format(idx, error))\\n\\n self.analyzer_state.transition_to_error(analyzer['_id'], 'error when executing validator:\\\\n' + '\\\\n'.join((str(error) for error in errors)))\\n else:\\n print(\\\"successfully commited analyzer {} run with action id {}. {} records inserted\\\".format(analyzer['_id'], action_id, valid_count))\\n self.analyzer_state.transition(analyzer['_id'], 'validating', 'sensing', {'action_id': action_id})\",\n \"def newAnalyzer():\\n analyzer = {'crimes': None,\\n 'dateIndex': None,\\n 'autors': None,\\n 'instrumentalness': None,\\n 'tempo':None,\\n 'liveness':None,\\n 'speechiness':None,\\n 'danceability':None,\\n 'valence':None,\\n 'loudness':None,\\n 'acousticness':None,\\n 'energy':None,\\n 'generos':None\\n }\\n\\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n\\n analyzer['autors'] = om.newMap(omaptype='RBT',\\n comparefunction=compareAUTOR)\\n\\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['tempo'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['liveness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['danceability'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n analyzer['valence'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['loudness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['energy'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n\\n analyzer['generos']= m.newMap(11,\\n maptype='CHAINING',\\n loadfactor=4.0)\\n \\n return analyzer\",\n \"def create_analysis_tools(self):\\r\\n raise NotImplementedError()\",\n \"def create_phases(inputs):\\n feed_tensors = inputs.values()\\n\\n remaining_analyzers = tf.get_collection(analyzers.ANALYZER_COLLECTION)\\n analyzer_output_ready = {}\\n for analyzer in remaining_analyzers:\\n for tensor in analyzer.outputs:\\n analyzer_output_ready[tensor] = False\\n\\n # Construct `AnalyzerInfo`s, removing any tensors that are analyzer outputs\\n # from the ASSET_FILEPATHS collection. These tensors will be replaced and\\n # the replacements will be added to the ASSET_FILEPATHS. Setting\\n # AnalyzerOutputInfo.is_asset instructs the implementation to do this.\\n asset_filepaths_collection = tf.get_collection_ref(\\n tf.GraphKeys.ASSET_FILEPATHS)\\n asset_filepaths = collections.OrderedDict(\\n (tensor, True)\\n for tensor in tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS))\\n\\n phases = []\\n while remaining_analyzers:\\n analyzer_inputs = []\\n for analyzer in remaining_analyzers:\\n analyzer_inputs.extend(analyzer.inputs)\\n ready_init_ops, ready_analyzer_inputs = (\\n graph_tools.determine_ready_tensors_and_table_initializers(\\n tf.get_default_graph(), analyzer_inputs, feed_tensors,\\n analyzer_output_ready))\\n ready_analyzer_inputs = set(ready_analyzer_inputs)\\n\\n new_remaining_analyzers = []\\n analyzer_infos = []\\n for analyzer in remaining_analyzers:\\n if all(tensor in ready_analyzer_inputs for tensor in analyzer.inputs):\\n input_tensor_names = [tensor.name for tensor in analyzer.inputs]\\n output_infos = [\\n AnalyzerOutputInfo(tensor.name, asset_filepaths.pop(tensor, False))\\n for tensor in analyzer.outputs]\\n analyzer_infos.append(AnalyzerInfo(\\n input_tensor_names, analyzer.attributes, output_infos))\\n\\n for tensor in analyzer.outputs:\\n analyzer_output_ready[tensor] = True\\n else:\\n new_remaining_analyzers.append(analyzer)\\n phases.append(Phase(analyzer_infos, ready_init_ops))\\n\\n assert len(new_remaining_analyzers) < len(remaining_analyzers)\\n remaining_analyzers = new_remaining_analyzers\\n\\n del asset_filepaths_collection[:]\\n asset_filepaths_collection.extend(six.iterkeys(asset_filepaths))\\n\\n return phases\",\n \"def newAnalyzer():\\n analyzer = {'accidents': None,\\n 'dateIndex': None,\\n 'timeIndex': None\\n }\\n\\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['timeIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareTimes)\\n \\n return analyzer\",\n \"def testGetAnalyzerInstances(self):\\n analyzer_names = manager.AnalyzersManager.GetAnalyzerNames()\\n analyzers = manager.AnalyzersManager.GetAnalyzerInstances(analyzer_names)\\n self.assertEqual(len(analyzer_names), len(analyzers))\\n for analyzer in analyzers:\\n self.assertIsInstance(analyzer, interface.BaseAnalyzer)\",\n \"def newAnalyzer():\\n analyzer = {'accidents': None,\\n 'dateIndex': None\\n }\\n\\n analyzer['accidents'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='BST',\\n comparefunction=compareDates)\\n return analyzer\",\n \"def add_analyzer_arg(parser):\\n parser.add(\\\"analyzer\\\", nargs=\\\"+\\\",\\n help=\\\"Fully qualified package name with an analyzer. Current directory is \\\"\\n \\\"included into PYTHONPATH.\\\")\",\n \"def _build_analyzer():\\n analyzer = {}\\n for e in LOSSLESS:\\n analyzer[e] = lambda d, _ext, name: d.lossless.append(name)\\n for e in COMPRESSED:\\n analyzer[e] = lambda d, _ext, name: d.compressed.append(name)\\n for e in IMAGES:\\n analyzer[e] = lambda d, _ext, name: d.images.append(name)\\n for e in VIDEOS:\\n analyzer[e] = lambda d, _ext, name: d.videos.append(name)\\n\\n def _increment_ignored(d, _ext, _name):\\n d.ignored += 1 # Can't use assignment in lambda\\n\\n for e in IGNORE:\\n analyzer[e] = _increment_ignored\\n analyzer['cue'] = lambda d, _, name: d.cue.append(name)\\n\\n return analyzer\",\n \"def newAnalyzer():\\n analyzer = {'accidentes': None,\\n 'dateIndex': None\\n }\\n\\n analyzer['accidentes'] = lt.newList('SINGLE_LINKED', compareseverity)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n return analyzer\",\n \"def _generate_index_analysis(self, query_analysis, indexes):\\n needs_recommendation = True\\n full_indexes = []\\n partial_indexes = []\\n coverage = \\\"unknown\\\"\\n\\n if indexes is not None:\\n for index_key in indexes.keys():\\n index = indexes[index_key]\\n index_report = self._generate_index_report(index,\\n query_analysis)\\n if index_report['supported'] is True:\\n if index_report['coverage'] == 'full':\\n full_indexes.append(index_report)\\n if index_report['idealOrder']:\\n needs_recommendation = False\\n elif index_report['coverage'] == 'partial':\\n partial_indexes.append(index_report)\\n\\n if len(full_indexes) > 0:\\n coverage = \\\"full\\\"\\n elif (len(partial_indexes)) > 0:\\n coverage = \\\"partial\\\"\\n elif query_analysis['supported']:\\n coverage = \\\"none\\\"\\n\\n # INDEX ANALYSIS\\n return OrderedDict([('indexStatus', coverage),\\n ('fullIndexes', full_indexes),\\n ('partialIndexes', partial_indexes)])\",\n \"def run_analyzers(args: argparse.Namespace):\\n log = logging.getLogger(\\\"run\\\")\\n model_repository = create_model_repo_from_args(args)\\n log.info(\\\"Created %s\\\", model_repository)\\n if args.request_server == \\\"auto\\\":\\n data_request_address = \\\"%s:10301\\\" % args.server.split(\\\":\\\")[0]\\n else:\\n data_request_address = args.request_server\\n data_service = DataService(data_request_address)\\n log.info(\\\"Created %s\\\", data_service)\\n sys.path.append(os.getcwd())\\n manager = AnalyzerManager(\\n analyzers=[importlib.import_module(a).analyzer_class for a in args.analyzer],\\n model_repository=model_repository,\\n data_service=data_service,\\n )\\n sys.path = sys.path[:-1]\\n log.info(\\\"Created %s\\\", manager)\\n listener = EventListener(address=args.server, handlers=manager, n_workers=args.workers)\\n log.info(\\\"Created %s\\\", listener)\\n listener.start()\\n log.info(\\\"Listening %s\\\", args.server)\\n listener.block()\\n model_repository.shutdown()\\n data_service.shutdown()\",\n \"def _generate_index_analysis(self, query_analysis, indexes):\\r\\n needs_recommendation = True\\r\\n full_indexes = []\\r\\n partial_indexes = []\\r\\n coverage = \\\"unknown\\\"\\r\\n\\r\\n if indexes is not None:\\r\\n for index_key in indexes.keys():\\r\\n index = indexes[index_key]\\r\\n index_report = self._generate_index_report(index,\\r\\n query_analysis)\\r\\n if index_report['supported'] is True:\\r\\n if index_report['coverage'] == 'full':\\r\\n full_indexes.append(index_report)\\r\\n if index_report['idealOrder']:\\r\\n needs_recommendation = False\\r\\n elif index_report['coverage'] == 'partial':\\r\\n partial_indexes.append(index_report)\\r\\n\\r\\n if len(full_indexes) > 0:\\r\\n coverage = \\\"full\\\"\\r\\n elif (len(partial_indexes)) > 0:\\r\\n coverage = \\\"partial\\\"\\r\\n elif query_analysis['supported']:\\r\\n coverage = \\\"none\\\"\\r\\n\\r\\n # INDEX ANALYSIS\\r\\n return OrderedDict([('indexStatus', coverage),\\r\\n ('fullIndexes', full_indexes),\\r\\n ('partialIndexes', partial_indexes)])\",\n \"def build_analyzer(self):\\n\\t\\tanalyser = super(TfidfVectorizer, self).build_analyzer()\\n\\t\\treturn lambda doc: (lemmatizer.lemmatize(w) for w in analyser(doc))\",\n \"def get_learners(beam_width, min_covered_examples, max_rule_length):\\n ordered_entropy = Orange.classification.rules.CN2Learner()\\n\\n unordered = Orange.classification.rules.CN2UnorderedLearner()\\n\\n laplace = Orange.classification.rules.CN2Learner()\\n laplace.rule_finder.quality_evaluator = Orange.classification.rules.LaplaceAccuracyEvaluator()\\n\\n learners = [ordered_entropy, unordered, laplace]\\n labels = [\\\"ordered\\\", \\\"unordered\\\", \\\"laplace\\\"]\\n\\n for learner in learners:\\n \\\"\\\"\\\"\\n Parametrize the learners\\n \\\"\\\"\\\"\\n learner.rule_finder.search_algorithm.beam_width = beam_width\\n learner.rule_finder.general_validator.min_covered_examples = min_covered_examples\\n learner.rule_finder.general_validator.max_rule_length = max_rule_length\\n\\n return learners, labels\",\n \"def newAnalyzer():\\n analyzer = {'tracks': None,\\n 'songs': None,\\n 'artists': None,\\n 'char': None,\\n 'char2': None,\\n 'char3': None,\\n }\\n\\n analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)\\n analyzer['songs'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['artists'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char2'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char3'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char4'] = mp.newMap(numelements=50,\\n maptype='PROBING',\\n comparefunction=compareValue)\\n \\n return analyzer\",\n \"def required_tool_results():\\n return [MethylResultModule]\",\n \"def generate(env, daos_prefix, comp_prefix, args):\\n analyzer = Analyzer(env, daos_prefix, comp_prefix, args)\\n analyzer.analyze_on_exit()\",\n \"def list_resolver_rules(MaxResults=None, NextToken=None, Filters=None):\\n pass\",\n \"def handle(self, *args, **options):\\n chains = sorted(plugin.BY_REQUIREMENTS.keys())\\n for chain in chains:\\n print(chain, \\\"chain:\\\")\\n reqs = sorted(\\\"{} -> {}\\\".format(\\\", \\\".join(sorted(k)) or 'START',\\n \\\", \\\".join(sorted(v))) for k, v in\\n plugin.BY_REQUIREMENTS[chain].iteritems())\\n for req in reqs:\\n print(\\\"-\\\", req)\",\n \"def build_suggesters(DomainName=None):\\n pass\",\n \"def do_generate(self, args):\\n\\t\\t[lang.hierarchyLengths for lang in self.languages]\",\n \"def requires(self):\\n bamcls = self.parent()[0]\\n indexcls = ratatosk.lib.tools.samtools.Index\\n return [bamcls(target=self.source()[0])] + [CombineVariants(target=os.path.join(self.outdir, \\\"CombinedVariants.vcf\\\"))] + [indexcls(target=rreplace(self.source()[0], bamcls().sfx(), indexcls().sfx(), 1), parent_task=fullclassname(bamcls))]\",\n \"def main(aligner):\\n\\n # load config file\\n config = file_utils.load_json(\\n os.path.join(\\n os.path.dirname(os.path.realpath(__file__)),\\n 'config',\\n 'normal_config.json',\\n ))\\n\\n # map of type of analyses required before particular analysis can run\\n # note: keep this order to avoid checking requirements more than once\\n required_analyses_map = {\\n 'annotation': [\\n 'hmmcopy',\\n 'align',\\n ],\\n 'hmmcopy': ['align'],\\n 'align': [],\\n }\\n\\n # get colossus analysis information objects with status not complete\\n analyses = colossus_api.list(\\n \\\"analysis_information\\\",\\n analysis_run__run_status_ne=\\\"complete\\\",\\n aligner=aligner if aligner else config[\\\"default_aligner\\\"],\\n )\\n\\n for analysis in analyses:\\n # get library id\\n library_id = analysis[\\\"library\\\"][\\\"pool_id\\\"]\\n log.info(f\\\"{library_id}\\\")\\n\\n # skip analysis if marked as complete\\n status = analysis[\\\"analysis_run\\\"][\\\"run_status\\\"]\\n\\n # skip analyses older than this year\\n # parse off ending time range\\n last_updated_date = parser.parse(analysis[\\\"analysis_run\\\"][\\\"last_updated\\\"][:-6])\\n if last_updated_date < datetime(2020, 1, 1):\\n continue\\n\\n jira_ticket = analysis[\\\"analysis_jira_ticket\\\"]\\n log.info(f\\\"checking ticket {jira_ticket} library {library_id}\\\")\\n for analysis_type in required_analyses_map:\\n log.info(f\\\"checking requirements for {analysis_type}\\\")\\n # check if analysis exists on tantalus\\n try:\\n tantalus_analysis = tantalus_api.get(\\n 'analysis',\\n jira_ticket=jira_ticket,\\n analysis_type__name=analysis_type,\\n )\\n except:\\n tantalus_analysis = None\\n\\n if tantalus_analysis is not None:\\n # check if running or complete\\n status = tantalus_analysis[\\\"status\\\"]\\n if status in ('running', 'complete'):\\n log.info(f\\\"skipping {analysis_type} for {jira_ticket} since status is {status}\\\")\\n\\n # update run status on colossus\\n if analysis_type == \\\"annotation\\\" and status == \\\"complete\\\":\\n analysis_run_id = analysis[\\\"analysis_run\\\"][\\\"id\\\"]\\n analysis_run = colossus_api.get(\\\"analysis_run\\\", id=analysis_run_id)\\n colossus_api.update(\\\"analysis_run\\\", id=analysis_run_id, run_status=\\\"complete\\\")\\n\\n continue\\n\\n log.info(f\\\"running {analysis_type} in library {library_id} with ticket {jira_ticket}\\\")\\n # otherwise run analysis\\n saltant_utils.run_analysis(\\n tantalus_analysis['id'],\\n analysis_type,\\n jira_ticket,\\n config[\\\"scp_version\\\"],\\n library_id,\\n aligner if aligner else config[\\\"default_aligner\\\"],\\n config,\\n )\\n else:\\n # set boolean determining trigger of run\\n is_ready_to_create = True\\n # check if required completed analyses exist\\n for required_analysis_type in required_analyses_map[analysis_type]:\\n try:\\n required_analysis = tantalus_api.get(\\n 'analysis',\\n jira_ticket=jira_ticket,\\n analysis_type__name=required_analysis_type,\\n status=\\\"complete\\\",\\n )\\n except:\\n log.error(\\n f\\\"a completed {required_analysis_type} analysis is required to run before {analysis_type} runs for {jira_ticket}\\\"\\n )\\n # set boolean as false since analysis cannot be created yet\\n is_ready_to_create = False\\n break\\n\\n # create analysis and trigger on saltant if analysis creation has met requirements\\n if is_ready_to_create:\\n log.info(f\\\"creating {analysis_type} analysis for ticket {jira_ticket}\\\")\\n\\n try:\\n tantalus_utils.create_qc_analyses_from_library(\\n library_id,\\n jira_ticket,\\n config[\\\"scp_version\\\"],\\n analysis_type,\\n )\\n except Exception as e:\\n log.error(f\\\"failed to create {analysis_type} analysis for ticket {jira_ticket}\\\")\\n continue\\n tantalus_analysis = tantalus_api.get(\\n 'analysis',\\n jira_ticket=jira_ticket,\\n analysis_type__name=analysis_type,\\n )\\n\\n log.info(f\\\"running {analysis_type} in library {library_id} with ticket {jira_ticket}\\\")\\n saltant_utils.run_analysis(\\n tantalus_analysis['id'],\\n analysis_type,\\n jira_ticket,\\n config[\\\"scp_version\\\"],\\n library_id,\\n aligner if aligner else config[\\\"default_aligner\\\"],\\n config,\\n )\\n\\n # get completed analyses that need montage loading\\n analyses = colossus_api.list(\\n \\\"analysis_information\\\",\\n montage_status=\\\"Pending\\\",\\n analysis_run__run_status=\\\"complete\\\",\\n )\\n\\n for analysis in analyses:\\n # get library id\\n library_id = analysis[\\\"library\\\"][\\\"pool_id\\\"]\\n\\n # skip analyses older than this year\\n # parse off ending time range\\n last_updated_date = parser.parse(analysis[\\\"analysis_run\\\"][\\\"last_updated\\\"][:-6])\\n if last_updated_date < datetime(2020, 1, 1):\\n continue\\n\\n jira_ticket = analysis[\\\"analysis_jira_ticket\\\"]\\n update_jira_dlp(jira_ticket, \\\"M\\\")\\n # upload qc report to jira ticket\\n attach_qc_report(jira_ticket, library_id, config[\\\"storages\\\"])\\n\\n # load analysis into montage\\n load_ticket(jira_ticket)\",\n \"def help_analyze(self):\\n print(ANALYZE)\",\n \"def workflowLessTypes(self):\\n\\n tools = [c.getName() for c in\\n self.atgenerator.getGeneratedTools(self.package)\\n if not\\n utils.isTGVFalse(c.getTaggedValue('autoinstall'))]\\n tools.sort()\\n return tools\",\n \"def build_action_randomizers(cls, constants) -> List[ActionRandomizer]:\\n return []\",\n \"def existing_analysis_sweeps(self):\\n setup_list = self.existing_analysis_setups\\n sweep_list = []\\n s_type = self.solution_type\\n for el in setup_list:\\n sweep_list.append(el + \\\" : \\\" + s_type)\\n return sweep_list\",\n \"def list_rulesets(command):\\n namespace = app.main(command)\\n assert namespace.command == 'lr' or namespace.command == \\\"listrulesets\\\"\",\n \"def generate_sla_metrics(self):\\n for module in self.account_definitions:\\n\\n metric_spec = Definition.return_spec(\\n type_set='metric_set',\\n module=module\\n )\\n \\n metric_module = importlib.util.module_from_spec(metric_spec)\\n metric_spec.loader.exec_module(metric_module)\\n try:\\n self.metric_sets.append(metric_module.metric_set)\\n except AttributeError as _ex:\\n print(\\\"Module has no attribute metric_set\\\")\\n sla_spec = Definition.return_spec(\\n type_set='sla_set',\\n module=module\\n )\\n \\n sla_module = importlib.util.module_from_spec(sla_spec)\\n sla_spec.loader.exec_module(sla_module)\\n try:\\n self.sla_sets.append(sla_module.sla_set)\\n except AttributeError as _ex:\\n print(\\\"Module has no attribute sla_set\\\")\",\n \"def list_algorithms(CreationTimeAfter=None, CreationTimeBefore=None, MaxResults=None, NameContains=None, NextToken=None, SortBy=None, SortOrder=None):\\n pass\",\n \"def generateAssociationRule(freqSet):\",\n \"def analysis_setup(self):\\n pass\",\n \"def _make_suggestions(self):\\n\\n #build concordance based on current approved\\n concordance = dict()\\n for term in self.tree.get_children('approved'):\\n words = [word.strip(',.:;*').lower() \\\\\\n for word in str(self.tree.item(term)['values'][0]).split(' ')]\\n for word in words:\\n# if word == 'ad':\\n# messagebox.showwarning(\\\"word == 'ad'\\\",\\\"concordance={}\\\".format(concordance))\\n# pass\\n if word not in ['and', 'the', 'a', 'to', 'of'] \\\\\\n and not word.isdigit():\\n if word not in concordance:\\n concordance[word] = set([term, ])\\n else:\\n concordance[word].add(term)\\n# if word == 'ad':\\n# messagebox.showwarning(\\\"word 'ad' added?\\\",\\\"concordance={}\\\".format(concordance))\\n# pass\\n \\n \\n #so concordance now holds a list of words in approved terms along with\\\\\\n #list of index of terms() they occur in\\n \\n for term in self.tree.get_children('suggestions'):\\n self._look_in_concordance(term, concordance)\\n\\n for term in self.tree.get_children('unknown'):\\n self._look_in_concordance(term, concordance)\\n\\n self._collapse_all()\",\n \"def main(argv):\\n argparser = ArgumentParser(\\n description=\\\"Measure agreement of the sentiment corpus.\\\")\\n argparser.add_argument(\\\"dir1\\\",\\n help=\\\"directort containing annotations of the\\\"\\n \\\" first expert\\\")\\n argparser.add_argument(\\\"dir2\\\",\\n help=\\\"directort containing annotations of the\\\"\\n \\\" second expert\\\")\\n args = argparser.parse_args(argv)\\n\\n def check_arg(arg):\\n if not os.path.isdir(args.dir1) or not os.access(args.dir1, os.R_OK):\\n print(\\\"Invalid argument {!r} should be a readable\\\"\\n \\\" directory\\\".format(arg), file=sys.stderr)\\n\\n check_arg(args.dir1)\\n check_arg(args.dir2)\\n annotations = []\\n skip_ids = set()\\n for fpath1 in glob.iglob(os.path.join(args.dir1, \\\"*.tsv\\\")):\\n if not os.access(fpath1, os.R_OK):\\n print(\\\"WARNING: cannot read file {:s}\\\".format(\\n fpath1\\n ), file=sys.stderr)\\n continue\\n base_fpath1 = os.path.basename(fpath1)\\n fpath2 = os.path.join(args.dir2, base_fpath1)\\n if not os.path.exists(fpath2):\\n print(\\\"WARNING: file {:s} not found\\\".format(\\n fpath1\\n ), file=sys.stderr)\\n continue\\n elif not os.access(fpath2, os.R_OK):\\n print(\\\"WARNING: cannot read file {:s}\\\".format(\\n fpath1\\n ), file=sys.stderr)\\n continue\\n read_annotations(annotations, skip_ids, fpath1, 1)\\n read_annotations(annotations, skip_ids, fpath2, 2)\\n annotations = [(coder, item, label)\\n for (coder, item, label) in annotations\\n if item not in skip_ids]\\n at = AnnotationTask(annotations, distance=interval_distance)\\n # print(at.avg_Ao())\\n # print(at.Ae_kappa())\\n print(\\\"Cohen's Kappa: {:.4f}\\\\nKrippendorff's Alpha: {:4f}\\\".format(\\n at.kappa(), at.alpha()\\n ))\",\n \"def build_frequency_list(name_list):\\n analyzer = build_analyzer()\\n char_list = []\\n for name in name_list:\\n char_list += analyzer(name)\\n return char_list\",\n \"def testAnalyzerRegistration(self):\\n number_of_analyzers = len(manager.AnalyzersManager._analyzer_classes)\\n manager.AnalyzersManager.RegisterAnalyzer(TestAnalyzer)\\n self.assertEqual(\\n number_of_analyzers + 1, len(\\n manager.AnalyzersManager._analyzer_classes))\\n\\n with self.assertRaises(KeyError):\\n manager.AnalyzersManager.RegisterAnalyzer(TestAnalyzer)\\n\\n manager.AnalyzersManager.DeregisterAnalyzer(TestAnalyzer)\\n\\n self.assertEqual(\\n number_of_analyzers, len(manager.AnalyzersManager._analyzer_classes))\",\n \"def newAnalyzer():\\n analyzer = {'events': None,\\n \\\"musical_genre\\\":None,\\n 'artist_ID': None,\\n \\\"track_ID\\\": None,\\n \\\"instrumentalness\\\": None,\\n \\\"acousticness\\\": None,\\n \\\"liveness\\\": None,\\n \\\"speechiness\\\": None,\\n \\\"energy\\\":None,\\n \\\"danceability\\\": None,\\n \\\"valence\\\": None\\n }\\n # Listas\\n analyzer['events'] = lt.newList('ARRAY_LIST', compareIds)\\n\\n # RBT \\n analyzer[\\\"instrumentalness\\\"] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['acousticness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['liveness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['speechiness'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['energy'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['danceability'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['valence'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['tempo'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['created_at'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n analyzer['dates_u'] = om.newMap(omaptype='RBT',comparefunction=compareDates)\\n \\n\\n # Tablas de Hash \\n analyzer['artist_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer['track_ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer['ID'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer[\\\"musical_genre\\\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer[\\\"sen\\\"] = mp.newMap(15,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n analyzer['track_ID_S'] = mp.newMap(10000,maptype='PROBING',loadfactor=0.5, comparefunction=compareByName)\\n\\n return analyzer\",\n \"def create_rules(app_names, error):\\n # If no applications is given on the command line, generate the rules\\n # for all the registered applications\\n if not app_names:\\n app_names = [entry.name for entry in pkg_resources.iter_entry_points('nagare.applications')]\\n\\n package = pkg_resources.Requirement.parse('nagare')\\n static = pkg_resources.resource_filename(package, 'static')\\n\\n apps = [('nagare', static)] # Initialize the result tuple with the static contents of the framework\\n\\n for app_name in app_names:\\n (cfgfile, app, dist, aconf) = util.read_application(app_name, error)\\n\\n static = aconf['application'].get('static', os.path.join(dist.location, 'static') if dist else None)\\n\\n if static and os.path.isdir(static):\\n apps.append((aconf['application']['name'], static))\\n\\n return sorted(apps, key=lambda x: len(x[0]))\",\n \"def get_agency_terms(self):\\n return # osid.authentication.AgencyQueryInspector\",\n \"def compile_tuning(self, cf):\\n logger = self._logger\\n for s in cf.sections():\\n logger.debug('Collecting definitions for %s', s)\\n try:\\n rexp = cf.get(s, 'regex')\\n except ConfigParser.NoOptionError:\\n self._logger.error('Section \\\"%s\\\" missing \\\"regex\\\" option', s)\\n continue\\n try:\\n r = re.compile(rexp)\\n except:\\n self._logger.error('Section \\\"%s\\\" invalid \\\"regex\\\" option', s)\\n continue\\n try:\\n transfer = cf.getint(s, 'transfer')\\n except ConfigParser.NoOptionError:\\n self._logger.info('Section \\\"%s\\\" missing \\\"transfer\\\" option', s)\\n transfer = 512\\n except ValueError:\\n self._logger.error('Section \\\"%s\\\", \\\"transfer\\\" option must be integer', s)\\n continue\\n try:\\n readahead = cf.getint(s, 'readahead')\\n except ConfigParser.NoOptionError:\\n self._logger.info('Section \\\"%s\\\" missing \\\"readahead\\\" option', s)\\n readahead = 2*transfer\\n except ValueError:\\n self._logger.error('Section \\\"%s\\\", \\\"readahead\\\" option must be integer', s)\\n continue\\n try:\\n scheduler = cf.get(s, 'scheduler')\\n except ConfigParser.NoOptionError:\\n self._logger.info('Section \\\"%s\\\" missing \\\"scheduler\\\" option', s)\\n scheduler = None\\n opts = []\\n if scheduler == 'deadline':\\n opts = []\\n for deadOpt in ('fifo_batch', 'read_expire', 'write_expire', 'writes_starved', 'front_merges'):\\n try:\\n deadVal = cf.getint(s, deadOpt)\\n except ConfigParser.NoOptionError:\\n self._logger.info('Section \\\"%s\\\" missing \\\"%s\\\" option', s, deadOpt)\\n deadVal = None\\n except ValueError:\\n self.logger.error('Section \\\"%s\\\", \\\"%s\\\" option must be integer', s, deadOpt)\\n continue\\n opts += [ deadVal ]\\n self._lunMatch.append( ( r, transfer, readahead, scheduler, opts ) )\",\n \"def test_creation(self):\\n config = config_factory()\\n db = config.arango_db\\n\\n # Create analyzer\\n analyzer = ArangoAnalyzer(\\\"analyzer_sample\\\")\\n analyzer.set_stopwords(\\n language=\\\"english\\\",\\n custom_stopwords=[\\\"stop\\\", \\\"word\\\"],\\n include_default=False,\\n )\\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\\n\\n analyzer.create(db)\\n assert analyzer.__dict__ == {\\n \\\"name\\\": \\\"analyzer_sample\\\",\\n \\\"type\\\": \\\"text\\\",\\n \\\"features\\\": [\\\"frequency\\\", \\\"norm\\\", \\\"position\\\"],\\n \\\"locale\\\": \\\"en\\\",\\n \\\"case\\\": \\\"lower\\\",\\n \\\"stopwords\\\": [\\\"stop\\\", \\\"word\\\"],\\n \\\"accent\\\": False,\\n \\\"stemming\\\": True,\\n \\\"edge_ngram\\\": None,\\n \\\"delimiter\\\": \\\",\\\",\\n \\\"min\\\": 2,\\n \\\"max\\\": 5,\\n \\\"preserve_original\\\": False,\\n \\\"start_marker\\\": \\\"\\\",\\n \\\"end_marker\\\": \\\"\\\",\\n \\\"stem_type\\\": \\\"binary\\\",\\n }\\n assert isinstance(db.analyzer(\\\"analyzer_sample\\\"), dict)\",\n \"def get_all_parsers():\\n return [OptimizerFactory.get_parser(optimizer) for optimizer in OptimizerFactory.optimizers]\",\n \"def list_modules(lookup_paths: list = None):\\n result = []\\n\\n if lookup_paths is None:\\n lookup_paths = analyzer_paths()\\n\\n for path in lookup_paths:\\n analyzer_module_root = resource_filename(path, \\\"modules\\\")\\n # analyzer_root = os.path.join(anchore_module_root, \\\"modules\\\")\\n for f in os.listdir(analyzer_module_root):\\n thecmd = os.path.join(analyzer_module_root, f)\\n if re.match(r\\\".*\\\\.py$\\\", thecmd):\\n result.append(thecmd)\\n\\n result.sort(key=lambda x: analyzer_name_from_path(x))\\n return result\",\n \"def add_arguments_to_parser(parser):\\n\\n parser.add_argument('--analyzers',\\n nargs='+',\\n dest='analyzers',\\n metavar='ANALYZER',\\n required=False,\\n choices=analyzer_types.supported_analyzers,\\n default=list(analyzer_types.supported_analyzers.\\n keys()),\\n help=\\\"Show checkers only from the analyzers \\\"\\n \\\"specified.\\\")\\n\\n if get_diagtool_bin():\\n parser.add_argument('-w', '--warnings',\\n dest='show_warnings',\\n default=argparse.SUPPRESS,\\n action='store_true',\\n required=False,\\n help=\\\"Show available warning flags.\\\")\\n\\n parser.add_argument('--details',\\n dest='details',\\n default=argparse.SUPPRESS,\\n action='store_true',\\n required=False,\\n help=\\\"Show details about the checker, such as \\\"\\n \\\"description, if available.\\\")\\n\\n parser.add_argument('--profile',\\n dest='profile',\\n metavar='PROFILE/list',\\n required=False,\\n default=argparse.SUPPRESS,\\n help=\\\"List checkers enabled by the selected profile. \\\"\\n \\\"'list' is a special option showing details \\\"\\n \\\"about profiles collectively.\\\")\\n\\n parser.add_argument('--guideline',\\n dest='guideline',\\n nargs='*',\\n required=False,\\n default=None,\\n help=\\\"List checkers that report on a specific \\\"\\n \\\"guideline rule. Here you can add the guideline \\\"\\n \\\"name or the ID of a rule. Without additional \\\"\\n \\\"parameter, the available guidelines and their \\\"\\n \\\"corresponding rules will be listed.\\\")\\n\\n parser.add_argument('--checker-config',\\n dest='checker_config',\\n default=argparse.SUPPRESS,\\n action='store_true',\\n required=False,\\n help=\\\"Show checker configuration options for all \\\"\\n \\\"existing checkers supported by the analyzer. \\\"\\n \\\"These can be given to 'CodeChecker analyze \\\"\\n \\\"--checker-config'.\\\")\\n\\n filters = parser.add_mutually_exclusive_group(required=False)\\n\\n filters.add_argument('--only-enabled',\\n dest='only_enabled',\\n default=argparse.SUPPRESS,\\n action='store_true',\\n help=\\\"Show only the enabled checkers.\\\")\\n\\n filters.add_argument('--only-disabled',\\n dest='only_disabled',\\n default=argparse.SUPPRESS,\\n action='store_true',\\n help=\\\"Show only the disabled checkers.\\\")\\n\\n parser.add_argument('-o', '--output',\\n dest='output_format',\\n required=False,\\n default='rows',\\n choices=USER_FORMATS,\\n help=\\\"The format to list the applicable checkers as.\\\")\\n\\n logger.add_verbose_arguments(parser)\\n parser.set_defaults(func=main)\",\n \"def collect_rules():\\n all_rules = []\\n keys = ['mobileconfig',\\n 'macOS',\\n 'severity',\\n 'title',\\n 'check',\\n 'fix',\\n 'odv',\\n 'tags',\\n 'id',\\n 'references',\\n 'result',\\n 'discussion']\\n references = ['disa_stig',\\n 'cci',\\n 'cce',\\n '800-53r4',\\n 'srg']\\n\\n\\n for rule in glob.glob('../rules/**/*.yaml',recursive=True) + glob.glob('../custom/rules/**/*.yaml',recursive=True):\\n if \\\"supplemental\\\" in rule:\\n continue\\n rule_yaml = get_rule_yaml(rule, custom=False)\\n for key in keys:\\n try:\\n rule_yaml[key]\\n except:\\n rule_yaml.update({key: \\\"missing\\\"})\\n if key == \\\"references\\\":\\n for reference in references:\\n try:\\n rule_yaml[key][reference]\\n except:\\n rule_yaml[key].update({reference: [\\\"None\\\"]})\\n\\n if \\\"n_a\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"n_a\\\")\\n if \\\"inherent\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"inherent\\\")\\n if \\\"manual\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"manual\\\")\\n if \\\"none\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"none\\\")\\n if \\\"permanent\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"permanent\\\")\\n if \\\"supplemental\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"supplemental\\\")\\n if \\\"i386\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"i386\\\")\\n if \\\"arm64\\\" in rule_yaml['tags']:\\n rule_yaml['tags'].remove(\\\"arm64\\\")\\n\\n all_rules.append(MacSecurityRule(rule_yaml['title'].replace('|', '\\\\|'),\\n rule_yaml['id'].replace('|', '\\\\|'),\\n rule_yaml['severity'].replace('|', '\\\\|'),\\n rule_yaml['discussion'].replace('|', '\\\\|'),\\n rule_yaml['check'].replace('|', '\\\\|'),\\n rule_yaml['fix'].replace('|', '\\\\|'),\\n rule_yaml['references']['cci'],\\n rule_yaml['references']['cce'],\\n rule_yaml['references']['800-53r4'],\\n rule_yaml['references']['disa_stig'],\\n rule_yaml['references']['srg'],\\n rule_yaml['odv'],\\n rule_yaml['tags'],\\n rule_yaml['result'],\\n rule_yaml['mobileconfig'],\\n rule_yaml['mobileconfig_info']\\n ))\\n return all_rules\",\n \"def scan(self):\\n exclude_patterns_config = self.doxygen_config.get(\\\"EXCLUDE_PATTERNS\\\", [])\\n exclude_patterns = [\\n pattern.replace(\\\"*/\\\", \\\"**/\\\") for pattern in exclude_patterns_config\\n ]\\n file_patterns = self.doxygen_config.get(\\\"FILE_PATTERNS\\\", [\\\"*.c\\\", \\\"*.h\\\"])\\n if self.doxygen_config.get(\\\"RECURSIVE\\\", [\\\"YES\\\"]) == [\\\"YES\\\"]:\\n file_patterns = [f\\\"**/{pattern}\\\" for pattern in file_patterns]\\n nodes = []\\n names = []\\n for node in self.doxygen_input:\\n if os.path.isdir(node.abspath()):\\n for i in node.ant_glob(incl=file_patterns, excl=exclude_patterns):\\n nodes.append(i)\\n else:\\n nodes.append(node)\\n return (nodes, names)\",\n \"def check(self, manager):\\n for all_json in self.api_dir.rglob(ALL_JSON):\\n stem = all_json.relative_to(self.api_dir)\\n yield dict(\\n name=f\\\"validate:translation:{stem}\\\",\\n doc=f\\\"Validate {stem} with the JupyterLab Translation API\\\",\\n file_dep=[all_json],\\n actions=[(self.validate_one_json_file, [None, all_json])],\\n )\",\n \"def extract_rule_names(self):\\n if self.scanner == YARA:\\n return sorted({result['rule'] for result in self.results})\\n if self.scanner == CUSTOMS and 'matchedRules' in self.results:\\n return self.results['matchedRules']\\n # We do not have support for the remaining scanners (yet).\\n return []\",\n \"def _generate_recommendation(self,\\n query_analysis,\\n db_name,\\n collection_name):\\n index_rec = '{'\\n for query_field in query_analysis['analyzedFields']:\\n if query_field['fieldType'] is EQUIV_TYPE:\\n if len(index_rec) is not 1:\\n index_rec += ', '\\n index_rec += '\\\"' + query_field['fieldName'] + '\\\": 1'\\n for query_field in query_analysis['analyzedFields']:\\n if query_field['fieldType'] is SORT_TYPE:\\n if len(index_rec) is not 1:\\n index_rec += ', '\\n index_rec += '\\\"' + query_field['fieldName'] + '\\\": 1'\\n for query_field in query_analysis['analyzedFields']:\\n if query_field['fieldType'] is RANGE_TYPE:\\n if len(index_rec) is not 1:\\n index_rec += ', '\\n index_rec += '\\\"' + query_field['fieldName'] + '\\\": 1'\\n index_rec += '}'\\n\\n # RECOMMENDATION\\n return OrderedDict([('index',index_rec),\\n ('shellCommand', self.generate_shell_command(collection_name, index_rec))])\",\n \"def version(self) -> str:\\n return \\\" \\\".join(self._model_id(a) for a in self._analyzers)\",\n \"def gen_docs(jsons: [{}], char_wb: bool = False, add_taint: bool = False) -> [Learner.LabelledDocs]:\\n docs = []\\n taint_counts = 0\\n for flow in jsons:\\n line = Analyzer.filter_url_words(flow['url'])\\n if '_' in flow['taint']:\\n taint_counts += 1\\n if add_taint:\\n line = line + ' ' + 't_' + flow['taint']\\n label = 1 if flow['label'] == '1' else 0\\n real_label = 1 if flow['real_label'] == '1' else 0\\n if real_label != label:\\n logger.info(\\\"Flow's real label does not match the training label for %s, real_label = %d label = %d\\\",\\n flow['url'], real_label, label)\\n numeric = [flow[name] for name in Analyzer.numeric_features]\\n docs.append(Learner.LabelledDocs(line, label, numeric, real_label, char_wb=char_wb))\\n logger.info('The number of flows who have more than 1 taints: %d', taint_counts)\\n return docs\",\n \"def GenerateACLString(self):\\n target_string = ''\\n app_id = 100 # variable in ACL sentences.\\n\\n for terms in self.silverpeak_terms:\\n for term in terms:\\n for unit in term.GenerateUnitList():\\n if term.term.precedence:\\n for precedence in term.term.precedence:\\n target_string += self._GenerateACLLine(app_id, term,\\n unit, precedence)\\n app_id += 100\\n else:\\n target_string += self._GenerateACLLine(app_id, term, unit)\\n app_id += 100\\n # finalize the target string\\n target_string = self._FinalizeString(target_string, self.pre_string,\\n self.fixed_content)\\n return target_string\",\n \"def init():\\n # analyzer es utilizado para interactuar con el modelo\\n analyzer = model.newAnalyzer()\\n return analyzer\",\n \"def get_rules(cls) -> list:\\n return [factory() for factory in cls._rules_factories]\",\n \"def _generate_recommendation(self,\\r\\n query_analysis,\\r\\n db_name,\\r\\n collection_name):\\r\\n index_rec = '{'\\r\\n for query_field in query_analysis['analyzedFields']:\\r\\n if query_field['fieldType'] is EQUIV_TYPE:\\r\\n if len(index_rec) is not 1:\\r\\n index_rec += ', '\\r\\n index_rec += '\\\"' + query_field['fieldName'] + '\\\": 1'\\r\\n for query_field in query_analysis['analyzedFields']:\\r\\n if query_field['fieldType'] is SORT_TYPE:\\r\\n if len(index_rec) is not 1:\\r\\n index_rec += ', '\\r\\n index_rec += '\\\"' + query_field['fieldName'] + '\\\": 1'\\r\\n for query_field in query_analysis['analyzedFields']:\\r\\n if query_field['fieldType'] is RANGE_TYPE:\\r\\n if len(index_rec) is not 1:\\r\\n index_rec += ', '\\r\\n index_rec += '\\\"' + query_field['fieldName'] + '\\\": 1'\\r\\n index_rec += '}'\\r\\n\\r\\n # RECOMMENDATION\\r\\n return OrderedDict([('index',index_rec),\\r\\n ('shellCommand', self.generate_shell_command(collection_name, index_rec))])\",\n \"def get_rules():\\n rules = []\\n\\n for app_module in get_config('tipfy', 'apps_installed'):\\n try:\\n # Load the urls module from the app and extend our rules.\\n app_rules = import_string('%s.urls' % app_module)\\n rules.extend(app_rules.get_rules())\\n except ImportError:\\n pass\\n\\n return rules\",\n \"def build_corpus(self):\\n # #############################\\n\\n doc = metapy.index.Document()\\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\\n tok = metapy.analyzers.LowercaseFilter(tok)\\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\\n tok = metapy.analyzers.Porter2Filter(tok)\\n tok = metapy.analyzers.ListFilter(tok, \\\"lemur-stopwords.txt\\\", metapy.analyzers.ListFilter.Type.Reject)\\n collection = -1\\n\\n with open(self.documents_path) as file:\\n for num, line in enumerate(file):\\n l = line.strip()\\n c = int(l[0])\\n l = l[2:]\\n doc.content(l)\\n tok.set_content(doc.content())\\n if c != collection:\\n self.documents.append([])\\n collection = c\\n self.documents[c].append([token for token in tok])\\n self.number_of_collections = len(self.documents)\\n self.number_of_documents = len(self.documents[0])\\n #print(self.number_of_collections)\\n #print(self.number_of_documents)\\n #print(self.documents[0])\",\n \"def hrules(self):\\n ...\",\n \"def list_all_agencies():\\n return JsonResponse.create(StatusCode.OK, get_all_agencies())\",\n \"def get_all_rules(self):\\n\\n rules = set()\\n for a_dict in self.get_dicts():\\n rules = keywords.union(a_dict['rules'])\\n return sorted(keywords)\",\n \"def __all_Algs_ ( self ) :\\n _algs = self.algorithms()\\n\\n algs = []\\n for _a in _algs :\\n algs += [ self.algorithm ( _a ) ]\\n return algs\",\n \"def __all_Algs_ ( self ) :\\n _algs = self.algorithms()\\n\\n algs = []\\n for _a in _algs :\\n algs += [ self.algorithm ( _a ) ]\\n return algs\",\n \"def bookAlgorithms(config, visitor):\\n import imp\\n import os\\n\\n CLI = config.getFolder(\\\"CLI+\\\")\\n # flag indicating to run a robust analysis\\n robust = CLI.getTagBoolDefault(\\\"robust\\\",False)\\n # flag indicating to run a dummy analysis\\n dummy = CLI.getTagBoolDefault(\\\"dummy\\\",False)\\n\\n # book any algorithms\\n for algorithmscript_TString in config.getTagVString(\\\"algorithms.snippets\\\"):\\n QFramework.TQStringUtils.removeLeadingBlanks(algorithmscript_TString)\\n QFramework.TQStringUtils.removeTrailingBlanks(algorithmscript_TString)\\n QFramework.TQStringUtils.removeTrailingText(algorithmscript_TString, \\\".py\\\")\\n algorithmscript = algorithmscript_TString.Data()\\n found_modules = []\\n algorithmsDirs = config.getTagVStandardString(\\\"algorithms.directories\\\")\\n # search through the directories provided in the config\\n for algorithmsPath in algorithmsDirs:\\n module = QFramework.TQFolder.concatPaths(algorithmsPath, algorithmscript) + \\\".py\\\"\\n module = common.findConfigPath(module, False)\\n # findConfigPath returns \\\"\\\" if no module was found\\n if len(module) > 0:\\n # snippet was found in this directory -\\n # add its absolute path and the directory it was found in\\n # to a list in tuple form\\n found_modules.append((module, algorithmsPath))\\n if len(found_modules) == 0:\\n # check CommonAnalysisHelpers for an algorithm snippet as fall-back\\n CAHAlgorithmsDir = \\\"CommonAnalysisHelpers/share/algorithms\\\"\\n algorithmsDirs.push_back(CAHAlgorithmsDir)\\n module = QFramework.TQFolder.concatPaths(CAHAlgorithmsDir, algorithmscript) + \\\".py\\\"\\n module = QFramework.TQPathManager.findFileFromEnvVarWithoutExecDir(module, \\\"CAFCOREDIR\\\")\\n if len(module) > 0:\\n found_modules.append((module, CAHAlgorithmsDir))\\n print(len(found_modules))\\n # continue only if there was one match found\\n if len(found_modules) == 0:\\n QFramework.BREAK(\\\"No module found for '{:s}' in the custom algorithm directories provided:\\\\n{:s}\\\\n\\\".format(algorithmscript,', '.join(algorithmsDirs))+\\n \\\"Please make sure that there exists a snippet by the name of '{:s}.py' available in one of them.\\\\n\\\".format(algorithmscript))\\n elif len(found_modules) > 1:\\n QFramework.BREAK(\\\"Ambiguity detected while resolving custom algorithm snippet location. Multiple modules found for {:s} in the custom algorithm directories provided:\\\\n{:s}\\\\n\\\".format(algorithmscript,', '.join(algorithmsDirs))+\\n \\\"Consider placing the {:s}.py snippet only in a common directory if it's used by more than one (sub)analysis.\\\".format(algorithmscript))\\n abs_path = found_modules[0][0]\\n module_name = os.path.basename(abs_path).rstrip(\\\".py\\\")\\n relative_path = QFramework.TQFolder.concatPaths(found_modules[0][1], algorithmscript)+\\\".py\\\"\\n QFramework.START(\\\"l.\\\",\\\"loading algorithms from '{:s}'\\\".format(str(relative_path)))\\n try:\\n addalgorithms = imp.load_source(module_name, abs_path)\\n added = addalgorithms.addAlgorithms(visitor,config)\\n if added:\\n QFramework.END(QFramework.TQMessageStream.OK)\\n else:\\n QFramework.END(QFramework.TQMessageStream.FAIL)\\n QFramework.BREAK(\\\"unable to properly setup custom algorithms\\\")\\n except IOError as error:\\n QFramework.END(QFramework.TQMessageStream.FAIL)\\n QFramework.BREAK(\\\"unable to open file '{:s}' - please double-check!\\\\n\\\".format(abs_path)+\\\"Message from python:\\\\n\\\"+str(error))\\n except NameError as error:\\n QFramework.END(QFramework.TQMessageStream.FAIL)\\n if not robust and not dummy:\\n QFramework.BREAK(\\\"syntax error in algorithm snippet '{:s}' - please double-check!\\\\n\\\".format(abs_path)+\\\"Message from python:\\\\n\\\"+str(error))\\n except AttributeError as error:\\n QFramework.END(QFramework.TQMessageStream.FAIL)\\n if not robust and not dummy:\\n QFramework.BREAK(\\\"attribute error in algorithm snippet '{:s}' - please double-check!\\\\n\\\".format(abs_path)+\\n \\\"If the message from python below is\\\\n'module' object has no attribute 'addAlgorithms'\\\\nplease make sure that the snippet has the function addAlgorithms() defined.\\\\n\\\"\\n \\\"Message from python:\\\\n\\\"+str(error))\\n\\n # only try and do the xAOD skimming configuration below if we are running with the MCASV\\n # since all channels should be considered at the same time (e.g. systematic variations)\\n if isinstance(visitor,QFramework.TQMultiChannelAnalysisSampleVisitor):\\n # TODO: these two lines are also done in bookAnalysisJobs\\n xAODdumpingConfig = QFramework.TQTaggable()\\n dumpXAODs = (xAODdumpingConfig.importTagsWithoutPrefix(config,\\\"xAODdumping.\\\") > 0)\\n\\n jobID = CLI.getTagStringDefault(\\\"jobID\\\",\\\"analyze\\\")\\n\\n #add xAODskimmingAlgorithm if requested (only for MCASV as we'd have event duplications otherwise!)\\n #note: if we ever implement an option to limit the number of channels executed at the same time we must ensure this does not run in such a configuration!!!!\\n if dumpXAODs:\\n print(\\\"Setting up xAOD skimming Algorithm...\\\")\\n xAODskimmingAlg = ROOT.TQxAODskimmingAlgorithm()\\n xAODskimmingAlg.SetName(\\\"xAODdumper\\\")\\n if xAODdumpingConfig.hasTag(\\\"flagName\\\"): xAODskimmingAlg.setFlagName(xAODdumpingConfig.getTagStringDefault(\\\"flagName\\\",\\\"\\\"))\\n xAODskimmingAlg.setOutputDir( xAODdumpingConfig.getTagStringDefault(\\\"outputDir\\\",\\\"CAFxAODs\\\") )\\n xAODskimmingAlg.setFilePrefix(jobID+\\\"_\\\")\\n if config.hasTag(\\\"nameTagName\\\") : xAODskimmingAlg.setPrefix( config.getTagStringDefault( ROOT.TString(\\\"aliases.\\\")+config.getTagStringDefault(\\\"nameTagName\\\",\\\"\\\"), \\\"\\\" ) )\\n visitor.addAlgorithm( xAODskimmingAlg )\",\n \"def genlangs(self):\\r\\n raise NotImplementedError\",\n \"def requires(self):\\n return []\",\n \"def _compile_codecoolers(cls):\\n cls.codecoolers = Student.get_students() + Mentor.get_mentors() + Admin.get_admins()\",\n \"def antonyms_pipeline(config: SettingConfig) -> None:\\n raw_antonym_pairs = generate_antonym_pairs(config)\\n for pos in config.pos.keys():\\n processed_synonym_pairs = postprocess_pairs(raw_antonym_pairs[pos], config)\\n write_pairs(processed_synonym_pairs, config.constraints_root_path, pos, \\\"antonyms\\\")\",\n \"def required(cls):\\n return []\",\n \"def get_authorizers(self, ApiId: str, MaxResults: str = None, NextToken: str = None) -> Dict:\\n pass\",\n \"def make_control_knowledge(self, horizon):\\n\\n \\\"\\\"\\\" *** YOUR CODE HERE *** \\\"\\\"\\\"\\n\\n # ADD_RULE1_COUNT = 0\\n # ADD_RULE2_COUNT = 0\\n # ADD_RULE3_COUNT = 0\\n\\n close = list()\\n far = list()\\n\\n for g in self.problem.goal:\\n for p in self.problem.propositions:\\n if re.match(r'at\\\\spackage\\\\d+\\\\scity\\\\d+-\\\\d+', str(p)):\\n p_split = str(p).split()\\n g_split = str(g).split()\\n\\n # if \\\"at\\\" and \\\"package[oo]\\\" match\\n if p_split[0] == g_split[0] and p_split[1] == g_split[1]:\\n # also \\\"city[oo]-[xx]\\\" match\\n if p_split[2][:-2] == g_split[2][:-2]:\\n close.append(p)\\n else:\\n far.append(p)\\n\\n # Rule 1:\\n # ===============================\\n # If a package is at its goal location, then it must remain there.\\n # p@t and goal@t) -> p@t+1), where p is at(package, location)\\n # cnf: not p@t or not goal@t or p@t+1\\n\\n for g in self.problem.goal:\\n for t in range(0, horizon):\\n clause = list()\\n clause.append(-self.proposition_fluent_codes[(g, t)])\\n clause.append(self.proposition_fluent_codes[(g, t + 1)])\\n self.add_clause(clause, \\\"control\\\")\\n # ADD_RULE1_COUNT += 1\\n\\n for t in range(0, horizon):\\n for a in self.problem.actions:\\n\\n # Rule 2\\n # ===============================\\n\\n # RULE\\n # close -> do not load airplane\\n # p1: close@t\\n # p2: at the location of an airport @t\\n # p3: airplane at this location @t\\n # p4: plane is not loaded\\n # a: load this airplane\\n #\\n # p1@t and p2@t and p3@t and p4@t => a@t\\n # not p1@t or not p2@t or not p3@t or not p4@t or a@t\\n # cnf: not p@t or not a@t\\n if str(a).startswith('load-airplane'):\\n for i in close:\\n package = str(i).split()[1]\\n if str(a).split()[1] == package:\\n clause = list()\\n clause.append(\\n -self.proposition_fluent_codes[(i, t)])\\n clause.append(-self.action_fluent_codes[(a, t)])\\n self.add_clause(clause, \\\"control\\\")\\n # ADD_RULE2_COUNT += 1\\n\\n # Rule 3\\n # ===============================\\n\\n # RULE\\n # far -> do not unload airplane\\n # p@t -> not a@t, where p is far, a is unload-airplane\\n # cnf: not p@t or not a@t\\n if str(a).startswith('unload-airplane'):\\n for j in far:\\n package = str(j).split()[1]\\n if str(a).split()[1] == package:\\n clause = list()\\n clause.append(\\n -self.proposition_fluent_codes[(j, t)])\\n clause.append(-self.action_fluent_codes[(a, t)])\\n self.add_clause(clause, \\\"control\\\")\\n # ADD_RULE3_COUNT += 1\\n\\n # # RULE\\n # # if an airplane has a package on it and the package's\\n # # destination is close do not fly this airplane.\\n # # in fact, if the destination of package is far,\\n # # fly this plane to it.\\n # #\\n # # p1: package on airplane @ t\\n # # p2: package at a place @ t\\n # # p3: the place and the goal are in the same city\\n # # rule: p1@t and p2@t and p@3 => not fly plane@t\\n # # and unload the plane@t\\n #\\n # # not p1@t or not p2@t or not fly@t\\n # # not p1@t or not p2@t or unload\\n #\\n # # rule: p1@t and p2@t and not p3@t => fly plane@t and not\\n # # unload the plane@t\\n #\\n # if str(a).startswith('fly-airplane'):\\n # plane = str(a).split()[1]\\n # # loc_from = str(a).split()[2]\\n # for p1 in self.problem.propositions:\\n # if str(p1).startswith('in package') and str(p1).split()[2] == plane: # in package plane\\n # package = str(p1).split()[1]\\n # for p2 in self.problem.propositions:\\n # if p2 in close and str(p2).split()[1] == package: # at package location\\n # clause = list()\\n # clause.append(-self.proposition_fluent_codes[p1, t])\\n # clause.append(-self.proposition_fluent_codes[p2, t])\\n # clause.append(-self.action_fluent_codes[a, t])\\n # self.add_clause(clause, 'control')\\n # ADD_RULE2_COUNT += 1\\n #\\n #\\n # for g in self.problem.goal:\\n # if str(g).split()[1] == package:\\n # destination = str(g).split()[2]\\n # for do in self.problem.actions:\\n # # unload-airplane package00 plane00 city00-00\\n # if str(do).startswith('unload') and str(do).split()[1] == package and str(do).split()[2] == plane and str(do).split()[3] == destination:\\n # clause2 = list()\\n # clause2.append(-\\n # self.proposition_fluent_codes[\\n # p1, t])\\n # clause2.append(-\\n # self.proposition_fluent_codes[\\n # p2, t])\\n # clause2.append(\\n # self.action_fluent_codes[\\n # do, t])\\n # self.add_clause(clause2,\\n # 'control')\\n #\\n # ADD_RULE3_COUNT += 1\\n\\n # RULE\\n # if there is no package needs to be transferred at a location,\\n # and the location has a truck\\n # drive the truck to its airport\\n\\n # p1: (at package__ city__-__ /\\\\ (it is a goal)@t\\n # p2: (at truck__ city__-__)@t\\n # p3: (city__-__ is not airport)\\n # not p1/\\\\p2/\\\\p3 => drive_truck_to_its_airport@t\\n #\\n #\\n # CNF: p1 V not p2 V not p3 V drive_truck_to_its_airport@t\\n # if str(a).startswith('DRIVE-TRUCK'):\\n # for p1 in self.problem.goal:\\n # city = str(p1).split()[2]\\n # for p2 in self.problem.propositions:\\n # if str(p2).startswith('at truck') and str(p2).split()[2] == city:\\n # for p3 in self.problem.propositions:\\n # if str(p3).startswith('airport') and str(p3).split()[1] == city:\\n # clause = list()\\n # clause.append(self.proposition_fluent_codes[(p1, t)])\\n # clause.append(-self.proposition_fluent_codes[(p2, t)])\\n # clause.append(-self.proposition_fluent_codes[(p3, t)])\\n # clause.append(self.action_fluent_codes[(a, t)])\\n # self.add_clause(clause, \\\"control\\\")\\n\\n # RULE\\n # if there is an airplane is loaded with a package need\\n # transfer (to another city), fly airplane to the corresponding\\n # city.\\n\\n # p1: (at airplane__ city__-__)@t\\n # p2: (in package__ airplane__)@t\\n # p3: ( p2 is in far)\\n # p1/\\\\p2/\\\\p3 => fly_airplane_to_its_airport@t\\n #\\n #\\n # CNF: not p1@t V not p2@t V not p3@t V fly_plane_to_airport@t\\n\\n # print(\\\"ADDED RULE 1:\\\")\\n # print(ADD_RULE1_COUNT)\\n #\\n # print(\\\"ADDED RULE 2:\\\")\\n # print(ADD_RULE2_COUNT)\\n #\\n # print(\\\"ADDED RULE 3:\\\")\\n # print(ADD_RULE3_COUNT)\",\n \"def generate(self):\\n\\n\\t\\tfor datapoint in self.dataSet[:]:\\n\\t\\t\\trule, degree = self.makeRule(datapoint)\\n\\t\\t\\tself.generatedRules.append((rule, degree))\",\n \"def get_argparser_ctor_args():\\n\\n data_files_dir_path = analyzer_context.get_context().data_files_dir_path\\n config_dir_path = os.path.join(data_files_dir_path, 'config')\\n return {\\n 'prog': 'CodeChecker checkers',\\n 'formatter_class': arg.RawDescriptionDefaultHelpFormatter,\\n\\n # Description is shown when the command's help is queried directly\\n 'description': \\\"Get the list of checkers available and their enabled \\\"\\n \\\"status in the supported analyzers.\\\",\\n\\n # Epilogue is shown after the arguments when the help is queried\\n # directly.\\n 'epilog': \\\"\\\"\\\"\\nThe list of checkers that are enabled or disabled by default can be edited by\\nediting the file '{}'.\\n\\nEnvironment variables\\n------------------------------------------------\\n CC_SEVERITY_MAP_FILE Path of the checker-severity mapping config file.\\n Default: '{}'\\n CC_GUIDELINE_MAP_FILE Path of the checker-guideline mapping config file.\\n Default: '{}'\\n CC_PROFILE_MAP_FILE Path of the checker-profile mapping config file.\\n Default: '{}'\\n\\\"\\\"\\\".format(os.path.join(config_dir_path, 'checker_profile_map.json'),\\n os.path.join(config_dir_path, 'checker_severity_map.json'),\\n os.path.join(config_dir_path, 'checker_guideline_map.json'),\\n os.path.join(config_dir_path, 'checker_profile_map.json')),\\n\\n # Help is shown when the \\\"parent\\\" CodeChecker command lists the\\n # individual subcommands.\\n 'help': \\\"List the checkers available for code analysis.\\\"\\n }\",\n \"def _get_installed_targets(target_types):\\r\\n lines = [TargetsHelp.INSTALLED_TARGETS_HEADER]\\r\\n for target_type in sorted(target_types.keys()):\\r\\n if target_types[target_type].__doc__ is None:\\r\\n desc = 'Description unavailable.'\\r\\n else:\\r\\n desc = target_types[target_type].__doc__.split('\\\\n')[0]\\r\\n lines.append(' %s: %s' % (\\r\\n TargetsHelp.TARGET_TO_ALIAS[target_type].rjust(TargetsHelp.MAX_ALIAS_LEN), desc))\\r\\n return lines\",\n \"def setup(self):\\n declared = []\\n for obj in Rt.objective:\\n var_list = split(\\\"[+*/-]\\\", obj)\\n for v in var_list:\\n if v not in declared:\\n self.add_input(v)\\n declared.append(v)\\n self.add_output(\\\"Objective function \\\" + obj)\",\n \"def get_rules(cls):\\n raise NotImplementedError()\",\n \"def main():\\n parser = argparse.ArgumentParser(description='investigate code health and random statistics')\\n sub_parsers = parser.add_subparsers(dest='command_name', title='Commands', help='', metavar='<command>')\\n\\n sub = sub_parsers.add_parser('line-count', help='list line counts')\\n sub.add_argument('files', nargs='+', help='files or folders to look in')\\n sub.add_argument('--each', type=int, default=1)\\n sub.add_argument('--show', action='store_true')\\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\\n sub.set_defaults(func=handle_line_count)\\n\\n sub = sub_parsers.add_parser('include-list', help='list headers from files')\\n cc.add_argument(sub)\\n sub.add_argument('files', nargs='+')\\n sub.add_argument('--print', dest='print_files', action='store_true')\\n sub.add_argument('--print-stats', dest='print_stats', action='store_true')\\n sub.add_argument('--print-max', dest='print_max', action='store_true')\\n sub.add_argument('--no-list', dest='print_list', action='store_false')\\n sub.add_argument('--count', default=2, type=int, help=\\\"only print includes that are more or equal to <count>\\\")\\n sub.add_argument('--limit', nargs='+', help=\\\"limit search to theese files and folders\\\")\\n sub.set_defaults(func=handle_list)\\n\\n sub = sub_parsers.add_parser('include-gv', help='generate a graphviz of the includes')\\n cc.add_argument(sub)\\n sub.add_argument('files', nargs='+')\\n sub.add_argument('--limit', nargs='+', help=\\\"limit search to theese files and folders\\\")\\n sub.add_argument('--group', action='store_true', help=\\\"group output\\\")\\n sub.add_argument('--cluster', action='store_true', help=\\\"group output into clusters\\\")\\n sub.set_defaults(func=handle_gv)\\n\\n sub = sub_parsers.add_parser('list-indents', help='list the files with the maximum indents')\\n sub.add_argument('files', nargs='+')\\n sub.add_argument('--each', type=int, default=1, help='group counts')\\n sub.add_argument('--show', action='store_true', help='include files in list')\\n sub.add_argument('--hist', action='store_true', help='show simple histogram')\\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\\n sub.set_defaults(func=handle_list_indents)\\n\\n sub = sub_parsers.add_parser('missing-pragma-once', help='find headers with missing include guards')\\n sub.add_argument('files', nargs='+')\\n sub.set_defaults(func=handle_missing_include_guards)\\n\\n sub = sub_parsers.add_parser('missing-in-cmake', help='find files that existis on disk but missing in cmake')\\n sub.add_argument('files', nargs='+')\\n cc.add_argument(sub)\\n sub.set_defaults(func=handle_missing_in_cmake)\\n\\n sub = sub_parsers.add_parser('list-no-project-folders', help='find projects that have not set the solution folder')\\n sub.add_argument('files', nargs='+')\\n cc.add_argument(sub)\\n sub.set_defaults(func=handle_list_no_project_folder)\\n\\n sub = sub_parsers.add_parser('check-files', help=\\\"find files that doesn't match the name style\\\")\\n sub.add_argument('files', nargs='+')\\n cc.add_argument(sub)\\n sub.set_defaults(func=handle_check_files)\\n\\n args = parser.parse_args()\\n if args.command_name is not None:\\n args.func(args)\\n else:\\n parser.print_help()\",\n \"def test_get_analyzer(self):\\n analyzer_class = manager.AnalysisManager.get_analyzer('mockanalyzer')\\n self.assertEqual(analyzer_class, MockAnalyzer)\",\n \"def get_rules(self):\\n rules = []\\n for item in self.name:\\n rules.append(item)\\n return rules\",\n \"def list_resolver_rule_associations(MaxResults=None, NextToken=None, Filters=None):\\n pass\",\n \"def main():\\n\\t#ps = PackageScanner()\\n\\t#packages = ps.getInstalledPackages()\\n\\t#print(packages)\\n\\t#ps.saveScanResults()\\n\\n\\tan = Analyzer()\\n\\tan.loadFromFile(config.PKG_SCAN_DIR / config.PKG_SCAN_FILE)\\n\\t#an.loadFromPackageCont(packages)\\n\\tan.analyze()\\n\\tan.saveAnalysisResults()\",\n \"def test_analyzer():\\n import analyzer\\n\\n analyzer # Fake usage.\",\n \"def doc_analyzer(self, doc):\\n\\n if self.lowercase is None or self.lowercase == 'none':\\n lowercase = set()\\n elif self.lowercase in {'both', 'all'}:\\n lowercase = {'char', 'word'}\\n else: lowercase = {self.lowercase}\\n\\n # character n-grams\\n if 'char' in lowercase:\\n docfeat = self.get_ngrams(list(doc.lower()),\\n self.c_ngmin, self.c_ngmax)\\n else:\\n docfeat = self.get_ngrams(list(doc),\\n self.c_ngmin, self.c_ngmax)\\n # word n-grams\\n if 'word' in lowercase:\\n docfeat.extend(self.get_ngrams(self.tokenizer(doc.lower()),\\n self.w_ngmin, self.w_ngmax,\\n suffix=\\\"⅏\\\", separator=\\\" \\\"))\\n else:\\n docfeat.extend(self.get_ngrams(self.tokenizer(doc),\\n self.w_ngmin, self.w_ngmax,\\n suffix=\\\"⅏\\\", separator=\\\" \\\"))\\n return docfeat\",\n \"def get_checker_configs(self):\\n LOG.debug(\\\"Tidy extra args: %s\\\", self.analyzer_extra_arguments)\\n\\n res = []\\n\\n # Get config from the extra arguments if there is any.\\n try:\\n tidy_config_parser = argparse.ArgumentParser()\\n tidy_config_parser.add_argument('-config',\\n dest='tidy_config',\\n default='',\\n type=str)\\n\\n args, _ = tidy_config_parser.parse_known_args(\\n shlex.split(self.analyzer_extra_arguments))\\n\\n except Exception as ex:\\n LOG.debug('No config found in the tidy extra args.')\\n LOG.debug(ex)\\n return res\\n\\n try:\\n # Match for clang tidy analyzer names and attributes.\\n clang_tidy_checker_rex = r'^(?P<checker_name>([^.]+))' \\\\\\n r'\\\\.(?P<checker_attribute>([^.]+))$'\\n\\n tidy_pattern = re.compile(clang_tidy_checker_rex)\\n tidy_config = json.loads(args.tidy_config)\\n for checker_option in tidy_config.get('CheckOptions', []):\\n value = checker_option['value']\\n # We only store configs related to tidy checks. We run static\\n # analyzer separately, so it does not affect the SA invocation.\\n key_values_tidy = re.match(tidy_pattern, checker_option['key'])\\n if key_values_tidy:\\n checker_name = key_values_tidy.group('checker_name')\\n checker_attr = key_values_tidy.group('checker_attribute')\\n res.append((checker_name, checker_attr, value))\\n else:\\n LOG.debug('no match')\\n except ValueError as verr:\\n LOG.debug('Failed to parse config.')\\n LOG.debug(verr)\\n except Exception as ex:\\n LOG.debug('Failed to process config.')\\n LOG.debug(ex)\\n\\n return res\",\n \"def generate_schema_list():\\n src = os.path.join(os.path.dirname(__file__), '../schemas')\\n for root, dirs, files in os.walk(src):\\n for fname in files:\\n if not fname.endswith('.yaml'):\\n continue\\n if os.path.splitext(fname)[0] in (\\n 'draft-01', 'asdf-schema-1.0.0'):\\n continue\\n yield os.path.join(root, fname)\",\n \"def getAnalyzerIndex(self, name):\\n\\n self.ensureNotCreated()\\n\\n if not name in self.analyzers:\\n raise Exception('Analyzer %r is not present in the framework configuration' % name)\\n\\n return self.analyzers.index(name)\",\n \"def affixes(etymologies):\\n for affix in AFFIXES:\\n name = FIRST(affix.groupindex.keys())\\n print('building {} dictionary...'.format(name))\\n dictionary = defaultdict(list)\\n with ProgressBar(maxval=len(etymologies)) as progress:\\n for i, key in enumerate(etymologies):\\n for definition in etymologies[key]:\\n if definition.has_key(name):\\n dictionary[key].append(definition)\\n progress.update(i)\\n yield name, dictionary\",\n \"def __init__(self):\\n \\n self.label = \\\"ArcSDM Tools\\\"\\n self.alias = \\\"ArcSDM\\\" \\n\\n # List of tool classes associated with this toolbox\\n self.tools = [PartitionNNInputFiles, CombineNNOutputFiles, NeuralNetworkOutputFiles, NeuralNetworkInputFiles, \\n CalculateWeightsTool,SiteReductionTool,CategoricalMembershipToool,\\n CategoricalAndReclassTool, TOCFuzzificationTool, CalculateResponse, LogisticRegressionTool, Symbolize, \\n ROCTool, AgterbergChengCITest, AreaFrequencyTable, GetSDMValues, GrandWofe]\",\n \"def root_lex_analysis():\\n\\n return {\\\"cat\\\": \\\"VerbLex\\\",\\n \\\"vform\\\": \\\"bare\\\",\\n \\\"orthoForm\\\": [\\\"*RootForm\\\"]}\",\n \"def generateRules(L, support_data, min_confidence=0.5):\\r\\n rules = []\\r\\n for i in range(1, len(L)):\\r\\n for freqSet in L[i]:\\r\\n H1 = [frozenset([item]) for item in freqSet]\\r\\n if (i > 1):\\r\\n rules_from_conseq(freqSet, H1, support_data, rules, min_confidence)\\r\\n else:\\r\\n calc_confidence(freqSet, H1, support_data, rules, min_confidence)\\r\\n return rules\",\n \"def main():\\n if len(sys.argv) == 1:\\n print(\\\"No dependencies file to validate!\\\")\\n return\\n dependencies_file = sys.argv[1]\\n try:\\n dependencies = json.loads(open(dependencies_file, 'r').read())\\n except json.decoder.JSONDecodeError:\\n print(\\\"Invalid dependency file syntax! Make sure you don't have any commas at the end of your last dependency.\\\")\\n return\\n for dependency in dependencies:\\n if 'target_path' in dependency and 'repository' in dependency:\\n print(\\\"Validated {}\\\".format(dependency['target_path']))\\n suggest_edits(dependency)\\n elif 'target_path' not in dependency and 'repository' in dependency:\\n print(\\\"Define target_path for dependency {}\\\".format(dependency['repository']))\\n elif 'repository' not in dependency and 'target_path' in dependency:\\n print(\\\"Define repository for dependency {}\\\".format(dependency['target_path']))\\n else:\\n print(\\\"Invalid format, missing repository and target_path for dependency {}\\\".format(dependencies.index(dependency)))\",\n \"def populate_lexical():\\n in_dir = os.path.join(buildconfig.FORM_INDEX_DIR, 'refined')\\n frequency_cutoff = buildconfig.FREQUENCY_CUTOFF\\n\\n taxonomy = LanguageTaxonomy()\\n lemma_counter = 0\\n definition_counter = 0\\n\\n for letter in string.ascii_lowercase:\\n stdout.write('Inserting data for %s...\\\\n' % letter)\\n blocks = []\\n in_file = os.path.join(in_dir, letter + '.json')\\n with open(in_file, 'r') as filehandle:\\n for line in filehandle:\\n data = json.loads(line.strip())\\n blocks.append(BlockData(*data))\\n\\n lemmas = []\\n wordforms = []\\n definitions = []\\n for i, block in enumerate(blocks):\\n lang_node = taxonomy.node(language=block.language)\\n if lang_node is None:\\n language_id = None\\n else:\\n language_id = lang_node.id\\n\\n if block.definition and block.f2000 < frequency_cutoff:\\n definition_counter += 1\\n definitions.append(Definition(id=definition_counter,\\n text=block.definition[:100]))\\n definition_id = definition_counter\\n else:\\n definition_id = None\\n\\n lemma_counter += 1\\n lemmas.append(Lemma(id=lemma_counter,\\n lemma=block.lemma,\\n sort=block.sort,\\n wordclass=block.wordclass,\\n firstyear=block.start,\\n lastyear=block.end,\\n refentry=block.refentry,\\n refid=block.refid,\\n thesaurus_id=block.htlink,\\n language_id=language_id,\\n definition_id=definition_id,\\n f2000=_rounder(block.f2000),\\n f1950=_rounder(block.f1950),\\n f1900=_rounder(block.f1900),\\n f1850=_rounder(block.f1850),\\n f1800=_rounder(block.f1800),\\n f1750=_rounder(block.f1750),))\\n\\n for typelist in (block.standard_types,\\n block.variant_types,\\n block.alien_types):\\n for typeunit in typelist:\\n wordforms.append(Wordform(sort=typeunit[0],\\n wordform=typeunit[1],\\n wordclass=typeunit[2],\\n lemma_id=lemma_counter,\\n f2000=_rounder(typeunit[4]),\\n f1900=_rounder(typeunit[5]),\\n f1800=_rounder(typeunit[6]),))\\n\\n if i % 1000 == 0:\\n Definition.objects.bulk_create(definitions)\\n Lemma.objects.bulk_create(lemmas)\\n Wordform.objects.bulk_create(wordforms)\\n definitions = []\\n lemmas = []\\n wordforms = []\\n\\n Definition.objects.bulk_create(definitions)\\n Lemma.objects.bulk_create(lemmas)\\n Wordform.objects.bulk_create(wordforms)\",\n \"def getChecks(self):\\r\\n raise AbstractError\\r\\n return []\",\n \"def get_all_relaxed_candidates_after_generation(self, gen):\\n q = 'relaxed=1,extinct=0,generation<={0}'\\n entries = self.c.select(q.format(gen))\\n\\n trajs = []\\n for v in entries:\\n t = self.get_atoms(id=v.id)\\n t.info['confid'] = v.gaid\\n t.info['relax_id'] = v.id\\n trajs.append(t)\\n trajs.sort(key=lambda x: get_raw_score(x),\\n reverse=True)\\n return trajs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69321376","0.6514944","0.57968","0.5703347","0.5684623","0.5570689","0.54497045","0.53985465","0.5392003","0.5353222","0.53327596","0.52788645","0.525477","0.52528656","0.5236998","0.51388603","0.49939647","0.49732646","0.4960888","0.49380273","0.48753262","0.48728484","0.4861302","0.48188752","0.47884384","0.47407892","0.47406742","0.47404498","0.47338605","0.47313946","0.4715155","0.47123975","0.4698403","0.4697843","0.4696087","0.46957964","0.46953154","0.46946704","0.4682991","0.4677027","0.46753502","0.46704525","0.46666133","0.46565804","0.46543464","0.46539664","0.46491423","0.46209693","0.4619807","0.4589736","0.45856437","0.456377","0.45616168","0.45527118","0.4550333","0.45372045","0.45067814","0.45052773","0.4500371","0.44792753","0.4474925","0.44738615","0.44725853","0.44725853","0.4466693","0.4462817","0.445399","0.44502738","0.44502738","0.4446742","0.44421113","0.44419235","0.4425579","0.4412955","0.44075367","0.4406624","0.43995237","0.43971425","0.43947482","0.438944","0.4375336","0.43724695","0.437204","0.4371903","0.4368499","0.43678486","0.43677548","0.43668982","0.43454027","0.43410078","0.43396497","0.43342727","0.43331215","0.4323538","0.4321125","0.43126318","0.43113416","0.43108147","0.43106082","0.4309246"],"string":"[\n \"0.69321376\",\n \"0.6514944\",\n \"0.57968\",\n \"0.5703347\",\n \"0.5684623\",\n \"0.5570689\",\n \"0.54497045\",\n \"0.53985465\",\n \"0.5392003\",\n \"0.5353222\",\n \"0.53327596\",\n \"0.52788645\",\n \"0.525477\",\n \"0.52528656\",\n \"0.5236998\",\n \"0.51388603\",\n \"0.49939647\",\n \"0.49732646\",\n \"0.4960888\",\n \"0.49380273\",\n \"0.48753262\",\n \"0.48728484\",\n \"0.4861302\",\n \"0.48188752\",\n \"0.47884384\",\n \"0.47407892\",\n \"0.47406742\",\n \"0.47404498\",\n \"0.47338605\",\n \"0.47313946\",\n \"0.4715155\",\n \"0.47123975\",\n \"0.4698403\",\n \"0.4697843\",\n \"0.4696087\",\n \"0.46957964\",\n \"0.46953154\",\n \"0.46946704\",\n \"0.4682991\",\n \"0.4677027\",\n \"0.46753502\",\n \"0.46704525\",\n \"0.46666133\",\n \"0.46565804\",\n \"0.46543464\",\n \"0.46539664\",\n \"0.46491423\",\n \"0.46209693\",\n \"0.4619807\",\n \"0.4589736\",\n \"0.45856437\",\n \"0.456377\",\n \"0.45616168\",\n \"0.45527118\",\n \"0.4550333\",\n \"0.45372045\",\n \"0.45067814\",\n \"0.45052773\",\n \"0.4500371\",\n \"0.44792753\",\n \"0.4474925\",\n \"0.44738615\",\n \"0.44725853\",\n \"0.44725853\",\n \"0.4466693\",\n \"0.4462817\",\n \"0.445399\",\n \"0.44502738\",\n \"0.44502738\",\n \"0.4446742\",\n \"0.44421113\",\n \"0.44419235\",\n \"0.4425579\",\n \"0.4412955\",\n \"0.44075367\",\n \"0.4406624\",\n \"0.43995237\",\n \"0.43971425\",\n \"0.43947482\",\n \"0.438944\",\n \"0.4375336\",\n \"0.43724695\",\n \"0.437204\",\n \"0.4371903\",\n \"0.4368499\",\n \"0.43678486\",\n \"0.43677548\",\n \"0.43668982\",\n \"0.43454027\",\n \"0.43410078\",\n \"0.43396497\",\n \"0.43342727\",\n \"0.43331215\",\n \"0.4323538\",\n \"0.4321125\",\n \"0.43126318\",\n \"0.43113416\",\n \"0.43108147\",\n \"0.43106082\",\n \"0.4309246\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.46405792"},"document_rank":{"kind":"string","value":"47"}}},{"rowIdx":679,"cells":{"query":{"kind":"string","value":"Returns a list formed by all the analysis we need to calculate, without repetition, from the data in parameters."},"document":{"kind":"string","value":"def get_query_and_evaluation_analysis_types(self, parameters):\n queries = parameters[\"clustering\"][\"evaluation\"][\"query_types\"]\n queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))\n return list(set(queries))"},"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_analysis_list(self):\n analysys_list = []\n\n analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)\n\n for analysis_type in analysis_types:\n if analysis_type in self.all_possible_analysis:\n analysys_list.append(self.all_possible_analysis[analysis_type])\n else:\n print \"[WARNING]\", analysis_type, \"is not an allowed analysis type\"\n\n return analysys_list","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def parameters(self):\n return []","def get_evaluation_analysis_types(self, parameters):\n eval_types =[]\n for evaluation_criteria_id in parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"]:\n# for subcriteria in parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"][evaluation_criteria_id]:\n# eval_types.append(subcriteria)\n eval_types.extend(parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"][evaluation_criteria_id].keys())\n return list(set(eval_types))","def analyze():\n\n\t# Analyze the available data\n\tcoins = ''\n\tparams = []\n\tdone = False\n\twhile done != True:\n\t\tS_0_dat, K_dat, V_dat, T, coin = analysis.load()\n\t\ttheta, T = analysis.LM(S_0_dat, K_dat, V_dat, T)\n\t\tparams.append([theta, T, coin])\n\t\tif coins == '':\n\t\t\tcoins = coin\n\t\telse:\n\t\t\tcoins += ', ' + coin + '.'\n\t\ttry:\n\t\t\tprint(\"Current coins analyzed:\", coins)\n\t\t\tinp = input(\"Analyze another dataset? (y/n)\t\")\n\t\t\tif inp.lower() == 'y':\n\t\t\t\tcontinue\n\t\t\tif inp.lower() == 'n':\n\t\t\t\tprint(\"\\nFinal parameters:\", params)\n\t\t\t\tdone = True\n\t\texcept ValueError:\n\t\t\tprint(\"\\nUnable to interpret input. Please try again.\")\n\treturn params","def algorithm(df, params):\n\n output = {}\n\n # PUT YOUR OWN IMPLEMENTATION HERE\n # STORE YOUR ANALYSIS OUTPUT IN OUTPUT\n\n return output","def get_so_results(self, save=False):\n #Read through output files\n parameters=self.get_optimization_parameters(friendly=True)\n parameterRange = range(len(parameters))\n\n results = []\n\n for i in parameterRange:\n result = {\n 'name': parameters[i][0],\n 'max_result': '?',\n 'max_evals' : '?',\n 'max_cpu' : '?',\n 'min_result' : '?',\n 'min_evals' : '?',\n 'min_cpu' : '?',\n }\n #Read min and max files\n for max in [0, 1]:\n iterator = 0\n \n try:\n file = open(os.path.join(self.path, 'output_1.%d.txt' % (2*i + max)),'r')\n output=[None for r in range(4)]\n for f in file.readlines():\n value = f.rstrip('\\n') #Read the file line by line.\n #Line 0: seperator. Line 1: Evals. Line 2: Time. Line 3: result\n index=parameterRange.index(i)\n output[iterator] = value\n iterator = (iterator + 1)%4\n file.close()\n evals = output[1].split(' ')[2]\n cpu_time = output[2].split(' ')[2]\n sens_result = output[3]\n \n if max == 0:\n max_str = 'max'\n else:\n max_str = 'min'\n result[max_str + '_result'] = sens_result\n result[max_str + '_cpu'] = cpu_time\n result[max_str + '_evals'] = evals\n \n except:\n raise\n \n results.append(result)\n \n #Finally, if save==True, write these results to file results.txt\n if save:\n if not os.path.isfile(os.path.join(self.path, 'results.txt')):\n results_file = open(os.path.join(self.path, 'results.txt'), 'w')\n header_line = 'Parameter name\\tMin result\\tMax result\\tMin CPU time\\tMin Evals\\tMax CPU time\\tMax Evals\\n'\n results_file.write(header_line)\n for result in results:\n result_line = result['name'] + '\\t' + result['min_result'] + '\\t' + result['max_result'] + '\\t' + result['min_cpu'] + '\\t' + result['min_evals'] + '\\t' + result['max_cpu'] + '\\t' + result['max_evals'] + '\\n'\n results_file.write(result_line)\n results_file.close()\n return results","def _build_parsed_values(self):\n\n results = []\n\n # Process each of the instrument particle parameters\n for (name, encoding) in INSTRUMENT_PARTICLE_ENCODING_RULES:\n results.append(self._encode_value(name, self.raw_data.group(name), encoding))\n\n # # Set the internal timestamp\n internal_timestamp_unix = numpy.float(self.raw_data.group(\n DostaAbcdjmCsppParserDataParticleKey.PROFILER_TIMESTAMP))\n self.set_internal_timestamp(unix_time=internal_timestamp_unix)\n\n return results","def _get_parameters(self) -> list:\n return self.parameters","def _generate_evaluaters(self):\n evaluators = []\n for para_key in self.parameter[1]:\n for value in self.parameter[1][para_key]:\n evaluators.append(evaluaterSearch.evaluaterSearch(self.parameter[2], [para_key, value]))\n self.evaluators = evaluators","def config_params0(data,parameter):\n model = []\n #Range of value of p\n acf = sm.graphics.tsa.acf(data.diff().dropna())\n for i in range(len(acf)):\n acf[i] = abs(acf[i]*10)\n if (ceil(acf[i])) <= 2:\n p = range(ceil(acf[i])-1,ceil(acf[i])+2)\n break\n\n #range of value of q\n pacf = sm.graphics.tsa.pacf(data.diff().dropna())\n for i in range(len(pacf)):\n pacf[i] = abs(pacf[i]*10)\n if (ceil(pacf[i])) <= 2:\n q = range(ceil(pacf[i])-1,ceil(pacf[i])+2)\n break\n\n\t# define config lists\n p_params = p\n d_params = parameter['d']\n q_params = q\n m_params = parameter['m']\n #P_params = p\n #D_params = [0, 1]\n #Q_params = q\n \n pdq_m = list(itertools.product(p_params, d_params, q_params,m_params)) #Generate all different combinations of p, q and q triplets\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\n return params","def params(self):\n return [p for sublist in [o.params for o in self.obs] for p in sublist]","def get_params(self):\n return []","def get_data(self): \n self.improvement = []\n self.corrsq = []\n for filename in onlyfiles:\n mst = MST(filename, mypath=mypath)\n mst.estimate_correct_seqences()\n mst.estimate_improvement()\n self.mst.append(mst)\n\n self.corrsq.append(mst.corrsq)\n self.improvement.append(mst.improvement)\n\n\n\n print(f\"cor = {improvement}\")\n print(f\"improvement = {improvement}\")\n print(f\"mittelwert der improvement = {np.mean(improvement)}\")\n print(f\"Standardabweichung der lersteigung = {np.std(improvement)}\")","def totalFitness(self):\n result = []\n for i in self.populasi:\n result.append(self.fungsi(*self.dekodeKromosom(i)))\n return result","def _collect_params(self) -> np.ndarray:\n res = np.array([0.]*(self.dimensions))\n res[0] = self.model.rbf.variance\n res[1:-1] = self.model.rbf.lengthscale\n res[-1] = self.model.Gaussian_noise.variance\n return res","def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))","def generate_parameter_list(self) -> None:\n\n # simulation parameters from model\n model_parameter_ids = np.array(self.amici_model.getParameterIds())\n write_string_array(self.f, \"/parameters/modelParameterNames\",\n model_parameter_ids)\n print(Fore.CYAN + \"Number of model parameters:\",\n len(model_parameter_ids))\n\n print(Fore.CYAN + \"Number of optimization parameters:\",\n len(self.parameter_df))\n write_string_array(self.f, \"/parameters/parameterNames\",\n self.parameter_df.index.values[\n (self.parameter_df.estimate == 1)\n & ~self.parameter_df.index.isin(\n self.amici_model.getFixedParameterIds())])\n\n self.generate_simulation_to_optimization_parameter_mapping()\n\n self.f.flush()","def science_parameter_list(cls):\n result = []\n for key in cls.list():\n if key not in GliderParticleKey.list():\n result.append(key)\n\n return result","def getResults():\n return declList","def computeAnsSets(self, param_dict):\n self.writeSolverConfigFile(param_dict)\n # run the process\n process = subprocess.Popen(self.bash_cmd.split(), stdout=subprocess.PIPE)\n output = process.communicate()[0]\n return self.parseGeneratedProblems(output)","def getResults():","def _build_parsed_values(self):\r\n\r\n results = []\r\n\r\n # Process each of the instrument particle parameters\r\n for (name, index, encoding) in INSTRUMENT_PARTICLE_ENCODING_RULES:\r\n\r\n results.append(self._encode_value(name, self.raw_data.group(index), encoding))\r\n\r\n port_timestamp_unix = numpy.float(self.raw_data.group(\r\n DataMatchesGroupNumber.PROFILER_TIMESTAMP))\r\n self.set_port_timestamp(unix_time=float(port_timestamp_unix))\r\n\r\n self.contents[DataParticleKey.PREFERRED_TIMESTAMP] = DataParticleKey.PORT_TIMESTAMP\r\n\r\n timestamp_str= self.raw_data.group(DataMatchesGroupNumber.DATE) + \" \" + self.raw_data.group(DataMatchesGroupNumber.TIME)\r\n self.set_internal_timestamp(timestamp=timestamp_mmddyyhhmmss_to_ntp(timestamp_str))\r\n\r\n return results","def _get_all_data(self, start_date, end_date):\n return [self._prep_data(self._get_input_data(var, start_date,\n end_date),\n self.var.func_input_dtype)\n for n, var in enumerate(self.variables)]","def parameter_space():\n return [list(range(7, 17)),\n list(range(17, 27)),\n list(range(27, 37)),\n list(permutations(range(1, 5), 4))]","def movies(self):\n return self.data.groupby('Parameters')","def get_parameters(self):\n run_parameters = []\n max_clusters = self.parameters[\"clustering\"][\"evaluation\"][\"maximum_clusters\"]\n min_clusters = self.parameters[\"clustering\"][\"evaluation\"][\"minimum_clusters\"]\n hierarchicalAlgorithm = HierarchicalClusteringAlgorithm(self.distance_matrix)\n clusters_and_cutoff = hierarchicalTools.get_clusters_with_ranged_search(\n hierarchicalAlgorithm,\n 0.,\n self.distance_matrix.calculateMean(),\n min_clusters,\n max_clusters,\n ParametersGenerator.HIERARCHICAL_REFINEMENT_VALUE)\n clusterings = []\n cutoffs = []\n for numclusters in clusters_and_cutoff:\n clusterings.append(clusters_and_cutoff[numclusters][1])\n cutoffs.append(clusters_and_cutoff[numclusters][0])\n\n for cutoff in cutoffs:\n run_parameter = ParametersGenerator.get_base_parameters()\n run_parameter[\"method\"] = 'complete'\n run_parameter[\"cutoff\"] = cutoff\n run_parameters.append(run_parameter)\n\n return run_parameters, clusterings","def solution(self):\n return [(\"simple 1\", 1.),\n (\"simple 2\", 1.),\n (\"simple 3\", 1.),\n (\"simple 4\", 1.),\n (\"simple 5\", 1.),\n (\"simple 10\", 1.),\n (\"simple 15\", 1.),\n (\"thai 1\", 1.),\n (\"thai 2\", 1.),\n (\"thai 3\", 1.),\n (\"thai 4\", 1.),\n (\"thai 5\", 1.),\n (\"thai 10\", 1.),\n (\"thai 15\", 1.),\n ]","def _get_params(self, fluents):\n objects_all = set()\n for fluent in fluents:\n objects = fluent.replace(\"(\",\"\").replace(\")\",\"\").split(\" \")[1:]\n objects_all.update(objects)\n\n return objects_all","def showAll(self):\n (filteredInterpreters, filteredLanguages) = (self.getFilteredInterpreters(self.vertices), self.getFilteredLanguages(self.vertices))\n output = '--------Function showAll--------'\n output += '\\n\\nTotal no. of candidates: {totalCandidates}\\nTotal no. of languages: {totalLangugages}'.format(totalCandidates = len(filteredInterpreters), totalLangugages = len(filteredLanguages))\n output += '\\n\\nList of candidates:'\n for candidate in filteredInterpreters:\n output += '\\n\\n'+ candidate['value'].title()\n output += '\\n\\n\\nList of languages:'\n for language in filteredLanguages:\n output += '\\n\\n'+ language['value'].title()\n analysisOutput = '\\n\\n--------Function showAll--------\\n\\nWorst Complexity will be O(n) where n is the total no of nodes\\n\\n 3 iterations were executed twice to filter data for language and interpreters and third loop for creating output which was divided as a sum of no interpreters and the languages that can be spoken'\n analysisOutput += '\\n\\nHence, Runtime Complexity here will be = {n}'.format(n=len(self.vertices))\n self.printOutput(output)\n self.printAnalysis(analysisOutput)","def hetaira_results(self):\n \n if self.descriptors is not None:\n results = [[str(self.items[i]), str(self.ivalue(i)),\n str(self.jvalue(i))] for i in range(len(self.items))]\n else:\n results = [[str(self.items[i]), str(self.ivalue(i))]\n for i in range(len(self.items))]\n\n results.append(['dset', str(self.dset)])\n return results","def __benchmark__(cls):\n results = []\n used_argsets = []\n for args in cls.arguments:\n used_argsets.append(args)\n # for each given argument\n for method in cls.get_methods():\n # append an empty list for the results with this argument\n method_results = []\n # for each repetition\n for n in xrange(cls.repetitions):\n # append the results to the list for this argument set\n trial_results = cls._trial(method, args)\n method_results.append(trial_results)\n # append a Result to `results`.\n results.append(Result(method, args, method_results))\n return results, used_argsets","def _get_aggregated_results(self):\n gradients = self.gradients\n client_traj_infos = flatten_lists(self.client_traj_infos)\n client_opt_infos = self._combine_client_opt_infos(self.client_opt_infos)\n \n self.gradients = []\n self.client_traj_infos = []\n self.client_opt_infos = []\n\n return gradients, client_traj_infos, client_opt_infos","def get_analytically_computed_optimization_parameter_indices(self):\n indices = []\n if '/offsetParameterIndices' in self.f:\n indices.extend(self.f['/offsetParameterIndices'])\n\n if '/scalingParameterIndices' in self.f:\n indices.extend(self.f['/scalingParameterIndices'])\n\n if '/sigmaParameterIndices' in self.f:\n indices.extend(self.f['/sigmaParameterIndices'])\n\n return list(set(indices))","def parameters(self):\n return [o.parameters for o in self.obs]","def statistify(criteria):\n final = []\n for degree in criteria.keys():\n if degree == 'total':\n continue\n for num in range(0,criteria[degree]):\n final.append(int(degree.split('degree')[1]))\n return final","def get_results(self):\n result = [round(self.mr / self.test_size, 1), round(self.mrr / self.test_size, 3),\n round(self.hits1 / self.test_size, 3), round(self.hits3 / self.test_size, 3),\n round(self.hits5 / self.test_size, 3), round(self.hits10 / self.test_size, 3)]\n return result","def parameters(self):\n return [term.parameter for term in self.terms]","def get_parameter_values(self):\n obsPars = numpy.zeros(self.get_num_parameters())\n i = 0\n for p in self.parameters:\n obsPars[i] = p.read_value_in_fmu(self.fmu)\n i += 1\n return obsPars","def param(self):\n return []","def param(self):\n return []","def get_all_variables(self):\n return []","def get_pars(self):\n return [self.z, self.b, self.logN]","def reduce_data(self, ctx):\n self.baselines_type = ctx.get(\"baselines_type\")\n visibilities = ctx.get(\"visibilities\")\n p_signal = self.compute_power(visibilities)\n\n # Remember that the results of \"simulate\" can be used in two places: (i) the computeLikelihood method, and (ii)\n # as data saved to file. In case of the latter, it is useful to save extra variables to the dictionary to be\n # looked at for diagnosis, even though they are not required in computeLikelihood().\n return [dict(p_signal=p_signal, baselines=self.baselines, frequencies=self.frequencies,\n u=self.u, eta=self.eta)]\n #, nbl_uv=self.nbl_uv, nbl_uvnu=self.nbl_uvnu, nbl_u=self.nbl_u, grid_weights=self.grid_weights)]","def formatdata(data,Params):\n\tmndata = dict()\n\talltrials = np.array([])\n\tfor k in range(len(Params[\"conditions\"])):\n\t\tconditionmean = data[0,k].mean(axis = 0)\n\t\tmndata.update({Params[\"conditions\"][k]: {'data' : data[0,k].mean(axis = 0), 'cmax' : conditionmean.max(), 'cmin' : conditionmean.min()}})\n\treturn mndata","def getParameters(self):\n params = []\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\n # we do not get param of output module\n l = list(m.parameters())\n params.extend(l)\n\n one_dim = [p.view(p.numel()) for p in params]\n params = F.torch.cat(one_dim)\n return params","def getParameters(self):\n params = []\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\n # we do not get param of output module\n l = list(m.parameters())\n params.extend(l)\n\n one_dim = [p.view(p.numel()) for p in params]\n params = F.torch.cat(one_dim)\n return params","def getParameterList(self):\n inputList = []\n for name, n in zip(self._names, self._inputs):\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\n return inputList","def parameters(self):\n return self.pars","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def get_required_parameters(self) -> list:\n results = []\n if self.no_params or self.params_optional:\n return []\n else:\n for parameter, parameter_details in self.parameters.items():\n # Fixing issue #92\n # if parameter == \"effect\":\n # continue\n if not parameter_details.default_value:\n results.append(parameter_details.name)\n return results","def param(self):\r\n\r\n return []","def _fit_params(cls) -> List[str]:\n fsigs = set()\n for series_def in cls.__series__:\n fsigs.add(inspect.signature(series_def.fit_func))\n if len(fsigs) > 1:\n raise AnalysisError(\n \"Fit functions specified in the series definition have \"\n \"different function signature. They should receive \"\n \"the same parameter set for multi-objective function fit.\"\n )\n\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\n\n # remove fixed parameters\n if cls.__fixed_parameters__ is not None:\n for fixed_param in cls.__fixed_parameters__:\n try:\n fit_params.remove(fixed_param)\n except ValueError as ex:\n raise AnalysisError(\n f\"Defined fixed parameter {fixed_param} is not a fit function argument.\"\n \"Update series definition to ensure the parameter name is defined with \"\n f\"fit functions. Currently available parameters are {fit_params}.\"\n ) from ex\n\n return fit_params","def parameters(self):\n #print \"in instrument.parameter()\"\n return self._params","def curate_filter_info(self):\n filter_list = [\n self.sample_name, self.final_id, self.all_variant_count,\n self.filter_min_depth_count, self.filter_max_depth_count,\n self.filter_common_var_count, self.log_mut_count,\n self.cosmic_variant_counts, self.unknown_maf_count\n ]\n return filter_list","def _analyseVariables(self):\n self.unused_vars = []\n ffis_limited = False\n\n highest_rank = -1\n best_var = None\n count = 0\n\n # Need to get highest ranked variable (most dimensions) so that we can work out FFI\n for var in self.vars:\n msg = f\"Analysing: {var.name}\"\n self.output_message.append(msg)\n count = count + 1\n\n # get rank\n rank = len(var.shape)\n\n # Deal with singleton variables\n if rank == 0: \n self.rank_zero_vars.append(var)\n self.rank_zero_var_ids.append(var.name)\n continue\n\n # Update highest if highest found or if equals highest with bigger size\n try:\n var.size = var.size()\n best_var.size = best_var.size()\n except:\n pass\n\n if rank > highest_rank or (rank == highest_rank and var.size > best_var.size):\n highest_rank = rank\n best_var = var\n best_var_index = count - 1\n\n # If all are zero ranked variables or no vars identified/found then we cannot write any to NASA Ames and return ([], [])\n if len(self.rank_zero_vars) == len(self.vars) or best_var is None: \n return ([], [])\n\n # Now start to sort the variables into main and auxiliary \n vars_for_na = [best_var]\n aux_vars_for_na = []\n shape = best_var.shape\n number_of_dims = len(shape)\n self.na_dict[\"NIV\"] = number_of_dims\n\n # If 2D then do a quick test to see if 2310 is feasible (i.e. uniformly spaced 2nd axis)\n if number_of_dims == 2:\n\n ffis_limited = [2010, 2110]\n axis = xarray_utils.get_coord_by_index(best_var, 1)\n\n if xarray_utils.isUniformlySpaced(axis):\n ffis_limited.append(2310)\n\n # Get the axes for the main variable being used\n best_var_axes = xarray_utils.getAxisList(best_var)\n \n # Get other variables into a list and analyse them\n rest_of_the_vars = self.vars[:best_var_index] + self.vars[(best_var_index + 1):]\n\n for var in rest_of_the_vars:\n\n if var.name in self.rank_zero_var_ids: continue\n\n # What to do with variables that have different number of dimensions or different shape\n if len(var.shape) != number_of_dims or var.shape != shape: \n # Could it be an auxiliary variable?\n if len(var.shape) != 1: \n self.unused_vars.append(var)\n continue\n\n first_axis = xarray_utils.get_coord_by_index(var, 0)\n # Check if axis is identical to first axis of main best variable, if so, can be auxiliary var\n if not xarray_utils.areAxesIdentical(best_var_axes[0], first_axis):\n\n # If not identical, then it might still qualify as an auxiliary every n time points - valid for 1020\n if len(var.shape) == 1:\n nvpm = xarray_utils.isAxisRegularlySpacedSubsetOf(first_axis, best_var_axes[0])\n\n # NVPM is the number of implied values which is equal to (len(ax2)/len(ax1))\n if nvpm:\n ffis_limited = [1020]\n self.na_dict[\"NVPM\"] = nvpm\n else: # if returned False, i.e. not regular subset axis\n self.unused_vars.append(var)\n\n else:\n self.unused_vars.append(var)\n continue\n\n else:\n # This could be used as a standard auxiliary variable\n if ffis_limited in ([1020],):\n # Already fixed on 1020 and cannot collect incompatible FFI vars so do not use\n self.unused_vars.append(var)\n else:\n aux_vars_for_na.append(var) \n\n else:\n this_var_axes = xarray_utils.getAxisList(var)\n\n # Loop through dimensions\n for i in range(number_of_dims): \n\n if not xarray_utils.areAxesIdentical(best_var_axes[i], this_var_axes[i]):\n self.unused_vars.append(var)\n break\n else:\n # OK, I think the current variable is compatible to write with the best variable along with a NASA Ames file \n vars_for_na.append(var)\n\n # Send vars_for_na AND aux_vars_for_na to a method to check if they have previously been mapped \n # from NASA Ames. In which case we'll write them back in the order they were initially read from the input file.\n (vars_for_na, aux_vars_for_na) = \\\n self._reorderVarsIfPreviouslyNA(vars_for_na, aux_vars_for_na)\n\n # Get the FFI\n self.na_dict[\"FFI\"] = \\\n self._decideFileFormatIndex(number_of_dims, aux_vars_for_na, ffis_limited)\n\n return vars_for_na, aux_vars_for_na","def mes_fidelity_helper(X, parameters, output_file):\n\n pop_fractions = output_file['Population_Fraction']\n iterations = output_file['Iterations']\n\n chosen_fidelity = X[:, parameters['Dimension'][0]:]\n chosen_fidelity = np.sum(chosen_fidelity, axis=1).tolist()\n\n parameters['Population_Fraction'] = \\\n [pop_fractions[int(idx)] for idx in chosen_fidelity]\n parameters['Iterations'] = \\\n [iterations[int(idx)] for idx in chosen_fidelity]\n\n parameters = create_parameters(X, parameters)\n\n return parameters","def param(self):\r\n return []","def processParam(self,paramInfo):\n modelicaParam = []\n for eachline in paramInfo:\n eachline = eachline.split('.param')\n #Include ',' in between parameter\n #Removing leading and trailing space\n line = eachline[1].strip()\n line = line.split()\n final_line = ','.join(line)\n stat = 'parameter Real ' + final_line + ';'\n stat = stat.translate(maketrans('{}', ' '))\n modelicaParam.append(stat)\n return modelicaParam","def classifyParameters(self):\n\n arguments = []\n options = []\n outputs = []\n for parameter in self.parameters():\n if parameter.channel == 'output' and not (\n parameter.isExternalType() or parameter.typ == 'file'):\n outputs.append(parameter)\n elif parameter.index is not None:\n arguments.append(parameter)\n if parameter.flag is not None or parameter.longflag is not None:\n logger.warning(\"Parameter %s has both index=%d and flag set.\" % (\n parameter.identifier(), parameter.index))\n else:\n options.append(parameter)\n arguments.sort(key = lambda parameter: parameter.index)\n return (arguments, options, outputs)","def X(self)->list:","def parameter_optimization(self):\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \"w\")\n spl = len(self.para_list)\n for i, sp in enumerate(self.para_list):\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n self._run_backtest()\n stats = self.portfolio.get_statistics()\n tot_profit = float(stats[0][1])\n sharpe = float(stats[1][1])\n max_dd = float(stats[2][1])\n win_rate = float(stats[7][1].replace(\"%\", \"\"))\n profit_factor = float(stats[8][1])\n\n out.write(\n \"%s,%s,%s,%s,%s,%s,%s\\n\" %\n (sp[\"takeprofit\"], sp[\"period\"], tot_profit, sharpe, max_dd, win_rate, profit_factor)\n )\n out.close()","def get_params_as_list(self):\n\n\t\tparams = [self.shape_slope, self.z_thick, self.thick, self.length]\n\t\treturn params","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def analyse(self):\n pass","def _build_parsed_values(self):\n\n # Set the base metadata parsed values to the results to return\n results = self._build_metadata_parsed_values()\n\n data_match = self.raw_data[MetadataRawDataKey.DATA_MATCH]\n\n # Process each of the non common metadata particle parameters\n for (name, encoding) in NON_COMMON_METADATA_PARTICLE_ENCODING_RULES:\n results.append(self._encode_value(name, data_match.group(name), encoding))\n\n # Set the internal timestamp\n internal_timestamp_unix = numpy.float(data_match.group(\n DostaAbcdjmCsppParserDataParticleKey.PROFILER_TIMESTAMP))\n self.set_internal_timestamp(unix_time=internal_timestamp_unix)\n\n return results","def get_parList(self):\n parList = []\n for modelName in self._modelList:\n model = self.__modelDict[modelName]\n modelParDict = model.parFitDict\n for parName in modelParDict.keys():\n parList.append(modelParDict[parName][\"value\"])\n return parList","def prms(widget: QWidget) -> List:\n parameters = BaseTrain.prms(widget)\n return parameters","def get_measures(self) -> List[str]:\n result = []\n for elements in self._get_results_list():\n result.append(elements[2])\n return result","def parameters(self):","def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out","def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out","def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out","def extract_parameters(self) -> Dict[str, Set[str]]:\n regex = \"\\{([A-Za-z0-9_]+)\\}\"\n reserved_parameters = [\n \"output\",\n \"input\",\n \"output_vec\",\n \"input_vec\",\n \"df\",\n \"vec_open\",\n \"vec_close\",\n ]\n parameters = {}\n for scope in self.scopes:\n parameters[scope] = set(\n [\n x\n for x in re.findall(regex, self.call)\n if x not in reserved_parameters\n ]\n )\n return parameters","def analyze(data):\n ## Do welch periodogram here\n pass","def _build_results(self):\n results = {}\n cols = []\n for pol in POLLUTANTS:\n for adj in ADJUSTMENTS:\n cols.append(get_rate_column(pol, adjustment=adj, generated=False))\n cols.append(get_column(pol, adjustment=adj))\n cols.append(\"net_consumed_mwh\")\n for ba in self.regions:\n results[ba] = pd.DataFrame(\n index=self.generation.index, columns=cols, dtype=np.float64\n )\n return results","def config_params1(parameter):\n\n p = parameter['p']\n q = parameter['q']\n d = parameter['d']\n m = parameter['m']\n pdq_m = list(itertools.product(p, d, q,m)) #Generate all different combinations of p, q and q triplets\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\n return params","def _core_init_params(self) :\n\t\ta_list,b_list = [],[]\n\t\tg_list,h_list = [],[]\n\t\t\n\t\t\n\t\tfor eqnid,eqn in enumerate(self.equations) : \n\t\t\treg_p = self.regressors[eqnid]['prod']\n\t\t\treg_d = self.regressors[eqnid]['degrad']\n\t\t\th_eqn = self.initsol['h'][eqn-1]\n\t\t\tg_eqn = self.initsol['g'][eqn-1]\n\n\n\t\t\ta_list.append(self.initsol['alpha'][eqn-1])\n\t\t\tb_list.append(self.initsol['beta'][eqn-1])\n\t\t\t\n\t\t\tg_eqn = np.array([g_eqn[reg-1] for reg in reg_p])\n\t\t\th_eqn = np.array([h_eqn[reg-1] for reg in reg_d])\n\t\t\th_list.append(h_eqn)\n\t\t\tg_list.append(g_eqn)\n\t\n\t\treturn (a_list,b_list,g_list,h_list)","def parameters(self):\n ps = super().parameters()\n exclude = set(self.estimator.parameters())\n ps = (p for p in ps if not p in exclude)\n return ps","def _build_parsed_values(self):\n # need to exclude sci times\n return self._parsed_values(EngineeringTelemeteredDataParticle.keys_exclude_sci_times)","def _build_parsed_values(self):\n # need to exclude sci times\n return self._parsed_values(EngineeringRecoveredDataParticle.keys_exclude_sci_times)","def _get_params(self):\r\n return np.hstack((self.variance,self.lengthscale,self.period))","def _get_params(self):\r\n return np.hstack((self.variance,self.lengthscale,self.period))","def _get_params(self):\r\n return np.hstack((self.variance,self.lengthscale,self.period))","def _build_parsed_values(self):\n # need to exclude m times\n return self._parsed_values(EngineeringScienceRecoveredDataParticle.keys_exclude_times)","def get_parameter_estimation_parameters(self, friendly=True):\n #Get the sensitivities task:\n fitTask=self._getTask('parameterFitting')\n fitProblem = fitTask.find(xmlns + 'Problem')\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\n parameters = []\n for subGroup in optimizationItems:\n name = None\n lowerBound = None\n upperBound = None\n startValue = None\n \n for item in subGroup:\n if item.attrib['name'] == 'ObjectCN':\n name = item.attrib['value']\n elif item.attrib['name'] == 'UpperBound':\n upperBound = item.attrib['value']\n elif item.attrib['name'] == 'LowerBound':\n lowerBound = item.attrib['value']\n elif item.attrib['name'] == 'StartValue':\n startValue = item.attrib['value']\n assert name !=None\n assert lowerBound != None\n assert upperBound != None\n assert startValue != None\n \n if friendly:\n #Construct a user-friendly name for the parameter name using regexs\n #Look for a match for global parameters: Vector=Values[Test parameter],\n global_string = r'.*Vector=Values\\[(?P<name>.*)\\].*'\n global_string_re = re.compile(global_string)\n global_match = re.match(global_string_re, name)\n \n if global_match:\n name = global_match.group('name')\n \n #else check for a local match.\n #Vector=Reactions[Reaction] Parameter=k1\n local_string = r'.*Vector=Reactions\\[(?P<reaction>.*)\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\n local_string_re = re.compile(local_string)\n local_match = re.match(local_string_re, name)\n \n if local_match:\n reaction = local_match.group('reaction')\n parameter = local_match.group('parameter')\n name = '(%s).%s'%(reaction, parameter)\n\n parameters.append((name, lowerBound, upperBound, startValue))\n\n return parameters","def analyse(self, data=None):\n pass","def all_numeric_accumulations(request):\n return request.param","def get_visitor_analysis(self,params=['2017-7-13','2017-7-15','www.att.com']):\n result =[]\n res_from_query1 = []\n\n query_params = {'domain_name':params[2],'start_date':params[0],'end_date':params[1]}\n logging.debug(\"Query params are: end date {} and start date {} for domain {}\".format(params[1],params[0],params[2]))\n query1 = \"\"\"select\n A.pt_log_date,\n TO_CHAR(count(*),'fm999999999.00') as total,\n ROUND(count(A.adobe_visid_high_low),2) as mcVisIdHigh,\n count(case when D.parameter_value = '0' then null else 1 end) as mcVisIdHigh,\n count(B.parameter_value) as uuid,\n count(case when instr(B.parameter_value,'-') > 0 then null else B.parameter_value end) as auth_uuid\n from wt_logs A left outer join wt_log_parts B on A.key = B.key and A.distribution_key = B.distribution_key and B.parameter_name = 'prop48' and B.pt_log_date between :start_date and :end_date\n left outer join wt_log_parts D on A.key = D.key and A.distribution_key = D.distribution_key and D.parameter_name = 'mcVisIdHigh' and D.pt_log_date between :start_date and :end_date\n where A.pt_log_date between :start_date and :end_date\n and A.domain_name = :domain_name\n group by A.pt_log_date\"\"\".replace('\\n',' ')\n logging.info(\"The first query is {}\".format(query1))\n with vertica_python.connect(**conn_info) as connection:\n cur = connection.cursor()\n cur.execute(query1,query_params)\n for row in cur.iterate():\n res_from_query1.append(row)\n\n return(res_from_query1)","def getResultAll(i=None):","def evaluate(self, parameters):\n [f, df] = self.linear_model(parameters)\n return OrderedDict([('f', f-self.data_f), ('df', df-self.data_df)])","def get_analytical_parameter_table(\n self,\n hierarchical_candidate_ids: list,\n parameter_type: str) -> list:\n\n condition_id_to_index = {name: idx for idx, name in\n enumerate(self.condition_ids)}\n # need list, not ndarray\n condition_map_list = [list(x) for x in self.condition_map]\n\n use = []\n for index, row in self.measurement_df.iterrows():\n # TODO : must handle sigmas separately\n if parameter_type == 'observable':\n overrides = petab.split_parameter_replacement_list(\n row.observableParameters)\n elif parameter_type == 'noise':\n overrides = petab.split_parameter_replacement_list(\n row.noiseParameters)\n else:\n raise ValueError(\n \"type must be noise or observable, but got\" + parameter_type)\n\n sim_cond_idx = \\\n condition_id_to_index[row.simulationConditionId]\n preeq_cond_idx = self.NO_PREEQ_CONDITION_IDX\n if not np.isnan(row.preequilibrationConditionId):\n preeq_cond_idx = condition_id_to_index[\n row.preequilibrationConditionId]\n\n for s in overrides:\n #print(s, parametersForHierarchical)\n try:\n scalingIdx = hierarchical_candidate_ids.index(s)\n except ValueError:\n continue # current parameter not in list\n\n conditionIdx = condition_map_list.index(\n [preeq_cond_idx, sim_cond_idx])\n observableIdx = self.observable_ids.index(row.observableId)\n tup = (scalingIdx, conditionIdx, observableIdx)\n\n # Don't add a new line for each timepoint\n # We don't allow separate parameters for individual time-points\n # (Can be implemented via different observables)\n if not tup in use:\n use.append(tup)\n\n if not len(use):\n raise AssertionError(\"Candidates were: \" + str(hierarchical_candidate_ids) + \" but nothing usable found\")\n\n return use","def parameters(self):\n return [p for _, a in vars(self).items() for p in self._params(a)]","def compute_statistics(self):","def getMeasures():","def lego_sets():\n \n \n data_test=data_specific\n\n\n\n \n \n\n \n print(data_test)\n print(\"The size of the data is: \",len(data_test))\n \n \n \n # you must replace this line and return your own list\n return data_test","def _build_parsed_values(self):\n # need to exclude m times\n return self._parsed_values(EngineeringScienceTelemeteredDataParticle.keys_exclude_times)","def parameter_list(self):\n return [\n [encut, kpoint_mesh]\n for encut, kpoint_mesh in zip(\n self._job.iteration_frame.ENCUT, self._job.iteration_frame.KPOINT_MESH\n )\n ]","def params(self):\n params = []\n\n for v in vars(self).values():\n params.extend(self.__computeParams(v))\n\n if isinstance(v, list):\n for p in v:\n params.extend(self.__computeParams(p))\n\n return params","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"rAfb[1.0,-5.0, 5.0]\");\n self.modelBuilder.doVar(\"rA0[1.0, -5.0, 5.0]\");\n self.modelBuilder.doSet(\"POI\",\"rAfb,rA0\")\n self.modelBuilder.factory_('expr::mAfb(\"@0*@1\",eAfb,rAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0*@1)\",eA0,rA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')"],"string":"[\n \"def get_analysis_list(self):\\n analysys_list = []\\n\\n analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)\\n\\n for analysis_type in analysis_types:\\n if analysis_type in self.all_possible_analysis:\\n analysys_list.append(self.all_possible_analysis[analysis_type])\\n else:\\n print \\\"[WARNING]\\\", analysis_type, \\\"is not an allowed analysis type\\\"\\n\\n return analysys_list\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def parameters(self):\\n return []\",\n \"def get_evaluation_analysis_types(self, parameters):\\n eval_types =[]\\n for evaluation_criteria_id in parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"evaluation_criteria\\\"]:\\n# for subcriteria in parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"evaluation_criteria\\\"][evaluation_criteria_id]:\\n# eval_types.append(subcriteria)\\n eval_types.extend(parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"evaluation_criteria\\\"][evaluation_criteria_id].keys())\\n return list(set(eval_types))\",\n \"def analyze():\\n\\n\\t# Analyze the available data\\n\\tcoins = ''\\n\\tparams = []\\n\\tdone = False\\n\\twhile done != True:\\n\\t\\tS_0_dat, K_dat, V_dat, T, coin = analysis.load()\\n\\t\\ttheta, T = analysis.LM(S_0_dat, K_dat, V_dat, T)\\n\\t\\tparams.append([theta, T, coin])\\n\\t\\tif coins == '':\\n\\t\\t\\tcoins = coin\\n\\t\\telse:\\n\\t\\t\\tcoins += ', ' + coin + '.'\\n\\t\\ttry:\\n\\t\\t\\tprint(\\\"Current coins analyzed:\\\", coins)\\n\\t\\t\\tinp = input(\\\"Analyze another dataset? (y/n)\\t\\\")\\n\\t\\t\\tif inp.lower() == 'y':\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif inp.lower() == 'n':\\n\\t\\t\\t\\tprint(\\\"\\\\nFinal parameters:\\\", params)\\n\\t\\t\\t\\tdone = True\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint(\\\"\\\\nUnable to interpret input. Please try again.\\\")\\n\\treturn params\",\n \"def algorithm(df, params):\\n\\n output = {}\\n\\n # PUT YOUR OWN IMPLEMENTATION HERE\\n # STORE YOUR ANALYSIS OUTPUT IN OUTPUT\\n\\n return output\",\n \"def get_so_results(self, save=False):\\n #Read through output files\\n parameters=self.get_optimization_parameters(friendly=True)\\n parameterRange = range(len(parameters))\\n\\n results = []\\n\\n for i in parameterRange:\\n result = {\\n 'name': parameters[i][0],\\n 'max_result': '?',\\n 'max_evals' : '?',\\n 'max_cpu' : '?',\\n 'min_result' : '?',\\n 'min_evals' : '?',\\n 'min_cpu' : '?',\\n }\\n #Read min and max files\\n for max in [0, 1]:\\n iterator = 0\\n \\n try:\\n file = open(os.path.join(self.path, 'output_1.%d.txt' % (2*i + max)),'r')\\n output=[None for r in range(4)]\\n for f in file.readlines():\\n value = f.rstrip('\\\\n') #Read the file line by line.\\n #Line 0: seperator. Line 1: Evals. Line 2: Time. Line 3: result\\n index=parameterRange.index(i)\\n output[iterator] = value\\n iterator = (iterator + 1)%4\\n file.close()\\n evals = output[1].split(' ')[2]\\n cpu_time = output[2].split(' ')[2]\\n sens_result = output[3]\\n \\n if max == 0:\\n max_str = 'max'\\n else:\\n max_str = 'min'\\n result[max_str + '_result'] = sens_result\\n result[max_str + '_cpu'] = cpu_time\\n result[max_str + '_evals'] = evals\\n \\n except:\\n raise\\n \\n results.append(result)\\n \\n #Finally, if save==True, write these results to file results.txt\\n if save:\\n if not os.path.isfile(os.path.join(self.path, 'results.txt')):\\n results_file = open(os.path.join(self.path, 'results.txt'), 'w')\\n header_line = 'Parameter name\\\\tMin result\\\\tMax result\\\\tMin CPU time\\\\tMin Evals\\\\tMax CPU time\\\\tMax Evals\\\\n'\\n results_file.write(header_line)\\n for result in results:\\n result_line = result['name'] + '\\\\t' + result['min_result'] + '\\\\t' + result['max_result'] + '\\\\t' + result['min_cpu'] + '\\\\t' + result['min_evals'] + '\\\\t' + result['max_cpu'] + '\\\\t' + result['max_evals'] + '\\\\n'\\n results_file.write(result_line)\\n results_file.close()\\n return results\",\n \"def _build_parsed_values(self):\\n\\n results = []\\n\\n # Process each of the instrument particle parameters\\n for (name, encoding) in INSTRUMENT_PARTICLE_ENCODING_RULES:\\n results.append(self._encode_value(name, self.raw_data.group(name), encoding))\\n\\n # # Set the internal timestamp\\n internal_timestamp_unix = numpy.float(self.raw_data.group(\\n DostaAbcdjmCsppParserDataParticleKey.PROFILER_TIMESTAMP))\\n self.set_internal_timestamp(unix_time=internal_timestamp_unix)\\n\\n return results\",\n \"def _get_parameters(self) -> list:\\n return self.parameters\",\n \"def _generate_evaluaters(self):\\n evaluators = []\\n for para_key in self.parameter[1]:\\n for value in self.parameter[1][para_key]:\\n evaluators.append(evaluaterSearch.evaluaterSearch(self.parameter[2], [para_key, value]))\\n self.evaluators = evaluators\",\n \"def config_params0(data,parameter):\\n model = []\\n #Range of value of p\\n acf = sm.graphics.tsa.acf(data.diff().dropna())\\n for i in range(len(acf)):\\n acf[i] = abs(acf[i]*10)\\n if (ceil(acf[i])) <= 2:\\n p = range(ceil(acf[i])-1,ceil(acf[i])+2)\\n break\\n\\n #range of value of q\\n pacf = sm.graphics.tsa.pacf(data.diff().dropna())\\n for i in range(len(pacf)):\\n pacf[i] = abs(pacf[i]*10)\\n if (ceil(pacf[i])) <= 2:\\n q = range(ceil(pacf[i])-1,ceil(pacf[i])+2)\\n break\\n\\n\\t# define config lists\\n p_params = p\\n d_params = parameter['d']\\n q_params = q\\n m_params = parameter['m']\\n #P_params = p\\n #D_params = [0, 1]\\n #Q_params = q\\n \\n pdq_m = list(itertools.product(p_params, d_params, q_params,m_params)) #Generate all different combinations of p, q and q triplets\\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\\n return params\",\n \"def params(self):\\n return [p for sublist in [o.params for o in self.obs] for p in sublist]\",\n \"def get_params(self):\\n return []\",\n \"def get_data(self): \\n self.improvement = []\\n self.corrsq = []\\n for filename in onlyfiles:\\n mst = MST(filename, mypath=mypath)\\n mst.estimate_correct_seqences()\\n mst.estimate_improvement()\\n self.mst.append(mst)\\n\\n self.corrsq.append(mst.corrsq)\\n self.improvement.append(mst.improvement)\\n\\n\\n\\n print(f\\\"cor = {improvement}\\\")\\n print(f\\\"improvement = {improvement}\\\")\\n print(f\\\"mittelwert der improvement = {np.mean(improvement)}\\\")\\n print(f\\\"Standardabweichung der lersteigung = {np.std(improvement)}\\\")\",\n \"def totalFitness(self):\\n result = []\\n for i in self.populasi:\\n result.append(self.fungsi(*self.dekodeKromosom(i)))\\n return result\",\n \"def _collect_params(self) -> np.ndarray:\\n res = np.array([0.]*(self.dimensions))\\n res[0] = self.model.rbf.variance\\n res[1:-1] = self.model.rbf.lengthscale\\n res[-1] = self.model.Gaussian_noise.variance\\n return res\",\n \"def get_parameters_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([p for p, _ in self.expert_knowledge])))\",\n \"def generate_parameter_list(self) -> None:\\n\\n # simulation parameters from model\\n model_parameter_ids = np.array(self.amici_model.getParameterIds())\\n write_string_array(self.f, \\\"/parameters/modelParameterNames\\\",\\n model_parameter_ids)\\n print(Fore.CYAN + \\\"Number of model parameters:\\\",\\n len(model_parameter_ids))\\n\\n print(Fore.CYAN + \\\"Number of optimization parameters:\\\",\\n len(self.parameter_df))\\n write_string_array(self.f, \\\"/parameters/parameterNames\\\",\\n self.parameter_df.index.values[\\n (self.parameter_df.estimate == 1)\\n & ~self.parameter_df.index.isin(\\n self.amici_model.getFixedParameterIds())])\\n\\n self.generate_simulation_to_optimization_parameter_mapping()\\n\\n self.f.flush()\",\n \"def science_parameter_list(cls):\\n result = []\\n for key in cls.list():\\n if key not in GliderParticleKey.list():\\n result.append(key)\\n\\n return result\",\n \"def getResults():\\n return declList\",\n \"def computeAnsSets(self, param_dict):\\n self.writeSolverConfigFile(param_dict)\\n # run the process\\n process = subprocess.Popen(self.bash_cmd.split(), stdout=subprocess.PIPE)\\n output = process.communicate()[0]\\n return self.parseGeneratedProblems(output)\",\n \"def getResults():\",\n \"def _build_parsed_values(self):\\r\\n\\r\\n results = []\\r\\n\\r\\n # Process each of the instrument particle parameters\\r\\n for (name, index, encoding) in INSTRUMENT_PARTICLE_ENCODING_RULES:\\r\\n\\r\\n results.append(self._encode_value(name, self.raw_data.group(index), encoding))\\r\\n\\r\\n port_timestamp_unix = numpy.float(self.raw_data.group(\\r\\n DataMatchesGroupNumber.PROFILER_TIMESTAMP))\\r\\n self.set_port_timestamp(unix_time=float(port_timestamp_unix))\\r\\n\\r\\n self.contents[DataParticleKey.PREFERRED_TIMESTAMP] = DataParticleKey.PORT_TIMESTAMP\\r\\n\\r\\n timestamp_str= self.raw_data.group(DataMatchesGroupNumber.DATE) + \\\" \\\" + self.raw_data.group(DataMatchesGroupNumber.TIME)\\r\\n self.set_internal_timestamp(timestamp=timestamp_mmddyyhhmmss_to_ntp(timestamp_str))\\r\\n\\r\\n return results\",\n \"def _get_all_data(self, start_date, end_date):\\n return [self._prep_data(self._get_input_data(var, start_date,\\n end_date),\\n self.var.func_input_dtype)\\n for n, var in enumerate(self.variables)]\",\n \"def parameter_space():\\n return [list(range(7, 17)),\\n list(range(17, 27)),\\n list(range(27, 37)),\\n list(permutations(range(1, 5), 4))]\",\n \"def movies(self):\\n return self.data.groupby('Parameters')\",\n \"def get_parameters(self):\\n run_parameters = []\\n max_clusters = self.parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"maximum_clusters\\\"]\\n min_clusters = self.parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"minimum_clusters\\\"]\\n hierarchicalAlgorithm = HierarchicalClusteringAlgorithm(self.distance_matrix)\\n clusters_and_cutoff = hierarchicalTools.get_clusters_with_ranged_search(\\n hierarchicalAlgorithm,\\n 0.,\\n self.distance_matrix.calculateMean(),\\n min_clusters,\\n max_clusters,\\n ParametersGenerator.HIERARCHICAL_REFINEMENT_VALUE)\\n clusterings = []\\n cutoffs = []\\n for numclusters in clusters_and_cutoff:\\n clusterings.append(clusters_and_cutoff[numclusters][1])\\n cutoffs.append(clusters_and_cutoff[numclusters][0])\\n\\n for cutoff in cutoffs:\\n run_parameter = ParametersGenerator.get_base_parameters()\\n run_parameter[\\\"method\\\"] = 'complete'\\n run_parameter[\\\"cutoff\\\"] = cutoff\\n run_parameters.append(run_parameter)\\n\\n return run_parameters, clusterings\",\n \"def solution(self):\\n return [(\\\"simple 1\\\", 1.),\\n (\\\"simple 2\\\", 1.),\\n (\\\"simple 3\\\", 1.),\\n (\\\"simple 4\\\", 1.),\\n (\\\"simple 5\\\", 1.),\\n (\\\"simple 10\\\", 1.),\\n (\\\"simple 15\\\", 1.),\\n (\\\"thai 1\\\", 1.),\\n (\\\"thai 2\\\", 1.),\\n (\\\"thai 3\\\", 1.),\\n (\\\"thai 4\\\", 1.),\\n (\\\"thai 5\\\", 1.),\\n (\\\"thai 10\\\", 1.),\\n (\\\"thai 15\\\", 1.),\\n ]\",\n \"def _get_params(self, fluents):\\n objects_all = set()\\n for fluent in fluents:\\n objects = fluent.replace(\\\"(\\\",\\\"\\\").replace(\\\")\\\",\\\"\\\").split(\\\" \\\")[1:]\\n objects_all.update(objects)\\n\\n return objects_all\",\n \"def showAll(self):\\n (filteredInterpreters, filteredLanguages) = (self.getFilteredInterpreters(self.vertices), self.getFilteredLanguages(self.vertices))\\n output = '--------Function showAll--------'\\n output += '\\\\n\\\\nTotal no. of candidates: {totalCandidates}\\\\nTotal no. of languages: {totalLangugages}'.format(totalCandidates = len(filteredInterpreters), totalLangugages = len(filteredLanguages))\\n output += '\\\\n\\\\nList of candidates:'\\n for candidate in filteredInterpreters:\\n output += '\\\\n\\\\n'+ candidate['value'].title()\\n output += '\\\\n\\\\n\\\\nList of languages:'\\n for language in filteredLanguages:\\n output += '\\\\n\\\\n'+ language['value'].title()\\n analysisOutput = '\\\\n\\\\n--------Function showAll--------\\\\n\\\\nWorst Complexity will be O(n) where n is the total no of nodes\\\\n\\\\n 3 iterations were executed twice to filter data for language and interpreters and third loop for creating output which was divided as a sum of no interpreters and the languages that can be spoken'\\n analysisOutput += '\\\\n\\\\nHence, Runtime Complexity here will be = {n}'.format(n=len(self.vertices))\\n self.printOutput(output)\\n self.printAnalysis(analysisOutput)\",\n \"def hetaira_results(self):\\n \\n if self.descriptors is not None:\\n results = [[str(self.items[i]), str(self.ivalue(i)),\\n str(self.jvalue(i))] for i in range(len(self.items))]\\n else:\\n results = [[str(self.items[i]), str(self.ivalue(i))]\\n for i in range(len(self.items))]\\n\\n results.append(['dset', str(self.dset)])\\n return results\",\n \"def __benchmark__(cls):\\n results = []\\n used_argsets = []\\n for args in cls.arguments:\\n used_argsets.append(args)\\n # for each given argument\\n for method in cls.get_methods():\\n # append an empty list for the results with this argument\\n method_results = []\\n # for each repetition\\n for n in xrange(cls.repetitions):\\n # append the results to the list for this argument set\\n trial_results = cls._trial(method, args)\\n method_results.append(trial_results)\\n # append a Result to `results`.\\n results.append(Result(method, args, method_results))\\n return results, used_argsets\",\n \"def _get_aggregated_results(self):\\n gradients = self.gradients\\n client_traj_infos = flatten_lists(self.client_traj_infos)\\n client_opt_infos = self._combine_client_opt_infos(self.client_opt_infos)\\n \\n self.gradients = []\\n self.client_traj_infos = []\\n self.client_opt_infos = []\\n\\n return gradients, client_traj_infos, client_opt_infos\",\n \"def get_analytically_computed_optimization_parameter_indices(self):\\n indices = []\\n if '/offsetParameterIndices' in self.f:\\n indices.extend(self.f['/offsetParameterIndices'])\\n\\n if '/scalingParameterIndices' in self.f:\\n indices.extend(self.f['/scalingParameterIndices'])\\n\\n if '/sigmaParameterIndices' in self.f:\\n indices.extend(self.f['/sigmaParameterIndices'])\\n\\n return list(set(indices))\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def statistify(criteria):\\n final = []\\n for degree in criteria.keys():\\n if degree == 'total':\\n continue\\n for num in range(0,criteria[degree]):\\n final.append(int(degree.split('degree')[1]))\\n return final\",\n \"def get_results(self):\\n result = [round(self.mr / self.test_size, 1), round(self.mrr / self.test_size, 3),\\n round(self.hits1 / self.test_size, 3), round(self.hits3 / self.test_size, 3),\\n round(self.hits5 / self.test_size, 3), round(self.hits10 / self.test_size, 3)]\\n return result\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def get_parameter_values(self):\\n obsPars = numpy.zeros(self.get_num_parameters())\\n i = 0\\n for p in self.parameters:\\n obsPars[i] = p.read_value_in_fmu(self.fmu)\\n i += 1\\n return obsPars\",\n \"def param(self):\\n return []\",\n \"def param(self):\\n return []\",\n \"def get_all_variables(self):\\n return []\",\n \"def get_pars(self):\\n return [self.z, self.b, self.logN]\",\n \"def reduce_data(self, ctx):\\n self.baselines_type = ctx.get(\\\"baselines_type\\\")\\n visibilities = ctx.get(\\\"visibilities\\\")\\n p_signal = self.compute_power(visibilities)\\n\\n # Remember that the results of \\\"simulate\\\" can be used in two places: (i) the computeLikelihood method, and (ii)\\n # as data saved to file. In case of the latter, it is useful to save extra variables to the dictionary to be\\n # looked at for diagnosis, even though they are not required in computeLikelihood().\\n return [dict(p_signal=p_signal, baselines=self.baselines, frequencies=self.frequencies,\\n u=self.u, eta=self.eta)]\\n #, nbl_uv=self.nbl_uv, nbl_uvnu=self.nbl_uvnu, nbl_u=self.nbl_u, grid_weights=self.grid_weights)]\",\n \"def formatdata(data,Params):\\n\\tmndata = dict()\\n\\talltrials = np.array([])\\n\\tfor k in range(len(Params[\\\"conditions\\\"])):\\n\\t\\tconditionmean = data[0,k].mean(axis = 0)\\n\\t\\tmndata.update({Params[\\\"conditions\\\"][k]: {'data' : data[0,k].mean(axis = 0), 'cmax' : conditionmean.max(), 'cmin' : conditionmean.min()}})\\n\\treturn mndata\",\n \"def getParameters(self):\\n params = []\\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\\n # we do not get param of output module\\n l = list(m.parameters())\\n params.extend(l)\\n\\n one_dim = [p.view(p.numel()) for p in params]\\n params = F.torch.cat(one_dim)\\n return params\",\n \"def getParameters(self):\\n params = []\\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\\n # we do not get param of output module\\n l = list(m.parameters())\\n params.extend(l)\\n\\n one_dim = [p.view(p.numel()) for p in params]\\n params = F.torch.cat(one_dim)\\n return params\",\n \"def getParameterList(self):\\n inputList = []\\n for name, n in zip(self._names, self._inputs):\\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\\n return inputList\",\n \"def parameters(self):\\n return self.pars\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def get_required_parameters(self) -> list:\\n results = []\\n if self.no_params or self.params_optional:\\n return []\\n else:\\n for parameter, parameter_details in self.parameters.items():\\n # Fixing issue #92\\n # if parameter == \\\"effect\\\":\\n # continue\\n if not parameter_details.default_value:\\n results.append(parameter_details.name)\\n return results\",\n \"def param(self):\\r\\n\\r\\n return []\",\n \"def _fit_params(cls) -> List[str]:\\n fsigs = set()\\n for series_def in cls.__series__:\\n fsigs.add(inspect.signature(series_def.fit_func))\\n if len(fsigs) > 1:\\n raise AnalysisError(\\n \\\"Fit functions specified in the series definition have \\\"\\n \\\"different function signature. They should receive \\\"\\n \\\"the same parameter set for multi-objective function fit.\\\"\\n )\\n\\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\\n\\n # remove fixed parameters\\n if cls.__fixed_parameters__ is not None:\\n for fixed_param in cls.__fixed_parameters__:\\n try:\\n fit_params.remove(fixed_param)\\n except ValueError as ex:\\n raise AnalysisError(\\n f\\\"Defined fixed parameter {fixed_param} is not a fit function argument.\\\"\\n \\\"Update series definition to ensure the parameter name is defined with \\\"\\n f\\\"fit functions. Currently available parameters are {fit_params}.\\\"\\n ) from ex\\n\\n return fit_params\",\n \"def parameters(self):\\n #print \\\"in instrument.parameter()\\\"\\n return self._params\",\n \"def curate_filter_info(self):\\n filter_list = [\\n self.sample_name, self.final_id, self.all_variant_count,\\n self.filter_min_depth_count, self.filter_max_depth_count,\\n self.filter_common_var_count, self.log_mut_count,\\n self.cosmic_variant_counts, self.unknown_maf_count\\n ]\\n return filter_list\",\n \"def _analyseVariables(self):\\n self.unused_vars = []\\n ffis_limited = False\\n\\n highest_rank = -1\\n best_var = None\\n count = 0\\n\\n # Need to get highest ranked variable (most dimensions) so that we can work out FFI\\n for var in self.vars:\\n msg = f\\\"Analysing: {var.name}\\\"\\n self.output_message.append(msg)\\n count = count + 1\\n\\n # get rank\\n rank = len(var.shape)\\n\\n # Deal with singleton variables\\n if rank == 0: \\n self.rank_zero_vars.append(var)\\n self.rank_zero_var_ids.append(var.name)\\n continue\\n\\n # Update highest if highest found or if equals highest with bigger size\\n try:\\n var.size = var.size()\\n best_var.size = best_var.size()\\n except:\\n pass\\n\\n if rank > highest_rank or (rank == highest_rank and var.size > best_var.size):\\n highest_rank = rank\\n best_var = var\\n best_var_index = count - 1\\n\\n # If all are zero ranked variables or no vars identified/found then we cannot write any to NASA Ames and return ([], [])\\n if len(self.rank_zero_vars) == len(self.vars) or best_var is None: \\n return ([], [])\\n\\n # Now start to sort the variables into main and auxiliary \\n vars_for_na = [best_var]\\n aux_vars_for_na = []\\n shape = best_var.shape\\n number_of_dims = len(shape)\\n self.na_dict[\\\"NIV\\\"] = number_of_dims\\n\\n # If 2D then do a quick test to see if 2310 is feasible (i.e. uniformly spaced 2nd axis)\\n if number_of_dims == 2:\\n\\n ffis_limited = [2010, 2110]\\n axis = xarray_utils.get_coord_by_index(best_var, 1)\\n\\n if xarray_utils.isUniformlySpaced(axis):\\n ffis_limited.append(2310)\\n\\n # Get the axes for the main variable being used\\n best_var_axes = xarray_utils.getAxisList(best_var)\\n \\n # Get other variables into a list and analyse them\\n rest_of_the_vars = self.vars[:best_var_index] + self.vars[(best_var_index + 1):]\\n\\n for var in rest_of_the_vars:\\n\\n if var.name in self.rank_zero_var_ids: continue\\n\\n # What to do with variables that have different number of dimensions or different shape\\n if len(var.shape) != number_of_dims or var.shape != shape: \\n # Could it be an auxiliary variable?\\n if len(var.shape) != 1: \\n self.unused_vars.append(var)\\n continue\\n\\n first_axis = xarray_utils.get_coord_by_index(var, 0)\\n # Check if axis is identical to first axis of main best variable, if so, can be auxiliary var\\n if not xarray_utils.areAxesIdentical(best_var_axes[0], first_axis):\\n\\n # If not identical, then it might still qualify as an auxiliary every n time points - valid for 1020\\n if len(var.shape) == 1:\\n nvpm = xarray_utils.isAxisRegularlySpacedSubsetOf(first_axis, best_var_axes[0])\\n\\n # NVPM is the number of implied values which is equal to (len(ax2)/len(ax1))\\n if nvpm:\\n ffis_limited = [1020]\\n self.na_dict[\\\"NVPM\\\"] = nvpm\\n else: # if returned False, i.e. not regular subset axis\\n self.unused_vars.append(var)\\n\\n else:\\n self.unused_vars.append(var)\\n continue\\n\\n else:\\n # This could be used as a standard auxiliary variable\\n if ffis_limited in ([1020],):\\n # Already fixed on 1020 and cannot collect incompatible FFI vars so do not use\\n self.unused_vars.append(var)\\n else:\\n aux_vars_for_na.append(var) \\n\\n else:\\n this_var_axes = xarray_utils.getAxisList(var)\\n\\n # Loop through dimensions\\n for i in range(number_of_dims): \\n\\n if not xarray_utils.areAxesIdentical(best_var_axes[i], this_var_axes[i]):\\n self.unused_vars.append(var)\\n break\\n else:\\n # OK, I think the current variable is compatible to write with the best variable along with a NASA Ames file \\n vars_for_na.append(var)\\n\\n # Send vars_for_na AND aux_vars_for_na to a method to check if they have previously been mapped \\n # from NASA Ames. In which case we'll write them back in the order they were initially read from the input file.\\n (vars_for_na, aux_vars_for_na) = \\\\\\n self._reorderVarsIfPreviouslyNA(vars_for_na, aux_vars_for_na)\\n\\n # Get the FFI\\n self.na_dict[\\\"FFI\\\"] = \\\\\\n self._decideFileFormatIndex(number_of_dims, aux_vars_for_na, ffis_limited)\\n\\n return vars_for_na, aux_vars_for_na\",\n \"def mes_fidelity_helper(X, parameters, output_file):\\n\\n pop_fractions = output_file['Population_Fraction']\\n iterations = output_file['Iterations']\\n\\n chosen_fidelity = X[:, parameters['Dimension'][0]:]\\n chosen_fidelity = np.sum(chosen_fidelity, axis=1).tolist()\\n\\n parameters['Population_Fraction'] = \\\\\\n [pop_fractions[int(idx)] for idx in chosen_fidelity]\\n parameters['Iterations'] = \\\\\\n [iterations[int(idx)] for idx in chosen_fidelity]\\n\\n parameters = create_parameters(X, parameters)\\n\\n return parameters\",\n \"def param(self):\\r\\n return []\",\n \"def processParam(self,paramInfo):\\n modelicaParam = []\\n for eachline in paramInfo:\\n eachline = eachline.split('.param')\\n #Include ',' in between parameter\\n #Removing leading and trailing space\\n line = eachline[1].strip()\\n line = line.split()\\n final_line = ','.join(line)\\n stat = 'parameter Real ' + final_line + ';'\\n stat = stat.translate(maketrans('{}', ' '))\\n modelicaParam.append(stat)\\n return modelicaParam\",\n \"def classifyParameters(self):\\n\\n arguments = []\\n options = []\\n outputs = []\\n for parameter in self.parameters():\\n if parameter.channel == 'output' and not (\\n parameter.isExternalType() or parameter.typ == 'file'):\\n outputs.append(parameter)\\n elif parameter.index is not None:\\n arguments.append(parameter)\\n if parameter.flag is not None or parameter.longflag is not None:\\n logger.warning(\\\"Parameter %s has both index=%d and flag set.\\\" % (\\n parameter.identifier(), parameter.index))\\n else:\\n options.append(parameter)\\n arguments.sort(key = lambda parameter: parameter.index)\\n return (arguments, options, outputs)\",\n \"def X(self)->list:\",\n \"def parameter_optimization(self):\\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \\\"w\\\")\\n spl = len(self.para_list)\\n for i, sp in enumerate(self.para_list):\\n print(\\\"Strategy %s out of %s...\\\" % (i + 1, spl))\\n self._generate_trading_instances(sp)\\n self._run_backtest()\\n stats = self.portfolio.get_statistics()\\n tot_profit = float(stats[0][1])\\n sharpe = float(stats[1][1])\\n max_dd = float(stats[2][1])\\n win_rate = float(stats[7][1].replace(\\\"%\\\", \\\"\\\"))\\n profit_factor = float(stats[8][1])\\n\\n out.write(\\n \\\"%s,%s,%s,%s,%s,%s,%s\\\\n\\\" %\\n (sp[\\\"takeprofit\\\"], sp[\\\"period\\\"], tot_profit, sharpe, max_dd, win_rate, profit_factor)\\n )\\n out.close()\",\n \"def get_params_as_list(self):\\n\\n\\t\\tparams = [self.shape_slope, self.z_thick, self.thick, self.length]\\n\\t\\treturn params\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def analyse(self):\\n pass\",\n \"def _build_parsed_values(self):\\n\\n # Set the base metadata parsed values to the results to return\\n results = self._build_metadata_parsed_values()\\n\\n data_match = self.raw_data[MetadataRawDataKey.DATA_MATCH]\\n\\n # Process each of the non common metadata particle parameters\\n for (name, encoding) in NON_COMMON_METADATA_PARTICLE_ENCODING_RULES:\\n results.append(self._encode_value(name, data_match.group(name), encoding))\\n\\n # Set the internal timestamp\\n internal_timestamp_unix = numpy.float(data_match.group(\\n DostaAbcdjmCsppParserDataParticleKey.PROFILER_TIMESTAMP))\\n self.set_internal_timestamp(unix_time=internal_timestamp_unix)\\n\\n return results\",\n \"def get_parList(self):\\n parList = []\\n for modelName in self._modelList:\\n model = self.__modelDict[modelName]\\n modelParDict = model.parFitDict\\n for parName in modelParDict.keys():\\n parList.append(modelParDict[parName][\\\"value\\\"])\\n return parList\",\n \"def prms(widget: QWidget) -> List:\\n parameters = BaseTrain.prms(widget)\\n return parameters\",\n \"def get_measures(self) -> List[str]:\\n result = []\\n for elements in self._get_results_list():\\n result.append(elements[2])\\n return result\",\n \"def parameters(self):\",\n \"def get_all_variables(self):\\n out = []\\n for i in self.items:\\n out += i.get_all_variables()\\n return out\",\n \"def get_all_variables(self):\\n out = []\\n for i in self.items:\\n out += i.get_all_variables()\\n return out\",\n \"def get_all_variables(self):\\n out = []\\n for i in self.items:\\n out += i.get_all_variables()\\n return out\",\n \"def extract_parameters(self) -> Dict[str, Set[str]]:\\n regex = \\\"\\\\{([A-Za-z0-9_]+)\\\\}\\\"\\n reserved_parameters = [\\n \\\"output\\\",\\n \\\"input\\\",\\n \\\"output_vec\\\",\\n \\\"input_vec\\\",\\n \\\"df\\\",\\n \\\"vec_open\\\",\\n \\\"vec_close\\\",\\n ]\\n parameters = {}\\n for scope in self.scopes:\\n parameters[scope] = set(\\n [\\n x\\n for x in re.findall(regex, self.call)\\n if x not in reserved_parameters\\n ]\\n )\\n return parameters\",\n \"def analyze(data):\\n ## Do welch periodogram here\\n pass\",\n \"def _build_results(self):\\n results = {}\\n cols = []\\n for pol in POLLUTANTS:\\n for adj in ADJUSTMENTS:\\n cols.append(get_rate_column(pol, adjustment=adj, generated=False))\\n cols.append(get_column(pol, adjustment=adj))\\n cols.append(\\\"net_consumed_mwh\\\")\\n for ba in self.regions:\\n results[ba] = pd.DataFrame(\\n index=self.generation.index, columns=cols, dtype=np.float64\\n )\\n return results\",\n \"def config_params1(parameter):\\n\\n p = parameter['p']\\n q = parameter['q']\\n d = parameter['d']\\n m = parameter['m']\\n pdq_m = list(itertools.product(p, d, q,m)) #Generate all different combinations of p, q and q triplets\\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\\n return params\",\n \"def _core_init_params(self) :\\n\\t\\ta_list,b_list = [],[]\\n\\t\\tg_list,h_list = [],[]\\n\\t\\t\\n\\t\\t\\n\\t\\tfor eqnid,eqn in enumerate(self.equations) : \\n\\t\\t\\treg_p = self.regressors[eqnid]['prod']\\n\\t\\t\\treg_d = self.regressors[eqnid]['degrad']\\n\\t\\t\\th_eqn = self.initsol['h'][eqn-1]\\n\\t\\t\\tg_eqn = self.initsol['g'][eqn-1]\\n\\n\\n\\t\\t\\ta_list.append(self.initsol['alpha'][eqn-1])\\n\\t\\t\\tb_list.append(self.initsol['beta'][eqn-1])\\n\\t\\t\\t\\n\\t\\t\\tg_eqn = np.array([g_eqn[reg-1] for reg in reg_p])\\n\\t\\t\\th_eqn = np.array([h_eqn[reg-1] for reg in reg_d])\\n\\t\\t\\th_list.append(h_eqn)\\n\\t\\t\\tg_list.append(g_eqn)\\n\\t\\n\\t\\treturn (a_list,b_list,g_list,h_list)\",\n \"def parameters(self):\\n ps = super().parameters()\\n exclude = set(self.estimator.parameters())\\n ps = (p for p in ps if not p in exclude)\\n return ps\",\n \"def _build_parsed_values(self):\\n # need to exclude sci times\\n return self._parsed_values(EngineeringTelemeteredDataParticle.keys_exclude_sci_times)\",\n \"def _build_parsed_values(self):\\n # need to exclude sci times\\n return self._parsed_values(EngineeringRecoveredDataParticle.keys_exclude_sci_times)\",\n \"def _get_params(self):\\r\\n return np.hstack((self.variance,self.lengthscale,self.period))\",\n \"def _get_params(self):\\r\\n return np.hstack((self.variance,self.lengthscale,self.period))\",\n \"def _get_params(self):\\r\\n return np.hstack((self.variance,self.lengthscale,self.period))\",\n \"def _build_parsed_values(self):\\n # need to exclude m times\\n return self._parsed_values(EngineeringScienceRecoveredDataParticle.keys_exclude_times)\",\n \"def get_parameter_estimation_parameters(self, friendly=True):\\n #Get the sensitivities task:\\n fitTask=self._getTask('parameterFitting')\\n fitProblem = fitTask.find(xmlns + 'Problem')\\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\\n parameters = []\\n for subGroup in optimizationItems:\\n name = None\\n lowerBound = None\\n upperBound = None\\n startValue = None\\n \\n for item in subGroup:\\n if item.attrib['name'] == 'ObjectCN':\\n name = item.attrib['value']\\n elif item.attrib['name'] == 'UpperBound':\\n upperBound = item.attrib['value']\\n elif item.attrib['name'] == 'LowerBound':\\n lowerBound = item.attrib['value']\\n elif item.attrib['name'] == 'StartValue':\\n startValue = item.attrib['value']\\n assert name !=None\\n assert lowerBound != None\\n assert upperBound != None\\n assert startValue != None\\n \\n if friendly:\\n #Construct a user-friendly name for the parameter name using regexs\\n #Look for a match for global parameters: Vector=Values[Test parameter],\\n global_string = r'.*Vector=Values\\\\[(?P<name>.*)\\\\].*'\\n global_string_re = re.compile(global_string)\\n global_match = re.match(global_string_re, name)\\n \\n if global_match:\\n name = global_match.group('name')\\n \\n #else check for a local match.\\n #Vector=Reactions[Reaction] Parameter=k1\\n local_string = r'.*Vector=Reactions\\\\[(?P<reaction>.*)\\\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\\n local_string_re = re.compile(local_string)\\n local_match = re.match(local_string_re, name)\\n \\n if local_match:\\n reaction = local_match.group('reaction')\\n parameter = local_match.group('parameter')\\n name = '(%s).%s'%(reaction, parameter)\\n\\n parameters.append((name, lowerBound, upperBound, startValue))\\n\\n return parameters\",\n \"def analyse(self, data=None):\\n pass\",\n \"def all_numeric_accumulations(request):\\n return request.param\",\n \"def get_visitor_analysis(self,params=['2017-7-13','2017-7-15','www.att.com']):\\n result =[]\\n res_from_query1 = []\\n\\n query_params = {'domain_name':params[2],'start_date':params[0],'end_date':params[1]}\\n logging.debug(\\\"Query params are: end date {} and start date {} for domain {}\\\".format(params[1],params[0],params[2]))\\n query1 = \\\"\\\"\\\"select\\n A.pt_log_date,\\n TO_CHAR(count(*),'fm999999999.00') as total,\\n ROUND(count(A.adobe_visid_high_low),2) as mcVisIdHigh,\\n count(case when D.parameter_value = '0' then null else 1 end) as mcVisIdHigh,\\n count(B.parameter_value) as uuid,\\n count(case when instr(B.parameter_value,'-') > 0 then null else B.parameter_value end) as auth_uuid\\n from wt_logs A left outer join wt_log_parts B on A.key = B.key and A.distribution_key = B.distribution_key and B.parameter_name = 'prop48' and B.pt_log_date between :start_date and :end_date\\n left outer join wt_log_parts D on A.key = D.key and A.distribution_key = D.distribution_key and D.parameter_name = 'mcVisIdHigh' and D.pt_log_date between :start_date and :end_date\\n where A.pt_log_date between :start_date and :end_date\\n and A.domain_name = :domain_name\\n group by A.pt_log_date\\\"\\\"\\\".replace('\\\\n',' ')\\n logging.info(\\\"The first query is {}\\\".format(query1))\\n with vertica_python.connect(**conn_info) as connection:\\n cur = connection.cursor()\\n cur.execute(query1,query_params)\\n for row in cur.iterate():\\n res_from_query1.append(row)\\n\\n return(res_from_query1)\",\n \"def getResultAll(i=None):\",\n \"def evaluate(self, parameters):\\n [f, df] = self.linear_model(parameters)\\n return OrderedDict([('f', f-self.data_f), ('df', df-self.data_df)])\",\n \"def get_analytical_parameter_table(\\n self,\\n hierarchical_candidate_ids: list,\\n parameter_type: str) -> list:\\n\\n condition_id_to_index = {name: idx for idx, name in\\n enumerate(self.condition_ids)}\\n # need list, not ndarray\\n condition_map_list = [list(x) for x in self.condition_map]\\n\\n use = []\\n for index, row in self.measurement_df.iterrows():\\n # TODO : must handle sigmas separately\\n if parameter_type == 'observable':\\n overrides = petab.split_parameter_replacement_list(\\n row.observableParameters)\\n elif parameter_type == 'noise':\\n overrides = petab.split_parameter_replacement_list(\\n row.noiseParameters)\\n else:\\n raise ValueError(\\n \\\"type must be noise or observable, but got\\\" + parameter_type)\\n\\n sim_cond_idx = \\\\\\n condition_id_to_index[row.simulationConditionId]\\n preeq_cond_idx = self.NO_PREEQ_CONDITION_IDX\\n if not np.isnan(row.preequilibrationConditionId):\\n preeq_cond_idx = condition_id_to_index[\\n row.preequilibrationConditionId]\\n\\n for s in overrides:\\n #print(s, parametersForHierarchical)\\n try:\\n scalingIdx = hierarchical_candidate_ids.index(s)\\n except ValueError:\\n continue # current parameter not in list\\n\\n conditionIdx = condition_map_list.index(\\n [preeq_cond_idx, sim_cond_idx])\\n observableIdx = self.observable_ids.index(row.observableId)\\n tup = (scalingIdx, conditionIdx, observableIdx)\\n\\n # Don't add a new line for each timepoint\\n # We don't allow separate parameters for individual time-points\\n # (Can be implemented via different observables)\\n if not tup in use:\\n use.append(tup)\\n\\n if not len(use):\\n raise AssertionError(\\\"Candidates were: \\\" + str(hierarchical_candidate_ids) + \\\" but nothing usable found\\\")\\n\\n return use\",\n \"def parameters(self):\\n return [p for _, a in vars(self).items() for p in self._params(a)]\",\n \"def compute_statistics(self):\",\n \"def getMeasures():\",\n \"def lego_sets():\\n \\n \\n data_test=data_specific\\n\\n\\n\\n \\n \\n\\n \\n print(data_test)\\n print(\\\"The size of the data is: \\\",len(data_test))\\n \\n \\n \\n # you must replace this line and return your own list\\n return data_test\",\n \"def _build_parsed_values(self):\\n # need to exclude m times\\n return self._parsed_values(EngineeringScienceTelemeteredDataParticle.keys_exclude_times)\",\n \"def parameter_list(self):\\n return [\\n [encut, kpoint_mesh]\\n for encut, kpoint_mesh in zip(\\n self._job.iteration_frame.ENCUT, self._job.iteration_frame.KPOINT_MESH\\n )\\n ]\",\n \"def params(self):\\n params = []\\n\\n for v in vars(self).values():\\n params.extend(self.__computeParams(v))\\n\\n if isinstance(v, list):\\n for p in v:\\n params.extend(self.__computeParams(p))\\n\\n return params\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"rAfb[1.0,-5.0, 5.0]\\\");\\n self.modelBuilder.doVar(\\\"rA0[1.0, -5.0, 5.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"rAfb,rA0\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0*@1\\\",eAfb,rAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0*@1)\\\",eA0,rA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62758255","0.6011133","0.5986083","0.59858525","0.5896374","0.58353883","0.58151305","0.57914615","0.57429975","0.5728949","0.571652","0.57030505","0.5637215","0.5610428","0.55925506","0.559063","0.5552268","0.5549528","0.5538644","0.5536552","0.55355585","0.5533471","0.553217","0.552021","0.55054367","0.5495071","0.5490576","0.54834175","0.5474648","0.5459539","0.54557353","0.5441871","0.5438413","0.5432206","0.5424039","0.54140013","0.54105157","0.5410438","0.5400653","0.5398416","0.5398416","0.53980523","0.53824043","0.5380607","0.5377599","0.5377157","0.5377157","0.5373567","0.53653973","0.5361568","0.5355108","0.53504586","0.5350315","0.53487027","0.5344209","0.5342283","0.53416526","0.5335663","0.5334152","0.5330392","0.5318386","0.53163725","0.53119683","0.5311308","0.53099865","0.5300712","0.5290973","0.5290932","0.52906805","0.5288584","0.5286454","0.5286454","0.5286454","0.528501","0.52828187","0.527604","0.52728236","0.526726","0.5256161","0.5255839","0.52547604","0.5244705","0.5244705","0.5244705","0.52413064","0.5238148","0.5221344","0.5220702","0.52155274","0.52067006","0.5204912","0.5204108","0.51914275","0.51906705","0.51899236","0.51864463","0.5184155","0.5182448","0.51750374","0.5174901"],"string":"[\n \"0.62758255\",\n \"0.6011133\",\n \"0.5986083\",\n \"0.59858525\",\n \"0.5896374\",\n \"0.58353883\",\n \"0.58151305\",\n \"0.57914615\",\n \"0.57429975\",\n \"0.5728949\",\n \"0.571652\",\n \"0.57030505\",\n \"0.5637215\",\n \"0.5610428\",\n \"0.55925506\",\n \"0.559063\",\n \"0.5552268\",\n \"0.5549528\",\n \"0.5538644\",\n \"0.5536552\",\n \"0.55355585\",\n \"0.5533471\",\n \"0.553217\",\n \"0.552021\",\n \"0.55054367\",\n \"0.5495071\",\n \"0.5490576\",\n \"0.54834175\",\n \"0.5474648\",\n \"0.5459539\",\n \"0.54557353\",\n \"0.5441871\",\n \"0.5438413\",\n \"0.5432206\",\n \"0.5424039\",\n \"0.54140013\",\n \"0.54105157\",\n \"0.5410438\",\n \"0.5400653\",\n \"0.5398416\",\n \"0.5398416\",\n \"0.53980523\",\n \"0.53824043\",\n \"0.5380607\",\n \"0.5377599\",\n \"0.5377157\",\n \"0.5377157\",\n \"0.5373567\",\n \"0.53653973\",\n \"0.5361568\",\n \"0.5355108\",\n \"0.53504586\",\n \"0.5350315\",\n \"0.53487027\",\n \"0.5344209\",\n \"0.5342283\",\n \"0.53416526\",\n \"0.5335663\",\n \"0.5334152\",\n \"0.5330392\",\n \"0.5318386\",\n \"0.53163725\",\n \"0.53119683\",\n \"0.5311308\",\n \"0.53099865\",\n \"0.5300712\",\n \"0.5290973\",\n \"0.5290932\",\n \"0.52906805\",\n \"0.5288584\",\n \"0.5286454\",\n \"0.5286454\",\n \"0.5286454\",\n \"0.528501\",\n \"0.52828187\",\n \"0.527604\",\n \"0.52728236\",\n \"0.526726\",\n \"0.5256161\",\n \"0.5255839\",\n \"0.52547604\",\n \"0.5244705\",\n \"0.5244705\",\n \"0.5244705\",\n \"0.52413064\",\n \"0.5238148\",\n \"0.5221344\",\n \"0.5220702\",\n \"0.52155274\",\n \"0.52067006\",\n \"0.5204912\",\n \"0.5204108\",\n \"0.51914275\",\n \"0.51906705\",\n \"0.51899236\",\n \"0.51864463\",\n \"0.5184155\",\n \"0.5182448\",\n \"0.51750374\",\n \"0.5174901\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5309142"},"document_rank":{"kind":"string","value":"65"}}},{"rowIdx":680,"cells":{"query":{"kind":"string","value":"Returns a list formed by the evaluation types present in criteria."},"document":{"kind":"string","value":"def get_evaluation_analysis_types(self, parameters):\n eval_types =[]\n for evaluation_criteria_id in parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"]:\n# for subcriteria in parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"][evaluation_criteria_id]:\n# eval_types.append(subcriteria)\n eval_types.extend(parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"][evaluation_criteria_id].keys())\n return list(set(eval_types))"},"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_query_and_evaluation_analysis_types(self, parameters):\n queries = parameters[\"clustering\"][\"evaluation\"][\"query_types\"]\n queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))\n return list(set(queries))","def getResultDefs(self, type=None):\n results = self.results.values()\n\n if type:\n results = filter(lambda result: result.type == type, results)\n\n return results","def getTypesList():\n return Gw2Spidy._request('types')['results']","def report_type_choices():\n\n rts = report_types()\n rcs = report_categories()\n return [(c, [(rt.report_type, rt.name) for rt in rts if rt.category == c]) for c in rcs]","def filter_evaluations_by_type(self, type_):\n from .evaluation import Evaluation\n from .code_component import CodeComponent\n\n joined_eval = join(\n Evaluation.t, CodeComponent.t,\n ((Evaluation.m.trial_id == CodeComponent.m.trial_id) &\n (Evaluation.m.code_component_id == CodeComponent.m.id))\n )\n joined = join(\n Activation.t, joined_eval,\n ((Evaluation.m.trial_id == Activation.m.trial_id) &\n (Evaluation.m.activation_id == Activation.m.id))\n )\n query = (\n select([CodeComponent.m.name, Evaluation.m.repr])\n .select_from(joined)\n .where((Activation.m.trial_id == self.trial_id) &\n (Activation.m.id == self.id) &\n (CodeComponent.m.type == type_))\n )\n for result in relational.session.execute(query):\n yield result","def getType(self, terms):\n\n\t\treturn [i for i in xrange(len(self.toTYPE)) if terms in self.toTYPE[i]]","def to_criteria(self):\r\n c = []\r\n if self.minmax_criteria is not None:\r\n c.extend(self.minmax_criteria.values())\r\n\r\n return c","def statistify(criteria):\n final = []\n for degree in criteria.keys():\n if degree == 'total':\n continue\n for num in range(0,criteria[degree]):\n final.append(int(degree.split('degree')[1]))\n return final","def get_evaluators(categories):\n eval_metric_fn_keys = [EVAL_DEFAULT_METRIC]\n evaluators_list = []\n for eval_metric_fn_key in eval_metric_fn_keys:\n evaluators_list.append(\n EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](categories=categories))\n return evaluators_list","def get_data_types(self):\n data_types = set()\n for er in self.exercise_recordings:\n for data_type in er.data_types:\n if data_type not in data_types:\n data_types.add(data_type)\n return list(data_types)","def get_evaluators(eval_config, categories):\n eval_metric_fn_key = eval_config.metrics_set\n if eval_metric_fn_key not in EVAL_METRICS_CLASS_DICT:\n raise ValueError('Metric not found: {}'.format(eval_metric_fn_key))\n return [\n EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](\n categories=categories)\n ]","def _inferred_type_levels(self) -> list[str]:\n return [i.inferred_type for i in self.levels]","def get_rules_for_type(type):\n\n rules = get_db().execute('SELECT * FROM ruleset WHERE type=?', (type,)).fetchall()\n\n return rules","def getTypes():\n\t\n\ttranslationTable = []\n\tfor x in typePrimitive:\n\t\ttranslationTable.extend(x[0])\n\t\n\tid = 0\n\ttypes = []\n\tmax = 0\n\tfor x in typePrimitive:\n\t\t\n\t\tbinds = []\n\t\tfor y in x[2]:\n\t\t\tbinds.append(translationTable.index(y))\n\t\t\n\t\tif (x[4] != False) and (x[4] > max):\n\t\t\tmax = x[4]\n\t\t\t\n\t\t\n\t\ttypes.append({'name':x[0],'nSites':x[1],'binds':binds,'sym':x[3],'id':id,'max':x[4]})\n\t\tid+=1\n\t\n\treturn (max,types)","def types(self) -> list:\n if self._types is None:\n fdist = self.fdist # ranked order\n types_ = list(fdist.type.values)\n self._types = types_\n return self._types","def test_for_criteria(self):\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\n return values","def filter_criteria(self):\n return self.filter_nodes('//Validation/Criteria')","def get_types(self) -> List[str]:\n return sorted(list(self._radii.keys()))","def load_criterias():\r\n l = [ (p.id, p.name) for p in StockProperty.objects.all() ]\r\n l.insert(0, ('', 'Select to add criteria ...'))\r\n return l","def get_criteria(self):\n\n\t\treturn self.__criteria","def items(self):\n return self._rules_by_lhs.items()","def gettypes(self):\n return [str(self.sd.xlate(t[0])) for t in self.sd.types]","def get_check_types():","def getCriteriaItems( self ):\n # filter out empty strings\n result = []\n\n value = tuple( filter( None, self.value ) )\n if not value:\n return ()\n result.append( ( self.field, self.value ), )\n\n if self.operator is not None:\n result.append( ( '%s_operator' % self.field, self.operator ) )\n\n return tuple( result )","def get_filter_types(verbose=False):\n if verbose:\n pprint(filter_types)\n return filter_types","def getTypes(quizScores):\n challenger = 0\n collaborator = 0\n communicator = 0\n contributor = 0\n for x in range(18):\n questionScore = quizScores[x]\n challenger += int(questionScore[0])\n collaborator += int(questionScore[1])\n communicator += int(questionScore[2])\n contributor += int(questionScore[3])\n return [ challenger, collaborator, communicator, contributor]","def getProposalTypesVocab(self):\n list = DisplayList()\n # Acquire the types\n types = self.aq_inner.aq_parent.getProposalTypes()\n for type in types:\n list.add(type, type)\n return list","def opinion_type_list():\n for type_ in orm.DataFlagOpinionType.select():\n click.echo(type_.name)","def __get_condition_types(condition):\n branch_condition = condition.get(\"specification\")\n condition_type, condition_value = branch_condition.split(\".\")\n condition_types = condition_type.split(\" \")\n return condition_types","def criterion_type(self) -> str:\n return pulumi.get(self, \"criterion_type\")","def criterion_type(self) -> str:\n return pulumi.get(self, \"criterion_type\")","def criteria(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Criterion]:","def types(self) -> List[str]:\n return self._types","def get_results(self, context):\n results = [x for x in self.select_results(context)]\n if len(results) == 1:\n res = results[0]\n if isinstance(res, (bool, int, float, Decimal)):\n return res\n elif isinstance(res, tuple) or is_etree_element(res) or is_document_node(res):\n return results\n elif is_schema_node(res):\n return results\n elif self.symbol in ('text', 'node'):\n return results\n elif self.label in ('function', 'literal'):\n return res\n else:\n return results\n else:\n return results","def query_rule_types(self: object, parameters: dict = None, **kwargs) -> dict:\n # [GET] https://assets.falcon.crowdstrike.com/support/api/swagger.html#/custom-ioa/query-rule-types\n return process_service_request(\n calling_object=self,\n endpoints=Endpoints,\n operation_id=\"query_rule_types\",\n keywords=kwargs,\n params=parameters\n )","def getTypes():\n\n\t\tquery = \"\\\n\t\t\tSELECT\\\n\t\t\t\tid_item_container_type,\\\n\t\t\t\tlabel\\\n\t\t\tFROM\\\n\t\t\t\titem_container_type\\\n\t\t\"\n\n\t\treturn {t['id_item_container_type']: t['label'] for t in Model.fetchAllRows(query)}","def get_evaluations(self):\r\n return self.evaluations","def get_quality_checks(dict):\n\n quality_checks = {}\n for type in dict:\n list = []\n for qc in dict[type]:\n list.append(const.get_id(qc))\n quality_checks[type] = list\n\n return quality_checks","def sorter(row):\n criteria = []\n for value in row[1]: # Ignore enumeration\n criteria.append(\n (\n value is not None,\n \"\" if isinstance(value, Number) else type(value).__name__,\n value,\n )\n )\n return criteria","def search_functional_identifiers(self, criterion_type, criteria_list):\n check_type(value=criterion_type, allowed_types=str, var_name=\"criterion_type\", raise_exception=True)\n check_type(value=criteria_list, allowed_types=list, var_name=\"criteria_list\", raise_exception=True)\n\n my_filter = dict()\n my_filter[criterion_type] = criteria_list\n\n response = self.send(root_url=self.session.dm_url + self.root_url,\n verb=GenericClient.VERB.POST,\n template=TEMPLATES['search_functional_identifier_list'],\n data=my_filter,\n files=None)\n check_http_code(response)\n\n return response.json","def get_op_types_by_precision(self, precision):\n assert precision in list(self.cur_config['ops'].keys())\n\n return self.cur_config['ops'][precision]","def _get_criteria(self):\n for molecule in self.values():\n molecule.get_criterion()","def get_object_types(default_val=False):\n result = []\n if g.user:\n if default_val:\n result.append((\"1\", \"Please Select\"))\n\n for name, data in entity.Entity.__dict__.items():\n if not isinstance(data, str):\n continue\n if \"__\" in name[:2]:\n continue\n result.append((name, name))\n\n result.sort()\n\n return result","def relevant_classifications(self):\n return self.relevant_classes","def all_qtypes(cls):\n return sorted([subcls.QTYPE for subcls in all_subclasses(cls)])","def listVerificationTypes(self):\n return self.get_json('/verificationType')","def get_analysis_list(self):\n analysys_list = []\n\n analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)\n\n for analysis_type in analysis_types:\n if analysis_type in self.all_possible_analysis:\n analysys_list.append(self.all_possible_analysis[analysis_type])\n else:\n print \"[WARNING]\", analysis_type, \"is not an allowed analysis type\"\n\n return analysys_list","def _generate_evaluaters(self):\n evaluators = []\n for para_key in self.parameter[1]:\n for value in self.parameter[1][para_key]:\n evaluators.append(evaluaterSearch.evaluaterSearch(self.parameter[2], [para_key, value]))\n self.evaluators = evaluators","def get_types(*args, **kwargs) -> list:\n arg_types = []\n for arg in args:\n arg_types.append(type(arg))\n for values in kwargs.values():\n arg_types.append(type(values))\n return arg_types","def get_subterms(expr):\n av_expr = []\n expr_types = []\n if isinstance(expr, Term):\n if expr.subterms:\n for s in expr.subterms:\n new_av, new_type = get_subterms(s)\n av_expr += new_av\n expr_types += new_type\n new_type = expr.type\n expr_types.append(new_type)\n av_expr.append(expr)\n else:\n av_expr.append(expr)\n expr_types.append(expr.type)\n elif type(expr) != str:\n if expr.term:\n new_av, new_type = get_subterms(expr.term)\n av_expr += new_av\n expr_types += new_type\n return av_expr, expr_types","def evaluators(self) -> List[Evaluator]:\n return self._evaluators","def etypes(self): # -> list[str]:\n ...","def evaluate(self, csls, evals, mode=\"csls\"):\n metrics = {}\n for eval_func in evals:\n assert hasattr(self, eval_func), \\\n \"Eval Function {0} not found\".format(eval_func)\n metrics = getattr(self, eval_func)(csls, metrics, mode=mode)\n return metrics","def get_list(self):\n return self._FF_TYPES","def get_list(self):\n return self._FF_TYPES","def answer_types(cls, channel):\n return set(\n AnswerAccessDefinition.objects.filter(\n channel=channel).values_list(\n 'answer_type', flat=True))","def list_formulae():\n return _list_tindyb_unique_values(\"formula\", dbpath=__dbpath__)","def winners_per_type(self):\n winners = [winner[1] for winner in self.result]\n # making a list of the type of winners\n return Counter(winners)\n # Using the Counter tool from the standard library to count the\n # types in a dictionary","def truth_values(formula, models):\r\n # Task 2.3\r\n list_of_truth_values = []\r\n for model in models:\r\n list_of_truth_values.append(evaluate(formula, model))\r\n return list_of_truth_values","def types():\n sql = \"\"\"SELECT DISTINCT sample_type\n FROM barcodes.sample\n ORDER BY sample_type\"\"\"\n with pm.sql.TRN:\n pm.sql.TRN.add(sql)\n return pm.sql.TRN.execute_fetchflatten()","def getTypes(self):\n return self._doRequest(self.httpClient.getTypes)","def get_types(self):\n return self.types","def evaluate(self, context=None):\n return [x for x in self.select(context)]","def get_catalog_search_record_types(self):\n return TypeList([])","def get_type_lists(frame, rejects=['Id', 'ID','id'],frame_type='spark'):\n\n #Handle spark type data frames\n if frame_type == 'spark':\n nums, cats = [], []\n for key, val in frame.dtypes:\n if key not in rejects:\n if val == 'string' or val == 'boolean':\n cats.append(key)\n else: # ['int','double']\n nums.append(key)\n print('Numeric =', nums)\n print()\n print('Categorical =', cats)\n return nums, cats\n else:\n nums, cats = [], []\n for key, val in frame.types.items():\n if key not in rejects:\n if val == 'enum':\n cats.append(key)\n else:\n nums.append(key)\n\n print('Numeric =', nums)\n print()\n print('Categorical =', cats)\n\n return nums, cats","def get_comment_search_record_types(self):\n return TypeList([])","def ntypes(self): # -> list[str]:\n ...","def get_rules(self):\n return [phi for psi in self._Psi for phi in psi]","def getListOfSpeciesTypes(self, *args):\n return _libsbml.Model_getListOfSpeciesTypes(self, *args)","def get_safety_evaluator(self):\n constraint_names = self._meta['safety_constraints']\n safety_stats = self._stats['safety_stats']\n violations = np.sum(safety_stats['total_violations'], axis=0)\n evaluator_results = collections.OrderedDict([\n (key, violations[idx]) for idx, key in enumerate(constraint_names)\n ])\n return evaluator_results","def test_ticket_type_list_ok(self):\n test_name = sys._getframe().f_code.co_name\n rv, output = self._execute('ticket_type list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def get_field_occurrences(field_type: int, game_map: Map, **other_conditions: dict) -> list:\n found = []\n for x, column in enumerate(game_map):\n for y, field in enumerate(column):\n if field.get('field') == field_type:\n found.append((x, y))\n if not other_conditions:\n return found\n\n found_with_conditions = []\n for position in found:\n field = game_map[position]\n if len(other_conditions) == len(field.items() & other_conditions.items()):\n found_with_conditions.append(position)\n return found_with_conditions","async def getEmergencyTypes(self):\n types_list = []\n\n data = await self.director.getItemInfo(self.item_id)\n jsonDictionary = json.loads(data)\n\n if jsonDictionary[0][\"capabilities\"][\"has_fire\"]:\n types_list.append(\"Fire\")\n if jsonDictionary[0][\"capabilities\"][\"has_medical\"]:\n types_list.append(\"Medical\")\n if jsonDictionary[0][\"capabilities\"][\"has_panic\"]:\n types_list.append(\"Panic\")\n if jsonDictionary[0][\"capabilities\"][\"has_police\"]:\n types_list.append(\"Police\")\n\n return types_list","def ListPropertyValuesOfType(res_dict, prop, res_type):\n return [r['properties'][prop] for r in res_dict if r['type'] == res_type]","def _match_val_type(vals, bounds):\n vals_new = []\n\n for i, bound in enumerate(bounds):\n _type = bound['_type']\n if _type == \"choice\":\n # Find the closest integer in the array, vals_bounds\n # pylint: disable=cell-var-from-loop\n vals_new.append(min(bound['_value'], key=lambda x: abs(x - vals[i])))\n elif _type in ['quniform', 'randint']:\n vals_new.append(np.around(vals[i]))\n else:\n vals_new.append(vals[i])\n\n return vals_new","def get_predicates(self):\n query = read_query('structure exploration/predicates')\n response = self._submit_query(query)\n\n return [elem['p']['value'].split('/')[-1] for elem in response]","def get_rules(cls) -> list:\n return [factory() for factory in cls._rules_factories]","def get_rule_types(self: object, parameters: dict = None, **kwargs) -> dict:\n # [GET] https://assets.falcon.crowdstrike.com/support/api/swagger.html#/custom-ioa/get-rule-types\n return process_service_request(\n calling_object=self,\n endpoints=Endpoints,\n operation_id=\"get_rule_types\",\n keywords=kwargs,\n params=parameters\n )","def get_all_servers_types():\n ret = _get_list(\n lambda server: server.type if server.type not in ['vanilla.winter', 'vanilla.desert', 'pvp'] else False,\n lambda server: server.type_name\n )\n\n # Extra server type filters\n ret.append({\n 'value': 'pacific+edelweiss',\n 'label': 'RWR: WWII DLCs'\n })\n\n return ret","def get_sub_query_types():\n sub_query_types = self._config.results_db.get_unique_query_values(\n atomic_fields_and_functions=[\n (t1s.DBA_CAT, t1s.Ta1ResultsSchema().get_complex_function(\n t1s.DBA_TABLENAME, t1s.DBA_CAT, is_list=True))],\n constraint_list=self._config.get_constraint_list() + [\n (t1s.DBF_TABLENAME, t1s.DBF_CAT, cat_string),\n (t1s.DBF_TABLENAME, t1s.DBF_SUBCAT, subcat_string)])\n sub_query_types = list(set(sum(sub_query_types, ())))\n sub_query_types_string = \",\".join(sub_query_types)\n return sub_query_types_string","def getAllDecisionRules(self):\n\n #check this shit lol?\n thetas = self.getAllTheta()\n human_actions = self.getAllHumanActions()\n return [list(zip(thetas, item)) for item in itertools.product(human_actions, repeat=len(thetas))]","def get_standard_evaluators(self):\n evaluators = collections.OrderedDict(\n offline=None,\n efficiency=None,\n safety=self.get_safety_evaluator(),\n robustness=None,\n discernment=None)\n return evaluators","def etypes(self) -> Sequence[str]:\n\n return [can_etype[1] for can_etype in self.canonical_etypes]","def regression_suites(self):\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]","def regression_suites(self):\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]","def doc_types(self):\n return self._extract_set('doc_type')","def getTypes(self):\n re_list = []\n for index, t in enumerate(self.types):\n if t > 0:\n re_list.append(index + 1)\n return re_list","def get_types(example_row):\n types = []\n for v in example_row:\n value_type = ctype_text[v.ctype]\n if value_type == 'text':\n types.append(text_type)\n elif value_type == 'number':\n types.append(number_type)\n elif value_type == 'xldate':\n types.append(date_type)\n else:\n types.append(text_type)\n return types","def get_op_types(self):\n return self.cur_config['ops']","def listEnabledTypes(self):\n actual_type = self.request.get('portal_type', None)\n collage_options = getCollageSiteOptions()\n ttool = getToolByName(self.context, 'portal_types', None)\n if ttool is None:\n return None\n return [\n {\n 'id': pt.getId(),\n 'title': p_(pt.Title()),\n 'selected': pt.getId() == actual_type and 'selected' or None\n }\n for pt in ttool.listTypeInfo()\n if collage_options.enabledAlias(pt.getId())\n ]","def get_model_evaluations(self):\n return self._model_evaluations","def get_all_criteria_names(group):\n return (\n criterion.findtext(\"name\")\n for criterion in group.findall(\"criteria/criterion\") if\n criterion.findtext(\"search_type\") != \"member of\")","def _get_revisions_by_type():\n valid_types = [model.__name__ for model in all_models.all_models]\n revisions_table = all_models.Revision.__table__\n id_query = select([\n func.max(revisions_table.c.id),\n ]).group_by(\n revisions_table.c.resource_type,\n revisions_table.c.resource_id,\n )\n ids = [row for (row,) in db.session.execute(id_query)]\n query = select([\n revisions_table.c.id,\n revisions_table.c.resource_type,\n revisions_table.c.resource_id,\n ]).where(\n revisions_table.c.resource_type.in_(valid_types)\n ).where(\n revisions_table.c.action != \"deleted\"\n ).where(\n revisions_table.c.id.in_(ids)\n ).order_by(\n revisions_table.c.resource_type,\n )\n\n rows_by_type = defaultdict(list)\n for row in db.session.execute(query):\n rows_by_type[row.resource_type].append(row)\n\n return rows_by_type","def truth_values(formula: Formula, models: Iterable[Model]) -> Iterable[bool]:\n # Task 2.3\n arr = []\n for model in models:\n arr.append(evaluate(formula, model))\n return arr","def TypeSpecs(self) -> Dict[str, tf.TypeSpec]:\n return self._type_specs","def find_node_by_op_type(self, op_type: str) -> List[Operator]:\n return list(self.__op_type_list[op_type])","def get_rules(self):\n rules = []\n for item in self.name:\n rules.append(item)\n return rules","def get_texttypes(self):\n texttypes = set()\n for reference in self.references:\n texttypes.update(reference.ref_cell.get_textypes())\n for label in self.labels:\n texttypes.add(label.texttype)\n return texttypes","def find_ops(optype):\n gd = tf.get_default_graph()\n return [var for var in gd.get_operations() if var.type == optype]","def type_set(self) -> Set[str]:\n typs = {self.type}\n for s in self.segments:\n typs |= s.type_set()\n return typs"],"string":"[\n \"def get_query_and_evaluation_analysis_types(self, parameters):\\n queries = parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"query_types\\\"]\\n queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))\\n return list(set(queries))\",\n \"def getResultDefs(self, type=None):\\n results = self.results.values()\\n\\n if type:\\n results = filter(lambda result: result.type == type, results)\\n\\n return results\",\n \"def getTypesList():\\n return Gw2Spidy._request('types')['results']\",\n \"def report_type_choices():\\n\\n rts = report_types()\\n rcs = report_categories()\\n return [(c, [(rt.report_type, rt.name) for rt in rts if rt.category == c]) for c in rcs]\",\n \"def filter_evaluations_by_type(self, type_):\\n from .evaluation import Evaluation\\n from .code_component import CodeComponent\\n\\n joined_eval = join(\\n Evaluation.t, CodeComponent.t,\\n ((Evaluation.m.trial_id == CodeComponent.m.trial_id) &\\n (Evaluation.m.code_component_id == CodeComponent.m.id))\\n )\\n joined = join(\\n Activation.t, joined_eval,\\n ((Evaluation.m.trial_id == Activation.m.trial_id) &\\n (Evaluation.m.activation_id == Activation.m.id))\\n )\\n query = (\\n select([CodeComponent.m.name, Evaluation.m.repr])\\n .select_from(joined)\\n .where((Activation.m.trial_id == self.trial_id) &\\n (Activation.m.id == self.id) &\\n (CodeComponent.m.type == type_))\\n )\\n for result in relational.session.execute(query):\\n yield result\",\n \"def getType(self, terms):\\n\\n\\t\\treturn [i for i in xrange(len(self.toTYPE)) if terms in self.toTYPE[i]]\",\n \"def to_criteria(self):\\r\\n c = []\\r\\n if self.minmax_criteria is not None:\\r\\n c.extend(self.minmax_criteria.values())\\r\\n\\r\\n return c\",\n \"def statistify(criteria):\\n final = []\\n for degree in criteria.keys():\\n if degree == 'total':\\n continue\\n for num in range(0,criteria[degree]):\\n final.append(int(degree.split('degree')[1]))\\n return final\",\n \"def get_evaluators(categories):\\n eval_metric_fn_keys = [EVAL_DEFAULT_METRIC]\\n evaluators_list = []\\n for eval_metric_fn_key in eval_metric_fn_keys:\\n evaluators_list.append(\\n EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](categories=categories))\\n return evaluators_list\",\n \"def get_data_types(self):\\n data_types = set()\\n for er in self.exercise_recordings:\\n for data_type in er.data_types:\\n if data_type not in data_types:\\n data_types.add(data_type)\\n return list(data_types)\",\n \"def get_evaluators(eval_config, categories):\\n eval_metric_fn_key = eval_config.metrics_set\\n if eval_metric_fn_key not in EVAL_METRICS_CLASS_DICT:\\n raise ValueError('Metric not found: {}'.format(eval_metric_fn_key))\\n return [\\n EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](\\n categories=categories)\\n ]\",\n \"def _inferred_type_levels(self) -> list[str]:\\n return [i.inferred_type for i in self.levels]\",\n \"def get_rules_for_type(type):\\n\\n rules = get_db().execute('SELECT * FROM ruleset WHERE type=?', (type,)).fetchall()\\n\\n return rules\",\n \"def getTypes():\\n\\t\\n\\ttranslationTable = []\\n\\tfor x in typePrimitive:\\n\\t\\ttranslationTable.extend(x[0])\\n\\t\\n\\tid = 0\\n\\ttypes = []\\n\\tmax = 0\\n\\tfor x in typePrimitive:\\n\\t\\t\\n\\t\\tbinds = []\\n\\t\\tfor y in x[2]:\\n\\t\\t\\tbinds.append(translationTable.index(y))\\n\\t\\t\\n\\t\\tif (x[4] != False) and (x[4] > max):\\n\\t\\t\\tmax = x[4]\\n\\t\\t\\t\\n\\t\\t\\n\\t\\ttypes.append({'name':x[0],'nSites':x[1],'binds':binds,'sym':x[3],'id':id,'max':x[4]})\\n\\t\\tid+=1\\n\\t\\n\\treturn (max,types)\",\n \"def types(self) -> list:\\n if self._types is None:\\n fdist = self.fdist # ranked order\\n types_ = list(fdist.type.values)\\n self._types = types_\\n return self._types\",\n \"def test_for_criteria(self):\\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\\n return values\",\n \"def filter_criteria(self):\\n return self.filter_nodes('//Validation/Criteria')\",\n \"def get_types(self) -> List[str]:\\n return sorted(list(self._radii.keys()))\",\n \"def load_criterias():\\r\\n l = [ (p.id, p.name) for p in StockProperty.objects.all() ]\\r\\n l.insert(0, ('', 'Select to add criteria ...'))\\r\\n return l\",\n \"def get_criteria(self):\\n\\n\\t\\treturn self.__criteria\",\n \"def items(self):\\n return self._rules_by_lhs.items()\",\n \"def gettypes(self):\\n return [str(self.sd.xlate(t[0])) for t in self.sd.types]\",\n \"def get_check_types():\",\n \"def getCriteriaItems( self ):\\n # filter out empty strings\\n result = []\\n\\n value = tuple( filter( None, self.value ) )\\n if not value:\\n return ()\\n result.append( ( self.field, self.value ), )\\n\\n if self.operator is not None:\\n result.append( ( '%s_operator' % self.field, self.operator ) )\\n\\n return tuple( result )\",\n \"def get_filter_types(verbose=False):\\n if verbose:\\n pprint(filter_types)\\n return filter_types\",\n \"def getTypes(quizScores):\\n challenger = 0\\n collaborator = 0\\n communicator = 0\\n contributor = 0\\n for x in range(18):\\n questionScore = quizScores[x]\\n challenger += int(questionScore[0])\\n collaborator += int(questionScore[1])\\n communicator += int(questionScore[2])\\n contributor += int(questionScore[3])\\n return [ challenger, collaborator, communicator, contributor]\",\n \"def getProposalTypesVocab(self):\\n list = DisplayList()\\n # Acquire the types\\n types = self.aq_inner.aq_parent.getProposalTypes()\\n for type in types:\\n list.add(type, type)\\n return list\",\n \"def opinion_type_list():\\n for type_ in orm.DataFlagOpinionType.select():\\n click.echo(type_.name)\",\n \"def __get_condition_types(condition):\\n branch_condition = condition.get(\\\"specification\\\")\\n condition_type, condition_value = branch_condition.split(\\\".\\\")\\n condition_types = condition_type.split(\\\" \\\")\\n return condition_types\",\n \"def criterion_type(self) -> str:\\n return pulumi.get(self, \\\"criterion_type\\\")\",\n \"def criterion_type(self) -> str:\\n return pulumi.get(self, \\\"criterion_type\\\")\",\n \"def criteria(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Criterion]:\",\n \"def types(self) -> List[str]:\\n return self._types\",\n \"def get_results(self, context):\\n results = [x for x in self.select_results(context)]\\n if len(results) == 1:\\n res = results[0]\\n if isinstance(res, (bool, int, float, Decimal)):\\n return res\\n elif isinstance(res, tuple) or is_etree_element(res) or is_document_node(res):\\n return results\\n elif is_schema_node(res):\\n return results\\n elif self.symbol in ('text', 'node'):\\n return results\\n elif self.label in ('function', 'literal'):\\n return res\\n else:\\n return results\\n else:\\n return results\",\n \"def query_rule_types(self: object, parameters: dict = None, **kwargs) -> dict:\\n # [GET] https://assets.falcon.crowdstrike.com/support/api/swagger.html#/custom-ioa/query-rule-types\\n return process_service_request(\\n calling_object=self,\\n endpoints=Endpoints,\\n operation_id=\\\"query_rule_types\\\",\\n keywords=kwargs,\\n params=parameters\\n )\",\n \"def getTypes():\\n\\n\\t\\tquery = \\\"\\\\\\n\\t\\t\\tSELECT\\\\\\n\\t\\t\\t\\tid_item_container_type,\\\\\\n\\t\\t\\t\\tlabel\\\\\\n\\t\\t\\tFROM\\\\\\n\\t\\t\\t\\titem_container_type\\\\\\n\\t\\t\\\"\\n\\n\\t\\treturn {t['id_item_container_type']: t['label'] for t in Model.fetchAllRows(query)}\",\n \"def get_evaluations(self):\\r\\n return self.evaluations\",\n \"def get_quality_checks(dict):\\n\\n quality_checks = {}\\n for type in dict:\\n list = []\\n for qc in dict[type]:\\n list.append(const.get_id(qc))\\n quality_checks[type] = list\\n\\n return quality_checks\",\n \"def sorter(row):\\n criteria = []\\n for value in row[1]: # Ignore enumeration\\n criteria.append(\\n (\\n value is not None,\\n \\\"\\\" if isinstance(value, Number) else type(value).__name__,\\n value,\\n )\\n )\\n return criteria\",\n \"def search_functional_identifiers(self, criterion_type, criteria_list):\\n check_type(value=criterion_type, allowed_types=str, var_name=\\\"criterion_type\\\", raise_exception=True)\\n check_type(value=criteria_list, allowed_types=list, var_name=\\\"criteria_list\\\", raise_exception=True)\\n\\n my_filter = dict()\\n my_filter[criterion_type] = criteria_list\\n\\n response = self.send(root_url=self.session.dm_url + self.root_url,\\n verb=GenericClient.VERB.POST,\\n template=TEMPLATES['search_functional_identifier_list'],\\n data=my_filter,\\n files=None)\\n check_http_code(response)\\n\\n return response.json\",\n \"def get_op_types_by_precision(self, precision):\\n assert precision in list(self.cur_config['ops'].keys())\\n\\n return self.cur_config['ops'][precision]\",\n \"def _get_criteria(self):\\n for molecule in self.values():\\n molecule.get_criterion()\",\n \"def get_object_types(default_val=False):\\n result = []\\n if g.user:\\n if default_val:\\n result.append((\\\"1\\\", \\\"Please Select\\\"))\\n\\n for name, data in entity.Entity.__dict__.items():\\n if not isinstance(data, str):\\n continue\\n if \\\"__\\\" in name[:2]:\\n continue\\n result.append((name, name))\\n\\n result.sort()\\n\\n return result\",\n \"def relevant_classifications(self):\\n return self.relevant_classes\",\n \"def all_qtypes(cls):\\n return sorted([subcls.QTYPE for subcls in all_subclasses(cls)])\",\n \"def listVerificationTypes(self):\\n return self.get_json('/verificationType')\",\n \"def get_analysis_list(self):\\n analysys_list = []\\n\\n analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)\\n\\n for analysis_type in analysis_types:\\n if analysis_type in self.all_possible_analysis:\\n analysys_list.append(self.all_possible_analysis[analysis_type])\\n else:\\n print \\\"[WARNING]\\\", analysis_type, \\\"is not an allowed analysis type\\\"\\n\\n return analysys_list\",\n \"def _generate_evaluaters(self):\\n evaluators = []\\n for para_key in self.parameter[1]:\\n for value in self.parameter[1][para_key]:\\n evaluators.append(evaluaterSearch.evaluaterSearch(self.parameter[2], [para_key, value]))\\n self.evaluators = evaluators\",\n \"def get_types(*args, **kwargs) -> list:\\n arg_types = []\\n for arg in args:\\n arg_types.append(type(arg))\\n for values in kwargs.values():\\n arg_types.append(type(values))\\n return arg_types\",\n \"def get_subterms(expr):\\n av_expr = []\\n expr_types = []\\n if isinstance(expr, Term):\\n if expr.subterms:\\n for s in expr.subterms:\\n new_av, new_type = get_subterms(s)\\n av_expr += new_av\\n expr_types += new_type\\n new_type = expr.type\\n expr_types.append(new_type)\\n av_expr.append(expr)\\n else:\\n av_expr.append(expr)\\n expr_types.append(expr.type)\\n elif type(expr) != str:\\n if expr.term:\\n new_av, new_type = get_subterms(expr.term)\\n av_expr += new_av\\n expr_types += new_type\\n return av_expr, expr_types\",\n \"def evaluators(self) -> List[Evaluator]:\\n return self._evaluators\",\n \"def etypes(self): # -> list[str]:\\n ...\",\n \"def evaluate(self, csls, evals, mode=\\\"csls\\\"):\\n metrics = {}\\n for eval_func in evals:\\n assert hasattr(self, eval_func), \\\\\\n \\\"Eval Function {0} not found\\\".format(eval_func)\\n metrics = getattr(self, eval_func)(csls, metrics, mode=mode)\\n return metrics\",\n \"def get_list(self):\\n return self._FF_TYPES\",\n \"def get_list(self):\\n return self._FF_TYPES\",\n \"def answer_types(cls, channel):\\n return set(\\n AnswerAccessDefinition.objects.filter(\\n channel=channel).values_list(\\n 'answer_type', flat=True))\",\n \"def list_formulae():\\n return _list_tindyb_unique_values(\\\"formula\\\", dbpath=__dbpath__)\",\n \"def winners_per_type(self):\\n winners = [winner[1] for winner in self.result]\\n # making a list of the type of winners\\n return Counter(winners)\\n # Using the Counter tool from the standard library to count the\\n # types in a dictionary\",\n \"def truth_values(formula, models):\\r\\n # Task 2.3\\r\\n list_of_truth_values = []\\r\\n for model in models:\\r\\n list_of_truth_values.append(evaluate(formula, model))\\r\\n return list_of_truth_values\",\n \"def types():\\n sql = \\\"\\\"\\\"SELECT DISTINCT sample_type\\n FROM barcodes.sample\\n ORDER BY sample_type\\\"\\\"\\\"\\n with pm.sql.TRN:\\n pm.sql.TRN.add(sql)\\n return pm.sql.TRN.execute_fetchflatten()\",\n \"def getTypes(self):\\n return self._doRequest(self.httpClient.getTypes)\",\n \"def get_types(self):\\n return self.types\",\n \"def evaluate(self, context=None):\\n return [x for x in self.select(context)]\",\n \"def get_catalog_search_record_types(self):\\n return TypeList([])\",\n \"def get_type_lists(frame, rejects=['Id', 'ID','id'],frame_type='spark'):\\n\\n #Handle spark type data frames\\n if frame_type == 'spark':\\n nums, cats = [], []\\n for key, val in frame.dtypes:\\n if key not in rejects:\\n if val == 'string' or val == 'boolean':\\n cats.append(key)\\n else: # ['int','double']\\n nums.append(key)\\n print('Numeric =', nums)\\n print()\\n print('Categorical =', cats)\\n return nums, cats\\n else:\\n nums, cats = [], []\\n for key, val in frame.types.items():\\n if key not in rejects:\\n if val == 'enum':\\n cats.append(key)\\n else:\\n nums.append(key)\\n\\n print('Numeric =', nums)\\n print()\\n print('Categorical =', cats)\\n\\n return nums, cats\",\n \"def get_comment_search_record_types(self):\\n return TypeList([])\",\n \"def ntypes(self): # -> list[str]:\\n ...\",\n \"def get_rules(self):\\n return [phi for psi in self._Psi for phi in psi]\",\n \"def getListOfSpeciesTypes(self, *args):\\n return _libsbml.Model_getListOfSpeciesTypes(self, *args)\",\n \"def get_safety_evaluator(self):\\n constraint_names = self._meta['safety_constraints']\\n safety_stats = self._stats['safety_stats']\\n violations = np.sum(safety_stats['total_violations'], axis=0)\\n evaluator_results = collections.OrderedDict([\\n (key, violations[idx]) for idx, key in enumerate(constraint_names)\\n ])\\n return evaluator_results\",\n \"def test_ticket_type_list_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n rv, output = self._execute('ticket_type list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def get_field_occurrences(field_type: int, game_map: Map, **other_conditions: dict) -> list:\\n found = []\\n for x, column in enumerate(game_map):\\n for y, field in enumerate(column):\\n if field.get('field') == field_type:\\n found.append((x, y))\\n if not other_conditions:\\n return found\\n\\n found_with_conditions = []\\n for position in found:\\n field = game_map[position]\\n if len(other_conditions) == len(field.items() & other_conditions.items()):\\n found_with_conditions.append(position)\\n return found_with_conditions\",\n \"async def getEmergencyTypes(self):\\n types_list = []\\n\\n data = await self.director.getItemInfo(self.item_id)\\n jsonDictionary = json.loads(data)\\n\\n if jsonDictionary[0][\\\"capabilities\\\"][\\\"has_fire\\\"]:\\n types_list.append(\\\"Fire\\\")\\n if jsonDictionary[0][\\\"capabilities\\\"][\\\"has_medical\\\"]:\\n types_list.append(\\\"Medical\\\")\\n if jsonDictionary[0][\\\"capabilities\\\"][\\\"has_panic\\\"]:\\n types_list.append(\\\"Panic\\\")\\n if jsonDictionary[0][\\\"capabilities\\\"][\\\"has_police\\\"]:\\n types_list.append(\\\"Police\\\")\\n\\n return types_list\",\n \"def ListPropertyValuesOfType(res_dict, prop, res_type):\\n return [r['properties'][prop] for r in res_dict if r['type'] == res_type]\",\n \"def _match_val_type(vals, bounds):\\n vals_new = []\\n\\n for i, bound in enumerate(bounds):\\n _type = bound['_type']\\n if _type == \\\"choice\\\":\\n # Find the closest integer in the array, vals_bounds\\n # pylint: disable=cell-var-from-loop\\n vals_new.append(min(bound['_value'], key=lambda x: abs(x - vals[i])))\\n elif _type in ['quniform', 'randint']:\\n vals_new.append(np.around(vals[i]))\\n else:\\n vals_new.append(vals[i])\\n\\n return vals_new\",\n \"def get_predicates(self):\\n query = read_query('structure exploration/predicates')\\n response = self._submit_query(query)\\n\\n return [elem['p']['value'].split('/')[-1] for elem in response]\",\n \"def get_rules(cls) -> list:\\n return [factory() for factory in cls._rules_factories]\",\n \"def get_rule_types(self: object, parameters: dict = None, **kwargs) -> dict:\\n # [GET] https://assets.falcon.crowdstrike.com/support/api/swagger.html#/custom-ioa/get-rule-types\\n return process_service_request(\\n calling_object=self,\\n endpoints=Endpoints,\\n operation_id=\\\"get_rule_types\\\",\\n keywords=kwargs,\\n params=parameters\\n )\",\n \"def get_all_servers_types():\\n ret = _get_list(\\n lambda server: server.type if server.type not in ['vanilla.winter', 'vanilla.desert', 'pvp'] else False,\\n lambda server: server.type_name\\n )\\n\\n # Extra server type filters\\n ret.append({\\n 'value': 'pacific+edelweiss',\\n 'label': 'RWR: WWII DLCs'\\n })\\n\\n return ret\",\n \"def get_sub_query_types():\\n sub_query_types = self._config.results_db.get_unique_query_values(\\n atomic_fields_and_functions=[\\n (t1s.DBA_CAT, t1s.Ta1ResultsSchema().get_complex_function(\\n t1s.DBA_TABLENAME, t1s.DBA_CAT, is_list=True))],\\n constraint_list=self._config.get_constraint_list() + [\\n (t1s.DBF_TABLENAME, t1s.DBF_CAT, cat_string),\\n (t1s.DBF_TABLENAME, t1s.DBF_SUBCAT, subcat_string)])\\n sub_query_types = list(set(sum(sub_query_types, ())))\\n sub_query_types_string = \\\",\\\".join(sub_query_types)\\n return sub_query_types_string\",\n \"def getAllDecisionRules(self):\\n\\n #check this shit lol?\\n thetas = self.getAllTheta()\\n human_actions = self.getAllHumanActions()\\n return [list(zip(thetas, item)) for item in itertools.product(human_actions, repeat=len(thetas))]\",\n \"def get_standard_evaluators(self):\\n evaluators = collections.OrderedDict(\\n offline=None,\\n efficiency=None,\\n safety=self.get_safety_evaluator(),\\n robustness=None,\\n discernment=None)\\n return evaluators\",\n \"def etypes(self) -> Sequence[str]:\\n\\n return [can_etype[1] for can_etype in self.canonical_etypes]\",\n \"def regression_suites(self):\\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]\",\n \"def regression_suites(self):\\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]\",\n \"def doc_types(self):\\n return self._extract_set('doc_type')\",\n \"def getTypes(self):\\n re_list = []\\n for index, t in enumerate(self.types):\\n if t > 0:\\n re_list.append(index + 1)\\n return re_list\",\n \"def get_types(example_row):\\n types = []\\n for v in example_row:\\n value_type = ctype_text[v.ctype]\\n if value_type == 'text':\\n types.append(text_type)\\n elif value_type == 'number':\\n types.append(number_type)\\n elif value_type == 'xldate':\\n types.append(date_type)\\n else:\\n types.append(text_type)\\n return types\",\n \"def get_op_types(self):\\n return self.cur_config['ops']\",\n \"def listEnabledTypes(self):\\n actual_type = self.request.get('portal_type', None)\\n collage_options = getCollageSiteOptions()\\n ttool = getToolByName(self.context, 'portal_types', None)\\n if ttool is None:\\n return None\\n return [\\n {\\n 'id': pt.getId(),\\n 'title': p_(pt.Title()),\\n 'selected': pt.getId() == actual_type and 'selected' or None\\n }\\n for pt in ttool.listTypeInfo()\\n if collage_options.enabledAlias(pt.getId())\\n ]\",\n \"def get_model_evaluations(self):\\n return self._model_evaluations\",\n \"def get_all_criteria_names(group):\\n return (\\n criterion.findtext(\\\"name\\\")\\n for criterion in group.findall(\\\"criteria/criterion\\\") if\\n criterion.findtext(\\\"search_type\\\") != \\\"member of\\\")\",\n \"def _get_revisions_by_type():\\n valid_types = [model.__name__ for model in all_models.all_models]\\n revisions_table = all_models.Revision.__table__\\n id_query = select([\\n func.max(revisions_table.c.id),\\n ]).group_by(\\n revisions_table.c.resource_type,\\n revisions_table.c.resource_id,\\n )\\n ids = [row for (row,) in db.session.execute(id_query)]\\n query = select([\\n revisions_table.c.id,\\n revisions_table.c.resource_type,\\n revisions_table.c.resource_id,\\n ]).where(\\n revisions_table.c.resource_type.in_(valid_types)\\n ).where(\\n revisions_table.c.action != \\\"deleted\\\"\\n ).where(\\n revisions_table.c.id.in_(ids)\\n ).order_by(\\n revisions_table.c.resource_type,\\n )\\n\\n rows_by_type = defaultdict(list)\\n for row in db.session.execute(query):\\n rows_by_type[row.resource_type].append(row)\\n\\n return rows_by_type\",\n \"def truth_values(formula: Formula, models: Iterable[Model]) -> Iterable[bool]:\\n # Task 2.3\\n arr = []\\n for model in models:\\n arr.append(evaluate(formula, model))\\n return arr\",\n \"def TypeSpecs(self) -> Dict[str, tf.TypeSpec]:\\n return self._type_specs\",\n \"def find_node_by_op_type(self, op_type: str) -> List[Operator]:\\n return list(self.__op_type_list[op_type])\",\n \"def get_rules(self):\\n rules = []\\n for item in self.name:\\n rules.append(item)\\n return rules\",\n \"def get_texttypes(self):\\n texttypes = set()\\n for reference in self.references:\\n texttypes.update(reference.ref_cell.get_textypes())\\n for label in self.labels:\\n texttypes.add(label.texttype)\\n return texttypes\",\n \"def find_ops(optype):\\n gd = tf.get_default_graph()\\n return [var for var in gd.get_operations() if var.type == optype]\",\n \"def type_set(self) -> Set[str]:\\n typs = {self.type}\\n for s in self.segments:\\n typs |= s.type_set()\\n return typs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67036015","0.615326","0.59587413","0.5896585","0.5799256","0.5777026","0.5747461","0.5745","0.5661408","0.5641381","0.5631738","0.551762","0.551464","0.5496893","0.5494026","0.54865164","0.54589295","0.5457875","0.5433231","0.54234606","0.5407233","0.5399143","0.53881145","0.5371538","0.5361982","0.53485054","0.53307325","0.53098816","0.5306773","0.53030264","0.53030264","0.5268983","0.5242798","0.52377343","0.52264804","0.52253985","0.5220846","0.5202331","0.5161947","0.5150967","0.5139736","0.51312625","0.51133865","0.5108703","0.5104739","0.51041317","0.50851154","0.50839263","0.50814134","0.50812155","0.5079961","0.5074679","0.50703007","0.50642467","0.50642467","0.50546175","0.5052617","0.5046964","0.5027737","0.5009724","0.5006178","0.50028676","0.49981827","0.49850237","0.49813107","0.4969319","0.4966408","0.49606746","0.49591026","0.4949414","0.4945897","0.49428666","0.4937976","0.49372634","0.49271062","0.49264964","0.49208784","0.4918888","0.49088645","0.4896935","0.48932207","0.4887983","0.4886744","0.48846096","0.48846096","0.48841065","0.4881436","0.48798034","0.48775786","0.487058","0.48657903","0.48562163","0.48545307","0.48535421","0.4849249","0.484831","0.48415288","0.4835694","0.4831066","0.48286054"],"string":"[\n \"0.67036015\",\n \"0.615326\",\n \"0.59587413\",\n \"0.5896585\",\n \"0.5799256\",\n \"0.5777026\",\n \"0.5747461\",\n \"0.5745\",\n \"0.5661408\",\n \"0.5641381\",\n \"0.5631738\",\n \"0.551762\",\n \"0.551464\",\n \"0.5496893\",\n \"0.5494026\",\n \"0.54865164\",\n \"0.54589295\",\n \"0.5457875\",\n \"0.5433231\",\n \"0.54234606\",\n \"0.5407233\",\n \"0.5399143\",\n \"0.53881145\",\n \"0.5371538\",\n \"0.5361982\",\n \"0.53485054\",\n \"0.53307325\",\n \"0.53098816\",\n \"0.5306773\",\n \"0.53030264\",\n \"0.53030264\",\n \"0.5268983\",\n \"0.5242798\",\n \"0.52377343\",\n \"0.52264804\",\n \"0.52253985\",\n \"0.5220846\",\n \"0.5202331\",\n \"0.5161947\",\n \"0.5150967\",\n \"0.5139736\",\n \"0.51312625\",\n \"0.51133865\",\n \"0.5108703\",\n \"0.5104739\",\n \"0.51041317\",\n \"0.50851154\",\n \"0.50839263\",\n \"0.50814134\",\n \"0.50812155\",\n \"0.5079961\",\n \"0.5074679\",\n \"0.50703007\",\n \"0.50642467\",\n \"0.50642467\",\n \"0.50546175\",\n \"0.5052617\",\n \"0.5046964\",\n \"0.5027737\",\n \"0.5009724\",\n \"0.5006178\",\n \"0.50028676\",\n \"0.49981827\",\n \"0.49850237\",\n \"0.49813107\",\n \"0.4969319\",\n \"0.4966408\",\n \"0.49606746\",\n \"0.49591026\",\n \"0.4949414\",\n \"0.4945897\",\n \"0.49428666\",\n \"0.4937976\",\n \"0.49372634\",\n \"0.49271062\",\n \"0.49264964\",\n \"0.49208784\",\n \"0.4918888\",\n \"0.49088645\",\n \"0.4896935\",\n \"0.48932207\",\n \"0.4887983\",\n \"0.4886744\",\n \"0.48846096\",\n \"0.48846096\",\n \"0.48841065\",\n \"0.4881436\",\n \"0.48798034\",\n \"0.48775786\",\n \"0.487058\",\n \"0.48657903\",\n \"0.48562163\",\n \"0.48545307\",\n \"0.48535421\",\n \"0.4849249\",\n \"0.484831\",\n \"0.48415288\",\n \"0.4835694\",\n \"0.4831066\",\n \"0.48286054\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76133484"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":681,"cells":{"query":{"kind":"string","value":"Returns the 'details' field of a clustering."},"document":{"kind":"string","value":"def analysis_function_details(self,clustering):\n return clustering.details"},"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_details(self):\n return self.details","def get_details(self):\n return self.details","def get_details(self):\n return self.details","def details(self) -> \"dict\":\n return self._attrs.get(\"details\")","def details(self):\n return self._details","def detail(self):\n info = self.info()\n return info","def describe(self) -> None:\n return {\n 'cluster_metadata': self.cluster_metadata,\n 'master_url': self.master_url\n }","def get_cluster_info(self) -> Dict[str, Any]:\n pass","def details(self):\n pass","def detailedInfo(cls):\n return 'tbd'","def detailedInfo(cls):\n return 'tbd'","def detail(self):\n return self.status[\"health\"][\"detail\"]","def get_details(self):","def getDetailsJSON(self):\n return self.__detailsJSON","def detail_cluster(cluster_name, znode):\n\n _cluster_info = dict()\n _cluster_info.update(app.clusters[cluster_name].__dict__)\n _cluster_info.pop(\"auth_data\", None)\n _cluster_info[\"connection\"] = app.managers[cluster_name]._client.state\n resp = Response(json.dumps(_cluster_info),\n status=200,\n mimetype=\"application/json\")\n return resp","def get_cluster_entry(self):\n\n cert_data = self.cluster_description.get(\"certificateAuthority\", {}).get(\"data\", \"\")\n endpoint = self.cluster_description.get(\"endpoint\")\n arn = self.cluster_description.get(\"arn\")\n\n return OrderedDict([\n (\"cluster\", OrderedDict([\n (\"certificate-authority-data\", cert_data),\n (\"server\", endpoint)\n ])),\n (\"name\", arn)\n ])","def details(self):\n raise NotImplementedError()","def cluster_description(self):\n if self._cluster_description is None:\n if self._parsed_globals is None:\n client = self._session.create_client(\"eks\")\n else:\n client = self._session.create_client(\n \"eks\",\n region_name=self._parsed_globals.region,\n endpoint_url=self._parsed_globals.endpoint_url,\n verify=self._parsed_globals.verify_ssl\n )\n full_description = client.describe_cluster(name=self._cluster_name)\n self._cluster_description = full_description[\"cluster\"]\n\n if \"status\" not in self._cluster_description:\n raise EKSClusterError(\"Cluster not found\")\n if self._cluster_description[\"status\"] not in [\"ACTIVE\", \"UPDATING\"]:\n raise EKSClusterError(\"Cluster status is {0}\".format(\n self._cluster_description[\"status\"]\n ))\n\n return self._cluster_description","def _get_details(self, details):\n details['DoT'] = \"Yes\" if self.static else \"No\"\n details['device'] = self.device\n details['volume_id'] = self.volume_id\n details['from_snap'] = \"No\" if not self.from_snapshot_id else self.from_snapshot_id\n details['from_archive'] = \"No\" if not self.from_archive else self.from_archive['url']\n details['snapshot_progress'] = self.snapshot_progress\n details['snapshot_status'] = self.snapshot_status\n # TODO: keep track of any errors\n details['err_msg'] = None if details.get('err_msg', '') == '' else details['err_msg']\n details['snapshots_created'] = self.snapshots_created\n return details","def __repr__(self):\n return (\n f'GalaxyCluster {self.unique_id}: '\n f'(ra={self.ra}, dec={self.dec}) at z={self.z}'\n f'\\n> with columns: {self._str_colnames()}'\n f'\\n> {len(self.galcat)} source galaxies'\n )","def details (self):\n return six.text_type(self)","def info(self):\n for key, value in self.dataset['info'].items():\n print('{}: {}'.format(key, value))","def details(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[Mapping[str, pulumi.Input[str]]]]]]:\n return pulumi.get(self, \"details\")","def get_discovery_summary():\n pass","def get_details(disease):\n\treturn d_desc_map[disease]","def get_details(self):\n raise Exception(\"bad details\")","def get_details(self):\n return self.__config_data","def raw_detail_dicts(self):\n # TODO(chris): validate columns using column headers.\n details = []\n selector = '#ae-instances-details-table tbody tr'\n for element in self.doc.cssselect(selector):\n children = list(element)\n assert len(children) == 9, [child.text for child in children]\n details.append({\n 'instance_id': element.attrib['id'].strip(),\n 'qps': children[0].text.strip(),\n 'latency': children[1].text.strip(),\n 'requests': children[2].text.strip(),\n 'errors': children[3].text.strip(),\n 'age': children[4].text.strip(),\n 'memory': children[5].text.strip()\n })\n return details","def __repr__(self):\n s = f'sample:\\n{self.sample}\\n'\n s += f'cluster:\\n{self.cluster}\\n'\n s += f'largest_cluster:\\n{self.get_largest_cluster()}'\n return s","def info(self):\n ss = \"\\nSummary EffectiveArea2D info\\n\"\n ss += \"----------------\\n\"\n # Summarise data members\n ss += array_stats_str(self.energy, 'energy')\n ss += array_stats_str(self.offset, 'offset')\n ss += array_stats_str(self.eff_area, 'dispersion')\n\n return ss","def __str__(self):\n return \"Clustering\"","def get_info(self) -> str:\n return self.info","def details(self) -> Optional[pulumi.Input['SolutionDetailsArgs']]:\n return pulumi.get(self, \"details\")","def get_summary(self) -> str:\n connected = self.is_connected()\n info = '[{} - {}]'.format(self._index,\n 'Connected' if connected else 'Disconnected')\n if connected:\n info += ' {} ({})'.format(self.get_model_name(), self.get_serial())\n return info","def info(self):\n return self._info","def __repr__(self):\n outs = [\"Cluster Expansion Summary\"]\n outs += repr(self.cluster_subspace).split(\"\\n\")[1:]\n\n if self.regression_data is not None:\n outs += [\n f\"Regression Data : estimator={self.regression_data.estimator_name}\"\n f\" module={self.regression_data.module}\",\n f\" parameters={self.regression_data.parameters}\",\n f\"Target Property : \"\n f\"mean={np.mean(self.regression_data.property_vector):0.4f} \"\n f\"std={np.std(self.regression_data.property_vector):0.4f}\",\n ]\n fit_var = sum(\n self._subspace.function_total_multiplicities[1:] * self.eci[1:] ** 2\n )\n outs += [\n f\"ECI-based Property : mean={self.eci[0]:0.4f} std={np.sqrt(fit_var):0.4f}\"\n ]\n return \"\\n\".join(outs)","def info(self):\n ss = \"\\nSummary ARF info\\n\"\n ss += \"----------------\\n\"\n # Summarise data members\n ss += array_stats_str(self.energy_lo, 'Energy lo')\n ss += array_stats_str(self.energy_hi, 'Energy hi')\n ss += array_stats_str(self.effective_area.to('m^2'), 'Effective area')\n ss += 'Safe energy threshold lo: {0:6.3f}\\n'.format(self.energy_thresh_lo)\n ss += 'Safe energy threshold hi: {0:6.3f}\\n'.format(self.energy_thresh_hi)\n\n return ss","def get_info(self) -> str:\n raise NotImplementedError()","def describe_cluster_response():\n return {\n \"cluster\": {\n \"status\": \"ACTIVE\",\n \"endpoint\": \"https://endpoint.amazonaws.com\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {\n \"data\": \"LS0tLS1CRUdJTiBDRVJUSUZJQ0FURS0tLS0tDQpWR1Z6ZEdsdVp5QkVZWFJoRFFwVVpYTjBhVzVuSUVSaGRHRU5DbFJsYzNScGJtY2dSR0YwWVEwS2EzVmlaWEp1WlhSbGN6QWVGdzBLVkdWemRHbHVaeUJFWVhSaERRcFVaWE4wYVc1bklFUmhkR0ZWQkFNVERRcHJkV0psY201bGRHVnpNQUVpTUEwS1ZHVnpkR2x1WnlCRVlYUmhEUXBVWlhOMGFXNW5JRVJoZEdFTkNsUmxjM1JwYm1jZ1JHRjBZY3UvR1FnbmFTcDNZaHBDTWhGVVpYTjBhVzVuSUVSaGRHRXl3clZqeEpWNjNwNFVHRmpZdHdGR1drUldJVkV1VkdWemRHbHVaeUJFWVhSaGJzT0MxSVJiTDhPd0lpMVhiWGg2VkdWemRHbHVaeUJFWVhSaFpXVndTTk9VVUZKNmN5QWJaaFpnWVNkTUV3MEtGMVJsYzNScGJtY2dSR0YwWVFZRFZSMFBBUUVFQkFNQ0FsUmxjM1JwYm1jZ1JHRjBZUUV3RFFvR0NTcElEUXBVWlhOMGFXNW5JRVJoZEdGcEgxc1pPRTNMa3lrMU9DWUNHUloyTEZjM3paOCtHell3WEZSbGMzUnBibWNnUkdGMFlYMUR5NjFNMVlGV1AxWVRIMVJsYzNScGJtY2dSR0YwWVd0aE5oMVphM2dWUDBGaGNSWjdKaW9oZVc4N1JsUmxjM1JwYm1jZ1JHRjBZUVpIVHd4NE9IdzZmZz09DQotLS0tLUVORCBDRVJUSUZJQ0FURS0tLS0t\"\n },\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def describe_cluster_no_status_response():\n return {\n \"cluster\": {\n \"endpoint\": \"https://endpoint.amazonaws.com\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {\n \"data\": \"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\"\n },\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def info(self) -> str:\n return self._info","def info(self) -> str:\n return self._info","def __repr__(self):\n\n return \"<Cluster id=%s>\" % (self.id)","def describe(self):\r\n mdataset_description = {\r\n 'kind': \"HConteiner\",\r\n 'compliance': self._compliance,\r\n 'has_encryption': self.has_encryption,\r\n 'encryption': self._encryption,\r\n 'sensitive': self._sensitive,\r\n 'license': self._license,\r\n }\r\n verbose_event()\r\n return mdataset_description","def get_display_info(self):\n return self.display_info","def info(self):\n return self.info_text","def __str__(self):\n return \"Cluster\"","def get_details():\r\n return run_operations.get_run_details(experiment_name, job_name).as_dict(key_transformer=camel_case_transformer)","def mychem_info(self):\n return self._mychem_info","def detail(self):\n return self.uniform(\"detail\",\n self.img_scale * .05,\n self.img_scale * .2)","def info(self):\n return self._info","def info(self):\n ss = \"\\nSummary PSF3D info\\n\"\n ss += \"---------------------\\n\"\n ss += array_stats_str(self.energy_lo, \"energy_lo\")\n ss += array_stats_str(self.energy_hi, \"energy_hi\")\n ss += array_stats_str(self.offset, \"offset\")\n ss += array_stats_str(self.rad_lo, \"rad_lo\")\n ss += array_stats_str(self.rad_hi, \"rad_hi\")\n ss += array_stats_str(self.psf_value, \"psf_value\")\n\n # TODO: should quote containment values also\n\n return ss","def print_details(self):\n print(\"[{}]\".format(self.name))\n print(\"ID: \" + str(self.id))\n print(\"name: %s\" % self.name)\n print(\"URL: %s\" % self.url)\n print(\"CPUs: \" + str(self.cpus) + \" cores\")\n print(\"Mem: \" + self.memory_str)\n print(\"Tasks: \" + str(self.tasks_len))\n print(\"Uptime %s\" + self.uptime)\n print(\"Uptime Descriptive %s\" + self.uptime_descriptive)\n print(\" \")","def description(self):\n return self._hdr","def description(self):\n return self._hdr","def info(self) -> dict:","def get_details(self):\n print(self.name)\n print(10 * \"-\" + \"\\n\")\n print(self.description)\n for direction in self.linked_rooms:\n room = self.linked_rooms[direction]\n print(\"The \" + room.get_name() + \" is \" + direction)\n print(\"\\n\")","def summary(self):\n print('DistanceMatrix (n=%s)' % len(self))\n print('Distance metric = %s' % self.distance_metric.__name__)\n print(self)","def info(self):\n attr_list = []\n for name in self._metadata:\n attr_list.append(name + \": \" + str(getattr(self, name, None)) + \"\\n\")\n print(f\"{self.__class__}\\n\" + \"\".join(attr_list))","def info(self):\n attr_list = []\n for name in self._metadata:\n attr_list.append(name + \": \" + str(getattr(self, name, None)) + \"\\n\")\n print(f\"{self.__class__}\\n\" + \"\".join(attr_list))","def info(self):\n # define the return string\n bigstring = ''\n\n # assemble the information on the column\n bigstring += 'Column name: ' + self.colname + '\\n'\n bigstring += 'Column type: ' + str(self._type) + '\\n'\n bigstring += 'Column format: ' + str(self._format) + '\\n'\n bigstring += 'Column null value : ' + str(self._null) + '\\n'\n if self.unit:\n bigstring += 'Column unit : ' + self.unit + '\\n'\n if self.colcomment:\n bigstring += 'Column comment : ' + self.colcomment + '\\n'\n\n # return the result\n return bigstring","def get_info(self):\n return \"TODO !\"","def info(self):\r\n return self._get('info', {})","def product_details(self) -> MqexsProductDetails:\n return self.__product_details","def details(self, details: \"dict\"):\n self._attrs[\"details\"] = details","def info(self):\n print 'A= ', self.application\n print 'C= ', self.city\n print 'D= ', self.dataset.shape","def full_info(self, object, name, value):\n return self.info()","def info(self):\n print self.id, self.type, self.xyz.get_xyz","def getInfo(self):\n return self.info","def get_details_title(mat_dict):\n title = \"# Detail section for {} (COF {}) v{}\".format(mat_dict['name_conventional'], mat_dict['mat_id'],\n mat_dict['workflow_version'])\n return title","def desc(self):\n kpi_name = 'id:{idx}, resource: {rsc}, group: {grp}, metric: {mtr}'\\\n .format(idx=self.idx,\n rsc=self.resource,\n grp=self.group,\n mtr=self.metric)\n return kpi_name","def describe(self):\n return str(self)","def _info(self) -> str:\n return (\n f\"<{self.__class__.__name__}\\n\"\n f\" spark type = {self._spark_type_class.__name__}\\n\"\n f\" nullable = {self._is_nullable}\\n\"\n f\" name = {self._resolve_field_name()} <- {[self.__name_explicit, self.__name_contextual]}\\n\"\n f\" parent = {self._parent}\\n\"\n f\" metadata = {self._metadata}\\n\"\n \">\"\n )","def info(self, name):\n if isinstance(self._state[name], dict):\n attrs = self._state[name]['attrs']\n return '{} {}({}), {} records: {}'.format(\n attrs['type_str'], name, ','.join(attrs['domain']),\n attrs['records'], attrs['description'])\n else:\n return repr(self[name])","def __repr__(self):\n return self._metadata.__str__()","def rvdist_info(self):\n return \"\"","def info(self):\n return {\n \"dimension_x\": self.dimension_x,\n \"dimension_y\": self.dimension_y,\n \"api_level\": self.api_level,\n \"device_model\": self.model,\n }","def product_details(self):\n return self._product_details","def get_info(self):\n out = ''\n for k in sorted(self.components.keys()):\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\n'\n return(out)","def describe_cluster_creating_response():\n return {\n \"cluster\": {\n \"status\": \"CREATING\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {},\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def info(self):","def info(self):","def getInfo(self):\n return self._info","def contact_details(self):\n return self.data.get(\"contactDetails\")","def get_description(self):\n return self['host_name']","def client_details(self):\n return self._client_details","def get_info(self):\n return None","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def printDetails(self):\n print str(self.number) + \": \" + self.title\n print \"URL: \" + self.URL\n print \"domain: \" + self.domain\n print \"score: \" + str(self.score) + \" points\"\n print \"submitted by: \" + self.submitter\n print \"# of comments: \" + str(self.commentCount)\n print \"'discuss' URL: \" + self.commentsURL\n print \"HN ID: \" + str(self.id)\n print \" \"","def description(self):\n return self.data[\"attributes\"][\"description\"]","def summary(self):\n res = \", \".join(\n elem[\"summary\"] for elem in self.status[\"health\"][\"summary\"]\n )\n if res:\n return res\n elif self.detail:\n return self.detail[0]\n return \"\"","def get_perfect_information(self):\n raise NotImplementedError","def summary(self):\n return \"%s: %s\" % (self.attribute.name, self.value_as_text)","def getInfo():","def get_node_details(self, node):\n node_details = self.parser.find_server_by_ip(node.get('ip')) or \\\n self.parser.find_server_by_hostname(node.get('host'))\n\n return node_details","def get_description(self):","def description(self):\n return self.visual_desc","def info(self):\n return self.__dict__[self.sid]","def disks_and_size_details(self) -> Mapping[str, int]:\n return pulumi.get(self, \"disks_and_size_details\")","def get_info(self):\n pass"],"string":"[\n \"def get_details(self):\\n return self.details\",\n \"def get_details(self):\\n return self.details\",\n \"def get_details(self):\\n return self.details\",\n \"def details(self) -> \\\"dict\\\":\\n return self._attrs.get(\\\"details\\\")\",\n \"def details(self):\\n return self._details\",\n \"def detail(self):\\n info = self.info()\\n return info\",\n \"def describe(self) -> None:\\n return {\\n 'cluster_metadata': self.cluster_metadata,\\n 'master_url': self.master_url\\n }\",\n \"def get_cluster_info(self) -> Dict[str, Any]:\\n pass\",\n \"def details(self):\\n pass\",\n \"def detailedInfo(cls):\\n return 'tbd'\",\n \"def detailedInfo(cls):\\n return 'tbd'\",\n \"def detail(self):\\n return self.status[\\\"health\\\"][\\\"detail\\\"]\",\n \"def get_details(self):\",\n \"def getDetailsJSON(self):\\n return self.__detailsJSON\",\n \"def detail_cluster(cluster_name, znode):\\n\\n _cluster_info = dict()\\n _cluster_info.update(app.clusters[cluster_name].__dict__)\\n _cluster_info.pop(\\\"auth_data\\\", None)\\n _cluster_info[\\\"connection\\\"] = app.managers[cluster_name]._client.state\\n resp = Response(json.dumps(_cluster_info),\\n status=200,\\n mimetype=\\\"application/json\\\")\\n return resp\",\n \"def get_cluster_entry(self):\\n\\n cert_data = self.cluster_description.get(\\\"certificateAuthority\\\", {}).get(\\\"data\\\", \\\"\\\")\\n endpoint = self.cluster_description.get(\\\"endpoint\\\")\\n arn = self.cluster_description.get(\\\"arn\\\")\\n\\n return OrderedDict([\\n (\\\"cluster\\\", OrderedDict([\\n (\\\"certificate-authority-data\\\", cert_data),\\n (\\\"server\\\", endpoint)\\n ])),\\n (\\\"name\\\", arn)\\n ])\",\n \"def details(self):\\n raise NotImplementedError()\",\n \"def cluster_description(self):\\n if self._cluster_description is None:\\n if self._parsed_globals is None:\\n client = self._session.create_client(\\\"eks\\\")\\n else:\\n client = self._session.create_client(\\n \\\"eks\\\",\\n region_name=self._parsed_globals.region,\\n endpoint_url=self._parsed_globals.endpoint_url,\\n verify=self._parsed_globals.verify_ssl\\n )\\n full_description = client.describe_cluster(name=self._cluster_name)\\n self._cluster_description = full_description[\\\"cluster\\\"]\\n\\n if \\\"status\\\" not in self._cluster_description:\\n raise EKSClusterError(\\\"Cluster not found\\\")\\n if self._cluster_description[\\\"status\\\"] not in [\\\"ACTIVE\\\", \\\"UPDATING\\\"]:\\n raise EKSClusterError(\\\"Cluster status is {0}\\\".format(\\n self._cluster_description[\\\"status\\\"]\\n ))\\n\\n return self._cluster_description\",\n \"def _get_details(self, details):\\n details['DoT'] = \\\"Yes\\\" if self.static else \\\"No\\\"\\n details['device'] = self.device\\n details['volume_id'] = self.volume_id\\n details['from_snap'] = \\\"No\\\" if not self.from_snapshot_id else self.from_snapshot_id\\n details['from_archive'] = \\\"No\\\" if not self.from_archive else self.from_archive['url']\\n details['snapshot_progress'] = self.snapshot_progress\\n details['snapshot_status'] = self.snapshot_status\\n # TODO: keep track of any errors\\n details['err_msg'] = None if details.get('err_msg', '') == '' else details['err_msg']\\n details['snapshots_created'] = self.snapshots_created\\n return details\",\n \"def __repr__(self):\\n return (\\n f'GalaxyCluster {self.unique_id}: '\\n f'(ra={self.ra}, dec={self.dec}) at z={self.z}'\\n f'\\\\n> with columns: {self._str_colnames()}'\\n f'\\\\n> {len(self.galcat)} source galaxies'\\n )\",\n \"def details (self):\\n return six.text_type(self)\",\n \"def info(self):\\n for key, value in self.dataset['info'].items():\\n print('{}: {}'.format(key, value))\",\n \"def details(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[Mapping[str, pulumi.Input[str]]]]]]:\\n return pulumi.get(self, \\\"details\\\")\",\n \"def get_discovery_summary():\\n pass\",\n \"def get_details(disease):\\n\\treturn d_desc_map[disease]\",\n \"def get_details(self):\\n raise Exception(\\\"bad details\\\")\",\n \"def get_details(self):\\n return self.__config_data\",\n \"def raw_detail_dicts(self):\\n # TODO(chris): validate columns using column headers.\\n details = []\\n selector = '#ae-instances-details-table tbody tr'\\n for element in self.doc.cssselect(selector):\\n children = list(element)\\n assert len(children) == 9, [child.text for child in children]\\n details.append({\\n 'instance_id': element.attrib['id'].strip(),\\n 'qps': children[0].text.strip(),\\n 'latency': children[1].text.strip(),\\n 'requests': children[2].text.strip(),\\n 'errors': children[3].text.strip(),\\n 'age': children[4].text.strip(),\\n 'memory': children[5].text.strip()\\n })\\n return details\",\n \"def __repr__(self):\\n s = f'sample:\\\\n{self.sample}\\\\n'\\n s += f'cluster:\\\\n{self.cluster}\\\\n'\\n s += f'largest_cluster:\\\\n{self.get_largest_cluster()}'\\n return s\",\n \"def info(self):\\n ss = \\\"\\\\nSummary EffectiveArea2D info\\\\n\\\"\\n ss += \\\"----------------\\\\n\\\"\\n # Summarise data members\\n ss += array_stats_str(self.energy, 'energy')\\n ss += array_stats_str(self.offset, 'offset')\\n ss += array_stats_str(self.eff_area, 'dispersion')\\n\\n return ss\",\n \"def __str__(self):\\n return \\\"Clustering\\\"\",\n \"def get_info(self) -> str:\\n return self.info\",\n \"def details(self) -> Optional[pulumi.Input['SolutionDetailsArgs']]:\\n return pulumi.get(self, \\\"details\\\")\",\n \"def get_summary(self) -> str:\\n connected = self.is_connected()\\n info = '[{} - {}]'.format(self._index,\\n 'Connected' if connected else 'Disconnected')\\n if connected:\\n info += ' {} ({})'.format(self.get_model_name(), self.get_serial())\\n return info\",\n \"def info(self):\\n return self._info\",\n \"def __repr__(self):\\n outs = [\\\"Cluster Expansion Summary\\\"]\\n outs += repr(self.cluster_subspace).split(\\\"\\\\n\\\")[1:]\\n\\n if self.regression_data is not None:\\n outs += [\\n f\\\"Regression Data : estimator={self.regression_data.estimator_name}\\\"\\n f\\\" module={self.regression_data.module}\\\",\\n f\\\" parameters={self.regression_data.parameters}\\\",\\n f\\\"Target Property : \\\"\\n f\\\"mean={np.mean(self.regression_data.property_vector):0.4f} \\\"\\n f\\\"std={np.std(self.regression_data.property_vector):0.4f}\\\",\\n ]\\n fit_var = sum(\\n self._subspace.function_total_multiplicities[1:] * self.eci[1:] ** 2\\n )\\n outs += [\\n f\\\"ECI-based Property : mean={self.eci[0]:0.4f} std={np.sqrt(fit_var):0.4f}\\\"\\n ]\\n return \\\"\\\\n\\\".join(outs)\",\n \"def info(self):\\n ss = \\\"\\\\nSummary ARF info\\\\n\\\"\\n ss += \\\"----------------\\\\n\\\"\\n # Summarise data members\\n ss += array_stats_str(self.energy_lo, 'Energy lo')\\n ss += array_stats_str(self.energy_hi, 'Energy hi')\\n ss += array_stats_str(self.effective_area.to('m^2'), 'Effective area')\\n ss += 'Safe energy threshold lo: {0:6.3f}\\\\n'.format(self.energy_thresh_lo)\\n ss += 'Safe energy threshold hi: {0:6.3f}\\\\n'.format(self.energy_thresh_hi)\\n\\n return ss\",\n \"def get_info(self) -> str:\\n raise NotImplementedError()\",\n \"def describe_cluster_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"status\\\": \\\"ACTIVE\\\",\\n \\\"endpoint\\\": \\\"https://endpoint.amazonaws.com\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {\\n \\\"data\\\": \\\"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\\\"\\n },\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def describe_cluster_no_status_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"endpoint\\\": \\\"https://endpoint.amazonaws.com\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {\\n \\\"data\\\": \\\"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\\\"\\n },\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def info(self) -> str:\\n return self._info\",\n \"def info(self) -> str:\\n return self._info\",\n \"def __repr__(self):\\n\\n return \\\"<Cluster id=%s>\\\" % (self.id)\",\n \"def describe(self):\\r\\n mdataset_description = {\\r\\n 'kind': \\\"HConteiner\\\",\\r\\n 'compliance': self._compliance,\\r\\n 'has_encryption': self.has_encryption,\\r\\n 'encryption': self._encryption,\\r\\n 'sensitive': self._sensitive,\\r\\n 'license': self._license,\\r\\n }\\r\\n verbose_event()\\r\\n return mdataset_description\",\n \"def get_display_info(self):\\n return self.display_info\",\n \"def info(self):\\n return self.info_text\",\n \"def __str__(self):\\n return \\\"Cluster\\\"\",\n \"def get_details():\\r\\n return run_operations.get_run_details(experiment_name, job_name).as_dict(key_transformer=camel_case_transformer)\",\n \"def mychem_info(self):\\n return self._mychem_info\",\n \"def detail(self):\\n return self.uniform(\\\"detail\\\",\\n self.img_scale * .05,\\n self.img_scale * .2)\",\n \"def info(self):\\n return self._info\",\n \"def info(self):\\n ss = \\\"\\\\nSummary PSF3D info\\\\n\\\"\\n ss += \\\"---------------------\\\\n\\\"\\n ss += array_stats_str(self.energy_lo, \\\"energy_lo\\\")\\n ss += array_stats_str(self.energy_hi, \\\"energy_hi\\\")\\n ss += array_stats_str(self.offset, \\\"offset\\\")\\n ss += array_stats_str(self.rad_lo, \\\"rad_lo\\\")\\n ss += array_stats_str(self.rad_hi, \\\"rad_hi\\\")\\n ss += array_stats_str(self.psf_value, \\\"psf_value\\\")\\n\\n # TODO: should quote containment values also\\n\\n return ss\",\n \"def print_details(self):\\n print(\\\"[{}]\\\".format(self.name))\\n print(\\\"ID: \\\" + str(self.id))\\n print(\\\"name: %s\\\" % self.name)\\n print(\\\"URL: %s\\\" % self.url)\\n print(\\\"CPUs: \\\" + str(self.cpus) + \\\" cores\\\")\\n print(\\\"Mem: \\\" + self.memory_str)\\n print(\\\"Tasks: \\\" + str(self.tasks_len))\\n print(\\\"Uptime %s\\\" + self.uptime)\\n print(\\\"Uptime Descriptive %s\\\" + self.uptime_descriptive)\\n print(\\\" \\\")\",\n \"def description(self):\\n return self._hdr\",\n \"def description(self):\\n return self._hdr\",\n \"def info(self) -> dict:\",\n \"def get_details(self):\\n print(self.name)\\n print(10 * \\\"-\\\" + \\\"\\\\n\\\")\\n print(self.description)\\n for direction in self.linked_rooms:\\n room = self.linked_rooms[direction]\\n print(\\\"The \\\" + room.get_name() + \\\" is \\\" + direction)\\n print(\\\"\\\\n\\\")\",\n \"def summary(self):\\n print('DistanceMatrix (n=%s)' % len(self))\\n print('Distance metric = %s' % self.distance_metric.__name__)\\n print(self)\",\n \"def info(self):\\n attr_list = []\\n for name in self._metadata:\\n attr_list.append(name + \\\": \\\" + str(getattr(self, name, None)) + \\\"\\\\n\\\")\\n print(f\\\"{self.__class__}\\\\n\\\" + \\\"\\\".join(attr_list))\",\n \"def info(self):\\n attr_list = []\\n for name in self._metadata:\\n attr_list.append(name + \\\": \\\" + str(getattr(self, name, None)) + \\\"\\\\n\\\")\\n print(f\\\"{self.__class__}\\\\n\\\" + \\\"\\\".join(attr_list))\",\n \"def info(self):\\n # define the return string\\n bigstring = ''\\n\\n # assemble the information on the column\\n bigstring += 'Column name: ' + self.colname + '\\\\n'\\n bigstring += 'Column type: ' + str(self._type) + '\\\\n'\\n bigstring += 'Column format: ' + str(self._format) + '\\\\n'\\n bigstring += 'Column null value : ' + str(self._null) + '\\\\n'\\n if self.unit:\\n bigstring += 'Column unit : ' + self.unit + '\\\\n'\\n if self.colcomment:\\n bigstring += 'Column comment : ' + self.colcomment + '\\\\n'\\n\\n # return the result\\n return bigstring\",\n \"def get_info(self):\\n return \\\"TODO !\\\"\",\n \"def info(self):\\r\\n return self._get('info', {})\",\n \"def product_details(self) -> MqexsProductDetails:\\n return self.__product_details\",\n \"def details(self, details: \\\"dict\\\"):\\n self._attrs[\\\"details\\\"] = details\",\n \"def info(self):\\n print 'A= ', self.application\\n print 'C= ', self.city\\n print 'D= ', self.dataset.shape\",\n \"def full_info(self, object, name, value):\\n return self.info()\",\n \"def info(self):\\n print self.id, self.type, self.xyz.get_xyz\",\n \"def getInfo(self):\\n return self.info\",\n \"def get_details_title(mat_dict):\\n title = \\\"# Detail section for {} (COF {}) v{}\\\".format(mat_dict['name_conventional'], mat_dict['mat_id'],\\n mat_dict['workflow_version'])\\n return title\",\n \"def desc(self):\\n kpi_name = 'id:{idx}, resource: {rsc}, group: {grp}, metric: {mtr}'\\\\\\n .format(idx=self.idx,\\n rsc=self.resource,\\n grp=self.group,\\n mtr=self.metric)\\n return kpi_name\",\n \"def describe(self):\\n return str(self)\",\n \"def _info(self) -> str:\\n return (\\n f\\\"<{self.__class__.__name__}\\\\n\\\"\\n f\\\" spark type = {self._spark_type_class.__name__}\\\\n\\\"\\n f\\\" nullable = {self._is_nullable}\\\\n\\\"\\n f\\\" name = {self._resolve_field_name()} <- {[self.__name_explicit, self.__name_contextual]}\\\\n\\\"\\n f\\\" parent = {self._parent}\\\\n\\\"\\n f\\\" metadata = {self._metadata}\\\\n\\\"\\n \\\">\\\"\\n )\",\n \"def info(self, name):\\n if isinstance(self._state[name], dict):\\n attrs = self._state[name]['attrs']\\n return '{} {}({}), {} records: {}'.format(\\n attrs['type_str'], name, ','.join(attrs['domain']),\\n attrs['records'], attrs['description'])\\n else:\\n return repr(self[name])\",\n \"def __repr__(self):\\n return self._metadata.__str__()\",\n \"def rvdist_info(self):\\n return \\\"\\\"\",\n \"def info(self):\\n return {\\n \\\"dimension_x\\\": self.dimension_x,\\n \\\"dimension_y\\\": self.dimension_y,\\n \\\"api_level\\\": self.api_level,\\n \\\"device_model\\\": self.model,\\n }\",\n \"def product_details(self):\\n return self._product_details\",\n \"def get_info(self):\\n out = ''\\n for k in sorted(self.components.keys()):\\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\\\n'\\n return(out)\",\n \"def describe_cluster_creating_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"status\\\": \\\"CREATING\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {},\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def info(self):\",\n \"def info(self):\",\n \"def getInfo(self):\\n return self._info\",\n \"def contact_details(self):\\n return self.data.get(\\\"contactDetails\\\")\",\n \"def get_description(self):\\n return self['host_name']\",\n \"def client_details(self):\\n return self._client_details\",\n \"def get_info(self):\\n return None\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def printDetails(self):\\n print str(self.number) + \\\": \\\" + self.title\\n print \\\"URL: \\\" + self.URL\\n print \\\"domain: \\\" + self.domain\\n print \\\"score: \\\" + str(self.score) + \\\" points\\\"\\n print \\\"submitted by: \\\" + self.submitter\\n print \\\"# of comments: \\\" + str(self.commentCount)\\n print \\\"'discuss' URL: \\\" + self.commentsURL\\n print \\\"HN ID: \\\" + str(self.id)\\n print \\\" \\\"\",\n \"def description(self):\\n return self.data[\\\"attributes\\\"][\\\"description\\\"]\",\n \"def summary(self):\\n res = \\\", \\\".join(\\n elem[\\\"summary\\\"] for elem in self.status[\\\"health\\\"][\\\"summary\\\"]\\n )\\n if res:\\n return res\\n elif self.detail:\\n return self.detail[0]\\n return \\\"\\\"\",\n \"def get_perfect_information(self):\\n raise NotImplementedError\",\n \"def summary(self):\\n return \\\"%s: %s\\\" % (self.attribute.name, self.value_as_text)\",\n \"def getInfo():\",\n \"def get_node_details(self, node):\\n node_details = self.parser.find_server_by_ip(node.get('ip')) or \\\\\\n self.parser.find_server_by_hostname(node.get('host'))\\n\\n return node_details\",\n \"def get_description(self):\",\n \"def description(self):\\n return self.visual_desc\",\n \"def info(self):\\n return self.__dict__[self.sid]\",\n \"def disks_and_size_details(self) -> Mapping[str, int]:\\n return pulumi.get(self, \\\"disks_and_size_details\\\")\",\n \"def get_info(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6636939","0.6636939","0.6636939","0.6543469","0.64725894","0.63629097","0.62274104","0.6106484","0.60739094","0.6035391","0.6035391","0.60135996","0.6000487","0.5963064","0.59448","0.5934605","0.5902071","0.5895396","0.58460885","0.5807865","0.57522964","0.57400453","0.5730994","0.5702703","0.56945956","0.5692235","0.56831974","0.5671745","0.5614645","0.5606766","0.5574266","0.55707467","0.5570531","0.5564634","0.5560177","0.5547312","0.553102","0.5526748","0.5517361","0.55122256","0.5508997","0.5508997","0.5505632","0.5493975","0.54908127","0.5484838","0.5482763","0.5476762","0.54646707","0.5456636","0.5451693","0.5443182","0.5420189","0.5418747","0.5418747","0.54152805","0.54118997","0.54086506","0.54046136","0.54046136","0.53980136","0.5395534","0.539425","0.53867245","0.538585","0.5379079","0.5377226","0.53640336","0.5357346","0.53547925","0.5341225","0.5330063","0.5324583","0.5321568","0.53178966","0.5315936","0.53077203","0.5307586","0.5302969","0.5301386","0.52992725","0.52992725","0.5298606","0.52983505","0.5297835","0.52977175","0.5292285","0.5290902","0.52884704","0.527069","0.5270553","0.5269551","0.52572894","0.52537","0.52535516","0.5253419","0.52526796","0.5250931","0.52437073","0.5236383"],"string":"[\n \"0.6636939\",\n \"0.6636939\",\n \"0.6636939\",\n \"0.6543469\",\n \"0.64725894\",\n \"0.63629097\",\n \"0.62274104\",\n \"0.6106484\",\n \"0.60739094\",\n \"0.6035391\",\n \"0.6035391\",\n \"0.60135996\",\n \"0.6000487\",\n \"0.5963064\",\n \"0.59448\",\n \"0.5934605\",\n \"0.5902071\",\n \"0.5895396\",\n \"0.58460885\",\n \"0.5807865\",\n \"0.57522964\",\n \"0.57400453\",\n \"0.5730994\",\n \"0.5702703\",\n \"0.56945956\",\n \"0.5692235\",\n \"0.56831974\",\n \"0.5671745\",\n \"0.5614645\",\n \"0.5606766\",\n \"0.5574266\",\n \"0.55707467\",\n \"0.5570531\",\n \"0.5564634\",\n \"0.5560177\",\n \"0.5547312\",\n \"0.553102\",\n \"0.5526748\",\n \"0.5517361\",\n \"0.55122256\",\n \"0.5508997\",\n \"0.5508997\",\n \"0.5505632\",\n \"0.5493975\",\n \"0.54908127\",\n \"0.5484838\",\n \"0.5482763\",\n \"0.5476762\",\n \"0.54646707\",\n \"0.5456636\",\n \"0.5451693\",\n \"0.5443182\",\n \"0.5420189\",\n \"0.5418747\",\n \"0.5418747\",\n \"0.54152805\",\n \"0.54118997\",\n \"0.54086506\",\n \"0.54046136\",\n \"0.54046136\",\n \"0.53980136\",\n \"0.5395534\",\n \"0.539425\",\n \"0.53867245\",\n \"0.538585\",\n \"0.5379079\",\n \"0.5377226\",\n \"0.53640336\",\n \"0.5357346\",\n \"0.53547925\",\n \"0.5341225\",\n \"0.5330063\",\n \"0.5324583\",\n \"0.5321568\",\n \"0.53178966\",\n \"0.5315936\",\n \"0.53077203\",\n \"0.5307586\",\n \"0.5302969\",\n \"0.5301386\",\n \"0.52992725\",\n \"0.52992725\",\n \"0.5298606\",\n \"0.52983505\",\n \"0.5297835\",\n \"0.52977175\",\n \"0.5292285\",\n \"0.5290902\",\n \"0.52884704\",\n \"0.527069\",\n \"0.5270553\",\n \"0.5269551\",\n \"0.52572894\",\n \"0.52537\",\n \"0.52535516\",\n \"0.5253419\",\n \"0.52526796\",\n \"0.5250931\",\n \"0.52437073\",\n \"0.5236383\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7595597"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":682,"cells":{"query":{"kind":"string","value":"Returns the number of cluster a clustering has."},"document":{"kind":"string","value":"def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)"},"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_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def n_clusters(self):\n return len(self.clusters)","def n_clusters(self):\n return self.model.n_clusters","def count(self):\n\n if self.cluster:\n return self.cluster.count()\n\n return super().count()","def get_cluster_idx(_cluster):\n\n return _cluster.cluster_idx","def cluster_counter(self):\n return Counter(self.model.labels_.tolist())","def num_links(self):\n count=0.0\n for cluster in self.clusters:\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\n numberofmembers=self.clusters[cluster].number_of_members\n count+=numberofmembers\n return count","def node_count(self) -> int:\n return pulumi.get(self, \"node_count\")","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def getNbClusters( model):\r\n\r\n\tlabels = model.labels_\r\n\tlabelValues = []\r\n\tfor label in labels:\r\n\t\tif label not in labelValues and label != -1: labelValues.append(label)\r\n\tnbClusters = len( labelValues)\r\n\treturn nbClusters","def cluster_obs_count(self):\n return(self.merged_data.groupby(\n 'labels').count().transpose().iloc[0, :])","def node_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"node_count\")","def node_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"node_count\")","def set_nb_clusters(self):\n \n print(\"Finding the optimal number of clusters...\")\n \n sample = ro.r.matrix(self.df[self.df[\"filename\"].between(1, 4)][\"active_power\"].to_numpy())\n \n r=ro.r(\"\"\"\n check = function(matrix) {\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\n\n n_clust = n_clust$data\n\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\n return(max_cluster)\n }\n \"\"\")\n\n result = r(sample)\n self.conf[\"nb_clust\"] = int(result[0])\n \n print(f\"Optimal number of clusters is {self.conf['nb_clust']}\\n\")","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def number_of_nodes(self):\n return int(self._data['number_of_nodes'])","def data_center_count(self) -> int:\n return pulumi.get(self, \"data_center_count\")","def extract_cluster_size(line):\r\n cluster_size = line.split(\":\")[-1]\r\n\r\n try:\r\n cluster_size = int(cluster_size)\r\n except ValueError:\r\n return 0\r\n return cluster_size","def node_count(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"node_count\")","def node_count(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"node_count\")","def get_num_nodes(self):\n\n return sum(self.topology)","def get_count_all(cls, context, cluster_id):\n return cls.dbapi.get_cluster_nodegroup_count(context, cluster_id)","def clustering_factor(self):\n return self.unpack_dword(0x2C)","def num_nodes(self) -> int:\n return pulumi.get(self, \"num_nodes\")","def _num_nodes(self):\n return int(self._node_map[-1])","def node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"node_count\")","def get_cluster_indices(self,dataset, cluster_number):\n\t\tself.__init__(dataset, self.k)\n\t\tself.e_step() #got responsibilities\n\t\tmax_cluster = np.argmax(self.w, axis = 1)\n\t\tindices = []\n\t\tfor i in range(dataset.shape[0]):\n\t\t\tif max_cluster[i] == cluster_number:\n\t\t\t\tindices.append(i)\n\t\treturn indices","def _choose_clusters_num(database_type: str, synthetic_data_dim: int) -> int:\n data_dim: int = 1\n if database_type == DatabaseType.Synthetic:\n data_dim = synthetic_data_dim\n elif database_type in [DatabaseType.ThreeDRoadNetwork, DatabaseType.IndividualHouseholdElectricPowerConsumption]:\n data_dim = 2\n elif database_type == DatabaseType.HouseSalesInKingCounty:\n data_dim = 8\n return 2 * (data_dim + 1) ** 2 + 2","def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements","def node_count(self) -> int:\n return int(self.graph_tuple_stats.node_count or 0)","def cluster_id(self):\n return self._cluster_id","def cluster(self):\n return self._cluster","def cluster(self):\n return self._cluster","def node_count(self):\n return self._node_count","def get_number_of_clusters(df, use_pca, n_components):\n n_clusters = 10\n cluster_with_distances = []\n for i in range(n_clusters):\n pipe = _build_model(df, use_pca, n_components, use_kmeans=True, n_clusters=i + 1)\n cluster_with_distances.append(pipe.named_steps['kmeans'].inertia_)\n plt.figure(6, figsize=(12, 6))\n plt.plot(range(1, 11), cluster_with_distances, 'o')\n plt.plot(range(1, 11), cluster_with_distances, '-', alpha=0.5)\n plt.title('The Elbow Criterion')\n plt.xlabel('number of cluster')\n plt.ylabel('Sum of squared distances of samples to their closest cluster center')\n plt.show()","def number_of_nodes(self) -> int:\n return self.graph.number_of_nodes()","def test_count_reads_per_cluster(self):\n \n bedtool = pybedtools.BedTool(clipper.test_file(\"clip_analysis_test_peak_results.bed\"))\n \n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\n \n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)","def numNodes(self):\n res = 0\n for n in self.iternodes():\n res += 1\n return res","def number_of_data_nodes(self):\n return int(self._data['number_of_data_nodes'])","def get_sectors_per_cluster(self):\n\n\t\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\n\t\tif sectors_per_cluster_base == 0:\n\t\t\traise BootSectorException('Invalid cluster size (zero)')\n\n\t\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\n\t\t\tsectors_per_cluster_real = sectors_per_cluster_base\n\t\telse:\n\t\t\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\n\t\t\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\n\n\t\treturn sectors_per_cluster_real","def getNumberOfNeighbors(self, vertexNumber): \n\n return self.__degreeCount[vertexNumber]","def node_count(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"node_count\")","def cluster_id(self) -> str:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> str:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> str:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> str:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> str:\n return pulumi.get(self, \"cluster_id\")","def num_nodes(self):\n return len(self.successors)","def get_cluster_id(self):\n cmd = \"svcinfo lscluster -delim :\"\n\n output = self._svc_command(cmd)[0]\n\n if len(output) != 2:\n return None\n\n header = output[0].split(':')\n values = output[1].split(':')\n index = header.index(SVC_CLUSTER_ID)\n cluster_id = values[index]\n return cluster_id","def carn_count(self):\n return len(self.carnivores)","def get_neighbors_count(self, atom):\n return self._graph.get_connected_vertices_count(atom)","def get_cluster_status(boto3_client, cluster_identifier):\n return boto3_client.describe_clusters(\n ClusterIdentifier=cluster_identifier\n )","def cluster_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"cluster_id\")","def cluster_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"cluster_id\")","def count_nodes(self):\n\t\treturn self.__count_nodes(self)","def clustering(self) -> 'outputs.ClusteringResponse':\n return pulumi.get(self, \"clustering\")","def num_nodes(self) -> Optional[int]:\n return pulumi.get(self, \"num_nodes\")","def current_node_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"current_node_count\")","def get_num_nodes(self):\n return len(self._nodes)","def get_num_nodes(self):\n return len(self._nodes)","def num_of_node(self):\n \n try:\n return self.num_node\n except:\n print(\"ERROR: No graph exists\")","def num_shards(self) -> int:\n return self.db_nodes","def add_new_cluster(self):\n self.result.append(Cluster.Cluster())\n return len(self.result) - 1","def get_clusters(self):\r\n\r\n return self.__clusters","def analysis_function_num_clusters_to_percent(self,clustering,percent):\n return clustering.number_of_clusters_to_get_percent(percent)","def numa_nodes(self):\n return int(self.num_numa_nodes) # type: ignore","def node_count(self):\n return self._root.count()","def get_clust_num_perc(model, vis_perc=0.9):\n\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\n\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\n\n\ttot = model.allocmodel.Nk[idx].sum()\n\tcursum = 0\n\ti = 0\n\twhile cursum < tot*vis_perc:\n\t cursum += model.allocmodel.Nk[idx][i]\n\t i+=1\n\n\treturn i","def num_nodes(self):\n return self._grid","def cluster_cal(self):\n self.Cluster = []\n for i in range(self.nodenum):\n neighborhood_node = self.neighbor_node(i)\n Node_num = len(neighborhood_node)\n Count = self.neighbor_edge(neighborhood_node)\n if(Node_num == 0 or Node_num == 1):\n self.Cluster.append(0.5)\n else:\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\n \n self.cluster_coeff = np.average(self.Cluster)","def _num_conn_comp(graph):\n\n return nx.number_connected_components(graph)","def gke_cluster(self) -> Optional['outputs.MembershipEndpointGkeCluster']:\n return pulumi.get(self, \"gke_cluster\")","def finding_the_number_of_clusters(self, HC_tree_terminal_cosine, Dist_tree_terminal_cosine, which_one):\n max_clusters = min(11, len(self.sequences))\n ress_sil = []\n for i in tqdm(range(2, max_clusters), desc=f\"Finding the best number of clusters ({which_one})\", disable=1-self.verbose):\n assignment_tree_terminal_cosine = cluster.hierarchy.cut_tree(HC_tree_terminal_cosine,i).ravel() #.ravel makes it 1D array.\n ress_sil.append((silhouette_score(squareform(Dist_tree_terminal_cosine),\n assignment_tree_terminal_cosine,metric='cosine').round(3)*1000)/1000)\n if which_one == 'DT':\n self.C_DT = ress_sil.index(max(ress_sil)) + 2\n elif which_one == 'RF':\n self.C_RF = ress_sil.index(max(ress_sil)) + 2\n elif which_one == 'DT_position':\n self.C_DT_p = ress_sil.index(max(ress_sil)) + 2\n elif which_one == 'RF_position':\n self.C_RF_p = ress_sil.index(max(ress_sil)) + 2","def count_all_cluster_instances(cluster_name, predictive=False, exclude_node_label_keys=app_config[\"EXCLUDE_NODE_LABEL_KEYS\"]):\n\n # Get the K8s nodes on the cluster, while excluding nodes with certain label keys\n k8s_nodes = get_k8s_nodes(exclude_node_label_keys)\n\n count = 0\n asgs = get_all_asgs(cluster_name)\n for asg in asgs:\n instances = asg['Instances']\n if predictive:\n count += asg['DesiredCapacity']\n else:\n # Use the get_node_by_instance_id() function as it only returns the node if it is not excluded by K8s labels\n for instance in instances:\n instance_id = instance['InstanceId']\n try:\n get_node_by_instance_id(k8s_nodes, instance_id)\n count += 1\n except Exception:\n logger.info(\"Skipping instance {}\".format(instance_id))\n logger.info(\"{} asg instance count in cluster is: {}. K8s node count should match this number\".format(\"*** Predicted\" if predictive else \"Current\", count))\n return count","def Test_NumNodes(Graph_MD):\n N_Knoten = Graph_MD.number_of_nodes()\n \n return N_Knoten","def get_clust_cent(self):\r\n\r\n return self.__clust_cent","def num_nodes(self, ntype: str = None) -> int:\n if ntype:\n return self.num_nodes_dict[ntype]\n else:\n return self.total_number_of_nodes","def compute_num_nodes(graph):\n return len(graph.keys()) # return the number of nodes in the graph","def get_num_arcs(self):\n num_arcs = 0\n for node in self._nodes.values(): num_arcs += node._deg\n return (num_arcs / 2) + 1","def get_num_arcs(self):\n num_arcs = 0\n for node in self._nodes.values(): num_arcs += node._deg\n return (num_arcs / 2) + 1","def NodesCount(self):\n return len(self.nodes)","def max_node_count(self) -> int:\n return pulumi.get(self, \"max_node_count\")","def count(self):\n\t\treturn len(list(self.nodes))","def _num_nodes(self):\n return len(self._nid2partid)","def cluster_size(result, var):\n df=calculate_cluster_size(result, var)\n df['cus']=df.index\n return df","def num_node_groups(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"num_node_groups\")","def get_n_workers(self):\n return self.df.worker.nunique()","def get_ncores(self):\n return self._ncores","def calc_Nw(cluster_labels):\n\n cluster_labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Nw = []\n for label in labels_set:\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\n Nw.append(n_cluster_pairs)\n\n return int(np.sum(Nw))","def degree(self) -> int:\n return len(self.neighbours)","def degree(self) -> int:\n return len(self.neighbours)","def degree(self) -> int:\n return len(self.neighbours)","def get_cluster_id(options):\n cluster = options.cluster\n datacenter = get_datacenter(options)\n for item in datacenter.hostFolder.childEntity:\n if (item.name == cluster):\n return item._GetMoId()","def get_number_of_cheeses(self):\n number = 0\n for i in range(len(self._stools)):\n number += len(self._stools[i])\n return number","def get_cluster_indices(dataset, cluster_number, GMM_model):\n\tGMM_model.data = dataset.copy()\n\tGMM_model.m, GMM_model.n = dataset.shape\n\tGMM_model.w = np.asmatrix(np.empty((GMM_model.m, GMM_model.k), dtype=float))\n\tGMM_model.e_step() #got responsibilities\n\tmax_cluster = np.argmax(GMM_model.w, axis = 1)\n\tindices = []\n\tfor i in range(dataset.shape[0]):\n\t\tif max_cluster[i] == cluster_number:\n\t\t\tindices.append(i)\n\treturn indices","def num_slaves(self) -> int:\n raise NotImplementedError","def num_neighbors(self):\n return self._num_neighbors"],"string":"[\n \"def get_cluster_count(self) -> int:\\n return len(self.get_all_cluster_ids())\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def n_clusters(self):\\n return len(self.clusters)\",\n \"def n_clusters(self):\\n return self.model.n_clusters\",\n \"def count(self):\\n\\n if self.cluster:\\n return self.cluster.count()\\n\\n return super().count()\",\n \"def get_cluster_idx(_cluster):\\n\\n return _cluster.cluster_idx\",\n \"def cluster_counter(self):\\n return Counter(self.model.labels_.tolist())\",\n \"def num_links(self):\\n count=0.0\\n for cluster in self.clusters:\\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\\n numberofmembers=self.clusters[cluster].number_of_members\\n count+=numberofmembers\\n return count\",\n \"def node_count(self) -> int:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def getNbClusters( model):\\r\\n\\r\\n\\tlabels = model.labels_\\r\\n\\tlabelValues = []\\r\\n\\tfor label in labels:\\r\\n\\t\\tif label not in labelValues and label != -1: labelValues.append(label)\\r\\n\\tnbClusters = len( labelValues)\\r\\n\\treturn nbClusters\",\n \"def cluster_obs_count(self):\\n return(self.merged_data.groupby(\\n 'labels').count().transpose().iloc[0, :])\",\n \"def node_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def node_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def set_nb_clusters(self):\\n \\n print(\\\"Finding the optimal number of clusters...\\\")\\n \\n sample = ro.r.matrix(self.df[self.df[\\\"filename\\\"].between(1, 4)][\\\"active_power\\\"].to_numpy())\\n \\n r=ro.r(\\\"\\\"\\\"\\n check = function(matrix) {\\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\\n\\n n_clust = n_clust$data\\n\\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\\n return(max_cluster)\\n }\\n \\\"\\\"\\\")\\n\\n result = r(sample)\\n self.conf[\\\"nb_clust\\\"] = int(result[0])\\n \\n print(f\\\"Optimal number of clusters is {self.conf['nb_clust']}\\\\n\\\")\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def number_of_nodes(self):\\n return int(self._data['number_of_nodes'])\",\n \"def data_center_count(self) -> int:\\n return pulumi.get(self, \\\"data_center_count\\\")\",\n \"def extract_cluster_size(line):\\r\\n cluster_size = line.split(\\\":\\\")[-1]\\r\\n\\r\\n try:\\r\\n cluster_size = int(cluster_size)\\r\\n except ValueError:\\r\\n return 0\\r\\n return cluster_size\",\n \"def node_count(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def node_count(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def get_num_nodes(self):\\n\\n return sum(self.topology)\",\n \"def get_count_all(cls, context, cluster_id):\\n return cls.dbapi.get_cluster_nodegroup_count(context, cluster_id)\",\n \"def clustering_factor(self):\\n return self.unpack_dword(0x2C)\",\n \"def num_nodes(self) -> int:\\n return pulumi.get(self, \\\"num_nodes\\\")\",\n \"def _num_nodes(self):\\n return int(self._node_map[-1])\",\n \"def node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def get_cluster_indices(self,dataset, cluster_number):\\n\\t\\tself.__init__(dataset, self.k)\\n\\t\\tself.e_step() #got responsibilities\\n\\t\\tmax_cluster = np.argmax(self.w, axis = 1)\\n\\t\\tindices = []\\n\\t\\tfor i in range(dataset.shape[0]):\\n\\t\\t\\tif max_cluster[i] == cluster_number:\\n\\t\\t\\t\\tindices.append(i)\\n\\t\\treturn indices\",\n \"def _choose_clusters_num(database_type: str, synthetic_data_dim: int) -> int:\\n data_dim: int = 1\\n if database_type == DatabaseType.Synthetic:\\n data_dim = synthetic_data_dim\\n elif database_type in [DatabaseType.ThreeDRoadNetwork, DatabaseType.IndividualHouseholdElectricPowerConsumption]:\\n data_dim = 2\\n elif database_type == DatabaseType.HouseSalesInKingCounty:\\n data_dim = 8\\n return 2 * (data_dim + 1) ** 2 + 2\",\n \"def analysis_function_total_elements(self,clustering):\\n return clustering.total_number_of_elements\",\n \"def node_count(self) -> int:\\n return int(self.graph_tuple_stats.node_count or 0)\",\n \"def cluster_id(self):\\n return self._cluster_id\",\n \"def cluster(self):\\n return self._cluster\",\n \"def cluster(self):\\n return self._cluster\",\n \"def node_count(self):\\n return self._node_count\",\n \"def get_number_of_clusters(df, use_pca, n_components):\\n n_clusters = 10\\n cluster_with_distances = []\\n for i in range(n_clusters):\\n pipe = _build_model(df, use_pca, n_components, use_kmeans=True, n_clusters=i + 1)\\n cluster_with_distances.append(pipe.named_steps['kmeans'].inertia_)\\n plt.figure(6, figsize=(12, 6))\\n plt.plot(range(1, 11), cluster_with_distances, 'o')\\n plt.plot(range(1, 11), cluster_with_distances, '-', alpha=0.5)\\n plt.title('The Elbow Criterion')\\n plt.xlabel('number of cluster')\\n plt.ylabel('Sum of squared distances of samples to their closest cluster center')\\n plt.show()\",\n \"def number_of_nodes(self) -> int:\\n return self.graph.number_of_nodes()\",\n \"def test_count_reads_per_cluster(self):\\n \\n bedtool = pybedtools.BedTool(clipper.test_file(\\\"clip_analysis_test_peak_results.bed\\\"))\\n \\n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\\n \\n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)\",\n \"def numNodes(self):\\n res = 0\\n for n in self.iternodes():\\n res += 1\\n return res\",\n \"def number_of_data_nodes(self):\\n return int(self._data['number_of_data_nodes'])\",\n \"def get_sectors_per_cluster(self):\\n\\n\\t\\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\\n\\t\\tif sectors_per_cluster_base == 0:\\n\\t\\t\\traise BootSectorException('Invalid cluster size (zero)')\\n\\n\\t\\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\\n\\t\\t\\tsectors_per_cluster_real = sectors_per_cluster_base\\n\\t\\telse:\\n\\t\\t\\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\\n\\t\\t\\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\\n\\n\\t\\treturn sectors_per_cluster_real\",\n \"def getNumberOfNeighbors(self, vertexNumber): \\n\\n return self.__degreeCount[vertexNumber]\",\n \"def node_count(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def cluster_id(self) -> str:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> str:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> str:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> str:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> str:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def num_nodes(self):\\n return len(self.successors)\",\n \"def get_cluster_id(self):\\n cmd = \\\"svcinfo lscluster -delim :\\\"\\n\\n output = self._svc_command(cmd)[0]\\n\\n if len(output) != 2:\\n return None\\n\\n header = output[0].split(':')\\n values = output[1].split(':')\\n index = header.index(SVC_CLUSTER_ID)\\n cluster_id = values[index]\\n return cluster_id\",\n \"def carn_count(self):\\n return len(self.carnivores)\",\n \"def get_neighbors_count(self, atom):\\n return self._graph.get_connected_vertices_count(atom)\",\n \"def get_cluster_status(boto3_client, cluster_identifier):\\n return boto3_client.describe_clusters(\\n ClusterIdentifier=cluster_identifier\\n )\",\n \"def cluster_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def cluster_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"cluster_id\\\")\",\n \"def count_nodes(self):\\n\\t\\treturn self.__count_nodes(self)\",\n \"def clustering(self) -> 'outputs.ClusteringResponse':\\n return pulumi.get(self, \\\"clustering\\\")\",\n \"def num_nodes(self) -> Optional[int]:\\n return pulumi.get(self, \\\"num_nodes\\\")\",\n \"def current_node_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"current_node_count\\\")\",\n \"def get_num_nodes(self):\\n return len(self._nodes)\",\n \"def get_num_nodes(self):\\n return len(self._nodes)\",\n \"def num_of_node(self):\\n \\n try:\\n return self.num_node\\n except:\\n print(\\\"ERROR: No graph exists\\\")\",\n \"def num_shards(self) -> int:\\n return self.db_nodes\",\n \"def add_new_cluster(self):\\n self.result.append(Cluster.Cluster())\\n return len(self.result) - 1\",\n \"def get_clusters(self):\\r\\n\\r\\n return self.__clusters\",\n \"def analysis_function_num_clusters_to_percent(self,clustering,percent):\\n return clustering.number_of_clusters_to_get_percent(percent)\",\n \"def numa_nodes(self):\\n return int(self.num_numa_nodes) # type: ignore\",\n \"def node_count(self):\\n return self._root.count()\",\n \"def get_clust_num_perc(model, vis_perc=0.9):\\n\\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\\n\\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\\n\\n\\ttot = model.allocmodel.Nk[idx].sum()\\n\\tcursum = 0\\n\\ti = 0\\n\\twhile cursum < tot*vis_perc:\\n\\t cursum += model.allocmodel.Nk[idx][i]\\n\\t i+=1\\n\\n\\treturn i\",\n \"def num_nodes(self):\\n return self._grid\",\n \"def cluster_cal(self):\\n self.Cluster = []\\n for i in range(self.nodenum):\\n neighborhood_node = self.neighbor_node(i)\\n Node_num = len(neighborhood_node)\\n Count = self.neighbor_edge(neighborhood_node)\\n if(Node_num == 0 or Node_num == 1):\\n self.Cluster.append(0.5)\\n else:\\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\\n \\n self.cluster_coeff = np.average(self.Cluster)\",\n \"def _num_conn_comp(graph):\\n\\n return nx.number_connected_components(graph)\",\n \"def gke_cluster(self) -> Optional['outputs.MembershipEndpointGkeCluster']:\\n return pulumi.get(self, \\\"gke_cluster\\\")\",\n \"def finding_the_number_of_clusters(self, HC_tree_terminal_cosine, Dist_tree_terminal_cosine, which_one):\\n max_clusters = min(11, len(self.sequences))\\n ress_sil = []\\n for i in tqdm(range(2, max_clusters), desc=f\\\"Finding the best number of clusters ({which_one})\\\", disable=1-self.verbose):\\n assignment_tree_terminal_cosine = cluster.hierarchy.cut_tree(HC_tree_terminal_cosine,i).ravel() #.ravel makes it 1D array.\\n ress_sil.append((silhouette_score(squareform(Dist_tree_terminal_cosine),\\n assignment_tree_terminal_cosine,metric='cosine').round(3)*1000)/1000)\\n if which_one == 'DT':\\n self.C_DT = ress_sil.index(max(ress_sil)) + 2\\n elif which_one == 'RF':\\n self.C_RF = ress_sil.index(max(ress_sil)) + 2\\n elif which_one == 'DT_position':\\n self.C_DT_p = ress_sil.index(max(ress_sil)) + 2\\n elif which_one == 'RF_position':\\n self.C_RF_p = ress_sil.index(max(ress_sil)) + 2\",\n \"def count_all_cluster_instances(cluster_name, predictive=False, exclude_node_label_keys=app_config[\\\"EXCLUDE_NODE_LABEL_KEYS\\\"]):\\n\\n # Get the K8s nodes on the cluster, while excluding nodes with certain label keys\\n k8s_nodes = get_k8s_nodes(exclude_node_label_keys)\\n\\n count = 0\\n asgs = get_all_asgs(cluster_name)\\n for asg in asgs:\\n instances = asg['Instances']\\n if predictive:\\n count += asg['DesiredCapacity']\\n else:\\n # Use the get_node_by_instance_id() function as it only returns the node if it is not excluded by K8s labels\\n for instance in instances:\\n instance_id = instance['InstanceId']\\n try:\\n get_node_by_instance_id(k8s_nodes, instance_id)\\n count += 1\\n except Exception:\\n logger.info(\\\"Skipping instance {}\\\".format(instance_id))\\n logger.info(\\\"{} asg instance count in cluster is: {}. K8s node count should match this number\\\".format(\\\"*** Predicted\\\" if predictive else \\\"Current\\\", count))\\n return count\",\n \"def Test_NumNodes(Graph_MD):\\n N_Knoten = Graph_MD.number_of_nodes()\\n \\n return N_Knoten\",\n \"def get_clust_cent(self):\\r\\n\\r\\n return self.__clust_cent\",\n \"def num_nodes(self, ntype: str = None) -> int:\\n if ntype:\\n return self.num_nodes_dict[ntype]\\n else:\\n return self.total_number_of_nodes\",\n \"def compute_num_nodes(graph):\\n return len(graph.keys()) # return the number of nodes in the graph\",\n \"def get_num_arcs(self):\\n num_arcs = 0\\n for node in self._nodes.values(): num_arcs += node._deg\\n return (num_arcs / 2) + 1\",\n \"def get_num_arcs(self):\\n num_arcs = 0\\n for node in self._nodes.values(): num_arcs += node._deg\\n return (num_arcs / 2) + 1\",\n \"def NodesCount(self):\\n return len(self.nodes)\",\n \"def max_node_count(self) -> int:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def count(self):\\n\\t\\treturn len(list(self.nodes))\",\n \"def _num_nodes(self):\\n return len(self._nid2partid)\",\n \"def cluster_size(result, var):\\n df=calculate_cluster_size(result, var)\\n df['cus']=df.index\\n return df\",\n \"def num_node_groups(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"num_node_groups\\\")\",\n \"def get_n_workers(self):\\n return self.df.worker.nunique()\",\n \"def get_ncores(self):\\n return self._ncores\",\n \"def calc_Nw(cluster_labels):\\n\\n cluster_labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Nw = []\\n for label in labels_set:\\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\\n Nw.append(n_cluster_pairs)\\n\\n return int(np.sum(Nw))\",\n \"def degree(self) -> int:\\n return len(self.neighbours)\",\n \"def degree(self) -> int:\\n return len(self.neighbours)\",\n \"def degree(self) -> int:\\n return len(self.neighbours)\",\n \"def get_cluster_id(options):\\n cluster = options.cluster\\n datacenter = get_datacenter(options)\\n for item in datacenter.hostFolder.childEntity:\\n if (item.name == cluster):\\n return item._GetMoId()\",\n \"def get_number_of_cheeses(self):\\n number = 0\\n for i in range(len(self._stools)):\\n number += len(self._stools[i])\\n return number\",\n \"def get_cluster_indices(dataset, cluster_number, GMM_model):\\n\\tGMM_model.data = dataset.copy()\\n\\tGMM_model.m, GMM_model.n = dataset.shape\\n\\tGMM_model.w = np.asmatrix(np.empty((GMM_model.m, GMM_model.k), dtype=float))\\n\\tGMM_model.e_step() #got responsibilities\\n\\tmax_cluster = np.argmax(GMM_model.w, axis = 1)\\n\\tindices = []\\n\\tfor i in range(dataset.shape[0]):\\n\\t\\tif max_cluster[i] == cluster_number:\\n\\t\\t\\tindices.append(i)\\n\\treturn indices\",\n \"def num_slaves(self) -> int:\\n raise NotImplementedError\",\n \"def num_neighbors(self):\\n return self._num_neighbors\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8762908","0.84918106","0.80784905","0.77352","0.71900326","0.7170602","0.7130262","0.70022637","0.6933635","0.68955094","0.6862841","0.6851633","0.68233424","0.68233424","0.6755365","0.6752718","0.66931707","0.66912186","0.6680638","0.66151196","0.66151196","0.6610706","0.65861267","0.6539488","0.6513963","0.649287","0.6439858","0.6420169","0.6417206","0.64149934","0.6413359","0.64035124","0.64019495","0.64019495","0.6382798","0.6356664","0.6350437","0.6315022","0.62987363","0.6288998","0.6269595","0.6236746","0.6226667","0.621742","0.621742","0.621742","0.621742","0.621742","0.6215246","0.6206762","0.61992633","0.61982834","0.61785","0.6176574","0.6176574","0.6176574","0.6176574","0.61754096","0.61500186","0.61457163","0.6131266","0.6126058","0.6126058","0.6125346","0.61201376","0.61135364","0.61032087","0.6099808","0.6083013","0.60783225","0.6071571","0.60682833","0.6066472","0.6062612","0.60620993","0.60593003","0.6055207","0.60528344","0.60498375","0.60378444","0.60370386","0.6021506","0.6021506","0.6019029","0.60128736","0.60126686","0.6000916","0.5997344","0.5995471","0.599225","0.59873134","0.59751415","0.59690964","0.59690964","0.59690964","0.59677","0.59660614","0.5963094","0.5960791","0.59604096"],"string":"[\n \"0.8762908\",\n \"0.84918106\",\n \"0.80784905\",\n \"0.77352\",\n \"0.71900326\",\n \"0.7170602\",\n \"0.7130262\",\n \"0.70022637\",\n \"0.6933635\",\n \"0.68955094\",\n \"0.6862841\",\n \"0.6851633\",\n \"0.68233424\",\n \"0.68233424\",\n \"0.6755365\",\n \"0.6752718\",\n \"0.66931707\",\n \"0.66912186\",\n \"0.6680638\",\n \"0.66151196\",\n \"0.66151196\",\n \"0.6610706\",\n \"0.65861267\",\n \"0.6539488\",\n \"0.6513963\",\n \"0.649287\",\n \"0.6439858\",\n \"0.6420169\",\n \"0.6417206\",\n \"0.64149934\",\n \"0.6413359\",\n \"0.64035124\",\n \"0.64019495\",\n \"0.64019495\",\n \"0.6382798\",\n \"0.6356664\",\n \"0.6350437\",\n \"0.6315022\",\n \"0.62987363\",\n \"0.6288998\",\n \"0.6269595\",\n \"0.6236746\",\n \"0.6226667\",\n \"0.621742\",\n \"0.621742\",\n \"0.621742\",\n \"0.621742\",\n \"0.621742\",\n \"0.6215246\",\n \"0.6206762\",\n \"0.61992633\",\n \"0.61982834\",\n \"0.61785\",\n \"0.6176574\",\n \"0.6176574\",\n \"0.6176574\",\n \"0.6176574\",\n \"0.61754096\",\n \"0.61500186\",\n \"0.61457163\",\n \"0.6131266\",\n \"0.6126058\",\n \"0.6126058\",\n \"0.6125346\",\n \"0.61201376\",\n \"0.61135364\",\n \"0.61032087\",\n \"0.6099808\",\n \"0.6083013\",\n \"0.60783225\",\n \"0.6071571\",\n \"0.60682833\",\n \"0.6066472\",\n \"0.6062612\",\n \"0.60620993\",\n \"0.60593003\",\n \"0.6055207\",\n \"0.60528344\",\n \"0.60498375\",\n \"0.60378444\",\n \"0.60370386\",\n \"0.6021506\",\n \"0.6021506\",\n \"0.6019029\",\n \"0.60128736\",\n \"0.60126686\",\n \"0.6000916\",\n \"0.5997344\",\n \"0.5995471\",\n \"0.599225\",\n \"0.59873134\",\n \"0.59751415\",\n \"0.59690964\",\n \"0.59690964\",\n \"0.59690964\",\n \"0.59677\",\n \"0.59660614\",\n \"0.5963094\",\n \"0.5960791\",\n \"0.59604096\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81173706"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":683,"cells":{"query":{"kind":"string","value":"Returns the number of elements that are clusterized in this clustering (which may not be the total number of elements of the dataset if there were noisy elements)"},"document":{"kind":"string","value":"def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements"},"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 n_clusters(self):\n return len(self.clusters)","def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())","def count_elements_in_dataset(dataset):\n return dataset.count()","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)","def num_elements(self):\n return self.subset.num_elements()","def cluster_obs_count(self):\n return(self.merged_data.groupby(\n 'labels').count().transpose().iloc[0, :])","def element_size(self):\n vecs = (\n self.nodes[self.elements[:, :4], :][:, 1:, :]\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\n )\n return np.abs(np.linalg.det(vecs)) / 6","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def n_clusters(self):\n return self.model.n_clusters","def nsites(self) -> int:\n return len(self.A)","def valency(self):\n return len(self.neighbors())","def n_points(self):\n\n if self.data_reduced:\n return len(self.data_reduced[0])\n else:\n return 0","def elements_count(self):\n return self.__elements_count","def getNNodesTot(self):\n nNodesTot = 0\n for iElt in Elements._all:\n nNodesTot += len(iElt.coord)\n return nNodesTot","def count(self):\n\t\treturn len(list(self.nodes))","def __len__(self):\n return len(self.dataset) * self.samples_per_pair","def dissimilarity(clusters):\n totDist = 0\n for c in clusters:\n totDist += c.variability()\n return totDist","def __len__(self):\n return len(self.centroid_vector)","def count(self):\n\n if self.cluster:\n return self.cluster.count()\n\n return super().count()","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def n_good_features_(self):\n return np.sum(self.important_features_)","def GetNumberOfElements(self, assoc):\n result = 0\n for dataset in self:\n result += dataset.GetNumberOfElements(assoc)\n return int(result)","def n_elements(self) -> int:\n n_elem = np.prod(self.shape)\n if self.n_timesteps > 1:\n n_elem = int(n_elem / self.n_timesteps)\n return n_elem","def __len__(self):\n return len(np.where(np.logical_not(self.data.mask))[0])","def count(self):\n return len(self._elements)","def count_nodes(self):\n\t\treturn self.__count_nodes(self)","def voxel_count(self):\n return self.cols * self.rows * self.sections","def size(self):\n\t\treturn len(self.nodes)","def element_count(self):\n return self._internal.get_element_count()","def __len__(self):\r\n if self.is_superset:\r\n length = 0\r\n for ds in self.data:\r\n length += len(ds)\r\n return length\r\n else:\r\n return len(self.data)","def size(self):\n try:\n return len(self._adjacency_list)\n except Exception as error:\n print(f'An error occurred: {error}')","def size(self) -> Tuple[groupable, pdarray]:\n return self.count()","def count(self):\r\n return self.data_array.size","def numnems(self):\n count = 0\n for o in self._objs.values():\n count += len(o.netifs())\n return count","def N(self):\n return len(self.cavity_grid.cavities) + 1","def noOfElem(classObj, index):\r\n return len(classObj.dataSet[:, index])","def __len__(self):\n return np.size(self.A,0)","def get_nodes_pixel_count(self):\n sum_count = self.pixel_count\n for i in range(8):\n node = self.children[i]\n if node:\n sum_count += node.pixel_count\n return sum_count","def getNumElements(self):\n return 0","def __len__(self) -> float:\n return len(self.elements)","def get_num_nodes(self):\n\n return sum(self.topology)","def getNbClusters( model):\r\n\r\n\tlabels = model.labels_\r\n\tlabelValues = []\r\n\tfor label in labels:\r\n\t\tif label not in labelValues and label != -1: labelValues.append(label)\r\n\tnbClusters = len( labelValues)\r\n\treturn nbClusters","def getNumElements(self):\n return 1","def number_of_electrodes(self):\n return self._pre_kernel.shape[1]","def n_thres(self):\n return np.size(self.thres)","def get_num_instances(im, non_building_labels):\n return np.setdiff1d(im, non_building_labels)","def __len__(self):\n return len(self.train) + len(self.val) + len(self.test)","def num_complementary_regions(self):\n g = self._get_puncturefinder_graph()\n # return g.connected_components_number()\n return nx.number_connected_components(g)","def dimension(self):\r\n a = 0\r\n for x in self.faces():\r\n if (len(x) > a):\r\n a = len(x) \r\n return a-1","def n_cs(self):\n return np.size(self._cs, 0)","def get_num_features(self):\r\n \r\n return len(self[0]['x'])","def Nnodes(self):\n return len(self.nodes)","def size(self):\n return len(self._adjacency_list)","def nspatials(self):\n return int(len(self)/2)","def size(self):\n return len(self.edges())","def size(self):\n return sum(elem.size for elem in self)","def number_of_data_nodes(self):\n return int(self._data['number_of_data_nodes'])","def num_actual_nodes(tree):\n return (tree.n_node_samples > 0).sum()","def getNumElements(self):\n return 1","def purity_score(clusters, classes):\n clusters = np.array(clusters)\n classes = np.array(classes)\n A = np.c_[(clusters,classes)]\n\n n_accurate = 0.\n\n for j in np.unique(A[:,0]):\n z = A[A[:,0] == j, 1]\n x = np.argmax(np.bincount(z))\n n_accurate += len(z[z == x])\n\n return n_accurate / A.shape[0]","def NodesCount(self):\n return len(self.nodes)","def __len__(self) -> int:\n return len(self.nodes)","def cluster_counter(self):\n return Counter(self.model.labels_.tolist())","def dim(self):\n if self._classifier is None:\n with self:\n return self._classifier.features_dim\n\n return self._classifier.features_dim","def __len__(self):\n\n if self.is_finite_set:\n size = 0\n for set in self.sets:\n size += len(set)\n return size\n else:\n raise ValueError(\"'%s' is not a finite set.\" % self)","def return_num_edges(self):\n return sum(map(lambda x: len(x),self.__adj))","def _num_nodes(self):\n return len(self._nid2partid)","def num_links(self):\n count=0.0\n for cluster in self.clusters:\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\n numberofmembers=self.clusters[cluster].number_of_members\n count+=numberofmembers\n return count","def get_length(self):\n if self.opt.num_buckets > 1:\n return sum([len(bucket) for bucket in self.data])\n else:\n return len(self.data)","def get_total_item_size(dataset):\n total_items = 0\n for element in dataset:\n total_items += 1\n return total_items","def get_nb_element_per_dimension(recipe):\n return len(recipe[\"r\"]), len(recipe[\"c\"]), len(recipe[\"z\"])","def __len__(self) -> int:\r\n return len(self._nodes)","def __len__(self):\n return self._dataset.size(dirs=self._dirs)","def get_node_count(self) -> Iterable:\n return len([i for i in self.all_nodes_as_iterable()])","def element_count(self):\n return len(self.elements) + len(self.virtual_elements)","def num_nodes(self):\n return len(self.successors)","def num_nodes(self):\n return len(self.nodes)","def size(self):\r\n return len(self._train_datas)","def essential_node_count(self) -> int:\n return sum(\n 1 for n in self.graph.nodes() if n.kind() not in self._EXCLUDED_NODE_KINDS\n )","def size(self):\n return len(self._adjacency_list.keys())","def n_cf(self):\n return np.size(self._ref_ii, 0)","def interval_cardinality(self):\n return len(list(self.lower_contained_intervals()))","def size(self):\n\n return len(self._adjacency_list)","def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)","def data_size(self) -> int:\n return len(self.__labels)","def numpoints(self):\n return len(self.pars) + 1 # so dof is 1","def calc_Nw(cluster_labels):\n\n cluster_labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Nw = []\n for label in labels_set:\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\n Nw.append(n_cluster_pairs)\n\n return int(np.sum(Nw))","def numNodes(self):\n res = 0\n for n in self.iternodes():\n res += 1\n return res","def __len__(self):\n if self._train:\n return len(self._train_data)\n return len(self._test_data)","def __len__(self):\n if self._train:\n return len(self._train_data)\n return len(self._test_data)","def __len__(self):\n\n return len(self.labels)","def count_unique_features(self):\n return N_UNIQUE_FEATS","def _N(self):\n return len(self._array)","def size(self):\n ret = 0\n for ii in self.__data:\n ret += int(ii.get_size())\n return ret","def __len__(self):\n return self.count_of(CUBA.NODE)","def countEdges(self):\n return numpy.count_nonzero(self.supportArray) / 2","def num_nodes(self):\n return ((len(self.tensor_u)+1) * (len(self.tensor_v)+1) *\n (len(self.tensor_w)+1))","def __len__(self) -> int:\n return len(self.__elements)","def length(self):\n # TODO: Count number of key-value entries in each of the buckets\n return self.size\n # for bucket in self.buckets():"],"string":"[\n \"def n_clusters(self):\\n return len(self.clusters)\",\n \"def get_cluster_count(self) -> int:\\n return len(self.get_all_cluster_ids())\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def analysis_function_num_clusters(self,clustering):\\n return len(clustering.clusters)\",\n \"def num_elements(self):\\n return self.subset.num_elements()\",\n \"def cluster_obs_count(self):\\n return(self.merged_data.groupby(\\n 'labels').count().transpose().iloc[0, :])\",\n \"def element_size(self):\\n vecs = (\\n self.nodes[self.elements[:, :4], :][:, 1:, :]\\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\\n )\\n return np.abs(np.linalg.det(vecs)) / 6\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def n_clusters(self):\\n return self.model.n_clusters\",\n \"def nsites(self) -> int:\\n return len(self.A)\",\n \"def valency(self):\\n return len(self.neighbors())\",\n \"def n_points(self):\\n\\n if self.data_reduced:\\n return len(self.data_reduced[0])\\n else:\\n return 0\",\n \"def elements_count(self):\\n return self.__elements_count\",\n \"def getNNodesTot(self):\\n nNodesTot = 0\\n for iElt in Elements._all:\\n nNodesTot += len(iElt.coord)\\n return nNodesTot\",\n \"def count(self):\\n\\t\\treturn len(list(self.nodes))\",\n \"def __len__(self):\\n return len(self.dataset) * self.samples_per_pair\",\n \"def dissimilarity(clusters):\\n totDist = 0\\n for c in clusters:\\n totDist += c.variability()\\n return totDist\",\n \"def __len__(self):\\n return len(self.centroid_vector)\",\n \"def count(self):\\n\\n if self.cluster:\\n return self.cluster.count()\\n\\n return super().count()\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def n_good_features_(self):\\n return np.sum(self.important_features_)\",\n \"def GetNumberOfElements(self, assoc):\\n result = 0\\n for dataset in self:\\n result += dataset.GetNumberOfElements(assoc)\\n return int(result)\",\n \"def n_elements(self) -> int:\\n n_elem = np.prod(self.shape)\\n if self.n_timesteps > 1:\\n n_elem = int(n_elem / self.n_timesteps)\\n return n_elem\",\n \"def __len__(self):\\n return len(np.where(np.logical_not(self.data.mask))[0])\",\n \"def count(self):\\n return len(self._elements)\",\n \"def count_nodes(self):\\n\\t\\treturn self.__count_nodes(self)\",\n \"def voxel_count(self):\\n return self.cols * self.rows * self.sections\",\n \"def size(self):\\n\\t\\treturn len(self.nodes)\",\n \"def element_count(self):\\n return self._internal.get_element_count()\",\n \"def __len__(self):\\r\\n if self.is_superset:\\r\\n length = 0\\r\\n for ds in self.data:\\r\\n length += len(ds)\\r\\n return length\\r\\n else:\\r\\n return len(self.data)\",\n \"def size(self):\\n try:\\n return len(self._adjacency_list)\\n except Exception as error:\\n print(f'An error occurred: {error}')\",\n \"def size(self) -> Tuple[groupable, pdarray]:\\n return self.count()\",\n \"def count(self):\\r\\n return self.data_array.size\",\n \"def numnems(self):\\n count = 0\\n for o in self._objs.values():\\n count += len(o.netifs())\\n return count\",\n \"def N(self):\\n return len(self.cavity_grid.cavities) + 1\",\n \"def noOfElem(classObj, index):\\r\\n return len(classObj.dataSet[:, index])\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def get_nodes_pixel_count(self):\\n sum_count = self.pixel_count\\n for i in range(8):\\n node = self.children[i]\\n if node:\\n sum_count += node.pixel_count\\n return sum_count\",\n \"def getNumElements(self):\\n return 0\",\n \"def __len__(self) -> float:\\n return len(self.elements)\",\n \"def get_num_nodes(self):\\n\\n return sum(self.topology)\",\n \"def getNbClusters( model):\\r\\n\\r\\n\\tlabels = model.labels_\\r\\n\\tlabelValues = []\\r\\n\\tfor label in labels:\\r\\n\\t\\tif label not in labelValues and label != -1: labelValues.append(label)\\r\\n\\tnbClusters = len( labelValues)\\r\\n\\treturn nbClusters\",\n \"def getNumElements(self):\\n return 1\",\n \"def number_of_electrodes(self):\\n return self._pre_kernel.shape[1]\",\n \"def n_thres(self):\\n return np.size(self.thres)\",\n \"def get_num_instances(im, non_building_labels):\\n return np.setdiff1d(im, non_building_labels)\",\n \"def __len__(self):\\n return len(self.train) + len(self.val) + len(self.test)\",\n \"def num_complementary_regions(self):\\n g = self._get_puncturefinder_graph()\\n # return g.connected_components_number()\\n return nx.number_connected_components(g)\",\n \"def dimension(self):\\r\\n a = 0\\r\\n for x in self.faces():\\r\\n if (len(x) > a):\\r\\n a = len(x) \\r\\n return a-1\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def get_num_features(self):\\r\\n \\r\\n return len(self[0]['x'])\",\n \"def Nnodes(self):\\n return len(self.nodes)\",\n \"def size(self):\\n return len(self._adjacency_list)\",\n \"def nspatials(self):\\n return int(len(self)/2)\",\n \"def size(self):\\n return len(self.edges())\",\n \"def size(self):\\n return sum(elem.size for elem in self)\",\n \"def number_of_data_nodes(self):\\n return int(self._data['number_of_data_nodes'])\",\n \"def num_actual_nodes(tree):\\n return (tree.n_node_samples > 0).sum()\",\n \"def getNumElements(self):\\n return 1\",\n \"def purity_score(clusters, classes):\\n clusters = np.array(clusters)\\n classes = np.array(classes)\\n A = np.c_[(clusters,classes)]\\n\\n n_accurate = 0.\\n\\n for j in np.unique(A[:,0]):\\n z = A[A[:,0] == j, 1]\\n x = np.argmax(np.bincount(z))\\n n_accurate += len(z[z == x])\\n\\n return n_accurate / A.shape[0]\",\n \"def NodesCount(self):\\n return len(self.nodes)\",\n \"def __len__(self) -> int:\\n return len(self.nodes)\",\n \"def cluster_counter(self):\\n return Counter(self.model.labels_.tolist())\",\n \"def dim(self):\\n if self._classifier is None:\\n with self:\\n return self._classifier.features_dim\\n\\n return self._classifier.features_dim\",\n \"def __len__(self):\\n\\n if self.is_finite_set:\\n size = 0\\n for set in self.sets:\\n size += len(set)\\n return size\\n else:\\n raise ValueError(\\\"'%s' is not a finite set.\\\" % self)\",\n \"def return_num_edges(self):\\n return sum(map(lambda x: len(x),self.__adj))\",\n \"def _num_nodes(self):\\n return len(self._nid2partid)\",\n \"def num_links(self):\\n count=0.0\\n for cluster in self.clusters:\\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\\n numberofmembers=self.clusters[cluster].number_of_members\\n count+=numberofmembers\\n return count\",\n \"def get_length(self):\\n if self.opt.num_buckets > 1:\\n return sum([len(bucket) for bucket in self.data])\\n else:\\n return len(self.data)\",\n \"def get_total_item_size(dataset):\\n total_items = 0\\n for element in dataset:\\n total_items += 1\\n return total_items\",\n \"def get_nb_element_per_dimension(recipe):\\n return len(recipe[\\\"r\\\"]), len(recipe[\\\"c\\\"]), len(recipe[\\\"z\\\"])\",\n \"def __len__(self) -> int:\\r\\n return len(self._nodes)\",\n \"def __len__(self):\\n return self._dataset.size(dirs=self._dirs)\",\n \"def get_node_count(self) -> Iterable:\\n return len([i for i in self.all_nodes_as_iterable()])\",\n \"def element_count(self):\\n return len(self.elements) + len(self.virtual_elements)\",\n \"def num_nodes(self):\\n return len(self.successors)\",\n \"def num_nodes(self):\\n return len(self.nodes)\",\n \"def size(self):\\r\\n return len(self._train_datas)\",\n \"def essential_node_count(self) -> int:\\n return sum(\\n 1 for n in self.graph.nodes() if n.kind() not in self._EXCLUDED_NODE_KINDS\\n )\",\n \"def size(self):\\n return len(self._adjacency_list.keys())\",\n \"def n_cf(self):\\n return np.size(self._ref_ii, 0)\",\n \"def interval_cardinality(self):\\n return len(list(self.lower_contained_intervals()))\",\n \"def size(self):\\n\\n return len(self._adjacency_list)\",\n \"def get_number_of_atoms_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return len(v.data.stoichiometry)\",\n \"def data_size(self) -> int:\\n return len(self.__labels)\",\n \"def numpoints(self):\\n return len(self.pars) + 1 # so dof is 1\",\n \"def calc_Nw(cluster_labels):\\n\\n cluster_labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Nw = []\\n for label in labels_set:\\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\\n Nw.append(n_cluster_pairs)\\n\\n return int(np.sum(Nw))\",\n \"def numNodes(self):\\n res = 0\\n for n in self.iternodes():\\n res += 1\\n return res\",\n \"def __len__(self):\\n if self._train:\\n return len(self._train_data)\\n return len(self._test_data)\",\n \"def __len__(self):\\n if self._train:\\n return len(self._train_data)\\n return len(self._test_data)\",\n \"def __len__(self):\\n\\n return len(self.labels)\",\n \"def count_unique_features(self):\\n return N_UNIQUE_FEATS\",\n \"def _N(self):\\n return len(self._array)\",\n \"def size(self):\\n ret = 0\\n for ii in self.__data:\\n ret += int(ii.get_size())\\n return ret\",\n \"def __len__(self):\\n return self.count_of(CUBA.NODE)\",\n \"def countEdges(self):\\n return numpy.count_nonzero(self.supportArray) / 2\",\n \"def num_nodes(self):\\n return ((len(self.tensor_u)+1) * (len(self.tensor_v)+1) *\\n (len(self.tensor_w)+1))\",\n \"def __len__(self) -> int:\\n return len(self.__elements)\",\n \"def length(self):\\n # TODO: Count number of key-value entries in each of the buckets\\n return self.size\\n # for bucket in self.buckets():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7475261","0.73660713","0.7281601","0.7209829","0.7184535","0.71797055","0.71275187","0.7005667","0.6999668","0.69551873","0.68624055","0.68511283","0.67983764","0.6792057","0.6780994","0.6774845","0.6771099","0.66886616","0.66632557","0.66320395","0.66132414","0.6564767","0.6555667","0.6509118","0.6487408","0.64816576","0.64807916","0.6468132","0.64678425","0.64630216","0.64625275","0.6456934","0.6443169","0.6433817","0.64320695","0.6430267","0.6425387","0.64124244","0.64114904","0.64039147","0.6394186","0.6386561","0.6381392","0.6377842","0.63628834","0.63565457","0.635478","0.6352624","0.6344664","0.6340512","0.63376904","0.6329972","0.6325249","0.6319735","0.6308754","0.63025564","0.6300754","0.62910634","0.629054","0.62886375","0.62876904","0.62873334","0.62842995","0.6280591","0.62802994","0.62797505","0.6278884","0.62764573","0.62726575","0.62720245","0.6270812","0.6267656","0.6264363","0.62582725","0.62497246","0.6246999","0.6246279","0.6238815","0.62386554","0.6237936","0.62348354","0.6232878","0.6229474","0.6227602","0.6226656","0.622173","0.62187964","0.620858","0.62078416","0.620067","0.620067","0.61803454","0.6176319","0.6169932","0.61662686","0.61651","0.6163326","0.61632836","0.61628973","0.6160111"],"string":"[\n \"0.7475261\",\n \"0.73660713\",\n \"0.7281601\",\n \"0.7209829\",\n \"0.7184535\",\n \"0.71797055\",\n \"0.71275187\",\n \"0.7005667\",\n \"0.6999668\",\n \"0.69551873\",\n \"0.68624055\",\n \"0.68511283\",\n \"0.67983764\",\n \"0.6792057\",\n \"0.6780994\",\n \"0.6774845\",\n \"0.6771099\",\n \"0.66886616\",\n \"0.66632557\",\n \"0.66320395\",\n \"0.66132414\",\n \"0.6564767\",\n \"0.6555667\",\n \"0.6509118\",\n \"0.6487408\",\n \"0.64816576\",\n \"0.64807916\",\n \"0.6468132\",\n \"0.64678425\",\n \"0.64630216\",\n \"0.64625275\",\n \"0.6456934\",\n \"0.6443169\",\n \"0.6433817\",\n \"0.64320695\",\n \"0.6430267\",\n \"0.6425387\",\n \"0.64124244\",\n \"0.64114904\",\n \"0.64039147\",\n \"0.6394186\",\n \"0.6386561\",\n \"0.6381392\",\n \"0.6377842\",\n \"0.63628834\",\n \"0.63565457\",\n \"0.635478\",\n \"0.6352624\",\n \"0.6344664\",\n \"0.6340512\",\n \"0.63376904\",\n \"0.6329972\",\n \"0.6325249\",\n \"0.6319735\",\n \"0.6308754\",\n \"0.63025564\",\n \"0.6300754\",\n \"0.62910634\",\n \"0.629054\",\n \"0.62886375\",\n \"0.62876904\",\n \"0.62873334\",\n \"0.62842995\",\n \"0.6280591\",\n \"0.62802994\",\n \"0.62797505\",\n \"0.6278884\",\n \"0.62764573\",\n \"0.62726575\",\n \"0.62720245\",\n \"0.6270812\",\n \"0.6267656\",\n \"0.6264363\",\n \"0.62582725\",\n \"0.62497246\",\n \"0.6246999\",\n \"0.6246279\",\n \"0.6238815\",\n \"0.62386554\",\n \"0.6237936\",\n \"0.62348354\",\n \"0.6232878\",\n \"0.6229474\",\n \"0.6227602\",\n \"0.6226656\",\n \"0.622173\",\n \"0.62187964\",\n \"0.620858\",\n \"0.62078416\",\n \"0.620067\",\n \"0.620067\",\n \"0.61803454\",\n \"0.6176319\",\n \"0.6169932\",\n \"0.61662686\",\n \"0.61651\",\n \"0.6163326\",\n \"0.61632836\",\n \"0.61628973\",\n \"0.6160111\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74841356"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":684,"cells":{"query":{"kind":"string","value":"Returns the percentage of elements of the clustering that are in the 4 bigger clusters."},"document":{"kind":"string","value":"def analysis_function_top_4(self,clustering):\n clustering.sort_clusters_by_size()\n total = 0\n percents = clustering.get_population_percent_of_n_bigger_clusters(4)\n for p in percents:\n total = total+p\n return total"},"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 dissimilarity(clusters):\n totDist = 0\n for c in clusters:\n totDist += c.variability()\n return totDist","def purity_score(clusters, classes):\n clusters = np.array(clusters)\n classes = np.array(classes)\n A = np.c_[(clusters,classes)]\n\n n_accurate = 0.\n\n for j in np.unique(A[:,0]):\n z = A[A[:,0] == j, 1]\n x = np.argmax(np.bincount(z))\n n_accurate += len(z[z == x])\n\n return n_accurate / A.shape[0]","def analysis_function_num_clusters_to_percent(self,clustering,percent):\n return clustering.number_of_clusters_to_get_percent(percent)","def purity(clusters, classes):\n\n d = defaultdict(list)\n\n # Get a list of class numbers of all examples in a cluster.\n for k, v in zip(clusters, classes):\n d[k].append(v)\n\n mayority = 0\n\n # Count the mayority class number and add it up over all clusters.\n for k in d:\n mayority += Counter(d[k]).most_common(1)[0][1]\n\n return float(mayority) / len(clusters)","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def test_clusters(trained_data, centroids):\n\n for c in range(len(centroids)):\n count_1 = 0\n count_0 = 0\n for p in range(len(trained_data)):\n if trained_data[p][-2] == 0 and trained_data[p][-1] == centroids[c]:\n count_0 += 1\n if trained_data[p][-2] == 1 and trained_data[p][-1] == centroids[c]:\n count_1 += 1\n print (\"Centroid \", c+1, \":\", centroids[c])\n print(\"Number of 1's: \", count_1)\n print(\"Number of 0's: \", count_0)\n print(\"Percent 1's: \", round((count_1/(count_1 + count_0))*100,2))\n print(\"Percent 0's: \", round((count_0 / (count_1 + count_0)) * 100,2))\n print(\"****************\")","def _calculate_cluster_measures(\n arr4d,\n threshold,\n bin_struct,\n two_sided_test=False,\n):\n n_regressors = arr4d.shape[3]\n\n max_sizes = np.zeros(n_regressors, int)\n max_masses = np.zeros(n_regressors, float)\n\n for i_regressor in range(n_regressors):\n arr3d = arr4d[..., i_regressor].copy()\n\n if two_sided_test:\n arr3d[np.abs(arr3d) <= threshold] = 0\n else:\n arr3d[arr3d <= threshold] = 0\n\n labeled_arr3d, _ = label(arr3d > 0, bin_struct)\n\n if two_sided_test:\n # Label positive and negative clusters separately\n n_positive_clusters = np.max(labeled_arr3d)\n temp_labeled_arr3d, _ = label(\n arr3d < 0,\n bin_struct,\n )\n temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters\n labeled_arr3d = labeled_arr3d + temp_labeled_arr3d\n del temp_labeled_arr3d\n\n clust_vals, clust_sizes = np.unique(labeled_arr3d, return_counts=True)\n assert clust_vals[0] == 0\n\n clust_vals = clust_vals[1:] # First cluster is zeros in matrix\n clust_sizes = clust_sizes[1:]\n\n # Cluster mass-based inference\n max_mass = 0\n for unique_val in clust_vals:\n ss_vals = np.abs(arr3d[labeled_arr3d == unique_val]) - threshold\n max_mass = np.maximum(max_mass, np.sum(ss_vals))\n\n # Cluster size-based inference\n max_size = 0\n if clust_sizes.size:\n max_size = np.max(clust_sizes)\n\n max_sizes[i_regressor], max_masses[i_regressor] = max_size, max_mass\n\n return max_sizes, max_masses","def get_clust_num_perc(model, vis_perc=0.9):\n\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\n\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\n\n\ttot = model.allocmodel.Nk[idx].sum()\n\tcursum = 0\n\ti = 0\n\twhile cursum < tot*vis_perc:\n\t cursum += model.allocmodel.Nk[idx][i]\n\t i+=1\n\n\treturn i","def compute_cluster_class_fractions(k_means_model, y):\n\n n_classes = y.shape[1]\n class_labels = utils.one_hot_to_index(y)\n cluster_labels = k_means_model.labels_\n\n class_clustroid_counts = np.zeros((n_classes, K))\n for i in range(len(class_labels)):\n class_clustroid_counts[class_labels[i], cluster_labels[i]] += 1\n\n class_clustroid_fractions = class_clustroid_counts / np.sum(class_clustroid_counts, axis=1).reshape(n_classes, 1)\n\n print(\"\\n---- Class Clustroid Distribution ----\")\n for i in range(n_classes):\n print(\"Class {}: {}\".format(i, class_clustroid_fractions[i, :]))","def calculate_cluster_size(result, var):\n \n cluster_results=pd.DataFrame(result[var].value_counts())\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\"ratio\"})\n return cluster_results.join(ratio)","def clust_strength(mat,groups):\n cluster_strengths = []\n for group in range(len(np.unique(groups))):\n this_cluster = mat[groups==group,:]\n this_cluster_mean = np.mean(this_cluster,axis=0)\n all_dists = mat - this_cluster_mean\n out_dists = np.linalg.norm(all_dists[groups!=group],axis=1)\n in_dists = np.linalg.norm(all_dists[groups==group],axis=1)\n this_strength = np.mean(out_dists)/np.mean(in_dists)\n cluster_strengths.append(this_strength)\n \n return np.mean(cluster_strengths)","def clusterAlgorithm(values):\n clusterMap = dict()\n for value in values:\n if value[2] not in clusterMap.keys():\n clusterMap[value[2]] = []\n clusterMap[value[2]].append(value)\n frequency = [float(len(clusterMap[value[2]])) for value in values]\n total = sum(frequency)\n weightValues = [freq / total for freq in frequency]\n print sum(weightValues)\n lightValues = [value[1] for value in values]\n return np.average(lightValues, weights = weightValues)","def get_susceptibility(clusters):\n\n # If there is no or only one cluster then there is no finite cluster\n if len(clusters) <= 1:\n return np.nan\n\n # Remove largest, i.e. infinite, cluster\n clusters.remove(max(clusters))\n\n sizes = np.array(list(set(clusters)))\n n_s = []\n\n for size in sizes:\n n_s.append(clusters.count(size))\n\n temp = sizes * n_s\n S = np.sum(sizes * temp) / np.sum(temp)\n\n return S","def clusterSize(l, scheme, clustertype='fluid'):\n clist = findClusters(l, scheme, clustertype)\n \n avglists=[]\n for i in clist:\n avglist=[]\n for l in i:\n avglist.append(np.mean(l))\n avglists.append(np.mean(avglist))\n return avglists","def assign_labels_to_centroids(self):\n labelled_centroids = []\n for i in range(len(self.final_clusters)):\n labels = map(lambda x: x[0], self.final_clusters[i])\n # pick the most common label\n most_common = Counter(labels).most_common(1)[0][0]\n c = np.round(len([item for item in self.final_clusters[i] if item[0]==1])/len(self.final_clusters[i]),2)\n if c>=0.46:\n most_common = 1.0\n centroid = (most_common, self.final_centroids[i])\n labelled_centroids.append(centroid)\n\n self.labelled_centroids = labelled_centroids\n print(\"cluster_0: \", np.round(len([item for item in self.final_clusters[0] if item[0]==1])/len(self.final_clusters[0]),2), \"size_0: \", len(self.final_clusters[0]))\n print(\"cluster_1: \", np.round(len([item for item in self.final_clusters[1] if item[0]==1])/len(self.final_clusters[1]),2), \"size_1: \", len(self.final_clusters[1]))\n #print(\"cluster_2: \", np.round(len([item for item in self.final_clusters[2] if item[0]==1])/len(self.final_clusters[2]),2), \"size_2: \", len(self.final_clusters[2]))\n #print(\"cluster_3: \", np.round(len([item for item in self.final_clusters[3] if item[0]==1])/len(self.final_clusters[3]),2), \"size_2: \", len(self.final_clusters[3]))","def evaluateClusters( features, labels):\r\n\r\n\treturn silhouette_score( features, labels)","def compute_total_bipartiteness(hypergraph, clusters):\n bipartiteness_sum = 0\n for i in range(len(clusters)):\n for j in range(i + 1, len(clusters)):\n bipartiteness_sum += hypergraph_bipartiteness(hypergraph, clusters[i], clusters[j])\n return bipartiteness_sum","def davies_bouldin_score(self):\r\n print(colored(\"The davies bouldin score of the clustering is %0.002f\\n\" %(davies_bouldin_score(self.X, self.labels)),color = 'red', attrs=['bold']))\r\n print()\r\n print(colored(\"The points in each cluster are : \",color = 'yellow', attrs=['bold']))\r\n print(collections.Counter(self.labels))","def get_optimal_clusters(cell,threshold=140):\n\n\t#\tTurn image to numpy array\n\tpic = image_to_matrix(cell)\n\n\t#\tGet the array of coordinates of dark dots\n\tdots = get_threshold_dots(pic,threshold)\n\n\tscores = []\n\n\tfor n_clusters in range(1,10):\n\t\tclusters = kmeans.kmeans(pic,pic.shape[0],pic.shape[1],50,n_clusters,threshold)\n\t\tprint clusters\n\n\t\tsquare_sum_array = [0]*n_clusters\n\t\tcount_array = [0]*n_clusters\n\n\t\tfor dot in dots:\n\t\t\tdistance_array = [kmeans.euclid_distance(dot,cluster) for cluster in clusters]\n\t\t\tmin_index = distance_array.index(min(distance_array))\n\t\t\tsquare_sum_array[min_index] += kmeans.euclid_distance(clusters[min_index],dot)\n\t\t\tcount_array[min_index] += 1\n\n\t\tvariances = [square_sum/(count+0.001) for square_sum, count in zip(square_sum_array,count_array)]\n\n\t\tprint variances\n\t\tscores.append(sum(variances)/len(variances))\n\n\treturn scores","def diversion_score(X, offspring_list):\r\n similarity_sum = 0\r\n if len(offspring_list[0]) == 2:\r\n offspring_list = [(parent_a, offspring, parent_a) for (parent_a, offspring) in offspring_list]\r\n for (parent_a, offspring, parent_b) in offspring_list:\r\n similarity_sum += max(icc(parent_a, offspring), icc(parent_b, offspring))\r\n return (1 - (((similarity_sum / len(offspring_list)) + 1) / 2)) * 100 # move from [-1,1] to [0,2], then to [0,1], then inverse, finally move to [0,100]\r","def ds_ratio(group):\n nix_count = (group=='nix').sum()\n top_count = (group=='top').sum()\n ratio = nix_count/(nix_count+top_count) #could smooth this\n return ratio","def percenter(rank, max_rank):\n\treturn 100 * (rank/(max_rank or 1))","def analysis_function_top_percent(self,clustering):\n clustering.sort_clusters_by_size()\n return clustering.get_population_percent_of_cluster(0)","def calculate_priors(trainingLabels):\r\n sum = 0\r\n priors = {}\r\n totalSamples = len(trainingLabels)\r\n classes = set(trainingLabels)\r\n for cls in classes:\r\n numCls = len(filter(lambda x: x == cls, trainingLabels))\r\n sum += numCls\r\n priors[cls] = float(numCls) / float(totalSamples)\r\n \r\n # Sanity check: valid partitioning\r\n assert(sum == totalSamples)\r\n\r\n return priors","def cluster_cal(self):\n self.Cluster = []\n for i in range(self.nodenum):\n neighborhood_node = self.neighbor_node(i)\n Node_num = len(neighborhood_node)\n Count = self.neighbor_edge(neighborhood_node)\n if(Node_num == 0 or Node_num == 1):\n self.Cluster.append(0.5)\n else:\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\n \n self.cluster_coeff = np.average(self.Cluster)","def get_cluster_to_split(clusters):\n\treturn max(clusters.items(), key=lambda x: x[1].get_distortion())[1]","def avg_sil_width(cluster_vec, centroid):\n tot_dis = 0\n for i in xrange(len(cluster_vec)):\n cos_dis = cos_sim(cluster_vec[i], centroid)\n tot_dis = tot_dis + cos_dis\n return tot_dis/len(cluster_vec)","def contains_percentage_of(self, other: 'Interval') -> float:\n if other.length == 0:\n return other.a in self\n intersection = Interval.intersection([self, other])\n return intersection.length / other.length if intersection else 0.0","def cluster_mcc_ratio(result, cluster_names, var, n=5):\n rel1=get_cluster_country_distr(result, var)\n clusters=calculate_cluster_size(result, var)\n hours=hours_tusc(result, var)\n res=\"\"\n for i in zip(clusters.index, cluster_names[:len(clusters)]):\n res=res+f\"By the number of unique visitors the {i[1]} cluster's top 5 countries are; \"\n rel=rel1.sort_values(i[0],ascending=False)[:n]\n for j in range(0,5):\n if j!=n-1:\n res=res+f'{rel[i[0]].index[j]} ({rel[i[0]][j]}%), '\n else:\n res=res+f'and {rel[i[0]].index[j]} ({rel[i[0]][j]}%). '\n res=res+f'This cluster spends on average {int(hours.hrs_in_tusc[i[0]])} days in Tuscany, '\n res=res+get_places_at_least4_hours(result, i[0], var)\n res=res+ cluster_airport_result(result, i[0], var)\n return res","def compute_clusters(self, p: float):\n pass","def get_utilization(self):\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\n # Remove overlapping prefixes from list of children\n networks = cidr_merge([c.prefix for c in child_prefixes])\n children_size = float(0)\n for p in networks:\n children_size += p.size\n return int(children_size / self.prefix.size * 100)","def calculate_cost(self, medoids, clusters):\n cost = 0.0\n for i in range(0, len(medoids)):\n for j in range(0, len(clusters[i])):\n cost += distance.sqeuclidean(medoids[i], clusters[i][j])\n return cost\n pass","def average_consensus(self, cluster):\n\t\tcenterk = 0\n\t\tindex = 0\n\t\tfor value in cluster:\n\t\t\tcenterk += value\n\t\t\tindex += 1\n\t\tcenterk = centerk / index\n\t\treturn centerk","def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements","def calculate_sample_silhouette(self):\n sum_samples = 0\n for cluster in self.cluster_lst:\n sum_samples += self.sum_silhouette(cluster)\n sample_size = len(self.samples)\n return sum_samples/sample_size","def get_modularity(adjacency, clusters):\n total_weight = np.sum(adjacency)\n e = get_clusters_adjacencies(adjacency, clusters)\n e = e / total_weight\n a = np.sum(e, axis=1)\n return np.sum(e.diagonal() - np.power(a, 2))","def _assign_clusters(self):\n\n dist = np.zeros((self.k, ))\n distortion = 0\n\n for index in range(0, self.data.shape[0]):\n for i in range(0, self.k):\n dist[i] = np.linalg.norm(self.data[index] - self.centroids[i])\n\n self.assigned_clusters[index] = np.argmin(dist)\n distortion += np.min(dist)\n\n return distortion","def get_modularity3(adjacency, clusters):\n\n rows, cols = adjacency.shape\n num_ids = adjacency.shape[0]\n id_to_cluster = get_id_to_cluster(clusters, num_ids)\n degrees = np.sum(adjacency, axis=1)\n total_weight = np.sum(adjacency)\n sum = 0\n for i in range(rows):\n for j in range(cols):\n if id_to_cluster[i] == id_to_cluster[j]:\n sum += adjacency[i, j] - (degrees[i] * degrees[j]) / total_weight\n sum = sum / total_weight\n return sum","def compute_variance(\n data, cluster_labels, centroids, alpha=10, debug=False, num_class=None\n):\n\n k = len(centroids) if num_class is None else num_class\n phis = torch.zeros(k)\n for c in range(k):\n cluster_points = data[cluster_labels == c]\n c_len = len(cluster_points)\n if c_len == 0:\n phis[c] = -1\n elif c_len == 1:\n phis[c] = 0.05\n else:\n phis[c] = torch.sum(torch.norm(cluster_points - centroids[c], dim=1)) / (\n c_len * np.log(c_len + alpha)\n )\n if phis[c] < 0.05:\n phis[c] = 0.05\n\n if debug:\n print(\"size-phi:\", end=\" \")\n for i in range(k):\n size = (cluster_labels == i).sum().item()\n print(f\"{size}[phi={phis[i].item():.3f}]\", end=\", \")\n print(\"\\n\")\n\n return phis","def cluster_membership_occupancy(data):\n \n \n \n n_clusters = len(set(data['clusters'])-{-1}) # since -1 element denotes noice\n\n if n_clusters == 0:\n membership=[Cluster_Membership_Features()]\n membership = pd.DataFrame([o.__dict__ for o in membership])\n areas=[Cluster_Area_Features()]\n areas = pd.DataFrame([o.__dict__ for o in areas])\n density=[Cluster_Density_Features()]\n density = pd.DataFrame([o.__dict__ for o in density])\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\n density], axis=1)\n \n elif n_clusters ==1:\n #obtain_total_cluster_areas_set_everything_else_to_default\n membership=[Cluster_Membership_Features()]\n membership = pd.DataFrame([o.__dict__ for o in membership])\n d = dict(tuple(data.groupby('clusters')))\n d.pop(-1, None)\n \n try:\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\n except:\n cluster_chull_areas=[0,0,0]\n \n Total_cluster_area=np.sum(cluster_chull_areas)\n areas=[Cluster_Area_Features([Total_cluster_area,0,0,0,0,0,0,0,0])]\n areas = pd.DataFrame([o.__dict__ for o in areas])\n density=[Cluster_Density_Features()]\n density = pd.DataFrame([o.__dict__ for o in density])\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\n density], axis=1)\n \n elif n_clusters >1:\n #Summarizing the cluster membership distribution characteristics\n cluster_size_nums=np.delete(np.array(data.groupby(['clusters']).size()),0)\n (cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD)= distribution_statistics(cluster_size_nums)\n\n #For each cluster calculate the area by calculating the area of the convex hull of cluster members\n # Note: concavehull implementation here might be a good addition as it will provide more imformative values. \n\n d = dict(tuple(data.groupby('clusters')))\n d.pop(-1, None)\n try:\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\n except:\n cluster_chull_areas=[0,0,0,0,0]\n \n\n (avg_cluster_area,min_cluster_area,max_cluster_area,\n std_cluster_area,CV_cluster_area,CD_cluster_area,\n IQR_cluster_area,Quartile_CD_cluster_area)= distribution_statistics(cluster_chull_areas)\n Total_cluster_area=np.sum(cluster_chull_areas)\n\n #Calculate cluster density: number of nuclei/ convex area of cluster\n cluster_density=np.divide(cluster_size_nums,cluster_chull_areas)\n (avg_cluster_density,min_cluster_density,max_cluster_density,\n std_cluster_density,CV_cluster_density,CD_cluster_density,\n IQR_cluster_density,Quartile_CD_cluster_density)= distribution_statistics(cluster_density)\n\n #return dataframe of features\n membership=[Cluster_Membership_Features([cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD])]\n membership = pd.DataFrame([o.__dict__ for o in membership])\n areas=[Cluster_Area_Features([Total_cluster_area,\n avg_cluster_area,min_cluster_area,max_cluster_area,\n std_cluster_area,CV_cluster_area,CD_cluster_area,\n IQR_cluster_area,Quartile_CD_cluster_area])]\n areas = pd.DataFrame([o.__dict__ for o in areas])\n density=[Cluster_Density_Features([avg_cluster_density,min_cluster_density,max_cluster_density,\n std_cluster_density,CV_cluster_density,CD_cluster_density,\n IQR_cluster_density,Quartile_CD_cluster_density])]\n density = pd.DataFrame([o.__dict__ for o in density])\n\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\n density], axis=1)\n return all_features","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def calc_priors(categories, data):\n counts = np.zeros(categories)\n for val in range(categories):\n counts[val] = np.count_nonzero(data.labels == val)\n return counts / len(data.labels)","def calc_Nw(cluster_labels):\n\n cluster_labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Nw = []\n for label in labels_set:\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\n Nw.append(n_cluster_pairs)\n\n return int(np.sum(Nw))","def test_determine_k(self):\n test_dir_name = os.path.dirname(__file__)\n feat_array_fn = os.path.join(\n test_dir_name, \"data\", \"four_clusters.csv\")\n df = pd.read_csv(feat_array_fn)\n feat_array = df[[\"x\", \"y\"]].values\n\n clusterer = Clusterer(feat_array_fn, \"/dev/null\", [])\n best_k = clusterer._determine_k(feat_array, 9)\n\n self.assertEqual(best_k, 4)\n\n feat_array_fn = os.path.join(\n test_dir_name, \"data\", \"iris.csv\")\n df = pd.read_csv(feat_array_fn)\n feat_array = df[[\n \"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\",\n \"Petal.Width\"]].values\n\n clusterer = Clusterer(feat_array_fn, \"/dev/null\", [])\n best_k = clusterer._determine_k(feat_array, 9)\n\n self.assertEqual(best_k, 2)","def group_size_dist(self):\n return self.group_sizes() / self.group_sizes().sum()","def purity_score(label, pred):\n \n df = pd.concat([label, pd.DataFrame(pred)], axis=1)\n df.set_axis(['label', 'pred'], axis=1, inplace=True)\n \n s = 0\n\n for x, cluster in df.groupby('pred'):\n s += cluster['label'].value_counts().iloc[0] # adding the most occuring class in a cluster\n\n return s / label.shape[0]","def d50(clones, num_Reads): \n\n\n d50_amount = num_Reads/2\n read_count=0\n for i in clones:\n read_count+=clones[i].num_reads\n if read_count>=d50_amount:\n return i/float(len(clones))","def fraction_mislabeled_nodes(labels, labels_pred):\n G1 = partition_indicator(labels)\n G2 = partition_indicator(labels_pred)\n\n # cost is minimized, overlap maximized\n cost_matrix = -G1.T.dot(G2)\n row_ind, col_ind = linear_sum_assignment(cost_matrix.A)\n cost = -cost_matrix[row_ind, col_ind].sum()\n\n return 1 - (cost / len(labels))","def calculate_enrichment_factor_optimal(molecules, ranked_dataset_percentage_cutoff, pic50_cutoff):\n\n ratio = sum(molecules[\"pIC50\"] >= pic50_cutoff) / len(molecules) * 100\n if ranked_dataset_percentage_cutoff <= ratio:\n enrichment_factor_optimal = round(100 / ratio * ranked_dataset_percentage_cutoff, 1)\n else:\n enrichment_factor_optimal = 100.0\n return enrichment_factor_optimal","def leaf_nodes_to_search_percent(self) -> Optional[int]:\n return pulumi.get(self, \"leaf_nodes_to_search_percent\")","def _calculate_cluster_probs(self, dist_mat, temperature):\n dist_mat = np.square(dist_mat) # euclidean distance -> squared euclidean distance\n n_samples = dist_mat.shape[0]\n n_clusters = dist_mat.shape[1]\n cluster_probs = np.zeros(shape=(n_samples, n_clusters))\n for i in range(n_clusters):\n for j in range(n_samples):\n normalizer = sum(self.marginal_probs[k] * np.exp(-dist_mat[j, k] / temperature) for k in range(n_clusters))\n cluster_probs[j, i] = self.marginal_probs[i] * np.exp(-dist_mat[j, i] / temperature) / normalizer\n return cluster_probs","def berger_parker_d(counts):\n return counts.max()/float(counts.sum())","def solidity(cnt):\n\tarea = cv2.contourArea(cnt)\n\thull = cv2.convexHull(cnt)\n\thull_area = cv2.contourArea(hull)\n\treturn float(area) / hull_area","def find_avg(centroids, short_cut=False, sim_scores=None):\n \n total_sim = 0.0\n total_comparisons = 0\n \n if short_cut:\n total_comparisons = len(sim_scores)\n \n for score in sim_scores:\n total_sim += score\n \n return (total_sim / total_comparisons)\n\n length = len(centroids)\n\n for i in xrange(0, length):\n for j in xrange(i + 1, length):\n total_sim += similarity(centroids[i], centroids[j])\n total_comparisons += 1\n\n return (total_sim / total_comparisons)","def evaluate_clusters(self, cluster_formulas, value='weighted_sum'):\n num_elems = len(self.labels)\n total_val = {}\n num_cl = len(cluster_formulas)\n clustered_points_num = 0\n print(\"\\n\\n\")\n print(\"Sufficiently big clusters: {}\".format(num_cl))\n for c, formula, val in cluster_formulas:\n c_size = len([l for l in self.labels if l == c])\n clustered_points_num += c_size\n\n if value == 'weighted_sum':\n total_val[c] = val * c_size / num_elems\n elif value == 'sum':\n total_val[c] = val * 1\n\n clust_val = sum(total_val.values())\n self.clustering_value = total_val\n print(\"Value of clustering: {}\".format(clust_val))\n return clust_val","def calculate_cluster_fitness(self, cluster_id: ObjectId):\n\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\n\n cluster_fitness = 0\n\n for genome in genomes:\n cluster_fitness += genome.fitness\n if cluster_fitness == 0:\n return 0\n\n return cluster_fitness / len(list(genomes))","def element_size(self):\n vecs = (\n self.nodes[self.elements[:, :4], :][:, 1:, :]\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\n )\n return np.abs(np.linalg.det(vecs)) / 6","def calculate_cluster_silhouette(self, cluster):\n cluster_size = len(cluster.get_samples())\n return self.sum_silhouette(cluster) / cluster_size","def clustering_coefficient(graph):\r\n count = 0\r\n sumOfClusteringCoefficients = 0\r\n for vertex in graph:\r\n count += 1\r\n sumOfClusteringCoefficients += local_clustering_coefficient(graph, vertex)\r\n return sumOfClusteringCoefficients / count","def _cluster_hitprobability(self, x, y):\n hm_count = np.zeros_like(y).astype(float)\n hm = np.zeros_like(y).astype(float)\n #skf = StratifiedShuffleSplit(n_splits=self.n_iter, test_size=self.shuffle_test_split, random_state=self.random_state)\n\n ind = self._cluster(x, 35)\n\n for cluster_id in np.unique(ind):\n test = np.argwhere(ind == cluster_id)[:, 0]\n train = np.argwhere(ind != cluster_id)[:, 0]\n #print test\n self.basemodel.fit(x[train, :], y[train], hyperparams_optim=False)\n hm_count[test] += 1.\n hm[test] += (self.basemodel.predict(x[test, :]) == y[test]).astype(float)\n\n proba = hm / hm_count\n if self.verbose:\n # print('H/M count:')\n # print(hm_count)\n print('Proba:')\n print(proba)\n self.basemodel.fit(x, y, hyperparams_optim=False)\n return proba","def clustering_factor(self):\n return self.unpack_dword(0x2C)","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def estimate_label_proportion(source_loader,target_loader,feat_extract,cuda,n_clusters,cluster_param): \n feat_extract.eval()\n #n_clusters = 3\n from sklearn.cluster import AgglomerativeClustering\n \n \n X_s,y_s = extract_feature(source_loader,feat_extract,cuda) \n X_t,y_t = extract_feature(target_loader,feat_extract,cuda) \n \n \n \n cluster = AgglomerativeClustering(n_clusters=n_clusters,linkage=cluster_param)\n label_t = cluster.fit_predict(X_t)\n #print(np.unique(label_t))\n mean_mat_S, num_in_class_S = extract_prototypes(X_s,y_s,n_clusters)\n mean_mat_T, num_in_class_T = extract_prototypes(X_t,label_t,n_clusters)\n \n \"\"\"\n We assume that prototypes of classes have been transported in some in the feature\n space \n \"\"\"\n \n import ot\n M = ot.dist(mean_mat_S, mean_mat_T)\n M /= M.max()\n \n n_1 = n_clusters\n a = np.ones((n_1,)) / n_1\n b = np.ones((n_1,)) / n_1\n \n \n gamma = ot.emd(a,b,M)\n nb_sample_S = [ np.sum(y_s==i) for i in range(n_clusters) ]\n proportion_T = num_in_class_T/np.sum(num_in_class_T)\n assignement_source_to_target = gamma.argmax(axis=1)\n \n # proportions are arranged directly per class\n proportion_T = proportion_T[assignement_source_to_target]\n print(proportion_T,assignement_source_to_target)\n \n\n return proportion_T,nb_sample_S, assignement_source_to_target","def heuristic_3_partition(game, player) -> float:\n\n partition_possible_factor = get_partition_possible_factor(game, player)\n\n return float(partition_possible_factor)","def fitness(individual, n_clusters=3, n_seeds=5):\n\n dataframe = common.scale_dataframe(individual)\n corr = abs(individual.dataframe.corr().iloc[0, 1])\n differences = []\n for seed in range(n_seeds):\n km = KMeans(n_clusters, random_state=seed).fit(dataframe)\n differences.append(silhouette_score(dataframe, km.labels_) - corr)\n\n best = max(differences)\n return best","def percentage_hapaxes(corpus_parts, corpus):\n percentage_h = []\n count = 0\n dv = divide_corpus(corpus, 10)\n hapax_parts = hapaxes_parts(corpus_parts)\n for x in hapax_parts:\n percentage_h.append(percentage(x, len(dv[count])))\n count += 1\n return percentage_h","def compute_clusters(self, documents):\n ###TODO\n for d in range(0, len(documents)):\n maxi = 999999999\n for cid in range(0, len(self.means)):\n dist = self.distance(documents[d], self.means[cid], self.norms[cid])\n if dist < maxi:\n maxi = dist\n clust = cid \n self.cluster[d] = clust","def __bayesian_information_criterion(data, clusters, centers):\n\n scores = [0.0] * len(clusters) # splitting criterion\n dimension = len(data[0])\n\n # estimation of the noise variance in the data set\n sigma = 0.0\n K = len(clusters)\n N = 0.0\n\n for index_cluster in range(0, len(clusters)):\n for index_object in clusters[index_cluster]:\n sigma += (euclidean_distance(data[index_object], centers[index_cluster])) # It works\n\n N += len(clusters[index_cluster])\n\n if (N - K != 0):\n sigma /= (N - K)\n\n # splitting criterion\n for index_cluster in range(0, len(clusters)):\n n = len(clusters[index_cluster])\n\n if (sigma > 0.0):\n scores[index_cluster] = n * math.log(n) - n * math.log(N) - n * math.log(\n 2.0 * np.pi) / 2.0 - n * dimension * math.log(sigma) / 2.0 - (n - K) / 2.0\n\n return sum(scores)","def calc_sw(X, cluster_labels):\n\n labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Sw = []\n for label in labels_set:\n # Loop through each cluster and calculate within cluster distance\n pairs = np.where(labels == label)\n pairs_distance = pdist(X[pairs[0]])\n within_cluster_distance = np.sum(pairs_distance, axis=0)\n Sw.append(within_cluster_distance)\n\n return np.sum(Sw)","def compute_distortion(cluster_list, data_table):\n\tdistortion = 0\n\tfor cluster in cluster_list:\n\t\tdistortion += cluster.cluster_error(data_table)\n\treturn distortion","def pie_chart_score(self, grouped):\n picked_scenario = self.scenario_dict[\"%d\" % (self.scenario_num-1)]\n distinct_enum_X = self.data_dict[picked_scenario[\"X\"]]['distinct_enum']\n score = 0\n if min(grouped) < 0:\n score = 0\n elif distinct_enum_X == 1:\n score = 0\n elif picked_scenario[\"Agg_func_Y\"] == \"avg\":\n score = 0\n elif distinct_enum_X >= 2 and distinct_enum_X <= 8:\n score += self.calculate_entropy(self.data_dict[picked_scenario[\"Y\"]]) / 8\n elif distinct_enum_X > 8:\n score += 4 * (self.calculate_entropy(self.data_dict[picked_scenario[\"Y\"]])) / distinct_enum_X\n if score > 3:\n score = 3\n return score","def _distorted_distance(self):\n distance = 0\n for i, pixel in enumerate(self.training_set):\n distance += self._euclid_distance(\n pixel, self.clusters[self.labels[i]], axis=0)\n return distance","def classProbs(observation, tree, classes):\n res = classify(observation, tree) #res = results\n total = sum(res.values())\n probs = []\n for c in classes:\n if c in res.keys():\n probs.append(float(res[c])/total)\n else:\n probs.append(0)\n return probs","def hausd95(result, reference, voxelspacing=None, connectivity=1):\n hd1 = __surface_distances(result, reference, voxelspacing, connectivity)\n hd2 = __surface_distances(reference, result, voxelspacing, connectivity)\n hd95 = np.percentile(np.hstack((hd1, hd2)), 95)\n return hd95","def score(self, dataset: List[Read]) -> int:\n kmers_in_common = 0\n for read in dataset:\n for kmer in read.kmers(self.k):\n if self.filter.contains(kmer):\n kmers_in_common += 1\n return self.filter.num_items_in_filter - kmers_in_common","def _fraction_latency(self, users_distances):\n\n users_desired_latency = np.array(list(map(lambda a: self.services_desired_latency[a],\n self.users_services)))\n check = users_distances < users_desired_latency\n fraction = np.count_nonzero(check==True) / self.num_of_users\n return fraction","def SquareClusteringCoefficient(graph):\n coef = np.mean(list(nx.square_clustering(graph).values()))\n return coef","def percent_accuracy(self, test_set, predicted_values):\r\n\r\n correct = 0\r\n for i in range(len(test_set)):\r\n if test_set[i].classification == predicted_values[i]:\r\n correct += 1\r\n return correct / len(test_set)","def __cluster_simi(self, i, j):\n sum_ = 0.\n for si in self.__indexclusters[i]:\n for sj in self.__indexclusters[j]:\n simi = self.__sample_simi(si, sj)\n sum_ += simi\n return sum_ / (len(self.__indexclusters[i]) * len(self.__indexclusters[j]))","def occupation_distribution(data):","def one_dimension_val_clutering(vals, max_distance=5):\n vals = sorted(vals)\n clusters = []\n for (idx, i) in enumerate(vals):\n cluster = [j for j in vals if abs(j - i) < max_distance]\n clusters.append(cluster)\n clusters = sorted(clusters, key=len, reverse=True)\n cluster = clusters[0]\n if len(cluster) / len(vals) > 0.6 or len(cluster) >= 3:\n return cluster\n else:\n return []","def check_correctness_statistics(classifier_out, mode, image_type):\n labels = image_type.image_data[mode].labels\n num_correct = 0\n total = len(classifier_out)\n for index, label in classifier_out:\n if labels[index] == label:\n num_correct += 1\n return (num_correct / total) * 100","def compute_distortion(cluster_list, data_table):\r\n distortion = 0\r\n \r\n for cluster in cluster_list:\r\n distortion += cluster.cluster_error(data_table)\r\n\r\n return distortion","def get_proportion_of_unique_lemmas(self):\n lemmas = self.blob.words.lemmatize()\n return len(set(lemmas)) / float(len(self.blob.words))","def FindScale(self):\n\n ## 6 and from the cv code the distance is 6 then we are good\n print(\"TODO: Very hard\")","def calculate_percent_match(primers,\n seq_count,\n exclude_seq_count=1):\n # Calculate percent of sequences that are 'hit' by each primer\n for n in range(len(primers)):\n # Calculate percent perfect match\n primers[n].percent_match=float(primers[n].match_count/seq_count)\n primers[n].non_specific_percent=\\\n float(primers[n].non_specific_hits/exclude_seq_count)\n \n return primers","def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)","def variance_ratio_criterion(cls, cluster_labels, pca_reduced, cluster_size, names: list):\n score = metrics.calinski_harabaz_score(pca_reduced, cluster_labels)\n return ClusterMetricScore('calinski harabaz', score, cluster_size, cluster_labels, names)","def getBeliefDistribution(self):\n # This essentially gives a point to a location for each particle there, then \n # normalizes the point values so they add up to 1.\n dist = util.Counter()\n for part in self.particles: dist[part] += 1\n dist.normalize()\n return dist","def good_clusters(block_path, cutoff=CLUSTER_ACCURACY_CUTOFF):\n accuracies, cluster_accuracies = morphs.load.cluster_accuracies()\n block_cluster_accuracies = cluster_accuracies[block_path]\n return block_cluster_accuracies[\n block_cluster_accuracies.accuracy > cutoff\n ].index.values","def calc_verts_median(verts):\n return ft.reduce(operator.add, [v.co for v in verts]) / len(verts)","def overlap_score(labels, labels_pred):\n raw_overlap = 1-fraction_mislabeled_nodes(labels, labels_pred)\n partition_true = np.array(labels).astype(int)\n partition_pred = np.array(labels_pred).astype(int)\n num_nodes = partition_pred.size\n num_groups = partition_true.max() + 1\n\n chance_level = 0.\n for i in range(num_groups):\n temp = np.sum(i == partition_true) / num_nodes\n if temp > chance_level:\n chance_level = temp\n\n score = (raw_overlap - chance_level) / (1 - chance_level)\n if score <= 0:\n score = 0\n\n return score","def get_modularity_other_c(A, cluster_indices):\n # define the number of nodes in the graph and the number of clusters\n n = len(cluster_indices)\n nclusters = max(cluster_indices) + 1\n # define the row sums of the adjacency matrix\n row_sums = [sum(row) for row in A]\n # define one half of the sum of all entries in the adjacency matrix\n m = sum(row_sums) / 2.0\n # define the modularity\n Q = 0\n for i in range(n):\n for j in range(n):\n if cluster_indices[i] == cluster_indices[j]:\n Q += (A[i][j] - row_sums[i] * row_sums[j] / (2*m)) / (2*m)\n return Q","def get_percentile(data_list, score, kind='weak'):\n n = len(data_list)\n\n if kind == 'strict':\n return len([i for i in data_list if i < score]) / float(n) * 100\n elif kind == 'weak':\n return len([i for i in data_list if i <= score]) / float(n) * 100\n elif kind == 'mean':\n return (len([i for i in data_list if i < score]) + len([i for i in data_list if i <= score])) * 50 / float(n)\n else:\n raise ValueError(\"The kind kwarg must be 'strict', 'weak' or 'mean'. You can also opt to leave it out and rely on the default method.\")","def cluster_partition_distance(individual, test_data, truth_data, name=None):\r\n distance_sum = 0\r\n max_sum = 0\r\n for test_clusters, truth_clusters in zip(test_data, truth_data):\r\n # Get last column of target data\r\n test_clusters = test_clusters[-1].flatten()\r\n\r\n p1_dict = {}\r\n for i, x in enumerate(test_clusters):\r\n if x not in p1_dict:\r\n p1_dict[x] = []\r\n p1_dict[x].append(i)\r\n\r\n p2_dict = {}\r\n for i, x in enumerate(truth_clusters):\r\n if x not in p2_dict:\r\n p2_dict[x] = []\r\n p2_dict[x].append(i)\r\n\r\n p1 = list(p1_dict.values())\r\n p2 = list(p2_dict.values())\r\n d = _fast_partition_distance(p1, p2, len(test_clusters))\r\n if d is None:\r\n d = _partition_distance(p1, p2, len(test_clusters))\r\n distance_sum += d\r\n max_sum += len(test_clusters) - 1\r\n return distance_sum / max_sum","def generate_clusters(df):\n\n df_size = df.shape[0]\n print(df_size)\n n_clusters = 0\n percent_min_pts = 0.105\n min_clusters = 3\n while (n_clusters != min_clusters):\n print(\"percent_min_pts\", percent_min_pts)\n min_cluster_pts = math.floor(df_size * percent_min_pts)\n print(\"min_cluster_pts\", min_cluster_pts)\n\n clusterer = hdbscan.HDBSCAN(min_cluster_size=min_cluster_pts)\n print(df.head())\n clusterer.fit(df)\n cluster_groups = {}\n labels = clusterer.labels_\n for i in labels:\n if cluster_groups.get(i):\n cluster_groups[i] = cluster_groups[i] + 1\n else:\n cluster_groups[i] = 1\n print(\"cluster_groups\", cluster_groups)\n n_clusters = len(set(labels))\n print(\"n_clusters\", n_clusters)\n multiplier = abs(n_clusters - min_clusters) * 0.001\n print(\"multiplier\", multiplier)\n if n_clusters > min_clusters:\n percent_min_pts += multiplier\n else:\n percent_min_pts -= multiplier\n print(\"percent_min_pts\", percent_min_pts)\n return labels","def avarage_for_group(data: Dict[int, int]) -> float:\n values = data.values()\n summary = sum(values)\n return summary // len(data)","def pct_bust(data):\n return round((data[\"new_total\"] > 21).sum() / len(data), 3)","def compute_means(self):\n ###TODO\n vector_means = []\n for doc in self.fin_clust.values():\n vec = defaultdict(float)\n for d_id in doc:\n doc_keys = self.docs[d_id].keys()\n for key in self.docs[d_id]:\n vec[key] = vec[key] + self.docs[d_id][key]\n tot = len(doc)\n x = defaultdict(float)\n for k,v in vec.items():\n x[k] = float(v)/tot\n vec = Counter(x)\n vector_means.append(vec)\n return vector_means","def calc_accuracy(truth, guess, n):\r\n if len(truth) == len(guess):\r\n guess = _reorder_clusters(truth, guess, intersect_allowed=False)\r\n\r\n num_correct = 0\r\n for i in range(len(truth)):\r\n num_correct += len(truth[i].intersection(guess[i]))\r\n return 100.0 * (num_correct/n)\r\n return 0.0"],"string":"[\n \"def dissimilarity(clusters):\\n totDist = 0\\n for c in clusters:\\n totDist += c.variability()\\n return totDist\",\n \"def purity_score(clusters, classes):\\n clusters = np.array(clusters)\\n classes = np.array(classes)\\n A = np.c_[(clusters,classes)]\\n\\n n_accurate = 0.\\n\\n for j in np.unique(A[:,0]):\\n z = A[A[:,0] == j, 1]\\n x = np.argmax(np.bincount(z))\\n n_accurate += len(z[z == x])\\n\\n return n_accurate / A.shape[0]\",\n \"def analysis_function_num_clusters_to_percent(self,clustering,percent):\\n return clustering.number_of_clusters_to_get_percent(percent)\",\n \"def purity(clusters, classes):\\n\\n d = defaultdict(list)\\n\\n # Get a list of class numbers of all examples in a cluster.\\n for k, v in zip(clusters, classes):\\n d[k].append(v)\\n\\n mayority = 0\\n\\n # Count the mayority class number and add it up over all clusters.\\n for k in d:\\n mayority += Counter(d[k]).most_common(1)[0][1]\\n\\n return float(mayority) / len(clusters)\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def test_clusters(trained_data, centroids):\\n\\n for c in range(len(centroids)):\\n count_1 = 0\\n count_0 = 0\\n for p in range(len(trained_data)):\\n if trained_data[p][-2] == 0 and trained_data[p][-1] == centroids[c]:\\n count_0 += 1\\n if trained_data[p][-2] == 1 and trained_data[p][-1] == centroids[c]:\\n count_1 += 1\\n print (\\\"Centroid \\\", c+1, \\\":\\\", centroids[c])\\n print(\\\"Number of 1's: \\\", count_1)\\n print(\\\"Number of 0's: \\\", count_0)\\n print(\\\"Percent 1's: \\\", round((count_1/(count_1 + count_0))*100,2))\\n print(\\\"Percent 0's: \\\", round((count_0 / (count_1 + count_0)) * 100,2))\\n print(\\\"****************\\\")\",\n \"def _calculate_cluster_measures(\\n arr4d,\\n threshold,\\n bin_struct,\\n two_sided_test=False,\\n):\\n n_regressors = arr4d.shape[3]\\n\\n max_sizes = np.zeros(n_regressors, int)\\n max_masses = np.zeros(n_regressors, float)\\n\\n for i_regressor in range(n_regressors):\\n arr3d = arr4d[..., i_regressor].copy()\\n\\n if two_sided_test:\\n arr3d[np.abs(arr3d) <= threshold] = 0\\n else:\\n arr3d[arr3d <= threshold] = 0\\n\\n labeled_arr3d, _ = label(arr3d > 0, bin_struct)\\n\\n if two_sided_test:\\n # Label positive and negative clusters separately\\n n_positive_clusters = np.max(labeled_arr3d)\\n temp_labeled_arr3d, _ = label(\\n arr3d < 0,\\n bin_struct,\\n )\\n temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters\\n labeled_arr3d = labeled_arr3d + temp_labeled_arr3d\\n del temp_labeled_arr3d\\n\\n clust_vals, clust_sizes = np.unique(labeled_arr3d, return_counts=True)\\n assert clust_vals[0] == 0\\n\\n clust_vals = clust_vals[1:] # First cluster is zeros in matrix\\n clust_sizes = clust_sizes[1:]\\n\\n # Cluster mass-based inference\\n max_mass = 0\\n for unique_val in clust_vals:\\n ss_vals = np.abs(arr3d[labeled_arr3d == unique_val]) - threshold\\n max_mass = np.maximum(max_mass, np.sum(ss_vals))\\n\\n # Cluster size-based inference\\n max_size = 0\\n if clust_sizes.size:\\n max_size = np.max(clust_sizes)\\n\\n max_sizes[i_regressor], max_masses[i_regressor] = max_size, max_mass\\n\\n return max_sizes, max_masses\",\n \"def get_clust_num_perc(model, vis_perc=0.9):\\n\\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\\n\\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\\n\\n\\ttot = model.allocmodel.Nk[idx].sum()\\n\\tcursum = 0\\n\\ti = 0\\n\\twhile cursum < tot*vis_perc:\\n\\t cursum += model.allocmodel.Nk[idx][i]\\n\\t i+=1\\n\\n\\treturn i\",\n \"def compute_cluster_class_fractions(k_means_model, y):\\n\\n n_classes = y.shape[1]\\n class_labels = utils.one_hot_to_index(y)\\n cluster_labels = k_means_model.labels_\\n\\n class_clustroid_counts = np.zeros((n_classes, K))\\n for i in range(len(class_labels)):\\n class_clustroid_counts[class_labels[i], cluster_labels[i]] += 1\\n\\n class_clustroid_fractions = class_clustroid_counts / np.sum(class_clustroid_counts, axis=1).reshape(n_classes, 1)\\n\\n print(\\\"\\\\n---- Class Clustroid Distribution ----\\\")\\n for i in range(n_classes):\\n print(\\\"Class {}: {}\\\".format(i, class_clustroid_fractions[i, :]))\",\n \"def calculate_cluster_size(result, var):\\n \\n cluster_results=pd.DataFrame(result[var].value_counts())\\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\\\"ratio\\\"})\\n return cluster_results.join(ratio)\",\n \"def clust_strength(mat,groups):\\n cluster_strengths = []\\n for group in range(len(np.unique(groups))):\\n this_cluster = mat[groups==group,:]\\n this_cluster_mean = np.mean(this_cluster,axis=0)\\n all_dists = mat - this_cluster_mean\\n out_dists = np.linalg.norm(all_dists[groups!=group],axis=1)\\n in_dists = np.linalg.norm(all_dists[groups==group],axis=1)\\n this_strength = np.mean(out_dists)/np.mean(in_dists)\\n cluster_strengths.append(this_strength)\\n \\n return np.mean(cluster_strengths)\",\n \"def clusterAlgorithm(values):\\n clusterMap = dict()\\n for value in values:\\n if value[2] not in clusterMap.keys():\\n clusterMap[value[2]] = []\\n clusterMap[value[2]].append(value)\\n frequency = [float(len(clusterMap[value[2]])) for value in values]\\n total = sum(frequency)\\n weightValues = [freq / total for freq in frequency]\\n print sum(weightValues)\\n lightValues = [value[1] for value in values]\\n return np.average(lightValues, weights = weightValues)\",\n \"def get_susceptibility(clusters):\\n\\n # If there is no or only one cluster then there is no finite cluster\\n if len(clusters) <= 1:\\n return np.nan\\n\\n # Remove largest, i.e. infinite, cluster\\n clusters.remove(max(clusters))\\n\\n sizes = np.array(list(set(clusters)))\\n n_s = []\\n\\n for size in sizes:\\n n_s.append(clusters.count(size))\\n\\n temp = sizes * n_s\\n S = np.sum(sizes * temp) / np.sum(temp)\\n\\n return S\",\n \"def clusterSize(l, scheme, clustertype='fluid'):\\n clist = findClusters(l, scheme, clustertype)\\n \\n avglists=[]\\n for i in clist:\\n avglist=[]\\n for l in i:\\n avglist.append(np.mean(l))\\n avglists.append(np.mean(avglist))\\n return avglists\",\n \"def assign_labels_to_centroids(self):\\n labelled_centroids = []\\n for i in range(len(self.final_clusters)):\\n labels = map(lambda x: x[0], self.final_clusters[i])\\n # pick the most common label\\n most_common = Counter(labels).most_common(1)[0][0]\\n c = np.round(len([item for item in self.final_clusters[i] if item[0]==1])/len(self.final_clusters[i]),2)\\n if c>=0.46:\\n most_common = 1.0\\n centroid = (most_common, self.final_centroids[i])\\n labelled_centroids.append(centroid)\\n\\n self.labelled_centroids = labelled_centroids\\n print(\\\"cluster_0: \\\", np.round(len([item for item in self.final_clusters[0] if item[0]==1])/len(self.final_clusters[0]),2), \\\"size_0: \\\", len(self.final_clusters[0]))\\n print(\\\"cluster_1: \\\", np.round(len([item for item in self.final_clusters[1] if item[0]==1])/len(self.final_clusters[1]),2), \\\"size_1: \\\", len(self.final_clusters[1]))\\n #print(\\\"cluster_2: \\\", np.round(len([item for item in self.final_clusters[2] if item[0]==1])/len(self.final_clusters[2]),2), \\\"size_2: \\\", len(self.final_clusters[2]))\\n #print(\\\"cluster_3: \\\", np.round(len([item for item in self.final_clusters[3] if item[0]==1])/len(self.final_clusters[3]),2), \\\"size_2: \\\", len(self.final_clusters[3]))\",\n \"def evaluateClusters( features, labels):\\r\\n\\r\\n\\treturn silhouette_score( features, labels)\",\n \"def compute_total_bipartiteness(hypergraph, clusters):\\n bipartiteness_sum = 0\\n for i in range(len(clusters)):\\n for j in range(i + 1, len(clusters)):\\n bipartiteness_sum += hypergraph_bipartiteness(hypergraph, clusters[i], clusters[j])\\n return bipartiteness_sum\",\n \"def davies_bouldin_score(self):\\r\\n print(colored(\\\"The davies bouldin score of the clustering is %0.002f\\\\n\\\" %(davies_bouldin_score(self.X, self.labels)),color = 'red', attrs=['bold']))\\r\\n print()\\r\\n print(colored(\\\"The points in each cluster are : \\\",color = 'yellow', attrs=['bold']))\\r\\n print(collections.Counter(self.labels))\",\n \"def get_optimal_clusters(cell,threshold=140):\\n\\n\\t#\\tTurn image to numpy array\\n\\tpic = image_to_matrix(cell)\\n\\n\\t#\\tGet the array of coordinates of dark dots\\n\\tdots = get_threshold_dots(pic,threshold)\\n\\n\\tscores = []\\n\\n\\tfor n_clusters in range(1,10):\\n\\t\\tclusters = kmeans.kmeans(pic,pic.shape[0],pic.shape[1],50,n_clusters,threshold)\\n\\t\\tprint clusters\\n\\n\\t\\tsquare_sum_array = [0]*n_clusters\\n\\t\\tcount_array = [0]*n_clusters\\n\\n\\t\\tfor dot in dots:\\n\\t\\t\\tdistance_array = [kmeans.euclid_distance(dot,cluster) for cluster in clusters]\\n\\t\\t\\tmin_index = distance_array.index(min(distance_array))\\n\\t\\t\\tsquare_sum_array[min_index] += kmeans.euclid_distance(clusters[min_index],dot)\\n\\t\\t\\tcount_array[min_index] += 1\\n\\n\\t\\tvariances = [square_sum/(count+0.001) for square_sum, count in zip(square_sum_array,count_array)]\\n\\n\\t\\tprint variances\\n\\t\\tscores.append(sum(variances)/len(variances))\\n\\n\\treturn scores\",\n \"def diversion_score(X, offspring_list):\\r\\n similarity_sum = 0\\r\\n if len(offspring_list[0]) == 2:\\r\\n offspring_list = [(parent_a, offspring, parent_a) for (parent_a, offspring) in offspring_list]\\r\\n for (parent_a, offspring, parent_b) in offspring_list:\\r\\n similarity_sum += max(icc(parent_a, offspring), icc(parent_b, offspring))\\r\\n return (1 - (((similarity_sum / len(offspring_list)) + 1) / 2)) * 100 # move from [-1,1] to [0,2], then to [0,1], then inverse, finally move to [0,100]\\r\",\n \"def ds_ratio(group):\\n nix_count = (group=='nix').sum()\\n top_count = (group=='top').sum()\\n ratio = nix_count/(nix_count+top_count) #could smooth this\\n return ratio\",\n \"def percenter(rank, max_rank):\\n\\treturn 100 * (rank/(max_rank or 1))\",\n \"def analysis_function_top_percent(self,clustering):\\n clustering.sort_clusters_by_size()\\n return clustering.get_population_percent_of_cluster(0)\",\n \"def calculate_priors(trainingLabels):\\r\\n sum = 0\\r\\n priors = {}\\r\\n totalSamples = len(trainingLabels)\\r\\n classes = set(trainingLabels)\\r\\n for cls in classes:\\r\\n numCls = len(filter(lambda x: x == cls, trainingLabels))\\r\\n sum += numCls\\r\\n priors[cls] = float(numCls) / float(totalSamples)\\r\\n \\r\\n # Sanity check: valid partitioning\\r\\n assert(sum == totalSamples)\\r\\n\\r\\n return priors\",\n \"def cluster_cal(self):\\n self.Cluster = []\\n for i in range(self.nodenum):\\n neighborhood_node = self.neighbor_node(i)\\n Node_num = len(neighborhood_node)\\n Count = self.neighbor_edge(neighborhood_node)\\n if(Node_num == 0 or Node_num == 1):\\n self.Cluster.append(0.5)\\n else:\\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\\n \\n self.cluster_coeff = np.average(self.Cluster)\",\n \"def get_cluster_to_split(clusters):\\n\\treturn max(clusters.items(), key=lambda x: x[1].get_distortion())[1]\",\n \"def avg_sil_width(cluster_vec, centroid):\\n tot_dis = 0\\n for i in xrange(len(cluster_vec)):\\n cos_dis = cos_sim(cluster_vec[i], centroid)\\n tot_dis = tot_dis + cos_dis\\n return tot_dis/len(cluster_vec)\",\n \"def contains_percentage_of(self, other: 'Interval') -> float:\\n if other.length == 0:\\n return other.a in self\\n intersection = Interval.intersection([self, other])\\n return intersection.length / other.length if intersection else 0.0\",\n \"def cluster_mcc_ratio(result, cluster_names, var, n=5):\\n rel1=get_cluster_country_distr(result, var)\\n clusters=calculate_cluster_size(result, var)\\n hours=hours_tusc(result, var)\\n res=\\\"\\\"\\n for i in zip(clusters.index, cluster_names[:len(clusters)]):\\n res=res+f\\\"By the number of unique visitors the {i[1]} cluster's top 5 countries are; \\\"\\n rel=rel1.sort_values(i[0],ascending=False)[:n]\\n for j in range(0,5):\\n if j!=n-1:\\n res=res+f'{rel[i[0]].index[j]} ({rel[i[0]][j]}%), '\\n else:\\n res=res+f'and {rel[i[0]].index[j]} ({rel[i[0]][j]}%). '\\n res=res+f'This cluster spends on average {int(hours.hrs_in_tusc[i[0]])} days in Tuscany, '\\n res=res+get_places_at_least4_hours(result, i[0], var)\\n res=res+ cluster_airport_result(result, i[0], var)\\n return res\",\n \"def compute_clusters(self, p: float):\\n pass\",\n \"def get_utilization(self):\\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\\n # Remove overlapping prefixes from list of children\\n networks = cidr_merge([c.prefix for c in child_prefixes])\\n children_size = float(0)\\n for p in networks:\\n children_size += p.size\\n return int(children_size / self.prefix.size * 100)\",\n \"def calculate_cost(self, medoids, clusters):\\n cost = 0.0\\n for i in range(0, len(medoids)):\\n for j in range(0, len(clusters[i])):\\n cost += distance.sqeuclidean(medoids[i], clusters[i][j])\\n return cost\\n pass\",\n \"def average_consensus(self, cluster):\\n\\t\\tcenterk = 0\\n\\t\\tindex = 0\\n\\t\\tfor value in cluster:\\n\\t\\t\\tcenterk += value\\n\\t\\t\\tindex += 1\\n\\t\\tcenterk = centerk / index\\n\\t\\treturn centerk\",\n \"def analysis_function_total_elements(self,clustering):\\n return clustering.total_number_of_elements\",\n \"def calculate_sample_silhouette(self):\\n sum_samples = 0\\n for cluster in self.cluster_lst:\\n sum_samples += self.sum_silhouette(cluster)\\n sample_size = len(self.samples)\\n return sum_samples/sample_size\",\n \"def get_modularity(adjacency, clusters):\\n total_weight = np.sum(adjacency)\\n e = get_clusters_adjacencies(adjacency, clusters)\\n e = e / total_weight\\n a = np.sum(e, axis=1)\\n return np.sum(e.diagonal() - np.power(a, 2))\",\n \"def _assign_clusters(self):\\n\\n dist = np.zeros((self.k, ))\\n distortion = 0\\n\\n for index in range(0, self.data.shape[0]):\\n for i in range(0, self.k):\\n dist[i] = np.linalg.norm(self.data[index] - self.centroids[i])\\n\\n self.assigned_clusters[index] = np.argmin(dist)\\n distortion += np.min(dist)\\n\\n return distortion\",\n \"def get_modularity3(adjacency, clusters):\\n\\n rows, cols = adjacency.shape\\n num_ids = adjacency.shape[0]\\n id_to_cluster = get_id_to_cluster(clusters, num_ids)\\n degrees = np.sum(adjacency, axis=1)\\n total_weight = np.sum(adjacency)\\n sum = 0\\n for i in range(rows):\\n for j in range(cols):\\n if id_to_cluster[i] == id_to_cluster[j]:\\n sum += adjacency[i, j] - (degrees[i] * degrees[j]) / total_weight\\n sum = sum / total_weight\\n return sum\",\n \"def compute_variance(\\n data, cluster_labels, centroids, alpha=10, debug=False, num_class=None\\n):\\n\\n k = len(centroids) if num_class is None else num_class\\n phis = torch.zeros(k)\\n for c in range(k):\\n cluster_points = data[cluster_labels == c]\\n c_len = len(cluster_points)\\n if c_len == 0:\\n phis[c] = -1\\n elif c_len == 1:\\n phis[c] = 0.05\\n else:\\n phis[c] = torch.sum(torch.norm(cluster_points - centroids[c], dim=1)) / (\\n c_len * np.log(c_len + alpha)\\n )\\n if phis[c] < 0.05:\\n phis[c] = 0.05\\n\\n if debug:\\n print(\\\"size-phi:\\\", end=\\\" \\\")\\n for i in range(k):\\n size = (cluster_labels == i).sum().item()\\n print(f\\\"{size}[phi={phis[i].item():.3f}]\\\", end=\\\", \\\")\\n print(\\\"\\\\n\\\")\\n\\n return phis\",\n \"def cluster_membership_occupancy(data):\\n \\n \\n \\n n_clusters = len(set(data['clusters'])-{-1}) # since -1 element denotes noice\\n\\n if n_clusters == 0:\\n membership=[Cluster_Membership_Features()]\\n membership = pd.DataFrame([o.__dict__ for o in membership])\\n areas=[Cluster_Area_Features()]\\n areas = pd.DataFrame([o.__dict__ for o in areas])\\n density=[Cluster_Density_Features()]\\n density = pd.DataFrame([o.__dict__ for o in density])\\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\\n density], axis=1)\\n \\n elif n_clusters ==1:\\n #obtain_total_cluster_areas_set_everything_else_to_default\\n membership=[Cluster_Membership_Features()]\\n membership = pd.DataFrame([o.__dict__ for o in membership])\\n d = dict(tuple(data.groupby('clusters')))\\n d.pop(-1, None)\\n \\n try:\\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\\n except:\\n cluster_chull_areas=[0,0,0]\\n \\n Total_cluster_area=np.sum(cluster_chull_areas)\\n areas=[Cluster_Area_Features([Total_cluster_area,0,0,0,0,0,0,0,0])]\\n areas = pd.DataFrame([o.__dict__ for o in areas])\\n density=[Cluster_Density_Features()]\\n density = pd.DataFrame([o.__dict__ for o in density])\\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\\n density], axis=1)\\n \\n elif n_clusters >1:\\n #Summarizing the cluster membership distribution characteristics\\n cluster_size_nums=np.delete(np.array(data.groupby(['clusters']).size()),0)\\n (cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD)= distribution_statistics(cluster_size_nums)\\n\\n #For each cluster calculate the area by calculating the area of the convex hull of cluster members\\n # Note: concavehull implementation here might be a good addition as it will provide more imformative values. \\n\\n d = dict(tuple(data.groupby('clusters')))\\n d.pop(-1, None)\\n try:\\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\\n except:\\n cluster_chull_areas=[0,0,0,0,0]\\n \\n\\n (avg_cluster_area,min_cluster_area,max_cluster_area,\\n std_cluster_area,CV_cluster_area,CD_cluster_area,\\n IQR_cluster_area,Quartile_CD_cluster_area)= distribution_statistics(cluster_chull_areas)\\n Total_cluster_area=np.sum(cluster_chull_areas)\\n\\n #Calculate cluster density: number of nuclei/ convex area of cluster\\n cluster_density=np.divide(cluster_size_nums,cluster_chull_areas)\\n (avg_cluster_density,min_cluster_density,max_cluster_density,\\n std_cluster_density,CV_cluster_density,CD_cluster_density,\\n IQR_cluster_density,Quartile_CD_cluster_density)= distribution_statistics(cluster_density)\\n\\n #return dataframe of features\\n membership=[Cluster_Membership_Features([cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD])]\\n membership = pd.DataFrame([o.__dict__ for o in membership])\\n areas=[Cluster_Area_Features([Total_cluster_area,\\n avg_cluster_area,min_cluster_area,max_cluster_area,\\n std_cluster_area,CV_cluster_area,CD_cluster_area,\\n IQR_cluster_area,Quartile_CD_cluster_area])]\\n areas = pd.DataFrame([o.__dict__ for o in areas])\\n density=[Cluster_Density_Features([avg_cluster_density,min_cluster_density,max_cluster_density,\\n std_cluster_density,CV_cluster_density,CD_cluster_density,\\n IQR_cluster_density,Quartile_CD_cluster_density])]\\n density = pd.DataFrame([o.__dict__ for o in density])\\n\\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\\n density], axis=1)\\n return all_features\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def calc_priors(categories, data):\\n counts = np.zeros(categories)\\n for val in range(categories):\\n counts[val] = np.count_nonzero(data.labels == val)\\n return counts / len(data.labels)\",\n \"def calc_Nw(cluster_labels):\\n\\n cluster_labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Nw = []\\n for label in labels_set:\\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\\n Nw.append(n_cluster_pairs)\\n\\n return int(np.sum(Nw))\",\n \"def test_determine_k(self):\\n test_dir_name = os.path.dirname(__file__)\\n feat_array_fn = os.path.join(\\n test_dir_name, \\\"data\\\", \\\"four_clusters.csv\\\")\\n df = pd.read_csv(feat_array_fn)\\n feat_array = df[[\\\"x\\\", \\\"y\\\"]].values\\n\\n clusterer = Clusterer(feat_array_fn, \\\"/dev/null\\\", [])\\n best_k = clusterer._determine_k(feat_array, 9)\\n\\n self.assertEqual(best_k, 4)\\n\\n feat_array_fn = os.path.join(\\n test_dir_name, \\\"data\\\", \\\"iris.csv\\\")\\n df = pd.read_csv(feat_array_fn)\\n feat_array = df[[\\n \\\"Sepal.Length\\\", \\\"Sepal.Width\\\", \\\"Petal.Length\\\",\\n \\\"Petal.Width\\\"]].values\\n\\n clusterer = Clusterer(feat_array_fn, \\\"/dev/null\\\", [])\\n best_k = clusterer._determine_k(feat_array, 9)\\n\\n self.assertEqual(best_k, 2)\",\n \"def group_size_dist(self):\\n return self.group_sizes() / self.group_sizes().sum()\",\n \"def purity_score(label, pred):\\n \\n df = pd.concat([label, pd.DataFrame(pred)], axis=1)\\n df.set_axis(['label', 'pred'], axis=1, inplace=True)\\n \\n s = 0\\n\\n for x, cluster in df.groupby('pred'):\\n s += cluster['label'].value_counts().iloc[0] # adding the most occuring class in a cluster\\n\\n return s / label.shape[0]\",\n \"def d50(clones, num_Reads): \\n\\n\\n d50_amount = num_Reads/2\\n read_count=0\\n for i in clones:\\n read_count+=clones[i].num_reads\\n if read_count>=d50_amount:\\n return i/float(len(clones))\",\n \"def fraction_mislabeled_nodes(labels, labels_pred):\\n G1 = partition_indicator(labels)\\n G2 = partition_indicator(labels_pred)\\n\\n # cost is minimized, overlap maximized\\n cost_matrix = -G1.T.dot(G2)\\n row_ind, col_ind = linear_sum_assignment(cost_matrix.A)\\n cost = -cost_matrix[row_ind, col_ind].sum()\\n\\n return 1 - (cost / len(labels))\",\n \"def calculate_enrichment_factor_optimal(molecules, ranked_dataset_percentage_cutoff, pic50_cutoff):\\n\\n ratio = sum(molecules[\\\"pIC50\\\"] >= pic50_cutoff) / len(molecules) * 100\\n if ranked_dataset_percentage_cutoff <= ratio:\\n enrichment_factor_optimal = round(100 / ratio * ranked_dataset_percentage_cutoff, 1)\\n else:\\n enrichment_factor_optimal = 100.0\\n return enrichment_factor_optimal\",\n \"def leaf_nodes_to_search_percent(self) -> Optional[int]:\\n return pulumi.get(self, \\\"leaf_nodes_to_search_percent\\\")\",\n \"def _calculate_cluster_probs(self, dist_mat, temperature):\\n dist_mat = np.square(dist_mat) # euclidean distance -> squared euclidean distance\\n n_samples = dist_mat.shape[0]\\n n_clusters = dist_mat.shape[1]\\n cluster_probs = np.zeros(shape=(n_samples, n_clusters))\\n for i in range(n_clusters):\\n for j in range(n_samples):\\n normalizer = sum(self.marginal_probs[k] * np.exp(-dist_mat[j, k] / temperature) for k in range(n_clusters))\\n cluster_probs[j, i] = self.marginal_probs[i] * np.exp(-dist_mat[j, i] / temperature) / normalizer\\n return cluster_probs\",\n \"def berger_parker_d(counts):\\n return counts.max()/float(counts.sum())\",\n \"def solidity(cnt):\\n\\tarea = cv2.contourArea(cnt)\\n\\thull = cv2.convexHull(cnt)\\n\\thull_area = cv2.contourArea(hull)\\n\\treturn float(area) / hull_area\",\n \"def find_avg(centroids, short_cut=False, sim_scores=None):\\n \\n total_sim = 0.0\\n total_comparisons = 0\\n \\n if short_cut:\\n total_comparisons = len(sim_scores)\\n \\n for score in sim_scores:\\n total_sim += score\\n \\n return (total_sim / total_comparisons)\\n\\n length = len(centroids)\\n\\n for i in xrange(0, length):\\n for j in xrange(i + 1, length):\\n total_sim += similarity(centroids[i], centroids[j])\\n total_comparisons += 1\\n\\n return (total_sim / total_comparisons)\",\n \"def evaluate_clusters(self, cluster_formulas, value='weighted_sum'):\\n num_elems = len(self.labels)\\n total_val = {}\\n num_cl = len(cluster_formulas)\\n clustered_points_num = 0\\n print(\\\"\\\\n\\\\n\\\")\\n print(\\\"Sufficiently big clusters: {}\\\".format(num_cl))\\n for c, formula, val in cluster_formulas:\\n c_size = len([l for l in self.labels if l == c])\\n clustered_points_num += c_size\\n\\n if value == 'weighted_sum':\\n total_val[c] = val * c_size / num_elems\\n elif value == 'sum':\\n total_val[c] = val * 1\\n\\n clust_val = sum(total_val.values())\\n self.clustering_value = total_val\\n print(\\\"Value of clustering: {}\\\".format(clust_val))\\n return clust_val\",\n \"def calculate_cluster_fitness(self, cluster_id: ObjectId):\\n\\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\\n\\n cluster_fitness = 0\\n\\n for genome in genomes:\\n cluster_fitness += genome.fitness\\n if cluster_fitness == 0:\\n return 0\\n\\n return cluster_fitness / len(list(genomes))\",\n \"def element_size(self):\\n vecs = (\\n self.nodes[self.elements[:, :4], :][:, 1:, :]\\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\\n )\\n return np.abs(np.linalg.det(vecs)) / 6\",\n \"def calculate_cluster_silhouette(self, cluster):\\n cluster_size = len(cluster.get_samples())\\n return self.sum_silhouette(cluster) / cluster_size\",\n \"def clustering_coefficient(graph):\\r\\n count = 0\\r\\n sumOfClusteringCoefficients = 0\\r\\n for vertex in graph:\\r\\n count += 1\\r\\n sumOfClusteringCoefficients += local_clustering_coefficient(graph, vertex)\\r\\n return sumOfClusteringCoefficients / count\",\n \"def _cluster_hitprobability(self, x, y):\\n hm_count = np.zeros_like(y).astype(float)\\n hm = np.zeros_like(y).astype(float)\\n #skf = StratifiedShuffleSplit(n_splits=self.n_iter, test_size=self.shuffle_test_split, random_state=self.random_state)\\n\\n ind = self._cluster(x, 35)\\n\\n for cluster_id in np.unique(ind):\\n test = np.argwhere(ind == cluster_id)[:, 0]\\n train = np.argwhere(ind != cluster_id)[:, 0]\\n #print test\\n self.basemodel.fit(x[train, :], y[train], hyperparams_optim=False)\\n hm_count[test] += 1.\\n hm[test] += (self.basemodel.predict(x[test, :]) == y[test]).astype(float)\\n\\n proba = hm / hm_count\\n if self.verbose:\\n # print('H/M count:')\\n # print(hm_count)\\n print('Proba:')\\n print(proba)\\n self.basemodel.fit(x, y, hyperparams_optim=False)\\n return proba\",\n \"def clustering_factor(self):\\n return self.unpack_dword(0x2C)\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def estimate_label_proportion(source_loader,target_loader,feat_extract,cuda,n_clusters,cluster_param): \\n feat_extract.eval()\\n #n_clusters = 3\\n from sklearn.cluster import AgglomerativeClustering\\n \\n \\n X_s,y_s = extract_feature(source_loader,feat_extract,cuda) \\n X_t,y_t = extract_feature(target_loader,feat_extract,cuda) \\n \\n \\n \\n cluster = AgglomerativeClustering(n_clusters=n_clusters,linkage=cluster_param)\\n label_t = cluster.fit_predict(X_t)\\n #print(np.unique(label_t))\\n mean_mat_S, num_in_class_S = extract_prototypes(X_s,y_s,n_clusters)\\n mean_mat_T, num_in_class_T = extract_prototypes(X_t,label_t,n_clusters)\\n \\n \\\"\\\"\\\"\\n We assume that prototypes of classes have been transported in some in the feature\\n space \\n \\\"\\\"\\\"\\n \\n import ot\\n M = ot.dist(mean_mat_S, mean_mat_T)\\n M /= M.max()\\n \\n n_1 = n_clusters\\n a = np.ones((n_1,)) / n_1\\n b = np.ones((n_1,)) / n_1\\n \\n \\n gamma = ot.emd(a,b,M)\\n nb_sample_S = [ np.sum(y_s==i) for i in range(n_clusters) ]\\n proportion_T = num_in_class_T/np.sum(num_in_class_T)\\n assignement_source_to_target = gamma.argmax(axis=1)\\n \\n # proportions are arranged directly per class\\n proportion_T = proportion_T[assignement_source_to_target]\\n print(proportion_T,assignement_source_to_target)\\n \\n\\n return proportion_T,nb_sample_S, assignement_source_to_target\",\n \"def heuristic_3_partition(game, player) -> float:\\n\\n partition_possible_factor = get_partition_possible_factor(game, player)\\n\\n return float(partition_possible_factor)\",\n \"def fitness(individual, n_clusters=3, n_seeds=5):\\n\\n dataframe = common.scale_dataframe(individual)\\n corr = abs(individual.dataframe.corr().iloc[0, 1])\\n differences = []\\n for seed in range(n_seeds):\\n km = KMeans(n_clusters, random_state=seed).fit(dataframe)\\n differences.append(silhouette_score(dataframe, km.labels_) - corr)\\n\\n best = max(differences)\\n return best\",\n \"def percentage_hapaxes(corpus_parts, corpus):\\n percentage_h = []\\n count = 0\\n dv = divide_corpus(corpus, 10)\\n hapax_parts = hapaxes_parts(corpus_parts)\\n for x in hapax_parts:\\n percentage_h.append(percentage(x, len(dv[count])))\\n count += 1\\n return percentage_h\",\n \"def compute_clusters(self, documents):\\n ###TODO\\n for d in range(0, len(documents)):\\n maxi = 999999999\\n for cid in range(0, len(self.means)):\\n dist = self.distance(documents[d], self.means[cid], self.norms[cid])\\n if dist < maxi:\\n maxi = dist\\n clust = cid \\n self.cluster[d] = clust\",\n \"def __bayesian_information_criterion(data, clusters, centers):\\n\\n scores = [0.0] * len(clusters) # splitting criterion\\n dimension = len(data[0])\\n\\n # estimation of the noise variance in the data set\\n sigma = 0.0\\n K = len(clusters)\\n N = 0.0\\n\\n for index_cluster in range(0, len(clusters)):\\n for index_object in clusters[index_cluster]:\\n sigma += (euclidean_distance(data[index_object], centers[index_cluster])) # It works\\n\\n N += len(clusters[index_cluster])\\n\\n if (N - K != 0):\\n sigma /= (N - K)\\n\\n # splitting criterion\\n for index_cluster in range(0, len(clusters)):\\n n = len(clusters[index_cluster])\\n\\n if (sigma > 0.0):\\n scores[index_cluster] = n * math.log(n) - n * math.log(N) - n * math.log(\\n 2.0 * np.pi) / 2.0 - n * dimension * math.log(sigma) / 2.0 - (n - K) / 2.0\\n\\n return sum(scores)\",\n \"def calc_sw(X, cluster_labels):\\n\\n labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Sw = []\\n for label in labels_set:\\n # Loop through each cluster and calculate within cluster distance\\n pairs = np.where(labels == label)\\n pairs_distance = pdist(X[pairs[0]])\\n within_cluster_distance = np.sum(pairs_distance, axis=0)\\n Sw.append(within_cluster_distance)\\n\\n return np.sum(Sw)\",\n \"def compute_distortion(cluster_list, data_table):\\n\\tdistortion = 0\\n\\tfor cluster in cluster_list:\\n\\t\\tdistortion += cluster.cluster_error(data_table)\\n\\treturn distortion\",\n \"def pie_chart_score(self, grouped):\\n picked_scenario = self.scenario_dict[\\\"%d\\\" % (self.scenario_num-1)]\\n distinct_enum_X = self.data_dict[picked_scenario[\\\"X\\\"]]['distinct_enum']\\n score = 0\\n if min(grouped) < 0:\\n score = 0\\n elif distinct_enum_X == 1:\\n score = 0\\n elif picked_scenario[\\\"Agg_func_Y\\\"] == \\\"avg\\\":\\n score = 0\\n elif distinct_enum_X >= 2 and distinct_enum_X <= 8:\\n score += self.calculate_entropy(self.data_dict[picked_scenario[\\\"Y\\\"]]) / 8\\n elif distinct_enum_X > 8:\\n score += 4 * (self.calculate_entropy(self.data_dict[picked_scenario[\\\"Y\\\"]])) / distinct_enum_X\\n if score > 3:\\n score = 3\\n return score\",\n \"def _distorted_distance(self):\\n distance = 0\\n for i, pixel in enumerate(self.training_set):\\n distance += self._euclid_distance(\\n pixel, self.clusters[self.labels[i]], axis=0)\\n return distance\",\n \"def classProbs(observation, tree, classes):\\n res = classify(observation, tree) #res = results\\n total = sum(res.values())\\n probs = []\\n for c in classes:\\n if c in res.keys():\\n probs.append(float(res[c])/total)\\n else:\\n probs.append(0)\\n return probs\",\n \"def hausd95(result, reference, voxelspacing=None, connectivity=1):\\n hd1 = __surface_distances(result, reference, voxelspacing, connectivity)\\n hd2 = __surface_distances(reference, result, voxelspacing, connectivity)\\n hd95 = np.percentile(np.hstack((hd1, hd2)), 95)\\n return hd95\",\n \"def score(self, dataset: List[Read]) -> int:\\n kmers_in_common = 0\\n for read in dataset:\\n for kmer in read.kmers(self.k):\\n if self.filter.contains(kmer):\\n kmers_in_common += 1\\n return self.filter.num_items_in_filter - kmers_in_common\",\n \"def _fraction_latency(self, users_distances):\\n\\n users_desired_latency = np.array(list(map(lambda a: self.services_desired_latency[a],\\n self.users_services)))\\n check = users_distances < users_desired_latency\\n fraction = np.count_nonzero(check==True) / self.num_of_users\\n return fraction\",\n \"def SquareClusteringCoefficient(graph):\\n coef = np.mean(list(nx.square_clustering(graph).values()))\\n return coef\",\n \"def percent_accuracy(self, test_set, predicted_values):\\r\\n\\r\\n correct = 0\\r\\n for i in range(len(test_set)):\\r\\n if test_set[i].classification == predicted_values[i]:\\r\\n correct += 1\\r\\n return correct / len(test_set)\",\n \"def __cluster_simi(self, i, j):\\n sum_ = 0.\\n for si in self.__indexclusters[i]:\\n for sj in self.__indexclusters[j]:\\n simi = self.__sample_simi(si, sj)\\n sum_ += simi\\n return sum_ / (len(self.__indexclusters[i]) * len(self.__indexclusters[j]))\",\n \"def occupation_distribution(data):\",\n \"def one_dimension_val_clutering(vals, max_distance=5):\\n vals = sorted(vals)\\n clusters = []\\n for (idx, i) in enumerate(vals):\\n cluster = [j for j in vals if abs(j - i) < max_distance]\\n clusters.append(cluster)\\n clusters = sorted(clusters, key=len, reverse=True)\\n cluster = clusters[0]\\n if len(cluster) / len(vals) > 0.6 or len(cluster) >= 3:\\n return cluster\\n else:\\n return []\",\n \"def check_correctness_statistics(classifier_out, mode, image_type):\\n labels = image_type.image_data[mode].labels\\n num_correct = 0\\n total = len(classifier_out)\\n for index, label in classifier_out:\\n if labels[index] == label:\\n num_correct += 1\\n return (num_correct / total) * 100\",\n \"def compute_distortion(cluster_list, data_table):\\r\\n distortion = 0\\r\\n \\r\\n for cluster in cluster_list:\\r\\n distortion += cluster.cluster_error(data_table)\\r\\n\\r\\n return distortion\",\n \"def get_proportion_of_unique_lemmas(self):\\n lemmas = self.blob.words.lemmatize()\\n return len(set(lemmas)) / float(len(self.blob.words))\",\n \"def FindScale(self):\\n\\n ## 6 and from the cv code the distance is 6 then we are good\\n print(\\\"TODO: Very hard\\\")\",\n \"def calculate_percent_match(primers,\\n seq_count,\\n exclude_seq_count=1):\\n # Calculate percent of sequences that are 'hit' by each primer\\n for n in range(len(primers)):\\n # Calculate percent perfect match\\n primers[n].percent_match=float(primers[n].match_count/seq_count)\\n primers[n].non_specific_percent=\\\\\\n float(primers[n].non_specific_hits/exclude_seq_count)\\n \\n return primers\",\n \"def analysis_function_num_clusters(self,clustering):\\n return len(clustering.clusters)\",\n \"def variance_ratio_criterion(cls, cluster_labels, pca_reduced, cluster_size, names: list):\\n score = metrics.calinski_harabaz_score(pca_reduced, cluster_labels)\\n return ClusterMetricScore('calinski harabaz', score, cluster_size, cluster_labels, names)\",\n \"def getBeliefDistribution(self):\\n # This essentially gives a point to a location for each particle there, then \\n # normalizes the point values so they add up to 1.\\n dist = util.Counter()\\n for part in self.particles: dist[part] += 1\\n dist.normalize()\\n return dist\",\n \"def good_clusters(block_path, cutoff=CLUSTER_ACCURACY_CUTOFF):\\n accuracies, cluster_accuracies = morphs.load.cluster_accuracies()\\n block_cluster_accuracies = cluster_accuracies[block_path]\\n return block_cluster_accuracies[\\n block_cluster_accuracies.accuracy > cutoff\\n ].index.values\",\n \"def calc_verts_median(verts):\\n return ft.reduce(operator.add, [v.co for v in verts]) / len(verts)\",\n \"def overlap_score(labels, labels_pred):\\n raw_overlap = 1-fraction_mislabeled_nodes(labels, labels_pred)\\n partition_true = np.array(labels).astype(int)\\n partition_pred = np.array(labels_pred).astype(int)\\n num_nodes = partition_pred.size\\n num_groups = partition_true.max() + 1\\n\\n chance_level = 0.\\n for i in range(num_groups):\\n temp = np.sum(i == partition_true) / num_nodes\\n if temp > chance_level:\\n chance_level = temp\\n\\n score = (raw_overlap - chance_level) / (1 - chance_level)\\n if score <= 0:\\n score = 0\\n\\n return score\",\n \"def get_modularity_other_c(A, cluster_indices):\\n # define the number of nodes in the graph and the number of clusters\\n n = len(cluster_indices)\\n nclusters = max(cluster_indices) + 1\\n # define the row sums of the adjacency matrix\\n row_sums = [sum(row) for row in A]\\n # define one half of the sum of all entries in the adjacency matrix\\n m = sum(row_sums) / 2.0\\n # define the modularity\\n Q = 0\\n for i in range(n):\\n for j in range(n):\\n if cluster_indices[i] == cluster_indices[j]:\\n Q += (A[i][j] - row_sums[i] * row_sums[j] / (2*m)) / (2*m)\\n return Q\",\n \"def get_percentile(data_list, score, kind='weak'):\\n n = len(data_list)\\n\\n if kind == 'strict':\\n return len([i for i in data_list if i < score]) / float(n) * 100\\n elif kind == 'weak':\\n return len([i for i in data_list if i <= score]) / float(n) * 100\\n elif kind == 'mean':\\n return (len([i for i in data_list if i < score]) + len([i for i in data_list if i <= score])) * 50 / float(n)\\n else:\\n raise ValueError(\\\"The kind kwarg must be 'strict', 'weak' or 'mean'. You can also opt to leave it out and rely on the default method.\\\")\",\n \"def cluster_partition_distance(individual, test_data, truth_data, name=None):\\r\\n distance_sum = 0\\r\\n max_sum = 0\\r\\n for test_clusters, truth_clusters in zip(test_data, truth_data):\\r\\n # Get last column of target data\\r\\n test_clusters = test_clusters[-1].flatten()\\r\\n\\r\\n p1_dict = {}\\r\\n for i, x in enumerate(test_clusters):\\r\\n if x not in p1_dict:\\r\\n p1_dict[x] = []\\r\\n p1_dict[x].append(i)\\r\\n\\r\\n p2_dict = {}\\r\\n for i, x in enumerate(truth_clusters):\\r\\n if x not in p2_dict:\\r\\n p2_dict[x] = []\\r\\n p2_dict[x].append(i)\\r\\n\\r\\n p1 = list(p1_dict.values())\\r\\n p2 = list(p2_dict.values())\\r\\n d = _fast_partition_distance(p1, p2, len(test_clusters))\\r\\n if d is None:\\r\\n d = _partition_distance(p1, p2, len(test_clusters))\\r\\n distance_sum += d\\r\\n max_sum += len(test_clusters) - 1\\r\\n return distance_sum / max_sum\",\n \"def generate_clusters(df):\\n\\n df_size = df.shape[0]\\n print(df_size)\\n n_clusters = 0\\n percent_min_pts = 0.105\\n min_clusters = 3\\n while (n_clusters != min_clusters):\\n print(\\\"percent_min_pts\\\", percent_min_pts)\\n min_cluster_pts = math.floor(df_size * percent_min_pts)\\n print(\\\"min_cluster_pts\\\", min_cluster_pts)\\n\\n clusterer = hdbscan.HDBSCAN(min_cluster_size=min_cluster_pts)\\n print(df.head())\\n clusterer.fit(df)\\n cluster_groups = {}\\n labels = clusterer.labels_\\n for i in labels:\\n if cluster_groups.get(i):\\n cluster_groups[i] = cluster_groups[i] + 1\\n else:\\n cluster_groups[i] = 1\\n print(\\\"cluster_groups\\\", cluster_groups)\\n n_clusters = len(set(labels))\\n print(\\\"n_clusters\\\", n_clusters)\\n multiplier = abs(n_clusters - min_clusters) * 0.001\\n print(\\\"multiplier\\\", multiplier)\\n if n_clusters > min_clusters:\\n percent_min_pts += multiplier\\n else:\\n percent_min_pts -= multiplier\\n print(\\\"percent_min_pts\\\", percent_min_pts)\\n return labels\",\n \"def avarage_for_group(data: Dict[int, int]) -> float:\\n values = data.values()\\n summary = sum(values)\\n return summary // len(data)\",\n \"def pct_bust(data):\\n return round((data[\\\"new_total\\\"] > 21).sum() / len(data), 3)\",\n \"def compute_means(self):\\n ###TODO\\n vector_means = []\\n for doc in self.fin_clust.values():\\n vec = defaultdict(float)\\n for d_id in doc:\\n doc_keys = self.docs[d_id].keys()\\n for key in self.docs[d_id]:\\n vec[key] = vec[key] + self.docs[d_id][key]\\n tot = len(doc)\\n x = defaultdict(float)\\n for k,v in vec.items():\\n x[k] = float(v)/tot\\n vec = Counter(x)\\n vector_means.append(vec)\\n return vector_means\",\n \"def calc_accuracy(truth, guess, n):\\r\\n if len(truth) == len(guess):\\r\\n guess = _reorder_clusters(truth, guess, intersect_allowed=False)\\r\\n\\r\\n num_correct = 0\\r\\n for i in range(len(truth)):\\r\\n num_correct += len(truth[i].intersection(guess[i]))\\r\\n return 100.0 * (num_correct/n)\\r\\n return 0.0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68239075","0.66668284","0.6481002","0.6474694","0.6268896","0.6198322","0.61978954","0.6153407","0.6145863","0.61173046","0.6088561","0.6067123","0.60514313","0.605035","0.60220045","0.6007003","0.59786797","0.5954374","0.5944898","0.591898","0.5904012","0.58934736","0.5888239","0.58542675","0.58466184","0.58107376","0.57912296","0.5785383","0.5767912","0.57631725","0.57616186","0.57545954","0.5744635","0.57412815","0.5735015","0.5714543","0.5708779","0.5708463","0.57064724","0.5692788","0.5682287","0.56805253","0.5661653","0.5643415","0.5630227","0.5623516","0.56189984","0.5607578","0.5595044","0.55923426","0.5582394","0.5580711","0.5579882","0.557808","0.5571734","0.556507","0.5545937","0.5536173","0.5532594","0.5531841","0.5530475","0.5513373","0.5511623","0.5507246","0.5507121","0.5496146","0.549497","0.5486816","0.5482418","0.5481227","0.5477074","0.54646385","0.5461252","0.5459336","0.5457425","0.5452532","0.5450086","0.543752","0.54371226","0.5436107","0.54346436","0.54304904","0.5428129","0.54216015","0.54107815","0.5406267","0.54025877","0.53959966","0.53912663","0.53885216","0.5383635","0.5382499","0.53818893","0.5375736","0.53704715","0.5369408","0.53573924","0.5354119","0.5350135","0.53449774"],"string":"[\n \"0.68239075\",\n \"0.66668284\",\n \"0.6481002\",\n \"0.6474694\",\n \"0.6268896\",\n \"0.6198322\",\n \"0.61978954\",\n \"0.6153407\",\n \"0.6145863\",\n \"0.61173046\",\n \"0.6088561\",\n \"0.6067123\",\n \"0.60514313\",\n \"0.605035\",\n \"0.60220045\",\n \"0.6007003\",\n \"0.59786797\",\n \"0.5954374\",\n \"0.5944898\",\n \"0.591898\",\n \"0.5904012\",\n \"0.58934736\",\n \"0.5888239\",\n \"0.58542675\",\n \"0.58466184\",\n \"0.58107376\",\n \"0.57912296\",\n \"0.5785383\",\n \"0.5767912\",\n \"0.57631725\",\n \"0.57616186\",\n \"0.57545954\",\n \"0.5744635\",\n \"0.57412815\",\n \"0.5735015\",\n \"0.5714543\",\n \"0.5708779\",\n \"0.5708463\",\n \"0.57064724\",\n \"0.5692788\",\n \"0.5682287\",\n \"0.56805253\",\n \"0.5661653\",\n \"0.5643415\",\n \"0.5630227\",\n \"0.5623516\",\n \"0.56189984\",\n \"0.5607578\",\n \"0.5595044\",\n \"0.55923426\",\n \"0.5582394\",\n \"0.5580711\",\n \"0.5579882\",\n \"0.557808\",\n \"0.5571734\",\n \"0.556507\",\n \"0.5545937\",\n \"0.5536173\",\n \"0.5532594\",\n \"0.5531841\",\n \"0.5530475\",\n \"0.5513373\",\n \"0.5511623\",\n \"0.5507246\",\n \"0.5507121\",\n \"0.5496146\",\n \"0.549497\",\n \"0.5486816\",\n \"0.5482418\",\n \"0.5481227\",\n \"0.5477074\",\n \"0.54646385\",\n \"0.5461252\",\n \"0.5459336\",\n \"0.5457425\",\n \"0.5452532\",\n \"0.5450086\",\n \"0.543752\",\n \"0.54371226\",\n \"0.5436107\",\n \"0.54346436\",\n \"0.54304904\",\n \"0.5428129\",\n \"0.54216015\",\n \"0.54107815\",\n \"0.5406267\",\n \"0.54025877\",\n \"0.53959966\",\n \"0.53912663\",\n \"0.53885216\",\n \"0.5383635\",\n \"0.5382499\",\n \"0.53818893\",\n \"0.5375736\",\n \"0.53704715\",\n \"0.5369408\",\n \"0.53573924\",\n \"0.5354119\",\n \"0.5350135\",\n \"0.53449774\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7220413"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":685,"cells":{"query":{"kind":"string","value":"Returns the maximum number of clusters needed to have a percent of the total number of clustered elements."},"document":{"kind":"string","value":"def analysis_function_num_clusters_to_percent(self,clustering,percent):\n return clustering.number_of_clusters_to_get_percent(percent)"},"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 cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def get_clust_num_perc(model, vis_perc=0.9):\n\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\n\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\n\n\ttot = model.allocmodel.Nk[idx].sum()\n\tcursum = 0\n\ti = 0\n\twhile cursum < tot*vis_perc:\n\t cursum += model.allocmodel.Nk[idx][i]\n\t i+=1\n\n\treturn i","def n_clusters(self):\n return len(self.clusters)","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def set_nb_clusters(self):\n \n print(\"Finding the optimal number of clusters...\")\n \n sample = ro.r.matrix(self.df[self.df[\"filename\"].between(1, 4)][\"active_power\"].to_numpy())\n \n r=ro.r(\"\"\"\n check = function(matrix) {\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\n\n n_clust = n_clust$data\n\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\n return(max_cluster)\n }\n \"\"\")\n\n result = r(sample)\n self.conf[\"nb_clust\"] = int(result[0])\n \n print(f\"Optimal number of clusters is {self.conf['nb_clust']}\\n\")","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())","def purity_score(clusters, classes):\n clusters = np.array(clusters)\n classes = np.array(classes)\n A = np.c_[(clusters,classes)]\n\n n_accurate = 0.\n\n for j in np.unique(A[:,0]):\n z = A[A[:,0] == j, 1]\n x = np.argmax(np.bincount(z))\n n_accurate += len(z[z == x])\n\n return n_accurate / A.shape[0]","def n_clusters(self):\n return self.model.n_clusters","def calculate_cluster_size(result, var):\n \n cluster_results=pd.DataFrame(result[var].value_counts())\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\"ratio\"})\n return cluster_results.join(ratio)","def analysis_function_top_4(self,clustering):\n clustering.sort_clusters_by_size()\n total = 0\n percents = clustering.get_population_percent_of_n_bigger_clusters(4)\n for p in percents:\n total = total+p\n return total","def max_individuals(self) -> int:\n return self.group_size.upper * self.groups_allowed","def compute_total_bipartiteness(hypergraph, clusters):\n bipartiteness_sum = 0\n for i in range(len(clusters)):\n for j in range(i + 1, len(clusters)):\n bipartiteness_sum += hypergraph_bipartiteness(hypergraph, clusters[i], clusters[j])\n return bipartiteness_sum","def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)","def total_max_node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"total_max_node_count\")","def getNbClusters( model):\r\n\r\n\tlabels = model.labels_\r\n\tlabelValues = []\r\n\tfor label in labels:\r\n\t\tif label not in labelValues and label != -1: labelValues.append(label)\r\n\tnbClusters = len( labelValues)\r\n\treturn nbClusters","def calc_Nw(cluster_labels):\n\n cluster_labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Nw = []\n for label in labels_set:\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\n Nw.append(n_cluster_pairs)\n\n return int(np.sum(Nw))","def max_node_count(self) -> int:\n return pulumi.get(self, \"max_node_count\")","def get_susceptibility(clusters):\n\n # If there is no or only one cluster then there is no finite cluster\n if len(clusters) <= 1:\n return np.nan\n\n # Remove largest, i.e. infinite, cluster\n clusters.remove(max(clusters))\n\n sizes = np.array(list(set(clusters)))\n n_s = []\n\n for size in sizes:\n n_s.append(clusters.count(size))\n\n temp = sizes * n_s\n S = np.sum(sizes * temp) / np.sum(temp)\n\n return S","def maxCluster(clusters):\n try:\n return clusters[np.argmax(map(len, clusters))]\n except:\n return None","def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements","def get_number_of_clusters(df, use_pca, n_components):\n n_clusters = 10\n cluster_with_distances = []\n for i in range(n_clusters):\n pipe = _build_model(df, use_pca, n_components, use_kmeans=True, n_clusters=i + 1)\n cluster_with_distances.append(pipe.named_steps['kmeans'].inertia_)\n plt.figure(6, figsize=(12, 6))\n plt.plot(range(1, 11), cluster_with_distances, 'o')\n plt.plot(range(1, 11), cluster_with_distances, '-', alpha=0.5)\n plt.title('The Elbow Criterion')\n plt.xlabel('number of cluster')\n plt.ylabel('Sum of squared distances of samples to their closest cluster center')\n plt.show()","def max_size(self):\n size = 1\n for idx in self.config.index_specs:\n size *= len(idx.distribution)\n return size","def get_total_n_cpu(self) -> int:","def get_nb_workers(ratio):\n return max(1, int(CPU_COUNT * ratio))","def maxContigLength(self):\n\t\tstats = self.scores()\n\t\treturn stats['largestContig']","def node_count_max(self) -> int:\n return int(self.graph_tuple_stats.node_count_max or 0)","def max_edges_in(i, cluster_bounds, insertion_factor):\n # for j in range(1, len(cluster_bounds)):\n # if(i < cluster_bounds[j] and i >= cluster_bounds[j-1]):\n # break\n bounds = get_cluster_bounds(i, cluster_bounds)\n #n_c = cluster_bounds[j] - cluster_bounds[j-1]\n n_c = bounds[1] - bounds[0]\n return np.int64(n_c * insertion_factor)","def num_links(self):\n count=0.0\n for cluster in self.clusters:\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\n numberofmembers=self.clusters[cluster].number_of_members\n count+=numberofmembers\n return count","def max_cardinality():\r\n #create a list containing the number of each vertex involvement.\r\n array = []\r\n for i in adj:\r\n array += [i[0],i[1]]\r\n\r\n #compute the degree by counting the involment\r\n degree = Counter(array).most_common()\r\n\r\n #retrieve the degree only\r\n degree_ = [ i[1] for i in degree]\r\n\r\n degree_ = np.array(degree_)\r\n \r\n max_m = None\r\n \r\n #check if m is valid\r\n for i in range(degree[0][1]+2)[2:]:\r\n \r\n #valid if there are at least m vertex with degree equals to at least m-1 \r\n if i < len(np.where(degree_>=i-1)[0]):\r\n max_m = i\r\n else:\r\n break\r\n max_m += 1\r\n print(f'maximum possible clique cardinality :{max_m}')\r\n return max_m","def percenter(rank, max_rank):\n\treturn 100 * (rank/(max_rank or 1))","def get_cluster_to_split(clusters):\n\treturn max(clusters.items(), key=lambda x: x[1].get_distortion())[1]","def dissimilarity(clusters):\n totDist = 0\n for c in clusters:\n totDist += c.variability()\n return totDist","def get_p_inf(clusters, shape):\n\n if len(clusters) == 0:\n return 0\n\n else:\n return max(clusters) / (shape[0] * shape[1])","def calculate_cluster_fitness(self, cluster_id: ObjectId):\n\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\n\n cluster_fitness = 0\n\n for genome in genomes:\n cluster_fitness += genome.fitness\n if cluster_fitness == 0:\n return 0\n\n return cluster_fitness / len(list(genomes))","def maximum_number_of_workers(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"maximum_number_of_workers\")","def get_largest_cluster(self) -> tuple:\n flat_cluster = self.cluster.reshape(-1)\n true_clusters = np.extract(flat_cluster > 0, flat_cluster)\n counts = np.bincount(true_clusters)\n largest = np.argmax(counts)\n size = counts[largest]\n return largest, size","def compute_cluster_class_fractions(k_means_model, y):\n\n n_classes = y.shape[1]\n class_labels = utils.one_hot_to_index(y)\n cluster_labels = k_means_model.labels_\n\n class_clustroid_counts = np.zeros((n_classes, K))\n for i in range(len(class_labels)):\n class_clustroid_counts[class_labels[i], cluster_labels[i]] += 1\n\n class_clustroid_fractions = class_clustroid_counts / np.sum(class_clustroid_counts, axis=1).reshape(n_classes, 1)\n\n print(\"\\n---- Class Clustroid Distribution ----\")\n for i in range(n_classes):\n print(\"Class {}: {}\".format(i, class_clustroid_fractions[i, :]))","def get_lcc_size(G,seed_nodes):\n\n # getting subgraph that only consists of the black_nodes\n g = nx.subgraph(G,list(seed_nodes))\n\n if g.number_of_nodes() != 0:\n # get all components \n max_CC = max(nx.connected_component_subgraphs(g), key=len)\n return len(max_CC.nodes()) # size of largest connected component\"\n\n else:\n return 0","def determine_jobs_per_pool(numpools, totaljobs):\n cluster = os.environ['CC_CLUSTER']\n if cluster in ['graham', 'beluga']:\n jobs_per_pool = math.floor(totaljobs / numpools)\n else:\n jobs_per_pool = totaljobs\n return jobs_per_pool","def purity(clusters, classes):\n\n d = defaultdict(list)\n\n # Get a list of class numbers of all examples in a cluster.\n for k, v in zip(clusters, classes):\n d[k].append(v)\n\n mayority = 0\n\n # Count the mayority class number and add it up over all clusters.\n for k in d:\n mayority += Counter(d[k]).most_common(1)[0][1]\n\n return float(mayority) / len(clusters)","def get_sectors_per_cluster(self):\n\n\t\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\n\t\tif sectors_per_cluster_base == 0:\n\t\t\traise BootSectorException('Invalid cluster size (zero)')\n\n\t\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\n\t\t\tsectors_per_cluster_real = sectors_per_cluster_base\n\t\telse:\n\t\t\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\n\t\t\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\n\n\t\treturn sectors_per_cluster_real","def get_typical_size(workers: List[List[int]]) -> int:\n size = 0\n for worker in workers:\n size = max([size,\n np.abs(worker[2]-worker[0]),\n np.abs(worker[3]-worker[1])])\n \n return size","def max_num_neighbors(self):\n return self._max_num_neighbors","def test_count_reads_per_cluster(self):\n \n bedtool = pybedtools.BedTool(clipper.test_file(\"clip_analysis_test_peak_results.bed\"))\n \n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\n \n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)","def find_count_divisor(this_list):\n max_found = this_list[0][1]\n count = 0\n\n while max_found/50 > 0:\n max_found -= 50\n count += 1\n\n return count","def get_total_collisions(self):\n return self.count_collisions","def largest_cc_size(ugraph):\n\tconnected = cc_visited(ugraph)\n\tmaxnum = 0\n\tfor content in connected:\n\t\tmaxnum = max(maxnum,len(content))\n\treturn maxnum","def group_size_dist(self):\n return self.group_sizes() / self.group_sizes().sum()","def clusterSize(l, scheme, clustertype='fluid'):\n clist = findClusters(l, scheme, clustertype)\n \n avglists=[]\n for i in clist:\n avglist=[]\n for l in i:\n avglist.append(np.mean(l))\n avglists.append(np.mean(avglist))\n return avglists","def analysis_function_top_percent(self,clustering):\n clustering.sort_clusters_by_size()\n return clustering.get_population_percent_of_cluster(0)","def get_utilization(self):\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\n # Remove overlapping prefixes from list of children\n networks = cidr_merge([c.prefix for c in child_prefixes])\n children_size = float(0)\n for p in networks:\n children_size += p.size\n return int(children_size / self.prefix.size * 100)","def maximumDominationCount(leaf):\n maximumDominationCount = np.nanmax(leaf.calDominationCount())\n return maximumDominationCount","def _get_max_gpu_processes(self):\n mem_usage = self._get_gpu_mem_usage()\n print('Mem Usage:', mem_usage)\n\n num_processes = int(1 / mem_usage)\n return num_processes","def largest_cc_size(ugraph):\n if not ugraph:\n return 0\n return max(len(cc) for cc in cc_visited(ugraph))","def node_size(graph):\n adj = nx.betweenness_centrality(graph)\n return np.array([x * 1e3 for x in adj.values()])","def get_modularity(adjacency, clusters):\n total_weight = np.sum(adjacency)\n e = get_clusters_adjacencies(adjacency, clusters)\n e = e / total_weight\n a = np.sum(e, axis=1)\n return np.sum(e.diagonal() - np.power(a, 2))","def __len__(self) -> int:\n\n length = self.n_classes * 100\n\n return length","def getMaxMancount(self):\n return self.__size * 20","def compute_optimal_block_maximum(block_counts) -> int:\n q1, q3 = compute_quartiles(block_counts)\n iqr = q3 - q1\n high_threshold = q3 + 1.5 * iqr\n return high_threshold","def clustering_factor(self):\n return self.unpack_dword(0x2C)","def maximum_number_of_workers(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"maximum_number_of_workers\")","def optimalBinSize(x):\n interquartile = np.diff(np.prctile(x, [25, 75]))\n return 2. * interquartile * len(x)**(-1./3)","def calc_dim(s):\n s = s.detach().numpy()\n dim = 0\n # calculate how much 90% would be\n s_square = [i ** 2 for i in s]\n sum_square = sum(s_square)\n goal = .9 * sum_square\n # find 90%\n count = 0\n while count < goal:\n count += s_square[dim]\n dim += 1\n return dim # return this many dimensions","def largest_cc_size(ugraph):\n ccomp = cc_visited(ugraph)\n if len(ccomp) == 0:\n return 0\n \n return max([len(s) for s in ccomp])","def max_node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_node_count\")","def cluster_size(result, var):\n df=calculate_cluster_size(result, var)\n df['cus']=df.index\n return df","def get_max_rows_per_partition() -> int:\n pass","def get_max_combination(total_cuts):\n max_pieces = 0\n for i in range(total_cuts):\n result = i * (total_cuts - i)\n if result > max_pieces:\n max_pieces = result\n print(max_pieces)","def mem_per_core(self):\n return self.mem_per_node / self.cores_per_node","def get_max_lb(self):\n max_lb = 0\n for arc in self.arc_info.keys():\n if self.arc_info[arc][\"lower_bound\"] > max_lb:\n max_lb = self.arc_info[arc][\"lower_bound\"]\n return max_lb","def getCpuNum(self):\n return len(psutil.cpu_percent(interval=None, percpu=True))","def berger_parker_d(counts):\n return counts.max()/float(counts.sum())","def calculate_enrichment_factor_optimal(molecules, ranked_dataset_percentage_cutoff, pic50_cutoff):\n\n ratio = sum(molecules[\"pIC50\"] >= pic50_cutoff) / len(molecules) * 100\n if ranked_dataset_percentage_cutoff <= ratio:\n enrichment_factor_optimal = round(100 / ratio * ranked_dataset_percentage_cutoff, 1)\n else:\n enrichment_factor_optimal = 100.0\n return enrichment_factor_optimal","def largest_cc_size(ugraph):\r\n\ttotal_list = cc_visited(ugraph)\r\n\tmax_length_list = []\r\n\tfor each_list in total_list:\r\n\t\tif len(max_length_list) < len(each_list):\r\n\t\t\tmax_length_list = each_list\r\n\treturn len(max_length_list)","def num_largest_coeffs_for_energy_percent(a, p):\n # compute energies\n a = jnp.conj(a) * a\n # sort in descending order\n a = jnp.sort(a)[::-1]\n # total energy\n s = jnp.sum(a) * 1.\n # normalize\n a = a / s\n # convert to a cmf\n cmf = jnp.cumsum(a)\n # the quantile value\n q = (p - 1e-10) / 100\n # find the index\n index = jnp.argmax(cmf >= q)\n return index + 1","def n_points(self):\n\n if self.data_reduced:\n return len(self.data_reduced[0])\n else:\n return 0","def get_max_num_onsets():\r\n \"\"\" based on the numbers above, should equal to 932945... \"\"\"\r\n c1 = len(gen_onset_c1())\r\n c2 = len(gen_onset_c2())\r\n c3 = len(gen_onset_c3_c4())\r\n c4 = len(gen_onset_c3_c4())\r\n temp = c1\r\n temp = temp + ( c1 * c2 )\r\n temp = temp + ( c1 * c3 )\r\n temp = temp + ( c1 * c2 * c3 )\r\n temp = temp + ( c1 * c3 * c4 )\r\n temp = temp + ( c1 * c2 * c3 * c4 )\r\n return temp","def max_node_size(self):\n return self.max_node_capacity","def clustering_coefficient(graph):\r\n count = 0\r\n sumOfClusteringCoefficients = 0\r\n for vertex in graph:\r\n count += 1\r\n sumOfClusteringCoefficients += local_clustering_coefficient(graph, vertex)\r\n return sumOfClusteringCoefficients / count","def largest_part_size():\n return usb_part_size(largest_partition())","def control_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.control_edge_count_max or 0)","def avg_sil_width(cluster_vec, centroid):\n tot_dis = 0\n for i in xrange(len(cluster_vec)):\n cos_dis = cos_sim(cluster_vec[i], centroid)\n tot_dis = tot_dis + cos_dis\n return tot_dis/len(cluster_vec)","def _get_max_cpu_usage(self) -> Optional[int]:\n max_cpu_usage = self._get_cgroups_max_cpu_usage()\n if not max_cpu_usage:\n # if no cgroups limit is in place, then maximum possible cpu usage depends on the number of available cpus\n max_cpu_usage = psutil.cpu_count() * 1000000 # number of cpus * microseconds in one second\n return max_cpu_usage","def local_clustering_coefficient(graph, vertex):\r\n edge_count = 0\r\n for neighbour1 in graph[vertex]:\r\n for neighbour2 in graph[vertex]: #look at each pair of neighbours of vertex\r\n if neighbour1 in graph[neighbour2]: #if the neighbours are joined to each other by an edge\r\n edge_count += 1 #add one to the edge count\r\n degree = len(graph[vertex]) #count how many neighbours vertex has\r\n return edge_count / (degree * (degree - 1)) #note factor of 2 missing as each edge counted twice\r","def get_maxdist(self, pixel_size):\n\n total_area = self.minnpix_cluster*pixel_size**2.\n\n radius = ((np.sqrt(total_area)/2.))\n if radius > 1.0:\n radius = int(radius)\n else:\n radius = round_to_1(radius)\n dist = np.sqrt(2.*float(radius)**2.)\n dist = dist+(0.05*dist)\n\n return dist","def n_cs(self):\n return np.size(self._cs, 0)","def maximum_elastic_worker_count(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"maximum_elastic_worker_count\")","def largest_bucket(self):\n size = 0\n for i in self.__buckets:\n if i.size() > size:\n size = i.size()\n return size","def abs_max_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_max_cool_setpoint_limit\", 3200)","def csize(grades):\n\tp = 0\n\tfor k in grades:\n\t\tl = _comb(n,k)\n\t\tp += l\n\treturn p","def csize(grades):\n\tp = 0\n\tfor k in grades:\n\t\tl = _comb(n,k)\n\t\tp += l\n\treturn p","def abs_max_heat_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_max_heat_setpoint_limit\", 3000)","def cluster_cal(self):\n self.Cluster = []\n for i in range(self.nodenum):\n neighborhood_node = self.neighbor_node(i)\n Node_num = len(neighborhood_node)\n Count = self.neighbor_edge(neighborhood_node)\n if(Node_num == 0 or Node_num == 1):\n self.Cluster.append(0.5)\n else:\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\n \n self.cluster_coeff = np.average(self.Cluster)","def optimal_nr_of_hyperplanes(graph, L):\n\n max_degree = max(dict(graph.degree()).values())\n k = find_number_of_vector_colors_from_vector_coloring(graph, L)\n opt_nr_of_hyperplanes = 2\n try:\n opt_nr_of_hyperplanes = 2 + int(math.ceil(math.log(max_degree, k)))\n except ValueError:\n logging.info(\"math domain error\")\n\n return max(1, opt_nr_of_hyperplanes - 2)","def getNNodesTot(self):\n nNodesTot = 0\n for iElt in Elements._all:\n nNodesTot += len(iElt.coord)\n return nNodesTot","def get_optimal_clusters(cell,threshold=140):\n\n\t#\tTurn image to numpy array\n\tpic = image_to_matrix(cell)\n\n\t#\tGet the array of coordinates of dark dots\n\tdots = get_threshold_dots(pic,threshold)\n\n\tscores = []\n\n\tfor n_clusters in range(1,10):\n\t\tclusters = kmeans.kmeans(pic,pic.shape[0],pic.shape[1],50,n_clusters,threshold)\n\t\tprint clusters\n\n\t\tsquare_sum_array = [0]*n_clusters\n\t\tcount_array = [0]*n_clusters\n\n\t\tfor dot in dots:\n\t\t\tdistance_array = [kmeans.euclid_distance(dot,cluster) for cluster in clusters]\n\t\t\tmin_index = distance_array.index(min(distance_array))\n\t\t\tsquare_sum_array[min_index] += kmeans.euclid_distance(clusters[min_index],dot)\n\t\t\tcount_array[min_index] += 1\n\n\t\tvariances = [square_sum/(count+0.001) for square_sum, count in zip(square_sum_array,count_array)]\n\n\t\tprint variances\n\t\tscores.append(sum(variances)/len(variances))\n\n\treturn scores","def nb_murs_total(self):\r\n murs_pleins=0\r\n for x in range(0,self.largeur):\r\n for y in range(0,self.hauteur):\r\n murs_pleins+=self.matrice_cases[x][y].nb_murs_pleins()\r\n \r\n return int((murs_pleins-self.hauteur*2-self.largeur*2)/2)","def reduction_ratio(links_pred, *total):\n\n n_max = full_index_size(*total)\n\n if isinstance(links_pred, pandas.MultiIndex):\n links_pred = len(links_pred)\n\n if links_pred > n_max:\n raise ValueError(\"n has to be smaller of equal n_max\")\n\n return 1 - links_pred / n_max","def element_size(self):\n vecs = (\n self.nodes[self.elements[:, :4], :][:, 1:, :]\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\n )\n return np.abs(np.linalg.det(vecs)) / 6"],"string":"[\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def get_clust_num_perc(model, vis_perc=0.9):\\n\\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\\n\\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\\n\\n\\ttot = model.allocmodel.Nk[idx].sum()\\n\\tcursum = 0\\n\\ti = 0\\n\\twhile cursum < tot*vis_perc:\\n\\t cursum += model.allocmodel.Nk[idx][i]\\n\\t i+=1\\n\\n\\treturn i\",\n \"def n_clusters(self):\\n return len(self.clusters)\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def set_nb_clusters(self):\\n \\n print(\\\"Finding the optimal number of clusters...\\\")\\n \\n sample = ro.r.matrix(self.df[self.df[\\\"filename\\\"].between(1, 4)][\\\"active_power\\\"].to_numpy())\\n \\n r=ro.r(\\\"\\\"\\\"\\n check = function(matrix) {\\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\\n\\n n_clust = n_clust$data\\n\\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\\n return(max_cluster)\\n }\\n \\\"\\\"\\\")\\n\\n result = r(sample)\\n self.conf[\\\"nb_clust\\\"] = int(result[0])\\n \\n print(f\\\"Optimal number of clusters is {self.conf['nb_clust']}\\\\n\\\")\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def get_cluster_count(self) -> int:\\n return len(self.get_all_cluster_ids())\",\n \"def purity_score(clusters, classes):\\n clusters = np.array(clusters)\\n classes = np.array(classes)\\n A = np.c_[(clusters,classes)]\\n\\n n_accurate = 0.\\n\\n for j in np.unique(A[:,0]):\\n z = A[A[:,0] == j, 1]\\n x = np.argmax(np.bincount(z))\\n n_accurate += len(z[z == x])\\n\\n return n_accurate / A.shape[0]\",\n \"def n_clusters(self):\\n return self.model.n_clusters\",\n \"def calculate_cluster_size(result, var):\\n \\n cluster_results=pd.DataFrame(result[var].value_counts())\\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\\\"ratio\\\"})\\n return cluster_results.join(ratio)\",\n \"def analysis_function_top_4(self,clustering):\\n clustering.sort_clusters_by_size()\\n total = 0\\n percents = clustering.get_population_percent_of_n_bigger_clusters(4)\\n for p in percents:\\n total = total+p\\n return total\",\n \"def max_individuals(self) -> int:\\n return self.group_size.upper * self.groups_allowed\",\n \"def compute_total_bipartiteness(hypergraph, clusters):\\n bipartiteness_sum = 0\\n for i in range(len(clusters)):\\n for j in range(i + 1, len(clusters)):\\n bipartiteness_sum += hypergraph_bipartiteness(hypergraph, clusters[i], clusters[j])\\n return bipartiteness_sum\",\n \"def analysis_function_num_clusters(self,clustering):\\n return len(clustering.clusters)\",\n \"def total_max_node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"total_max_node_count\\\")\",\n \"def getNbClusters( model):\\r\\n\\r\\n\\tlabels = model.labels_\\r\\n\\tlabelValues = []\\r\\n\\tfor label in labels:\\r\\n\\t\\tif label not in labelValues and label != -1: labelValues.append(label)\\r\\n\\tnbClusters = len( labelValues)\\r\\n\\treturn nbClusters\",\n \"def calc_Nw(cluster_labels):\\n\\n cluster_labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Nw = []\\n for label in labels_set:\\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\\n Nw.append(n_cluster_pairs)\\n\\n return int(np.sum(Nw))\",\n \"def max_node_count(self) -> int:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def get_susceptibility(clusters):\\n\\n # If there is no or only one cluster then there is no finite cluster\\n if len(clusters) <= 1:\\n return np.nan\\n\\n # Remove largest, i.e. infinite, cluster\\n clusters.remove(max(clusters))\\n\\n sizes = np.array(list(set(clusters)))\\n n_s = []\\n\\n for size in sizes:\\n n_s.append(clusters.count(size))\\n\\n temp = sizes * n_s\\n S = np.sum(sizes * temp) / np.sum(temp)\\n\\n return S\",\n \"def maxCluster(clusters):\\n try:\\n return clusters[np.argmax(map(len, clusters))]\\n except:\\n return None\",\n \"def analysis_function_total_elements(self,clustering):\\n return clustering.total_number_of_elements\",\n \"def get_number_of_clusters(df, use_pca, n_components):\\n n_clusters = 10\\n cluster_with_distances = []\\n for i in range(n_clusters):\\n pipe = _build_model(df, use_pca, n_components, use_kmeans=True, n_clusters=i + 1)\\n cluster_with_distances.append(pipe.named_steps['kmeans'].inertia_)\\n plt.figure(6, figsize=(12, 6))\\n plt.plot(range(1, 11), cluster_with_distances, 'o')\\n plt.plot(range(1, 11), cluster_with_distances, '-', alpha=0.5)\\n plt.title('The Elbow Criterion')\\n plt.xlabel('number of cluster')\\n plt.ylabel('Sum of squared distances of samples to their closest cluster center')\\n plt.show()\",\n \"def max_size(self):\\n size = 1\\n for idx in self.config.index_specs:\\n size *= len(idx.distribution)\\n return size\",\n \"def get_total_n_cpu(self) -> int:\",\n \"def get_nb_workers(ratio):\\n return max(1, int(CPU_COUNT * ratio))\",\n \"def maxContigLength(self):\\n\\t\\tstats = self.scores()\\n\\t\\treturn stats['largestContig']\",\n \"def node_count_max(self) -> int:\\n return int(self.graph_tuple_stats.node_count_max or 0)\",\n \"def max_edges_in(i, cluster_bounds, insertion_factor):\\n # for j in range(1, len(cluster_bounds)):\\n # if(i < cluster_bounds[j] and i >= cluster_bounds[j-1]):\\n # break\\n bounds = get_cluster_bounds(i, cluster_bounds)\\n #n_c = cluster_bounds[j] - cluster_bounds[j-1]\\n n_c = bounds[1] - bounds[0]\\n return np.int64(n_c * insertion_factor)\",\n \"def num_links(self):\\n count=0.0\\n for cluster in self.clusters:\\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\\n numberofmembers=self.clusters[cluster].number_of_members\\n count+=numberofmembers\\n return count\",\n \"def max_cardinality():\\r\\n #create a list containing the number of each vertex involvement.\\r\\n array = []\\r\\n for i in adj:\\r\\n array += [i[0],i[1]]\\r\\n\\r\\n #compute the degree by counting the involment\\r\\n degree = Counter(array).most_common()\\r\\n\\r\\n #retrieve the degree only\\r\\n degree_ = [ i[1] for i in degree]\\r\\n\\r\\n degree_ = np.array(degree_)\\r\\n \\r\\n max_m = None\\r\\n \\r\\n #check if m is valid\\r\\n for i in range(degree[0][1]+2)[2:]:\\r\\n \\r\\n #valid if there are at least m vertex with degree equals to at least m-1 \\r\\n if i < len(np.where(degree_>=i-1)[0]):\\r\\n max_m = i\\r\\n else:\\r\\n break\\r\\n max_m += 1\\r\\n print(f'maximum possible clique cardinality :{max_m}')\\r\\n return max_m\",\n \"def percenter(rank, max_rank):\\n\\treturn 100 * (rank/(max_rank or 1))\",\n \"def get_cluster_to_split(clusters):\\n\\treturn max(clusters.items(), key=lambda x: x[1].get_distortion())[1]\",\n \"def dissimilarity(clusters):\\n totDist = 0\\n for c in clusters:\\n totDist += c.variability()\\n return totDist\",\n \"def get_p_inf(clusters, shape):\\n\\n if len(clusters) == 0:\\n return 0\\n\\n else:\\n return max(clusters) / (shape[0] * shape[1])\",\n \"def calculate_cluster_fitness(self, cluster_id: ObjectId):\\n\\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\\n\\n cluster_fitness = 0\\n\\n for genome in genomes:\\n cluster_fitness += genome.fitness\\n if cluster_fitness == 0:\\n return 0\\n\\n return cluster_fitness / len(list(genomes))\",\n \"def maximum_number_of_workers(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"maximum_number_of_workers\\\")\",\n \"def get_largest_cluster(self) -> tuple:\\n flat_cluster = self.cluster.reshape(-1)\\n true_clusters = np.extract(flat_cluster > 0, flat_cluster)\\n counts = np.bincount(true_clusters)\\n largest = np.argmax(counts)\\n size = counts[largest]\\n return largest, size\",\n \"def compute_cluster_class_fractions(k_means_model, y):\\n\\n n_classes = y.shape[1]\\n class_labels = utils.one_hot_to_index(y)\\n cluster_labels = k_means_model.labels_\\n\\n class_clustroid_counts = np.zeros((n_classes, K))\\n for i in range(len(class_labels)):\\n class_clustroid_counts[class_labels[i], cluster_labels[i]] += 1\\n\\n class_clustroid_fractions = class_clustroid_counts / np.sum(class_clustroid_counts, axis=1).reshape(n_classes, 1)\\n\\n print(\\\"\\\\n---- Class Clustroid Distribution ----\\\")\\n for i in range(n_classes):\\n print(\\\"Class {}: {}\\\".format(i, class_clustroid_fractions[i, :]))\",\n \"def get_lcc_size(G,seed_nodes):\\n\\n # getting subgraph that only consists of the black_nodes\\n g = nx.subgraph(G,list(seed_nodes))\\n\\n if g.number_of_nodes() != 0:\\n # get all components \\n max_CC = max(nx.connected_component_subgraphs(g), key=len)\\n return len(max_CC.nodes()) # size of largest connected component\\\"\\n\\n else:\\n return 0\",\n \"def determine_jobs_per_pool(numpools, totaljobs):\\n cluster = os.environ['CC_CLUSTER']\\n if cluster in ['graham', 'beluga']:\\n jobs_per_pool = math.floor(totaljobs / numpools)\\n else:\\n jobs_per_pool = totaljobs\\n return jobs_per_pool\",\n \"def purity(clusters, classes):\\n\\n d = defaultdict(list)\\n\\n # Get a list of class numbers of all examples in a cluster.\\n for k, v in zip(clusters, classes):\\n d[k].append(v)\\n\\n mayority = 0\\n\\n # Count the mayority class number and add it up over all clusters.\\n for k in d:\\n mayority += Counter(d[k]).most_common(1)[0][1]\\n\\n return float(mayority) / len(clusters)\",\n \"def get_sectors_per_cluster(self):\\n\\n\\t\\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\\n\\t\\tif sectors_per_cluster_base == 0:\\n\\t\\t\\traise BootSectorException('Invalid cluster size (zero)')\\n\\n\\t\\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\\n\\t\\t\\tsectors_per_cluster_real = sectors_per_cluster_base\\n\\t\\telse:\\n\\t\\t\\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\\n\\t\\t\\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\\n\\n\\t\\treturn sectors_per_cluster_real\",\n \"def get_typical_size(workers: List[List[int]]) -> int:\\n size = 0\\n for worker in workers:\\n size = max([size,\\n np.abs(worker[2]-worker[0]),\\n np.abs(worker[3]-worker[1])])\\n \\n return size\",\n \"def max_num_neighbors(self):\\n return self._max_num_neighbors\",\n \"def test_count_reads_per_cluster(self):\\n \\n bedtool = pybedtools.BedTool(clipper.test_file(\\\"clip_analysis_test_peak_results.bed\\\"))\\n \\n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\\n \\n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)\",\n \"def find_count_divisor(this_list):\\n max_found = this_list[0][1]\\n count = 0\\n\\n while max_found/50 > 0:\\n max_found -= 50\\n count += 1\\n\\n return count\",\n \"def get_total_collisions(self):\\n return self.count_collisions\",\n \"def largest_cc_size(ugraph):\\n\\tconnected = cc_visited(ugraph)\\n\\tmaxnum = 0\\n\\tfor content in connected:\\n\\t\\tmaxnum = max(maxnum,len(content))\\n\\treturn maxnum\",\n \"def group_size_dist(self):\\n return self.group_sizes() / self.group_sizes().sum()\",\n \"def clusterSize(l, scheme, clustertype='fluid'):\\n clist = findClusters(l, scheme, clustertype)\\n \\n avglists=[]\\n for i in clist:\\n avglist=[]\\n for l in i:\\n avglist.append(np.mean(l))\\n avglists.append(np.mean(avglist))\\n return avglists\",\n \"def analysis_function_top_percent(self,clustering):\\n clustering.sort_clusters_by_size()\\n return clustering.get_population_percent_of_cluster(0)\",\n \"def get_utilization(self):\\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\\n # Remove overlapping prefixes from list of children\\n networks = cidr_merge([c.prefix for c in child_prefixes])\\n children_size = float(0)\\n for p in networks:\\n children_size += p.size\\n return int(children_size / self.prefix.size * 100)\",\n \"def maximumDominationCount(leaf):\\n maximumDominationCount = np.nanmax(leaf.calDominationCount())\\n return maximumDominationCount\",\n \"def _get_max_gpu_processes(self):\\n mem_usage = self._get_gpu_mem_usage()\\n print('Mem Usage:', mem_usage)\\n\\n num_processes = int(1 / mem_usage)\\n return num_processes\",\n \"def largest_cc_size(ugraph):\\n if not ugraph:\\n return 0\\n return max(len(cc) for cc in cc_visited(ugraph))\",\n \"def node_size(graph):\\n adj = nx.betweenness_centrality(graph)\\n return np.array([x * 1e3 for x in adj.values()])\",\n \"def get_modularity(adjacency, clusters):\\n total_weight = np.sum(adjacency)\\n e = get_clusters_adjacencies(adjacency, clusters)\\n e = e / total_weight\\n a = np.sum(e, axis=1)\\n return np.sum(e.diagonal() - np.power(a, 2))\",\n \"def __len__(self) -> int:\\n\\n length = self.n_classes * 100\\n\\n return length\",\n \"def getMaxMancount(self):\\n return self.__size * 20\",\n \"def compute_optimal_block_maximum(block_counts) -> int:\\n q1, q3 = compute_quartiles(block_counts)\\n iqr = q3 - q1\\n high_threshold = q3 + 1.5 * iqr\\n return high_threshold\",\n \"def clustering_factor(self):\\n return self.unpack_dword(0x2C)\",\n \"def maximum_number_of_workers(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"maximum_number_of_workers\\\")\",\n \"def optimalBinSize(x):\\n interquartile = np.diff(np.prctile(x, [25, 75]))\\n return 2. * interquartile * len(x)**(-1./3)\",\n \"def calc_dim(s):\\n s = s.detach().numpy()\\n dim = 0\\n # calculate how much 90% would be\\n s_square = [i ** 2 for i in s]\\n sum_square = sum(s_square)\\n goal = .9 * sum_square\\n # find 90%\\n count = 0\\n while count < goal:\\n count += s_square[dim]\\n dim += 1\\n return dim # return this many dimensions\",\n \"def largest_cc_size(ugraph):\\n ccomp = cc_visited(ugraph)\\n if len(ccomp) == 0:\\n return 0\\n \\n return max([len(s) for s in ccomp])\",\n \"def max_node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def cluster_size(result, var):\\n df=calculate_cluster_size(result, var)\\n df['cus']=df.index\\n return df\",\n \"def get_max_rows_per_partition() -> int:\\n pass\",\n \"def get_max_combination(total_cuts):\\n max_pieces = 0\\n for i in range(total_cuts):\\n result = i * (total_cuts - i)\\n if result > max_pieces:\\n max_pieces = result\\n print(max_pieces)\",\n \"def mem_per_core(self):\\n return self.mem_per_node / self.cores_per_node\",\n \"def get_max_lb(self):\\n max_lb = 0\\n for arc in self.arc_info.keys():\\n if self.arc_info[arc][\\\"lower_bound\\\"] > max_lb:\\n max_lb = self.arc_info[arc][\\\"lower_bound\\\"]\\n return max_lb\",\n \"def getCpuNum(self):\\n return len(psutil.cpu_percent(interval=None, percpu=True))\",\n \"def berger_parker_d(counts):\\n return counts.max()/float(counts.sum())\",\n \"def calculate_enrichment_factor_optimal(molecules, ranked_dataset_percentage_cutoff, pic50_cutoff):\\n\\n ratio = sum(molecules[\\\"pIC50\\\"] >= pic50_cutoff) / len(molecules) * 100\\n if ranked_dataset_percentage_cutoff <= ratio:\\n enrichment_factor_optimal = round(100 / ratio * ranked_dataset_percentage_cutoff, 1)\\n else:\\n enrichment_factor_optimal = 100.0\\n return enrichment_factor_optimal\",\n \"def largest_cc_size(ugraph):\\r\\n\\ttotal_list = cc_visited(ugraph)\\r\\n\\tmax_length_list = []\\r\\n\\tfor each_list in total_list:\\r\\n\\t\\tif len(max_length_list) < len(each_list):\\r\\n\\t\\t\\tmax_length_list = each_list\\r\\n\\treturn len(max_length_list)\",\n \"def num_largest_coeffs_for_energy_percent(a, p):\\n # compute energies\\n a = jnp.conj(a) * a\\n # sort in descending order\\n a = jnp.sort(a)[::-1]\\n # total energy\\n s = jnp.sum(a) * 1.\\n # normalize\\n a = a / s\\n # convert to a cmf\\n cmf = jnp.cumsum(a)\\n # the quantile value\\n q = (p - 1e-10) / 100\\n # find the index\\n index = jnp.argmax(cmf >= q)\\n return index + 1\",\n \"def n_points(self):\\n\\n if self.data_reduced:\\n return len(self.data_reduced[0])\\n else:\\n return 0\",\n \"def get_max_num_onsets():\\r\\n \\\"\\\"\\\" based on the numbers above, should equal to 932945... \\\"\\\"\\\"\\r\\n c1 = len(gen_onset_c1())\\r\\n c2 = len(gen_onset_c2())\\r\\n c3 = len(gen_onset_c3_c4())\\r\\n c4 = len(gen_onset_c3_c4())\\r\\n temp = c1\\r\\n temp = temp + ( c1 * c2 )\\r\\n temp = temp + ( c1 * c3 )\\r\\n temp = temp + ( c1 * c2 * c3 )\\r\\n temp = temp + ( c1 * c3 * c4 )\\r\\n temp = temp + ( c1 * c2 * c3 * c4 )\\r\\n return temp\",\n \"def max_node_size(self):\\n return self.max_node_capacity\",\n \"def clustering_coefficient(graph):\\r\\n count = 0\\r\\n sumOfClusteringCoefficients = 0\\r\\n for vertex in graph:\\r\\n count += 1\\r\\n sumOfClusteringCoefficients += local_clustering_coefficient(graph, vertex)\\r\\n return sumOfClusteringCoefficients / count\",\n \"def largest_part_size():\\n return usb_part_size(largest_partition())\",\n \"def control_edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.control_edge_count_max or 0)\",\n \"def avg_sil_width(cluster_vec, centroid):\\n tot_dis = 0\\n for i in xrange(len(cluster_vec)):\\n cos_dis = cos_sim(cluster_vec[i], centroid)\\n tot_dis = tot_dis + cos_dis\\n return tot_dis/len(cluster_vec)\",\n \"def _get_max_cpu_usage(self) -> Optional[int]:\\n max_cpu_usage = self._get_cgroups_max_cpu_usage()\\n if not max_cpu_usage:\\n # if no cgroups limit is in place, then maximum possible cpu usage depends on the number of available cpus\\n max_cpu_usage = psutil.cpu_count() * 1000000 # number of cpus * microseconds in one second\\n return max_cpu_usage\",\n \"def local_clustering_coefficient(graph, vertex):\\r\\n edge_count = 0\\r\\n for neighbour1 in graph[vertex]:\\r\\n for neighbour2 in graph[vertex]: #look at each pair of neighbours of vertex\\r\\n if neighbour1 in graph[neighbour2]: #if the neighbours are joined to each other by an edge\\r\\n edge_count += 1 #add one to the edge count\\r\\n degree = len(graph[vertex]) #count how many neighbours vertex has\\r\\n return edge_count / (degree * (degree - 1)) #note factor of 2 missing as each edge counted twice\\r\",\n \"def get_maxdist(self, pixel_size):\\n\\n total_area = self.minnpix_cluster*pixel_size**2.\\n\\n radius = ((np.sqrt(total_area)/2.))\\n if radius > 1.0:\\n radius = int(radius)\\n else:\\n radius = round_to_1(radius)\\n dist = np.sqrt(2.*float(radius)**2.)\\n dist = dist+(0.05*dist)\\n\\n return dist\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def maximum_elastic_worker_count(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"maximum_elastic_worker_count\\\")\",\n \"def largest_bucket(self):\\n size = 0\\n for i in self.__buckets:\\n if i.size() > size:\\n size = i.size()\\n return size\",\n \"def abs_max_cool_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_max_cool_setpoint_limit\\\", 3200)\",\n \"def csize(grades):\\n\\tp = 0\\n\\tfor k in grades:\\n\\t\\tl = _comb(n,k)\\n\\t\\tp += l\\n\\treturn p\",\n \"def csize(grades):\\n\\tp = 0\\n\\tfor k in grades:\\n\\t\\tl = _comb(n,k)\\n\\t\\tp += l\\n\\treturn p\",\n \"def abs_max_heat_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_max_heat_setpoint_limit\\\", 3000)\",\n \"def cluster_cal(self):\\n self.Cluster = []\\n for i in range(self.nodenum):\\n neighborhood_node = self.neighbor_node(i)\\n Node_num = len(neighborhood_node)\\n Count = self.neighbor_edge(neighborhood_node)\\n if(Node_num == 0 or Node_num == 1):\\n self.Cluster.append(0.5)\\n else:\\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\\n \\n self.cluster_coeff = np.average(self.Cluster)\",\n \"def optimal_nr_of_hyperplanes(graph, L):\\n\\n max_degree = max(dict(graph.degree()).values())\\n k = find_number_of_vector_colors_from_vector_coloring(graph, L)\\n opt_nr_of_hyperplanes = 2\\n try:\\n opt_nr_of_hyperplanes = 2 + int(math.ceil(math.log(max_degree, k)))\\n except ValueError:\\n logging.info(\\\"math domain error\\\")\\n\\n return max(1, opt_nr_of_hyperplanes - 2)\",\n \"def getNNodesTot(self):\\n nNodesTot = 0\\n for iElt in Elements._all:\\n nNodesTot += len(iElt.coord)\\n return nNodesTot\",\n \"def get_optimal_clusters(cell,threshold=140):\\n\\n\\t#\\tTurn image to numpy array\\n\\tpic = image_to_matrix(cell)\\n\\n\\t#\\tGet the array of coordinates of dark dots\\n\\tdots = get_threshold_dots(pic,threshold)\\n\\n\\tscores = []\\n\\n\\tfor n_clusters in range(1,10):\\n\\t\\tclusters = kmeans.kmeans(pic,pic.shape[0],pic.shape[1],50,n_clusters,threshold)\\n\\t\\tprint clusters\\n\\n\\t\\tsquare_sum_array = [0]*n_clusters\\n\\t\\tcount_array = [0]*n_clusters\\n\\n\\t\\tfor dot in dots:\\n\\t\\t\\tdistance_array = [kmeans.euclid_distance(dot,cluster) for cluster in clusters]\\n\\t\\t\\tmin_index = distance_array.index(min(distance_array))\\n\\t\\t\\tsquare_sum_array[min_index] += kmeans.euclid_distance(clusters[min_index],dot)\\n\\t\\t\\tcount_array[min_index] += 1\\n\\n\\t\\tvariances = [square_sum/(count+0.001) for square_sum, count in zip(square_sum_array,count_array)]\\n\\n\\t\\tprint variances\\n\\t\\tscores.append(sum(variances)/len(variances))\\n\\n\\treturn scores\",\n \"def nb_murs_total(self):\\r\\n murs_pleins=0\\r\\n for x in range(0,self.largeur):\\r\\n for y in range(0,self.hauteur):\\r\\n murs_pleins+=self.matrice_cases[x][y].nb_murs_pleins()\\r\\n \\r\\n return int((murs_pleins-self.hauteur*2-self.largeur*2)/2)\",\n \"def reduction_ratio(links_pred, *total):\\n\\n n_max = full_index_size(*total)\\n\\n if isinstance(links_pred, pandas.MultiIndex):\\n links_pred = len(links_pred)\\n\\n if links_pred > n_max:\\n raise ValueError(\\\"n has to be smaller of equal n_max\\\")\\n\\n return 1 - links_pred / n_max\",\n \"def element_size(self):\\n vecs = (\\n self.nodes[self.elements[:, :4], :][:, 1:, :]\\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\\n )\\n return np.abs(np.linalg.det(vecs)) / 6\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73421067","0.72231865","0.6814632","0.6761012","0.67437637","0.6737779","0.6622332","0.66150904","0.6535396","0.649745","0.64956445","0.6430533","0.64143646","0.6354163","0.63429755","0.63404","0.6339005","0.632302","0.6261867","0.62327045","0.62271553","0.6199023","0.61949694","0.61724746","0.61688817","0.6168732","0.6147854","0.6138262","0.6125837","0.61169857","0.6106808","0.60965633","0.60560846","0.6038405","0.60346556","0.5998857","0.5997318","0.59752667","0.5973794","0.59673345","0.5958641","0.59400076","0.59305626","0.5922549","0.5919258","0.59191257","0.5914993","0.5908023","0.5905588","0.5884294","0.588338","0.5864176","0.5857997","0.5835185","0.58074844","0.58055365","0.58001447","0.5791224","0.5785508","0.5783607","0.5775817","0.5748888","0.5743911","0.57412124","0.574053","0.5727498","0.56947994","0.56703895","0.5668831","0.566237","0.56609434","0.56603837","0.56581837","0.5656916","0.5654458","0.5644952","0.5643117","0.5632182","0.5628959","0.5627479","0.56190115","0.5618644","0.56151783","0.5612925","0.5612541","0.56115025","0.5608499","0.5607013","0.56026787","0.5601381","0.55999595","0.55999595","0.55995685","0.559881","0.5598412","0.5596695","0.559567","0.5592091","0.55809957","0.55712235"],"string":"[\n \"0.73421067\",\n \"0.72231865\",\n \"0.6814632\",\n \"0.6761012\",\n \"0.67437637\",\n \"0.6737779\",\n \"0.6622332\",\n \"0.66150904\",\n \"0.6535396\",\n \"0.649745\",\n \"0.64956445\",\n \"0.6430533\",\n \"0.64143646\",\n \"0.6354163\",\n \"0.63429755\",\n \"0.63404\",\n \"0.6339005\",\n \"0.632302\",\n \"0.6261867\",\n \"0.62327045\",\n \"0.62271553\",\n \"0.6199023\",\n \"0.61949694\",\n \"0.61724746\",\n \"0.61688817\",\n \"0.6168732\",\n \"0.6147854\",\n \"0.6138262\",\n \"0.6125837\",\n \"0.61169857\",\n \"0.6106808\",\n \"0.60965633\",\n \"0.60560846\",\n \"0.6038405\",\n \"0.60346556\",\n \"0.5998857\",\n \"0.5997318\",\n \"0.59752667\",\n \"0.5973794\",\n \"0.59673345\",\n \"0.5958641\",\n \"0.59400076\",\n \"0.59305626\",\n \"0.5922549\",\n \"0.5919258\",\n \"0.59191257\",\n \"0.5914993\",\n \"0.5908023\",\n \"0.5905588\",\n \"0.5884294\",\n \"0.588338\",\n \"0.5864176\",\n \"0.5857997\",\n \"0.5835185\",\n \"0.58074844\",\n \"0.58055365\",\n \"0.58001447\",\n \"0.5791224\",\n \"0.5785508\",\n \"0.5783607\",\n \"0.5775817\",\n \"0.5748888\",\n \"0.5743911\",\n \"0.57412124\",\n \"0.574053\",\n \"0.5727498\",\n \"0.56947994\",\n \"0.56703895\",\n \"0.5668831\",\n \"0.566237\",\n \"0.56609434\",\n \"0.56603837\",\n \"0.56581837\",\n \"0.5656916\",\n \"0.5654458\",\n \"0.5644952\",\n \"0.5643117\",\n \"0.5632182\",\n \"0.5628959\",\n \"0.5627479\",\n \"0.56190115\",\n \"0.5618644\",\n \"0.56151783\",\n \"0.5612925\",\n \"0.5612541\",\n \"0.56115025\",\n \"0.5608499\",\n \"0.5607013\",\n \"0.56026787\",\n \"0.5601381\",\n \"0.55999595\",\n \"0.55999595\",\n \"0.55995685\",\n \"0.559881\",\n \"0.5598412\",\n \"0.5596695\",\n \"0.559567\",\n \"0.5592091\",\n \"0.55809957\",\n \"0.55712235\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6772024"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":686,"cells":{"query":{"kind":"string","value":"Returns the percent of elements over the total number of elements of the clustering, that have been clustered into the bigger cluster."},"document":{"kind":"string","value":"def analysis_function_top_percent(self,clustering):\n clustering.sort_clusters_by_size()\n return clustering.get_population_percent_of_cluster(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 analysis_function_num_clusters_to_percent(self,clustering,percent):\n return clustering.number_of_clusters_to_get_percent(percent)","def analysis_function_top_4(self,clustering):\n clustering.sort_clusters_by_size()\n total = 0\n percents = clustering.get_population_percent_of_n_bigger_clusters(4)\n for p in percents:\n total = total+p\n return total","def get_clust_num_perc(model, vis_perc=0.9):\n\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\n\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\n\n\ttot = model.allocmodel.Nk[idx].sum()\n\tcursum = 0\n\ti = 0\n\twhile cursum < tot*vis_perc:\n\t cursum += model.allocmodel.Nk[idx][i]\n\t i+=1\n\n\treturn i","def calculate_cluster_size(result, var):\n \n cluster_results=pd.DataFrame(result[var].value_counts())\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\"ratio\"})\n return cluster_results.join(ratio)","def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements","def dissimilarity(clusters):\n totDist = 0\n for c in clusters:\n totDist += c.variability()\n return totDist","def purity_score(clusters, classes):\n clusters = np.array(clusters)\n classes = np.array(classes)\n A = np.c_[(clusters,classes)]\n\n n_accurate = 0.\n\n for j in np.unique(A[:,0]):\n z = A[A[:,0] == j, 1]\n x = np.argmax(np.bincount(z))\n n_accurate += len(z[z == x])\n\n return n_accurate / A.shape[0]","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def cluster_cal(self):\n self.Cluster = []\n for i in range(self.nodenum):\n neighborhood_node = self.neighbor_node(i)\n Node_num = len(neighborhood_node)\n Count = self.neighbor_edge(neighborhood_node)\n if(Node_num == 0 or Node_num == 1):\n self.Cluster.append(0.5)\n else:\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\n \n self.cluster_coeff = np.average(self.Cluster)","def compute_total_bipartiteness(hypergraph, clusters):\n bipartiteness_sum = 0\n for i in range(len(clusters)):\n for j in range(i + 1, len(clusters)):\n bipartiteness_sum += hypergraph_bipartiteness(hypergraph, clusters[i], clusters[j])\n return bipartiteness_sum","def get_modularity(adjacency, clusters):\n total_weight = np.sum(adjacency)\n e = get_clusters_adjacencies(adjacency, clusters)\n e = e / total_weight\n a = np.sum(e, axis=1)\n return np.sum(e.diagonal() - np.power(a, 2))","def get_susceptibility(clusters):\n\n # If there is no or only one cluster then there is no finite cluster\n if len(clusters) <= 1:\n return np.nan\n\n # Remove largest, i.e. infinite, cluster\n clusters.remove(max(clusters))\n\n sizes = np.array(list(set(clusters)))\n n_s = []\n\n for size in sizes:\n n_s.append(clusters.count(size))\n\n temp = sizes * n_s\n S = np.sum(sizes * temp) / np.sum(temp)\n\n return S","def contains_percentage_of(self, other: 'Interval') -> float:\n if other.length == 0:\n return other.a in self\n intersection = Interval.intersection([self, other])\n return intersection.length / other.length if intersection else 0.0","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def assign_labels_to_centroids(self):\n labelled_centroids = []\n for i in range(len(self.final_clusters)):\n labels = map(lambda x: x[0], self.final_clusters[i])\n # pick the most common label\n most_common = Counter(labels).most_common(1)[0][0]\n c = np.round(len([item for item in self.final_clusters[i] if item[0]==1])/len(self.final_clusters[i]),2)\n if c>=0.46:\n most_common = 1.0\n centroid = (most_common, self.final_centroids[i])\n labelled_centroids.append(centroid)\n\n self.labelled_centroids = labelled_centroids\n print(\"cluster_0: \", np.round(len([item for item in self.final_clusters[0] if item[0]==1])/len(self.final_clusters[0]),2), \"size_0: \", len(self.final_clusters[0]))\n print(\"cluster_1: \", np.round(len([item for item in self.final_clusters[1] if item[0]==1])/len(self.final_clusters[1]),2), \"size_1: \", len(self.final_clusters[1]))\n #print(\"cluster_2: \", np.round(len([item for item in self.final_clusters[2] if item[0]==1])/len(self.final_clusters[2]),2), \"size_2: \", len(self.final_clusters[2]))\n #print(\"cluster_3: \", np.round(len([item for item in self.final_clusters[3] if item[0]==1])/len(self.final_clusters[3]),2), \"size_2: \", len(self.final_clusters[3]))","def compute_cluster_class_fractions(k_means_model, y):\n\n n_classes = y.shape[1]\n class_labels = utils.one_hot_to_index(y)\n cluster_labels = k_means_model.labels_\n\n class_clustroid_counts = np.zeros((n_classes, K))\n for i in range(len(class_labels)):\n class_clustroid_counts[class_labels[i], cluster_labels[i]] += 1\n\n class_clustroid_fractions = class_clustroid_counts / np.sum(class_clustroid_counts, axis=1).reshape(n_classes, 1)\n\n print(\"\\n---- Class Clustroid Distribution ----\")\n for i in range(n_classes):\n print(\"Class {}: {}\".format(i, class_clustroid_fractions[i, :]))","def calculate_cluster_fitness(self, cluster_id: ObjectId):\n\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\n\n cluster_fitness = 0\n\n for genome in genomes:\n cluster_fitness += genome.fitness\n if cluster_fitness == 0:\n return 0\n\n return cluster_fitness / len(list(genomes))","def purity(clusters, classes):\n\n d = defaultdict(list)\n\n # Get a list of class numbers of all examples in a cluster.\n for k, v in zip(clusters, classes):\n d[k].append(v)\n\n mayority = 0\n\n # Count the mayority class number and add it up over all clusters.\n for k in d:\n mayority += Counter(d[k]).most_common(1)[0][1]\n\n return float(mayority) / len(clusters)","def get_utilization(self):\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\n # Remove overlapping prefixes from list of children\n networks = cidr_merge([c.prefix for c in child_prefixes])\n children_size = float(0)\n for p in networks:\n children_size += p.size\n return int(children_size / self.prefix.size * 100)","def percenter(rank, max_rank):\n\treturn 100 * (rank/(max_rank or 1))","def clust_strength(mat,groups):\n cluster_strengths = []\n for group in range(len(np.unique(groups))):\n this_cluster = mat[groups==group,:]\n this_cluster_mean = np.mean(this_cluster,axis=0)\n all_dists = mat - this_cluster_mean\n out_dists = np.linalg.norm(all_dists[groups!=group],axis=1)\n in_dists = np.linalg.norm(all_dists[groups==group],axis=1)\n this_strength = np.mean(out_dists)/np.mean(in_dists)\n cluster_strengths.append(this_strength)\n \n return np.mean(cluster_strengths)","def leaf_nodes_to_search_percent(self) -> Optional[int]:\n return pulumi.get(self, \"leaf_nodes_to_search_percent\")","def group_size_dist(self):\n return self.group_sizes() / self.group_sizes().sum()","def clusterAlgorithm(values):\n clusterMap = dict()\n for value in values:\n if value[2] not in clusterMap.keys():\n clusterMap[value[2]] = []\n clusterMap[value[2]].append(value)\n frequency = [float(len(clusterMap[value[2]])) for value in values]\n total = sum(frequency)\n weightValues = [freq / total for freq in frequency]\n print sum(weightValues)\n lightValues = [value[1] for value in values]\n return np.average(lightValues, weights = weightValues)","def _cluster_hitprobability(self, x, y):\n hm_count = np.zeros_like(y).astype(float)\n hm = np.zeros_like(y).astype(float)\n #skf = StratifiedShuffleSplit(n_splits=self.n_iter, test_size=self.shuffle_test_split, random_state=self.random_state)\n\n ind = self._cluster(x, 35)\n\n for cluster_id in np.unique(ind):\n test = np.argwhere(ind == cluster_id)[:, 0]\n train = np.argwhere(ind != cluster_id)[:, 0]\n #print test\n self.basemodel.fit(x[train, :], y[train], hyperparams_optim=False)\n hm_count[test] += 1.\n hm[test] += (self.basemodel.predict(x[test, :]) == y[test]).astype(float)\n\n proba = hm / hm_count\n if self.verbose:\n # print('H/M count:')\n # print(hm_count)\n print('Proba:')\n print(proba)\n self.basemodel.fit(x, y, hyperparams_optim=False)\n return proba","def get_modularity_other_c(A, cluster_indices):\n # define the number of nodes in the graph and the number of clusters\n n = len(cluster_indices)\n nclusters = max(cluster_indices) + 1\n # define the row sums of the adjacency matrix\n row_sums = [sum(row) for row in A]\n # define one half of the sum of all entries in the adjacency matrix\n m = sum(row_sums) / 2.0\n # define the modularity\n Q = 0\n for i in range(n):\n for j in range(n):\n if cluster_indices[i] == cluster_indices[j]:\n Q += (A[i][j] - row_sums[i] * row_sums[j] / (2*m)) / (2*m)\n return Q","def compute_clusters(self, p: float):\n pass","def analysis_function_mean_cluster_size(self,clustering):\n sizes = get_cluster_sizes(clustering.clusters)[1]\n return numpy.mean(sizes)","def getBeliefDistribution(self):\n # This essentially gives a point to a location for each particle there, then \n # normalizes the point values so they add up to 1.\n dist = util.Counter()\n for part in self.particles: dist[part] += 1\n dist.normalize()\n return dist","def clusterSize(l, scheme, clustertype='fluid'):\n clist = findClusters(l, scheme, clustertype)\n \n avglists=[]\n for i in clist:\n avglist=[]\n for l in i:\n avglist.append(np.mean(l))\n avglists.append(np.mean(avglist))\n return avglists","def average_consensus(self, cluster):\n\t\tcenterk = 0\n\t\tindex = 0\n\t\tfor value in cluster:\n\t\t\tcenterk += value\n\t\t\tindex += 1\n\t\tcenterk = centerk / index\n\t\treturn centerk","def inter_cost(cluster):\n def _p2p(point):\n _freq_sum = 0\n for pt in cluster.points:\n if point != pt:\n _freq_sum += point.frequency(pt)\n return _freq_sum\n\n return int(sum(map(_p2p, cluster.points)))","def describe_reduce_nb(col, a, perc, ddof, *args):\n a = a[~np.isnan(a)]\n out = np.empty(5 + len(perc), dtype=np.float_)\n out[0] = len(a)\n if len(a) > 0:\n out[1] = np.mean(a)\n out[2] = nanstd_1d_nb(a, ddof=ddof)\n out[3] = np.min(a)\n out[4:-1] = np.percentile(a, perc * 100)\n out[4 + len(perc)] = np.max(a)\n else:\n out[1:] = np.nan\n return out","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def test_clusters(trained_data, centroids):\n\n for c in range(len(centroids)):\n count_1 = 0\n count_0 = 0\n for p in range(len(trained_data)):\n if trained_data[p][-2] == 0 and trained_data[p][-1] == centroids[c]:\n count_0 += 1\n if trained_data[p][-2] == 1 and trained_data[p][-1] == centroids[c]:\n count_1 += 1\n print (\"Centroid \", c+1, \":\", centroids[c])\n print(\"Number of 1's: \", count_1)\n print(\"Number of 0's: \", count_0)\n print(\"Percent 1's: \", round((count_1/(count_1 + count_0))*100,2))\n print(\"Percent 0's: \", round((count_0 / (count_1 + count_0)) * 100,2))\n print(\"****************\")","def occupation_distribution(data):","def __cluster_simi(self, i, j):\n sum_ = 0.\n for si in self.__indexclusters[i]:\n for sj in self.__indexclusters[j]:\n simi = self.__sample_simi(si, sj)\n sum_ += simi\n return sum_ / (len(self.__indexclusters[i]) * len(self.__indexclusters[j]))","def _calculate_cluster_probs(self, dist_mat, temperature):\n dist_mat = np.square(dist_mat) # euclidean distance -> squared euclidean distance\n n_samples = dist_mat.shape[0]\n n_clusters = dist_mat.shape[1]\n cluster_probs = np.zeros(shape=(n_samples, n_clusters))\n for i in range(n_clusters):\n for j in range(n_samples):\n normalizer = sum(self.marginal_probs[k] * np.exp(-dist_mat[j, k] / temperature) for k in range(n_clusters))\n cluster_probs[j, i] = self.marginal_probs[i] * np.exp(-dist_mat[j, i] / temperature) / normalizer\n return cluster_probs","def get_modularity3(adjacency, clusters):\n\n rows, cols = adjacency.shape\n num_ids = adjacency.shape[0]\n id_to_cluster = get_id_to_cluster(clusters, num_ids)\n degrees = np.sum(adjacency, axis=1)\n total_weight = np.sum(adjacency)\n sum = 0\n for i in range(rows):\n for j in range(cols):\n if id_to_cluster[i] == id_to_cluster[j]:\n sum += adjacency[i, j] - (degrees[i] * degrees[j]) / total_weight\n sum = sum / total_weight\n return sum","def _assign_clusters(self):\n\n dist = np.zeros((self.k, ))\n distortion = 0\n\n for index in range(0, self.data.shape[0]):\n for i in range(0, self.k):\n dist[i] = np.linalg.norm(self.data[index] - self.centroids[i])\n\n self.assigned_clusters[index] = np.argmin(dist)\n distortion += np.min(dist)\n\n return distortion","def estimate_label_proportion(source_loader,target_loader,feat_extract,cuda,n_clusters,cluster_param): \n feat_extract.eval()\n #n_clusters = 3\n from sklearn.cluster import AgglomerativeClustering\n \n \n X_s,y_s = extract_feature(source_loader,feat_extract,cuda) \n X_t,y_t = extract_feature(target_loader,feat_extract,cuda) \n \n \n \n cluster = AgglomerativeClustering(n_clusters=n_clusters,linkage=cluster_param)\n label_t = cluster.fit_predict(X_t)\n #print(np.unique(label_t))\n mean_mat_S, num_in_class_S = extract_prototypes(X_s,y_s,n_clusters)\n mean_mat_T, num_in_class_T = extract_prototypes(X_t,label_t,n_clusters)\n \n \"\"\"\n We assume that prototypes of classes have been transported in some in the feature\n space \n \"\"\"\n \n import ot\n M = ot.dist(mean_mat_S, mean_mat_T)\n M /= M.max()\n \n n_1 = n_clusters\n a = np.ones((n_1,)) / n_1\n b = np.ones((n_1,)) / n_1\n \n \n gamma = ot.emd(a,b,M)\n nb_sample_S = [ np.sum(y_s==i) for i in range(n_clusters) ]\n proportion_T = num_in_class_T/np.sum(num_in_class_T)\n assignement_source_to_target = gamma.argmax(axis=1)\n \n # proportions are arranged directly per class\n proportion_T = proportion_T[assignement_source_to_target]\n print(proportion_T,assignement_source_to_target)\n \n\n return proportion_T,nb_sample_S, assignement_source_to_target","def clustering_coefficient(graph):\r\n count = 0\r\n sumOfClusteringCoefficients = 0\r\n for vertex in graph:\r\n count += 1\r\n sumOfClusteringCoefficients += local_clustering_coefficient(graph, vertex)\r\n return sumOfClusteringCoefficients / count","def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)","def davies_bouldin_score(self):\r\n print(colored(\"The davies bouldin score of the clustering is %0.002f\\n\" %(davies_bouldin_score(self.X, self.labels)),color = 'red', attrs=['bold']))\r\n print()\r\n print(colored(\"The points in each cluster are : \",color = 'yellow', attrs=['bold']))\r\n print(collections.Counter(self.labels))","def calculate_cost(self, medoids, clusters):\n cost = 0.0\n for i in range(0, len(medoids)):\n for j in range(0, len(clusters[i])):\n cost += distance.sqeuclidean(medoids[i], clusters[i][j])\n return cost\n pass","def get_cluster_to_split(clusters):\n\treturn max(clusters.items(), key=lambda x: x[1].get_distortion())[1]","def d50(clones, num_Reads): \n\n\n d50_amount = num_Reads/2\n read_count=0\n for i in clones:\n read_count+=clones[i].num_reads\n if read_count>=d50_amount:\n return i/float(len(clones))","def _calculate_cluster_measures(\n arr4d,\n threshold,\n bin_struct,\n two_sided_test=False,\n):\n n_regressors = arr4d.shape[3]\n\n max_sizes = np.zeros(n_regressors, int)\n max_masses = np.zeros(n_regressors, float)\n\n for i_regressor in range(n_regressors):\n arr3d = arr4d[..., i_regressor].copy()\n\n if two_sided_test:\n arr3d[np.abs(arr3d) <= threshold] = 0\n else:\n arr3d[arr3d <= threshold] = 0\n\n labeled_arr3d, _ = label(arr3d > 0, bin_struct)\n\n if two_sided_test:\n # Label positive and negative clusters separately\n n_positive_clusters = np.max(labeled_arr3d)\n temp_labeled_arr3d, _ = label(\n arr3d < 0,\n bin_struct,\n )\n temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters\n labeled_arr3d = labeled_arr3d + temp_labeled_arr3d\n del temp_labeled_arr3d\n\n clust_vals, clust_sizes = np.unique(labeled_arr3d, return_counts=True)\n assert clust_vals[0] == 0\n\n clust_vals = clust_vals[1:] # First cluster is zeros in matrix\n clust_sizes = clust_sizes[1:]\n\n # Cluster mass-based inference\n max_mass = 0\n for unique_val in clust_vals:\n ss_vals = np.abs(arr3d[labeled_arr3d == unique_val]) - threshold\n max_mass = np.maximum(max_mass, np.sum(ss_vals))\n\n # Cluster size-based inference\n max_size = 0\n if clust_sizes.size:\n max_size = np.max(clust_sizes)\n\n max_sizes[i_regressor], max_masses[i_regressor] = max_size, max_mass\n\n return max_sizes, max_masses","def calc_Nw(cluster_labels):\n\n cluster_labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Nw = []\n for label in labels_set:\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\n Nw.append(n_cluster_pairs)\n\n return int(np.sum(Nw))","def avg_sil_width(cluster_vec, centroid):\n tot_dis = 0\n for i in xrange(len(cluster_vec)):\n cos_dis = cos_sim(cluster_vec[i], centroid)\n tot_dis = tot_dis + cos_dis\n return tot_dis/len(cluster_vec)","def calculate_sample_silhouette(self):\n sum_samples = 0\n for cluster in self.cluster_lst:\n sum_samples += self.sum_silhouette(cluster)\n sample_size = len(self.samples)\n return sum_samples/sample_size","def print_cluster(self, cluster, value):\n total = 0\n ham = 0\n spam = 0\n for message in cluster:\n if self.spamorham[self.ids[message]] == 'ham':\n ham += 1\n elif self.spamorham[self.ids[message]] == 'spam':\n spam += 1\n else:\n print(\"ERROR!\")\n total += 1\n\n print(\"Total number of messages in the {0} cluster: {1}\\n\"\n \"Percentage of SPAM messages in the {2} cluster: {3}\\n\"\n \"Percentage of HAM messages in the {4} cluster: {5}\".format(value, total, value,\n str((float(spam) / total) * 100), value,\n str((float(ham) / total) * 100)))","def SquareClusteringCoefficient(graph):\n coef = np.mean(list(nx.square_clustering(graph).values()))\n return coef","def percent_processed(self):\n try:\n return (self.pos / self.data_encap.size) * 100.0\n except ZeroDivisionError:\n return 100.0","def _overlap_energy(self, this, that):\n if not this.overlaps(that):\n return 0.0\n\n return min(10.0 / this.rank, 10.0 / that.rank)","def purity_score(label, pred):\n \n df = pd.concat([label, pd.DataFrame(pred)], axis=1)\n df.set_axis(['label', 'pred'], axis=1, inplace=True)\n \n s = 0\n\n for x, cluster in df.groupby('pred'):\n s += cluster['label'].value_counts().iloc[0] # adding the most occuring class in a cluster\n\n return s / label.shape[0]","def get_optimal_clusters(cell,threshold=140):\n\n\t#\tTurn image to numpy array\n\tpic = image_to_matrix(cell)\n\n\t#\tGet the array of coordinates of dark dots\n\tdots = get_threshold_dots(pic,threshold)\n\n\tscores = []\n\n\tfor n_clusters in range(1,10):\n\t\tclusters = kmeans.kmeans(pic,pic.shape[0],pic.shape[1],50,n_clusters,threshold)\n\t\tprint clusters\n\n\t\tsquare_sum_array = [0]*n_clusters\n\t\tcount_array = [0]*n_clusters\n\n\t\tfor dot in dots:\n\t\t\tdistance_array = [kmeans.euclid_distance(dot,cluster) for cluster in clusters]\n\t\t\tmin_index = distance_array.index(min(distance_array))\n\t\t\tsquare_sum_array[min_index] += kmeans.euclid_distance(clusters[min_index],dot)\n\t\t\tcount_array[min_index] += 1\n\n\t\tvariances = [square_sum/(count+0.001) for square_sum, count in zip(square_sum_array,count_array)]\n\n\t\tprint variances\n\t\tscores.append(sum(variances)/len(variances))\n\n\treturn scores","def percentage_hapaxes(corpus_parts, corpus):\n percentage_h = []\n count = 0\n dv = divide_corpus(corpus, 10)\n hapax_parts = hapaxes_parts(corpus_parts)\n for x in hapax_parts:\n percentage_h.append(percentage(x, len(dv[count])))\n count += 1\n return percentage_h","def disaggregate_by_cluster(self):\n # wt = np.zeros((1, self.ds.shape[1]))\n # total = np.zeros((self.n_ahead, self.ds.shape[1]))\n \n agg_cluster_ds = np.zeros((self.n_ahead+1, self.n_clusters))\n agg_cluster_ds[0] = self.ds_agg_by_c[-1]\n agg_cluster_ds[1:] = self.ds_c_for\n cluster_perc_change = np.diff(agg_cluster_ds, axis = 0) / agg_cluster_ds[:-1]\n\n cluster_scaling_vector = np.zeros((2, self.ds.shape[1]))\n\n # break down proportionally -> don't work well\n # for c in range(self.n_clusters):\n # c_m = self.ds.iloc[-self.cluster_n_period:, np.where(self.ds_c == c)[0]]\n # c_sum = sum(c_m)\n # indiv_sum = np.sum(c_m, axis = 0)\n # wt[:,np.where(self.ds_c == c)[0]] = (indiv_sum/c_sum)\n # total[:,np.where(self.ds_c == c)[0]] = np.reshape(\n # np.repeat(self.ds_c_for[:,c], c_m.shape[1]), (self.n_ahead, c_m.shape[1]))\n \n # multiply by the perc change\n \n for i in range(self.ds_c.shape[0]):\n cluster_scaling_vector[:,i] = cluster_perc_change[:,self.ds_c[i]]\n cluster_scaling_vector = cluster_scaling_vector+1\n cluster_scaling_vector = np.array(cluster_scaling_vector)\n \n self.ds_for = self.ds.copy()\n\n for yr in range(self.n_ahead)[::-1]:\n # forecast on foretasted number\n yr_ind = self.ds_for.index[-(yr+1)]\n self.ds_for.ix[yr_ind] = self.ds_for.iloc[-(yr+2),:].values * cluster_scaling_vector[-(yr+1)]\n\n # self.ds_for.iloc[-(self.n_ahead):,:] = self.ds_for.iloc[-(self.n_ahead+1):-1,:].values * np.array(cluster_scaling_vector)\n\n # if negative -> 0\n self.ds_for[self.ds_for < 0] = 0","def dim_reduction( M ):\n tot_count_per_type = M.sum(axis = 1)\n tot_count = float(tot_count_per_type.sum())\n sorted_index = np.argsort(tot_count_per_type)\n threshold = 0.01\n accu = 0\n for i in range(len(sorted_index)):\n perc = float(tot_count_per_type[sorted_index[i]])/tot_count\n accu = accu + perc\n if accu > threshold:\n break;\n \n return sorted_index[0:i]","def in_xi(self, sample, cluster, cluster_size):\n sum_distance = 0\n for cur_sample in cluster.get_samples():\n if sample.get_s_id() != cur_sample.get_s_id():\n if cur_sample.get_s_id() < sample.get_s_id():\n sum_distance += self.distance_dict[(cur_sample.get_s_id(), sample.get_s_id())]\n elif cur_sample.get_s_id() > sample.get_s_id():\n sum_distance += self.distance_dict[(sample.get_s_id(), cur_sample.get_s_id())]\n return sum_distance / (cluster_size - 1)","def num_links(self):\n count=0.0\n for cluster in self.clusters:\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\n numberofmembers=self.clusters[cluster].number_of_members\n count+=numberofmembers\n return count","def calc_sw(X, cluster_labels):\n\n labels = np.array(cluster_labels)\n labels_set = set(cluster_labels)\n n_labels = len(labels_set)\n\n Sw = []\n for label in labels_set:\n # Loop through each cluster and calculate within cluster distance\n pairs = np.where(labels == label)\n pairs_distance = pdist(X[pairs[0]])\n within_cluster_distance = np.sum(pairs_distance, axis=0)\n Sw.append(within_cluster_distance)\n\n return np.sum(Sw)","def cluster_mcc_ratio(result, cluster_names, var, n=5):\n rel1=get_cluster_country_distr(result, var)\n clusters=calculate_cluster_size(result, var)\n hours=hours_tusc(result, var)\n res=\"\"\n for i in zip(clusters.index, cluster_names[:len(clusters)]):\n res=res+f\"By the number of unique visitors the {i[1]} cluster's top 5 countries are; \"\n rel=rel1.sort_values(i[0],ascending=False)[:n]\n for j in range(0,5):\n if j!=n-1:\n res=res+f'{rel[i[0]].index[j]} ({rel[i[0]][j]}%), '\n else:\n res=res+f'and {rel[i[0]].index[j]} ({rel[i[0]][j]}%). '\n res=res+f'This cluster spends on average {int(hours.hrs_in_tusc[i[0]])} days in Tuscany, '\n res=res+get_places_at_least4_hours(result, i[0], var)\n res=res+ cluster_airport_result(result, i[0], var)\n return res","def percentage(self):\n temp = self.cpu_freq_time_spent.copy()\n for i in self.cpu_freq_time_spent:\n total = 0\n for j in self.cpu_freq_time_spent[i]:\n total += self.cpu_freq_time_spent[i][j]\n for j in self.cpu_freq_time_spent[i]:\n if total != 0:\n temp[i][j] = self.cpu_freq_time_spent[i][j] * 100 / total\n else:\n temp[i][j] = 0\n return temp","def test_count_reads_per_cluster(self):\n \n bedtool = pybedtools.BedTool(clipper.test_file(\"clip_analysis_test_peak_results.bed\"))\n \n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\n \n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)","def GlobalClusteringCoefficient(graph):\n coef = np.mean(list(nx.clustering(graph).values()))\n return coef","def diversion_score(X, offspring_list):\r\n similarity_sum = 0\r\n if len(offspring_list[0]) == 2:\r\n offspring_list = [(parent_a, offspring, parent_a) for (parent_a, offspring) in offspring_list]\r\n for (parent_a, offspring, parent_b) in offspring_list:\r\n similarity_sum += max(icc(parent_a, offspring), icc(parent_b, offspring))\r\n return (1 - (((similarity_sum / len(offspring_list)) + 1) / 2)) * 100 # move from [-1,1] to [0,2], then to [0,1], then inverse, finally move to [0,100]\r","def _calculate_score(lsh, minhash, total_num_events):\n neighbours = lsh.query(minhash)\n return float(len(neighbours)) / float(total_num_events)","def ds_ratio(group):\n nix_count = (group=='nix').sum()\n top_count = (group=='top').sum()\n ratio = nix_count/(nix_count+top_count) #could smooth this\n return ratio","def evaluate_clusters(self, cluster_formulas, value='weighted_sum'):\n num_elems = len(self.labels)\n total_val = {}\n num_cl = len(cluster_formulas)\n clustered_points_num = 0\n print(\"\\n\\n\")\n print(\"Sufficiently big clusters: {}\".format(num_cl))\n for c, formula, val in cluster_formulas:\n c_size = len([l for l in self.labels if l == c])\n clustered_points_num += c_size\n\n if value == 'weighted_sum':\n total_val[c] = val * c_size / num_elems\n elif value == 'sum':\n total_val[c] = val * 1\n\n clust_val = sum(total_val.values())\n self.clustering_value = total_val\n print(\"Value of clustering: {}\".format(clust_val))\n return clust_val","def percent_overlap(items1, items2, k = None):\n if k is None:\n k = max([len(items1), len(items2)])\n assert k > 0 and k <= max([len(items1), len(items2)]), 'k is out of bounds!'\n items1_set, items2_set = set(items1[:k]), set(items2[:k])\n return len(items1_set & items2_set) / len(items1_set | items2_set)","def percentage(count, total):\n return count / total * 100","def cluster_size(result, var):\n df=calculate_cluster_size(result, var)\n df['cus']=df.index\n return df","def calculate_cluster_silhouette(self, cluster):\n cluster_size = len(cluster.get_samples())\n return self.sum_silhouette(cluster) / cluster_size","def percentage(self):\n return sum(self.chunk_percentage) / self.total_steps","def get_sectors_per_cluster(self):\n\n\t\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\n\t\tif sectors_per_cluster_base == 0:\n\t\t\traise BootSectorException('Invalid cluster size (zero)')\n\n\t\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\n\t\t\tsectors_per_cluster_real = sectors_per_cluster_base\n\t\telse:\n\t\t\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\n\t\t\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\n\n\t\treturn sectors_per_cluster_real","def pct_bust(data):\n return round((data[\"new_total\"] > 21).sum() / len(data), 3)","def valency(self):\n return len(self.neighbors())","def _avg_cluster_hitprobability(self, x, y, n_clusters=30):\n\n # Compute the individual Hit probability\n proba = self._shufflesplit(x, y)\n\n # average the individual hit probability for each cluster\n ind = self._cluster(x, x.shape[0]/2.)\n\n avg_proba = np.copy(proba)\n\n for cluster in np.unique(ind):\n mask_ = ind == cluster\n avg_proba[mask_] = avg_proba[mask_].mean()\n\n return avg_proba","def cluster_partition_distance(individual, test_data, truth_data, name=None):\r\n distance_sum = 0\r\n max_sum = 0\r\n for test_clusters, truth_clusters in zip(test_data, truth_data):\r\n # Get last column of target data\r\n test_clusters = test_clusters[-1].flatten()\r\n\r\n p1_dict = {}\r\n for i, x in enumerate(test_clusters):\r\n if x not in p1_dict:\r\n p1_dict[x] = []\r\n p1_dict[x].append(i)\r\n\r\n p2_dict = {}\r\n for i, x in enumerate(truth_clusters):\r\n if x not in p2_dict:\r\n p2_dict[x] = []\r\n p2_dict[x].append(i)\r\n\r\n p1 = list(p1_dict.values())\r\n p2 = list(p2_dict.values())\r\n d = _fast_partition_distance(p1, p2, len(test_clusters))\r\n if d is None:\r\n d = _partition_distance(p1, p2, len(test_clusters))\r\n distance_sum += d\r\n max_sum += len(test_clusters) - 1\r\n return distance_sum / max_sum","def hausd95(result, reference, voxelspacing=None, connectivity=1):\n hd1 = __surface_distances(result, reference, voxelspacing, connectivity)\n hd2 = __surface_distances(reference, result, voxelspacing, connectivity)\n hd95 = np.percentile(np.hstack((hd1, hd2)), 95)\n return hd95","def cluster_membership_occupancy(data):\n \n \n \n n_clusters = len(set(data['clusters'])-{-1}) # since -1 element denotes noice\n\n if n_clusters == 0:\n membership=[Cluster_Membership_Features()]\n membership = pd.DataFrame([o.__dict__ for o in membership])\n areas=[Cluster_Area_Features()]\n areas = pd.DataFrame([o.__dict__ for o in areas])\n density=[Cluster_Density_Features()]\n density = pd.DataFrame([o.__dict__ for o in density])\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\n density], axis=1)\n \n elif n_clusters ==1:\n #obtain_total_cluster_areas_set_everything_else_to_default\n membership=[Cluster_Membership_Features()]\n membership = pd.DataFrame([o.__dict__ for o in membership])\n d = dict(tuple(data.groupby('clusters')))\n d.pop(-1, None)\n \n try:\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\n except:\n cluster_chull_areas=[0,0,0]\n \n Total_cluster_area=np.sum(cluster_chull_areas)\n areas=[Cluster_Area_Features([Total_cluster_area,0,0,0,0,0,0,0,0])]\n areas = pd.DataFrame([o.__dict__ for o in areas])\n density=[Cluster_Density_Features()]\n density = pd.DataFrame([o.__dict__ for o in density])\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\n density], axis=1)\n \n elif n_clusters >1:\n #Summarizing the cluster membership distribution characteristics\n cluster_size_nums=np.delete(np.array(data.groupby(['clusters']).size()),0)\n (cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD)= distribution_statistics(cluster_size_nums)\n\n #For each cluster calculate the area by calculating the area of the convex hull of cluster members\n # Note: concavehull implementation here might be a good addition as it will provide more imformative values. \n\n d = dict(tuple(data.groupby('clusters')))\n d.pop(-1, None)\n try:\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\n except:\n cluster_chull_areas=[0,0,0,0,0]\n \n\n (avg_cluster_area,min_cluster_area,max_cluster_area,\n std_cluster_area,CV_cluster_area,CD_cluster_area,\n IQR_cluster_area,Quartile_CD_cluster_area)= distribution_statistics(cluster_chull_areas)\n Total_cluster_area=np.sum(cluster_chull_areas)\n\n #Calculate cluster density: number of nuclei/ convex area of cluster\n cluster_density=np.divide(cluster_size_nums,cluster_chull_areas)\n (avg_cluster_density,min_cluster_density,max_cluster_density,\n std_cluster_density,CV_cluster_density,CD_cluster_density,\n IQR_cluster_density,Quartile_CD_cluster_density)= distribution_statistics(cluster_density)\n\n #return dataframe of features\n membership=[Cluster_Membership_Features([cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD])]\n membership = pd.DataFrame([o.__dict__ for o in membership])\n areas=[Cluster_Area_Features([Total_cluster_area,\n avg_cluster_area,min_cluster_area,max_cluster_area,\n std_cluster_area,CV_cluster_area,CD_cluster_area,\n IQR_cluster_area,Quartile_CD_cluster_area])]\n areas = pd.DataFrame([o.__dict__ for o in areas])\n density=[Cluster_Density_Features([avg_cluster_density,min_cluster_density,max_cluster_density,\n std_cluster_density,CV_cluster_density,CD_cluster_density,\n IQR_cluster_density,Quartile_CD_cluster_density])]\n density = pd.DataFrame([o.__dict__ for o in density])\n\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\n density], axis=1)\n return all_features","def element_size(self):\n vecs = (\n self.nodes[self.elements[:, :4], :][:, 1:, :]\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\n )\n return np.abs(np.linalg.det(vecs)) / 6","def calculate_enrichment_factor(enrichment, ranked_dataset_percentage_cutoff):\n\n # Keep only molecules that meet the cutoff\n enrichment = enrichment[\n enrichment[\"% ranked dataset\"] <= ranked_dataset_percentage_cutoff / 100\n ]\n # Get highest percentage of actives and the corresponding percentage of actives\n highest_enrichment = enrichment.iloc[-1]\n enrichment_factor = round(100 * float(highest_enrichment[\"% true actives identified\"]), 1)\n return enrichment_factor","def media(self):\n self.kmeans = [[] for i in range(0,self.cluster_number)]\n for i in range(self.cluster_number):\n for j in range(0,len(self.cluster[i][0])):\n self.kmeans[i].append(np.sum(self.cluster[i][::,j:j+1:])/len(self.cluster[i][::,j:j+1:]))","def modularity():\n\n q = 0.0\n for idx in range(0, node_count):\n if _tot[idx] > 0.0:\n q += (_in[idx] / m - math.pow(_tot[idx] / m, 2))\n return q","def lscoreatpercentile (inlist, percent):\r\n if percent > 1:\r\n print \"\\nDividing percent>1 by 100 in lscoreatpercentile().\\n\"\r\n percent = percent / 100.0\r\n targetcf = percent*len(inlist)\r\n h, lrl, binsize, extras = histogram(inlist)\r\n cumhist = cumsum(copy.deepcopy(h))\r\n for i in range(len(cumhist)):\r\n if cumhist[i] >= targetcf:\r\n break\r\n score = binsize * ((targetcf - cumhist[i-1]) / float(h[i])) + (lrl+binsize*i)\r\n return score","def compute_distortion(cluster_list, data_table):\n\tdistortion = 0\n\tfor cluster in cluster_list:\n\t\tdistortion += cluster.cluster_error(data_table)\n\treturn distortion","def local_clustering_coefficient(graph, vertex):\r\n edge_count = 0\r\n for neighbour1 in graph[vertex]:\r\n for neighbour2 in graph[vertex]: #look at each pair of neighbours of vertex\r\n if neighbour1 in graph[neighbour2]: #if the neighbours are joined to each other by an edge\r\n edge_count += 1 #add one to the edge count\r\n degree = len(graph[vertex]) #count how many neighbours vertex has\r\n return edge_count / (degree * (degree - 1)) #note factor of 2 missing as each edge counted twice\r","def compute_distortion(cluster_list, data_table):\r\n distortion = 0\r\n \r\n for cluster in cluster_list:\r\n distortion += cluster.cluster_error(data_table)\r\n\r\n return distortion","def _calc_perc(arr: np.array, p: Sequence[float] = None):\n if p is None:\n p = [50]\n\n nan_count = np.isnan(arr).sum(axis=-1)\n out = np.moveaxis(np.percentile(arr, p, axis=-1), 0, -1)\n nans = (nan_count > 0) & (nan_count < arr.shape[-1])\n if np.any(nans):\n out_mask = np.stack([nans] * len(p), axis=-1)\n # arr1 = arr.reshape(int(arr.size / arr.shape[-1]), arr.shape[-1])\n # only use nanpercentile where we need it (slow performance compared to standard) :\n out[out_mask] = np.moveaxis(\n np.nanpercentile(arr[nans], p, axis=-1), 0, -1\n ).ravel()\n return out","def berger_parker_d(counts):\n return counts.max()/float(counts.sum())","def n_percentage_part(percentage_level, counted_od):\n total = 0.0\n od_num = 0\n percentage_od = count_to_percentage(counted_od)\n if percentage_level == 1.0:\n od_num = len(counted_od)\n else:\n for i in percentage_od:\n if total < percentage_level:\n total += i\n else:\n od_num = percentage_od.index(i)\n break\n return od_num","def percent_passing(self) -> float:\n num_meas = Enumerable(self.mlc_meas).select_many(lambda m: m.passed).count()\n num_pass = (\n Enumerable(self.mlc_meas)\n .select_many(lambda m: m.passed)\n .count(lambda p: bool(p) is True)\n )\n return float(100 * num_pass / num_meas)","def metric_value(x, y):\n return (sum(np.all(x == y, axis=1) * 1) / len(x)) * 100","def gap_statistic(X, cluster_nums, ref_num=10, cluster_method=None):\n if cluster_method is None:\n def k_means(data, cluster_nums):\n \"\"\"\n http://scikit-learn.org/stable/modules/clustering.html#k-means\n \"\"\"\n from sklearn.cluster import KMeans\n\n labels_list = []\n for cluster_num in cluster_nums:\n kmeans = KMeans(cluster_num, random_state=0, n_init=10).fit(data)\n labels_list.append(kmeans.labels_ + 1)\n print('KMeans finished: {}'.format(cluster_num))\n return labels_list\n\n cluster_method = k_means\n\n print('Start: calculate W\\u2096s')\n Wks = []\n labels_list = cluster_method(X, cluster_nums)\n for labels in labels_list:\n Wks.append(elbow_score(X, labels))\n Wks = np.array(Wks)\n Wks_log = np.log(Wks)\n print('Finish: calculate W\\u2096s')\n\n print(\"Start: calculate references' W\\u2096s\")\n Wks_refs_log = []\n minimums = np.atleast_2d(np.min(X, axis=0))\n maximums = np.atleast_2d(np.max(X, axis=0))\n bounding_box = np.r_[minimums, maximums]\n for i in range(ref_num):\n X_ref = uniform_box_sampling(X.shape[0], bounding_box)\n labels_list_ref = cluster_method(X_ref, cluster_nums)\n Wks_ref_log = []\n for labels in labels_list_ref:\n Wks_ref_log.append(np.log(elbow_score(X_ref, labels)))\n Wks_refs_log.append(Wks_ref_log)\n print('Finish reference: {}/{}'.format(i+1, ref_num))\n print(\"Finish: calculate references' W\\u2096s\")\n\n print('Start: calculate gaps')\n Wks_refs_log = np.array(Wks_refs_log)\n Wks_refs_log_mean = np.mean(Wks_refs_log, axis=0)\n Wks_refs_log_std = np.std(Wks_refs_log, axis=0)\n gaps = Wks_refs_log_mean - Wks_log\n print('Finish: calculate gaps')\n\n print('Start: select optimal k')\n s = Wks_refs_log_std * np.sqrt(1 + 1.0 / ref_num)\n idx_selected = np.where(gaps[:-1] >= gaps[1:] - s[1:])[0][0]\n k_selected = cluster_nums[idx_selected]\n print('Finish: select optimal k')\n\n return labels_list, Wks, Wks_refs_log_mean, gaps, s, k_selected","def compute_sufficient_stats(self):\n self.counts = (np.sum(self.resp, axis=0) + 10e-30)\n # print(self.counts)\n for k in range(self.k):\n self.means[k] = np.sum(self.resp[n, k] * self.x[n] for n in range(self.n)) / self.counts[k]\n self.covars[k] = np.sum(self.resp[n, k] * (self.x[n] - self.means[k]) @ (self.x[n] - self.means[k]).T\n for n in range(self.n)) / self.counts[k]\n self.covars[k] = np.nan_to_num(self.covars[k])\n self.means[k] = np.nan_to_num(self.means[k])","def fraction_mislabeled_nodes(labels, labels_pred):\n G1 = partition_indicator(labels)\n G2 = partition_indicator(labels_pred)\n\n # cost is minimized, overlap maximized\n cost_matrix = -G1.T.dot(G2)\n row_ind, col_ind = linear_sum_assignment(cost_matrix.A)\n cost = -cost_matrix[row_ind, col_ind].sum()\n\n return 1 - (cost / len(labels))","def clusterValues( values, relS=0.1 , refScaleAbs='range' ):\n if len(values)==0:\n return []\n if len(values.shape)==1:\n sortedV = numpy.stack([ values , numpy.arange(len(values))] ,1)\n else:\n # Assume value.shape = (N,2) and index are ok\n sortedV = values \n sortedV = sortedV[ numpy.argsort(sortedV[:,0]) ]\n\n sortedVV = sortedV[:,0]\n refScale = sortedVV[-1]-sortedVV[0]\n #sortedVV += 2*min(sortedVV)) # shift to avoid numerical issues around 0\n\n #print sortedVV\n class Cluster:\n def __init__(self, delta, sum, indices):\n self.delta = delta\n self.sum = sum\n self.N=len(indices)\n self.indices = indices\n def size(self):\n return self.delta/refScale\n \n def combine(self, c):\n #print ' combine ', self.indices[0], c.indices[-1], ' -> ', sortedVV[c.indices[-1]] - sortedVV[self.indices[0]]\n newC = Cluster(sortedVV[c.indices[-1]] - sortedVV[self.indices[0]],\n self.sum+c.sum,\n self.indices+c.indices)\n return newC\n\n def originIndices(self):\n return tuple(int(sortedV[i][1]) for i in self.indices)\n\n def size_local(self):\n return self.delta / sum( sortedVV[i] for i in self.indices) *len(self.indices)\n def size_range(self):\n return self.delta/refScale\n def size_abs(self):\n return self.delta\n\n if refScaleAbs=='range':\n Cluster.size = size_range\n elif refScaleAbs=='local':\n Cluster.size = size_local\n elif refScaleAbs=='abs':\n Cluster.size = size_abs\n \n class ClusterPair:\n next=None\n prev=None\n def __init__(self, c1, c2 ):\n self.c1=c1\n self.c2=c2\n self.refresh()\n def refresh(self):\n self.potentialC =self.c1.combine(self.c2)\n self.size = self.potentialC.size()\n def setC1(self, c1):\n self.c1=c1\n self.refresh()\n def setC2(self, c2):\n self.c2=c2\n self.refresh()\n \n #ave = 0.5*(sortedVV[1:,0]+sortedV[:-1,0])\n #deltaR = (sortedV[1:,0]-sortedV[:-1,0])/ave\n\n cList = [Cluster(0,v,(i,)) for (i,v) in enumerate(sortedVV) ]\n cpList = [ ClusterPair( c, cList[i+1] ) for (i,c) in enumerate(cList[:-1]) ]\n resetPrevNextSegment( cpList )\n\n #print cpList\n def reduceCL( cList ):\n if len(cList)<=1:\n return cList\n cp = min(cList, key=lambda cp:cp.size) \n #print '==', cp.size , relS, cp.c1.indices , cp.c2.indices, cp.potentialC.indices\n\n while cp.size < relS:\n if cp.next:\n cp.next.setC1(cp.potentialC)\n cp.next.prev = cp.prev\n if cp.prev:\n cp.prev.setC2(cp.potentialC)\n cp.prev.next = cp.next\n cList.remove(cp)\n if len(cList)<2:\n break\n cp = min(cList, key=lambda cp:cp.size) \n #print ' -----> ', [ (cp.c1.indices , cp.c2.indices) for cp in cList]\n return cList\n\n cpList = reduceCL(cpList)\n if len(cpList)==1:\n cp = cpList[0]\n if cp.potentialC.size()<relS:\n return [ cp.potentialC.originIndices() ]\n #print cpList\n if cpList==[]:\n return []\n finalCL = [ cp.c1.originIndices() for cp in cpList ]+[ cpList[-1].c2.originIndices() ]\n return finalCL"],"string":"[\n \"def analysis_function_num_clusters_to_percent(self,clustering,percent):\\n return clustering.number_of_clusters_to_get_percent(percent)\",\n \"def analysis_function_top_4(self,clustering):\\n clustering.sort_clusters_by_size()\\n total = 0\\n percents = clustering.get_population_percent_of_n_bigger_clusters(4)\\n for p in percents:\\n total = total+p\\n return total\",\n \"def get_clust_num_perc(model, vis_perc=0.9):\\n\\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\\n\\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\\n\\n\\ttot = model.allocmodel.Nk[idx].sum()\\n\\tcursum = 0\\n\\ti = 0\\n\\twhile cursum < tot*vis_perc:\\n\\t cursum += model.allocmodel.Nk[idx][i]\\n\\t i+=1\\n\\n\\treturn i\",\n \"def calculate_cluster_size(result, var):\\n \\n cluster_results=pd.DataFrame(result[var].value_counts())\\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\\\"ratio\\\"})\\n return cluster_results.join(ratio)\",\n \"def analysis_function_total_elements(self,clustering):\\n return clustering.total_number_of_elements\",\n \"def dissimilarity(clusters):\\n totDist = 0\\n for c in clusters:\\n totDist += c.variability()\\n return totDist\",\n \"def purity_score(clusters, classes):\\n clusters = np.array(clusters)\\n classes = np.array(classes)\\n A = np.c_[(clusters,classes)]\\n\\n n_accurate = 0.\\n\\n for j in np.unique(A[:,0]):\\n z = A[A[:,0] == j, 1]\\n x = np.argmax(np.bincount(z))\\n n_accurate += len(z[z == x])\\n\\n return n_accurate / A.shape[0]\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def cluster_cal(self):\\n self.Cluster = []\\n for i in range(self.nodenum):\\n neighborhood_node = self.neighbor_node(i)\\n Node_num = len(neighborhood_node)\\n Count = self.neighbor_edge(neighborhood_node)\\n if(Node_num == 0 or Node_num == 1):\\n self.Cluster.append(0.5)\\n else:\\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\\n \\n self.cluster_coeff = np.average(self.Cluster)\",\n \"def compute_total_bipartiteness(hypergraph, clusters):\\n bipartiteness_sum = 0\\n for i in range(len(clusters)):\\n for j in range(i + 1, len(clusters)):\\n bipartiteness_sum += hypergraph_bipartiteness(hypergraph, clusters[i], clusters[j])\\n return bipartiteness_sum\",\n \"def get_modularity(adjacency, clusters):\\n total_weight = np.sum(adjacency)\\n e = get_clusters_adjacencies(adjacency, clusters)\\n e = e / total_weight\\n a = np.sum(e, axis=1)\\n return np.sum(e.diagonal() - np.power(a, 2))\",\n \"def get_susceptibility(clusters):\\n\\n # If there is no or only one cluster then there is no finite cluster\\n if len(clusters) <= 1:\\n return np.nan\\n\\n # Remove largest, i.e. infinite, cluster\\n clusters.remove(max(clusters))\\n\\n sizes = np.array(list(set(clusters)))\\n n_s = []\\n\\n for size in sizes:\\n n_s.append(clusters.count(size))\\n\\n temp = sizes * n_s\\n S = np.sum(sizes * temp) / np.sum(temp)\\n\\n return S\",\n \"def contains_percentage_of(self, other: 'Interval') -> float:\\n if other.length == 0:\\n return other.a in self\\n intersection = Interval.intersection([self, other])\\n return intersection.length / other.length if intersection else 0.0\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def assign_labels_to_centroids(self):\\n labelled_centroids = []\\n for i in range(len(self.final_clusters)):\\n labels = map(lambda x: x[0], self.final_clusters[i])\\n # pick the most common label\\n most_common = Counter(labels).most_common(1)[0][0]\\n c = np.round(len([item for item in self.final_clusters[i] if item[0]==1])/len(self.final_clusters[i]),2)\\n if c>=0.46:\\n most_common = 1.0\\n centroid = (most_common, self.final_centroids[i])\\n labelled_centroids.append(centroid)\\n\\n self.labelled_centroids = labelled_centroids\\n print(\\\"cluster_0: \\\", np.round(len([item for item in self.final_clusters[0] if item[0]==1])/len(self.final_clusters[0]),2), \\\"size_0: \\\", len(self.final_clusters[0]))\\n print(\\\"cluster_1: \\\", np.round(len([item for item in self.final_clusters[1] if item[0]==1])/len(self.final_clusters[1]),2), \\\"size_1: \\\", len(self.final_clusters[1]))\\n #print(\\\"cluster_2: \\\", np.round(len([item for item in self.final_clusters[2] if item[0]==1])/len(self.final_clusters[2]),2), \\\"size_2: \\\", len(self.final_clusters[2]))\\n #print(\\\"cluster_3: \\\", np.round(len([item for item in self.final_clusters[3] if item[0]==1])/len(self.final_clusters[3]),2), \\\"size_2: \\\", len(self.final_clusters[3]))\",\n \"def compute_cluster_class_fractions(k_means_model, y):\\n\\n n_classes = y.shape[1]\\n class_labels = utils.one_hot_to_index(y)\\n cluster_labels = k_means_model.labels_\\n\\n class_clustroid_counts = np.zeros((n_classes, K))\\n for i in range(len(class_labels)):\\n class_clustroid_counts[class_labels[i], cluster_labels[i]] += 1\\n\\n class_clustroid_fractions = class_clustroid_counts / np.sum(class_clustroid_counts, axis=1).reshape(n_classes, 1)\\n\\n print(\\\"\\\\n---- Class Clustroid Distribution ----\\\")\\n for i in range(n_classes):\\n print(\\\"Class {}: {}\\\".format(i, class_clustroid_fractions[i, :]))\",\n \"def calculate_cluster_fitness(self, cluster_id: ObjectId):\\n\\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\\n\\n cluster_fitness = 0\\n\\n for genome in genomes:\\n cluster_fitness += genome.fitness\\n if cluster_fitness == 0:\\n return 0\\n\\n return cluster_fitness / len(list(genomes))\",\n \"def purity(clusters, classes):\\n\\n d = defaultdict(list)\\n\\n # Get a list of class numbers of all examples in a cluster.\\n for k, v in zip(clusters, classes):\\n d[k].append(v)\\n\\n mayority = 0\\n\\n # Count the mayority class number and add it up over all clusters.\\n for k in d:\\n mayority += Counter(d[k]).most_common(1)[0][1]\\n\\n return float(mayority) / len(clusters)\",\n \"def get_utilization(self):\\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\\n # Remove overlapping prefixes from list of children\\n networks = cidr_merge([c.prefix for c in child_prefixes])\\n children_size = float(0)\\n for p in networks:\\n children_size += p.size\\n return int(children_size / self.prefix.size * 100)\",\n \"def percenter(rank, max_rank):\\n\\treturn 100 * (rank/(max_rank or 1))\",\n \"def clust_strength(mat,groups):\\n cluster_strengths = []\\n for group in range(len(np.unique(groups))):\\n this_cluster = mat[groups==group,:]\\n this_cluster_mean = np.mean(this_cluster,axis=0)\\n all_dists = mat - this_cluster_mean\\n out_dists = np.linalg.norm(all_dists[groups!=group],axis=1)\\n in_dists = np.linalg.norm(all_dists[groups==group],axis=1)\\n this_strength = np.mean(out_dists)/np.mean(in_dists)\\n cluster_strengths.append(this_strength)\\n \\n return np.mean(cluster_strengths)\",\n \"def leaf_nodes_to_search_percent(self) -> Optional[int]:\\n return pulumi.get(self, \\\"leaf_nodes_to_search_percent\\\")\",\n \"def group_size_dist(self):\\n return self.group_sizes() / self.group_sizes().sum()\",\n \"def clusterAlgorithm(values):\\n clusterMap = dict()\\n for value in values:\\n if value[2] not in clusterMap.keys():\\n clusterMap[value[2]] = []\\n clusterMap[value[2]].append(value)\\n frequency = [float(len(clusterMap[value[2]])) for value in values]\\n total = sum(frequency)\\n weightValues = [freq / total for freq in frequency]\\n print sum(weightValues)\\n lightValues = [value[1] for value in values]\\n return np.average(lightValues, weights = weightValues)\",\n \"def _cluster_hitprobability(self, x, y):\\n hm_count = np.zeros_like(y).astype(float)\\n hm = np.zeros_like(y).astype(float)\\n #skf = StratifiedShuffleSplit(n_splits=self.n_iter, test_size=self.shuffle_test_split, random_state=self.random_state)\\n\\n ind = self._cluster(x, 35)\\n\\n for cluster_id in np.unique(ind):\\n test = np.argwhere(ind == cluster_id)[:, 0]\\n train = np.argwhere(ind != cluster_id)[:, 0]\\n #print test\\n self.basemodel.fit(x[train, :], y[train], hyperparams_optim=False)\\n hm_count[test] += 1.\\n hm[test] += (self.basemodel.predict(x[test, :]) == y[test]).astype(float)\\n\\n proba = hm / hm_count\\n if self.verbose:\\n # print('H/M count:')\\n # print(hm_count)\\n print('Proba:')\\n print(proba)\\n self.basemodel.fit(x, y, hyperparams_optim=False)\\n return proba\",\n \"def get_modularity_other_c(A, cluster_indices):\\n # define the number of nodes in the graph and the number of clusters\\n n = len(cluster_indices)\\n nclusters = max(cluster_indices) + 1\\n # define the row sums of the adjacency matrix\\n row_sums = [sum(row) for row in A]\\n # define one half of the sum of all entries in the adjacency matrix\\n m = sum(row_sums) / 2.0\\n # define the modularity\\n Q = 0\\n for i in range(n):\\n for j in range(n):\\n if cluster_indices[i] == cluster_indices[j]:\\n Q += (A[i][j] - row_sums[i] * row_sums[j] / (2*m)) / (2*m)\\n return Q\",\n \"def compute_clusters(self, p: float):\\n pass\",\n \"def analysis_function_mean_cluster_size(self,clustering):\\n sizes = get_cluster_sizes(clustering.clusters)[1]\\n return numpy.mean(sizes)\",\n \"def getBeliefDistribution(self):\\n # This essentially gives a point to a location for each particle there, then \\n # normalizes the point values so they add up to 1.\\n dist = util.Counter()\\n for part in self.particles: dist[part] += 1\\n dist.normalize()\\n return dist\",\n \"def clusterSize(l, scheme, clustertype='fluid'):\\n clist = findClusters(l, scheme, clustertype)\\n \\n avglists=[]\\n for i in clist:\\n avglist=[]\\n for l in i:\\n avglist.append(np.mean(l))\\n avglists.append(np.mean(avglist))\\n return avglists\",\n \"def average_consensus(self, cluster):\\n\\t\\tcenterk = 0\\n\\t\\tindex = 0\\n\\t\\tfor value in cluster:\\n\\t\\t\\tcenterk += value\\n\\t\\t\\tindex += 1\\n\\t\\tcenterk = centerk / index\\n\\t\\treturn centerk\",\n \"def inter_cost(cluster):\\n def _p2p(point):\\n _freq_sum = 0\\n for pt in cluster.points:\\n if point != pt:\\n _freq_sum += point.frequency(pt)\\n return _freq_sum\\n\\n return int(sum(map(_p2p, cluster.points)))\",\n \"def describe_reduce_nb(col, a, perc, ddof, *args):\\n a = a[~np.isnan(a)]\\n out = np.empty(5 + len(perc), dtype=np.float_)\\n out[0] = len(a)\\n if len(a) > 0:\\n out[1] = np.mean(a)\\n out[2] = nanstd_1d_nb(a, ddof=ddof)\\n out[3] = np.min(a)\\n out[4:-1] = np.percentile(a, perc * 100)\\n out[4 + len(perc)] = np.max(a)\\n else:\\n out[1:] = np.nan\\n return out\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def test_clusters(trained_data, centroids):\\n\\n for c in range(len(centroids)):\\n count_1 = 0\\n count_0 = 0\\n for p in range(len(trained_data)):\\n if trained_data[p][-2] == 0 and trained_data[p][-1] == centroids[c]:\\n count_0 += 1\\n if trained_data[p][-2] == 1 and trained_data[p][-1] == centroids[c]:\\n count_1 += 1\\n print (\\\"Centroid \\\", c+1, \\\":\\\", centroids[c])\\n print(\\\"Number of 1's: \\\", count_1)\\n print(\\\"Number of 0's: \\\", count_0)\\n print(\\\"Percent 1's: \\\", round((count_1/(count_1 + count_0))*100,2))\\n print(\\\"Percent 0's: \\\", round((count_0 / (count_1 + count_0)) * 100,2))\\n print(\\\"****************\\\")\",\n \"def occupation_distribution(data):\",\n \"def __cluster_simi(self, i, j):\\n sum_ = 0.\\n for si in self.__indexclusters[i]:\\n for sj in self.__indexclusters[j]:\\n simi = self.__sample_simi(si, sj)\\n sum_ += simi\\n return sum_ / (len(self.__indexclusters[i]) * len(self.__indexclusters[j]))\",\n \"def _calculate_cluster_probs(self, dist_mat, temperature):\\n dist_mat = np.square(dist_mat) # euclidean distance -> squared euclidean distance\\n n_samples = dist_mat.shape[0]\\n n_clusters = dist_mat.shape[1]\\n cluster_probs = np.zeros(shape=(n_samples, n_clusters))\\n for i in range(n_clusters):\\n for j in range(n_samples):\\n normalizer = sum(self.marginal_probs[k] * np.exp(-dist_mat[j, k] / temperature) for k in range(n_clusters))\\n cluster_probs[j, i] = self.marginal_probs[i] * np.exp(-dist_mat[j, i] / temperature) / normalizer\\n return cluster_probs\",\n \"def get_modularity3(adjacency, clusters):\\n\\n rows, cols = adjacency.shape\\n num_ids = adjacency.shape[0]\\n id_to_cluster = get_id_to_cluster(clusters, num_ids)\\n degrees = np.sum(adjacency, axis=1)\\n total_weight = np.sum(adjacency)\\n sum = 0\\n for i in range(rows):\\n for j in range(cols):\\n if id_to_cluster[i] == id_to_cluster[j]:\\n sum += adjacency[i, j] - (degrees[i] * degrees[j]) / total_weight\\n sum = sum / total_weight\\n return sum\",\n \"def _assign_clusters(self):\\n\\n dist = np.zeros((self.k, ))\\n distortion = 0\\n\\n for index in range(0, self.data.shape[0]):\\n for i in range(0, self.k):\\n dist[i] = np.linalg.norm(self.data[index] - self.centroids[i])\\n\\n self.assigned_clusters[index] = np.argmin(dist)\\n distortion += np.min(dist)\\n\\n return distortion\",\n \"def estimate_label_proportion(source_loader,target_loader,feat_extract,cuda,n_clusters,cluster_param): \\n feat_extract.eval()\\n #n_clusters = 3\\n from sklearn.cluster import AgglomerativeClustering\\n \\n \\n X_s,y_s = extract_feature(source_loader,feat_extract,cuda) \\n X_t,y_t = extract_feature(target_loader,feat_extract,cuda) \\n \\n \\n \\n cluster = AgglomerativeClustering(n_clusters=n_clusters,linkage=cluster_param)\\n label_t = cluster.fit_predict(X_t)\\n #print(np.unique(label_t))\\n mean_mat_S, num_in_class_S = extract_prototypes(X_s,y_s,n_clusters)\\n mean_mat_T, num_in_class_T = extract_prototypes(X_t,label_t,n_clusters)\\n \\n \\\"\\\"\\\"\\n We assume that prototypes of classes have been transported in some in the feature\\n space \\n \\\"\\\"\\\"\\n \\n import ot\\n M = ot.dist(mean_mat_S, mean_mat_T)\\n M /= M.max()\\n \\n n_1 = n_clusters\\n a = np.ones((n_1,)) / n_1\\n b = np.ones((n_1,)) / n_1\\n \\n \\n gamma = ot.emd(a,b,M)\\n nb_sample_S = [ np.sum(y_s==i) for i in range(n_clusters) ]\\n proportion_T = num_in_class_T/np.sum(num_in_class_T)\\n assignement_source_to_target = gamma.argmax(axis=1)\\n \\n # proportions are arranged directly per class\\n proportion_T = proportion_T[assignement_source_to_target]\\n print(proportion_T,assignement_source_to_target)\\n \\n\\n return proportion_T,nb_sample_S, assignement_source_to_target\",\n \"def clustering_coefficient(graph):\\r\\n count = 0\\r\\n sumOfClusteringCoefficients = 0\\r\\n for vertex in graph:\\r\\n count += 1\\r\\n sumOfClusteringCoefficients += local_clustering_coefficient(graph, vertex)\\r\\n return sumOfClusteringCoefficients / count\",\n \"def analysis_function_num_clusters(self,clustering):\\n return len(clustering.clusters)\",\n \"def davies_bouldin_score(self):\\r\\n print(colored(\\\"The davies bouldin score of the clustering is %0.002f\\\\n\\\" %(davies_bouldin_score(self.X, self.labels)),color = 'red', attrs=['bold']))\\r\\n print()\\r\\n print(colored(\\\"The points in each cluster are : \\\",color = 'yellow', attrs=['bold']))\\r\\n print(collections.Counter(self.labels))\",\n \"def calculate_cost(self, medoids, clusters):\\n cost = 0.0\\n for i in range(0, len(medoids)):\\n for j in range(0, len(clusters[i])):\\n cost += distance.sqeuclidean(medoids[i], clusters[i][j])\\n return cost\\n pass\",\n \"def get_cluster_to_split(clusters):\\n\\treturn max(clusters.items(), key=lambda x: x[1].get_distortion())[1]\",\n \"def d50(clones, num_Reads): \\n\\n\\n d50_amount = num_Reads/2\\n read_count=0\\n for i in clones:\\n read_count+=clones[i].num_reads\\n if read_count>=d50_amount:\\n return i/float(len(clones))\",\n \"def _calculate_cluster_measures(\\n arr4d,\\n threshold,\\n bin_struct,\\n two_sided_test=False,\\n):\\n n_regressors = arr4d.shape[3]\\n\\n max_sizes = np.zeros(n_regressors, int)\\n max_masses = np.zeros(n_regressors, float)\\n\\n for i_regressor in range(n_regressors):\\n arr3d = arr4d[..., i_regressor].copy()\\n\\n if two_sided_test:\\n arr3d[np.abs(arr3d) <= threshold] = 0\\n else:\\n arr3d[arr3d <= threshold] = 0\\n\\n labeled_arr3d, _ = label(arr3d > 0, bin_struct)\\n\\n if two_sided_test:\\n # Label positive and negative clusters separately\\n n_positive_clusters = np.max(labeled_arr3d)\\n temp_labeled_arr3d, _ = label(\\n arr3d < 0,\\n bin_struct,\\n )\\n temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters\\n labeled_arr3d = labeled_arr3d + temp_labeled_arr3d\\n del temp_labeled_arr3d\\n\\n clust_vals, clust_sizes = np.unique(labeled_arr3d, return_counts=True)\\n assert clust_vals[0] == 0\\n\\n clust_vals = clust_vals[1:] # First cluster is zeros in matrix\\n clust_sizes = clust_sizes[1:]\\n\\n # Cluster mass-based inference\\n max_mass = 0\\n for unique_val in clust_vals:\\n ss_vals = np.abs(arr3d[labeled_arr3d == unique_val]) - threshold\\n max_mass = np.maximum(max_mass, np.sum(ss_vals))\\n\\n # Cluster size-based inference\\n max_size = 0\\n if clust_sizes.size:\\n max_size = np.max(clust_sizes)\\n\\n max_sizes[i_regressor], max_masses[i_regressor] = max_size, max_mass\\n\\n return max_sizes, max_masses\",\n \"def calc_Nw(cluster_labels):\\n\\n cluster_labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Nw = []\\n for label in labels_set:\\n n_examples = np.sum(np.where(cluster_labels == label, 1, 0))\\n n_cluster_pairs = n_examples * (n_examples - 1) / 2 # Combinations\\n Nw.append(n_cluster_pairs)\\n\\n return int(np.sum(Nw))\",\n \"def avg_sil_width(cluster_vec, centroid):\\n tot_dis = 0\\n for i in xrange(len(cluster_vec)):\\n cos_dis = cos_sim(cluster_vec[i], centroid)\\n tot_dis = tot_dis + cos_dis\\n return tot_dis/len(cluster_vec)\",\n \"def calculate_sample_silhouette(self):\\n sum_samples = 0\\n for cluster in self.cluster_lst:\\n sum_samples += self.sum_silhouette(cluster)\\n sample_size = len(self.samples)\\n return sum_samples/sample_size\",\n \"def print_cluster(self, cluster, value):\\n total = 0\\n ham = 0\\n spam = 0\\n for message in cluster:\\n if self.spamorham[self.ids[message]] == 'ham':\\n ham += 1\\n elif self.spamorham[self.ids[message]] == 'spam':\\n spam += 1\\n else:\\n print(\\\"ERROR!\\\")\\n total += 1\\n\\n print(\\\"Total number of messages in the {0} cluster: {1}\\\\n\\\"\\n \\\"Percentage of SPAM messages in the {2} cluster: {3}\\\\n\\\"\\n \\\"Percentage of HAM messages in the {4} cluster: {5}\\\".format(value, total, value,\\n str((float(spam) / total) * 100), value,\\n str((float(ham) / total) * 100)))\",\n \"def SquareClusteringCoefficient(graph):\\n coef = np.mean(list(nx.square_clustering(graph).values()))\\n return coef\",\n \"def percent_processed(self):\\n try:\\n return (self.pos / self.data_encap.size) * 100.0\\n except ZeroDivisionError:\\n return 100.0\",\n \"def _overlap_energy(self, this, that):\\n if not this.overlaps(that):\\n return 0.0\\n\\n return min(10.0 / this.rank, 10.0 / that.rank)\",\n \"def purity_score(label, pred):\\n \\n df = pd.concat([label, pd.DataFrame(pred)], axis=1)\\n df.set_axis(['label', 'pred'], axis=1, inplace=True)\\n \\n s = 0\\n\\n for x, cluster in df.groupby('pred'):\\n s += cluster['label'].value_counts().iloc[0] # adding the most occuring class in a cluster\\n\\n return s / label.shape[0]\",\n \"def get_optimal_clusters(cell,threshold=140):\\n\\n\\t#\\tTurn image to numpy array\\n\\tpic = image_to_matrix(cell)\\n\\n\\t#\\tGet the array of coordinates of dark dots\\n\\tdots = get_threshold_dots(pic,threshold)\\n\\n\\tscores = []\\n\\n\\tfor n_clusters in range(1,10):\\n\\t\\tclusters = kmeans.kmeans(pic,pic.shape[0],pic.shape[1],50,n_clusters,threshold)\\n\\t\\tprint clusters\\n\\n\\t\\tsquare_sum_array = [0]*n_clusters\\n\\t\\tcount_array = [0]*n_clusters\\n\\n\\t\\tfor dot in dots:\\n\\t\\t\\tdistance_array = [kmeans.euclid_distance(dot,cluster) for cluster in clusters]\\n\\t\\t\\tmin_index = distance_array.index(min(distance_array))\\n\\t\\t\\tsquare_sum_array[min_index] += kmeans.euclid_distance(clusters[min_index],dot)\\n\\t\\t\\tcount_array[min_index] += 1\\n\\n\\t\\tvariances = [square_sum/(count+0.001) for square_sum, count in zip(square_sum_array,count_array)]\\n\\n\\t\\tprint variances\\n\\t\\tscores.append(sum(variances)/len(variances))\\n\\n\\treturn scores\",\n \"def percentage_hapaxes(corpus_parts, corpus):\\n percentage_h = []\\n count = 0\\n dv = divide_corpus(corpus, 10)\\n hapax_parts = hapaxes_parts(corpus_parts)\\n for x in hapax_parts:\\n percentage_h.append(percentage(x, len(dv[count])))\\n count += 1\\n return percentage_h\",\n \"def disaggregate_by_cluster(self):\\n # wt = np.zeros((1, self.ds.shape[1]))\\n # total = np.zeros((self.n_ahead, self.ds.shape[1]))\\n \\n agg_cluster_ds = np.zeros((self.n_ahead+1, self.n_clusters))\\n agg_cluster_ds[0] = self.ds_agg_by_c[-1]\\n agg_cluster_ds[1:] = self.ds_c_for\\n cluster_perc_change = np.diff(agg_cluster_ds, axis = 0) / agg_cluster_ds[:-1]\\n\\n cluster_scaling_vector = np.zeros((2, self.ds.shape[1]))\\n\\n # break down proportionally -> don't work well\\n # for c in range(self.n_clusters):\\n # c_m = self.ds.iloc[-self.cluster_n_period:, np.where(self.ds_c == c)[0]]\\n # c_sum = sum(c_m)\\n # indiv_sum = np.sum(c_m, axis = 0)\\n # wt[:,np.where(self.ds_c == c)[0]] = (indiv_sum/c_sum)\\n # total[:,np.where(self.ds_c == c)[0]] = np.reshape(\\n # np.repeat(self.ds_c_for[:,c], c_m.shape[1]), (self.n_ahead, c_m.shape[1]))\\n \\n # multiply by the perc change\\n \\n for i in range(self.ds_c.shape[0]):\\n cluster_scaling_vector[:,i] = cluster_perc_change[:,self.ds_c[i]]\\n cluster_scaling_vector = cluster_scaling_vector+1\\n cluster_scaling_vector = np.array(cluster_scaling_vector)\\n \\n self.ds_for = self.ds.copy()\\n\\n for yr in range(self.n_ahead)[::-1]:\\n # forecast on foretasted number\\n yr_ind = self.ds_for.index[-(yr+1)]\\n self.ds_for.ix[yr_ind] = self.ds_for.iloc[-(yr+2),:].values * cluster_scaling_vector[-(yr+1)]\\n\\n # self.ds_for.iloc[-(self.n_ahead):,:] = self.ds_for.iloc[-(self.n_ahead+1):-1,:].values * np.array(cluster_scaling_vector)\\n\\n # if negative -> 0\\n self.ds_for[self.ds_for < 0] = 0\",\n \"def dim_reduction( M ):\\n tot_count_per_type = M.sum(axis = 1)\\n tot_count = float(tot_count_per_type.sum())\\n sorted_index = np.argsort(tot_count_per_type)\\n threshold = 0.01\\n accu = 0\\n for i in range(len(sorted_index)):\\n perc = float(tot_count_per_type[sorted_index[i]])/tot_count\\n accu = accu + perc\\n if accu > threshold:\\n break;\\n \\n return sorted_index[0:i]\",\n \"def in_xi(self, sample, cluster, cluster_size):\\n sum_distance = 0\\n for cur_sample in cluster.get_samples():\\n if sample.get_s_id() != cur_sample.get_s_id():\\n if cur_sample.get_s_id() < sample.get_s_id():\\n sum_distance += self.distance_dict[(cur_sample.get_s_id(), sample.get_s_id())]\\n elif cur_sample.get_s_id() > sample.get_s_id():\\n sum_distance += self.distance_dict[(sample.get_s_id(), cur_sample.get_s_id())]\\n return sum_distance / (cluster_size - 1)\",\n \"def num_links(self):\\n count=0.0\\n for cluster in self.clusters:\\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\\n numberofmembers=self.clusters[cluster].number_of_members\\n count+=numberofmembers\\n return count\",\n \"def calc_sw(X, cluster_labels):\\n\\n labels = np.array(cluster_labels)\\n labels_set = set(cluster_labels)\\n n_labels = len(labels_set)\\n\\n Sw = []\\n for label in labels_set:\\n # Loop through each cluster and calculate within cluster distance\\n pairs = np.where(labels == label)\\n pairs_distance = pdist(X[pairs[0]])\\n within_cluster_distance = np.sum(pairs_distance, axis=0)\\n Sw.append(within_cluster_distance)\\n\\n return np.sum(Sw)\",\n \"def cluster_mcc_ratio(result, cluster_names, var, n=5):\\n rel1=get_cluster_country_distr(result, var)\\n clusters=calculate_cluster_size(result, var)\\n hours=hours_tusc(result, var)\\n res=\\\"\\\"\\n for i in zip(clusters.index, cluster_names[:len(clusters)]):\\n res=res+f\\\"By the number of unique visitors the {i[1]} cluster's top 5 countries are; \\\"\\n rel=rel1.sort_values(i[0],ascending=False)[:n]\\n for j in range(0,5):\\n if j!=n-1:\\n res=res+f'{rel[i[0]].index[j]} ({rel[i[0]][j]}%), '\\n else:\\n res=res+f'and {rel[i[0]].index[j]} ({rel[i[0]][j]}%). '\\n res=res+f'This cluster spends on average {int(hours.hrs_in_tusc[i[0]])} days in Tuscany, '\\n res=res+get_places_at_least4_hours(result, i[0], var)\\n res=res+ cluster_airport_result(result, i[0], var)\\n return res\",\n \"def percentage(self):\\n temp = self.cpu_freq_time_spent.copy()\\n for i in self.cpu_freq_time_spent:\\n total = 0\\n for j in self.cpu_freq_time_spent[i]:\\n total += self.cpu_freq_time_spent[i][j]\\n for j in self.cpu_freq_time_spent[i]:\\n if total != 0:\\n temp[i][j] = self.cpu_freq_time_spent[i][j] * 100 / total\\n else:\\n temp[i][j] = 0\\n return temp\",\n \"def test_count_reads_per_cluster(self):\\n \\n bedtool = pybedtools.BedTool(clipper.test_file(\\\"clip_analysis_test_peak_results.bed\\\"))\\n \\n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\\n \\n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)\",\n \"def GlobalClusteringCoefficient(graph):\\n coef = np.mean(list(nx.clustering(graph).values()))\\n return coef\",\n \"def diversion_score(X, offspring_list):\\r\\n similarity_sum = 0\\r\\n if len(offspring_list[0]) == 2:\\r\\n offspring_list = [(parent_a, offspring, parent_a) for (parent_a, offspring) in offspring_list]\\r\\n for (parent_a, offspring, parent_b) in offspring_list:\\r\\n similarity_sum += max(icc(parent_a, offspring), icc(parent_b, offspring))\\r\\n return (1 - (((similarity_sum / len(offspring_list)) + 1) / 2)) * 100 # move from [-1,1] to [0,2], then to [0,1], then inverse, finally move to [0,100]\\r\",\n \"def _calculate_score(lsh, minhash, total_num_events):\\n neighbours = lsh.query(minhash)\\n return float(len(neighbours)) / float(total_num_events)\",\n \"def ds_ratio(group):\\n nix_count = (group=='nix').sum()\\n top_count = (group=='top').sum()\\n ratio = nix_count/(nix_count+top_count) #could smooth this\\n return ratio\",\n \"def evaluate_clusters(self, cluster_formulas, value='weighted_sum'):\\n num_elems = len(self.labels)\\n total_val = {}\\n num_cl = len(cluster_formulas)\\n clustered_points_num = 0\\n print(\\\"\\\\n\\\\n\\\")\\n print(\\\"Sufficiently big clusters: {}\\\".format(num_cl))\\n for c, formula, val in cluster_formulas:\\n c_size = len([l for l in self.labels if l == c])\\n clustered_points_num += c_size\\n\\n if value == 'weighted_sum':\\n total_val[c] = val * c_size / num_elems\\n elif value == 'sum':\\n total_val[c] = val * 1\\n\\n clust_val = sum(total_val.values())\\n self.clustering_value = total_val\\n print(\\\"Value of clustering: {}\\\".format(clust_val))\\n return clust_val\",\n \"def percent_overlap(items1, items2, k = None):\\n if k is None:\\n k = max([len(items1), len(items2)])\\n assert k > 0 and k <= max([len(items1), len(items2)]), 'k is out of bounds!'\\n items1_set, items2_set = set(items1[:k]), set(items2[:k])\\n return len(items1_set & items2_set) / len(items1_set | items2_set)\",\n \"def percentage(count, total):\\n return count / total * 100\",\n \"def cluster_size(result, var):\\n df=calculate_cluster_size(result, var)\\n df['cus']=df.index\\n return df\",\n \"def calculate_cluster_silhouette(self, cluster):\\n cluster_size = len(cluster.get_samples())\\n return self.sum_silhouette(cluster) / cluster_size\",\n \"def percentage(self):\\n return sum(self.chunk_percentage) / self.total_steps\",\n \"def get_sectors_per_cluster(self):\\n\\n\\t\\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\\n\\t\\tif sectors_per_cluster_base == 0:\\n\\t\\t\\traise BootSectorException('Invalid cluster size (zero)')\\n\\n\\t\\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\\n\\t\\t\\tsectors_per_cluster_real = sectors_per_cluster_base\\n\\t\\telse:\\n\\t\\t\\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\\n\\t\\t\\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\\n\\n\\t\\treturn sectors_per_cluster_real\",\n \"def pct_bust(data):\\n return round((data[\\\"new_total\\\"] > 21).sum() / len(data), 3)\",\n \"def valency(self):\\n return len(self.neighbors())\",\n \"def _avg_cluster_hitprobability(self, x, y, n_clusters=30):\\n\\n # Compute the individual Hit probability\\n proba = self._shufflesplit(x, y)\\n\\n # average the individual hit probability for each cluster\\n ind = self._cluster(x, x.shape[0]/2.)\\n\\n avg_proba = np.copy(proba)\\n\\n for cluster in np.unique(ind):\\n mask_ = ind == cluster\\n avg_proba[mask_] = avg_proba[mask_].mean()\\n\\n return avg_proba\",\n \"def cluster_partition_distance(individual, test_data, truth_data, name=None):\\r\\n distance_sum = 0\\r\\n max_sum = 0\\r\\n for test_clusters, truth_clusters in zip(test_data, truth_data):\\r\\n # Get last column of target data\\r\\n test_clusters = test_clusters[-1].flatten()\\r\\n\\r\\n p1_dict = {}\\r\\n for i, x in enumerate(test_clusters):\\r\\n if x not in p1_dict:\\r\\n p1_dict[x] = []\\r\\n p1_dict[x].append(i)\\r\\n\\r\\n p2_dict = {}\\r\\n for i, x in enumerate(truth_clusters):\\r\\n if x not in p2_dict:\\r\\n p2_dict[x] = []\\r\\n p2_dict[x].append(i)\\r\\n\\r\\n p1 = list(p1_dict.values())\\r\\n p2 = list(p2_dict.values())\\r\\n d = _fast_partition_distance(p1, p2, len(test_clusters))\\r\\n if d is None:\\r\\n d = _partition_distance(p1, p2, len(test_clusters))\\r\\n distance_sum += d\\r\\n max_sum += len(test_clusters) - 1\\r\\n return distance_sum / max_sum\",\n \"def hausd95(result, reference, voxelspacing=None, connectivity=1):\\n hd1 = __surface_distances(result, reference, voxelspacing, connectivity)\\n hd2 = __surface_distances(reference, result, voxelspacing, connectivity)\\n hd95 = np.percentile(np.hstack((hd1, hd2)), 95)\\n return hd95\",\n \"def cluster_membership_occupancy(data):\\n \\n \\n \\n n_clusters = len(set(data['clusters'])-{-1}) # since -1 element denotes noice\\n\\n if n_clusters == 0:\\n membership=[Cluster_Membership_Features()]\\n membership = pd.DataFrame([o.__dict__ for o in membership])\\n areas=[Cluster_Area_Features()]\\n areas = pd.DataFrame([o.__dict__ for o in areas])\\n density=[Cluster_Density_Features()]\\n density = pd.DataFrame([o.__dict__ for o in density])\\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\\n density], axis=1)\\n \\n elif n_clusters ==1:\\n #obtain_total_cluster_areas_set_everything_else_to_default\\n membership=[Cluster_Membership_Features()]\\n membership = pd.DataFrame([o.__dict__ for o in membership])\\n d = dict(tuple(data.groupby('clusters')))\\n d.pop(-1, None)\\n \\n try:\\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\\n except:\\n cluster_chull_areas=[0,0,0]\\n \\n Total_cluster_area=np.sum(cluster_chull_areas)\\n areas=[Cluster_Area_Features([Total_cluster_area,0,0,0,0,0,0,0,0])]\\n areas = pd.DataFrame([o.__dict__ for o in areas])\\n density=[Cluster_Density_Features()]\\n density = pd.DataFrame([o.__dict__ for o in density])\\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\\n density], axis=1)\\n \\n elif n_clusters >1:\\n #Summarizing the cluster membership distribution characteristics\\n cluster_size_nums=np.delete(np.array(data.groupby(['clusters']).size()),0)\\n (cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD)= distribution_statistics(cluster_size_nums)\\n\\n #For each cluster calculate the area by calculating the area of the convex hull of cluster members\\n # Note: concavehull implementation here might be a good addition as it will provide more imformative values. \\n\\n d = dict(tuple(data.groupby('clusters')))\\n d.pop(-1, None)\\n try:\\n cluster_chull_areas=[ss.ConvexHull(np.column_stack([d[i]['X'].array,d[i]['Y'].array])).volume for i in d.keys()]\\n except:\\n cluster_chull_areas=[0,0,0,0,0]\\n \\n\\n (avg_cluster_area,min_cluster_area,max_cluster_area,\\n std_cluster_area,CV_cluster_area,CD_cluster_area,\\n IQR_cluster_area,Quartile_CD_cluster_area)= distribution_statistics(cluster_chull_areas)\\n Total_cluster_area=np.sum(cluster_chull_areas)\\n\\n #Calculate cluster density: number of nuclei/ convex area of cluster\\n cluster_density=np.divide(cluster_size_nums,cluster_chull_areas)\\n (avg_cluster_density,min_cluster_density,max_cluster_density,\\n std_cluster_density,CV_cluster_density,CD_cluster_density,\\n IQR_cluster_density,Quartile_CD_cluster_density)= distribution_statistics(cluster_density)\\n\\n #return dataframe of features\\n membership=[Cluster_Membership_Features([cluster_size_nums_avg,cluster_size_nums_min,cluster_size_nums_max,\\n cluster_size_nums_std,cluster_size_nums_cv,cluster_size_nums_cd,\\n cluster_size_nums_IQR,cluster_size_nums_Quartile_CD])]\\n membership = pd.DataFrame([o.__dict__ for o in membership])\\n areas=[Cluster_Area_Features([Total_cluster_area,\\n avg_cluster_area,min_cluster_area,max_cluster_area,\\n std_cluster_area,CV_cluster_area,CD_cluster_area,\\n IQR_cluster_area,Quartile_CD_cluster_area])]\\n areas = pd.DataFrame([o.__dict__ for o in areas])\\n density=[Cluster_Density_Features([avg_cluster_density,min_cluster_density,max_cluster_density,\\n std_cluster_density,CV_cluster_density,CD_cluster_density,\\n IQR_cluster_density,Quartile_CD_cluster_density])]\\n density = pd.DataFrame([o.__dict__ for o in density])\\n\\n all_features = pd.concat([membership.reset_index(drop=True), areas.reset_index(drop=True),\\n density], axis=1)\\n return all_features\",\n \"def element_size(self):\\n vecs = (\\n self.nodes[self.elements[:, :4], :][:, 1:, :]\\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\\n )\\n return np.abs(np.linalg.det(vecs)) / 6\",\n \"def calculate_enrichment_factor(enrichment, ranked_dataset_percentage_cutoff):\\n\\n # Keep only molecules that meet the cutoff\\n enrichment = enrichment[\\n enrichment[\\\"% ranked dataset\\\"] <= ranked_dataset_percentage_cutoff / 100\\n ]\\n # Get highest percentage of actives and the corresponding percentage of actives\\n highest_enrichment = enrichment.iloc[-1]\\n enrichment_factor = round(100 * float(highest_enrichment[\\\"% true actives identified\\\"]), 1)\\n return enrichment_factor\",\n \"def media(self):\\n self.kmeans = [[] for i in range(0,self.cluster_number)]\\n for i in range(self.cluster_number):\\n for j in range(0,len(self.cluster[i][0])):\\n self.kmeans[i].append(np.sum(self.cluster[i][::,j:j+1:])/len(self.cluster[i][::,j:j+1:]))\",\n \"def modularity():\\n\\n q = 0.0\\n for idx in range(0, node_count):\\n if _tot[idx] > 0.0:\\n q += (_in[idx] / m - math.pow(_tot[idx] / m, 2))\\n return q\",\n \"def lscoreatpercentile (inlist, percent):\\r\\n if percent > 1:\\r\\n print \\\"\\\\nDividing percent>1 by 100 in lscoreatpercentile().\\\\n\\\"\\r\\n percent = percent / 100.0\\r\\n targetcf = percent*len(inlist)\\r\\n h, lrl, binsize, extras = histogram(inlist)\\r\\n cumhist = cumsum(copy.deepcopy(h))\\r\\n for i in range(len(cumhist)):\\r\\n if cumhist[i] >= targetcf:\\r\\n break\\r\\n score = binsize * ((targetcf - cumhist[i-1]) / float(h[i])) + (lrl+binsize*i)\\r\\n return score\",\n \"def compute_distortion(cluster_list, data_table):\\n\\tdistortion = 0\\n\\tfor cluster in cluster_list:\\n\\t\\tdistortion += cluster.cluster_error(data_table)\\n\\treturn distortion\",\n \"def local_clustering_coefficient(graph, vertex):\\r\\n edge_count = 0\\r\\n for neighbour1 in graph[vertex]:\\r\\n for neighbour2 in graph[vertex]: #look at each pair of neighbours of vertex\\r\\n if neighbour1 in graph[neighbour2]: #if the neighbours are joined to each other by an edge\\r\\n edge_count += 1 #add one to the edge count\\r\\n degree = len(graph[vertex]) #count how many neighbours vertex has\\r\\n return edge_count / (degree * (degree - 1)) #note factor of 2 missing as each edge counted twice\\r\",\n \"def compute_distortion(cluster_list, data_table):\\r\\n distortion = 0\\r\\n \\r\\n for cluster in cluster_list:\\r\\n distortion += cluster.cluster_error(data_table)\\r\\n\\r\\n return distortion\",\n \"def _calc_perc(arr: np.array, p: Sequence[float] = None):\\n if p is None:\\n p = [50]\\n\\n nan_count = np.isnan(arr).sum(axis=-1)\\n out = np.moveaxis(np.percentile(arr, p, axis=-1), 0, -1)\\n nans = (nan_count > 0) & (nan_count < arr.shape[-1])\\n if np.any(nans):\\n out_mask = np.stack([nans] * len(p), axis=-1)\\n # arr1 = arr.reshape(int(arr.size / arr.shape[-1]), arr.shape[-1])\\n # only use nanpercentile where we need it (slow performance compared to standard) :\\n out[out_mask] = np.moveaxis(\\n np.nanpercentile(arr[nans], p, axis=-1), 0, -1\\n ).ravel()\\n return out\",\n \"def berger_parker_d(counts):\\n return counts.max()/float(counts.sum())\",\n \"def n_percentage_part(percentage_level, counted_od):\\n total = 0.0\\n od_num = 0\\n percentage_od = count_to_percentage(counted_od)\\n if percentage_level == 1.0:\\n od_num = len(counted_od)\\n else:\\n for i in percentage_od:\\n if total < percentage_level:\\n total += i\\n else:\\n od_num = percentage_od.index(i)\\n break\\n return od_num\",\n \"def percent_passing(self) -> float:\\n num_meas = Enumerable(self.mlc_meas).select_many(lambda m: m.passed).count()\\n num_pass = (\\n Enumerable(self.mlc_meas)\\n .select_many(lambda m: m.passed)\\n .count(lambda p: bool(p) is True)\\n )\\n return float(100 * num_pass / num_meas)\",\n \"def metric_value(x, y):\\n return (sum(np.all(x == y, axis=1) * 1) / len(x)) * 100\",\n \"def gap_statistic(X, cluster_nums, ref_num=10, cluster_method=None):\\n if cluster_method is None:\\n def k_means(data, cluster_nums):\\n \\\"\\\"\\\"\\n http://scikit-learn.org/stable/modules/clustering.html#k-means\\n \\\"\\\"\\\"\\n from sklearn.cluster import KMeans\\n\\n labels_list = []\\n for cluster_num in cluster_nums:\\n kmeans = KMeans(cluster_num, random_state=0, n_init=10).fit(data)\\n labels_list.append(kmeans.labels_ + 1)\\n print('KMeans finished: {}'.format(cluster_num))\\n return labels_list\\n\\n cluster_method = k_means\\n\\n print('Start: calculate W\\\\u2096s')\\n Wks = []\\n labels_list = cluster_method(X, cluster_nums)\\n for labels in labels_list:\\n Wks.append(elbow_score(X, labels))\\n Wks = np.array(Wks)\\n Wks_log = np.log(Wks)\\n print('Finish: calculate W\\\\u2096s')\\n\\n print(\\\"Start: calculate references' W\\\\u2096s\\\")\\n Wks_refs_log = []\\n minimums = np.atleast_2d(np.min(X, axis=0))\\n maximums = np.atleast_2d(np.max(X, axis=0))\\n bounding_box = np.r_[minimums, maximums]\\n for i in range(ref_num):\\n X_ref = uniform_box_sampling(X.shape[0], bounding_box)\\n labels_list_ref = cluster_method(X_ref, cluster_nums)\\n Wks_ref_log = []\\n for labels in labels_list_ref:\\n Wks_ref_log.append(np.log(elbow_score(X_ref, labels)))\\n Wks_refs_log.append(Wks_ref_log)\\n print('Finish reference: {}/{}'.format(i+1, ref_num))\\n print(\\\"Finish: calculate references' W\\\\u2096s\\\")\\n\\n print('Start: calculate gaps')\\n Wks_refs_log = np.array(Wks_refs_log)\\n Wks_refs_log_mean = np.mean(Wks_refs_log, axis=0)\\n Wks_refs_log_std = np.std(Wks_refs_log, axis=0)\\n gaps = Wks_refs_log_mean - Wks_log\\n print('Finish: calculate gaps')\\n\\n print('Start: select optimal k')\\n s = Wks_refs_log_std * np.sqrt(1 + 1.0 / ref_num)\\n idx_selected = np.where(gaps[:-1] >= gaps[1:] - s[1:])[0][0]\\n k_selected = cluster_nums[idx_selected]\\n print('Finish: select optimal k')\\n\\n return labels_list, Wks, Wks_refs_log_mean, gaps, s, k_selected\",\n \"def compute_sufficient_stats(self):\\n self.counts = (np.sum(self.resp, axis=0) + 10e-30)\\n # print(self.counts)\\n for k in range(self.k):\\n self.means[k] = np.sum(self.resp[n, k] * self.x[n] for n in range(self.n)) / self.counts[k]\\n self.covars[k] = np.sum(self.resp[n, k] * (self.x[n] - self.means[k]) @ (self.x[n] - self.means[k]).T\\n for n in range(self.n)) / self.counts[k]\\n self.covars[k] = np.nan_to_num(self.covars[k])\\n self.means[k] = np.nan_to_num(self.means[k])\",\n \"def fraction_mislabeled_nodes(labels, labels_pred):\\n G1 = partition_indicator(labels)\\n G2 = partition_indicator(labels_pred)\\n\\n # cost is minimized, overlap maximized\\n cost_matrix = -G1.T.dot(G2)\\n row_ind, col_ind = linear_sum_assignment(cost_matrix.A)\\n cost = -cost_matrix[row_ind, col_ind].sum()\\n\\n return 1 - (cost / len(labels))\",\n \"def clusterValues( values, relS=0.1 , refScaleAbs='range' ):\\n if len(values)==0:\\n return []\\n if len(values.shape)==1:\\n sortedV = numpy.stack([ values , numpy.arange(len(values))] ,1)\\n else:\\n # Assume value.shape = (N,2) and index are ok\\n sortedV = values \\n sortedV = sortedV[ numpy.argsort(sortedV[:,0]) ]\\n\\n sortedVV = sortedV[:,0]\\n refScale = sortedVV[-1]-sortedVV[0]\\n #sortedVV += 2*min(sortedVV)) # shift to avoid numerical issues around 0\\n\\n #print sortedVV\\n class Cluster:\\n def __init__(self, delta, sum, indices):\\n self.delta = delta\\n self.sum = sum\\n self.N=len(indices)\\n self.indices = indices\\n def size(self):\\n return self.delta/refScale\\n \\n def combine(self, c):\\n #print ' combine ', self.indices[0], c.indices[-1], ' -> ', sortedVV[c.indices[-1]] - sortedVV[self.indices[0]]\\n newC = Cluster(sortedVV[c.indices[-1]] - sortedVV[self.indices[0]],\\n self.sum+c.sum,\\n self.indices+c.indices)\\n return newC\\n\\n def originIndices(self):\\n return tuple(int(sortedV[i][1]) for i in self.indices)\\n\\n def size_local(self):\\n return self.delta / sum( sortedVV[i] for i in self.indices) *len(self.indices)\\n def size_range(self):\\n return self.delta/refScale\\n def size_abs(self):\\n return self.delta\\n\\n if refScaleAbs=='range':\\n Cluster.size = size_range\\n elif refScaleAbs=='local':\\n Cluster.size = size_local\\n elif refScaleAbs=='abs':\\n Cluster.size = size_abs\\n \\n class ClusterPair:\\n next=None\\n prev=None\\n def __init__(self, c1, c2 ):\\n self.c1=c1\\n self.c2=c2\\n self.refresh()\\n def refresh(self):\\n self.potentialC =self.c1.combine(self.c2)\\n self.size = self.potentialC.size()\\n def setC1(self, c1):\\n self.c1=c1\\n self.refresh()\\n def setC2(self, c2):\\n self.c2=c2\\n self.refresh()\\n \\n #ave = 0.5*(sortedVV[1:,0]+sortedV[:-1,0])\\n #deltaR = (sortedV[1:,0]-sortedV[:-1,0])/ave\\n\\n cList = [Cluster(0,v,(i,)) for (i,v) in enumerate(sortedVV) ]\\n cpList = [ ClusterPair( c, cList[i+1] ) for (i,c) in enumerate(cList[:-1]) ]\\n resetPrevNextSegment( cpList )\\n\\n #print cpList\\n def reduceCL( cList ):\\n if len(cList)<=1:\\n return cList\\n cp = min(cList, key=lambda cp:cp.size) \\n #print '==', cp.size , relS, cp.c1.indices , cp.c2.indices, cp.potentialC.indices\\n\\n while cp.size < relS:\\n if cp.next:\\n cp.next.setC1(cp.potentialC)\\n cp.next.prev = cp.prev\\n if cp.prev:\\n cp.prev.setC2(cp.potentialC)\\n cp.prev.next = cp.next\\n cList.remove(cp)\\n if len(cList)<2:\\n break\\n cp = min(cList, key=lambda cp:cp.size) \\n #print ' -----> ', [ (cp.c1.indices , cp.c2.indices) for cp in cList]\\n return cList\\n\\n cpList = reduceCL(cpList)\\n if len(cpList)==1:\\n cp = cpList[0]\\n if cp.potentialC.size()<relS:\\n return [ cp.potentialC.originIndices() ]\\n #print cpList\\n if cpList==[]:\\n return []\\n finalCL = [ cp.c1.originIndices() for cp in cpList ]+[ cpList[-1].c2.originIndices() ]\\n return finalCL\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7153438","0.704848","0.6598043","0.65899014","0.65830696","0.6483606","0.6449112","0.6421873","0.63327456","0.62887275","0.6227568","0.6170871","0.61646694","0.6152447","0.6065469","0.6052555","0.6034332","0.60112214","0.6000157","0.5957048","0.5954154","0.59512043","0.5926732","0.5926423","0.5921683","0.5915802","0.5901626","0.5896818","0.58966404","0.58849305","0.58721614","0.587037","0.5857933","0.5845804","0.5840319","0.5836977","0.5822742","0.5812218","0.5797974","0.579589","0.574924","0.5745349","0.5733645","0.5727552","0.57212174","0.57196945","0.57092184","0.5706276","0.5703501","0.57015884","0.5688196","0.5687761","0.56852114","0.56756467","0.567427","0.5668201","0.5641451","0.5633625","0.56327474","0.5632171","0.56264544","0.5624381","0.56226003","0.56129193","0.5608246","0.55882025","0.5583245","0.55824345","0.5578064","0.5577199","0.55747426","0.55712044","0.55575585","0.55500025","0.5549561","0.55480504","0.55465287","0.5534835","0.5532562","0.55306304","0.5517971","0.55165815","0.55107313","0.5507788","0.5504864","0.55045176","0.5500046","0.5497609","0.5493939","0.5493054","0.5489692","0.54893225","0.5486331","0.5486219","0.54861754","0.54842144","0.5482547","0.54811805","0.5479472","0.5476514"],"string":"[\n \"0.7153438\",\n \"0.704848\",\n \"0.6598043\",\n \"0.65899014\",\n \"0.65830696\",\n \"0.6483606\",\n \"0.6449112\",\n \"0.6421873\",\n \"0.63327456\",\n \"0.62887275\",\n \"0.6227568\",\n \"0.6170871\",\n \"0.61646694\",\n \"0.6152447\",\n \"0.6065469\",\n \"0.6052555\",\n \"0.6034332\",\n \"0.60112214\",\n \"0.6000157\",\n \"0.5957048\",\n \"0.5954154\",\n \"0.59512043\",\n \"0.5926732\",\n \"0.5926423\",\n \"0.5921683\",\n \"0.5915802\",\n \"0.5901626\",\n \"0.5896818\",\n \"0.58966404\",\n \"0.58849305\",\n \"0.58721614\",\n \"0.587037\",\n \"0.5857933\",\n \"0.5845804\",\n \"0.5840319\",\n \"0.5836977\",\n \"0.5822742\",\n \"0.5812218\",\n \"0.5797974\",\n \"0.579589\",\n \"0.574924\",\n \"0.5745349\",\n \"0.5733645\",\n \"0.5727552\",\n \"0.57212174\",\n \"0.57196945\",\n \"0.57092184\",\n \"0.5706276\",\n \"0.5703501\",\n \"0.57015884\",\n \"0.5688196\",\n \"0.5687761\",\n \"0.56852114\",\n \"0.56756467\",\n \"0.567427\",\n \"0.5668201\",\n \"0.5641451\",\n \"0.5633625\",\n \"0.56327474\",\n \"0.5632171\",\n \"0.56264544\",\n \"0.5624381\",\n \"0.56226003\",\n \"0.56129193\",\n \"0.5608246\",\n \"0.55882025\",\n \"0.5583245\",\n \"0.55824345\",\n \"0.5578064\",\n \"0.5577199\",\n \"0.55747426\",\n \"0.55712044\",\n \"0.55575585\",\n \"0.55500025\",\n \"0.5549561\",\n \"0.55480504\",\n \"0.55465287\",\n \"0.5534835\",\n \"0.5532562\",\n \"0.55306304\",\n \"0.5517971\",\n \"0.55165815\",\n \"0.55107313\",\n \"0.5507788\",\n \"0.5504864\",\n \"0.55045176\",\n \"0.5500046\",\n \"0.5497609\",\n \"0.5493939\",\n \"0.5493054\",\n \"0.5489692\",\n \"0.54893225\",\n \"0.5486331\",\n \"0.5486219\",\n \"0.54861754\",\n \"0.54842144\",\n \"0.5482547\",\n \"0.54811805\",\n \"0.5479472\",\n \"0.5476514\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6579574"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":687,"cells":{"query":{"kind":"string","value":"Returns the percent of noise elements in the dataset."},"document":{"kind":"string","value":"def analysis_function_noise_level(self, clustering, total_elements):\n return 100.-(clustering.total_number_of_elements/float(total_elements))*100."},"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 _compute_noise_level(self, data):\n noise = max(data)\n noise_min = 2600\n noise_max = 4095\n ratio = (noise - noise_min)/(noise_max - noise_min)\n return int(ratio*100)","def getNoiseVar(img,fraction=0.95):\n last_val = np.percentile(img,fraction)\n #si(img<last_val,title=\"Pixel values considered as noise\")\n return np.var(img[img<last_val])","def noise(self):\n # Extract parameters\n pzs = self.params[0]\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\n return 1.0 / ngals","def calculate_sample_silhouette(self):\n sum_samples = 0\n for cluster in self.cluster_lst:\n sum_samples += self.sum_silhouette(cluster)\n sample_size = len(self.samples)\n return sum_samples/sample_size","def noise_level(data):\n length=len(data) - 2\n dev=[]\n for i in range(1,length - 1):\n dev.append((abs(data[i] - data[i-1]) + abs(data[i] - data[i + 1]))/2)\n dev.sort()\n return dev[round(0.9*length)]","def noise(self, freq: int, /) -> None:","def mdape(self) -> float:\n return float(np.median(np.abs(self._percentage_error())) * 100)","def density(self):\r\n return self.count_ones() / float(self.xspan * self.yspan)","def rmdspe(self) -> float:\n return float(np.sqrt(np.median(np.square(self._percentage_error()))) * 100.0)","def noise(self):\n # Extract parameters\n pzs = self.params[0]\n # retrieve number of galaxies in each bins\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\n if isinstance(self.config[\"sigma_e\"], list):\n sigma_e = np.array([s for s in self.config[\"sigma_e\"]])\n else:\n sigma_e = self.config[\"sigma_e\"]\n return sigma_e ** 2 / ngals","def mean(self):\r\n\t\treturn sum(self.sample)/len(self.sample)","def var(x):\n length = len(x)\n\n if length == 0:\n return None\n result = 0.0\n m = TinyStatistician.mean(x)\n for i in x:\n result += (i - m) ** 2\n\n return result / length","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def get_percentage_missing(series):\n num = series.isnull().sum()\n den = len(series)\n return round(num / den, 2)","def get_percentage_missing(series):\n num = series.isnull().sum()\n den = len(series)\n return round(num / den, 2)","def get_percent_wet():\n # Create an ADS1115 ADC (16-bit) instance.\n adc = Adafruit_ADS1x15.ADS1115()\n\n GAIN = 1\n DRY = 20280 # 100% Dry\n WET = 10140 # 100% Wet\n\n value = adc.read_adc(0, gain=GAIN)\n \n # print \"value: %d\" % value\n \n percent_dry = ((value - WET)*100)/(DRY-WET)\n percent_wet = 100 - percent_dry\n\n return percent_wet","def get_percentage_missing(series):\n num = series.isnull().sum()\n den = len(series)\n return round(num/den*100, 2)","def noise(self):\n return self._noise","def sampling_ratio(self):\n return self.coincidences / self.n","def get_percent(self, n):\n controlled = 0.00\n for i in range(len(self.tile_contents)):\n if(self.tile_contents[i].player_number == n):\n controlled += 1.00\n \n return float(controlled / self.paint_blocks)","def noiseFraction(truth_i3, measured_i3, tolerance):\n if (measured_i3.getNumberMolecules() == 0):\n return [0, truth_i3.getNumberMolecules()]\n \n noise_locs = 0\n total_locs = 0\n for i in range(truth_i3.getNumberFrames()):\n t_locs = truth_i3.getMoleculesInFrame(i+1)\n m_locs = measured_i3.getMoleculesInFrame(i+1, good_only = False)\n \n dist = utilC.peakToPeakDist(m_locs['xc'], m_locs['yc'], t_locs['xc'], t_locs['yc'])\n\n noise_locs += numpy.count_nonzero((dist > tolerance))\n total_locs += dist.size\n\n return [noise_locs, total_locs]","def prob1(n):\n\n # create a giant draw from a normal distribution\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\n\n # mask the values\n mask = random_draws > 3\n\n return np.sum(mask)/float(n)","def _ion_densities_datafiles(self):\n ne = self.ne_in\n nD = self.ni_in[0,:]\n nC = (ne-nD)/6.\n print(\"nC/nD: \"+str(np.mean(nC/nD)*100.)+\" %\")\n self.ni_in[0,:] = nD\n self.ni_in[1,:] = nC","def get_estimated_noise(self):\n return self.gp_core.noise_var","def add_noise(self, noise):\n if noise > 0.0:\n for key in self.counts:\n self.counts[key] *= 1.0 + noise * np.random.random_sample()","def smape(self) -> float:\n _temp = np.sum(2 * np.abs(self.predicted - self.true) / (np.abs(self.true) + np.abs(self.predicted)))\n return float(100 / len(self.true) * _temp)","def percentages(self) -> pandas.Series:\n if self._percentages is None:\n scalar = 1 if self.use_fraction else 100\n self._percentages = scalar * self.counts/self.total\n return self._percentages","def percent_passing(self) -> float:\n num_meas = Enumerable(self.mlc_meas).select_many(lambda m: m.passed).count()\n num_pass = (\n Enumerable(self.mlc_meas)\n .select_many(lambda m: m.passed)\n .count(lambda p: bool(p) is True)\n )\n return float(100 * num_pass / num_meas)","def density(self, x):\n\t\tN = len(self.train_data)\n\t\tpoints = list(self.train_data)\n\t\tdists = [np.linalg.norm(x-point)**2 for point in points]\n\t\texps = [np.exp(-dist / (2 * (self.bandwidth ** 2))) for dist in dists]\n\t\tunnormalized_sum = sum(exps)\n\t\tprobability = (1 / N) * self.normalizing_constant() * unnormalized_sum\n\t\treturn probability","def prob(self, w):\n return self.counts[w] / self.total_count","def get_noise_distribution(corpus: List[str],\n vocabulary: np.ndarray,\n dist_alpha: float\n ) -> List[int]:\n all_words = [word for text in corpus for word in text]\n arr = np.array(list(map(\n lambda x: all_words.count(x)**dist_alpha, vocabulary\n )))\n return arr/arr.sum() # frequencies, normalised, in order of vocabulary","def mean(data):\n n = len(data)\n return sum(data)/float(n)","def estimate_noiseperbl(data):\n\n # define noise per baseline for data seen by detect_bispectra or image\n datamean = data.mean(axis=2).imag # use imaginary part to estimate noise without calibrated, on-axis signal\n noiseperbl = datamean.std() # measure single noise for input to detect_bispectra\n logger.debug('Measured noise per baseline of {0:.3f}'.format(noiseperbl))\n return noiseperbl","def nats(self) -> float:\n return self.entropy()","def theoretical_effective(dataset):\n return float(sum(dataset))/len(dataset)","def dominance(counts):\n freqs = counts/float(counts.sum())\n return (freqs*freqs).sum()","def mean(data):\n n = len(data)\n if n < 1:\n return 0\n return sum(data)/float(n)","def get_percent_interest(self):\n return self.__percentage_interest","def density(self):\n return self.nnz / self.size","def pct(self):\n\t\treturn self.bottle.pct()","def prob4():\n\n\n N = 500000\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\n\n h = lambda x: x[0] < -1 and x[1] > 1\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\n\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\n\n return 1./N * np.sum(probability)","def get_estimated_noise(self):\n return self.gp_core.likelihood.noise.item()","def probability(self, samples):\n pass","def _ion_densities(self):\n nD = self.ne_in*(6-self.zeff_in)/(5.)\n nC = self.ne_in*(self.zeff_in-1)/(30.)\n nC[np.where(nC<0)]=0.\n print(\"nC/nD: \"+str(np.mean(nC/nD)*100.)+\" %\")\n self.ni_in[0,:] = nD\n self.ni_in[1,:] = nC","def get_probability_loss(self):\n return sum(self._loss)/len(self._loss)","def variation_statistic(gene_data: pd.DataFrame) -> pd.Series:\n statistic = gene_data.std(axis=1) / gene_data.mean(axis=1)\n # statistic = gene_data.std(axis=1)\n # TODO How to deal with 0 expressed genes? Are they informative?????\n return statistic.replace(np.nan, 0)","def ds_ratio(group):\n nix_count = (group=='nix').sum()\n top_count = (group=='top').sum()\n ratio = nix_count/(nix_count+top_count) #could smooth this\n return ratio","def addNoise_amp(array,counts):\r\n if array.dtype == 'complex' :\r\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\r\n else :\r\n if np.float64(counts) == 0.0e0 :\r\n arrayout = np.copy(array)\r\n elif np.float64(counts) < 0.0e0 :\r\n print 'bg.addNoise : warning counts < 0'\r\n else :\r\n arrayout = np.zeros(array.shape)\r\n arrayout = np.square(normalise(array))\r\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\r\n arrayout = np.sqrt(arrayout)\r\n tot = np.sum(np.abs(array)**2)\r\n arrayout = normalise(arrayout,tot)\r\n return arrayout","def get_percent(self):\n if not (self.votes and self.score):\n return 0\n return 100 * (self.get_rating() / self.field.range)","def find_scales_dominant(wav, no_good, dataset=None):\n dataset_dic = {\n\n 'MFG' : -17, # purely empirical, sorry\n 'MSG' : -8,\n 'GRIDSAT' : -20,\n 'neutral' : 0\n }\n\n wll = wav['t']\n\n power_img = np.sum(wll, axis=0)\n power_img[no_good] = 0\n\n\n smaller = dataset_dic[dataset]\n thresh_p = np.sum((wav['scales'] + smaller) ** .5) # set different power threshold adjustments to datasets\n try:\n power_img[(power_img < np.percentile(power_img[power_img > 1], 25)) | (power_img < (thresh_p))] = 0\n except IndexError:\n return\n\n labels, numL = label(power_img)\n u, inv = np.unique(labels, return_inverse=True)\n\n for inds in u:\n if inds == 0:\n continue\n\n arr = power_img.copy()\n arr[np.where(labels != inds)] = 0\n power_img.flat[np.argmax(arr)] = -999\n\n return power_img","def qc_NoiseRank(spread):\n\n variance = spread*spread # for a Gaussian\n \n if (variance <= 0.2):\n qc_label = 'good'\n elif (0.2 < variance < 0.25):\n qc_label = 'ok'\n else:\n qc_label = 'bad'\n \n return qc_label","def calculate_probability(self):\n return 0","def density(self):\n return self.nnz/self.dim","def get_noise(self):\n\n n = self.qubic.get_noise().ravel()\n n = np.r_[n, self.planck.get_noise().ravel()]\n\n return n","def trimean(data):\n p_25, p_50, p_75 = percentile(data, [25, 50, 75], axis=0)\n\n return (p_25 + 2 * p_50 + p_75) / 4","def samplePercNaN(df, specie = \"Caenorhabditis elegans OX=6239\", sample = \"S01\"):\n spec = rawSpecies(df = df, specie = specie)\n nonDecoy = spec[spec[\"EG.IsDecoy\"] == False] \n sample = sample\n sampleDat = nonDecoy[nonDecoy[\"R.Condition\"].str[-3:] == sample] \n perc_NaN = sampleDat[\"PG.Quantity\"].isna().sum() / (sampleDat[\"PG.Quantity\"].count() + sampleDat[\"PG.Quantity\"].isna().sum())\n return perc_NaN","def addNoise(array,counts):\r\n if array.dtype == 'complex' :\r\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\r\n else :\r\n if np.float64(counts) == 0.0e0 :\r\n arrayout = np.zeros(array.shape, dtype=arrayout.dtype)\r\n elif np.float64(counts) < 0.0e0 :\r\n print 'bg.addNoise : warning counts < 0'\r\n elif np.float64(counts) > 1.0e9 :\r\n arrayout = np.zeros(array.shape)\r\n arrayout = normaliseInt(array)\r\n arrayout = np.random.normal(arrayout*np.float64(counts),np.sqrt(arrayout*np.float64(counts)))/np.float64(counts)\r\n tot = np.sum(array)\r\n arrayout = normaliseInt(arrayout,tot)\r\n else :\r\n arrayout = np.zeros(array.shape)\r\n arrayout = normaliseInt(array)\r\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\r\n tot = np.sum(array)\r\n arrayout = normaliseInt(arrayout,tot)\r\n return arrayout","def number_densities(self) -> u.m**-3:\n try:\n return (self.n_elem * self.ionic_fractions).to(u.m**-3)\n except Exception: # noqa: BLE001\n return np.full(self.atomic_number + 1, np.nan) * u.m**-3","def noiseFraction(truth_h5, measured_h5, tolerance):\n if (measured_h5.getNLocalizations() == 0):\n return [0, truth_h5.getNLocalizations()]\n \n noise_locs = 0\n total_locs = 0\n for i in range(truth_h5.getMovieLength()):\n t_locs = truth_h5.getLocalizationsInFrame(i)\n m_locs = measured_h5.getLocalizationsInFrame(i)\n\n if bool(t_locs) and bool(m_locs):\n dist = iaUtilsC.peakToPeakDistAndIndex(m_locs['x'], m_locs['y'],\n t_locs['x'], t_locs['y'],\n max_distance = tolerance)[0]\n\n noise_locs += numpy.count_nonzero((dist < 0.0))\n total_locs += dist.size\n elif bool(m_locs):\n noise_locs += m_locs['x'].size\n total_locs += m_locs['x'].size\n\n return [noise_locs, total_locs]","def noiseReduction(self):\n pass","def stdProbabilityNorm(self):\n return 0.5","def entropy(self):\r\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)","def sample_percent(self, percentage):\n count = int(len(self.features) * (percentage / 100))\n indices = np.random.randint(0, high=len(self.features), size=count)\n return ProcessedImageData(self.features[indices], self.labels[indices], indices)","def pc_nproduced_avg(self) -> \"float\":\n return _beamforming_swig.randomsampler_sptr_pc_nproduced_avg(self)","def variance(self):\n return 1 / self.count() * sum((number-self.average())**2 for number in self.numbers)","def white_noise():\n return random.randint(-32767, 32767)","def chance_of_rain(self):\r\n raise NotImplementedError","def menhinick(counts):\n return observed_species(counts)/sqrt(counts.sum())","def get_estimated_noise(self):\n raise NotImplementedError('Abstract Method.')","def score(self, data):\n return np.mean( np.log( mvn.getSamplePointDensity(self.dataFrame_, self.H_, pd.DataFrame(data)) ) )","def nanPercentage(df):\n rows = df.shape[0]\n cols = df.shape[1]\n tot = rows*cols\n \n nanNum = 0\n for i in range(df.shape[0]):\n nanNum = nanNum + np.sum ( pd.isnull(df.iloc[i]) )\n logger.debug ('nan %d tot %d ' % (nanNum, tot) )\n perc = (100*nanNum) / (tot * 1.0)\n return perc","def percent(self):\r\n return self._percent","def dissimilarity(clusters):\n totDist = 0\n for c in clusters:\n totDist += c.variability()\n return totDist","def tail_ratio(returns):\n\n return np.abs(np.percentile(returns, 95)) / \\\n np.abs(np.percentile(returns, 5))","def die(world, percentage):\n\n \n infected = np.sum((world >= 1) & (world <= 10))\n to_die = percentage * infected\n if to_die < 1:\n to_die = 0\n else:\n to_die = to_die\n to_die = np.round(to_die).astype(int)\n\n\n indizes = [] # Für die Koordinaten der infizierten Zellen\n for i, v in np.ndenumerate(world):\n if v in range(1, 11):\n indizes.append(i)\n #Ziehe Stichprobe aus den infizierten Zellen und setze sie auf 300\n sample = random.sample(indizes, to_die)\n for i in sample:\n world[i] = 300\n \n return world","def mape(self) -> float:\n return float(np.mean(np.abs((self.true - self.predicted) / self.true)) * 100)","def population_density(self) -> float:\n return self.population / self.area","def get_percent(self):\n return self.percent","def pct_bust(data):\n return round((data[\"new_total\"] > 21).sum() / len(data), 3)","def get_fraction_significant_p_value_gray_screen(group):\n fraction_significant_p_value_gray_screen = len(group[group.p_value_gray_screen < 0.05]) / float(len(group))\n return pd.Series({'fraction_significant_p_value_gray_screen': fraction_significant_p_value_gray_screen})","def main():\n x, y = create_simple_classification_dataset(5)\n count = 0\n for i in y:\n if i[0] == 0:\n count += 1\n print('percent of zeros', count / len(y)) # confirm that the distribution isn't too skewed.","def noise_profile(nf, gain, ffs, df):\n\n h_mWThz = 1e-3 * h * (1e14)**2\n nf_lin = db2lin(nf)\n g_lin = db2lin(gain)\n ase = h_mWThz * df * ffs * (nf_lin * g_lin - 1)\n asedb = lin2db(ase)\n\n return asedb","def entropy(self):\n n = len(self.y)\n sum_ = 0\n for i in np.unique(self.y):\n v = len(self.y[self.y == i])\n sum_ += -((v/n) * log2(v/n))\n return sum_","def copyCounterWithNoise(self, counter, prob):\n \"\"\"\n counter = deepcopy(counter)\n for i in range(self.nObjectTypes):\n noise = np.random.randint(1,5)\n if np.random.random() < prob:\n if np.random.random() > 0.5:\n counter[i] += noise # false detections\n else:\n counter[i] -= noise # missed detections\n if counter[i] < 0:\n counter[i]=0\n return counter\n \"\"\"\n counter = deepcopy(counter)\n for i in range(self.nObjectTypes):\n noise = np.random.randint(1,5)\n if np.random.random() > prob:\n continue\n if counter[i] == 0:\n # Noise could only be +1\n counter[i] = noise\n else:\n # Noise could be either +1 or -1\n if np.random.random() > 0.5:\n counter[i] += noise\n else:\n counter[i] -= noise\n if counter[i] < 0:\n counter[i]=0\n return counter","def percent_frequencies(self):\n word_count = 0\n local = self.frequencies()\n for key in local.keys():\n i = local[key]\n word_count += int(i)\n for key in local.keys():\n i = local[key]\n percentage = float(i) / float(word_count)\n local[key] = percentage\n return local","def normpdf(x, mean, sd):\n var = float(sd)**2\n denom = (2*math.pi*var)**.5\n num = math.exp(-(float(x)-float(mean))**2/(2*var))\n return num/denom","def test_noise_equiv_bandwidth():\n win = windows.blackmanharris(2000)\n assert np.isclose(2, 1.0 / utils.noise_equivalent_bandwidth(win), rtol=1e-2)","def fracSeen(data):\n itemSet = set()\n numSeen = []\n for x in data:\n itemSet.add(x)\n numSeen.append(len(itemSet))\n\n fracSeen = [x/numSeen[-1] for x in numSeen]\n return fracSeen","def get_mean(data, n=-1):\n \n return round((sum(data)/n),1)","def entropy(data):\n strings, lens = Counter(data), np.float(len(data))\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())","def pulse_width_percent(self) -> float:","def _basic_probability(count: int, sequence_total_count: int) -> float:\n return float(count) / sequence_total_count","def __measurement_prob(angle, measurement, noise):\n return ParticleFilter.Gaussian(angle, noise, measurement)","def percent_processed(self):\n try:\n return (self.pos / self.data_encap.size) * 100.0\n except ZeroDivisionError:\n return 100.0","def percentage(my_list, item):\n return 100.0 * frequency(my_list, item)","def intensity(self) -> int:","def pc_noutput_items_avg(self) -> \"float\":\n return _beamforming_swig.randomsampler_sptr_pc_noutput_items_avg(self)","def mean_deviation(self):\r\n\t\t_mean = sum(self.sample)/len(self.sample)\r\n\t\treturn sum(map(lambda x: abs(x - _mean), self.sample))/len(self.sample)","def salt_and_pepper_noise(image, prob):\n output = np.zeros(image.shape,np.uint8)\n thres = 1 - prob \n for i in range(image.shape[0]):\n for j in range(image.shape[1]):\n rdn = random.random()\n if rdn < prob:\n output[i][j] = 0\n elif rdn > thres:\n output[i][j] = 255\n else:\n output[i][j] = image[i][j]\n return output","def sampling_frequency(self) -> int:\n return int(1 / self.x_scale)"],"string":"[\n \"def _compute_noise_level(self, data):\\n noise = max(data)\\n noise_min = 2600\\n noise_max = 4095\\n ratio = (noise - noise_min)/(noise_max - noise_min)\\n return int(ratio*100)\",\n \"def getNoiseVar(img,fraction=0.95):\\n last_val = np.percentile(img,fraction)\\n #si(img<last_val,title=\\\"Pixel values considered as noise\\\")\\n return np.var(img[img<last_val])\",\n \"def noise(self):\\n # Extract parameters\\n pzs = self.params[0]\\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\\n return 1.0 / ngals\",\n \"def calculate_sample_silhouette(self):\\n sum_samples = 0\\n for cluster in self.cluster_lst:\\n sum_samples += self.sum_silhouette(cluster)\\n sample_size = len(self.samples)\\n return sum_samples/sample_size\",\n \"def noise_level(data):\\n length=len(data) - 2\\n dev=[]\\n for i in range(1,length - 1):\\n dev.append((abs(data[i] - data[i-1]) + abs(data[i] - data[i + 1]))/2)\\n dev.sort()\\n return dev[round(0.9*length)]\",\n \"def noise(self, freq: int, /) -> None:\",\n \"def mdape(self) -> float:\\n return float(np.median(np.abs(self._percentage_error())) * 100)\",\n \"def density(self):\\r\\n return self.count_ones() / float(self.xspan * self.yspan)\",\n \"def rmdspe(self) -> float:\\n return float(np.sqrt(np.median(np.square(self._percentage_error()))) * 100.0)\",\n \"def noise(self):\\n # Extract parameters\\n pzs = self.params[0]\\n # retrieve number of galaxies in each bins\\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\\n if isinstance(self.config[\\\"sigma_e\\\"], list):\\n sigma_e = np.array([s for s in self.config[\\\"sigma_e\\\"]])\\n else:\\n sigma_e = self.config[\\\"sigma_e\\\"]\\n return sigma_e ** 2 / ngals\",\n \"def mean(self):\\r\\n\\t\\treturn sum(self.sample)/len(self.sample)\",\n \"def var(x):\\n length = len(x)\\n\\n if length == 0:\\n return None\\n result = 0.0\\n m = TinyStatistician.mean(x)\\n for i in x:\\n result += (i - m) ** 2\\n\\n return result / length\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def get_percentage_missing(series):\\n num = series.isnull().sum()\\n den = len(series)\\n return round(num / den, 2)\",\n \"def get_percentage_missing(series):\\n num = series.isnull().sum()\\n den = len(series)\\n return round(num / den, 2)\",\n \"def get_percent_wet():\\n # Create an ADS1115 ADC (16-bit) instance.\\n adc = Adafruit_ADS1x15.ADS1115()\\n\\n GAIN = 1\\n DRY = 20280 # 100% Dry\\n WET = 10140 # 100% Wet\\n\\n value = adc.read_adc(0, gain=GAIN)\\n \\n # print \\\"value: %d\\\" % value\\n \\n percent_dry = ((value - WET)*100)/(DRY-WET)\\n percent_wet = 100 - percent_dry\\n\\n return percent_wet\",\n \"def get_percentage_missing(series):\\n num = series.isnull().sum()\\n den = len(series)\\n return round(num/den*100, 2)\",\n \"def noise(self):\\n return self._noise\",\n \"def sampling_ratio(self):\\n return self.coincidences / self.n\",\n \"def get_percent(self, n):\\n controlled = 0.00\\n for i in range(len(self.tile_contents)):\\n if(self.tile_contents[i].player_number == n):\\n controlled += 1.00\\n \\n return float(controlled / self.paint_blocks)\",\n \"def noiseFraction(truth_i3, measured_i3, tolerance):\\n if (measured_i3.getNumberMolecules() == 0):\\n return [0, truth_i3.getNumberMolecules()]\\n \\n noise_locs = 0\\n total_locs = 0\\n for i in range(truth_i3.getNumberFrames()):\\n t_locs = truth_i3.getMoleculesInFrame(i+1)\\n m_locs = measured_i3.getMoleculesInFrame(i+1, good_only = False)\\n \\n dist = utilC.peakToPeakDist(m_locs['xc'], m_locs['yc'], t_locs['xc'], t_locs['yc'])\\n\\n noise_locs += numpy.count_nonzero((dist > tolerance))\\n total_locs += dist.size\\n\\n return [noise_locs, total_locs]\",\n \"def prob1(n):\\n\\n # create a giant draw from a normal distribution\\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\\n\\n # mask the values\\n mask = random_draws > 3\\n\\n return np.sum(mask)/float(n)\",\n \"def _ion_densities_datafiles(self):\\n ne = self.ne_in\\n nD = self.ni_in[0,:]\\n nC = (ne-nD)/6.\\n print(\\\"nC/nD: \\\"+str(np.mean(nC/nD)*100.)+\\\" %\\\")\\n self.ni_in[0,:] = nD\\n self.ni_in[1,:] = nC\",\n \"def get_estimated_noise(self):\\n return self.gp_core.noise_var\",\n \"def add_noise(self, noise):\\n if noise > 0.0:\\n for key in self.counts:\\n self.counts[key] *= 1.0 + noise * np.random.random_sample()\",\n \"def smape(self) -> float:\\n _temp = np.sum(2 * np.abs(self.predicted - self.true) / (np.abs(self.true) + np.abs(self.predicted)))\\n return float(100 / len(self.true) * _temp)\",\n \"def percentages(self) -> pandas.Series:\\n if self._percentages is None:\\n scalar = 1 if self.use_fraction else 100\\n self._percentages = scalar * self.counts/self.total\\n return self._percentages\",\n \"def percent_passing(self) -> float:\\n num_meas = Enumerable(self.mlc_meas).select_many(lambda m: m.passed).count()\\n num_pass = (\\n Enumerable(self.mlc_meas)\\n .select_many(lambda m: m.passed)\\n .count(lambda p: bool(p) is True)\\n )\\n return float(100 * num_pass / num_meas)\",\n \"def density(self, x):\\n\\t\\tN = len(self.train_data)\\n\\t\\tpoints = list(self.train_data)\\n\\t\\tdists = [np.linalg.norm(x-point)**2 for point in points]\\n\\t\\texps = [np.exp(-dist / (2 * (self.bandwidth ** 2))) for dist in dists]\\n\\t\\tunnormalized_sum = sum(exps)\\n\\t\\tprobability = (1 / N) * self.normalizing_constant() * unnormalized_sum\\n\\t\\treturn probability\",\n \"def prob(self, w):\\n return self.counts[w] / self.total_count\",\n \"def get_noise_distribution(corpus: List[str],\\n vocabulary: np.ndarray,\\n dist_alpha: float\\n ) -> List[int]:\\n all_words = [word for text in corpus for word in text]\\n arr = np.array(list(map(\\n lambda x: all_words.count(x)**dist_alpha, vocabulary\\n )))\\n return arr/arr.sum() # frequencies, normalised, in order of vocabulary\",\n \"def mean(data):\\n n = len(data)\\n return sum(data)/float(n)\",\n \"def estimate_noiseperbl(data):\\n\\n # define noise per baseline for data seen by detect_bispectra or image\\n datamean = data.mean(axis=2).imag # use imaginary part to estimate noise without calibrated, on-axis signal\\n noiseperbl = datamean.std() # measure single noise for input to detect_bispectra\\n logger.debug('Measured noise per baseline of {0:.3f}'.format(noiseperbl))\\n return noiseperbl\",\n \"def nats(self) -> float:\\n return self.entropy()\",\n \"def theoretical_effective(dataset):\\n return float(sum(dataset))/len(dataset)\",\n \"def dominance(counts):\\n freqs = counts/float(counts.sum())\\n return (freqs*freqs).sum()\",\n \"def mean(data):\\n n = len(data)\\n if n < 1:\\n return 0\\n return sum(data)/float(n)\",\n \"def get_percent_interest(self):\\n return self.__percentage_interest\",\n \"def density(self):\\n return self.nnz / self.size\",\n \"def pct(self):\\n\\t\\treturn self.bottle.pct()\",\n \"def prob4():\\n\\n\\n N = 500000\\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\\n\\n h = lambda x: x[0] < -1 and x[1] > 1\\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\\n\\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\\n\\n return 1./N * np.sum(probability)\",\n \"def get_estimated_noise(self):\\n return self.gp_core.likelihood.noise.item()\",\n \"def probability(self, samples):\\n pass\",\n \"def _ion_densities(self):\\n nD = self.ne_in*(6-self.zeff_in)/(5.)\\n nC = self.ne_in*(self.zeff_in-1)/(30.)\\n nC[np.where(nC<0)]=0.\\n print(\\\"nC/nD: \\\"+str(np.mean(nC/nD)*100.)+\\\" %\\\")\\n self.ni_in[0,:] = nD\\n self.ni_in[1,:] = nC\",\n \"def get_probability_loss(self):\\n return sum(self._loss)/len(self._loss)\",\n \"def variation_statistic(gene_data: pd.DataFrame) -> pd.Series:\\n statistic = gene_data.std(axis=1) / gene_data.mean(axis=1)\\n # statistic = gene_data.std(axis=1)\\n # TODO How to deal with 0 expressed genes? Are they informative?????\\n return statistic.replace(np.nan, 0)\",\n \"def ds_ratio(group):\\n nix_count = (group=='nix').sum()\\n top_count = (group=='top').sum()\\n ratio = nix_count/(nix_count+top_count) #could smooth this\\n return ratio\",\n \"def addNoise_amp(array,counts):\\r\\n if array.dtype == 'complex' :\\r\\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\\r\\n else :\\r\\n if np.float64(counts) == 0.0e0 :\\r\\n arrayout = np.copy(array)\\r\\n elif np.float64(counts) < 0.0e0 :\\r\\n print 'bg.addNoise : warning counts < 0'\\r\\n else :\\r\\n arrayout = np.zeros(array.shape)\\r\\n arrayout = np.square(normalise(array))\\r\\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\\r\\n arrayout = np.sqrt(arrayout)\\r\\n tot = np.sum(np.abs(array)**2)\\r\\n arrayout = normalise(arrayout,tot)\\r\\n return arrayout\",\n \"def get_percent(self):\\n if not (self.votes and self.score):\\n return 0\\n return 100 * (self.get_rating() / self.field.range)\",\n \"def find_scales_dominant(wav, no_good, dataset=None):\\n dataset_dic = {\\n\\n 'MFG' : -17, # purely empirical, sorry\\n 'MSG' : -8,\\n 'GRIDSAT' : -20,\\n 'neutral' : 0\\n }\\n\\n wll = wav['t']\\n\\n power_img = np.sum(wll, axis=0)\\n power_img[no_good] = 0\\n\\n\\n smaller = dataset_dic[dataset]\\n thresh_p = np.sum((wav['scales'] + smaller) ** .5) # set different power threshold adjustments to datasets\\n try:\\n power_img[(power_img < np.percentile(power_img[power_img > 1], 25)) | (power_img < (thresh_p))] = 0\\n except IndexError:\\n return\\n\\n labels, numL = label(power_img)\\n u, inv = np.unique(labels, return_inverse=True)\\n\\n for inds in u:\\n if inds == 0:\\n continue\\n\\n arr = power_img.copy()\\n arr[np.where(labels != inds)] = 0\\n power_img.flat[np.argmax(arr)] = -999\\n\\n return power_img\",\n \"def qc_NoiseRank(spread):\\n\\n variance = spread*spread # for a Gaussian\\n \\n if (variance <= 0.2):\\n qc_label = 'good'\\n elif (0.2 < variance < 0.25):\\n qc_label = 'ok'\\n else:\\n qc_label = 'bad'\\n \\n return qc_label\",\n \"def calculate_probability(self):\\n return 0\",\n \"def density(self):\\n return self.nnz/self.dim\",\n \"def get_noise(self):\\n\\n n = self.qubic.get_noise().ravel()\\n n = np.r_[n, self.planck.get_noise().ravel()]\\n\\n return n\",\n \"def trimean(data):\\n p_25, p_50, p_75 = percentile(data, [25, 50, 75], axis=0)\\n\\n return (p_25 + 2 * p_50 + p_75) / 4\",\n \"def samplePercNaN(df, specie = \\\"Caenorhabditis elegans OX=6239\\\", sample = \\\"S01\\\"):\\n spec = rawSpecies(df = df, specie = specie)\\n nonDecoy = spec[spec[\\\"EG.IsDecoy\\\"] == False] \\n sample = sample\\n sampleDat = nonDecoy[nonDecoy[\\\"R.Condition\\\"].str[-3:] == sample] \\n perc_NaN = sampleDat[\\\"PG.Quantity\\\"].isna().sum() / (sampleDat[\\\"PG.Quantity\\\"].count() + sampleDat[\\\"PG.Quantity\\\"].isna().sum())\\n return perc_NaN\",\n \"def addNoise(array,counts):\\r\\n if array.dtype == 'complex' :\\r\\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\\r\\n else :\\r\\n if np.float64(counts) == 0.0e0 :\\r\\n arrayout = np.zeros(array.shape, dtype=arrayout.dtype)\\r\\n elif np.float64(counts) < 0.0e0 :\\r\\n print 'bg.addNoise : warning counts < 0'\\r\\n elif np.float64(counts) > 1.0e9 :\\r\\n arrayout = np.zeros(array.shape)\\r\\n arrayout = normaliseInt(array)\\r\\n arrayout = np.random.normal(arrayout*np.float64(counts),np.sqrt(arrayout*np.float64(counts)))/np.float64(counts)\\r\\n tot = np.sum(array)\\r\\n arrayout = normaliseInt(arrayout,tot)\\r\\n else :\\r\\n arrayout = np.zeros(array.shape)\\r\\n arrayout = normaliseInt(array)\\r\\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\\r\\n tot = np.sum(array)\\r\\n arrayout = normaliseInt(arrayout,tot)\\r\\n return arrayout\",\n \"def number_densities(self) -> u.m**-3:\\n try:\\n return (self.n_elem * self.ionic_fractions).to(u.m**-3)\\n except Exception: # noqa: BLE001\\n return np.full(self.atomic_number + 1, np.nan) * u.m**-3\",\n \"def noiseFraction(truth_h5, measured_h5, tolerance):\\n if (measured_h5.getNLocalizations() == 0):\\n return [0, truth_h5.getNLocalizations()]\\n \\n noise_locs = 0\\n total_locs = 0\\n for i in range(truth_h5.getMovieLength()):\\n t_locs = truth_h5.getLocalizationsInFrame(i)\\n m_locs = measured_h5.getLocalizationsInFrame(i)\\n\\n if bool(t_locs) and bool(m_locs):\\n dist = iaUtilsC.peakToPeakDistAndIndex(m_locs['x'], m_locs['y'],\\n t_locs['x'], t_locs['y'],\\n max_distance = tolerance)[0]\\n\\n noise_locs += numpy.count_nonzero((dist < 0.0))\\n total_locs += dist.size\\n elif bool(m_locs):\\n noise_locs += m_locs['x'].size\\n total_locs += m_locs['x'].size\\n\\n return [noise_locs, total_locs]\",\n \"def noiseReduction(self):\\n pass\",\n \"def stdProbabilityNorm(self):\\n return 0.5\",\n \"def entropy(self):\\r\\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)\",\n \"def sample_percent(self, percentage):\\n count = int(len(self.features) * (percentage / 100))\\n indices = np.random.randint(0, high=len(self.features), size=count)\\n return ProcessedImageData(self.features[indices], self.labels[indices], indices)\",\n \"def pc_nproduced_avg(self) -> \\\"float\\\":\\n return _beamforming_swig.randomsampler_sptr_pc_nproduced_avg(self)\",\n \"def variance(self):\\n return 1 / self.count() * sum((number-self.average())**2 for number in self.numbers)\",\n \"def white_noise():\\n return random.randint(-32767, 32767)\",\n \"def chance_of_rain(self):\\r\\n raise NotImplementedError\",\n \"def menhinick(counts):\\n return observed_species(counts)/sqrt(counts.sum())\",\n \"def get_estimated_noise(self):\\n raise NotImplementedError('Abstract Method.')\",\n \"def score(self, data):\\n return np.mean( np.log( mvn.getSamplePointDensity(self.dataFrame_, self.H_, pd.DataFrame(data)) ) )\",\n \"def nanPercentage(df):\\n rows = df.shape[0]\\n cols = df.shape[1]\\n tot = rows*cols\\n \\n nanNum = 0\\n for i in range(df.shape[0]):\\n nanNum = nanNum + np.sum ( pd.isnull(df.iloc[i]) )\\n logger.debug ('nan %d tot %d ' % (nanNum, tot) )\\n perc = (100*nanNum) / (tot * 1.0)\\n return perc\",\n \"def percent(self):\\r\\n return self._percent\",\n \"def dissimilarity(clusters):\\n totDist = 0\\n for c in clusters:\\n totDist += c.variability()\\n return totDist\",\n \"def tail_ratio(returns):\\n\\n return np.abs(np.percentile(returns, 95)) / \\\\\\n np.abs(np.percentile(returns, 5))\",\n \"def die(world, percentage):\\n\\n \\n infected = np.sum((world >= 1) & (world <= 10))\\n to_die = percentage * infected\\n if to_die < 1:\\n to_die = 0\\n else:\\n to_die = to_die\\n to_die = np.round(to_die).astype(int)\\n\\n\\n indizes = [] # Für die Koordinaten der infizierten Zellen\\n for i, v in np.ndenumerate(world):\\n if v in range(1, 11):\\n indizes.append(i)\\n #Ziehe Stichprobe aus den infizierten Zellen und setze sie auf 300\\n sample = random.sample(indizes, to_die)\\n for i in sample:\\n world[i] = 300\\n \\n return world\",\n \"def mape(self) -> float:\\n return float(np.mean(np.abs((self.true - self.predicted) / self.true)) * 100)\",\n \"def population_density(self) -> float:\\n return self.population / self.area\",\n \"def get_percent(self):\\n return self.percent\",\n \"def pct_bust(data):\\n return round((data[\\\"new_total\\\"] > 21).sum() / len(data), 3)\",\n \"def get_fraction_significant_p_value_gray_screen(group):\\n fraction_significant_p_value_gray_screen = len(group[group.p_value_gray_screen < 0.05]) / float(len(group))\\n return pd.Series({'fraction_significant_p_value_gray_screen': fraction_significant_p_value_gray_screen})\",\n \"def main():\\n x, y = create_simple_classification_dataset(5)\\n count = 0\\n for i in y:\\n if i[0] == 0:\\n count += 1\\n print('percent of zeros', count / len(y)) # confirm that the distribution isn't too skewed.\",\n \"def noise_profile(nf, gain, ffs, df):\\n\\n h_mWThz = 1e-3 * h * (1e14)**2\\n nf_lin = db2lin(nf)\\n g_lin = db2lin(gain)\\n ase = h_mWThz * df * ffs * (nf_lin * g_lin - 1)\\n asedb = lin2db(ase)\\n\\n return asedb\",\n \"def entropy(self):\\n n = len(self.y)\\n sum_ = 0\\n for i in np.unique(self.y):\\n v = len(self.y[self.y == i])\\n sum_ += -((v/n) * log2(v/n))\\n return sum_\",\n \"def copyCounterWithNoise(self, counter, prob):\\n \\\"\\\"\\\"\\n counter = deepcopy(counter)\\n for i in range(self.nObjectTypes):\\n noise = np.random.randint(1,5)\\n if np.random.random() < prob:\\n if np.random.random() > 0.5:\\n counter[i] += noise # false detections\\n else:\\n counter[i] -= noise # missed detections\\n if counter[i] < 0:\\n counter[i]=0\\n return counter\\n \\\"\\\"\\\"\\n counter = deepcopy(counter)\\n for i in range(self.nObjectTypes):\\n noise = np.random.randint(1,5)\\n if np.random.random() > prob:\\n continue\\n if counter[i] == 0:\\n # Noise could only be +1\\n counter[i] = noise\\n else:\\n # Noise could be either +1 or -1\\n if np.random.random() > 0.5:\\n counter[i] += noise\\n else:\\n counter[i] -= noise\\n if counter[i] < 0:\\n counter[i]=0\\n return counter\",\n \"def percent_frequencies(self):\\n word_count = 0\\n local = self.frequencies()\\n for key in local.keys():\\n i = local[key]\\n word_count += int(i)\\n for key in local.keys():\\n i = local[key]\\n percentage = float(i) / float(word_count)\\n local[key] = percentage\\n return local\",\n \"def normpdf(x, mean, sd):\\n var = float(sd)**2\\n denom = (2*math.pi*var)**.5\\n num = math.exp(-(float(x)-float(mean))**2/(2*var))\\n return num/denom\",\n \"def test_noise_equiv_bandwidth():\\n win = windows.blackmanharris(2000)\\n assert np.isclose(2, 1.0 / utils.noise_equivalent_bandwidth(win), rtol=1e-2)\",\n \"def fracSeen(data):\\n itemSet = set()\\n numSeen = []\\n for x in data:\\n itemSet.add(x)\\n numSeen.append(len(itemSet))\\n\\n fracSeen = [x/numSeen[-1] for x in numSeen]\\n return fracSeen\",\n \"def get_mean(data, n=-1):\\n \\n return round((sum(data)/n),1)\",\n \"def entropy(data):\\n strings, lens = Counter(data), np.float(len(data))\\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())\",\n \"def pulse_width_percent(self) -> float:\",\n \"def _basic_probability(count: int, sequence_total_count: int) -> float:\\n return float(count) / sequence_total_count\",\n \"def __measurement_prob(angle, measurement, noise):\\n return ParticleFilter.Gaussian(angle, noise, measurement)\",\n \"def percent_processed(self):\\n try:\\n return (self.pos / self.data_encap.size) * 100.0\\n except ZeroDivisionError:\\n return 100.0\",\n \"def percentage(my_list, item):\\n return 100.0 * frequency(my_list, item)\",\n \"def intensity(self) -> int:\",\n \"def pc_noutput_items_avg(self) -> \\\"float\\\":\\n return _beamforming_swig.randomsampler_sptr_pc_noutput_items_avg(self)\",\n \"def mean_deviation(self):\\r\\n\\t\\t_mean = sum(self.sample)/len(self.sample)\\r\\n\\t\\treturn sum(map(lambda x: abs(x - _mean), self.sample))/len(self.sample)\",\n \"def salt_and_pepper_noise(image, prob):\\n output = np.zeros(image.shape,np.uint8)\\n thres = 1 - prob \\n for i in range(image.shape[0]):\\n for j in range(image.shape[1]):\\n rdn = random.random()\\n if rdn < prob:\\n output[i][j] = 0\\n elif rdn > thres:\\n output[i][j] = 255\\n else:\\n output[i][j] = image[i][j]\\n return output\",\n \"def sampling_frequency(self) -> int:\\n return int(1 / self.x_scale)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68183047","0.67847115","0.6347133","0.62229025","0.61716366","0.61085075","0.6024723","0.59983605","0.59494674","0.5946805","0.5945206","0.5942025","0.5883616","0.58745587","0.58745587","0.5860042","0.5856353","0.5841199","0.5834648","0.57891965","0.5787481","0.5779676","0.5754733","0.5739616","0.5738536","0.5734329","0.5733115","0.57128024","0.5706441","0.5677128","0.56746167","0.56584126","0.565517","0.5646461","0.56399137","0.56369793","0.56289685","0.56271696","0.5621887","0.5619539","0.5602273","0.5588275","0.558633","0.55799896","0.5562771","0.55593","0.55535597","0.55517733","0.55502284","0.5539951","0.553784","0.55372566","0.5527414","0.5524691","0.55224955","0.55221075","0.55201876","0.55174595","0.55141395","0.551336","0.55103344","0.5485136","0.5482127","0.5481422","0.5480001","0.54757494","0.5472683","0.5470905","0.5467766","0.5466083","0.5464174","0.5460244","0.5458011","0.5451941","0.5450008","0.5448966","0.5446524","0.5443406","0.5435541","0.5430954","0.54283017","0.5427817","0.5424463","0.54225093","0.5421148","0.541611","0.5413816","0.5412646","0.5412563","0.54112935","0.54090303","0.5405418","0.54045916","0.5403542","0.5402787","0.53958315","0.53947634","0.53865856","0.53851086","0.53808147"],"string":"[\n \"0.68183047\",\n \"0.67847115\",\n \"0.6347133\",\n \"0.62229025\",\n \"0.61716366\",\n \"0.61085075\",\n \"0.6024723\",\n \"0.59983605\",\n \"0.59494674\",\n \"0.5946805\",\n \"0.5945206\",\n \"0.5942025\",\n \"0.5883616\",\n \"0.58745587\",\n \"0.58745587\",\n \"0.5860042\",\n \"0.5856353\",\n \"0.5841199\",\n \"0.5834648\",\n \"0.57891965\",\n \"0.5787481\",\n \"0.5779676\",\n \"0.5754733\",\n \"0.5739616\",\n \"0.5738536\",\n \"0.5734329\",\n \"0.5733115\",\n \"0.57128024\",\n \"0.5706441\",\n \"0.5677128\",\n \"0.56746167\",\n \"0.56584126\",\n \"0.565517\",\n \"0.5646461\",\n \"0.56399137\",\n \"0.56369793\",\n \"0.56289685\",\n \"0.56271696\",\n \"0.5621887\",\n \"0.5619539\",\n \"0.5602273\",\n \"0.5588275\",\n \"0.558633\",\n \"0.55799896\",\n \"0.5562771\",\n \"0.55593\",\n \"0.55535597\",\n \"0.55517733\",\n \"0.55502284\",\n \"0.5539951\",\n \"0.553784\",\n \"0.55372566\",\n \"0.5527414\",\n \"0.5524691\",\n \"0.55224955\",\n \"0.55221075\",\n \"0.55201876\",\n \"0.55174595\",\n \"0.55141395\",\n \"0.551336\",\n \"0.55103344\",\n \"0.5485136\",\n \"0.5482127\",\n \"0.5481422\",\n \"0.5480001\",\n \"0.54757494\",\n \"0.5472683\",\n \"0.5470905\",\n \"0.5467766\",\n \"0.5466083\",\n \"0.5464174\",\n \"0.5460244\",\n \"0.5458011\",\n \"0.5451941\",\n \"0.5450008\",\n \"0.5448966\",\n \"0.5446524\",\n \"0.5443406\",\n \"0.5435541\",\n \"0.5430954\",\n \"0.54283017\",\n \"0.5427817\",\n \"0.5424463\",\n \"0.54225093\",\n \"0.5421148\",\n \"0.541611\",\n \"0.5413816\",\n \"0.5412646\",\n \"0.5412563\",\n \"0.54112935\",\n \"0.54090303\",\n \"0.5405418\",\n \"0.54045916\",\n \"0.5403542\",\n \"0.5402787\",\n \"0.53958315\",\n \"0.53947634\",\n \"0.53865856\",\n \"0.53851086\",\n \"0.53808147\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70178664"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":688,"cells":{"query":{"kind":"string","value":"Returns the mean cluster size."},"document":{"kind":"string","value":"def analysis_function_mean_cluster_size(self,clustering):\n sizes = get_cluster_sizes(clustering.clusters)[1]\n return numpy.mean(sizes)"},"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 mean_cluster(self, labelled_cluster):\n sum_of_points = self.sum_cluster(labelled_cluster)\n size_cluster = len(labelled_cluster)\n if self.sigma_cl1:\n size_cluster += np.sqrt(2)*self.sigma_cl1*np.random.randn()\n mean_of_points = sum_of_points * (1.0 / size_cluster)\n return mean_of_points","def meanContigLength(self):\n\t\tstats = self.scores()\n\t\treturn stats['meanContig']","def avgsize_c(self):\n return self._avgsize_c","def clusterSize(l, scheme, clustertype='fluid'):\n clist = findClusters(l, scheme, clustertype)\n \n avglists=[]\n for i in clist:\n avglist=[]\n for l in i:\n avglist.append(np.mean(l))\n avglists.append(np.mean(avglist))\n return avglists","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def mean(self):\n mean = sum(self.data)/self.size\n return mean","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())","def avg_sil_width(cluster_vec, centroid):\n tot_dis = 0\n for i in xrange(len(cluster_vec)):\n cos_dis = cos_sim(cluster_vec[i], centroid)\n tot_dis = tot_dis + cos_dis\n return tot_dis/len(cluster_vec)","def meshsize_avg(self):\n nspans = self.numspans\n support = abs(self.kv[-1] - self.kv[0])\n return support / nspans","def average_consensus(self, cluster):\n\t\tcenterk = 0\n\t\tindex = 0\n\t\tfor value in cluster:\n\t\t\tcenterk += value\n\t\t\tindex += 1\n\t\tcenterk = centerk / index\n\t\treturn centerk","def average_size(self):\n sizes = []\n for i in range(self.params.num_trees):\n with tf.device(self.device_assigner.get_device(i)):\n sizes.append(self.trees[i].size())\n return tf.reduce_mean(tf.pack(sizes))","def n_clusters(self):\n return len(self.clusters)","def graph_data_size_avg(self) -> float:\n return float(self.graph_tuple_stats.graph_data_size_avg or 0)","def group_size_dist(self):\n return self.group_sizes() / self.group_sizes().sum()","def avg_net(self) -> float:\n return torch.mean(self.units.net)","def average_city_size(self):\r\n average = 0\r\n total = 0\r\n for code, node in self.vertices.items():\r\n average += node.population\r\n total += 1\r\n return average // total","def ensemble_mean(self):\n return self.mean(dim='mem')","def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)","def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements","def n_clusters(self):\n return self.model.n_clusters","def getCentroid(cluster):\n try:\n return np.mean(cluster, axis = 0)\n except:\n return None","def get_cluster_average(cls, indices, dist_mat):\n distances = cls.get_all_distances(indices, dist_mat)\n return np.mean(distances)","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def avgcpu(self):\n return (self._total_cpu['value'] / self._total_cpu['count']) if self._total_cpu['count'] else 0","def extract_cluster_size(line):\r\n cluster_size = line.split(\":\")[-1]\r\n\r\n try:\r\n cluster_size = int(cluster_size)\r\n except ValueError:\r\n return 0\r\n return cluster_size","def get_clust_cent(self):\r\n\r\n return self.__clust_cent","def cf_mean(self):\n return self['capacity_factor'] / 100","def cf_mean(self):\n return self['capacity_factor'] / 100","def mean(self):\n return self._mean","def mean(self):\n return self._mean","def get_total(self):\n return int(self.total_cores)","def get_total_n_cpu(self) -> int:","def mean(self) -> float:\n return self._data.mean()","def calculate_cluster_size(result, var):\n \n cluster_results=pd.DataFrame(result[var].value_counts())\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\"ratio\"})\n return cluster_results.join(ratio)","def meanZmArea(self):\n sumArea = 0\n for site in self.sites:\n sumArea = sumArea + site.siteZmArea\n meanArea = sumArea / self.countSites()\n return meanArea","def get_mean(self):\n return self.serie.mean()","def avg_hops(self):\n return self._avg_hops","def kmean(X,initial_centroids,max_iters):\n m = np.size(X,0)\n K = np.size(initial_centroids,0)\n centroids = initial_centroids\n idx = np.zeros((m,1))\n for i in range(1,max_iters):\n idx = nearest_cluster(X,centroids)\n centroids = update_centroids(X,idx,K)\n return centroids,idx","def mean(self) -> float:\n return self._interval_sum / len(self.intervals)","def get_avg_word_length(self):\n words = self.blob.words\n average_word_length = np.mean(np.array([len(word) for word in words]))\n return average_word_length","def compute_centroid(data):\n return sum(data[:]) / len(data)","def mean_value(self):\n\n return self._system.mean()","def k_mean_cluster(num_anchors, boxes, convergence_threshold=1e-9):\n # Randomly pick 5 boxes as centroids\n centroids = np.array(random.sample(boxes, k=num_anchors))\n # Clustering until reaching loss_convergence threshold\n centroids, prev_errors = k_mean(boxes, centroids)\n while True:\n centroids, errors = k_mean(boxes, centroids)\n if abs(errors - prev_errors) <= convergence_threshold:\n break\n else:\n prev_errors = errors\n\n avg_iou = np.mean(np.max(compute_iou(boxes, centroids), axis=-1))\n return centroids, avg_iou","def meanMolarMass(self):\n return _cantera.phase_meanmolwt(self._phase_id)","def get_average_neighbors(self,radius):\n return np.mean([agent.n_neighbors(radius) for agent in self.agents])","def mean_radius(self):\n return self._mean_radius","def avg_packet_size(self):\n result = 0\n try:\n result = sum(self.fcip_doc['packet_lengths'])/float(len(self.fcip_doc['packet_lengths']))\n except:\n pass\n return result","def getMean(self):\n return self.mean","def cluster_cal(self):\n self.Cluster = []\n for i in range(self.nodenum):\n neighborhood_node = self.neighbor_node(i)\n Node_num = len(neighborhood_node)\n Count = self.neighbor_edge(neighborhood_node)\n if(Node_num == 0 or Node_num == 1):\n self.Cluster.append(0.5)\n else:\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\n \n self.cluster_coeff = np.average(self.Cluster)","def average_length_of_gene(self):\n return sum([len(e) for e in self.population]) / len(self.population)","def get_mean_degree(self):\n\n return np.mean(self.graph.degree())","def cluster_means_scaled(self):\n if self.evaluate_by is not None:\n return(self.merged_scaled_data.groupby(\n 'labels').mean().sort_values(self.evaluate_by).transpose())\n else:\n return(self.merged_scaled_data.groupby(\n 'labels').mean().transpose())","def calculate_cluster_fitness(self, cluster_id: ObjectId):\n\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\n\n cluster_fitness = 0\n\n for genome in genomes:\n cluster_fitness += genome.fitness\n if cluster_fitness == 0:\n return 0\n\n return cluster_fitness / len(list(genomes))","def mean(self):\n return self.sum / self.sum_weights","def total_min_node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"total_min_node_count\")","def totalsize(self):\n return sum([sz for sz in self.iterate()])","def size(self) -> int:\n\n return self.sizes.sum()","def mean(self):\n return self.vmean","def mean_sample_length(dataset, n_points, n_neighbors, n_resamplings):\r\n\tlength = 0\r\n\tfor _ in range(n_resamplings):\r\n\t\tsample = random.sample(range(len(dataset)), n_points)\r\n\t\tsample_kNN = kneighbors_graph(dataset[sample], 5, mode='distance', include_self=False, n_jobs=-1)\r\n\t\tlength += np.sum(sample_kNN)\r\n\treturn length/n_resamplings","def _compute_centroids(self):\n\n for i in range(0, self.k):\n cluster = np.argwhere(self.assigned_clusters == i)\n cluster_points = self.data[cluster].squeeze()\n self.centroids[i] = np.mean(cluster_points, axis=0)","def mean(self):\r\n\t\treturn sum(self.sample)/len(self.sample)","def centroid(self):\n return self.contours_to_matrix().mean(axis=0)","def data_center_count(self) -> int:\n return pulumi.get(self, \"data_center_count\")","def mean(self):\n\n return self._reduce_for_stat_function(F.mean, only_numeric=True)","def average_length(corpus):\n token_size = 0\n for i in corpus:\n token_size += len(i)\n return token_size/len(corpus)","def clust_strength(mat,groups):\n cluster_strengths = []\n for group in range(len(np.unique(groups))):\n this_cluster = mat[groups==group,:]\n this_cluster_mean = np.mean(this_cluster,axis=0)\n all_dists = mat - this_cluster_mean\n out_dists = np.linalg.norm(all_dists[groups!=group],axis=1)\n in_dists = np.linalg.norm(all_dists[groups==group],axis=1)\n this_strength = np.mean(out_dists)/np.mean(in_dists)\n cluster_strengths.append(this_strength)\n \n return np.mean(cluster_strengths)","def calcCentroid(self):\n size = len(self.vectors)\n # zip all features together\n zipped = zip(*self.vectors)\n # Calculate the mean for each feature/column\n centroid = [math.fsum(column)/size for column in zipped]\n \n return centroid","def get_clust_num_perc(model, vis_perc=0.9):\n\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\n\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\n\n\ttot = model.allocmodel.Nk[idx].sum()\n\tcursum = 0\n\ti = 0\n\twhile cursum < tot*vis_perc:\n\t cursum += model.allocmodel.Nk[idx][i]\n\t i+=1\n\n\treturn i","def nrmse_mean(self) -> float:\n return float(self.rmse() / np.mean(self.true))","def train_size(self) -> int:\n return int(self.data_size * self.__train_fraction)","def get_utilization(self):\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\n # Remove overlapping prefixes from list of children\n networks = cidr_merge([c.prefix for c in child_prefixes])\n children_size = float(0)\n for p in networks:\n children_size += p.size\n return int(children_size / self.prefix.size * 100)","def find_average_doc_length(self):\n avg_doc_length_result = self.es.search(index=\"ap_dataset\", doc_type=\"document\",\n body={\"aggs\": {\"avg_doc_length\": {\"avg\": {\"script\": \"doc['doclength'].values\"}}}})\n avg_doc_length = avg_doc_length_result['aggregations']['avg_doc_length']['value']\n return avg_doc_length","def assign_to_current_mean(img: np.ndarray, clustermask: np.ndarray) -> float:\n\n rows, cols = img.shape[:2]\n distances = np.zeros((numclusters, 1))\n overall_dist = 0\n\n for i in range(rows):\n for j in range(cols):\n distances = distance(img[i, j, :]) # returned shape: (numclusters, 1)\n \n k = np.argmin(distances) # closest cluster\n clustermask.itemset((i, j), k) # update cluster mask\n overall_dist += distances[k, 0] # sum distance\n\n return overall_dist","def get_avg_sentence_length(self):\n sentences = self.blob.sentences\n average_sentence_length = np.mean(np.array([len(sentence.words) for sentence in sentences]))\n return average_sentence_length","def get_average(self) -> float:\n return sum(self._scores) / len(self._scores)","def compute_means(self):\n ###TODO\n vector_means = []\n for doc in self.fin_clust.values():\n vec = defaultdict(float)\n for d_id in doc:\n doc_keys = self.docs[d_id].keys()\n for key in self.docs[d_id]:\n vec[key] = vec[key] + self.docs[d_id][key]\n tot = len(doc)\n x = defaultdict(float)\n for k,v in vec.items():\n x[k] = float(v)/tot\n vec = Counter(x)\n vector_means.append(vec)\n return vector_means","def size(self):\n futures = self.client.map(_call_size, self.vecDask, pure=False)\n sizes = self.client.gather(futures)\n return np.sum(sizes)","def compute_cluster_ensemble(var, indicesOnCluster, maxIndices, indicesToParticle): #{{{\n\n num_clusters = maxIndices.shape[0]\n if len(var.shape) == 1:\n meanvar = np.zeros((num_clusters,))\n elif len(var.shape) == 2:\n meanvar = np.zeros((var.shape[0],num_clusters))\n else:\n warnings.warn('did not have correct shape for ' + str(var) + ' with len(var.shape)='+ str(len(var.shape)))\n meanvar = None\n\n for aCluster, maxInd in enumerate(maxIndices):\n # get particles in cluster\n particles = indicesToParticle[indicesOnCluster[aCluster,0:maxInd]]\n\n # compute mean depending upon size of array\n if len(var.shape) == 1:\n meanvar[aCluster] = np.mean(var[particles])\n if len(var.shape) == 2:\n meanvar[:,aCluster] = np.mean(var[:,particles], axis=1)\n\n return meanvar #}}}","def mean(self):\n return self._summarize(lambda c: c.mean)","def group_size(self):\n return self._gsize","def get_sectors_per_cluster(self):\n\n\t\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\n\t\tif sectors_per_cluster_base == 0:\n\t\t\traise BootSectorException('Invalid cluster size (zero)')\n\n\t\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\n\t\t\tsectors_per_cluster_real = sectors_per_cluster_base\n\t\telse:\n\t\t\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\n\t\t\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\n\n\t\treturn sectors_per_cluster_real","def compute_mean_response_length(self):\n mean_response_length = 0\n for row in self.responses:\n mean_response_length += len(row.response)\n return round(mean_response_length / len(self.responses), 2)","def mean_min_distance_to_cluster(actual_activation,\n cluster_activation, n_cluster_samples):\n if cluster_activation.ndim == 4:\n actual_activation = actual_activation.dimshuffle('x',0,1,2)\n squared_distances = T.mean(T.square(cluster_activation -\n actual_activation), axis=(1,2,3))\n elif cluster_activation.ndim == 2:\n actual_activation = actual_activation.dimshuffle('x',0)\n squared_distances = T.mean(T.square(cluster_activation -\n actual_activation), axis=(1,))\n \n \n squared_distances_sorted = T.sort(squared_distances)\n \n distance = T.mean(squared_distances_sorted[:n_cluster_samples])\n return distance","def size(self) -> pulumi.Output[float]:\n return pulumi.get(self, \"size\")","def getCentroid(self):\r\n return self._centroid","def get_avg(self) -> float:\n if self._cur_elem_count < 1:\n return 0\n self._mtx.acquire()\n avg = self._sum / float(self._cur_elem_count)\n self._mtx.release()\n return avg","def mean(self):\r\n return np.mean(self.data_array)","def avg_latency(self):\n return self._avg_latency","def avg_latency(self):\n return self._avg_latency","def find_avg(centroids, short_cut=False, sim_scores=None):\n \n total_sim = 0.0\n total_comparisons = 0\n \n if short_cut:\n total_comparisons = len(sim_scores)\n \n for score in sim_scores:\n total_sim += score\n \n return (total_sim / total_comparisons)\n\n length = len(centroids)\n\n for i in xrange(0, length):\n for j in xrange(i + 1, length):\n total_sim += similarity(centroids[i], centroids[j])\n total_comparisons += 1\n\n return (total_sim / total_comparisons)","def get_insert_size_mean(self, ctx, params):\n # ctx is the context object\n # return variables are: returnVal\n #BEGIN get_insert_size_mean\n\n if 'workspace_name' not in params:\n raise ValueError('Parameter workspace_name is not set in input arguments')\n workspace_name = params['workspace_name']\n if 'id' not in params:\n raise ValueError('Parameter id is not set in input arguments')\n objid = params['id']\n\n token = ctx['token']\n wsClient = workspaceService(self.workspaceURL, token=token)\n try:\n\n objref = workspace_name + '/' + str(objid)\n\n # Note that results from the workspace are returned in a list\n returnVal = wsClient.get_objects([{'ref': objref}])[0]\n\n print \"get_insert_size_mean returnVal \" + str(returnVal)\n\n if returnVal is not None:\n if returnVal['data']['single_genome'] is not None:\n returnVal = returnVal['data']['single_genome']\n\n # print \"is_single_genome issingle \" + str(returnVal)\n except:\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lines = traceback.format_exception(exc_type, exc_value, exc_traceback)\n orig_error = ''.join(' ' + line for line in lines)\n raise ValueError('Error from workspace:\\n' + orig_error)\n\n #END get_insert_size_mean\n\n # At some point might do deeper type checking...\n if not isinstance(returnVal, float):\n raise ValueError('Method get_insert_size_mean return value ' +\n 'returnVal is not type float as required.')\n # return the results\n return [returnVal]","def get_total_distributed(self) -> int:\n return self._total_distributed.get()","def centroid(clusters):\n centroids = list(map(\n lambda cluster: tuple(map(\n lambda x, cluster=cluster: x / len(cluster['vertices']),\n sum(map(\n numpy.array,\n cluster['vertices'])))),\n clusters))\n return centroids","def mem_per_core(self):\n return self.mem_per_node / self.cores_per_node","def num_cores(self):\n return self.cores_per_socket * self.sockets_per_node * self.num_nodes","def __len__(self):\n return len(self.centroid_vector)","def get_num_nodes(self):\n\n return sum(self.topology)","def getNNodesTot(self):\n nNodesTot = 0\n for iElt in Elements._all:\n nNodesTot += len(iElt.coord)\n return nNodesTot","def compute(self): \r\n hist = self.confusion_matrix\r\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\r\n mean_iu = np.nanmean(iu)\r\n return mean_iu"],"string":"[\n \"def mean_cluster(self, labelled_cluster):\\n sum_of_points = self.sum_cluster(labelled_cluster)\\n size_cluster = len(labelled_cluster)\\n if self.sigma_cl1:\\n size_cluster += np.sqrt(2)*self.sigma_cl1*np.random.randn()\\n mean_of_points = sum_of_points * (1.0 / size_cluster)\\n return mean_of_points\",\n \"def meanContigLength(self):\\n\\t\\tstats = self.scores()\\n\\t\\treturn stats['meanContig']\",\n \"def avgsize_c(self):\\n return self._avgsize_c\",\n \"def clusterSize(l, scheme, clustertype='fluid'):\\n clist = findClusters(l, scheme, clustertype)\\n \\n avglists=[]\\n for i in clist:\\n avglist=[]\\n for l in i:\\n avglist.append(np.mean(l))\\n avglists.append(np.mean(avglist))\\n return avglists\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def mean(self):\\n mean = sum(self.data)/self.size\\n return mean\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def get_cluster_count(self) -> int:\\n return len(self.get_all_cluster_ids())\",\n \"def avg_sil_width(cluster_vec, centroid):\\n tot_dis = 0\\n for i in xrange(len(cluster_vec)):\\n cos_dis = cos_sim(cluster_vec[i], centroid)\\n tot_dis = tot_dis + cos_dis\\n return tot_dis/len(cluster_vec)\",\n \"def meshsize_avg(self):\\n nspans = self.numspans\\n support = abs(self.kv[-1] - self.kv[0])\\n return support / nspans\",\n \"def average_consensus(self, cluster):\\n\\t\\tcenterk = 0\\n\\t\\tindex = 0\\n\\t\\tfor value in cluster:\\n\\t\\t\\tcenterk += value\\n\\t\\t\\tindex += 1\\n\\t\\tcenterk = centerk / index\\n\\t\\treturn centerk\",\n \"def average_size(self):\\n sizes = []\\n for i in range(self.params.num_trees):\\n with tf.device(self.device_assigner.get_device(i)):\\n sizes.append(self.trees[i].size())\\n return tf.reduce_mean(tf.pack(sizes))\",\n \"def n_clusters(self):\\n return len(self.clusters)\",\n \"def graph_data_size_avg(self) -> float:\\n return float(self.graph_tuple_stats.graph_data_size_avg or 0)\",\n \"def group_size_dist(self):\\n return self.group_sizes() / self.group_sizes().sum()\",\n \"def avg_net(self) -> float:\\n return torch.mean(self.units.net)\",\n \"def average_city_size(self):\\r\\n average = 0\\r\\n total = 0\\r\\n for code, node in self.vertices.items():\\r\\n average += node.population\\r\\n total += 1\\r\\n return average // total\",\n \"def ensemble_mean(self):\\n return self.mean(dim='mem')\",\n \"def analysis_function_num_clusters(self,clustering):\\n return len(clustering.clusters)\",\n \"def analysis_function_total_elements(self,clustering):\\n return clustering.total_number_of_elements\",\n \"def n_clusters(self):\\n return self.model.n_clusters\",\n \"def getCentroid(cluster):\\n try:\\n return np.mean(cluster, axis = 0)\\n except:\\n return None\",\n \"def get_cluster_average(cls, indices, dist_mat):\\n distances = cls.get_all_distances(indices, dist_mat)\\n return np.mean(distances)\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def avgcpu(self):\\n return (self._total_cpu['value'] / self._total_cpu['count']) if self._total_cpu['count'] else 0\",\n \"def extract_cluster_size(line):\\r\\n cluster_size = line.split(\\\":\\\")[-1]\\r\\n\\r\\n try:\\r\\n cluster_size = int(cluster_size)\\r\\n except ValueError:\\r\\n return 0\\r\\n return cluster_size\",\n \"def get_clust_cent(self):\\r\\n\\r\\n return self.__clust_cent\",\n \"def cf_mean(self):\\n return self['capacity_factor'] / 100\",\n \"def cf_mean(self):\\n return self['capacity_factor'] / 100\",\n \"def mean(self):\\n return self._mean\",\n \"def mean(self):\\n return self._mean\",\n \"def get_total(self):\\n return int(self.total_cores)\",\n \"def get_total_n_cpu(self) -> int:\",\n \"def mean(self) -> float:\\n return self._data.mean()\",\n \"def calculate_cluster_size(result, var):\\n \\n cluster_results=pd.DataFrame(result[var].value_counts())\\n ratio=np.round(cluster_results/cluster_results.sum()*100, 2).rename(columns={var:\\\"ratio\\\"})\\n return cluster_results.join(ratio)\",\n \"def meanZmArea(self):\\n sumArea = 0\\n for site in self.sites:\\n sumArea = sumArea + site.siteZmArea\\n meanArea = sumArea / self.countSites()\\n return meanArea\",\n \"def get_mean(self):\\n return self.serie.mean()\",\n \"def avg_hops(self):\\n return self._avg_hops\",\n \"def kmean(X,initial_centroids,max_iters):\\n m = np.size(X,0)\\n K = np.size(initial_centroids,0)\\n centroids = initial_centroids\\n idx = np.zeros((m,1))\\n for i in range(1,max_iters):\\n idx = nearest_cluster(X,centroids)\\n centroids = update_centroids(X,idx,K)\\n return centroids,idx\",\n \"def mean(self) -> float:\\n return self._interval_sum / len(self.intervals)\",\n \"def get_avg_word_length(self):\\n words = self.blob.words\\n average_word_length = np.mean(np.array([len(word) for word in words]))\\n return average_word_length\",\n \"def compute_centroid(data):\\n return sum(data[:]) / len(data)\",\n \"def mean_value(self):\\n\\n return self._system.mean()\",\n \"def k_mean_cluster(num_anchors, boxes, convergence_threshold=1e-9):\\n # Randomly pick 5 boxes as centroids\\n centroids = np.array(random.sample(boxes, k=num_anchors))\\n # Clustering until reaching loss_convergence threshold\\n centroids, prev_errors = k_mean(boxes, centroids)\\n while True:\\n centroids, errors = k_mean(boxes, centroids)\\n if abs(errors - prev_errors) <= convergence_threshold:\\n break\\n else:\\n prev_errors = errors\\n\\n avg_iou = np.mean(np.max(compute_iou(boxes, centroids), axis=-1))\\n return centroids, avg_iou\",\n \"def meanMolarMass(self):\\n return _cantera.phase_meanmolwt(self._phase_id)\",\n \"def get_average_neighbors(self,radius):\\n return np.mean([agent.n_neighbors(radius) for agent in self.agents])\",\n \"def mean_radius(self):\\n return self._mean_radius\",\n \"def avg_packet_size(self):\\n result = 0\\n try:\\n result = sum(self.fcip_doc['packet_lengths'])/float(len(self.fcip_doc['packet_lengths']))\\n except:\\n pass\\n return result\",\n \"def getMean(self):\\n return self.mean\",\n \"def cluster_cal(self):\\n self.Cluster = []\\n for i in range(self.nodenum):\\n neighborhood_node = self.neighbor_node(i)\\n Node_num = len(neighborhood_node)\\n Count = self.neighbor_edge(neighborhood_node)\\n if(Node_num == 0 or Node_num == 1):\\n self.Cluster.append(0.5)\\n else:\\n self.Cluster.append(Count/(Node_num*(Node_num - 1)))\\n \\n self.cluster_coeff = np.average(self.Cluster)\",\n \"def average_length_of_gene(self):\\n return sum([len(e) for e in self.population]) / len(self.population)\",\n \"def get_mean_degree(self):\\n\\n return np.mean(self.graph.degree())\",\n \"def cluster_means_scaled(self):\\n if self.evaluate_by is not None:\\n return(self.merged_scaled_data.groupby(\\n 'labels').mean().sort_values(self.evaluate_by).transpose())\\n else:\\n return(self.merged_scaled_data.groupby(\\n 'labels').mean().transpose())\",\n \"def calculate_cluster_fitness(self, cluster_id: ObjectId):\\n\\n genomes = self.genome_repository.get_genomes_in_cluster(cluster_id)\\n\\n cluster_fitness = 0\\n\\n for genome in genomes:\\n cluster_fitness += genome.fitness\\n if cluster_fitness == 0:\\n return 0\\n\\n return cluster_fitness / len(list(genomes))\",\n \"def mean(self):\\n return self.sum / self.sum_weights\",\n \"def total_min_node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"total_min_node_count\\\")\",\n \"def totalsize(self):\\n return sum([sz for sz in self.iterate()])\",\n \"def size(self) -> int:\\n\\n return self.sizes.sum()\",\n \"def mean(self):\\n return self.vmean\",\n \"def mean_sample_length(dataset, n_points, n_neighbors, n_resamplings):\\r\\n\\tlength = 0\\r\\n\\tfor _ in range(n_resamplings):\\r\\n\\t\\tsample = random.sample(range(len(dataset)), n_points)\\r\\n\\t\\tsample_kNN = kneighbors_graph(dataset[sample], 5, mode='distance', include_self=False, n_jobs=-1)\\r\\n\\t\\tlength += np.sum(sample_kNN)\\r\\n\\treturn length/n_resamplings\",\n \"def _compute_centroids(self):\\n\\n for i in range(0, self.k):\\n cluster = np.argwhere(self.assigned_clusters == i)\\n cluster_points = self.data[cluster].squeeze()\\n self.centroids[i] = np.mean(cluster_points, axis=0)\",\n \"def mean(self):\\r\\n\\t\\treturn sum(self.sample)/len(self.sample)\",\n \"def centroid(self):\\n return self.contours_to_matrix().mean(axis=0)\",\n \"def data_center_count(self) -> int:\\n return pulumi.get(self, \\\"data_center_count\\\")\",\n \"def mean(self):\\n\\n return self._reduce_for_stat_function(F.mean, only_numeric=True)\",\n \"def average_length(corpus):\\n token_size = 0\\n for i in corpus:\\n token_size += len(i)\\n return token_size/len(corpus)\",\n \"def clust_strength(mat,groups):\\n cluster_strengths = []\\n for group in range(len(np.unique(groups))):\\n this_cluster = mat[groups==group,:]\\n this_cluster_mean = np.mean(this_cluster,axis=0)\\n all_dists = mat - this_cluster_mean\\n out_dists = np.linalg.norm(all_dists[groups!=group],axis=1)\\n in_dists = np.linalg.norm(all_dists[groups==group],axis=1)\\n this_strength = np.mean(out_dists)/np.mean(in_dists)\\n cluster_strengths.append(this_strength)\\n \\n return np.mean(cluster_strengths)\",\n \"def calcCentroid(self):\\n size = len(self.vectors)\\n # zip all features together\\n zipped = zip(*self.vectors)\\n # Calculate the mean for each feature/column\\n centroid = [math.fsum(column)/size for column in zipped]\\n \\n return centroid\",\n \"def get_clust_num_perc(model, vis_perc=0.9):\\n\\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\\n\\tidx = np.argsort(-model.allocmodel.Nk)[0:nc]\\n\\n\\ttot = model.allocmodel.Nk[idx].sum()\\n\\tcursum = 0\\n\\ti = 0\\n\\twhile cursum < tot*vis_perc:\\n\\t cursum += model.allocmodel.Nk[idx][i]\\n\\t i+=1\\n\\n\\treturn i\",\n \"def nrmse_mean(self) -> float:\\n return float(self.rmse() / np.mean(self.true))\",\n \"def train_size(self) -> int:\\n return int(self.data_size * self.__train_fraction)\",\n \"def get_utilization(self):\\n child_prefixes = Prefix.objects.filter(prefix__net_contained_or_equal=str(self.prefix))\\n # Remove overlapping prefixes from list of children\\n networks = cidr_merge([c.prefix for c in child_prefixes])\\n children_size = float(0)\\n for p in networks:\\n children_size += p.size\\n return int(children_size / self.prefix.size * 100)\",\n \"def find_average_doc_length(self):\\n avg_doc_length_result = self.es.search(index=\\\"ap_dataset\\\", doc_type=\\\"document\\\",\\n body={\\\"aggs\\\": {\\\"avg_doc_length\\\": {\\\"avg\\\": {\\\"script\\\": \\\"doc['doclength'].values\\\"}}}})\\n avg_doc_length = avg_doc_length_result['aggregations']['avg_doc_length']['value']\\n return avg_doc_length\",\n \"def assign_to_current_mean(img: np.ndarray, clustermask: np.ndarray) -> float:\\n\\n rows, cols = img.shape[:2]\\n distances = np.zeros((numclusters, 1))\\n overall_dist = 0\\n\\n for i in range(rows):\\n for j in range(cols):\\n distances = distance(img[i, j, :]) # returned shape: (numclusters, 1)\\n \\n k = np.argmin(distances) # closest cluster\\n clustermask.itemset((i, j), k) # update cluster mask\\n overall_dist += distances[k, 0] # sum distance\\n\\n return overall_dist\",\n \"def get_avg_sentence_length(self):\\n sentences = self.blob.sentences\\n average_sentence_length = np.mean(np.array([len(sentence.words) for sentence in sentences]))\\n return average_sentence_length\",\n \"def get_average(self) -> float:\\n return sum(self._scores) / len(self._scores)\",\n \"def compute_means(self):\\n ###TODO\\n vector_means = []\\n for doc in self.fin_clust.values():\\n vec = defaultdict(float)\\n for d_id in doc:\\n doc_keys = self.docs[d_id].keys()\\n for key in self.docs[d_id]:\\n vec[key] = vec[key] + self.docs[d_id][key]\\n tot = len(doc)\\n x = defaultdict(float)\\n for k,v in vec.items():\\n x[k] = float(v)/tot\\n vec = Counter(x)\\n vector_means.append(vec)\\n return vector_means\",\n \"def size(self):\\n futures = self.client.map(_call_size, self.vecDask, pure=False)\\n sizes = self.client.gather(futures)\\n return np.sum(sizes)\",\n \"def compute_cluster_ensemble(var, indicesOnCluster, maxIndices, indicesToParticle): #{{{\\n\\n num_clusters = maxIndices.shape[0]\\n if len(var.shape) == 1:\\n meanvar = np.zeros((num_clusters,))\\n elif len(var.shape) == 2:\\n meanvar = np.zeros((var.shape[0],num_clusters))\\n else:\\n warnings.warn('did not have correct shape for ' + str(var) + ' with len(var.shape)='+ str(len(var.shape)))\\n meanvar = None\\n\\n for aCluster, maxInd in enumerate(maxIndices):\\n # get particles in cluster\\n particles = indicesToParticle[indicesOnCluster[aCluster,0:maxInd]]\\n\\n # compute mean depending upon size of array\\n if len(var.shape) == 1:\\n meanvar[aCluster] = np.mean(var[particles])\\n if len(var.shape) == 2:\\n meanvar[:,aCluster] = np.mean(var[:,particles], axis=1)\\n\\n return meanvar #}}}\",\n \"def mean(self):\\n return self._summarize(lambda c: c.mean)\",\n \"def group_size(self):\\n return self._gsize\",\n \"def get_sectors_per_cluster(self):\\n\\n\\t\\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\\n\\t\\tif sectors_per_cluster_base == 0:\\n\\t\\t\\traise BootSectorException('Invalid cluster size (zero)')\\n\\n\\t\\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\\n\\t\\t\\tsectors_per_cluster_real = sectors_per_cluster_base\\n\\t\\telse:\\n\\t\\t\\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\\n\\t\\t\\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\\n\\n\\t\\treturn sectors_per_cluster_real\",\n \"def compute_mean_response_length(self):\\n mean_response_length = 0\\n for row in self.responses:\\n mean_response_length += len(row.response)\\n return round(mean_response_length / len(self.responses), 2)\",\n \"def mean_min_distance_to_cluster(actual_activation,\\n cluster_activation, n_cluster_samples):\\n if cluster_activation.ndim == 4:\\n actual_activation = actual_activation.dimshuffle('x',0,1,2)\\n squared_distances = T.mean(T.square(cluster_activation -\\n actual_activation), axis=(1,2,3))\\n elif cluster_activation.ndim == 2:\\n actual_activation = actual_activation.dimshuffle('x',0)\\n squared_distances = T.mean(T.square(cluster_activation -\\n actual_activation), axis=(1,))\\n \\n \\n squared_distances_sorted = T.sort(squared_distances)\\n \\n distance = T.mean(squared_distances_sorted[:n_cluster_samples])\\n return distance\",\n \"def size(self) -> pulumi.Output[float]:\\n return pulumi.get(self, \\\"size\\\")\",\n \"def getCentroid(self):\\r\\n return self._centroid\",\n \"def get_avg(self) -> float:\\n if self._cur_elem_count < 1:\\n return 0\\n self._mtx.acquire()\\n avg = self._sum / float(self._cur_elem_count)\\n self._mtx.release()\\n return avg\",\n \"def mean(self):\\r\\n return np.mean(self.data_array)\",\n \"def avg_latency(self):\\n return self._avg_latency\",\n \"def avg_latency(self):\\n return self._avg_latency\",\n \"def find_avg(centroids, short_cut=False, sim_scores=None):\\n \\n total_sim = 0.0\\n total_comparisons = 0\\n \\n if short_cut:\\n total_comparisons = len(sim_scores)\\n \\n for score in sim_scores:\\n total_sim += score\\n \\n return (total_sim / total_comparisons)\\n\\n length = len(centroids)\\n\\n for i in xrange(0, length):\\n for j in xrange(i + 1, length):\\n total_sim += similarity(centroids[i], centroids[j])\\n total_comparisons += 1\\n\\n return (total_sim / total_comparisons)\",\n \"def get_insert_size_mean(self, ctx, params):\\n # ctx is the context object\\n # return variables are: returnVal\\n #BEGIN get_insert_size_mean\\n\\n if 'workspace_name' not in params:\\n raise ValueError('Parameter workspace_name is not set in input arguments')\\n workspace_name = params['workspace_name']\\n if 'id' not in params:\\n raise ValueError('Parameter id is not set in input arguments')\\n objid = params['id']\\n\\n token = ctx['token']\\n wsClient = workspaceService(self.workspaceURL, token=token)\\n try:\\n\\n objref = workspace_name + '/' + str(objid)\\n\\n # Note that results from the workspace are returned in a list\\n returnVal = wsClient.get_objects([{'ref': objref}])[0]\\n\\n print \\\"get_insert_size_mean returnVal \\\" + str(returnVal)\\n\\n if returnVal is not None:\\n if returnVal['data']['single_genome'] is not None:\\n returnVal = returnVal['data']['single_genome']\\n\\n # print \\\"is_single_genome issingle \\\" + str(returnVal)\\n except:\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lines = traceback.format_exception(exc_type, exc_value, exc_traceback)\\n orig_error = ''.join(' ' + line for line in lines)\\n raise ValueError('Error from workspace:\\\\n' + orig_error)\\n\\n #END get_insert_size_mean\\n\\n # At some point might do deeper type checking...\\n if not isinstance(returnVal, float):\\n raise ValueError('Method get_insert_size_mean return value ' +\\n 'returnVal is not type float as required.')\\n # return the results\\n return [returnVal]\",\n \"def get_total_distributed(self) -> int:\\n return self._total_distributed.get()\",\n \"def centroid(clusters):\\n centroids = list(map(\\n lambda cluster: tuple(map(\\n lambda x, cluster=cluster: x / len(cluster['vertices']),\\n sum(map(\\n numpy.array,\\n cluster['vertices'])))),\\n clusters))\\n return centroids\",\n \"def mem_per_core(self):\\n return self.mem_per_node / self.cores_per_node\",\n \"def num_cores(self):\\n return self.cores_per_socket * self.sockets_per_node * self.num_nodes\",\n \"def __len__(self):\\n return len(self.centroid_vector)\",\n \"def get_num_nodes(self):\\n\\n return sum(self.topology)\",\n \"def getNNodesTot(self):\\n nNodesTot = 0\\n for iElt in Elements._all:\\n nNodesTot += len(iElt.coord)\\n return nNodesTot\",\n \"def compute(self): \\r\\n hist = self.confusion_matrix\\r\\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\\r\\n mean_iu = np.nanmean(iu)\\r\\n return mean_iu\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7243587","0.7179832","0.7015998","0.7009933","0.68922645","0.68876517","0.684724","0.6808377","0.669405","0.6671428","0.6667033","0.6651806","0.65882844","0.6482646","0.63869536","0.63827366","0.63742137","0.63045746","0.6303817","0.62123394","0.61937535","0.61916125","0.6186689","0.6182542","0.618072","0.61401653","0.6137394","0.6104162","0.6104162","0.6092552","0.6092552","0.60821295","0.6077454","0.6044927","0.60269886","0.5993478","0.5986319","0.59649414","0.59624803","0.595281","0.59385246","0.5932969","0.5908111","0.5876624","0.58603084","0.58601516","0.585777","0.5856976","0.5856251","0.5845542","0.5830276","0.5815427","0.58153605","0.58131456","0.58100456","0.5805519","0.58031666","0.5798056","0.57971174","0.57949406","0.57882005","0.57876503","0.57706314","0.5770221","0.57467306","0.57463014","0.57405955","0.5722507","0.57032484","0.5681643","0.5676549","0.5669544","0.5669448","0.56686467","0.5667061","0.5665254","0.5658459","0.56547993","0.5652273","0.56429666","0.563285","0.56299233","0.56297314","0.5616537","0.5610085","0.56086594","0.56074643","0.5606431","0.5601822","0.5601822","0.5588715","0.5588563","0.55882394","0.558775","0.55836505","0.5571456","0.5569384","0.5564178","0.5563764","0.556266"],"string":"[\n \"0.7243587\",\n \"0.7179832\",\n \"0.7015998\",\n \"0.7009933\",\n \"0.68922645\",\n \"0.68876517\",\n \"0.684724\",\n \"0.6808377\",\n \"0.669405\",\n \"0.6671428\",\n \"0.6667033\",\n \"0.6651806\",\n \"0.65882844\",\n \"0.6482646\",\n \"0.63869536\",\n \"0.63827366\",\n \"0.63742137\",\n \"0.63045746\",\n \"0.6303817\",\n \"0.62123394\",\n \"0.61937535\",\n \"0.61916125\",\n \"0.6186689\",\n \"0.6182542\",\n \"0.618072\",\n \"0.61401653\",\n \"0.6137394\",\n \"0.6104162\",\n \"0.6104162\",\n \"0.6092552\",\n \"0.6092552\",\n \"0.60821295\",\n \"0.6077454\",\n \"0.6044927\",\n \"0.60269886\",\n \"0.5993478\",\n \"0.5986319\",\n \"0.59649414\",\n \"0.59624803\",\n \"0.595281\",\n \"0.59385246\",\n \"0.5932969\",\n \"0.5908111\",\n \"0.5876624\",\n \"0.58603084\",\n \"0.58601516\",\n \"0.585777\",\n \"0.5856976\",\n \"0.5856251\",\n \"0.5845542\",\n \"0.5830276\",\n \"0.5815427\",\n \"0.58153605\",\n \"0.58131456\",\n \"0.58100456\",\n \"0.5805519\",\n \"0.58031666\",\n \"0.5798056\",\n \"0.57971174\",\n \"0.57949406\",\n \"0.57882005\",\n \"0.57876503\",\n \"0.57706314\",\n \"0.5770221\",\n \"0.57467306\",\n \"0.57463014\",\n \"0.57405955\",\n \"0.5722507\",\n \"0.57032484\",\n \"0.5681643\",\n \"0.5676549\",\n \"0.5669544\",\n \"0.5669448\",\n \"0.56686467\",\n \"0.5667061\",\n \"0.5665254\",\n \"0.5658459\",\n \"0.56547993\",\n \"0.5652273\",\n \"0.56429666\",\n \"0.563285\",\n \"0.56299233\",\n \"0.56297314\",\n \"0.5616537\",\n \"0.5610085\",\n \"0.56086594\",\n \"0.56074643\",\n \"0.5606431\",\n \"0.5601822\",\n \"0.5601822\",\n \"0.5588715\",\n \"0.5588563\",\n \"0.55882394\",\n \"0.558775\",\n \"0.55836505\",\n \"0.5571456\",\n \"0.5569384\",\n \"0.5564178\",\n \"0.5563764\",\n \"0.556266\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8215371"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":689,"cells":{"query":{"kind":"string","value":"Creates a calculator using a class."},"document":{"kind":"string","value":"def evaluate_with_calculator(self, clustering, key_args):\n calculator = key_args['class']()\n return calculator.evaluate(clustering, key_args['matrix'])"},"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 main():\n model = Calculator()","def __init__(self, mode=\"Normal\"):\n\n self.mode = mode\n # minor optimization\n # apparently [] is 5x faster than list()\n # https://youtu.be/YjHsOrOOSuI?t=19m30s\n self.button = []\n \"\"\"button: List of buttons.\"\"\"\n\n self.numbers = (\"789456123\")\n \"\"\"numbers: basic numberpad layout of ints\"\"\"\n\n self.root = tk.Tk()\n self.root.title('Michael\\'s Calculator')\n\n self.menu = self.root.winfo_toplevel()\n self.root.menuBar = tk.Menu(self.menu)\n self.menu['menu'] = self.root.menuBar\n\n self.root.subMenu = tk.Menu(self.root.menuBar)\n self.root.menuBar.add_cascade(label='View', menu=self.root.subMenu)\n self.root.subMenu.add_command(label='Basic')\n self.root.subMenu.add_command(label='Scientific')\n\n self.display = tk.Entry(self.root, width=15)\n self.display.grid(row=0, column=0, columnspan=5)\n self.create_number_buttons()\n self.create_operation_buttons()\n\n self.root.mainloop()","def calculator(**pars):\n # paying for parameter conversion each time to keep life simple, if not fast\n pars = revert_pars(model_info, pars)\n for k, v in pars.items():\n parts = k.split('.') # polydispersity components\n if len(parts) == 2:\n model.dispersion[parts[0]][parts[1]] = v\n else:\n model.setParam(k, v)\n return theory()","def create(cls, **dictionary):\n if cls.__name__ == 'Square':\n object = cls(1)\n object.update(**dictionary)\n return object\n\n if cls.__name__ == 'Rectangle':\n object = cls(1, 2)\n object.update(**dictionary)\n return object","def calculator(operation): \n \n operation = MATH[operation]\n a = int(request.args.get(\"a\"))\n b = int(request.args.get(\"b\"))\n total = operation(a, b)\n\n return f\"<h1>TOTAL: {total}</h1>\"","def make_fee_class():\r\n fee = 1\r\n return make_class(locals())","def withMaker(cls):\n nodeName = cls.__name__.decode(\"utf-8\")\n if cls.__init__ is object.__init__:\n names = ()\n else:\n names = inspect.getargspec(cls.__init__).args[1:]\n verb = nodeName\n if getattr(cls, \"fromMonte\", None) is not None:\n verb += \".fromMonte\"\n arglist = u\", \".join([u\"args[%s]\" % i for i in range(len(names))])\n runverb = 'RUN_%s' % len(names)\n src = \"\"\"\\\n @audited.DF\n class %sMaker(Object):\n def printOn(self, out):\n out.call(u\"print\", [StrObject(u\"<kernel make%s>\")])\n def recv(self, atom, args):\n if atom is %s:\n return %s(%s)\n raise Refused(self, atom, args)\n \"\"\" % (nodeName, nodeName, runverb, verb, arglist)\n d = globals()\n exec textwrap.dedent(src) in d\n cls.nodeMaker = d[nodeName + \"Maker\"]()\n return cls","def __init__(self):\n\n #Creates the window.\n EasyFrame.__init__(self, \"Calculator\", resizable = False)\n\n #Creates the data model Calculator.\n self.calculator = Calculator()\n\n #Keeps track of when the user enters an input.\n self.operatorEntered = False\n\n #Creates the colored panels.\n for column in range(5):\n numberBarPanel = self.addPanel(row = 0, column = column, background = \"black\")\n\n for row in range(1, 6):\n symbolPanel = self.addPanel(row = row, column = 4, background = 'orange')\n\n for column in range (4):\n topPanel = self.addPanel(row = 1, column = column, background = \"snow3\")\n\n for row in range (2, 6):\n for column in range (0, 3):\n numberPanel = self.addPanel(row = row, column = column, background = \"snow2\")\n\n #Creates fonts.\n barFont = Font(family = \"San Francisco\", size = 20)\n \n #Creates the number bar at the top of the calculator.\n self.digits = self.addLabel(\"0\", row= 0, column= 0, columnspan= 5, sticky= \"E\", background = 'black', foreground = 'white', font = barFont)\n\n #Creates button for clear.\n self.clearButton= self.addButton(text= \"AC\", row= 1, column= 0, command= self.clearCommand)\n self.clearButton['background'] = 'snow3'\n \n #Creates button for +/-.\n negativeButton= self.addButton(text= \"+/-\", row= 1, column= 1, command= self.negativeCommand)\n negativeButton['background'] = 'snow3'\n \n #Creates button for %\n percentButton= self.addButton(text= \"%\", row= 1, column= 2, command= self.percentCommand)\n percentButton['background'] = 'snow3'\n\n #Creates side row of operator symbols.\n sideSymbols= [\"/\", \"X\", \"-\", \"+\", \"=\"]\n row= 1\n for symbol in sideSymbols:\n symbolButton = self.addButton(text= symbol, row= row, column= 4)\n symbolButton[\"command\"] = self.operatorCommand(symbol)\n symbolButton['foreground'] = 'white'\n symbolButton['background'] = 'orange'\n row += 1\n \n #Goes through and creates a grid with numbers 1-9.\n digit= 7\n for row in range(2, 5):\n for column in range(3):\n numberButton = self.addButton(str(digit), row, column)\n numberButton[\"command\"] = self.numberCommand(str(digit))\n numberButton['background'] = 'snow2'\n if digit == 9:\n digit = 3\n elif digit == 6:\n digit = 0\n digit += 1\n\n #Creates 0 button.\n zeroButton = self.addButton(text= \"0 \", row= 5, column = 0, columnspan = 2, command = self.numberCommand(\"0\"))\n zeroButton['background'] = 'snow2'\n\n #Creates . button.\n self.decimalButton = self.addButton(text= \".\", row = 5, column = 2, command = self.decimalCommand, state = 'normal')\n self.decimalButton['background'] = 'snow2'","def __init__(self, operation, operand):\n self.operation = operation\n self.right = operand","def test_module(self):\n calculator = Calculator(Adder(), Subtracter(), Multiplier(), Divider())\n\n calculator.enter_number(5)\n calculator.enter_number(2)\n\n # Tests\n # Add:\n self.assertEqual(7, calculator.add())\n\n # Subtract:\n calculator.enter_number(5)\n self.assertEqual(2, calculator.subtract())\n\n # Multiply:\n calculator.enter_number(5)\n self.assertEqual(10, calculator.multiply())\n\n # Divide:\n calculator.enter_number(1)\n self.assertEqual(10, calculator.divide())","def test_calculator_add():\n # Arrange by instantiating the calc class\n calc = Calculator()\n # Act by calling the method to be tested\n calc.add(4)\n # Assert that the results are correct\n assert calc.result == 4","def beta_create_Calculator_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\n request_serializers = {\n ('calculator.Calculator', 'Add'): CalculateRequest.SerializeToString,\n ('calculator.Calculator', 'Divide'): CalculateRequest.SerializeToString,\n ('calculator.Calculator', 'Multiply'): CalculateRequest.SerializeToString,\n ('calculator.Calculator', 'Subtract'): CalculateRequest.SerializeToString,\n }\n response_deserializers = {\n ('calculator.Calculator', 'Add'): CalculateResponse.FromString,\n ('calculator.Calculator', 'Divide'): CalculateResponse.FromString,\n ('calculator.Calculator', 'Multiply'): CalculateResponse.FromString,\n ('calculator.Calculator', 'Subtract'): CalculateResponse.FromString,\n }\n cardinalities = {\n 'Add': cardinality.Cardinality.UNARY_UNARY,\n 'Divide': cardinality.Cardinality.UNARY_UNARY,\n 'Multiply': cardinality.Cardinality.UNARY_UNARY,\n 'Subtract': cardinality.Cardinality.UNARY_UNARY,\n }\n stub_options = beta_implementations.stub_options(host=host, metadata_transformer=metadata_transformer, request_serializers=request_serializers, response_deserializers=response_deserializers, thread_pool=pool, thread_pool_size=pool_size)\n return beta_implementations.dynamic_stub(channel, 'calculator.Calculator', cardinalities, options=stub_options)","def class_test():\n\n class Zulu(object):\n x = 6\n y = 6\n\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n @classmethod\n def add(cls):\n print(cls.x + cls.y)\n\n kowabunga = Zulu(5, 5)\n\n kowabunga.add()","def __init__(self):\r\n EasyFrame.__init__(self, title=\"Investment Calculator\")\r\n self.addLabel(text=\"Initial amount\", row=0, column=0)\r\n self.addLabel(text=\"Number of years\", row=1, column=0)\r\n self.addLabel(text=\"Interest rate in %\", row=2, column=0)\r\n self.amount = self.addFloatField(value=0.0, row=0, column=1)\r\n self.period = self.addIntegerField(value=0, row=1, column=1)\r\n self.rate = self.addIntegerField(value=0, row=2, column=1)\r\n\r\n self.outputArea = self.addTextArea(\"\", row=4, column=0, columnspan=2, width=50, height=15)\r\n\r\n self.compute = self.addButton(text=\"Compute\", row=3, column=0, columnspan=2, command=self.compute)","def __init__(self):\n Cmd.__init__(self)\n self.calc = ReversePolishCalc()","def makeCalc(self, dataSet):\n\n #cyl = sasmodels.core.load_model_info('cylinder')\n #hs = sasmodels.core.load_model_info('hardsphere')\n #cylhs = sasmodels.core.load_model_info('cylinder@hardsphere')\n cylhmsa = sasmodels.core.load_model_info('cylinder@hayter_msa')\n\n # Build using c version instead of python. Avoids pyopencl\n model = sasmodels.core.build_model(cylhmsa, platform='dll')\n self.calculator = sasmodels.direct_model.DirectModel(dataSet, model)\n\n return","def create(cls, **dictionary):\n new_inst = cls.__new__(cls)\n if cls.__name__ == \"Rectangle\":\n new_inst.__init__(42, 98)\n elif cls.__name__ == \"Square\":\n new_inst.__init__(42)\n new_inst.update(**dictionary)\n return new_inst","def __init__(self, *args):\n this = _ida_hexrays.new_operand_locator_t(*args)\n try: self.this.append(this)\n except: self.this = this","def create(cls, **dictionary):\n if cls.__name__ == \"Rectangle\":\n dummy = cls(1, 2)\n dummy.update(**dictionary)\n return dummy\n if cls.__name__ == \"Square\":\n dummy = cls(1)\n dummy.update(**dictionary)\n return dummy","def create(cls, **props: Any) -> 'ResolverOp':\n real_instance = super().__call__()\n cls._check_kwargs(props)\n for name, value in props.items():\n setattr(real_instance, name, value)\n return real_instance","def create(cls, **props: Any) -> 'ResolverOp':\n real_instance = super().__call__()\n cls._check_kwargs(props)\n for name, value in props.items():\n setattr(real_instance, name, value)\n return real_instance","def make_operators(self):\n self.relationship_operator = Operators.RelationshipOperator(self)\n self.infection_operator = Operators.InfectionOperator(self)\n self.time_operator = Operators.TimeOperator(self)","def create(cls, **dictionary):\n if cls.__name__ == \"Rectangle\":\n object = cls(1, 1)\n object.update(**dictionary)\n return object\n\n if cls.__name__ == \"Square\":\n object = cls(1)\n object.update(**dictionary)\n return object","def create_expr(self, exprcls, ast, params=None, nopush=False):\n if params is None:\n expr = exprcls(self.current_parent, ast=ast)\n else:\n expr = exprcls(self.current_parent, ast=ast, **params)\n if not nopush:\n self.push_state(expr)\n return expr","def create(cls, **dictionary):\n if cls.__name__ == \"Rectangle\":\n new_class = cls(1, 2)\n else:\n new_class = cls(1)\n new_class.update(**dictionary)\n return new_class","def __call__(self):\r\n new_node = Node()\r\n new_node.op = self\r\n return new_node","def __init__(self, op, expression1, expression2):\n LinearExpression.__init__(self)\n\n self.op = op\n self.expression1 = expression1\n self.expression2 = expression2","def main():\n #------------------------------------- Functions\n def add(text):\n \"\"\"\n This will add to the display, and be the go to function of most buttons.\n We'll want to add in conditions for what buttons go.\n \"\"\"\n orig = dispb[\"text\"]\n new = orig + text\n ops = [\"+\",\"-\",\"*\",\"/\"]\n # conditions\n # length 21\n if len(new) > 21:\n dispb[\"text\"] = orig\n return 0\n \n # one calc at a time\n if len(orig) > 0:\n if (orig[-1] in ops) & (text in ops):\n dispb[\"text\"] = orig\n return 0\n\n dispb[\"text\"] = new\n return 0\n \n def clear():\n dispb[\"text\"] = \"\"\n return 0\n \n def backspace():\n dispb[\"text\"] = dispb[\"text\"][:len(dispb[\"text\"])-1]\n return 0\n \n def equals():\n try:\n dispb[\"text\"] = str(eval(dispb[\"text\"]))\n except:\n dispb[\"text\"]=\"ERROR, clear display\"\n \n #------------------------------------- UI\n \n # title and start\n calc = tk.Tk()\n calc.title(\"Calculator\")\n # size\n calc.geometry(\"255x235\")\n #calc.columnconfigure(range(3), weight=1, minsize=50)\n #calc.rowconfigure(range(1,4), weight=1, minsize=48)\n \n # Icon\n calc.iconbitmap('Icon.ico')#'Icon.ico')\n \n \n calcarea = tk.Frame(master=calc)\n calcarea.pack(padx=5, pady=10)\n \n # display box\n disp = tk.Frame(\n master = calcarea\n )\n disp.grid(row = 0, column = 0, columnspan = 3)\n dispb = tk.Label(\n master = disp,\n text = '',\n fg = 'black',\n bg = 'white',\n borderwidth = 1,\n relief = 'solid',\n height = 2,\n width = 19\n )\n dispb.pack()\n \n # number buttons\n num1 = tk.Frame(\n master=calcarea\n )\n num1.grid(row = 3, column = 0)\n num1b = tk.Button(\n master = num1,\n text = 1,\n width = 5,\n height = 2,\n command = lambda: add(\"1\")\n ).pack()\n # the pack is what adds it to the UI\n # two \n num2 = tk.Frame(\n master=calcarea\n )\n num2.grid(row = 3, column = 1)\n num2b = tk.Button(\n master = num2,\n text = \"2\",\n width = 5,\n height = 2,\n command = lambda: add(\"2\")\n ).pack()\n \n # three \n num3 = tk.Frame(\n master=calcarea\n )\n num3.grid(row = 3, column = 2)\n num3b = tk.Button(\n master = num3,\n text = \"3\",\n width = 5,\n height = 2,\n command = lambda: add(\"3\")\n ).pack()\n \n # four \n num4 = tk.Frame(\n master=calcarea\n )\n num4.grid(row = 2, column = 0)\n num4b = tk.Button(\n master = num4,\n text = \"4\",\n width = 5,\n height = 2,\n command = lambda: add(\"4\")\n ).pack()\n \n # five \n num5 = tk.Frame(\n master=calcarea\n )\n num5.grid(row = 2, column = 1)\n num5b = tk.Button(\n master = num5,\n text = \"5\",\n width = 5,\n height = 2,\n command = lambda: add(\"5\")\n ).pack()\n \n # six \n num6 = tk.Frame(\n master=calcarea\n )\n num6.grid(row = 2, column = 2)\n num6b = tk.Button(\n master = num6,\n text = \"6\",\n width = 5,\n height = 2,\n command = lambda: add(\"6\")\n ).pack()\n \n # seven \n num7 = tk.Frame(\n master=calcarea\n )\n num7.grid(row = 1, column = 0)\n num7b = tk.Button(\n master = num7,\n text = \"7\",\n width = 5,\n height = 2,\n command = lambda: add(\"7\")\n ).pack()\n \n # eight \n num8 = tk.Frame(\n master=calcarea\n )\n num8.grid(row = 1, column = 1)\n num8b = tk.Button(\n master = num8,\n text = \"8\",\n width = 5,\n height = 2,\n command = lambda: add(\"8\")\n ).pack()\n \n # nine \n num9 = tk.Frame(\n master=calcarea\n )\n num9.grid(row = 1, column = 2)\n num9b = tk.Button(\n master = num9,\n text = \"9\",\n width = 5,\n height = 2,\n command = lambda: add(\"9\")\n ).pack()\n \n # zero\n num0 = tk.Frame(\n master = calcarea\n )\n num0.grid(row = 4, column = 0)\n num0b = tk.Button(\n master = num0,\n text = 0,\n width = 5,\n height = 2,\n command = lambda: add(\"0\")\n ).pack()\n \n # period\n dot = tk.Frame(\n master = calcarea\n )\n dot.grid(row = 4, column = 1)\n dotb = tk.Button(\n master = dot,\n text = \".\",\n width = 5,\n height = 2,\n command = lambda: add(\".\")\n ).pack()\n \n # equal sign\n eq = tk.Frame(\n master = calcarea\n )\n eq.grid(row = 4, column = 2, columnspan = 2)\n eqb = tk.Button(\n master = eq,\n text = \"=\",\n width = 11,\n height = 2,\n command = equals\n ).pack()\n \n # plus sign\n plus = tk.Frame(\n master = calcarea\n )\n plus.grid(row = 3, column = 4, rowspan = 2)\n plusb = tk.Button(\n master = plus,\n text = \"+\",\n width = 5,\n height = 5,\n command = lambda: add(\"+\")\n ).pack()\n \n # minus sign\n minu = tk.Frame(\n master = calcarea\n )\n minu.grid(row = 3, column = 3)\n minub = tk.Button(\n master = minu,\n text = \"-\",\n width = 5,\n height = 2,\n command = lambda: add(\"-\")\n ).pack()\n \n # multiplication\n mult = tk.Frame(\n master = calcarea\n )\n mult.grid(row = 2, column = 3)\n multb = tk.Button(\n master = mult,\n text = \"*\",\n width = 5,\n height = 2,\n command = lambda: add(\"*\")\n ).pack()\n \n # division\n div = tk.Frame(\n master = calcarea\n )\n div.grid(row = 2, column = 4)\n divb = tk.Button(\n master = div,\n text = \"/\",\n width = 5,\n height = 2,\n command = lambda: add(\"/\")\n ).pack()\n \n # left parentheses\n lefp = tk.Frame(\n master = calcarea\n )\n lefp.grid(row = 1, column = 3)\n lefpb = tk.Button(\n master = lefp,\n text = \"(\",\n width = 5,\n height = 2,\n command = lambda: add(\"(\")\n ).pack()\n \n # right paraentheses\n rigp = tk.Frame(\n master = calcarea\n )\n rigp.grid(row = 1, column = 4)\n rigpb = tk.Button(\n master = rigp,\n text = \")\",\n width = 5,\n height = 2,\n command = lambda: add(\")\")\n ).pack()\n \n # Clear button\n Clr = tk.Frame(\n master = calcarea\n )\n Clr.grid(row = 0, column = 3)\n Clrb = tk.Button(\n master = Clr,\n text = \"C\",\n width = 5,\n height = 2,\n command = clear\n ).pack()\n \n # backspace\n bck = tk.Frame(\n master = calcarea\n )\n bck.grid(row = 0, column = 4)\n bckb = tk.Button(\n master = bck,\n text = \"\\N{RIGHTWARDS BLACK ARROW}\",\n width = 5,\n height = 2,\n command = backspace\n ).pack()\n \n # This is what kicks the whole thing off, lets it wait for commands.\n calc.mainloop()","def create(cls, **dictionary):\n if cls.__name__ == \"Square\":\n dummy = cls(1)\n if cls.__name__ == \"Rectangle\":\n dummy = cls(1, 1)\n dummy.update(**dictionary)\n return dummy","def main():\r\n # Create an instance of the MyCallCostCalc class.\r\n my_callcost = MyCallCostCalc()","def New(*args, **kargs):\n obj = itkSingleValuedCostFunction.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def calculate(numbers, operator):\n \n if operator == 'add':\n return add(prepare_numbers(numbers))\n elif operator == 'subtract':\n return subtract(prepare_numbers(numbers))\n elif operator == 'multiply':\n return multiply(prepare_numbers(numbers))\n elif operator == 'divide':\n return divide(prepare_numbers(numbers))\n elif operator == 'remainder':\n return remainder(prepare_numbers(numbers))\n elif operator == 'power':\n return power(prepare_numbers(numbers))","def New(*args, **kargs):\n obj = itkCostFunction.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def __call__(self):\n new_node = Node()\n new_node.op = self\n return new_node","def get_calculator(settings_path, logging = True):\n settings = SettingsReader(settings_path)\n if settings.get(\"energy_calculator\", \"code\") == \"psi4\":\n return Psi4Calculator(settings_path, logging)\n elif settings.get(\"energy_calculator\", \"code\") == \"qchem\":\n return QchemCalculator(settings_path, logging)\n else:\n raise NoSuchLibraryError(settings.get(\"energy_calculator\", \"code\"))","def __init__(self):\n self.currentTotal = None\n self.previousOperator = None\n self.previousOperand = None","def __init__(self, op, value):\n self.op = op\n self.value = value","def operation_class(self):\n clazzname = self.name.title() + \"Op\"\n if clazzname == \"NopOp\":\n clazz = BaseOp\n else:\n clazz = globals()[clazzname]\n return clazz","def create(cls, **dictionary):\n if cls.__name__ == \"Rectangle\":\n dummy = cls(1, 1)\n elif cls.__name__ == \"Square\":\n dummy = cls(1)\n dummy.update(**dictionary)\n return dummy","def test_get_operator_class(self):\n Node = collections.namedtuple(\"Node\", \"op_type\")\n\n op_types = [\"Sum\", \"AveragePool\", \"Mean\"]\n for op_type in op_types:\n node = Node(op_type)\n operator = onnx_converter._get_operator_class(node.op_type, {})\n self.assertTrue(\n issubclass(operator, crypten.nn.Module),\n f\"{op_type} operator class {operator} is not a CrypTen module.\",\n )\n # check conv\n kernel_shapes = [[1], [3, 3]]\n node = Node(\"Conv\")\n for kernel_shape in kernel_shapes:\n attributes = {\"kernel_shape\": kernel_shape}\n operator = onnx_converter._get_operator_class(node.op_type, attributes)\n\n # check invalid op_types\n invalid_types = [(\"Convolution\", {\"kernel_shape\": [3, 3, 3]}), (\"Banana\", {})]\n for invalid_type, attr in invalid_types:\n with self.assertRaises(ValueError):\n node = Node(invalid_type)\n operator = onnx_converter._get_operator_class(node.op_type, attr)","def __init__(self, opToken, leftOper, rightOper):\n self.operator = opToken\n self.leftOperand = leftOper\n self.rightOperand = rightOper","def __init__(self, operations = []):\n self.operations = operations","def infectious_math(cls):\n for prp in [\n \"__add__\", \"__radd__\",\n \"__sub__\", \"__rsub__\",\n \"__mul__\", \"__rmul__\",\n \"__matmul__\", \"__rmatmul__\",\n \"__truediv__\", \"__rtruediv__\",\n \"__floordiv__\", \"__rfloordiv__\",\n \"__mod__\", \"__rmod__\",\n \"__divmod__\", \"__rdivmod__\",\n \"__pow__\", \"__rpow__\",\n \"__lshift__\", \"__rlshift__\",\n \"__rshift__\", \"__rrshift__\",\n \"__and__\", \"__rand__\",\n \"__xor__\", \"__rxor__\",\n \"__or__\", \"__ror__\",\n\n \"__neg__\", \"__rneg__\",\n \"__pos__\", \"__rpos__\",\n \"__abs__\", \"__rabs__\",\n \"__invert__\", \"__rinvert__\",\n ]:\n if hasattr(cls, prp):\n def_impl = getattr(cls, prp)\n def augment(method):\n def augmented(self, *args, **kwargs):\n nonlocal method\n result = method.__get__(self).__call__(*args, **kwargs)\n return cls(result)\n return augmented\n replacement = augment(def_impl)\n replacement.__name__ = prp\n setattr(cls, prp, replacement)\n\n return cls","def create(cls, **dictionary):\n if cls.__name__ == \"Rectangle\":\n r_dummy = cls(2, 4)\n r_dummy.update(**dictionary)\n return (r_dummy)\n\n if cls.__name__ == \"Square\":\n s_dummy = cls(3)\n s_dummy.update(**dictionary)\n return (s_dummy)","def create(cls, **dictionary):\n if cls.__name__ == 'Rectangle':\n dummy = cls(1, 1)\n elif cls.__name__ == 'Square':\n dummy = cls(1)\n dummy.update(**dictionary)\n return dummy","def main():\n pycalcApp = QApplication(sys.argv)\n pycalcView = PyCalcUi()\n pycalcView.show()\n model = evaluateExpression\n PyCalcController(model=model, view=pycalcView)\n sys.exit(pycalcApp.exec())","def build(cls, real, imag):\n return cls(f\"{real} {imag}\")","def New(*args, **kargs):\n obj = itkMultipleValuedCostFunction.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def New(*args, **kargs):\n obj = itkMultipleValuedCostFunction.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def create(cls, **dictionary):\n if cls.__name__ == 'Rectangle':\n dummy = cls(1, 1)\n if cls.__name__ == 'Square':\n dummy = cls(1)\n dummy.update(**dictionary)\n return dummy","def main():\n app = QApplication(sys.argv)\n calc = Calculator()\n calc.show()\n calc.setFixedSize(calc.size())\n sys.exit(app.exec_())","def _new_instance(self):\n return self.__class__(self._fmodule)","def _new_instance(self):\n return self.__class__(self._fmodule)","def __init__(self, operation, constargs, randomargs):\n Operation.__init__(self)\n self.operation = operation\n self.constargs = constargs\n self.randomargs = randomargs\n if type(operation) is str:\n import CCAugmentation.outputs as cca_out\n import CCAugmentation.transformations as cca_trans\n self.operation = eval(self._get_op_str())\n self.args = {'operation': self.operation.__name__, 'constargs': constargs, 'randomargs': randomargs}","def createWidgets(self):\n num_positions = [ (3,0), (2,0), (2,1), (2,2), (1,0), \\\n (1,1), (1,2), (0,0), (0,1), (0,2) ] \n\n op_info = { '+': (3, 3), '-': (2, 3), '*': (1, 3), '/': (0, 3) }\n self.operators = {}\n\n #Creates the 10 number buttons.\n for i in range(10):\n button = Button(self, text=str(i), height=2, width=5)\n button['command'] = lambda i=i: self.handle_num_general(str(i))\n r, c = num_positions[i][0], num_positions[i][1]\n button.grid(row=r, column=c)\n\n #Creates the 4 operator buttons.\n for op,position in op_info.iteritems():\n button = Button(self, text=str(op), height=2, width=5)\n button['command'] = lambda op=op: self.handle_op_general(op)\n button.grid(row=position[0], column=position[1])\n self.operators[op] = button\n\n self.equals = Button(self, text='=', height=2, width=5)\n self.equals['command'] = self.perform_op\n self.equals.grid(row=3, column=2)\n\n self.clear = Button(self, text='C', height=2, width=5)\n self.clear['command'] = lambda: self.reset('0') \n self.clear.grid(row=3, column=1)\n\n self.change_equals_state(DISABLED)\n self.change_ops_state(DISABLED)","def calculator(): \n\n #asks for user's input \n user_input = raw_input(\"Type in the math expression and two numbers like + 1 2 : \")\n #splits the user's input into a list\n math_op = user_input.split(\" \")\n\n #pulls the appropriate function based on the user's input\n if math_op[0] == '+':\n print add(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == '-':\n print subtract(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == '*':\n print multiply(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == '/':\n print divide(int(math_op[1]), int(math_op[2]))\n \n elif math_op[0] == \"square\":\n print square(int(math_op[1]))\n\n elif math_op[0] == 'cube':\n print cube(int(math_op[1]))\n\n elif math_op[0] == 'pow':\n print power(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == 'mod':\n print mod(int(math_op[1]), int(math_op[2]))\n\n else:\n print \"That is not a valid input. Please try any of the following operator: + - * / square cube pow mod.\"","def eval(cls, *args):\n raise NotImplementedError(\"subclasses need to override this method\")","def __init__(self, name, operator, values):\n self.name = name\n self.operator = operator\n self.values = values","def basic_calculator():\r\n\r\n num1 = input(\"Enter first number: \") # taking input\r\n\r\n # handling the exception of typecasting the value of 'num1' to float\r\n try:\r\n num1 = float(num1)\r\n except ValueError:\r\n print(\"Error: Input numeric values.\\nTry Again!\")\r\n exit()\r\n\r\n num2 = input(\"Enter second number: \") # taking input\r\n\r\n # handling the exception of typecasting the value of 'num2' to float\r\n try:\r\n num2 = float(num2)\r\n except ValueError:\r\n print(\"Error: Input numeric values.\\nTry Again!\")\r\n exit()\r\n\r\n # Asking user for the operation\r\n print(\"Select the operation:\")\r\n print(\"Type:\")\r\n print(\"1 for Addition\\n2 for Subtraction\\n3 for Multiplication\\n4 for Division\\n5 for Integer Division\\n6 for Power\")\r\n choice = input(\"Enter your choice: \")\r\n\r\n result = 0.0\r\n\r\n # Performing the operation and providing the result\r\n if choice == '1':\r\n result = num1 + num2\r\n elif choice == '2':\r\n result = num1 - num2\r\n elif choice == '3':\r\n result = num1 * num2\r\n elif choice == '4':\r\n result = num1 / num2\r\n elif choice == '5':\r\n result = num1 // num2\r\n elif choice == '6':\r\n result = num1 ** num2\r\n else:\r\n print(\"Wrong Input! Try Again.\")\r\n exit()\r\n\r\n print(f'\\nThe result is: {result}')","def New():\n Self = $classname()\n Self._initialize_()\n Self._update_()\n return Self","def from_operator(operation=debug):\r\n\r\n def C(*things):\r\n return Container(freezed(operation), list(things), [], [], [], [])\r\n return C","def __new__(cls, ctx):\n return cls.__run(cls, ctx)","def make_instance(cls):\r\n def get_value(name):\r\n if name in attributes:\r\n return attributes[name]\r\n else:\r\n value = cls['get'](name)\r\n return bind_method(value, instance)\r\n\r\n def set_value(name, value):\r\n attributes[name] = value\r\n\r\n attributes = {}\r\n instance = {'get': get_value, 'set': set_value}\r\n return instance","def parse_calc_cmd(self, line):\n self.E_str = \"parse_calc_cmd\"\n # Clean up the line\n line, any_vars = self.find_vars_in_str(line)\n words = line.split()\n _, calc_type, _, var_name, _, new_var_name = words\n\n # Get the variable to calculate the property with\n Var = getattr(self, var_name)\n\n # Check the required metadata has been set\n required_metadata = f_dicts.calc_fncs[calc_type].required_metadata\n Var = getattr(self, var_name)\n for attr in required_metadata:\n if attr not in Var.metadata and attr not in f_dicts.calc_fncs[calc_type]._defaults:\n err_msg = f\"'{attr}' required for calculation of '{calc_type}'\"\n err_msg += \"\\n\\nPlease set it with the following syntax:\\n\\t\"\n err_msg += f\"{var_name}['{attr}'] = <value>\"\n err_msg += f\" or by using a set command.\"\n self.print_error(err_msg)\n\n\n Calc_Obj = f_dicts.calc_fncs[calc_type](Var)\n\n Calc_Obj.calc()\n\n # Create a new variable type\n New_Var = inp_types.Variable(Calc_Obj.name, Calc_Obj, Calc_Obj.metadata)\n setattr(self, new_var_name, New_Var)\n if new_var_name not in self.variables:\n self.variables.append(new_var_name)","def new_calc_target(self, day, block, which):\n calc = Calculated(\n target_date=day.day,\n target_block=which,\n name=block.name,\n color=block.color,\n note=block.note\n )\n for service in block.services:\n calc.services.append(CalculatedService(\n name=service.name,\n start_time=service.start_time\n ))\n return calc","def setup_class(self):\n class SubCosmology(Cosmology):\n\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\n Tcmb0 = Parameter(unit=u.K)\n\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\n super().__init__(name=name, meta=meta)\n self._H0 = H0\n self._Tcmb0 = Tcmb0\n\n self.cls = SubCosmology\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\"a\": \"b\"})","def __new__(cls, value_str):\n\n value_str = value_str.strip()\n if value_str[0] == '[' and value_str[-1] == ']':\n value = literal_eval(value_str)\n else:\n value_str = value_str.split()\n assert len(value_str) >= 1\n\n try:\n value = float(value_str[0])\n except ValueError:\n value = value_str[0]\n\n #False positive, pylint does not see attributes\n #defined in __new__\n #pylint: disable=attribute-defined-outside-init\n if isinstance(value, float):\n if len(value_str) != 1:\n if len(value_str) == 4:\n unit = Unit(value_str[3])\n else:\n unit = Unit('')\n\n if value_str[1] == '+/-':\n plus_error = float(value_str[2])\n minus_error = plus_error\n else:\n plus_error = float(value_str[1])\n minus_error = abs(float(value_str[2]))\n result = super().__new__(cls, value, unit)\n result.plus_error = plus_error\n result.minus_error = minus_error\n return result","def setUp(self):\n self.adder = Adder()\n self.subtracter = Subtracter()\n self.multiplier = Multiplier()\n self.divider = Divider()\n\n self.calculator = Calculator(self.adder, self.subtracter,\n self.multiplier, self.divider)","def createMath(self, *args):\n return _libsbml.ASTBasePlugin_createMath(self, *args)","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHDUS.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def __init__(self, operand_string):\n\n # String to hold the operand literal\n self.op_string = operand_string\n\n # Integer value of the operand\n self.op_value = int(operand_string)","def from_string_expr(cls, expr):\n if \"*\" in expr:\n ch = \"*\"\n op = \"cross\"\n elif \"+\" in expr:\n ch = \"+\"\n op = \"blend\"\n elif \"/\" in expr:\n ch = \"/\"\n op = \"nest\"\n factors = [cls(s.strip()) for s in expr.split(ch)]\n return cls(op=op, factors=factors)","def __new__(mcs, name, parent, attr):\n def add_numeric_op(attr_name, op):\n \"\"\"Create an attribute named attr_name that calls\n _numeric_op(self, other, op).\"\"\"\n def closure(self, other):\n return VTKCompositeDataArray._numeric_op(self, other, op)\n closure.__name__ = attr_name\n attr[attr_name] = closure\n\n def add_reverse_numeric_op(attr_name, op):\n \"\"\"Create an attribute named attr_name that calls\n _reverse_numeric_op(self, other, op).\"\"\"\n def closure(self, other):\n return VTKCompositeDataArray._reverse_numeric_op(self, other, op)\n closure.__name__ = attr_name\n attr[attr_name] = closure\n\n def add_default_reverse_numeric_op(op_name):\n \"\"\"Adds '__r[op_name]__' attribute that uses operator.[op_name]\"\"\"\n add_reverse_numeric_op(\"__r%s__\"%op_name, getattr(operator, op_name))\n\n def add_default_numeric_op(op_name):\n \"\"\"Adds '__[op_name]__' attribute that uses operator.[op_name]\"\"\"\n add_numeric_op(\"__%s__\"%op_name, getattr(operator, op_name))\n\n def add_default_numeric_ops(op_name):\n \"\"\"Call both add_default_numeric_op and add_default_reverse_numeric_op.\"\"\"\n add_default_numeric_op(op_name)\n add_default_reverse_numeric_op(op_name)\n\n add_default_numeric_ops(\"add\")\n add_default_numeric_ops(\"sub\")\n add_default_numeric_ops(\"mul\")\n add_default_numeric_ops(\"truediv\")\n add_default_numeric_ops(\"floordiv\")\n add_default_numeric_ops(\"mod\")\n add_default_numeric_ops(\"pow\")\n add_default_numeric_ops(\"lshift\")\n add_default_numeric_ops(\"rshift\")\n add_numeric_op(\"__and__\", operator.and_)\n add_reverse_numeric_op(\"__rand__\", operator.and_)\n add_default_numeric_ops(\"xor\")\n add_numeric_op(\"__or__\", operator.or_)\n add_reverse_numeric_op(\"__ror__\", operator.or_)\n\n add_default_numeric_op(\"lt\")\n add_default_numeric_op(\"le\")\n add_default_numeric_op(\"eq\")\n add_default_numeric_op(\"ne\")\n add_default_numeric_op(\"ge\")\n add_default_numeric_op(\"gt\")\n return type.__new__(mcs, name, parent, attr)","def calculations(self):\n return Calculation.find(self)","def create_instance(self,name):\n print \"INFO : new %s\" % name\n return self.get_class(name)()","def operator(app):\n return car(app)","def R(cls, *args, **kwargs):\n return cls(*args, model_type='regressor', **kwargs)","def some_operation(self):\n\n # Call the factory method to create a Product object.\n product = self.factory_method()\n\n # Now, use the product.\n product.operation()","def new(cls):\n return cls()","def __init__(self, math, variables=None, first_derivatives=None,\n second_derivatives=None, name=None):\n self.math = math\n if variables is not None:\n self.variables = variables\n else:\n # expr_manip still returns Sets instead of sets; cast the\n # result to set mostly because this annoys me.\n self.variables = set(em.extract_vars(self.math))\n self._first_derivatives = {}\n if first_derivatives:\n self._first_derivatives.update(first_derivatives)\n self._second_derivatives = {}\n if second_derivatives:\n self._second_derivatives.update(second_derivatives)\n self._code = None\n self._first_derivative_code = {}\n self._second_derivative_code = {}\n self.name = name\n if self.name:\n self.tag = name\n elif len(self.math) < 100:\n self.tag = \"'%s'\" % self.math\n else:\n self.tag = \"'%s...'\" % self.math[:100]","def create_operator(statement_a, operator, statement_b):\n return S(statement_a=statement_a, operator=operator, statement_b=statement_b)","def run_calc(self):\n\n from openquake.calculators import base, getters\n from openquake.baselib import config, performance, zeromq\n if self.vtag >= 11:\n from openquake.baselib import version\n else:\n from openquake.baselib import __version__ as version\n\n with self.calculator._monitor:\n self.calculator._monitor.username = ''\n try:\n # Pre-execute setups\n self.calculator.pre_execute()\n\n #self.calculator.datastore.swmr_on()\n oq = self.calculator.oqparam\n dstore = self.calculator.datastore\n self.calculator.set_param()\n self.calculator.offset = 0\n\n # Source model\n #print('self.__dict__ = ')\n #print(self.calculator.__dict__)\n if oq.hazard_calculation_id: # from ruptures\n dstore.parent = self.calculator.datastore.read(\n oq.hazard_calculation_id)\n elif hasattr(self.calculator, 'csm'): # from sources\n self.calculator_build_events_from_sources()\n #self.calculator.build_events_from_sources()\n if (oq.ground_motion_fields is False and oq.hazard_curves_from_gmfs is False):\n return {}\n elif 'rupture_model' not in oq.inputs:\n logging.warning(\n 'There is no rupture_model, the calculator will just '\n 'import data without performing any calculation')\n fake = logictree.FullLogicTree.fake()\n dstore['full_lt'] = fake # needed to expose the outputs\n dstore['weights'] = [1.]\n return {}\n else: # scenario\n self.calculator._read_scenario_ruptures()\n if (oq.ground_motion_fields is False and oq.hazard_curves_from_gmfs is False):\n return {}\n\n # Intensity measure models\n if oq.ground_motion_fields:\n if self.vtag >= 12:\n imts = oq.get_primary_imtls()\n nrups = len(dstore['ruptures'])\n base.create_gmf_data(dstore, imts, oq.get_sec_imts())\n dstore.create_dset('gmf_data/sigma_epsilon',\n getters.sig_eps_dt(oq.imtls))\n dstore.create_dset('gmf_data/time_by_rup',\n getters.time_dt, (nrups,), fillvalue=None)\n elif self.vtag == 11:\n imts = oq.get_primary_imtls()\n nrups = len(dstore['ruptures'])\n base.create_gmf_data(dstore, len(imts), oq.get_sec_imts())\n dstore.create_dset('gmf_data/sigma_epsilon',\n getters.sig_eps_dt(oq.imtls))\n dstore.create_dset('gmf_data/time_by_rup',\n getters.time_dt, (nrups,), fillvalue=None)\n else:\n pass\n\n # Prepare inputs for GmfGetter\n nr = len(dstore['ruptures'])\n logging.info('Reading {:_d} ruptures'.format(nr))\n if self.vtag >= 12:\n rgetters = getters.get_rupture_getters(dstore, oq.concurrent_tasks * 1.25,\n srcfilter=self.calculator.srcfilter)\n elif self.vtag == 11:\n rgetters = getters.gen_rupture_getters(dstore, oq.concurrent_tasks)\n else:\n rgetters = getters.gen_rupture_getters(dstore, self.calculator.srcfilter, oq.concurrent_tasks)\n\n \n args = [(rgetter, self.calculator.param) for rgetter in rgetters]\n mon = performance.Monitor()\n mon.version = version\n mon.config = config\n rcvr = 'tcp://%s:%s' % (config.dbserver.listen,\n config.dbserver.receiver_ports)\n skt = zeromq.Socket(rcvr, zeromq.zmq.PULL, 'bind').__enter__()\n mon.backurl = 'tcp://%s:%s' % (config.dbserver.host, skt.port)\n mon = mon.new(\n operation='total ' + self.calculator.core_task.__func__.__name__, measuremem=True)\n mon.weight = getattr(args[0], 'weight', 1.) # used in task_info\n mon.task_no = 1 # initialize the task number\n args += (mon,)\n\n self.args = args\n self.mon = mon\n self.dstore = dstore\n\n finally:\n print('FetchOpenQuake: OpenQuake Hazard Calculator defined.')\n # parallel.Starmap.shutdown()","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHDUC.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def classFactory(iface): # pylint: disable=invalid-name\n #\n from .nuclear_energy_plant_radiation_module import energy_plant_radiation_class\n return energy_plant_radiation_class(iface)","def build_evaluator(cfg: CfgNode) -> EvaluatorBase:\n name = cfg[\"name\"]\n evaluator = simple_build(name, cfg, EVALUATORS)\n return evaluator","def _new_instance(cls: Type[T], mod_dir: str, home_dir: str,\n state_dir: str, mod_reg: Dict[str, \"Definition\"]) -> T:\n assert cls.name\n mod = cls(mod_dir, home_dir, state.load_state(state_dir, cls.name),\n logging.get_logger(cls.name))\n for dep_name in cls.required:\n dep = mod_reg[dep_name]\n setattr(mod, dep_name, _Protector(dep))\n for dep_name in cls.optional:\n opt_dep = mod_reg.get(dep_name)\n if not opt_dep:\n _LOG.info(\n f\"optional dependency {dep_name} of {cls.name} not available\"\n )\n setattr(mod, dep_name, None)\n continue\n setattr(mod, dep_name, _Protector(opt_dep))\n\n return mod","def simple_calculator(calculation):\n\n\n operations = {'+': lambda x,y: x + y,'-': lambda x,y: x-y,'*': lambda x,y: x * y,'/': lambda x,y: x/y}\n \n def is_numeric(x):\n\n try:\n float(x)\n int(x)\n except:\n return False\n else:\n return True\n \n\n values = calculation.split()\n print(values)\n if is_numeric(values[0]) and is_numeric(values[2]) and values[1] in operations:\n operation = operations[values[1]]\n try:\n return operation(float(values[0]),float(values[2]))\n except ZeroDivisionError:\n raise ValueError(\"Division by zero\")\n\n\n raise ValueError(\"Invalid Operation\")","def cmd_calculation():","def disassembler_factory():\n \n ir_id, ir_cls, dis_cls = find_current_arch()\n \n class disassembler(dis_cls, ir_cls): # disassembler (host) class must be left-most.\n def __init__(self, ir_id):\n self.ir_id = ir_id\n dis_cls.__init__(self)\n ir_cls.__init__(self)\n return\n \n dis = disassembler(ir_id)\n \n return dis","def __new__(mcs, name, parent, attr):\n def add_numeric_op(attr_name):\n \"\"\"Create an attribute named attr_name that calls\n _numeric_op(self, other, op).\"\"\"\n def closure(self, other):\n return VTKArray._numeric_op(self, other, attr_name)\n closure.__name__ = attr_name\n attr[attr_name] = closure\n\n def add_default_numeric_op(op_name):\n \"\"\"Adds '__[op_name]__' attribute that uses operator.[op_name]\"\"\"\n add_numeric_op(\"__%s__\"%op_name)\n\n def add_reverse_numeric_op(attr_name):\n \"\"\"Create an attribute named attr_name that calls\n _reverse_numeric_op(self, other, op).\"\"\"\n def closure(self, other):\n return VTKArray._reverse_numeric_op(self, other, attr_name)\n closure.__name__ = attr_name\n attr[attr_name] = closure\n\n def add_default_reverse_numeric_op(op_name):\n \"\"\"Adds '__r[op_name]__' attribute that uses operator.[op_name]\"\"\"\n add_reverse_numeric_op(\"__r%s__\"%op_name)\n\n def add_default_numeric_ops(op_name):\n \"\"\"Call both add_default_numeric_op and add_default_reverse_numeric_op.\"\"\"\n add_default_numeric_op(op_name)\n add_default_reverse_numeric_op(op_name)\n\n add_default_numeric_ops(\"add\")\n add_default_numeric_ops(\"sub\")\n add_default_numeric_ops(\"mul\")\n add_default_numeric_ops(\"truediv\")\n add_default_numeric_ops(\"floordiv\")\n add_default_numeric_ops(\"mod\")\n add_default_numeric_ops(\"pow\")\n add_default_numeric_ops(\"lshift\")\n add_default_numeric_ops(\"rshift\")\n add_numeric_op(\"and\")\n add_default_numeric_ops(\"xor\")\n add_numeric_op(\"or\")\n\n add_default_numeric_op(\"lt\")\n add_default_numeric_op(\"le\")\n add_default_numeric_op(\"eq\")\n add_default_numeric_op(\"ne\")\n add_default_numeric_op(\"ge\")\n add_default_numeric_op(\"gt\")\n return type.__new__(mcs, name, parent, attr)","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHFF.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def create(cls, **dictionary):\n if cls.__name__ == \"Rectangle\":\n myDummy = cls(1, 1)\n elif cls.__name__ == \"Square\":\n myDummy = cls(1)\n myDummy.update(**dictionary)\n return myDummy","def createStandardInputObjFromCalcObjs(self, calcObjs):\n\t\treturn self._stdInpFromCalcObjs(self, calcObjs)","def main():\n CalculatorApp().mainloop()","def __init__(self, adder, subtracter, multiplier, divider):\n self.adder = adder\n self.subtracter = subtracter\n self.multiplier = multiplier\n self.divider = divider\n\n self.stack = []","def __init__(self, adder, subtracter, multiplier, divider):\n self.adder = adder\n self.subtracter = subtracter\n self.multiplier = multiplier\n self.divider = divider\n\n self.stack = []","def __new__(cls, *args, **kwargs):\n if cls.instance is None:\n cls.instance = super(_ZeroAmount, cls).__new__(cls)\n return cls.instance","def calculator():\n print(art.logo)\n # Changed 'int' to 'float' to do calculation for floating numbers as well\n num1 = float(input(\"Enter the first number : \"))\n end_calculation = False\n\n while not end_calculation:\n list_operators()\n operator = input(\"Pick an operation : \")\n num2 = float(input(\"Enter the next number : \"))\n calculation_fun = operations[operator]\n answer = round(calculation_fun(num1, num2), 2)\n print(f\"{num1} {operator} {num2} = {answer}\")\n\n wish_to_continue = input(\"Type 'Y' to Continue or Type 'N' to Exit : \").lower()\n if wish_to_continue == \"y\":\n num1 = answer\n else:\n # clear()\n end_calculation = True\n # recursive function call to restart the calculation freshly when user doesn't want to continue\n calculator()","def __init__(self, expression, result, is_singleton=False):\n\n self.expr = expression\n self.result = result\n self.is_singleton = is_singleton","def __init__(self, calcGrad, calcCost, input):\n\tself.calcGrad = calcGrad\n\tself.calcCost = calcCost\n\tself.input = np.asarray(input, dtype=np.float32)\n\tself.inp_shape = input.shape","def __new__(mcs, name, parent, attr):\n def _add_op(attr_name, op):\n \"\"\"Create an attribute named attr_name that calls\n _numeric_op(self, other, op).\"\"\"\n def closure(self, other):\n return VTKNoneArray._op(self, other, op)\n closure.__name__ = attr_name\n attr[attr_name] = closure\n\n def _add_default_reverse_op(op_name):\n \"\"\"Adds '__r[op_name]__' attribute that uses operator.[op_name]\"\"\"\n _add_op(\"__r%s__\"%op_name, getattr(operator, op_name))\n\n def _add_default_op(op_name):\n \"\"\"Adds '__[op_name]__' attribute that uses operator.[op_name]\"\"\"\n _add_op(\"__%s__\"%op_name, getattr(operator, op_name))\n\n def _add_default_ops(op_name):\n \"\"\"Call both add_default_numeric_op and add_default_reverse_numeric_op.\"\"\"\n _add_default_op(op_name)\n _add_default_reverse_op(op_name)\n\n _add_default_ops(\"add\")\n _add_default_ops(\"sub\")\n _add_default_ops(\"mul\")\n _add_default_ops(\"truediv\")\n _add_default_ops(\"floordiv\")\n _add_default_ops(\"mod\")\n _add_default_ops(\"pow\")\n _add_default_ops(\"lshift\")\n _add_default_ops(\"rshift\")\n _add_op(\"__and__\", operator.and_)\n _add_op(\"__rand__\", operator.and_)\n _add_default_ops(\"xor\")\n _add_op(\"__or__\", operator.or_)\n _add_op(\"__ror__\", operator.or_)\n\n _add_default_op(\"lt\")\n _add_default_op(\"le\")\n _add_default_op(\"eq\")\n _add_default_op(\"ne\")\n _add_default_op(\"ge\")\n _add_default_op(\"gt\")\n return type.__new__(mcs, name, parent, attr)"],"string":"[\n \"def main():\\n model = Calculator()\",\n \"def __init__(self, mode=\\\"Normal\\\"):\\n\\n self.mode = mode\\n # minor optimization\\n # apparently [] is 5x faster than list()\\n # https://youtu.be/YjHsOrOOSuI?t=19m30s\\n self.button = []\\n \\\"\\\"\\\"button: List of buttons.\\\"\\\"\\\"\\n\\n self.numbers = (\\\"789456123\\\")\\n \\\"\\\"\\\"numbers: basic numberpad layout of ints\\\"\\\"\\\"\\n\\n self.root = tk.Tk()\\n self.root.title('Michael\\\\'s Calculator')\\n\\n self.menu = self.root.winfo_toplevel()\\n self.root.menuBar = tk.Menu(self.menu)\\n self.menu['menu'] = self.root.menuBar\\n\\n self.root.subMenu = tk.Menu(self.root.menuBar)\\n self.root.menuBar.add_cascade(label='View', menu=self.root.subMenu)\\n self.root.subMenu.add_command(label='Basic')\\n self.root.subMenu.add_command(label='Scientific')\\n\\n self.display = tk.Entry(self.root, width=15)\\n self.display.grid(row=0, column=0, columnspan=5)\\n self.create_number_buttons()\\n self.create_operation_buttons()\\n\\n self.root.mainloop()\",\n \"def calculator(**pars):\\n # paying for parameter conversion each time to keep life simple, if not fast\\n pars = revert_pars(model_info, pars)\\n for k, v in pars.items():\\n parts = k.split('.') # polydispersity components\\n if len(parts) == 2:\\n model.dispersion[parts[0]][parts[1]] = v\\n else:\\n model.setParam(k, v)\\n return theory()\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == 'Square':\\n object = cls(1)\\n object.update(**dictionary)\\n return object\\n\\n if cls.__name__ == 'Rectangle':\\n object = cls(1, 2)\\n object.update(**dictionary)\\n return object\",\n \"def calculator(operation): \\n \\n operation = MATH[operation]\\n a = int(request.args.get(\\\"a\\\"))\\n b = int(request.args.get(\\\"b\\\"))\\n total = operation(a, b)\\n\\n return f\\\"<h1>TOTAL: {total}</h1>\\\"\",\n \"def make_fee_class():\\r\\n fee = 1\\r\\n return make_class(locals())\",\n \"def withMaker(cls):\\n nodeName = cls.__name__.decode(\\\"utf-8\\\")\\n if cls.__init__ is object.__init__:\\n names = ()\\n else:\\n names = inspect.getargspec(cls.__init__).args[1:]\\n verb = nodeName\\n if getattr(cls, \\\"fromMonte\\\", None) is not None:\\n verb += \\\".fromMonte\\\"\\n arglist = u\\\", \\\".join([u\\\"args[%s]\\\" % i for i in range(len(names))])\\n runverb = 'RUN_%s' % len(names)\\n src = \\\"\\\"\\\"\\\\\\n @audited.DF\\n class %sMaker(Object):\\n def printOn(self, out):\\n out.call(u\\\"print\\\", [StrObject(u\\\"<kernel make%s>\\\")])\\n def recv(self, atom, args):\\n if atom is %s:\\n return %s(%s)\\n raise Refused(self, atom, args)\\n \\\"\\\"\\\" % (nodeName, nodeName, runverb, verb, arglist)\\n d = globals()\\n exec textwrap.dedent(src) in d\\n cls.nodeMaker = d[nodeName + \\\"Maker\\\"]()\\n return cls\",\n \"def __init__(self):\\n\\n #Creates the window.\\n EasyFrame.__init__(self, \\\"Calculator\\\", resizable = False)\\n\\n #Creates the data model Calculator.\\n self.calculator = Calculator()\\n\\n #Keeps track of when the user enters an input.\\n self.operatorEntered = False\\n\\n #Creates the colored panels.\\n for column in range(5):\\n numberBarPanel = self.addPanel(row = 0, column = column, background = \\\"black\\\")\\n\\n for row in range(1, 6):\\n symbolPanel = self.addPanel(row = row, column = 4, background = 'orange')\\n\\n for column in range (4):\\n topPanel = self.addPanel(row = 1, column = column, background = \\\"snow3\\\")\\n\\n for row in range (2, 6):\\n for column in range (0, 3):\\n numberPanel = self.addPanel(row = row, column = column, background = \\\"snow2\\\")\\n\\n #Creates fonts.\\n barFont = Font(family = \\\"San Francisco\\\", size = 20)\\n \\n #Creates the number bar at the top of the calculator.\\n self.digits = self.addLabel(\\\"0\\\", row= 0, column= 0, columnspan= 5, sticky= \\\"E\\\", background = 'black', foreground = 'white', font = barFont)\\n\\n #Creates button for clear.\\n self.clearButton= self.addButton(text= \\\"AC\\\", row= 1, column= 0, command= self.clearCommand)\\n self.clearButton['background'] = 'snow3'\\n \\n #Creates button for +/-.\\n negativeButton= self.addButton(text= \\\"+/-\\\", row= 1, column= 1, command= self.negativeCommand)\\n negativeButton['background'] = 'snow3'\\n \\n #Creates button for %\\n percentButton= self.addButton(text= \\\"%\\\", row= 1, column= 2, command= self.percentCommand)\\n percentButton['background'] = 'snow3'\\n\\n #Creates side row of operator symbols.\\n sideSymbols= [\\\"/\\\", \\\"X\\\", \\\"-\\\", \\\"+\\\", \\\"=\\\"]\\n row= 1\\n for symbol in sideSymbols:\\n symbolButton = self.addButton(text= symbol, row= row, column= 4)\\n symbolButton[\\\"command\\\"] = self.operatorCommand(symbol)\\n symbolButton['foreground'] = 'white'\\n symbolButton['background'] = 'orange'\\n row += 1\\n \\n #Goes through and creates a grid with numbers 1-9.\\n digit= 7\\n for row in range(2, 5):\\n for column in range(3):\\n numberButton = self.addButton(str(digit), row, column)\\n numberButton[\\\"command\\\"] = self.numberCommand(str(digit))\\n numberButton['background'] = 'snow2'\\n if digit == 9:\\n digit = 3\\n elif digit == 6:\\n digit = 0\\n digit += 1\\n\\n #Creates 0 button.\\n zeroButton = self.addButton(text= \\\"0 \\\", row= 5, column = 0, columnspan = 2, command = self.numberCommand(\\\"0\\\"))\\n zeroButton['background'] = 'snow2'\\n\\n #Creates . button.\\n self.decimalButton = self.addButton(text= \\\".\\\", row = 5, column = 2, command = self.decimalCommand, state = 'normal')\\n self.decimalButton['background'] = 'snow2'\",\n \"def __init__(self, operation, operand):\\n self.operation = operation\\n self.right = operand\",\n \"def test_module(self):\\n calculator = Calculator(Adder(), Subtracter(), Multiplier(), Divider())\\n\\n calculator.enter_number(5)\\n calculator.enter_number(2)\\n\\n # Tests\\n # Add:\\n self.assertEqual(7, calculator.add())\\n\\n # Subtract:\\n calculator.enter_number(5)\\n self.assertEqual(2, calculator.subtract())\\n\\n # Multiply:\\n calculator.enter_number(5)\\n self.assertEqual(10, calculator.multiply())\\n\\n # Divide:\\n calculator.enter_number(1)\\n self.assertEqual(10, calculator.divide())\",\n \"def test_calculator_add():\\n # Arrange by instantiating the calc class\\n calc = Calculator()\\n # Act by calling the method to be tested\\n calc.add(4)\\n # Assert that the results are correct\\n assert calc.result == 4\",\n \"def beta_create_Calculator_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\\n request_serializers = {\\n ('calculator.Calculator', 'Add'): CalculateRequest.SerializeToString,\\n ('calculator.Calculator', 'Divide'): CalculateRequest.SerializeToString,\\n ('calculator.Calculator', 'Multiply'): CalculateRequest.SerializeToString,\\n ('calculator.Calculator', 'Subtract'): CalculateRequest.SerializeToString,\\n }\\n response_deserializers = {\\n ('calculator.Calculator', 'Add'): CalculateResponse.FromString,\\n ('calculator.Calculator', 'Divide'): CalculateResponse.FromString,\\n ('calculator.Calculator', 'Multiply'): CalculateResponse.FromString,\\n ('calculator.Calculator', 'Subtract'): CalculateResponse.FromString,\\n }\\n cardinalities = {\\n 'Add': cardinality.Cardinality.UNARY_UNARY,\\n 'Divide': cardinality.Cardinality.UNARY_UNARY,\\n 'Multiply': cardinality.Cardinality.UNARY_UNARY,\\n 'Subtract': cardinality.Cardinality.UNARY_UNARY,\\n }\\n stub_options = beta_implementations.stub_options(host=host, metadata_transformer=metadata_transformer, request_serializers=request_serializers, response_deserializers=response_deserializers, thread_pool=pool, thread_pool_size=pool_size)\\n return beta_implementations.dynamic_stub(channel, 'calculator.Calculator', cardinalities, options=stub_options)\",\n \"def class_test():\\n\\n class Zulu(object):\\n x = 6\\n y = 6\\n\\n def __init__(self, x, y):\\n self.x = x\\n self.y = y\\n\\n @classmethod\\n def add(cls):\\n print(cls.x + cls.y)\\n\\n kowabunga = Zulu(5, 5)\\n\\n kowabunga.add()\",\n \"def __init__(self):\\r\\n EasyFrame.__init__(self, title=\\\"Investment Calculator\\\")\\r\\n self.addLabel(text=\\\"Initial amount\\\", row=0, column=0)\\r\\n self.addLabel(text=\\\"Number of years\\\", row=1, column=0)\\r\\n self.addLabel(text=\\\"Interest rate in %\\\", row=2, column=0)\\r\\n self.amount = self.addFloatField(value=0.0, row=0, column=1)\\r\\n self.period = self.addIntegerField(value=0, row=1, column=1)\\r\\n self.rate = self.addIntegerField(value=0, row=2, column=1)\\r\\n\\r\\n self.outputArea = self.addTextArea(\\\"\\\", row=4, column=0, columnspan=2, width=50, height=15)\\r\\n\\r\\n self.compute = self.addButton(text=\\\"Compute\\\", row=3, column=0, columnspan=2, command=self.compute)\",\n \"def __init__(self):\\n Cmd.__init__(self)\\n self.calc = ReversePolishCalc()\",\n \"def makeCalc(self, dataSet):\\n\\n #cyl = sasmodels.core.load_model_info('cylinder')\\n #hs = sasmodels.core.load_model_info('hardsphere')\\n #cylhs = sasmodels.core.load_model_info('cylinder@hardsphere')\\n cylhmsa = sasmodels.core.load_model_info('cylinder@hayter_msa')\\n\\n # Build using c version instead of python. Avoids pyopencl\\n model = sasmodels.core.build_model(cylhmsa, platform='dll')\\n self.calculator = sasmodels.direct_model.DirectModel(dataSet, model)\\n\\n return\",\n \"def create(cls, **dictionary):\\n new_inst = cls.__new__(cls)\\n if cls.__name__ == \\\"Rectangle\\\":\\n new_inst.__init__(42, 98)\\n elif cls.__name__ == \\\"Square\\\":\\n new_inst.__init__(42)\\n new_inst.update(**dictionary)\\n return new_inst\",\n \"def __init__(self, *args):\\n this = _ida_hexrays.new_operand_locator_t(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Rectangle\\\":\\n dummy = cls(1, 2)\\n dummy.update(**dictionary)\\n return dummy\\n if cls.__name__ == \\\"Square\\\":\\n dummy = cls(1)\\n dummy.update(**dictionary)\\n return dummy\",\n \"def create(cls, **props: Any) -> 'ResolverOp':\\n real_instance = super().__call__()\\n cls._check_kwargs(props)\\n for name, value in props.items():\\n setattr(real_instance, name, value)\\n return real_instance\",\n \"def create(cls, **props: Any) -> 'ResolverOp':\\n real_instance = super().__call__()\\n cls._check_kwargs(props)\\n for name, value in props.items():\\n setattr(real_instance, name, value)\\n return real_instance\",\n \"def make_operators(self):\\n self.relationship_operator = Operators.RelationshipOperator(self)\\n self.infection_operator = Operators.InfectionOperator(self)\\n self.time_operator = Operators.TimeOperator(self)\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Rectangle\\\":\\n object = cls(1, 1)\\n object.update(**dictionary)\\n return object\\n\\n if cls.__name__ == \\\"Square\\\":\\n object = cls(1)\\n object.update(**dictionary)\\n return object\",\n \"def create_expr(self, exprcls, ast, params=None, nopush=False):\\n if params is None:\\n expr = exprcls(self.current_parent, ast=ast)\\n else:\\n expr = exprcls(self.current_parent, ast=ast, **params)\\n if not nopush:\\n self.push_state(expr)\\n return expr\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Rectangle\\\":\\n new_class = cls(1, 2)\\n else:\\n new_class = cls(1)\\n new_class.update(**dictionary)\\n return new_class\",\n \"def __call__(self):\\r\\n new_node = Node()\\r\\n new_node.op = self\\r\\n return new_node\",\n \"def __init__(self, op, expression1, expression2):\\n LinearExpression.__init__(self)\\n\\n self.op = op\\n self.expression1 = expression1\\n self.expression2 = expression2\",\n \"def main():\\n #------------------------------------- Functions\\n def add(text):\\n \\\"\\\"\\\"\\n This will add to the display, and be the go to function of most buttons.\\n We'll want to add in conditions for what buttons go.\\n \\\"\\\"\\\"\\n orig = dispb[\\\"text\\\"]\\n new = orig + text\\n ops = [\\\"+\\\",\\\"-\\\",\\\"*\\\",\\\"/\\\"]\\n # conditions\\n # length 21\\n if len(new) > 21:\\n dispb[\\\"text\\\"] = orig\\n return 0\\n \\n # one calc at a time\\n if len(orig) > 0:\\n if (orig[-1] in ops) & (text in ops):\\n dispb[\\\"text\\\"] = orig\\n return 0\\n\\n dispb[\\\"text\\\"] = new\\n return 0\\n \\n def clear():\\n dispb[\\\"text\\\"] = \\\"\\\"\\n return 0\\n \\n def backspace():\\n dispb[\\\"text\\\"] = dispb[\\\"text\\\"][:len(dispb[\\\"text\\\"])-1]\\n return 0\\n \\n def equals():\\n try:\\n dispb[\\\"text\\\"] = str(eval(dispb[\\\"text\\\"]))\\n except:\\n dispb[\\\"text\\\"]=\\\"ERROR, clear display\\\"\\n \\n #------------------------------------- UI\\n \\n # title and start\\n calc = tk.Tk()\\n calc.title(\\\"Calculator\\\")\\n # size\\n calc.geometry(\\\"255x235\\\")\\n #calc.columnconfigure(range(3), weight=1, minsize=50)\\n #calc.rowconfigure(range(1,4), weight=1, minsize=48)\\n \\n # Icon\\n calc.iconbitmap('Icon.ico')#'Icon.ico')\\n \\n \\n calcarea = tk.Frame(master=calc)\\n calcarea.pack(padx=5, pady=10)\\n \\n # display box\\n disp = tk.Frame(\\n master = calcarea\\n )\\n disp.grid(row = 0, column = 0, columnspan = 3)\\n dispb = tk.Label(\\n master = disp,\\n text = '',\\n fg = 'black',\\n bg = 'white',\\n borderwidth = 1,\\n relief = 'solid',\\n height = 2,\\n width = 19\\n )\\n dispb.pack()\\n \\n # number buttons\\n num1 = tk.Frame(\\n master=calcarea\\n )\\n num1.grid(row = 3, column = 0)\\n num1b = tk.Button(\\n master = num1,\\n text = 1,\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"1\\\")\\n ).pack()\\n # the pack is what adds it to the UI\\n # two \\n num2 = tk.Frame(\\n master=calcarea\\n )\\n num2.grid(row = 3, column = 1)\\n num2b = tk.Button(\\n master = num2,\\n text = \\\"2\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"2\\\")\\n ).pack()\\n \\n # three \\n num3 = tk.Frame(\\n master=calcarea\\n )\\n num3.grid(row = 3, column = 2)\\n num3b = tk.Button(\\n master = num3,\\n text = \\\"3\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"3\\\")\\n ).pack()\\n \\n # four \\n num4 = tk.Frame(\\n master=calcarea\\n )\\n num4.grid(row = 2, column = 0)\\n num4b = tk.Button(\\n master = num4,\\n text = \\\"4\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"4\\\")\\n ).pack()\\n \\n # five \\n num5 = tk.Frame(\\n master=calcarea\\n )\\n num5.grid(row = 2, column = 1)\\n num5b = tk.Button(\\n master = num5,\\n text = \\\"5\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"5\\\")\\n ).pack()\\n \\n # six \\n num6 = tk.Frame(\\n master=calcarea\\n )\\n num6.grid(row = 2, column = 2)\\n num6b = tk.Button(\\n master = num6,\\n text = \\\"6\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"6\\\")\\n ).pack()\\n \\n # seven \\n num7 = tk.Frame(\\n master=calcarea\\n )\\n num7.grid(row = 1, column = 0)\\n num7b = tk.Button(\\n master = num7,\\n text = \\\"7\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"7\\\")\\n ).pack()\\n \\n # eight \\n num8 = tk.Frame(\\n master=calcarea\\n )\\n num8.grid(row = 1, column = 1)\\n num8b = tk.Button(\\n master = num8,\\n text = \\\"8\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"8\\\")\\n ).pack()\\n \\n # nine \\n num9 = tk.Frame(\\n master=calcarea\\n )\\n num9.grid(row = 1, column = 2)\\n num9b = tk.Button(\\n master = num9,\\n text = \\\"9\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"9\\\")\\n ).pack()\\n \\n # zero\\n num0 = tk.Frame(\\n master = calcarea\\n )\\n num0.grid(row = 4, column = 0)\\n num0b = tk.Button(\\n master = num0,\\n text = 0,\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"0\\\")\\n ).pack()\\n \\n # period\\n dot = tk.Frame(\\n master = calcarea\\n )\\n dot.grid(row = 4, column = 1)\\n dotb = tk.Button(\\n master = dot,\\n text = \\\".\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\".\\\")\\n ).pack()\\n \\n # equal sign\\n eq = tk.Frame(\\n master = calcarea\\n )\\n eq.grid(row = 4, column = 2, columnspan = 2)\\n eqb = tk.Button(\\n master = eq,\\n text = \\\"=\\\",\\n width = 11,\\n height = 2,\\n command = equals\\n ).pack()\\n \\n # plus sign\\n plus = tk.Frame(\\n master = calcarea\\n )\\n plus.grid(row = 3, column = 4, rowspan = 2)\\n plusb = tk.Button(\\n master = plus,\\n text = \\\"+\\\",\\n width = 5,\\n height = 5,\\n command = lambda: add(\\\"+\\\")\\n ).pack()\\n \\n # minus sign\\n minu = tk.Frame(\\n master = calcarea\\n )\\n minu.grid(row = 3, column = 3)\\n minub = tk.Button(\\n master = minu,\\n text = \\\"-\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"-\\\")\\n ).pack()\\n \\n # multiplication\\n mult = tk.Frame(\\n master = calcarea\\n )\\n mult.grid(row = 2, column = 3)\\n multb = tk.Button(\\n master = mult,\\n text = \\\"*\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"*\\\")\\n ).pack()\\n \\n # division\\n div = tk.Frame(\\n master = calcarea\\n )\\n div.grid(row = 2, column = 4)\\n divb = tk.Button(\\n master = div,\\n text = \\\"/\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"/\\\")\\n ).pack()\\n \\n # left parentheses\\n lefp = tk.Frame(\\n master = calcarea\\n )\\n lefp.grid(row = 1, column = 3)\\n lefpb = tk.Button(\\n master = lefp,\\n text = \\\"(\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\"(\\\")\\n ).pack()\\n \\n # right paraentheses\\n rigp = tk.Frame(\\n master = calcarea\\n )\\n rigp.grid(row = 1, column = 4)\\n rigpb = tk.Button(\\n master = rigp,\\n text = \\\")\\\",\\n width = 5,\\n height = 2,\\n command = lambda: add(\\\")\\\")\\n ).pack()\\n \\n # Clear button\\n Clr = tk.Frame(\\n master = calcarea\\n )\\n Clr.grid(row = 0, column = 3)\\n Clrb = tk.Button(\\n master = Clr,\\n text = \\\"C\\\",\\n width = 5,\\n height = 2,\\n command = clear\\n ).pack()\\n \\n # backspace\\n bck = tk.Frame(\\n master = calcarea\\n )\\n bck.grid(row = 0, column = 4)\\n bckb = tk.Button(\\n master = bck,\\n text = \\\"\\\\N{RIGHTWARDS BLACK ARROW}\\\",\\n width = 5,\\n height = 2,\\n command = backspace\\n ).pack()\\n \\n # This is what kicks the whole thing off, lets it wait for commands.\\n calc.mainloop()\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Square\\\":\\n dummy = cls(1)\\n if cls.__name__ == \\\"Rectangle\\\":\\n dummy = cls(1, 1)\\n dummy.update(**dictionary)\\n return dummy\",\n \"def main():\\r\\n # Create an instance of the MyCallCostCalc class.\\r\\n my_callcost = MyCallCostCalc()\",\n \"def New(*args, **kargs):\\n obj = itkSingleValuedCostFunction.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def calculate(numbers, operator):\\n \\n if operator == 'add':\\n return add(prepare_numbers(numbers))\\n elif operator == 'subtract':\\n return subtract(prepare_numbers(numbers))\\n elif operator == 'multiply':\\n return multiply(prepare_numbers(numbers))\\n elif operator == 'divide':\\n return divide(prepare_numbers(numbers))\\n elif operator == 'remainder':\\n return remainder(prepare_numbers(numbers))\\n elif operator == 'power':\\n return power(prepare_numbers(numbers))\",\n \"def New(*args, **kargs):\\n obj = itkCostFunction.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def __call__(self):\\n new_node = Node()\\n new_node.op = self\\n return new_node\",\n \"def get_calculator(settings_path, logging = True):\\n settings = SettingsReader(settings_path)\\n if settings.get(\\\"energy_calculator\\\", \\\"code\\\") == \\\"psi4\\\":\\n return Psi4Calculator(settings_path, logging)\\n elif settings.get(\\\"energy_calculator\\\", \\\"code\\\") == \\\"qchem\\\":\\n return QchemCalculator(settings_path, logging)\\n else:\\n raise NoSuchLibraryError(settings.get(\\\"energy_calculator\\\", \\\"code\\\"))\",\n \"def __init__(self):\\n self.currentTotal = None\\n self.previousOperator = None\\n self.previousOperand = None\",\n \"def __init__(self, op, value):\\n self.op = op\\n self.value = value\",\n \"def operation_class(self):\\n clazzname = self.name.title() + \\\"Op\\\"\\n if clazzname == \\\"NopOp\\\":\\n clazz = BaseOp\\n else:\\n clazz = globals()[clazzname]\\n return clazz\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Rectangle\\\":\\n dummy = cls(1, 1)\\n elif cls.__name__ == \\\"Square\\\":\\n dummy = cls(1)\\n dummy.update(**dictionary)\\n return dummy\",\n \"def test_get_operator_class(self):\\n Node = collections.namedtuple(\\\"Node\\\", \\\"op_type\\\")\\n\\n op_types = [\\\"Sum\\\", \\\"AveragePool\\\", \\\"Mean\\\"]\\n for op_type in op_types:\\n node = Node(op_type)\\n operator = onnx_converter._get_operator_class(node.op_type, {})\\n self.assertTrue(\\n issubclass(operator, crypten.nn.Module),\\n f\\\"{op_type} operator class {operator} is not a CrypTen module.\\\",\\n )\\n # check conv\\n kernel_shapes = [[1], [3, 3]]\\n node = Node(\\\"Conv\\\")\\n for kernel_shape in kernel_shapes:\\n attributes = {\\\"kernel_shape\\\": kernel_shape}\\n operator = onnx_converter._get_operator_class(node.op_type, attributes)\\n\\n # check invalid op_types\\n invalid_types = [(\\\"Convolution\\\", {\\\"kernel_shape\\\": [3, 3, 3]}), (\\\"Banana\\\", {})]\\n for invalid_type, attr in invalid_types:\\n with self.assertRaises(ValueError):\\n node = Node(invalid_type)\\n operator = onnx_converter._get_operator_class(node.op_type, attr)\",\n \"def __init__(self, opToken, leftOper, rightOper):\\n self.operator = opToken\\n self.leftOperand = leftOper\\n self.rightOperand = rightOper\",\n \"def __init__(self, operations = []):\\n self.operations = operations\",\n \"def infectious_math(cls):\\n for prp in [\\n \\\"__add__\\\", \\\"__radd__\\\",\\n \\\"__sub__\\\", \\\"__rsub__\\\",\\n \\\"__mul__\\\", \\\"__rmul__\\\",\\n \\\"__matmul__\\\", \\\"__rmatmul__\\\",\\n \\\"__truediv__\\\", \\\"__rtruediv__\\\",\\n \\\"__floordiv__\\\", \\\"__rfloordiv__\\\",\\n \\\"__mod__\\\", \\\"__rmod__\\\",\\n \\\"__divmod__\\\", \\\"__rdivmod__\\\",\\n \\\"__pow__\\\", \\\"__rpow__\\\",\\n \\\"__lshift__\\\", \\\"__rlshift__\\\",\\n \\\"__rshift__\\\", \\\"__rrshift__\\\",\\n \\\"__and__\\\", \\\"__rand__\\\",\\n \\\"__xor__\\\", \\\"__rxor__\\\",\\n \\\"__or__\\\", \\\"__ror__\\\",\\n\\n \\\"__neg__\\\", \\\"__rneg__\\\",\\n \\\"__pos__\\\", \\\"__rpos__\\\",\\n \\\"__abs__\\\", \\\"__rabs__\\\",\\n \\\"__invert__\\\", \\\"__rinvert__\\\",\\n ]:\\n if hasattr(cls, prp):\\n def_impl = getattr(cls, prp)\\n def augment(method):\\n def augmented(self, *args, **kwargs):\\n nonlocal method\\n result = method.__get__(self).__call__(*args, **kwargs)\\n return cls(result)\\n return augmented\\n replacement = augment(def_impl)\\n replacement.__name__ = prp\\n setattr(cls, prp, replacement)\\n\\n return cls\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Rectangle\\\":\\n r_dummy = cls(2, 4)\\n r_dummy.update(**dictionary)\\n return (r_dummy)\\n\\n if cls.__name__ == \\\"Square\\\":\\n s_dummy = cls(3)\\n s_dummy.update(**dictionary)\\n return (s_dummy)\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == 'Rectangle':\\n dummy = cls(1, 1)\\n elif cls.__name__ == 'Square':\\n dummy = cls(1)\\n dummy.update(**dictionary)\\n return dummy\",\n \"def main():\\n pycalcApp = QApplication(sys.argv)\\n pycalcView = PyCalcUi()\\n pycalcView.show()\\n model = evaluateExpression\\n PyCalcController(model=model, view=pycalcView)\\n sys.exit(pycalcApp.exec())\",\n \"def build(cls, real, imag):\\n return cls(f\\\"{real} {imag}\\\")\",\n \"def New(*args, **kargs):\\n obj = itkMultipleValuedCostFunction.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def New(*args, **kargs):\\n obj = itkMultipleValuedCostFunction.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == 'Rectangle':\\n dummy = cls(1, 1)\\n if cls.__name__ == 'Square':\\n dummy = cls(1)\\n dummy.update(**dictionary)\\n return dummy\",\n \"def main():\\n app = QApplication(sys.argv)\\n calc = Calculator()\\n calc.show()\\n calc.setFixedSize(calc.size())\\n sys.exit(app.exec_())\",\n \"def _new_instance(self):\\n return self.__class__(self._fmodule)\",\n \"def _new_instance(self):\\n return self.__class__(self._fmodule)\",\n \"def __init__(self, operation, constargs, randomargs):\\n Operation.__init__(self)\\n self.operation = operation\\n self.constargs = constargs\\n self.randomargs = randomargs\\n if type(operation) is str:\\n import CCAugmentation.outputs as cca_out\\n import CCAugmentation.transformations as cca_trans\\n self.operation = eval(self._get_op_str())\\n self.args = {'operation': self.operation.__name__, 'constargs': constargs, 'randomargs': randomargs}\",\n \"def createWidgets(self):\\n num_positions = [ (3,0), (2,0), (2,1), (2,2), (1,0), \\\\\\n (1,1), (1,2), (0,0), (0,1), (0,2) ] \\n\\n op_info = { '+': (3, 3), '-': (2, 3), '*': (1, 3), '/': (0, 3) }\\n self.operators = {}\\n\\n #Creates the 10 number buttons.\\n for i in range(10):\\n button = Button(self, text=str(i), height=2, width=5)\\n button['command'] = lambda i=i: self.handle_num_general(str(i))\\n r, c = num_positions[i][0], num_positions[i][1]\\n button.grid(row=r, column=c)\\n\\n #Creates the 4 operator buttons.\\n for op,position in op_info.iteritems():\\n button = Button(self, text=str(op), height=2, width=5)\\n button['command'] = lambda op=op: self.handle_op_general(op)\\n button.grid(row=position[0], column=position[1])\\n self.operators[op] = button\\n\\n self.equals = Button(self, text='=', height=2, width=5)\\n self.equals['command'] = self.perform_op\\n self.equals.grid(row=3, column=2)\\n\\n self.clear = Button(self, text='C', height=2, width=5)\\n self.clear['command'] = lambda: self.reset('0') \\n self.clear.grid(row=3, column=1)\\n\\n self.change_equals_state(DISABLED)\\n self.change_ops_state(DISABLED)\",\n \"def calculator(): \\n\\n #asks for user's input \\n user_input = raw_input(\\\"Type in the math expression and two numbers like + 1 2 : \\\")\\n #splits the user's input into a list\\n math_op = user_input.split(\\\" \\\")\\n\\n #pulls the appropriate function based on the user's input\\n if math_op[0] == '+':\\n print add(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == '-':\\n print subtract(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == '*':\\n print multiply(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == '/':\\n print divide(int(math_op[1]), int(math_op[2]))\\n \\n elif math_op[0] == \\\"square\\\":\\n print square(int(math_op[1]))\\n\\n elif math_op[0] == 'cube':\\n print cube(int(math_op[1]))\\n\\n elif math_op[0] == 'pow':\\n print power(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == 'mod':\\n print mod(int(math_op[1]), int(math_op[2]))\\n\\n else:\\n print \\\"That is not a valid input. Please try any of the following operator: + - * / square cube pow mod.\\\"\",\n \"def eval(cls, *args):\\n raise NotImplementedError(\\\"subclasses need to override this method\\\")\",\n \"def __init__(self, name, operator, values):\\n self.name = name\\n self.operator = operator\\n self.values = values\",\n \"def basic_calculator():\\r\\n\\r\\n num1 = input(\\\"Enter first number: \\\") # taking input\\r\\n\\r\\n # handling the exception of typecasting the value of 'num1' to float\\r\\n try:\\r\\n num1 = float(num1)\\r\\n except ValueError:\\r\\n print(\\\"Error: Input numeric values.\\\\nTry Again!\\\")\\r\\n exit()\\r\\n\\r\\n num2 = input(\\\"Enter second number: \\\") # taking input\\r\\n\\r\\n # handling the exception of typecasting the value of 'num2' to float\\r\\n try:\\r\\n num2 = float(num2)\\r\\n except ValueError:\\r\\n print(\\\"Error: Input numeric values.\\\\nTry Again!\\\")\\r\\n exit()\\r\\n\\r\\n # Asking user for the operation\\r\\n print(\\\"Select the operation:\\\")\\r\\n print(\\\"Type:\\\")\\r\\n print(\\\"1 for Addition\\\\n2 for Subtraction\\\\n3 for Multiplication\\\\n4 for Division\\\\n5 for Integer Division\\\\n6 for Power\\\")\\r\\n choice = input(\\\"Enter your choice: \\\")\\r\\n\\r\\n result = 0.0\\r\\n\\r\\n # Performing the operation and providing the result\\r\\n if choice == '1':\\r\\n result = num1 + num2\\r\\n elif choice == '2':\\r\\n result = num1 - num2\\r\\n elif choice == '3':\\r\\n result = num1 * num2\\r\\n elif choice == '4':\\r\\n result = num1 / num2\\r\\n elif choice == '5':\\r\\n result = num1 // num2\\r\\n elif choice == '6':\\r\\n result = num1 ** num2\\r\\n else:\\r\\n print(\\\"Wrong Input! Try Again.\\\")\\r\\n exit()\\r\\n\\r\\n print(f'\\\\nThe result is: {result}')\",\n \"def New():\\n Self = $classname()\\n Self._initialize_()\\n Self._update_()\\n return Self\",\n \"def from_operator(operation=debug):\\r\\n\\r\\n def C(*things):\\r\\n return Container(freezed(operation), list(things), [], [], [], [])\\r\\n return C\",\n \"def __new__(cls, ctx):\\n return cls.__run(cls, ctx)\",\n \"def make_instance(cls):\\r\\n def get_value(name):\\r\\n if name in attributes:\\r\\n return attributes[name]\\r\\n else:\\r\\n value = cls['get'](name)\\r\\n return bind_method(value, instance)\\r\\n\\r\\n def set_value(name, value):\\r\\n attributes[name] = value\\r\\n\\r\\n attributes = {}\\r\\n instance = {'get': get_value, 'set': set_value}\\r\\n return instance\",\n \"def parse_calc_cmd(self, line):\\n self.E_str = \\\"parse_calc_cmd\\\"\\n # Clean up the line\\n line, any_vars = self.find_vars_in_str(line)\\n words = line.split()\\n _, calc_type, _, var_name, _, new_var_name = words\\n\\n # Get the variable to calculate the property with\\n Var = getattr(self, var_name)\\n\\n # Check the required metadata has been set\\n required_metadata = f_dicts.calc_fncs[calc_type].required_metadata\\n Var = getattr(self, var_name)\\n for attr in required_metadata:\\n if attr not in Var.metadata and attr not in f_dicts.calc_fncs[calc_type]._defaults:\\n err_msg = f\\\"'{attr}' required for calculation of '{calc_type}'\\\"\\n err_msg += \\\"\\\\n\\\\nPlease set it with the following syntax:\\\\n\\\\t\\\"\\n err_msg += f\\\"{var_name}['{attr}'] = <value>\\\"\\n err_msg += f\\\" or by using a set command.\\\"\\n self.print_error(err_msg)\\n\\n\\n Calc_Obj = f_dicts.calc_fncs[calc_type](Var)\\n\\n Calc_Obj.calc()\\n\\n # Create a new variable type\\n New_Var = inp_types.Variable(Calc_Obj.name, Calc_Obj, Calc_Obj.metadata)\\n setattr(self, new_var_name, New_Var)\\n if new_var_name not in self.variables:\\n self.variables.append(new_var_name)\",\n \"def new_calc_target(self, day, block, which):\\n calc = Calculated(\\n target_date=day.day,\\n target_block=which,\\n name=block.name,\\n color=block.color,\\n note=block.note\\n )\\n for service in block.services:\\n calc.services.append(CalculatedService(\\n name=service.name,\\n start_time=service.start_time\\n ))\\n return calc\",\n \"def setup_class(self):\\n class SubCosmology(Cosmology):\\n\\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\\n Tcmb0 = Parameter(unit=u.K)\\n\\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\\n super().__init__(name=name, meta=meta)\\n self._H0 = H0\\n self._Tcmb0 = Tcmb0\\n\\n self.cls = SubCosmology\\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def __new__(cls, value_str):\\n\\n value_str = value_str.strip()\\n if value_str[0] == '[' and value_str[-1] == ']':\\n value = literal_eval(value_str)\\n else:\\n value_str = value_str.split()\\n assert len(value_str) >= 1\\n\\n try:\\n value = float(value_str[0])\\n except ValueError:\\n value = value_str[0]\\n\\n #False positive, pylint does not see attributes\\n #defined in __new__\\n #pylint: disable=attribute-defined-outside-init\\n if isinstance(value, float):\\n if len(value_str) != 1:\\n if len(value_str) == 4:\\n unit = Unit(value_str[3])\\n else:\\n unit = Unit('')\\n\\n if value_str[1] == '+/-':\\n plus_error = float(value_str[2])\\n minus_error = plus_error\\n else:\\n plus_error = float(value_str[1])\\n minus_error = abs(float(value_str[2]))\\n result = super().__new__(cls, value, unit)\\n result.plus_error = plus_error\\n result.minus_error = minus_error\\n return result\",\n \"def setUp(self):\\n self.adder = Adder()\\n self.subtracter = Subtracter()\\n self.multiplier = Multiplier()\\n self.divider = Divider()\\n\\n self.calculator = Calculator(self.adder, self.subtracter,\\n self.multiplier, self.divider)\",\n \"def createMath(self, *args):\\n return _libsbml.ASTBasePlugin_createMath(self, *args)\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHDUS.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def __init__(self, operand_string):\\n\\n # String to hold the operand literal\\n self.op_string = operand_string\\n\\n # Integer value of the operand\\n self.op_value = int(operand_string)\",\n \"def from_string_expr(cls, expr):\\n if \\\"*\\\" in expr:\\n ch = \\\"*\\\"\\n op = \\\"cross\\\"\\n elif \\\"+\\\" in expr:\\n ch = \\\"+\\\"\\n op = \\\"blend\\\"\\n elif \\\"/\\\" in expr:\\n ch = \\\"/\\\"\\n op = \\\"nest\\\"\\n factors = [cls(s.strip()) for s in expr.split(ch)]\\n return cls(op=op, factors=factors)\",\n \"def __new__(mcs, name, parent, attr):\\n def add_numeric_op(attr_name, op):\\n \\\"\\\"\\\"Create an attribute named attr_name that calls\\n _numeric_op(self, other, op).\\\"\\\"\\\"\\n def closure(self, other):\\n return VTKCompositeDataArray._numeric_op(self, other, op)\\n closure.__name__ = attr_name\\n attr[attr_name] = closure\\n\\n def add_reverse_numeric_op(attr_name, op):\\n \\\"\\\"\\\"Create an attribute named attr_name that calls\\n _reverse_numeric_op(self, other, op).\\\"\\\"\\\"\\n def closure(self, other):\\n return VTKCompositeDataArray._reverse_numeric_op(self, other, op)\\n closure.__name__ = attr_name\\n attr[attr_name] = closure\\n\\n def add_default_reverse_numeric_op(op_name):\\n \\\"\\\"\\\"Adds '__r[op_name]__' attribute that uses operator.[op_name]\\\"\\\"\\\"\\n add_reverse_numeric_op(\\\"__r%s__\\\"%op_name, getattr(operator, op_name))\\n\\n def add_default_numeric_op(op_name):\\n \\\"\\\"\\\"Adds '__[op_name]__' attribute that uses operator.[op_name]\\\"\\\"\\\"\\n add_numeric_op(\\\"__%s__\\\"%op_name, getattr(operator, op_name))\\n\\n def add_default_numeric_ops(op_name):\\n \\\"\\\"\\\"Call both add_default_numeric_op and add_default_reverse_numeric_op.\\\"\\\"\\\"\\n add_default_numeric_op(op_name)\\n add_default_reverse_numeric_op(op_name)\\n\\n add_default_numeric_ops(\\\"add\\\")\\n add_default_numeric_ops(\\\"sub\\\")\\n add_default_numeric_ops(\\\"mul\\\")\\n add_default_numeric_ops(\\\"truediv\\\")\\n add_default_numeric_ops(\\\"floordiv\\\")\\n add_default_numeric_ops(\\\"mod\\\")\\n add_default_numeric_ops(\\\"pow\\\")\\n add_default_numeric_ops(\\\"lshift\\\")\\n add_default_numeric_ops(\\\"rshift\\\")\\n add_numeric_op(\\\"__and__\\\", operator.and_)\\n add_reverse_numeric_op(\\\"__rand__\\\", operator.and_)\\n add_default_numeric_ops(\\\"xor\\\")\\n add_numeric_op(\\\"__or__\\\", operator.or_)\\n add_reverse_numeric_op(\\\"__ror__\\\", operator.or_)\\n\\n add_default_numeric_op(\\\"lt\\\")\\n add_default_numeric_op(\\\"le\\\")\\n add_default_numeric_op(\\\"eq\\\")\\n add_default_numeric_op(\\\"ne\\\")\\n add_default_numeric_op(\\\"ge\\\")\\n add_default_numeric_op(\\\"gt\\\")\\n return type.__new__(mcs, name, parent, attr)\",\n \"def calculations(self):\\n return Calculation.find(self)\",\n \"def create_instance(self,name):\\n print \\\"INFO : new %s\\\" % name\\n return self.get_class(name)()\",\n \"def operator(app):\\n return car(app)\",\n \"def R(cls, *args, **kwargs):\\n return cls(*args, model_type='regressor', **kwargs)\",\n \"def some_operation(self):\\n\\n # Call the factory method to create a Product object.\\n product = self.factory_method()\\n\\n # Now, use the product.\\n product.operation()\",\n \"def new(cls):\\n return cls()\",\n \"def __init__(self, math, variables=None, first_derivatives=None,\\n second_derivatives=None, name=None):\\n self.math = math\\n if variables is not None:\\n self.variables = variables\\n else:\\n # expr_manip still returns Sets instead of sets; cast the\\n # result to set mostly because this annoys me.\\n self.variables = set(em.extract_vars(self.math))\\n self._first_derivatives = {}\\n if first_derivatives:\\n self._first_derivatives.update(first_derivatives)\\n self._second_derivatives = {}\\n if second_derivatives:\\n self._second_derivatives.update(second_derivatives)\\n self._code = None\\n self._first_derivative_code = {}\\n self._second_derivative_code = {}\\n self.name = name\\n if self.name:\\n self.tag = name\\n elif len(self.math) < 100:\\n self.tag = \\\"'%s'\\\" % self.math\\n else:\\n self.tag = \\\"'%s...'\\\" % self.math[:100]\",\n \"def create_operator(statement_a, operator, statement_b):\\n return S(statement_a=statement_a, operator=operator, statement_b=statement_b)\",\n \"def run_calc(self):\\n\\n from openquake.calculators import base, getters\\n from openquake.baselib import config, performance, zeromq\\n if self.vtag >= 11:\\n from openquake.baselib import version\\n else:\\n from openquake.baselib import __version__ as version\\n\\n with self.calculator._monitor:\\n self.calculator._monitor.username = ''\\n try:\\n # Pre-execute setups\\n self.calculator.pre_execute()\\n\\n #self.calculator.datastore.swmr_on()\\n oq = self.calculator.oqparam\\n dstore = self.calculator.datastore\\n self.calculator.set_param()\\n self.calculator.offset = 0\\n\\n # Source model\\n #print('self.__dict__ = ')\\n #print(self.calculator.__dict__)\\n if oq.hazard_calculation_id: # from ruptures\\n dstore.parent = self.calculator.datastore.read(\\n oq.hazard_calculation_id)\\n elif hasattr(self.calculator, 'csm'): # from sources\\n self.calculator_build_events_from_sources()\\n #self.calculator.build_events_from_sources()\\n if (oq.ground_motion_fields is False and oq.hazard_curves_from_gmfs is False):\\n return {}\\n elif 'rupture_model' not in oq.inputs:\\n logging.warning(\\n 'There is no rupture_model, the calculator will just '\\n 'import data without performing any calculation')\\n fake = logictree.FullLogicTree.fake()\\n dstore['full_lt'] = fake # needed to expose the outputs\\n dstore['weights'] = [1.]\\n return {}\\n else: # scenario\\n self.calculator._read_scenario_ruptures()\\n if (oq.ground_motion_fields is False and oq.hazard_curves_from_gmfs is False):\\n return {}\\n\\n # Intensity measure models\\n if oq.ground_motion_fields:\\n if self.vtag >= 12:\\n imts = oq.get_primary_imtls()\\n nrups = len(dstore['ruptures'])\\n base.create_gmf_data(dstore, imts, oq.get_sec_imts())\\n dstore.create_dset('gmf_data/sigma_epsilon',\\n getters.sig_eps_dt(oq.imtls))\\n dstore.create_dset('gmf_data/time_by_rup',\\n getters.time_dt, (nrups,), fillvalue=None)\\n elif self.vtag == 11:\\n imts = oq.get_primary_imtls()\\n nrups = len(dstore['ruptures'])\\n base.create_gmf_data(dstore, len(imts), oq.get_sec_imts())\\n dstore.create_dset('gmf_data/sigma_epsilon',\\n getters.sig_eps_dt(oq.imtls))\\n dstore.create_dset('gmf_data/time_by_rup',\\n getters.time_dt, (nrups,), fillvalue=None)\\n else:\\n pass\\n\\n # Prepare inputs for GmfGetter\\n nr = len(dstore['ruptures'])\\n logging.info('Reading {:_d} ruptures'.format(nr))\\n if self.vtag >= 12:\\n rgetters = getters.get_rupture_getters(dstore, oq.concurrent_tasks * 1.25,\\n srcfilter=self.calculator.srcfilter)\\n elif self.vtag == 11:\\n rgetters = getters.gen_rupture_getters(dstore, oq.concurrent_tasks)\\n else:\\n rgetters = getters.gen_rupture_getters(dstore, self.calculator.srcfilter, oq.concurrent_tasks)\\n\\n \\n args = [(rgetter, self.calculator.param) for rgetter in rgetters]\\n mon = performance.Monitor()\\n mon.version = version\\n mon.config = config\\n rcvr = 'tcp://%s:%s' % (config.dbserver.listen,\\n config.dbserver.receiver_ports)\\n skt = zeromq.Socket(rcvr, zeromq.zmq.PULL, 'bind').__enter__()\\n mon.backurl = 'tcp://%s:%s' % (config.dbserver.host, skt.port)\\n mon = mon.new(\\n operation='total ' + self.calculator.core_task.__func__.__name__, measuremem=True)\\n mon.weight = getattr(args[0], 'weight', 1.) # used in task_info\\n mon.task_no = 1 # initialize the task number\\n args += (mon,)\\n\\n self.args = args\\n self.mon = mon\\n self.dstore = dstore\\n\\n finally:\\n print('FetchOpenQuake: OpenQuake Hazard Calculator defined.')\\n # parallel.Starmap.shutdown()\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHDUC.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def classFactory(iface): # pylint: disable=invalid-name\\n #\\n from .nuclear_energy_plant_radiation_module import energy_plant_radiation_class\\n return energy_plant_radiation_class(iface)\",\n \"def build_evaluator(cfg: CfgNode) -> EvaluatorBase:\\n name = cfg[\\\"name\\\"]\\n evaluator = simple_build(name, cfg, EVALUATORS)\\n return evaluator\",\n \"def _new_instance(cls: Type[T], mod_dir: str, home_dir: str,\\n state_dir: str, mod_reg: Dict[str, \\\"Definition\\\"]) -> T:\\n assert cls.name\\n mod = cls(mod_dir, home_dir, state.load_state(state_dir, cls.name),\\n logging.get_logger(cls.name))\\n for dep_name in cls.required:\\n dep = mod_reg[dep_name]\\n setattr(mod, dep_name, _Protector(dep))\\n for dep_name in cls.optional:\\n opt_dep = mod_reg.get(dep_name)\\n if not opt_dep:\\n _LOG.info(\\n f\\\"optional dependency {dep_name} of {cls.name} not available\\\"\\n )\\n setattr(mod, dep_name, None)\\n continue\\n setattr(mod, dep_name, _Protector(opt_dep))\\n\\n return mod\",\n \"def simple_calculator(calculation):\\n\\n\\n operations = {'+': lambda x,y: x + y,'-': lambda x,y: x-y,'*': lambda x,y: x * y,'/': lambda x,y: x/y}\\n \\n def is_numeric(x):\\n\\n try:\\n float(x)\\n int(x)\\n except:\\n return False\\n else:\\n return True\\n \\n\\n values = calculation.split()\\n print(values)\\n if is_numeric(values[0]) and is_numeric(values[2]) and values[1] in operations:\\n operation = operations[values[1]]\\n try:\\n return operation(float(values[0]),float(values[2]))\\n except ZeroDivisionError:\\n raise ValueError(\\\"Division by zero\\\")\\n\\n\\n raise ValueError(\\\"Invalid Operation\\\")\",\n \"def cmd_calculation():\",\n \"def disassembler_factory():\\n \\n ir_id, ir_cls, dis_cls = find_current_arch()\\n \\n class disassembler(dis_cls, ir_cls): # disassembler (host) class must be left-most.\\n def __init__(self, ir_id):\\n self.ir_id = ir_id\\n dis_cls.__init__(self)\\n ir_cls.__init__(self)\\n return\\n \\n dis = disassembler(ir_id)\\n \\n return dis\",\n \"def __new__(mcs, name, parent, attr):\\n def add_numeric_op(attr_name):\\n \\\"\\\"\\\"Create an attribute named attr_name that calls\\n _numeric_op(self, other, op).\\\"\\\"\\\"\\n def closure(self, other):\\n return VTKArray._numeric_op(self, other, attr_name)\\n closure.__name__ = attr_name\\n attr[attr_name] = closure\\n\\n def add_default_numeric_op(op_name):\\n \\\"\\\"\\\"Adds '__[op_name]__' attribute that uses operator.[op_name]\\\"\\\"\\\"\\n add_numeric_op(\\\"__%s__\\\"%op_name)\\n\\n def add_reverse_numeric_op(attr_name):\\n \\\"\\\"\\\"Create an attribute named attr_name that calls\\n _reverse_numeric_op(self, other, op).\\\"\\\"\\\"\\n def closure(self, other):\\n return VTKArray._reverse_numeric_op(self, other, attr_name)\\n closure.__name__ = attr_name\\n attr[attr_name] = closure\\n\\n def add_default_reverse_numeric_op(op_name):\\n \\\"\\\"\\\"Adds '__r[op_name]__' attribute that uses operator.[op_name]\\\"\\\"\\\"\\n add_reverse_numeric_op(\\\"__r%s__\\\"%op_name)\\n\\n def add_default_numeric_ops(op_name):\\n \\\"\\\"\\\"Call both add_default_numeric_op and add_default_reverse_numeric_op.\\\"\\\"\\\"\\n add_default_numeric_op(op_name)\\n add_default_reverse_numeric_op(op_name)\\n\\n add_default_numeric_ops(\\\"add\\\")\\n add_default_numeric_ops(\\\"sub\\\")\\n add_default_numeric_ops(\\\"mul\\\")\\n add_default_numeric_ops(\\\"truediv\\\")\\n add_default_numeric_ops(\\\"floordiv\\\")\\n add_default_numeric_ops(\\\"mod\\\")\\n add_default_numeric_ops(\\\"pow\\\")\\n add_default_numeric_ops(\\\"lshift\\\")\\n add_default_numeric_ops(\\\"rshift\\\")\\n add_numeric_op(\\\"and\\\")\\n add_default_numeric_ops(\\\"xor\\\")\\n add_numeric_op(\\\"or\\\")\\n\\n add_default_numeric_op(\\\"lt\\\")\\n add_default_numeric_op(\\\"le\\\")\\n add_default_numeric_op(\\\"eq\\\")\\n add_default_numeric_op(\\\"ne\\\")\\n add_default_numeric_op(\\\"ge\\\")\\n add_default_numeric_op(\\\"gt\\\")\\n return type.__new__(mcs, name, parent, attr)\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHFF.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def create(cls, **dictionary):\\n if cls.__name__ == \\\"Rectangle\\\":\\n myDummy = cls(1, 1)\\n elif cls.__name__ == \\\"Square\\\":\\n myDummy = cls(1)\\n myDummy.update(**dictionary)\\n return myDummy\",\n \"def createStandardInputObjFromCalcObjs(self, calcObjs):\\n\\t\\treturn self._stdInpFromCalcObjs(self, calcObjs)\",\n \"def main():\\n CalculatorApp().mainloop()\",\n \"def __init__(self, adder, subtracter, multiplier, divider):\\n self.adder = adder\\n self.subtracter = subtracter\\n self.multiplier = multiplier\\n self.divider = divider\\n\\n self.stack = []\",\n \"def __init__(self, adder, subtracter, multiplier, divider):\\n self.adder = adder\\n self.subtracter = subtracter\\n self.multiplier = multiplier\\n self.divider = divider\\n\\n self.stack = []\",\n \"def __new__(cls, *args, **kwargs):\\n if cls.instance is None:\\n cls.instance = super(_ZeroAmount, cls).__new__(cls)\\n return cls.instance\",\n \"def calculator():\\n print(art.logo)\\n # Changed 'int' to 'float' to do calculation for floating numbers as well\\n num1 = float(input(\\\"Enter the first number : \\\"))\\n end_calculation = False\\n\\n while not end_calculation:\\n list_operators()\\n operator = input(\\\"Pick an operation : \\\")\\n num2 = float(input(\\\"Enter the next number : \\\"))\\n calculation_fun = operations[operator]\\n answer = round(calculation_fun(num1, num2), 2)\\n print(f\\\"{num1} {operator} {num2} = {answer}\\\")\\n\\n wish_to_continue = input(\\\"Type 'Y' to Continue or Type 'N' to Exit : \\\").lower()\\n if wish_to_continue == \\\"y\\\":\\n num1 = answer\\n else:\\n # clear()\\n end_calculation = True\\n # recursive function call to restart the calculation freshly when user doesn't want to continue\\n calculator()\",\n \"def __init__(self, expression, result, is_singleton=False):\\n\\n self.expr = expression\\n self.result = result\\n self.is_singleton = is_singleton\",\n \"def __init__(self, calcGrad, calcCost, input):\\n\\tself.calcGrad = calcGrad\\n\\tself.calcCost = calcCost\\n\\tself.input = np.asarray(input, dtype=np.float32)\\n\\tself.inp_shape = input.shape\",\n \"def __new__(mcs, name, parent, attr):\\n def _add_op(attr_name, op):\\n \\\"\\\"\\\"Create an attribute named attr_name that calls\\n _numeric_op(self, other, op).\\\"\\\"\\\"\\n def closure(self, other):\\n return VTKNoneArray._op(self, other, op)\\n closure.__name__ = attr_name\\n attr[attr_name] = closure\\n\\n def _add_default_reverse_op(op_name):\\n \\\"\\\"\\\"Adds '__r[op_name]__' attribute that uses operator.[op_name]\\\"\\\"\\\"\\n _add_op(\\\"__r%s__\\\"%op_name, getattr(operator, op_name))\\n\\n def _add_default_op(op_name):\\n \\\"\\\"\\\"Adds '__[op_name]__' attribute that uses operator.[op_name]\\\"\\\"\\\"\\n _add_op(\\\"__%s__\\\"%op_name, getattr(operator, op_name))\\n\\n def _add_default_ops(op_name):\\n \\\"\\\"\\\"Call both add_default_numeric_op and add_default_reverse_numeric_op.\\\"\\\"\\\"\\n _add_default_op(op_name)\\n _add_default_reverse_op(op_name)\\n\\n _add_default_ops(\\\"add\\\")\\n _add_default_ops(\\\"sub\\\")\\n _add_default_ops(\\\"mul\\\")\\n _add_default_ops(\\\"truediv\\\")\\n _add_default_ops(\\\"floordiv\\\")\\n _add_default_ops(\\\"mod\\\")\\n _add_default_ops(\\\"pow\\\")\\n _add_default_ops(\\\"lshift\\\")\\n _add_default_ops(\\\"rshift\\\")\\n _add_op(\\\"__and__\\\", operator.and_)\\n _add_op(\\\"__rand__\\\", operator.and_)\\n _add_default_ops(\\\"xor\\\")\\n _add_op(\\\"__or__\\\", operator.or_)\\n _add_op(\\\"__ror__\\\", operator.or_)\\n\\n _add_default_op(\\\"lt\\\")\\n _add_default_op(\\\"le\\\")\\n _add_default_op(\\\"eq\\\")\\n _add_default_op(\\\"ne\\\")\\n _add_default_op(\\\"ge\\\")\\n _add_default_op(\\\"gt\\\")\\n return type.__new__(mcs, name, parent, attr)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6303939","0.5606582","0.5468204","0.5322988","0.53078365","0.529308","0.5283356","0.5271009","0.52677727","0.5266936","0.5252997","0.5238419","0.5233305","0.5231769","0.5197301","0.51828855","0.5182773","0.5162779","0.51625335","0.51475465","0.51475465","0.5142594","0.51315063","0.512747","0.51260394","0.5125916","0.5114183","0.51043624","0.51041955","0.5100643","0.50856876","0.5074678","0.5065174","0.505762","0.5033035","0.5021856","0.5016422","0.5001367","0.49883187","0.4975265","0.49676546","0.49675006","0.496178","0.4961175","0.49468952","0.49457493","0.49444577","0.4943917","0.4943917","0.49434912","0.4930547","0.49191317","0.49191317","0.49040225","0.4885421","0.48683232","0.48602933","0.484954","0.4846732","0.48448187","0.4842286","0.48397857","0.4819495","0.48171175","0.4816521","0.48144796","0.4807072","0.48000586","0.4799887","0.47929788","0.47913298","0.47909614","0.47846982","0.478406","0.4783031","0.47814113","0.47791123","0.4777555","0.47668615","0.47636095","0.4758488","0.47579035","0.47576138","0.47439644","0.47438866","0.474275","0.4741818","0.4741164","0.4740029","0.4732702","0.47321844","0.47295067","0.47228393","0.4722288","0.47192174","0.47192174","0.47142357","0.47127345","0.47114635","0.47105315","0.4710372"],"string":"[\n \"0.6303939\",\n \"0.5606582\",\n \"0.5468204\",\n \"0.5322988\",\n \"0.53078365\",\n \"0.529308\",\n \"0.5283356\",\n \"0.5271009\",\n \"0.52677727\",\n \"0.5266936\",\n \"0.5252997\",\n \"0.5238419\",\n \"0.5233305\",\n \"0.5231769\",\n \"0.5197301\",\n \"0.51828855\",\n \"0.5182773\",\n \"0.5162779\",\n \"0.51625335\",\n \"0.51475465\",\n \"0.51475465\",\n \"0.5142594\",\n \"0.51315063\",\n \"0.512747\",\n \"0.51260394\",\n \"0.5125916\",\n \"0.5114183\",\n \"0.51043624\",\n \"0.51041955\",\n \"0.5100643\",\n \"0.50856876\",\n \"0.5074678\",\n \"0.5065174\",\n \"0.505762\",\n \"0.5033035\",\n \"0.5021856\",\n \"0.5016422\",\n \"0.5001367\",\n \"0.49883187\",\n \"0.4975265\",\n \"0.49676546\",\n \"0.49675006\",\n \"0.496178\",\n \"0.4961175\",\n \"0.49468952\",\n \"0.49457493\",\n \"0.49444577\",\n \"0.4943917\",\n \"0.4943917\",\n \"0.49434912\",\n \"0.4930547\",\n \"0.49191317\",\n \"0.49191317\",\n \"0.49040225\",\n \"0.4885421\",\n \"0.48683232\",\n \"0.48602933\",\n \"0.484954\",\n \"0.4846732\",\n \"0.48448187\",\n \"0.4842286\",\n \"0.48397857\",\n \"0.4819495\",\n \"0.48171175\",\n \"0.4816521\",\n \"0.48144796\",\n \"0.4807072\",\n \"0.48000586\",\n \"0.4799887\",\n \"0.47929788\",\n \"0.47913298\",\n \"0.47909614\",\n \"0.47846982\",\n \"0.478406\",\n \"0.4783031\",\n \"0.47814113\",\n \"0.47791123\",\n \"0.4777555\",\n \"0.47668615\",\n \"0.47636095\",\n \"0.4758488\",\n \"0.47579035\",\n \"0.47576138\",\n \"0.47439644\",\n \"0.47438866\",\n \"0.474275\",\n \"0.4741818\",\n \"0.4741164\",\n \"0.4740029\",\n \"0.4732702\",\n \"0.47321844\",\n \"0.47295067\",\n \"0.47228393\",\n \"0.4722288\",\n \"0.47192174\",\n \"0.47192174\",\n \"0.47142357\",\n \"0.47127345\",\n \"0.47114635\",\n \"0.47105315\",\n \"0.4710372\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":690,"cells":{"query":{"kind":"string","value":"This method create a project in pivotal tracker"},"document":{"kind":"string","value":"def create_project():\n client = RequestManager()\n project_name = \"\".join(choices(string.ascii_letters + string.digits, k=10))\n client.set_method(\"POST\")\n client.set_endpoint(\"/projects\")\n body = {\"name\": project_name}\n client.set_body(json.dumps(body))\n response = client.execute_request()\n STORED_ID['project_id'] = response.json()['id']"},"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 project_create(project):\n client.project.create(project)","def test_create_project(self):\n pass","def test_create_project(self):\n pass","def test_create_project(self):\n pass","def create_project(self, **kwargs):\n save = kwargs.get('save', True) \n if kwargs.has_key('save'):\n del(kwargs['save'])\n\n index = self.object_index()\n defaults = dict(slug = \"test-project-%s\" % index,\n basecamp_url = \"https://foo.basecamphq.com/projects/%s/log\" % index)\n defaults.update(kwargs)\n p = Project(**defaults)\n\n if save:\n p.save()\n self.assert_(p.id)\n return p","def _create_project(self):\n request = {\n \"project\": {\n \"description\": \"description\",\n \"enabled\": True,\n \"name\": uuid.uuid4().hex,\n \"domain_id\": \"default\",\n }\n }\n response = self.client.post(PROJECT_PATH, data=json.dumps(request),\n headers=HEADERS)\n\n if response.status_code == 201:\n return response.json()\n else:\n raise SystemExit(\"Failed to create project.\")","def createProject(self, payLoad):\n\n uri = \"/v1/projects/\" \n response = self.client.post(uri, payLoad)\n return response","def test_create_project_request(self):\n pass","def ktrack_project(ktrack_instance):\n project = ktrack_instance.create(\"project\", {\"name\": \"My_Test_Project\"})\n return project","def project_created_handler(event):\n obj = event.obj\n # submit Project after creation\n obj.workflow.start()","def create_project_info(data):\n\t\n\tproject = ProjectInfo()\n\tproject.name = data['name']\n\tproject.description = data['description']\n\tproject.start_date = data['start_date']\n\tproject.end_date = data['end_date']\n\tproject.save()\n\tprint ('Inserted')\n\treturn True","def test_new_project_existing(self):\n\n project = fake_clients.FakeProject(name=\"test_project\")\n\n setup_identity_cache(projects=[project])\n\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n headers = {\n \"project_name\": \"test_project\",\n \"project_id\": \"test_project_id\",\n \"roles\": \"admin,member\",\n \"username\": \"test@example.com\",\n \"user_id\": \"test_user_id\",\n \"authenticated\": True,\n }\n new_task = Task.objects.all()[0]\n url = \"/v1/tasks/\" + new_task.uuid\n response = self.client.post(\n url, {\"approved\": True}, format=\"json\", headers=headers\n )\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(response.json(), {\"errors\": [\"actions invalid\"]})","def test_project_creation(self):\n title = 'Project title'\n code = 'SCW-12345'\n project = self.create_project(\n title=title,\n code=code,\n institution=self.institution,\n tech_lead=self.project_owner,\n category=self.category,\n funding_source=self.funding_source,\n )\n self.assertTrue(isinstance(project, Project))\n self.assertEqual(project.__str__(), code + ' - ' + title)\n self.assertEqual(project.status, Project.AWAITING_APPROVAL)\n self.assertEqual(project.title, title)\n self.assertEqual(project.code, code)\n self.assertTrue(project.awaiting_approval())","def test_create_project(self):\n yield self.nodes[0].overlay.create_project(\"test\", \"specpointer\", \"01-02-03\", 300, \"EUR\", 5)\n yield self.deliver_messages()\n\n # Node 2 should know about this app request now\n projects = self.nodes[1].overlay.persistence.get_projects()\n self.assertTrue(projects)\n self.assertEqual(projects[0]['id'], 1)","def post_project_create(self, resource_dict):\n pass","def test_projects_post(self):\n project = Project()\n response = self.client.open('/project-tracker/projects',\n method='POST',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def _create_project(org, project_name):\n project = Project(\n org=org,\n name=project_name\n )\n project.save()\n return project","def create_project(projectname):\n auth_id = request.get_json().get(\"auth_id\")\n storage_accesses = request.get_json().get(\"storage_accesses\", [])\n response = jsonify(\n admin.create_project(\n current_app.scoped_session(), projectname, auth_id, storage_accesses\n )\n )\n return response","def test_add_project(self):\n pass","def create_project(self, pool, project, arg):\n self.verify_pool(pool)\n svc = self.project_path % (pool, project)\n ret = self.rclient.get(svc)\n if ret.status != restclient.Status.OK:\n svc = self.projects_path % pool\n ret = self.rclient.post(svc, arg)\n if ret.status != restclient.Status.CREATED:\n exception_msg = (_('Error creating project: '\n '%(project)s on '\n 'pool: %(pool)s '\n 'return code: %(ret.status)d '\n 'message: %(ret.data)s.')\n % {'project': project,\n 'pool': pool,\n 'ret.status': ret.status,\n 'ret.data': ret.data})\n raise exception.ShareBackendException(msg=exception_msg)","def create_new_project(project_name, token=None):\n session = konfuzio_session(token)\n url = create_new_project_url()\n new_project_data = {\"name\": project_name}\n r = session.post(url=url, json=new_project_data)\n return r","def create_project(self, **kwargs):\n _url = f\"{self.base_url}/projects\"\n if \"name\" not in kwargs:\n raise ValueError(\"Parameter 'name' is mandatory\")\n return self.http_call(\"post\", _url, json_data=kwargs).json()","def test_new_project(self):\n\n setup_identity_cache()\n\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n headers = {\n \"project_name\": \"test_project\",\n \"project_id\": \"test_project_id\",\n \"roles\": \"admin,member\",\n \"username\": \"test@example.com\",\n \"user_id\": \"test_user_id\",\n \"authenticated\": True,\n }\n new_task = Task.objects.all()[0]\n url = \"/v1/tasks/\" + new_task.uuid\n response = self.client.post(\n url, {\"approved\": True}, format=\"json\", headers=headers\n )\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n self.assertEqual(response.json(), {\"notes\": [\"created token\"]})\n\n new_project = fake_clients.identity_cache[\"new_projects\"][0]\n self.assertEqual(new_project.name, \"test_project\")\n\n new_token = Token.objects.all()[0]\n url = \"/v1/tokens/\" + new_token.token\n data = {\"password\": \"testpassword\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def test_create_project(client, session, tokens):\n response = client.post(\n \"/projects\",\n json={\n \"name\": \"New Project\",\n \"organizations\": [],\n \"teams\": [],\n \"users\": [],\n },\n headers={\"Authorization\": f\"Bearer {tokens['write']}\"},\n )\n assert response.status_code == 201\n project_id = response.json[\"id\"]\n assert Project.query.filter(Project.id == project_id).count() == 1","def test_create_project_target_enabled(self):\n self.assertEqual(Project.objects.count(), 2)\n url = reverse('projectroles:api_project_create')\n post_data = {\n 'title': NEW_PROJECT_TITLE,\n 'type': PROJECT_TYPE_PROJECT,\n 'parent': str(self.category.sodar_uuid),\n 'description': 'description',\n 'readme': 'readme',\n 'public_guest_access': False,\n 'owner': str(self.user.sodar_uuid),\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 201, msg=response.content)\n self.assertEqual(Project.objects.count(), 3)","def create(self):\n \n # create the sequence structure by calling the self.project.create\n self.project.create()","def create(self, token: Any):\n params = [token, ]\n method = \"ProjectAPI.Create\"\n self.__add_request(method, params, lambda payload: Definition.from_json(payload))","def post(self, data):\n conn = pecan.request.db_conn\n try:\n project = db_models.Project(**data.as_dict())\n return conn.create_project(request.context, project)\n except Exception:\n LOG.exception('Fail to create project: %s' % data.as_dict())\n raise exception.ProjectCreateFailed(project_id=data.project_id,\n user_id=data.user_id)","def test_duplicate_tasks_new_project(self):\n\n setup_identity_cache()\n\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_409_CONFLICT)\n\n data = {\"project_name\": \"test_project_2\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)","def test_projects_id_post(self):\n project = Project()\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\n method='POST',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def create_project(self, project):\n\n with self._transaction.cursor() as cur:\n if project.project_id is not None:\n id_ = project.project_id\n else:\n cur.execute(\"SELECT MAX(project_id) + 1 \"\n \"FROM barcodes.project\")\n id_ = cur.fetchone()[0]\n\n query = f\"\"\"\n INSERT INTO barcodes.project\n ({PROJECT_FIELDS})\n VALUES (\n %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n %s, %s, %s, %s, %s, %s, %s);\"\"\"\n\n cur.execute(query,\n [id_, project.project_name, project.is_microsetta,\n project.bank_samples, project.plating_start_date,\n project.contact_name, project.additional_contact_name,\n project.contact_email, project.deadlines,\n project.num_subjects, project.num_timepoints,\n project.start_date, project.disposition_comments,\n project.collection, project.is_fecal,\n project.is_saliva, project.is_skin, project.is_blood,\n project.is_other, project.do_16s,\n project.do_shallow_shotgun, project.do_shotgun,\n project.do_rt_qpcr, project.do_serology,\n project.do_metatranscriptomics,\n project.do_mass_spec, project.mass_spec_comments,\n project.mass_spec_contact_name,\n project.mass_spec_contact_email,\n project.do_other,\n project.branding_associated_instructions,\n project.branding_status, project.subproject_name,\n project.alias, project.sponsor, project.coordination,\n project.is_active])\n\n # if we made it this far, all is well\n return id_","def newproject():\n log('Criando novo projeto', yellow)\n log('Cria a conta no bitbucket com o nome do projeto vázio que o script se encarregará do resto', red)\n\n conta = raw_input('Digite o nome do projeto: ')\n\n local('echo \"clonando projeto %s\"' % bitbucket_repository)\n local('git clone {0} {1}{2}'.format(bitbucket_repository, folder_project_local, conta))\n local('cd {0}{1}'.format(folder_project_local, conta))\n local('mkvirtualenv {0}'.format(conta))\n local('setvirtualenvproject')\n local('pip install -r requirements.txt')\n local('rm -rf {0}{1}/.git'.format(folder_project_local, conta))\n local('rm -rf README.md')\n local('git init')\n local('git remote add origin git@bitbucket.org:{0}/{1}.git'.format(bitbucket_user, conta))","def create_project(self, name):\n project = self._post('/projects', data={'name': name})\n self.create_project_hook(project['id'], self.webhook_url + name)\n return project","def create_keystone_v3_project(self, **kwargs):\n LOG_OBJ.debug(\"Creating the project.\")\n print self.project_info\n\n _url = \"http://\" + self.host_ip + \":35357/v3/projects\"\n _headers = {'x-auth-token': self.cloud_admin_info[\"token_domain\"],\n 'content-type': 'application/json'}\n\n _project_info = {\"project\": {}}\n for argument in [\"name\", \"description\", \"domain_id\",\n \"enabled\", \"disabled\"]:\n try:\n _project_info['project'].update(\n {argument: kwargs[argument]})\n except KeyError:\n pass\n _body = json.dumps(_project_info)\n response = self.request(\"POST\", _url, _headers, _body)\n\n if response is None:\n LOG_OBJ.error(\"No response from Server while creating project\")\n print (\"No response from Server while creating project\")\n return response\n\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\" Creating project Failed with status %s \"\n \"and error : %s\" % (response.status, response.data))\n print (\" Creating project Failed with status %s and error : %s\" %\n (response.status, response.data))\n return response.status\n\n output = json.loads(response.data)\n LOG_OBJ.info(\"Project details : %s \" % output)\n print (\"Project details : %s \" % output)\n return output['project']['id']","def create_project(cls, title, code, institution, tech_lead, category, funding_source):\n project = Project.objects.create(\n title=title,\n description='Project description',\n legacy_hpcw_id='HPCW-12345',\n legacy_arcca_id='ARCCA-12345',\n code=code,\n institution=institution,\n institution_reference='BW-12345',\n department='School of Chemistry',\n pi='Project Principal Investigator',\n tech_lead=tech_lead,\n category=category,\n funding_source=funding_source,\n start_date=datetime.datetime.now(),\n end_date=datetime.datetime.now() + datetime.timedelta(days=10),\n economic_user=True,\n requirements_software='None',\n requirements_gateways='None',\n requirements_training='None',\n requirements_onboarding='None',\n allocation_rse=True,\n allocation_cputime='1000000',\n allocation_memory='100',\n allocation_storage_home='5000',\n allocation_storage_scartch='1000',\n notes='Project notes',\n )\n return project","def create_projects(self):\n if self.gl is None or self.config is None:\n print(\"No config/Gitlab found, please run connect first.\")\n exit(1)\n else:\n print(\"Starting Project creation.\")\n gl = self.gl\n config = self.config\n for project in config[\"projects\"]:\n # get the import url\n imp_url = config[\"projects\"][project][\"import_url\"]\n\n # Set rights/members/protected master\n if config[\"projects\"][project][\"owner_conf\"][\"owner\"] == \"all_users\":\n for user in self.users:\n print(\"Importing \\'\" + imp_url + \"\\' for user \\'\" + user.username + \"\\'\")\n pj = user.projects.create({'name': project,\n 'user_id': user.id,\n 'access_level': gitlab.OWNER_ACCESS,\n 'import_url': imp_url})\n elif config[\"projects\"][project][\"owner_conf\"][\"owner\"] == \"user\":\n for user in self.users:\n if user.username == config[\"projects\"][project][\"owner_conf\"][\"name\"]:\n print(\"Importing \\'\" + imp_url + \"\\' for user \\'\" + user.username + \"\\'\")\n pj = user.projects.create({'name': project,\n 'user_id': user.id,\n 'Access_level': gitlab.OWNER_ACCESS,\n 'import_url': imp_url})\n elif config[\"projects\"][project][\"owner_conf\"][\"owner\"] == \"group\":\n for group in self.groups:\n if group.name == config[\"projects\"][project][\"owner_conf\"][\"name\"]:\n print(\"Importing \\'\" + imp_url + \"\\' for group \\'\" + group.name + \"\\'\")\n pj = group.projects.create({'name': project,\n 'namespace_id': group.id,\n 'import_url': imp_url})\n else:\n print(\"Project owner Config is wrong, aborting\")\n exit(1)\n # Delete protected Master Branch\n if config[\"projects\"][project][\"protect_master_branch\"] == \"False\":\n print(\"Removing Project master Branch protection\")\n pj.protectedbranches.delete('master')","def NewProject (projectname):\n\tif projectname == \"\" or projectname == None:\n\t\tnewprojcode(projectname)\n\telse:\n\t\tnewprojCode_withNamed()","def _post_project(prj=None):\n template_path = (os.path.join(\n os.path.split(__file__)[0], \"post_project_template.xml\"))\n with open(template_path, 'r') as file:\n template = Template(file.read())\n response_xml = template.render(\n name=f\"Project_TEST_{datetime.now()}\",\n open_date=str(datetime.today().date()),\n res_uri=f\"{LIMS_API.tools.api.host}researchers/1\")\n\n prj_response = LIMS_API.tools.api.post(\n f\"{LIMS_API.tools.api.host}projects\", response_xml)\n\n prj_response_soup = BeautifulSoup(\n prj_response, \"xml\").find(\"prj:project\")\n prj = api_types.Project(\n prj_response_soup.find(\"name\"),\n DEFAULT_RES,\n datetime.today().date(),\n [],\n prj_response_soup[\"uri\"])\n\n return prj","def make_project(id):\n return {\n \"type\": \"Project\",\n \"metrics\": [],\n \"tags\": [],\n \"id\": id,\n \"description\": \"\",\n \"applicant\": \"\",\n }","def _create_dummy_project(self,projectname=\"testproject\"):\n # Create three types of users that exist: Root, can do anything, \n # projectadmin, cam do things to a project he or she owns. And logged in\n # user \n \n #created in _create_main_project_and_root.\n root = self.root\n # non-root users are created as if they signed up through the project, \n # to maximize test coverage. \n \n # A user who has created a project\n projectadmin = self._create_random_user(\"projectadmin_\")\n \n testproject = self._create_comicsite_in_admin(projectadmin,projectname)\n create_page_in_admin(testproject,\"testpage1\")\n create_page_in_admin(testproject,\"testpage2\")\n \n # a user who explicitly signed up to testproject\n participant = self._create_random_user(\"participant_\")\n self._register(participant,testproject)\n \n # a user who only signed up but did not register to any project\n registered_user = self._create_random_user(\"comicregistered_\")\n \n #TODO: How to do this gracefully? \n return [testproject,root,projectadmin,participant,registered_user]","def run(opts, args):\n create_new_project()","def test_create_account_project(self, create):\n row = {'PROJ_NAME1': 'Some Proj', 'PROJ_NO': '121-212',\n 'SECTOR': 'IT'}\n sync.create_account(row, None)\n self.assertTrue(create.called)\n account, row, issue_map = create.call_args[0]\n self.assertEqual(account.name, 'Some Proj')\n self.assertEqual(account.code, '121-212')\n self.assertEqual(account.category, Account.PROJECT)\n self.assertEqual(0, len(Account.objects.filter(pk=account.pk)))","def project():","def project():","def project():","def add_project(project):\n print('add_project: ' + str(project))\n try_insert_or_update(models.projects.insert(), # pylint: disable=no-value-for-parameter\n [dict(\n name=project['name'], path=project['name'], active=True, user_id=current_user.id)])\n return","def create_project(self, name, description=None):\n description = description or ''\n data = self._run(\n url_path=\"projects/add\",\n name=name,\n description=description\n )\n return data['result']['project']['id']","def create_new_project(self,\n customer_name,\n contract_date,\n project_info,\n project_datest,\n project_dateend,\n project_budget,\n project_actst=None,\n project_actend=None,\n project_cost=None):\n\n customer_info = self.query_customer(cus_name=customer_name)\n\n if customer_info:\n # Search for project manager in the same region as the customer.\n customer_region_id = customer_info[0][1]\n get_employee_query = \"select employee.emp_id, emp_lname, emp_fname from employee, \" \\\n \"empskill, skill, region where employee.emp_id = \" \\\n \"empskill.emp_id and empskill.skill_id = \" \\\n \"skill.skill_id and skill.skill_descrpt = \" \\\n \"'Project Manager' and region.region_id = \" \\\n \"employee.region_id and region.region_id = '{}' \"\n try:\n self.dbCursor.execute(\n get_employee_query.format(customer_region_id))\n employee_info = self.dbCursor.fetchall()\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n if len(employee_info) == 0:\n ErrorMessageWindow(\"No suitable project manager found!\")\n else:\n if customer_info and employee_info:\n if len(customer_info) > 1:\n MultiRowScreen(customer_info, \"project\")\n else:\n cus_id = customer_info[0][0]\n emp_id = employee_info[0][0]\n optional_inputs = [project_actst, project_actend,\n project_cost]\n\n query = \"insert into project(cus_id, emp_id, proj_date, \" \\\n \"proj_descrpt, proj_estdatest, proj_estdateend, \" \\\n \"proj_estbudget) values ('{}', '{}', '{}', '{}', \" \\\n \"'{}', '{}', '{}') \".format(cus_id,\n emp_id,\n contract_date,\n project_info,\n project_datest,\n project_dateend,\n project_budget)\n\n yes_options = False\n for item in optional_inputs:\n if item != \"\":\n yes_options = True\n\n if yes_options is False:\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n option_names = [\"proj_actdatest\",\n \"proj_actdateend\",\n \"proj_actcost\"]\n options_index = []\n filled_options = []\n\n index = 0\n for item in optional_inputs:\n if item != \"\":\n options_index.append(index)\n filled_options.append(item)\n index += 1\n update_query = \"update project set \"\n\n j = 0\n for i in options_index:\n if j < len(filled_options) - 1:\n update_query += \"{}='{}', \".format(\n option_names[i], filled_options[j]\n )\n else:\n update_query += \"{}='{}' \".format(\n option_names[i], filled_options[j]\n )\n j += 1\n\n try:\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n\n self.dbCursor.execute(update_query)\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n ErrorMessageWindow(\"Customer not found!\")","def create(self, request):\n lot = Lot.objects.get(pk=request.data[\"lotId\"])\n\n project = Project()\n project.name = request.data[\"name\"]\n project.estimatedCost = request.data[\"estimatedCost\"]\n project.estimatedCompletionDate = request.data[\"estimatedCompletionDate\"]\n project.lotId = lot\n #projectNote=projectNote\n\n\n try:\n project.save()\n serializer = ProjectSerializer(project, context={'request': request}) #converting data into json\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except ValidationError as ex:\n return Response({\"reason\": ex.message}, status=status.HTTP_400_BAD_REQUEST)","def pre_project_create(self, resource_dict):\n pass","def create_project(self, project_name=None, check=True):\n project_name = project_name or next(utils.generate_ids('project'))\n page_projects = self._page_projects()\n page_projects.button_create_project.click()\n\n with page_projects.form_create_project as form:\n form.field_name.value = project_name\n form.submit()\n\n if check:\n self.close_notification('success')\n page_projects.table_projects.row(\n name=project_name).wait_for_presence()\n\n return project_name","def post_project():\n\n title = request.form.get('title')\n description = request.form.get('description')\n max_grade = request.form.get('max_grade')\n\n hackbright.make_new_project(title, description, max_grade)\n\n flash(\"Successfully added new project.\")\n\n return redirect(\"/project?title={}\".format(title))","def test_new_project_existing_project_new_user(self):\n setup_identity_cache()\n\n # create signup#1 - project1 with user 1\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n # Create signup#2 - project1 with user 2\n data = {\"project_name\": \"test_project\", \"email\": \"test2@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n headers = {\n \"project_name\": \"admin_project\",\n \"project_id\": \"admin_project_id\",\n \"roles\": \"admin,member\",\n \"username\": \"admin\",\n \"user_id\": \"admin_id\",\n \"authenticated\": True,\n }\n # approve signup #1\n new_task1 = Task.objects.all()[0]\n url = \"/v1/tasks/\" + new_task1.uuid\n response = self.client.post(\n url, {\"approved\": True}, format=\"json\", headers=headers\n )\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n self.assertEqual(response.json(), {\"notes\": [\"created token\"]})\n\n # Attempt to approve signup #2\n new_task2 = Task.objects.all()[1]\n url = \"/v1/tasks/\" + new_task2.uuid\n response = self.client.post(\n url, {\"approved\": True}, format=\"json\", headers=headers\n )\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(response.json(), {\"errors\": [\"actions invalid\"]})","def test_create_project_root(self):\n self.assertEqual(Project.objects.count(), 2)\n url = reverse('projectroles:api_project_create')\n post_data = {\n 'title': NEW_PROJECT_TITLE,\n 'type': PROJECT_TYPE_PROJECT,\n 'parent': None,\n 'description': 'description',\n 'readme': 'readme',\n 'public_guest_access': False,\n 'owner': str(self.user.sodar_uuid),\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 400)\n self.assertEqual(Project.objects.count(), 2)","def create_project(conn, project):\r\n sql = ''' INSERT INTO rq_table(id, date, co2x, o2xx, temp, humi, type)\r\n VALUES(%s,%s,%s,%s,%s,%s,%s) '''\r\n cur = conn.cursor()\r\n cur.execute(sql, project)\r\n conn.commit()\r\n return cur.lastrowid","def create_project_if_necessary(ctx, org_name, project_name, ):\n org = cmd.get_one_organization_by_name(\n client=ctx.obj, organization_name=org_name)\n pprint(cmd.ensure_project(\n client=ctx.obj, project_name=project_name, organization_id=org.id))","def newProject(self):\n dialog = NewProjectDialog()\n if not dialog.name is None and not dialog.path is None:\n self._app.createProject(str(dialog.name), str(dialog.path))","def test_create_project_from_template(self):\n project_new = self.project_template.take_template()\n\n self.assertTrue(project_new)","def create_pcpp(account, row, issue_map):\n country_name = row['LOCATION']\n country = Country.objects.filter(name__iexact=country_name).first()\n if not country:\n logging.getLogger('peacecorps.sync_accounting').warning(\n \"%s: Country does not exist: %s\", row['PROJ_NO'], row['LOCATION'])\n issue = issue_map.find(row['SECTOR'])\n if not issue and row['SECTOR'] != 'None':\n logging.getLogger('peacecorps.sync_accounting').warning(\n \"%s: Sector does not exist: %s\", row['PROJ_NO'], row['SECTOR'])\n\n if country and (issue or row['SECTOR'] == 'None'):\n set_balances(row, account)\n account.save()\n\n volunteername = row['PCV_NAME']\n if volunteername.startswith(row['STATE']):\n volunteername = volunteername[len(row['STATE']):].strip()\n\n summary = clean_description(row['SUMMARY'])\n sirtrevorobj = {\"data\": [{\"type\": \"text\", \"data\": {\"text\": summary}}]}\n description = json.dumps(sirtrevorobj)\n\n project = Project(\n title=row['PROJ_NAME1'], country=country, account=account,\n volunteername=volunteername, volunteerhomestate=row['STATE'],\n description=description\n )\n if issue:\n project.overflow = issue.account\n project.save()\n project.campaigns.add(issue)\n else:\n project.save()","def new_project(self, rootdir=None):\n if rootdir is None:\n rootdir = Ui.instance().select_directory(user.home)\n if not os.path.exists(rootdir):\n os.makedirs(rootdir)\n\n print 'Weld.new_project in ', rootdir\n project = Project(rootdir)\n\n project.save()\n self.project = project\n self.current_project_path = rootdir\n Ui.instance().set_resources_draggable(True)\n Ui.instance().show_status('new project created')","def create_project(self, project_name: str, domain_id: str = None, classification_type: str = None) -> Project:\n if not self.validate():\n raise SettingCustomVisionAccessFailed\n trainer = self.get_trainer_obj()\n if not domain_id:\n domain_id = self.obj_detection_domain_id if domain_id is None else domain_id\n logger.info(\"Creating object detection project.\")\n try:\n project = trainer.create_project(name=project_name, domain_id=domain_id, classification_type=classification_type)\n return project\n except CustomVisionErrorException:\n raise SettingCustomVisionCannotCreateProject","def save_project(uid, song_notes, author_name, creation_date, project_name):","def testSessionCreate(self):\n success = False\n project = None\n\n try:\n project = self.session.create_project()\n\n success = True\n except Exception:\n pass\n\n self.failUnless(success)\n self.failIf(project is None)","def create_project(data):\n try:\n CheckConnection()\n with Connection.cursor() as cursor:\n name = str(data[\"name\"])\n end_date = str(data[\"enddate\"])\n team_size = str(data[\"teamsize\"])\n budget = str(data[\"budget\"])\n tools = str(data[\"tools\"])\n priority = str(data[\"priority\"])\n is_assignment_complete = str(0)\n\n query = \"INSERT INTO Project (ProjectName,ProjectEndDate,ProjectTeamSize,Budget,Tools,Priority,IsAssignmentComplete) VALUES (%s,%s,%s,%s,%s,%s,%s);\"\n cursor.execute(\n query,\n (\n name,\n end_date,\n team_size,\n budget,\n tools,\n priority,\n is_assignment_complete,\n ),\n )\n Connection.commit()\n return True\n\n except conn.Error as error:\n print(\"Failed to update record to database rollback: {}\".format(error))\n Connection.rollback()\n return False","async def create(self, ctx, name: str,\n owner: discord.Member = None) -> discord.Message:\n if ctx.projects.find_project(name):\n project = ctx.projects.find_project(name)\n if ctx.guild.get_Channel(int(project.get(\"channel\"))):\n return await ctx.send(\"A project with that name exists.\")\n else:\n await ctx.send(\"A project with this name exists but, a related\"\n \" project channel was not found. \"\n \"I will be overwriting the previous project.\")\n ctx.projects.delete_project(name)\n\n owner = owner if owner else ctx.author\n if not ctx.bot.db(\"guilds\").find(str(ctx.guild.id)):\n ctx.bot.db(\"guilds\").insert(str(ctx.guild.id), ctx.bot.empty_guild)\n\n # await ctx.send(\"Creating project channel...\")\n if not ctx.bot.db(\"guilds\").find(\n str(ctx.guild.id)).get(\"project_category\"):\n overwrites = {\n ctx.guild.default_role: discord.PermissionOverwrite(\n read_messages=False\n ),\n ctx.me: discord.PermissionOverwrite(\n read_messages=True,\n send_messages=True,\n manage_channels=True\n )\n }\n category = await ctx.guild.create_category(\"Flux Projects\",\n overwrites=overwrites)\n ctx.bot.db(\"guilds\").update(str(ctx.guild.id), {\n \"project_category\": str(category.id)})\n\n else:\n category = ctx.guild.get_channel(\n int(ctx.bot.db(\"guilds\").find(\n str(ctx.guild.id)).get(\"project_category\")))\n\n overwrites = {owner: discord.PermissionOverwrite(read_messages=True,\n send_messages=False,\n add_reactions=True),\n ctx.me: discord.PermissionOverwrite(read_messages=True,\n send_messages=True),\n ctx.guild.default_role: discord.PermissionOverwrite(\n read_messages=False)}\n\n channel = await ctx.guild.create_text_channel(f\"{name}-project\",\n category=category,\n overwrites=overwrites)\n await channel.send(f\"Project Owner: {owner}\")\n message = await channel.send(self.empty_progress_bar)\n await message.pin()\n res = ctx.projects.create_project(\n owner.id, owner.id, name, channel.id, message.id)\n if not res:\n return await ctx.send(\"An error has occurred. Use `.contact`\"\n \" with error: `ERR_PROJECT_STILL_EXISTS`\")\n return await ctx.send(\"Project created!\")","def test_create_project_target_remote(self):\n # Create source site\n source_site = self.make_site(\n name=REMOTE_SITE_NAME,\n url=REMOTE_SITE_URL,\n mode=SITE_MODE_SOURCE,\n description=REMOTE_SITE_DESC,\n secret=REMOTE_SITE_SECRET,\n )\n # Make category remote\n self.make_remote_project(\n project_uuid=self.category.sodar_uuid,\n project=self.category,\n site=source_site,\n level=SODAR_CONSTANTS['REMOTE_LEVEL_READ_ROLES'],\n )\n self.assertEqual(Project.objects.count(), 2)\n\n url = reverse('projectroles:api_project_create')\n post_data = {\n 'title': NEW_PROJECT_TITLE,\n 'type': PROJECT_TYPE_PROJECT,\n 'parent': str(self.category.sodar_uuid),\n 'description': 'description',\n 'readme': 'readme',\n 'public_guest_access': False,\n 'owner': str(self.user.sodar_uuid),\n }\n response = self.request_knox(url, method='POST', data=post_data)\n\n self.assertEqual(response.status_code, 403, msg=response.content)\n self.assertEqual(Project.objects.count(), 2)","def __init_project(self, presenter):\n # Get the project id correponding to the input presenter\n # (if it does not exists, create a new one)\n if presenter == None:\n raise Exception(\"Cannot publish tasks without specifying a presenter.\"\n \"Please use set_presenter() to specify a presenter.\")\n pbclient = self.cc.pbclient\n p = None\n #try:\n if len(pbclient.find_project(short_name = presenter.short_name)) > 0:\n # the presenter has been created\n p= pbclient.find_project(short_name = presenter.short_name)[0]\n elif len(pbclient.find_project(name = presenter.name)) > 0:\n # the presenter has been created\n p= pbclient.find_project(name = presenter.name)[0]\n else:\n # create a new project with the presente\n p = pbclient.create_project(presenter.name, presenter.short_name, presenter.description)\n\n try:\n self.project_id = p.id\n self.project_short_name = presenter.short_name\n self.project_name = presenter.name\n p= pbclient.find_project(short_name = presenter.short_name)[0]\n p.info['task_presenter'] = Template(presenter.template).safe_substitute(short_name = presenter.short_name, question = presenter.question)\n p.long_description = presenter.description\n p.name = presenter.name\n p.short_name = presenter.short_name\n pbclient.update_project(p)\n except:\n if type(p) is dict and \"exception_msg\" in p.keys():\n raise Exception(\"%s\" %(p[\"exception_msg\"]))\n else:\n print p\n raise","def test_save(self):\n p = self.create_project(save=False,\n basecamp_url='https://foo.basecamphq.com/projects/1701/log/',\n basecamp_id=None,\n name='')\n p.save()\n self.assertEqual(1701, p.basecamp_id)\n self.assertEqual(\"Kobol's Last Gleaming\", p.name)","def test_create_project_unknown_user(self):\n self.assertEqual(Project.objects.count(), 2)\n url = reverse('projectroles:api_project_create')\n post_data = {\n 'title': NEW_PROJECT_TITLE,\n 'type': PROJECT_TYPE_PROJECT,\n 'parent': str(self.category.sodar_uuid),\n 'description': 'description',\n 'readme': 'readme',\n 'public_guest_access': False,\n 'owner': INVALID_UUID,\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 400)\n self.assertEqual(Project.objects.count(), 2)","def create_project(self, conn, name, description=\"\"):\n group = conn.group.allocate(name, description)\n # returns Project object\n return group","def create(index):\n # Get the project root\n project_root = get_project_root()\n package_name = os.path.basename(project_root)\n logging.info(\"Creating package for current project: \" + package_name)\n Packager(package_name, project_root).create(index)","def create(self, project_name: str):\n \n #check naming-convention\n Utility.matchNamingConvention(project_name)\n\n projects_folder = self.config.projects_folder\n\n #check if folder already existsts\n Utility.checkNotOccupied(project_name, projects_folder)\n\n target_path = projects_folder+project_name\n\n project_godot_file_path = target_path+'/project.godot'\n\n os.mkdir(target_path)\n os.makedirs(target_path+'/bin/plugins', exist_ok=True)\n os.mknod(project_godot_file_path)\n\n project_godot_file = open(project_godot_file_path, mode='w')\n project_godot_file.write('[application]\\n\\nconfig/name=\"'+project_name+'\"\\n')","def test_new_project_existing_user(self):\n\n user = fake_clients.FakeUser(\n name=\"test@example.com\", password=\"123\", email=\"test@example.com\"\n )\n\n setup_identity_cache(users=[user])\n\n # unauthenticated sign up as existing user\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": user.email}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n # approve the sign-up as admin\n headers = {\n \"project_name\": \"admin_project\",\n \"project_id\": \"admin_project_id\",\n \"roles\": \"admin,member\",\n \"username\": \"admin\",\n \"user_id\": \"admin_id\",\n \"authenticated\": True,\n }\n new_task = Task.objects.all()[0]\n url = \"/v1/tasks/\" + new_task.uuid\n response = self.client.post(\n url, {\"approved\": True}, format=\"json\", headers=headers\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.json(), {\"notes\": [\"Task completed successfully.\"]})","def create_project_in_gme(self, active_node, projectName, contract_term, series):\n core = self.core\n projectMeta = self.META[\"Project\"]\n projectNode = core.create_child(active_node, projectMeta)\n core.set_attribute(projectNode, \"name\", projectName)\n core.set_attribute(projectNode, \"date\", date.today().strftime(\"%d/%m/%Y\"))\n core.set_attribute(projectNode, \"contract term\", int(contract_term))\n core.set_attribute(projectNode, \"series\", series)\n return projectNode","def test_fields_on_new_project(new_project) -> None:\n\n # assert isinstance(new_project.id, int) # not created yet, hasnt been committed\n assert isinstance(new_project.token, uuid.UUID)\n assert new_project.title == \"Lord of the Rings\"\n assert new_project.email == \"J. R. R. Tolkien\"\n assert new_project.phone == \"5558675309\"\n assert new_project.verification is None","def create_project(name=None, defaultJobTimeoutMinutes=None):\n pass","def test_projects_put(self):\n project = Project()\n response = self.client.open('/project-tracker/projects',\n method='PUT',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def create_new_project():\n readline.parse_and_bind('tab: complete')\n\n print \\\n\"\"\"\n xbmcswift2 - A micro-framework for creating XBMC plugins.\n xbmc@jonathanbeluch.com\n --\n\"\"\"\n print 'I\\'m going to ask you a few questions to get this project' \\\n ' started.'\n\n # noinspection PyDictCreation\n opts = {}\n\n # Plugin Name\n opts['plugin_name'] = get_valid_value(\n 'What is your plugin name?',\n validate_nonblank\n )\n\n # Plugin ID\n opts['plugin_id'] = get_valid_value(\n 'Enter your plugin id.',\n validate_pluginid,\n 'plugin.video.%s' % (opts['plugin_name'].lower().replace(' ', ''))\n )\n\n # Parent Directory\n opts['parent_dir'] = get_valid_value(\n 'Enter parent folder (where to create project)',\n validate_isfolder,\n getcwd()\n )\n opts['plugin_dir'] = os.path.join(opts['parent_dir'], opts['plugin_id'])\n assert not os.path.isdir(opts['plugin_dir']), \\\n 'A folder named %s already exists in %s.' % (opts['plugin_id'],\n opts['parent_dir'])\n\n # Provider\n opts['provider_name'] = get_valid_value(\n 'Enter provider name',\n validate_nonblank,\n )\n\n # Create the project folder by copying over skel\n copytree(SKEL, opts['plugin_dir'], ignore=ignore_patterns('*.pyc'))\n\n # Walk through all the new files and fill in with out options\n for root, dirs, files in os.walk(opts['plugin_dir']):\n for filename in files:\n update_file(os.path.join(root, filename), opts)\n\n print 'Projects successfully created in %s.' % opts['plugin_dir']\n print 'Done.'","def init_project(self,project_name,project_dir):\n projectkey = id_generator(10)\n if \"towercrane\" not in os.listdir(project_dir):\n print(f'Initializing project:\"{project_name}\" with projectkey: \"{projectkey}\" ')\n self.TowercraneConfig = {\"project_name\":project_name,\n \"projectkey\":projectkey,\n \"publicurl\":\"private_project\"\n }\n write_config(project_dir,self.TowercraneConfig)\n project_insert_report = self.db.create_project(project_name,project_dir,projectkey)\n print(project_insert_report)\n \n elif \"towercrane\" in os.listdir(project_dir):\n self.TowercraneConfig = read_config(project_dir)\n print(f'project:\"{self.TowercraneConfig[\"project_name\"]}\" with projectkey: \"{self.TowercraneConfig[\"projectkey\"]}\" Already Exists')","def createproject(project_name):\n app_clone_script = 'git clone https://github.com/jaarce/falcon-bp.git %s' % project_name\n subprocess.call(app_clone_script.split(' '))","def test_user_can_create_a_project(self):\n self.assertEqual(project_model.Project.objects.get(user=self.test_user).pk, self.test_project.pk)","def test_create_project_target_disabled(self):\n self.assertEqual(Project.objects.count(), 2)\n url = reverse('projectroles:api_project_create')\n post_data = {\n 'title': NEW_PROJECT_TITLE,\n 'type': PROJECT_TYPE_PROJECT,\n 'parent': str(self.category.sodar_uuid),\n 'description': 'description',\n 'readme': 'readme',\n 'public_guest_access': False,\n 'owner': str(self.user.sodar_uuid),\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 403, msg=response.content)\n self.assertEqual(Project.objects.count(), 2)","def create(self, request, *args, **kwargs):\n project = Project.objects.get(id=kwargs[\"projects_pk\"])\n self.check_object_permissions(request, project)\n\n serializer = self.get_serializer(data=request.data)\n if serializer.is_valid():\n serializer.save(permission=\"contributor\", role=\"Contributor\")\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def test_new_project_invalid_on_submit(self):\n\n setup_identity_cache()\n\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n headers = {\n \"project_name\": \"test_project\",\n \"project_id\": \"test_project_id\",\n \"roles\": \"admin,member\",\n \"username\": \"test@example.com\",\n \"user_id\": \"test_user_id\",\n \"authenticated\": True,\n }\n new_task = Task.objects.all()[0]\n url = \"/v1/tasks/\" + new_task.uuid\n response = self.client.post(\n url, {\"approved\": True}, format=\"json\", headers=headers\n )\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n self.assertEqual(response.data, {\"notes\": [\"created token\"]})\n self.assertEqual(len(mail.outbox), 3)\n\n fake_clients.identity_cache[\"projects\"] = {}\n\n new_token = Token.objects.all()[0]\n url = \"/v1/tokens/\" + new_token.token\n data = {\"password\": \"testpassword\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(len(mail.outbox), 3)","def test_notification_CreateProjectAndUser(self):\n setup_identity_cache()\n\n url = \"/v1/actions/CreateProjectAndUser\"\n data = {\"project_name\": \"test_project\", \"email\": \"test@example.com\"}\n response = self.client.post(url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n\n new_task = Task.objects.all()[0]\n\n headers = {\n \"project_name\": \"test_project\",\n \"project_id\": \"test_project_id\",\n \"roles\": \"admin,member\",\n \"username\": \"test@example.com\",\n \"user_id\": \"test_user_id\",\n \"authenticated\": True,\n }\n\n url = \"/v1/notifications\"\n response = self.client.get(url, headers=headers)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.json()[\"notifications\"][0][\"task\"], new_task.uuid)","def create_project(opts):\n if opts['django']:\n structure.create_django_proj(opts)\n if opts['cookiecutter_template']:\n structure.create_cookiecutter(opts)\n proj_struct = structure.make_structure(opts)\n structure.create_structure(proj_struct,\n update=opts['update'] or opts['force'])\n if not opts['update'] and not repo.is_git_repo(opts['project']):\n repo.init_commit_repo(opts['project'], proj_struct)","def create_new_python_project():\n\t# Create the different variables\n\tfolder_name = str(sys.argv[1])\n\tdir_name = my_project_folder + folder_name\n\tpy_file = dir_name + '/' + folder_name + '.py'\n\treadme_file = dir_name + '/' + 'README.md'\n\ttodo_file = dir_name + '/' + 'TODO.txt'\n\n\t# Create directory if it does not exist yet\n\tif not os.path.exists(dir_name):\n\t\tos.mkdir(dir_name)\n\t\tprint(\"Directory \" , dir_name , \" Created \")\n\n\t\t# Create Python file\n\t\tdata = ''\n\t\twith open(template_py, 'r') as file:\n\t\t\tdata += file.read()\n\n\t\twith open(py_file, 'w') as f:\n\t\t\tf.write(data)\n\t\t\tprint(\"Python file created\")\n\n\t\t# Create README file\n\t\tdata = ''\n\t\twith open(template_readme, 'r') as file:\n\t\t\tdata += file.read()\n\n\t\twith open(readme_file, 'w') as f:\n\t\t\tf.write(data)\n\t\t\tprint(\"Readme file created\")\n\n\t\t# Create Todo file\n\t\twith open(todo_file, 'w') as f:\n\t\t\tprint(\"TODO file created\")\n\n\t\t# Create Github repo\n\t\twith open(\".env\", \"r\") as f:\n\t\t\tdata = f.read()\n\n\t\tindex_1 = data.find('TOKEN=\"') + len('TOKEN=\"')\n\t\ttoken = data[index_1:-1]\n\t\tg = Github(token)\n\t\tuser = g.get_user()\n\t\trepo = user.create_repo(folder_name)\n\t\tprint(\"Succesfully created repository {}\".format(folder_name))\n\n\n\telse: \n\t\tprint(\"Directory \" , dir_name , \" already exists\")","def create_project(conn, project):\n sql = ''' INSERT INTO projects(name,begin_date,end_date)\n VALUES(?,?,?) '''\n cur = conn.cursor()\n cur.execute(sql, project)\n return cur.lastrowid","def create():","def create():","def _create_namespace(self):\n self.ocp.new_project(self.namespace)","def setup_project(client, project_template, do_auth=True):\n client = deepcopy(client)\n email = \"test@gmail.com\"\n password = \"test\"\n urls = URLS()\n project_config = project_template()\n\n # we work in empty database, so let's create business user and login\n user = User.objects.create(email=email)\n user.set_password(password) # set password without hash\n\n create_business(user)\n org = Organization.create_organization(created_by=user, title=user.first_name)\n user.active_organization = org\n user.save()\n\n if do_auth:\n\n assert signin(client, email, password).status_code == 302\n # create project\n with requests_mock.Mocker() as m:\n m.register_uri('POST', re.compile(r'ml\\.heartex\\.net/\\d+/validate'), text=json.dumps({'status': 'ok'}))\n m.register_uri('GET', re.compile(r'ml\\.heartex\\.net/\\d+/health'), text=json.dumps({'status': 'UP'}))\n r = client.post(urls.project_create, data=project_config)\n print('Project create with status code:', r.status_code)\n assert r.status_code == 201, f'Create project result should be redirect to the next page'\n\n # get project id and prepare url\n project = Project.objects.filter(title=project_config['title']).first()\n urls.set_project(project.pk)\n print('Project id:', project.id)\n\n client.project = project\n\n client.user = user\n client.urls = urls\n client.project_config = project_config\n client.org = org\n return client","def add_project(project, network, id):\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.add_project(project, network, id)\n if ret[constants.STATUS_CODE_KEY] == 200:\n click.echo(\"Success\")\n else:\n click.echo(ret[constants.MESSAGE_KEY])","def CreateProject(projectName='project'):\r\n projectName = input('''The project's name: ''')\r\n if not os.path.exists(projectName):\r\n os.mkdir(projectName)\r\n else:\r\n print('There is a file with the same name.')\r\n\r\n for dir in ['OPT', 'SCF', 'PHO']:\r\n if not os.path.exists(projectName + os.sep + dir):\r\n os.mkdir(projectName + os.sep + dir)","def project(projectname,targetamount):\n if (validatename(projectname) and validatenum(targetamount)):\n targetamount=float(targetamount)\n con = lite.connect(databasefile)\n with con:\n cur = con.cursor() \n cur.execute(\"SELECT Id FROM projects where name=?\", (projectname,))\n exists = cur.fetchone()\n if exists:\n click.echo(\"Project name already exists!\")\n sys.exit()\n cur.execute(\"INSERT INTO projects (Name, Tamount) VALUES (?, ?)\", (projectname, targetamount))\n click.echo(\"Added %s project with target of $%-.2f\" % (projectname, targetamount))","def test_create(self):\n\n responses.add(\n responses.POST,\n self.host + \"/manager\",\n json={\"path\": \"manager?project=ProjectTest\", \"action\": \"redirect\", \"status\": \"success\"},\n status=200\n )\n\n self.azk.create(self.project, self.description)","def add_project(self, project):\n c = self.conn.cursor()\n cursor = c.execute(\"INSERT INTO projects VALUES (null, ?, ?, ?, ?)\", (project['owner'],\n project['title'],\n datetime.now(), datetime.now(),))\n\n self.conn.commit()\n project_id = cursor.lastrowid\n\n self.conn.cursor().execute(\"INSERT INTO users_projects VALUES (?,?)\", (project['owner'], project_id),)\n self.conn.commit()\n return self.get_project(project_id)","async def create_project_flow(\n create_project_flow_request: CreateProjectFlow,\n token: str = Depends(oauth2_scheme),\n):\n try:\n logging.info(\"Calling /osairis/project_flow/create endpoint\")\n logging.debug(f\"Request: {create_project_flow_request}\")\n if decodeJWT(token=token):\n response = ProjectFlowController().create_project_flow_controller(\n request=create_project_flow_request\n )\n return CreateProjectFlowResponse(**response)\n else:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Invalid access token\",\n headers={\"WWW-Authenticate\": \"Bearer\"},\n )\n except Exception as error:\n logging.error(f\"Error in /osairis/project_flow/create endpoint: {error}\")\n raise error","def create(self, values):\n TYPE = self.env['anytracker.ticket.type']\n SEQUENCE = self.env['ir.sequence']\n types = TYPE.search([('code', '=', 'node')])\n values.update({\n 'number': SEQUENCE.sudo().next_by_code('anytracker.ticket')})\n if values.get('parent_id'):\n values['project_id'] = self.browse(\n values['parent_id']).project_id.id\n\n # project creation: auto-assign the 'node' type\n if not values.get('parent_id') and types:\n values['type'] = types[0].id\n\n # add myself to the project at creation\n if not values.get('parent_id'):\n p_ids = values.get('participant_ids', [(6, 0, [])])\n if self.env.uid not in p_ids:\n values['participant_ids'] = [\n (6, 0, [self.env.uid] + p_ids[0][2])]\n\n # replace ticket numbers with permalinks\n if 'description' in values:\n values['description'] = add_permalinks(\n self.env.cr.dbname, values['description'])\n\n ticket = super(Ticket, self).create(values)\n\n if not values.get('parent_id'):\n ticket.write({'project_id': ticket.id})\n\n # turn the parent into a node\n if 'parent_id' in values and values['parent_id'] and types:\n ticket.browse(values['parent_id']).write({'type': types[0].id})\n\n # subscribe the followers of the parent,\n # or the participants if this is a project\n # This allows to subscribe or unsubscribe to ticket subtrees\n if ticket.project_id.participant_ids:\n if ticket.parent_id:\n ticket.message_subscribe(\n ticket.parent_id.message_follower_ids.ids)\n else:\n ticket.message_subscribe_users(\n ticket.participant_ids.ids)\n\n return ticket","def save(self, project_id=None):\r\n if project_id is not None:\r\n project = Project.objects.get(pk=int(project_id))\r\n else:\r\n project = Project()\r\n # Fill out the data of the given project and prepare it\r\n # for saving into database.\r\n project.Name = self.cleaned_data['name']\r\n project.ProjectClient = self.cleaned_data['project_client']\r\n project.Start = self.cleaned_data['start']\r\n project.End = self.cleaned_data['end']\r\n project.ProjectManager = self.cleaned_data['project_manager']\r\n project.QualityAssurance = self.cleaned_data['quality_assurance']\r\n project.Price = self.cleaned_data['price']\r\n project.Segment = self.cleaned_data['segment']\r\n project.Type = self.cleaned_data['type']\r\n project.save()\r\n # If the item was just created, set up workflow for it\r\n if project_id is None:\r\n workflow = Workflow.objects.get(name='Project')\r\n utils.set_workflow(project, workflow)\r\n state = utils.get_state(project)\r\n project.Status = state\r\n project.save()\r\n return project"],"string":"[\n \"def project_create(project):\\n client.project.create(project)\",\n \"def test_create_project(self):\\n pass\",\n \"def test_create_project(self):\\n pass\",\n \"def test_create_project(self):\\n pass\",\n \"def create_project(self, **kwargs):\\n save = kwargs.get('save', True) \\n if kwargs.has_key('save'):\\n del(kwargs['save'])\\n\\n index = self.object_index()\\n defaults = dict(slug = \\\"test-project-%s\\\" % index,\\n basecamp_url = \\\"https://foo.basecamphq.com/projects/%s/log\\\" % index)\\n defaults.update(kwargs)\\n p = Project(**defaults)\\n\\n if save:\\n p.save()\\n self.assert_(p.id)\\n return p\",\n \"def _create_project(self):\\n request = {\\n \\\"project\\\": {\\n \\\"description\\\": \\\"description\\\",\\n \\\"enabled\\\": True,\\n \\\"name\\\": uuid.uuid4().hex,\\n \\\"domain_id\\\": \\\"default\\\",\\n }\\n }\\n response = self.client.post(PROJECT_PATH, data=json.dumps(request),\\n headers=HEADERS)\\n\\n if response.status_code == 201:\\n return response.json()\\n else:\\n raise SystemExit(\\\"Failed to create project.\\\")\",\n \"def createProject(self, payLoad):\\n\\n uri = \\\"/v1/projects/\\\" \\n response = self.client.post(uri, payLoad)\\n return response\",\n \"def test_create_project_request(self):\\n pass\",\n \"def ktrack_project(ktrack_instance):\\n project = ktrack_instance.create(\\\"project\\\", {\\\"name\\\": \\\"My_Test_Project\\\"})\\n return project\",\n \"def project_created_handler(event):\\n obj = event.obj\\n # submit Project after creation\\n obj.workflow.start()\",\n \"def create_project_info(data):\\n\\t\\n\\tproject = ProjectInfo()\\n\\tproject.name = data['name']\\n\\tproject.description = data['description']\\n\\tproject.start_date = data['start_date']\\n\\tproject.end_date = data['end_date']\\n\\tproject.save()\\n\\tprint ('Inserted')\\n\\treturn True\",\n \"def test_new_project_existing(self):\\n\\n project = fake_clients.FakeProject(name=\\\"test_project\\\")\\n\\n setup_identity_cache(projects=[project])\\n\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n headers = {\\n \\\"project_name\\\": \\\"test_project\\\",\\n \\\"project_id\\\": \\\"test_project_id\\\",\\n \\\"roles\\\": \\\"admin,member\\\",\\n \\\"username\\\": \\\"test@example.com\\\",\\n \\\"user_id\\\": \\\"test_user_id\\\",\\n \\\"authenticated\\\": True,\\n }\\n new_task = Task.objects.all()[0]\\n url = \\\"/v1/tasks/\\\" + new_task.uuid\\n response = self.client.post(\\n url, {\\\"approved\\\": True}, format=\\\"json\\\", headers=headers\\n )\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(response.json(), {\\\"errors\\\": [\\\"actions invalid\\\"]})\",\n \"def test_project_creation(self):\\n title = 'Project title'\\n code = 'SCW-12345'\\n project = self.create_project(\\n title=title,\\n code=code,\\n institution=self.institution,\\n tech_lead=self.project_owner,\\n category=self.category,\\n funding_source=self.funding_source,\\n )\\n self.assertTrue(isinstance(project, Project))\\n self.assertEqual(project.__str__(), code + ' - ' + title)\\n self.assertEqual(project.status, Project.AWAITING_APPROVAL)\\n self.assertEqual(project.title, title)\\n self.assertEqual(project.code, code)\\n self.assertTrue(project.awaiting_approval())\",\n \"def test_create_project(self):\\n yield self.nodes[0].overlay.create_project(\\\"test\\\", \\\"specpointer\\\", \\\"01-02-03\\\", 300, \\\"EUR\\\", 5)\\n yield self.deliver_messages()\\n\\n # Node 2 should know about this app request now\\n projects = self.nodes[1].overlay.persistence.get_projects()\\n self.assertTrue(projects)\\n self.assertEqual(projects[0]['id'], 1)\",\n \"def post_project_create(self, resource_dict):\\n pass\",\n \"def test_projects_post(self):\\n project = Project()\\n response = self.client.open('/project-tracker/projects',\\n method='POST',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def _create_project(org, project_name):\\n project = Project(\\n org=org,\\n name=project_name\\n )\\n project.save()\\n return project\",\n \"def create_project(projectname):\\n auth_id = request.get_json().get(\\\"auth_id\\\")\\n storage_accesses = request.get_json().get(\\\"storage_accesses\\\", [])\\n response = jsonify(\\n admin.create_project(\\n current_app.scoped_session(), projectname, auth_id, storage_accesses\\n )\\n )\\n return response\",\n \"def test_add_project(self):\\n pass\",\n \"def create_project(self, pool, project, arg):\\n self.verify_pool(pool)\\n svc = self.project_path % (pool, project)\\n ret = self.rclient.get(svc)\\n if ret.status != restclient.Status.OK:\\n svc = self.projects_path % pool\\n ret = self.rclient.post(svc, arg)\\n if ret.status != restclient.Status.CREATED:\\n exception_msg = (_('Error creating project: '\\n '%(project)s on '\\n 'pool: %(pool)s '\\n 'return code: %(ret.status)d '\\n 'message: %(ret.data)s.')\\n % {'project': project,\\n 'pool': pool,\\n 'ret.status': ret.status,\\n 'ret.data': ret.data})\\n raise exception.ShareBackendException(msg=exception_msg)\",\n \"def create_new_project(project_name, token=None):\\n session = konfuzio_session(token)\\n url = create_new_project_url()\\n new_project_data = {\\\"name\\\": project_name}\\n r = session.post(url=url, json=new_project_data)\\n return r\",\n \"def create_project(self, **kwargs):\\n _url = f\\\"{self.base_url}/projects\\\"\\n if \\\"name\\\" not in kwargs:\\n raise ValueError(\\\"Parameter 'name' is mandatory\\\")\\n return self.http_call(\\\"post\\\", _url, json_data=kwargs).json()\",\n \"def test_new_project(self):\\n\\n setup_identity_cache()\\n\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n headers = {\\n \\\"project_name\\\": \\\"test_project\\\",\\n \\\"project_id\\\": \\\"test_project_id\\\",\\n \\\"roles\\\": \\\"admin,member\\\",\\n \\\"username\\\": \\\"test@example.com\\\",\\n \\\"user_id\\\": \\\"test_user_id\\\",\\n \\\"authenticated\\\": True,\\n }\\n new_task = Task.objects.all()[0]\\n url = \\\"/v1/tasks/\\\" + new_task.uuid\\n response = self.client.post(\\n url, {\\\"approved\\\": True}, format=\\\"json\\\", headers=headers\\n )\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n self.assertEqual(response.json(), {\\\"notes\\\": [\\\"created token\\\"]})\\n\\n new_project = fake_clients.identity_cache[\\\"new_projects\\\"][0]\\n self.assertEqual(new_project.name, \\\"test_project\\\")\\n\\n new_token = Token.objects.all()[0]\\n url = \\\"/v1/tokens/\\\" + new_token.token\\n data = {\\\"password\\\": \\\"testpassword\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def test_create_project(client, session, tokens):\\n response = client.post(\\n \\\"/projects\\\",\\n json={\\n \\\"name\\\": \\\"New Project\\\",\\n \\\"organizations\\\": [],\\n \\\"teams\\\": [],\\n \\\"users\\\": [],\\n },\\n headers={\\\"Authorization\\\": f\\\"Bearer {tokens['write']}\\\"},\\n )\\n assert response.status_code == 201\\n project_id = response.json[\\\"id\\\"]\\n assert Project.query.filter(Project.id == project_id).count() == 1\",\n \"def test_create_project_target_enabled(self):\\n self.assertEqual(Project.objects.count(), 2)\\n url = reverse('projectroles:api_project_create')\\n post_data = {\\n 'title': NEW_PROJECT_TITLE,\\n 'type': PROJECT_TYPE_PROJECT,\\n 'parent': str(self.category.sodar_uuid),\\n 'description': 'description',\\n 'readme': 'readme',\\n 'public_guest_access': False,\\n 'owner': str(self.user.sodar_uuid),\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 201, msg=response.content)\\n self.assertEqual(Project.objects.count(), 3)\",\n \"def create(self):\\n \\n # create the sequence structure by calling the self.project.create\\n self.project.create()\",\n \"def create(self, token: Any):\\n params = [token, ]\\n method = \\\"ProjectAPI.Create\\\"\\n self.__add_request(method, params, lambda payload: Definition.from_json(payload))\",\n \"def post(self, data):\\n conn = pecan.request.db_conn\\n try:\\n project = db_models.Project(**data.as_dict())\\n return conn.create_project(request.context, project)\\n except Exception:\\n LOG.exception('Fail to create project: %s' % data.as_dict())\\n raise exception.ProjectCreateFailed(project_id=data.project_id,\\n user_id=data.user_id)\",\n \"def test_duplicate_tasks_new_project(self):\\n\\n setup_identity_cache()\\n\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_409_CONFLICT)\\n\\n data = {\\\"project_name\\\": \\\"test_project_2\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\",\n \"def test_projects_id_post(self):\\n project = Project()\\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\\n method='POST',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def create_project(self, project):\\n\\n with self._transaction.cursor() as cur:\\n if project.project_id is not None:\\n id_ = project.project_id\\n else:\\n cur.execute(\\\"SELECT MAX(project_id) + 1 \\\"\\n \\\"FROM barcodes.project\\\")\\n id_ = cur.fetchone()[0]\\n\\n query = f\\\"\\\"\\\"\\n INSERT INTO barcodes.project\\n ({PROJECT_FIELDS})\\n VALUES (\\n %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\\n %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\\n %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\\n %s, %s, %s, %s, %s, %s, %s);\\\"\\\"\\\"\\n\\n cur.execute(query,\\n [id_, project.project_name, project.is_microsetta,\\n project.bank_samples, project.plating_start_date,\\n project.contact_name, project.additional_contact_name,\\n project.contact_email, project.deadlines,\\n project.num_subjects, project.num_timepoints,\\n project.start_date, project.disposition_comments,\\n project.collection, project.is_fecal,\\n project.is_saliva, project.is_skin, project.is_blood,\\n project.is_other, project.do_16s,\\n project.do_shallow_shotgun, project.do_shotgun,\\n project.do_rt_qpcr, project.do_serology,\\n project.do_metatranscriptomics,\\n project.do_mass_spec, project.mass_spec_comments,\\n project.mass_spec_contact_name,\\n project.mass_spec_contact_email,\\n project.do_other,\\n project.branding_associated_instructions,\\n project.branding_status, project.subproject_name,\\n project.alias, project.sponsor, project.coordination,\\n project.is_active])\\n\\n # if we made it this far, all is well\\n return id_\",\n \"def newproject():\\n log('Criando novo projeto', yellow)\\n log('Cria a conta no bitbucket com o nome do projeto vázio que o script se encarregará do resto', red)\\n\\n conta = raw_input('Digite o nome do projeto: ')\\n\\n local('echo \\\"clonando projeto %s\\\"' % bitbucket_repository)\\n local('git clone {0} {1}{2}'.format(bitbucket_repository, folder_project_local, conta))\\n local('cd {0}{1}'.format(folder_project_local, conta))\\n local('mkvirtualenv {0}'.format(conta))\\n local('setvirtualenvproject')\\n local('pip install -r requirements.txt')\\n local('rm -rf {0}{1}/.git'.format(folder_project_local, conta))\\n local('rm -rf README.md')\\n local('git init')\\n local('git remote add origin git@bitbucket.org:{0}/{1}.git'.format(bitbucket_user, conta))\",\n \"def create_project(self, name):\\n project = self._post('/projects', data={'name': name})\\n self.create_project_hook(project['id'], self.webhook_url + name)\\n return project\",\n \"def create_keystone_v3_project(self, **kwargs):\\n LOG_OBJ.debug(\\\"Creating the project.\\\")\\n print self.project_info\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v3/projects\\\"\\n _headers = {'x-auth-token': self.cloud_admin_info[\\\"token_domain\\\"],\\n 'content-type': 'application/json'}\\n\\n _project_info = {\\\"project\\\": {}}\\n for argument in [\\\"name\\\", \\\"description\\\", \\\"domain_id\\\",\\n \\\"enabled\\\", \\\"disabled\\\"]:\\n try:\\n _project_info['project'].update(\\n {argument: kwargs[argument]})\\n except KeyError:\\n pass\\n _body = json.dumps(_project_info)\\n response = self.request(\\\"POST\\\", _url, _headers, _body)\\n\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while creating project\\\")\\n print (\\\"No response from Server while creating project\\\")\\n return response\\n\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\" Creating project Failed with status %s \\\"\\n \\\"and error : %s\\\" % (response.status, response.data))\\n print (\\\" Creating project Failed with status %s and error : %s\\\" %\\n (response.status, response.data))\\n return response.status\\n\\n output = json.loads(response.data)\\n LOG_OBJ.info(\\\"Project details : %s \\\" % output)\\n print (\\\"Project details : %s \\\" % output)\\n return output['project']['id']\",\n \"def create_project(cls, title, code, institution, tech_lead, category, funding_source):\\n project = Project.objects.create(\\n title=title,\\n description='Project description',\\n legacy_hpcw_id='HPCW-12345',\\n legacy_arcca_id='ARCCA-12345',\\n code=code,\\n institution=institution,\\n institution_reference='BW-12345',\\n department='School of Chemistry',\\n pi='Project Principal Investigator',\\n tech_lead=tech_lead,\\n category=category,\\n funding_source=funding_source,\\n start_date=datetime.datetime.now(),\\n end_date=datetime.datetime.now() + datetime.timedelta(days=10),\\n economic_user=True,\\n requirements_software='None',\\n requirements_gateways='None',\\n requirements_training='None',\\n requirements_onboarding='None',\\n allocation_rse=True,\\n allocation_cputime='1000000',\\n allocation_memory='100',\\n allocation_storage_home='5000',\\n allocation_storage_scartch='1000',\\n notes='Project notes',\\n )\\n return project\",\n \"def create_projects(self):\\n if self.gl is None or self.config is None:\\n print(\\\"No config/Gitlab found, please run connect first.\\\")\\n exit(1)\\n else:\\n print(\\\"Starting Project creation.\\\")\\n gl = self.gl\\n config = self.config\\n for project in config[\\\"projects\\\"]:\\n # get the import url\\n imp_url = config[\\\"projects\\\"][project][\\\"import_url\\\"]\\n\\n # Set rights/members/protected master\\n if config[\\\"projects\\\"][project][\\\"owner_conf\\\"][\\\"owner\\\"] == \\\"all_users\\\":\\n for user in self.users:\\n print(\\\"Importing \\\\'\\\" + imp_url + \\\"\\\\' for user \\\\'\\\" + user.username + \\\"\\\\'\\\")\\n pj = user.projects.create({'name': project,\\n 'user_id': user.id,\\n 'access_level': gitlab.OWNER_ACCESS,\\n 'import_url': imp_url})\\n elif config[\\\"projects\\\"][project][\\\"owner_conf\\\"][\\\"owner\\\"] == \\\"user\\\":\\n for user in self.users:\\n if user.username == config[\\\"projects\\\"][project][\\\"owner_conf\\\"][\\\"name\\\"]:\\n print(\\\"Importing \\\\'\\\" + imp_url + \\\"\\\\' for user \\\\'\\\" + user.username + \\\"\\\\'\\\")\\n pj = user.projects.create({'name': project,\\n 'user_id': user.id,\\n 'Access_level': gitlab.OWNER_ACCESS,\\n 'import_url': imp_url})\\n elif config[\\\"projects\\\"][project][\\\"owner_conf\\\"][\\\"owner\\\"] == \\\"group\\\":\\n for group in self.groups:\\n if group.name == config[\\\"projects\\\"][project][\\\"owner_conf\\\"][\\\"name\\\"]:\\n print(\\\"Importing \\\\'\\\" + imp_url + \\\"\\\\' for group \\\\'\\\" + group.name + \\\"\\\\'\\\")\\n pj = group.projects.create({'name': project,\\n 'namespace_id': group.id,\\n 'import_url': imp_url})\\n else:\\n print(\\\"Project owner Config is wrong, aborting\\\")\\n exit(1)\\n # Delete protected Master Branch\\n if config[\\\"projects\\\"][project][\\\"protect_master_branch\\\"] == \\\"False\\\":\\n print(\\\"Removing Project master Branch protection\\\")\\n pj.protectedbranches.delete('master')\",\n \"def NewProject (projectname):\\n\\tif projectname == \\\"\\\" or projectname == None:\\n\\t\\tnewprojcode(projectname)\\n\\telse:\\n\\t\\tnewprojCode_withNamed()\",\n \"def _post_project(prj=None):\\n template_path = (os.path.join(\\n os.path.split(__file__)[0], \\\"post_project_template.xml\\\"))\\n with open(template_path, 'r') as file:\\n template = Template(file.read())\\n response_xml = template.render(\\n name=f\\\"Project_TEST_{datetime.now()}\\\",\\n open_date=str(datetime.today().date()),\\n res_uri=f\\\"{LIMS_API.tools.api.host}researchers/1\\\")\\n\\n prj_response = LIMS_API.tools.api.post(\\n f\\\"{LIMS_API.tools.api.host}projects\\\", response_xml)\\n\\n prj_response_soup = BeautifulSoup(\\n prj_response, \\\"xml\\\").find(\\\"prj:project\\\")\\n prj = api_types.Project(\\n prj_response_soup.find(\\\"name\\\"),\\n DEFAULT_RES,\\n datetime.today().date(),\\n [],\\n prj_response_soup[\\\"uri\\\"])\\n\\n return prj\",\n \"def make_project(id):\\n return {\\n \\\"type\\\": \\\"Project\\\",\\n \\\"metrics\\\": [],\\n \\\"tags\\\": [],\\n \\\"id\\\": id,\\n \\\"description\\\": \\\"\\\",\\n \\\"applicant\\\": \\\"\\\",\\n }\",\n \"def _create_dummy_project(self,projectname=\\\"testproject\\\"):\\n # Create three types of users that exist: Root, can do anything, \\n # projectadmin, cam do things to a project he or she owns. And logged in\\n # user \\n \\n #created in _create_main_project_and_root.\\n root = self.root\\n # non-root users are created as if they signed up through the project, \\n # to maximize test coverage. \\n \\n # A user who has created a project\\n projectadmin = self._create_random_user(\\\"projectadmin_\\\")\\n \\n testproject = self._create_comicsite_in_admin(projectadmin,projectname)\\n create_page_in_admin(testproject,\\\"testpage1\\\")\\n create_page_in_admin(testproject,\\\"testpage2\\\")\\n \\n # a user who explicitly signed up to testproject\\n participant = self._create_random_user(\\\"participant_\\\")\\n self._register(participant,testproject)\\n \\n # a user who only signed up but did not register to any project\\n registered_user = self._create_random_user(\\\"comicregistered_\\\")\\n \\n #TODO: How to do this gracefully? \\n return [testproject,root,projectadmin,participant,registered_user]\",\n \"def run(opts, args):\\n create_new_project()\",\n \"def test_create_account_project(self, create):\\n row = {'PROJ_NAME1': 'Some Proj', 'PROJ_NO': '121-212',\\n 'SECTOR': 'IT'}\\n sync.create_account(row, None)\\n self.assertTrue(create.called)\\n account, row, issue_map = create.call_args[0]\\n self.assertEqual(account.name, 'Some Proj')\\n self.assertEqual(account.code, '121-212')\\n self.assertEqual(account.category, Account.PROJECT)\\n self.assertEqual(0, len(Account.objects.filter(pk=account.pk)))\",\n \"def project():\",\n \"def project():\",\n \"def project():\",\n \"def add_project(project):\\n print('add_project: ' + str(project))\\n try_insert_or_update(models.projects.insert(), # pylint: disable=no-value-for-parameter\\n [dict(\\n name=project['name'], path=project['name'], active=True, user_id=current_user.id)])\\n return\",\n \"def create_project(self, name, description=None):\\n description = description or ''\\n data = self._run(\\n url_path=\\\"projects/add\\\",\\n name=name,\\n description=description\\n )\\n return data['result']['project']['id']\",\n \"def create_new_project(self,\\n customer_name,\\n contract_date,\\n project_info,\\n project_datest,\\n project_dateend,\\n project_budget,\\n project_actst=None,\\n project_actend=None,\\n project_cost=None):\\n\\n customer_info = self.query_customer(cus_name=customer_name)\\n\\n if customer_info:\\n # Search for project manager in the same region as the customer.\\n customer_region_id = customer_info[0][1]\\n get_employee_query = \\\"select employee.emp_id, emp_lname, emp_fname from employee, \\\" \\\\\\n \\\"empskill, skill, region where employee.emp_id = \\\" \\\\\\n \\\"empskill.emp_id and empskill.skill_id = \\\" \\\\\\n \\\"skill.skill_id and skill.skill_descrpt = \\\" \\\\\\n \\\"'Project Manager' and region.region_id = \\\" \\\\\\n \\\"employee.region_id and region.region_id = '{}' \\\"\\n try:\\n self.dbCursor.execute(\\n get_employee_query.format(customer_region_id))\\n employee_info = self.dbCursor.fetchall()\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n if len(employee_info) == 0:\\n ErrorMessageWindow(\\\"No suitable project manager found!\\\")\\n else:\\n if customer_info and employee_info:\\n if len(customer_info) > 1:\\n MultiRowScreen(customer_info, \\\"project\\\")\\n else:\\n cus_id = customer_info[0][0]\\n emp_id = employee_info[0][0]\\n optional_inputs = [project_actst, project_actend,\\n project_cost]\\n\\n query = \\\"insert into project(cus_id, emp_id, proj_date, \\\" \\\\\\n \\\"proj_descrpt, proj_estdatest, proj_estdateend, \\\" \\\\\\n \\\"proj_estbudget) values ('{}', '{}', '{}', '{}', \\\" \\\\\\n \\\"'{}', '{}', '{}') \\\".format(cus_id,\\n emp_id,\\n contract_date,\\n project_info,\\n project_datest,\\n project_dateend,\\n project_budget)\\n\\n yes_options = False\\n for item in optional_inputs:\\n if item != \\\"\\\":\\n yes_options = True\\n\\n if yes_options is False:\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n option_names = [\\\"proj_actdatest\\\",\\n \\\"proj_actdateend\\\",\\n \\\"proj_actcost\\\"]\\n options_index = []\\n filled_options = []\\n\\n index = 0\\n for item in optional_inputs:\\n if item != \\\"\\\":\\n options_index.append(index)\\n filled_options.append(item)\\n index += 1\\n update_query = \\\"update project set \\\"\\n\\n j = 0\\n for i in options_index:\\n if j < len(filled_options) - 1:\\n update_query += \\\"{}='{}', \\\".format(\\n option_names[i], filled_options[j]\\n )\\n else:\\n update_query += \\\"{}='{}' \\\".format(\\n option_names[i], filled_options[j]\\n )\\n j += 1\\n\\n try:\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n\\n self.dbCursor.execute(update_query)\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n ErrorMessageWindow(\\\"Customer not found!\\\")\",\n \"def create(self, request):\\n lot = Lot.objects.get(pk=request.data[\\\"lotId\\\"])\\n\\n project = Project()\\n project.name = request.data[\\\"name\\\"]\\n project.estimatedCost = request.data[\\\"estimatedCost\\\"]\\n project.estimatedCompletionDate = request.data[\\\"estimatedCompletionDate\\\"]\\n project.lotId = lot\\n #projectNote=projectNote\\n\\n\\n try:\\n project.save()\\n serializer = ProjectSerializer(project, context={'request': request}) #converting data into json\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except ValidationError as ex:\\n return Response({\\\"reason\\\": ex.message}, status=status.HTTP_400_BAD_REQUEST)\",\n \"def pre_project_create(self, resource_dict):\\n pass\",\n \"def create_project(self, project_name=None, check=True):\\n project_name = project_name or next(utils.generate_ids('project'))\\n page_projects = self._page_projects()\\n page_projects.button_create_project.click()\\n\\n with page_projects.form_create_project as form:\\n form.field_name.value = project_name\\n form.submit()\\n\\n if check:\\n self.close_notification('success')\\n page_projects.table_projects.row(\\n name=project_name).wait_for_presence()\\n\\n return project_name\",\n \"def post_project():\\n\\n title = request.form.get('title')\\n description = request.form.get('description')\\n max_grade = request.form.get('max_grade')\\n\\n hackbright.make_new_project(title, description, max_grade)\\n\\n flash(\\\"Successfully added new project.\\\")\\n\\n return redirect(\\\"/project?title={}\\\".format(title))\",\n \"def test_new_project_existing_project_new_user(self):\\n setup_identity_cache()\\n\\n # create signup#1 - project1 with user 1\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n # Create signup#2 - project1 with user 2\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test2@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n headers = {\\n \\\"project_name\\\": \\\"admin_project\\\",\\n \\\"project_id\\\": \\\"admin_project_id\\\",\\n \\\"roles\\\": \\\"admin,member\\\",\\n \\\"username\\\": \\\"admin\\\",\\n \\\"user_id\\\": \\\"admin_id\\\",\\n \\\"authenticated\\\": True,\\n }\\n # approve signup #1\\n new_task1 = Task.objects.all()[0]\\n url = \\\"/v1/tasks/\\\" + new_task1.uuid\\n response = self.client.post(\\n url, {\\\"approved\\\": True}, format=\\\"json\\\", headers=headers\\n )\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n self.assertEqual(response.json(), {\\\"notes\\\": [\\\"created token\\\"]})\\n\\n # Attempt to approve signup #2\\n new_task2 = Task.objects.all()[1]\\n url = \\\"/v1/tasks/\\\" + new_task2.uuid\\n response = self.client.post(\\n url, {\\\"approved\\\": True}, format=\\\"json\\\", headers=headers\\n )\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(response.json(), {\\\"errors\\\": [\\\"actions invalid\\\"]})\",\n \"def test_create_project_root(self):\\n self.assertEqual(Project.objects.count(), 2)\\n url = reverse('projectroles:api_project_create')\\n post_data = {\\n 'title': NEW_PROJECT_TITLE,\\n 'type': PROJECT_TYPE_PROJECT,\\n 'parent': None,\\n 'description': 'description',\\n 'readme': 'readme',\\n 'public_guest_access': False,\\n 'owner': str(self.user.sodar_uuid),\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(Project.objects.count(), 2)\",\n \"def create_project(conn, project):\\r\\n sql = ''' INSERT INTO rq_table(id, date, co2x, o2xx, temp, humi, type)\\r\\n VALUES(%s,%s,%s,%s,%s,%s,%s) '''\\r\\n cur = conn.cursor()\\r\\n cur.execute(sql, project)\\r\\n conn.commit()\\r\\n return cur.lastrowid\",\n \"def create_project_if_necessary(ctx, org_name, project_name, ):\\n org = cmd.get_one_organization_by_name(\\n client=ctx.obj, organization_name=org_name)\\n pprint(cmd.ensure_project(\\n client=ctx.obj, project_name=project_name, organization_id=org.id))\",\n \"def newProject(self):\\n dialog = NewProjectDialog()\\n if not dialog.name is None and not dialog.path is None:\\n self._app.createProject(str(dialog.name), str(dialog.path))\",\n \"def test_create_project_from_template(self):\\n project_new = self.project_template.take_template()\\n\\n self.assertTrue(project_new)\",\n \"def create_pcpp(account, row, issue_map):\\n country_name = row['LOCATION']\\n country = Country.objects.filter(name__iexact=country_name).first()\\n if not country:\\n logging.getLogger('peacecorps.sync_accounting').warning(\\n \\\"%s: Country does not exist: %s\\\", row['PROJ_NO'], row['LOCATION'])\\n issue = issue_map.find(row['SECTOR'])\\n if not issue and row['SECTOR'] != 'None':\\n logging.getLogger('peacecorps.sync_accounting').warning(\\n \\\"%s: Sector does not exist: %s\\\", row['PROJ_NO'], row['SECTOR'])\\n\\n if country and (issue or row['SECTOR'] == 'None'):\\n set_balances(row, account)\\n account.save()\\n\\n volunteername = row['PCV_NAME']\\n if volunteername.startswith(row['STATE']):\\n volunteername = volunteername[len(row['STATE']):].strip()\\n\\n summary = clean_description(row['SUMMARY'])\\n sirtrevorobj = {\\\"data\\\": [{\\\"type\\\": \\\"text\\\", \\\"data\\\": {\\\"text\\\": summary}}]}\\n description = json.dumps(sirtrevorobj)\\n\\n project = Project(\\n title=row['PROJ_NAME1'], country=country, account=account,\\n volunteername=volunteername, volunteerhomestate=row['STATE'],\\n description=description\\n )\\n if issue:\\n project.overflow = issue.account\\n project.save()\\n project.campaigns.add(issue)\\n else:\\n project.save()\",\n \"def new_project(self, rootdir=None):\\n if rootdir is None:\\n rootdir = Ui.instance().select_directory(user.home)\\n if not os.path.exists(rootdir):\\n os.makedirs(rootdir)\\n\\n print 'Weld.new_project in ', rootdir\\n project = Project(rootdir)\\n\\n project.save()\\n self.project = project\\n self.current_project_path = rootdir\\n Ui.instance().set_resources_draggable(True)\\n Ui.instance().show_status('new project created')\",\n \"def create_project(self, project_name: str, domain_id: str = None, classification_type: str = None) -> Project:\\n if not self.validate():\\n raise SettingCustomVisionAccessFailed\\n trainer = self.get_trainer_obj()\\n if not domain_id:\\n domain_id = self.obj_detection_domain_id if domain_id is None else domain_id\\n logger.info(\\\"Creating object detection project.\\\")\\n try:\\n project = trainer.create_project(name=project_name, domain_id=domain_id, classification_type=classification_type)\\n return project\\n except CustomVisionErrorException:\\n raise SettingCustomVisionCannotCreateProject\",\n \"def save_project(uid, song_notes, author_name, creation_date, project_name):\",\n \"def testSessionCreate(self):\\n success = False\\n project = None\\n\\n try:\\n project = self.session.create_project()\\n\\n success = True\\n except Exception:\\n pass\\n\\n self.failUnless(success)\\n self.failIf(project is None)\",\n \"def create_project(data):\\n try:\\n CheckConnection()\\n with Connection.cursor() as cursor:\\n name = str(data[\\\"name\\\"])\\n end_date = str(data[\\\"enddate\\\"])\\n team_size = str(data[\\\"teamsize\\\"])\\n budget = str(data[\\\"budget\\\"])\\n tools = str(data[\\\"tools\\\"])\\n priority = str(data[\\\"priority\\\"])\\n is_assignment_complete = str(0)\\n\\n query = \\\"INSERT INTO Project (ProjectName,ProjectEndDate,ProjectTeamSize,Budget,Tools,Priority,IsAssignmentComplete) VALUES (%s,%s,%s,%s,%s,%s,%s);\\\"\\n cursor.execute(\\n query,\\n (\\n name,\\n end_date,\\n team_size,\\n budget,\\n tools,\\n priority,\\n is_assignment_complete,\\n ),\\n )\\n Connection.commit()\\n return True\\n\\n except conn.Error as error:\\n print(\\\"Failed to update record to database rollback: {}\\\".format(error))\\n Connection.rollback()\\n return False\",\n \"async def create(self, ctx, name: str,\\n owner: discord.Member = None) -> discord.Message:\\n if ctx.projects.find_project(name):\\n project = ctx.projects.find_project(name)\\n if ctx.guild.get_Channel(int(project.get(\\\"channel\\\"))):\\n return await ctx.send(\\\"A project with that name exists.\\\")\\n else:\\n await ctx.send(\\\"A project with this name exists but, a related\\\"\\n \\\" project channel was not found. \\\"\\n \\\"I will be overwriting the previous project.\\\")\\n ctx.projects.delete_project(name)\\n\\n owner = owner if owner else ctx.author\\n if not ctx.bot.db(\\\"guilds\\\").find(str(ctx.guild.id)):\\n ctx.bot.db(\\\"guilds\\\").insert(str(ctx.guild.id), ctx.bot.empty_guild)\\n\\n # await ctx.send(\\\"Creating project channel...\\\")\\n if not ctx.bot.db(\\\"guilds\\\").find(\\n str(ctx.guild.id)).get(\\\"project_category\\\"):\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(\\n read_messages=False\\n ),\\n ctx.me: discord.PermissionOverwrite(\\n read_messages=True,\\n send_messages=True,\\n manage_channels=True\\n )\\n }\\n category = await ctx.guild.create_category(\\\"Flux Projects\\\",\\n overwrites=overwrites)\\n ctx.bot.db(\\\"guilds\\\").update(str(ctx.guild.id), {\\n \\\"project_category\\\": str(category.id)})\\n\\n else:\\n category = ctx.guild.get_channel(\\n int(ctx.bot.db(\\\"guilds\\\").find(\\n str(ctx.guild.id)).get(\\\"project_category\\\")))\\n\\n overwrites = {owner: discord.PermissionOverwrite(read_messages=True,\\n send_messages=False,\\n add_reactions=True),\\n ctx.me: discord.PermissionOverwrite(read_messages=True,\\n send_messages=True),\\n ctx.guild.default_role: discord.PermissionOverwrite(\\n read_messages=False)}\\n\\n channel = await ctx.guild.create_text_channel(f\\\"{name}-project\\\",\\n category=category,\\n overwrites=overwrites)\\n await channel.send(f\\\"Project Owner: {owner}\\\")\\n message = await channel.send(self.empty_progress_bar)\\n await message.pin()\\n res = ctx.projects.create_project(\\n owner.id, owner.id, name, channel.id, message.id)\\n if not res:\\n return await ctx.send(\\\"An error has occurred. Use `.contact`\\\"\\n \\\" with error: `ERR_PROJECT_STILL_EXISTS`\\\")\\n return await ctx.send(\\\"Project created!\\\")\",\n \"def test_create_project_target_remote(self):\\n # Create source site\\n source_site = self.make_site(\\n name=REMOTE_SITE_NAME,\\n url=REMOTE_SITE_URL,\\n mode=SITE_MODE_SOURCE,\\n description=REMOTE_SITE_DESC,\\n secret=REMOTE_SITE_SECRET,\\n )\\n # Make category remote\\n self.make_remote_project(\\n project_uuid=self.category.sodar_uuid,\\n project=self.category,\\n site=source_site,\\n level=SODAR_CONSTANTS['REMOTE_LEVEL_READ_ROLES'],\\n )\\n self.assertEqual(Project.objects.count(), 2)\\n\\n url = reverse('projectroles:api_project_create')\\n post_data = {\\n 'title': NEW_PROJECT_TITLE,\\n 'type': PROJECT_TYPE_PROJECT,\\n 'parent': str(self.category.sodar_uuid),\\n 'description': 'description',\\n 'readme': 'readme',\\n 'public_guest_access': False,\\n 'owner': str(self.user.sodar_uuid),\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n\\n self.assertEqual(response.status_code, 403, msg=response.content)\\n self.assertEqual(Project.objects.count(), 2)\",\n \"def __init_project(self, presenter):\\n # Get the project id correponding to the input presenter\\n # (if it does not exists, create a new one)\\n if presenter == None:\\n raise Exception(\\\"Cannot publish tasks without specifying a presenter.\\\"\\n \\\"Please use set_presenter() to specify a presenter.\\\")\\n pbclient = self.cc.pbclient\\n p = None\\n #try:\\n if len(pbclient.find_project(short_name = presenter.short_name)) > 0:\\n # the presenter has been created\\n p= pbclient.find_project(short_name = presenter.short_name)[0]\\n elif len(pbclient.find_project(name = presenter.name)) > 0:\\n # the presenter has been created\\n p= pbclient.find_project(name = presenter.name)[0]\\n else:\\n # create a new project with the presente\\n p = pbclient.create_project(presenter.name, presenter.short_name, presenter.description)\\n\\n try:\\n self.project_id = p.id\\n self.project_short_name = presenter.short_name\\n self.project_name = presenter.name\\n p= pbclient.find_project(short_name = presenter.short_name)[0]\\n p.info['task_presenter'] = Template(presenter.template).safe_substitute(short_name = presenter.short_name, question = presenter.question)\\n p.long_description = presenter.description\\n p.name = presenter.name\\n p.short_name = presenter.short_name\\n pbclient.update_project(p)\\n except:\\n if type(p) is dict and \\\"exception_msg\\\" in p.keys():\\n raise Exception(\\\"%s\\\" %(p[\\\"exception_msg\\\"]))\\n else:\\n print p\\n raise\",\n \"def test_save(self):\\n p = self.create_project(save=False,\\n basecamp_url='https://foo.basecamphq.com/projects/1701/log/',\\n basecamp_id=None,\\n name='')\\n p.save()\\n self.assertEqual(1701, p.basecamp_id)\\n self.assertEqual(\\\"Kobol's Last Gleaming\\\", p.name)\",\n \"def test_create_project_unknown_user(self):\\n self.assertEqual(Project.objects.count(), 2)\\n url = reverse('projectroles:api_project_create')\\n post_data = {\\n 'title': NEW_PROJECT_TITLE,\\n 'type': PROJECT_TYPE_PROJECT,\\n 'parent': str(self.category.sodar_uuid),\\n 'description': 'description',\\n 'readme': 'readme',\\n 'public_guest_access': False,\\n 'owner': INVALID_UUID,\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(Project.objects.count(), 2)\",\n \"def create_project(self, conn, name, description=\\\"\\\"):\\n group = conn.group.allocate(name, description)\\n # returns Project object\\n return group\",\n \"def create(index):\\n # Get the project root\\n project_root = get_project_root()\\n package_name = os.path.basename(project_root)\\n logging.info(\\\"Creating package for current project: \\\" + package_name)\\n Packager(package_name, project_root).create(index)\",\n \"def create(self, project_name: str):\\n \\n #check naming-convention\\n Utility.matchNamingConvention(project_name)\\n\\n projects_folder = self.config.projects_folder\\n\\n #check if folder already existsts\\n Utility.checkNotOccupied(project_name, projects_folder)\\n\\n target_path = projects_folder+project_name\\n\\n project_godot_file_path = target_path+'/project.godot'\\n\\n os.mkdir(target_path)\\n os.makedirs(target_path+'/bin/plugins', exist_ok=True)\\n os.mknod(project_godot_file_path)\\n\\n project_godot_file = open(project_godot_file_path, mode='w')\\n project_godot_file.write('[application]\\\\n\\\\nconfig/name=\\\"'+project_name+'\\\"\\\\n')\",\n \"def test_new_project_existing_user(self):\\n\\n user = fake_clients.FakeUser(\\n name=\\\"test@example.com\\\", password=\\\"123\\\", email=\\\"test@example.com\\\"\\n )\\n\\n setup_identity_cache(users=[user])\\n\\n # unauthenticated sign up as existing user\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": user.email}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n # approve the sign-up as admin\\n headers = {\\n \\\"project_name\\\": \\\"admin_project\\\",\\n \\\"project_id\\\": \\\"admin_project_id\\\",\\n \\\"roles\\\": \\\"admin,member\\\",\\n \\\"username\\\": \\\"admin\\\",\\n \\\"user_id\\\": \\\"admin_id\\\",\\n \\\"authenticated\\\": True,\\n }\\n new_task = Task.objects.all()[0]\\n url = \\\"/v1/tasks/\\\" + new_task.uuid\\n response = self.client.post(\\n url, {\\\"approved\\\": True}, format=\\\"json\\\", headers=headers\\n )\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.json(), {\\\"notes\\\": [\\\"Task completed successfully.\\\"]})\",\n \"def create_project_in_gme(self, active_node, projectName, contract_term, series):\\n core = self.core\\n projectMeta = self.META[\\\"Project\\\"]\\n projectNode = core.create_child(active_node, projectMeta)\\n core.set_attribute(projectNode, \\\"name\\\", projectName)\\n core.set_attribute(projectNode, \\\"date\\\", date.today().strftime(\\\"%d/%m/%Y\\\"))\\n core.set_attribute(projectNode, \\\"contract term\\\", int(contract_term))\\n core.set_attribute(projectNode, \\\"series\\\", series)\\n return projectNode\",\n \"def test_fields_on_new_project(new_project) -> None:\\n\\n # assert isinstance(new_project.id, int) # not created yet, hasnt been committed\\n assert isinstance(new_project.token, uuid.UUID)\\n assert new_project.title == \\\"Lord of the Rings\\\"\\n assert new_project.email == \\\"J. R. R. Tolkien\\\"\\n assert new_project.phone == \\\"5558675309\\\"\\n assert new_project.verification is None\",\n \"def create_project(name=None, defaultJobTimeoutMinutes=None):\\n pass\",\n \"def test_projects_put(self):\\n project = Project()\\n response = self.client.open('/project-tracker/projects',\\n method='PUT',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def create_new_project():\\n readline.parse_and_bind('tab: complete')\\n\\n print \\\\\\n\\\"\\\"\\\"\\n xbmcswift2 - A micro-framework for creating XBMC plugins.\\n xbmc@jonathanbeluch.com\\n --\\n\\\"\\\"\\\"\\n print 'I\\\\'m going to ask you a few questions to get this project' \\\\\\n ' started.'\\n\\n # noinspection PyDictCreation\\n opts = {}\\n\\n # Plugin Name\\n opts['plugin_name'] = get_valid_value(\\n 'What is your plugin name?',\\n validate_nonblank\\n )\\n\\n # Plugin ID\\n opts['plugin_id'] = get_valid_value(\\n 'Enter your plugin id.',\\n validate_pluginid,\\n 'plugin.video.%s' % (opts['plugin_name'].lower().replace(' ', ''))\\n )\\n\\n # Parent Directory\\n opts['parent_dir'] = get_valid_value(\\n 'Enter parent folder (where to create project)',\\n validate_isfolder,\\n getcwd()\\n )\\n opts['plugin_dir'] = os.path.join(opts['parent_dir'], opts['plugin_id'])\\n assert not os.path.isdir(opts['plugin_dir']), \\\\\\n 'A folder named %s already exists in %s.' % (opts['plugin_id'],\\n opts['parent_dir'])\\n\\n # Provider\\n opts['provider_name'] = get_valid_value(\\n 'Enter provider name',\\n validate_nonblank,\\n )\\n\\n # Create the project folder by copying over skel\\n copytree(SKEL, opts['plugin_dir'], ignore=ignore_patterns('*.pyc'))\\n\\n # Walk through all the new files and fill in with out options\\n for root, dirs, files in os.walk(opts['plugin_dir']):\\n for filename in files:\\n update_file(os.path.join(root, filename), opts)\\n\\n print 'Projects successfully created in %s.' % opts['plugin_dir']\\n print 'Done.'\",\n \"def init_project(self,project_name,project_dir):\\n projectkey = id_generator(10)\\n if \\\"towercrane\\\" not in os.listdir(project_dir):\\n print(f'Initializing project:\\\"{project_name}\\\" with projectkey: \\\"{projectkey}\\\" ')\\n self.TowercraneConfig = {\\\"project_name\\\":project_name,\\n \\\"projectkey\\\":projectkey,\\n \\\"publicurl\\\":\\\"private_project\\\"\\n }\\n write_config(project_dir,self.TowercraneConfig)\\n project_insert_report = self.db.create_project(project_name,project_dir,projectkey)\\n print(project_insert_report)\\n \\n elif \\\"towercrane\\\" in os.listdir(project_dir):\\n self.TowercraneConfig = read_config(project_dir)\\n print(f'project:\\\"{self.TowercraneConfig[\\\"project_name\\\"]}\\\" with projectkey: \\\"{self.TowercraneConfig[\\\"projectkey\\\"]}\\\" Already Exists')\",\n \"def createproject(project_name):\\n app_clone_script = 'git clone https://github.com/jaarce/falcon-bp.git %s' % project_name\\n subprocess.call(app_clone_script.split(' '))\",\n \"def test_user_can_create_a_project(self):\\n self.assertEqual(project_model.Project.objects.get(user=self.test_user).pk, self.test_project.pk)\",\n \"def test_create_project_target_disabled(self):\\n self.assertEqual(Project.objects.count(), 2)\\n url = reverse('projectroles:api_project_create')\\n post_data = {\\n 'title': NEW_PROJECT_TITLE,\\n 'type': PROJECT_TYPE_PROJECT,\\n 'parent': str(self.category.sodar_uuid),\\n 'description': 'description',\\n 'readme': 'readme',\\n 'public_guest_access': False,\\n 'owner': str(self.user.sodar_uuid),\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 403, msg=response.content)\\n self.assertEqual(Project.objects.count(), 2)\",\n \"def create(self, request, *args, **kwargs):\\n project = Project.objects.get(id=kwargs[\\\"projects_pk\\\"])\\n self.check_object_permissions(request, project)\\n\\n serializer = self.get_serializer(data=request.data)\\n if serializer.is_valid():\\n serializer.save(permission=\\\"contributor\\\", role=\\\"Contributor\\\")\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def test_new_project_invalid_on_submit(self):\\n\\n setup_identity_cache()\\n\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n headers = {\\n \\\"project_name\\\": \\\"test_project\\\",\\n \\\"project_id\\\": \\\"test_project_id\\\",\\n \\\"roles\\\": \\\"admin,member\\\",\\n \\\"username\\\": \\\"test@example.com\\\",\\n \\\"user_id\\\": \\\"test_user_id\\\",\\n \\\"authenticated\\\": True,\\n }\\n new_task = Task.objects.all()[0]\\n url = \\\"/v1/tasks/\\\" + new_task.uuid\\n response = self.client.post(\\n url, {\\\"approved\\\": True}, format=\\\"json\\\", headers=headers\\n )\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n self.assertEqual(response.data, {\\\"notes\\\": [\\\"created token\\\"]})\\n self.assertEqual(len(mail.outbox), 3)\\n\\n fake_clients.identity_cache[\\\"projects\\\"] = {}\\n\\n new_token = Token.objects.all()[0]\\n url = \\\"/v1/tokens/\\\" + new_token.token\\n data = {\\\"password\\\": \\\"testpassword\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(len(mail.outbox), 3)\",\n \"def test_notification_CreateProjectAndUser(self):\\n setup_identity_cache()\\n\\n url = \\\"/v1/actions/CreateProjectAndUser\\\"\\n data = {\\\"project_name\\\": \\\"test_project\\\", \\\"email\\\": \\\"test@example.com\\\"}\\n response = self.client.post(url, data, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\\n\\n new_task = Task.objects.all()[0]\\n\\n headers = {\\n \\\"project_name\\\": \\\"test_project\\\",\\n \\\"project_id\\\": \\\"test_project_id\\\",\\n \\\"roles\\\": \\\"admin,member\\\",\\n \\\"username\\\": \\\"test@example.com\\\",\\n \\\"user_id\\\": \\\"test_user_id\\\",\\n \\\"authenticated\\\": True,\\n }\\n\\n url = \\\"/v1/notifications\\\"\\n response = self.client.get(url, headers=headers)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.json()[\\\"notifications\\\"][0][\\\"task\\\"], new_task.uuid)\",\n \"def create_project(opts):\\n if opts['django']:\\n structure.create_django_proj(opts)\\n if opts['cookiecutter_template']:\\n structure.create_cookiecutter(opts)\\n proj_struct = structure.make_structure(opts)\\n structure.create_structure(proj_struct,\\n update=opts['update'] or opts['force'])\\n if not opts['update'] and not repo.is_git_repo(opts['project']):\\n repo.init_commit_repo(opts['project'], proj_struct)\",\n \"def create_new_python_project():\\n\\t# Create the different variables\\n\\tfolder_name = str(sys.argv[1])\\n\\tdir_name = my_project_folder + folder_name\\n\\tpy_file = dir_name + '/' + folder_name + '.py'\\n\\treadme_file = dir_name + '/' + 'README.md'\\n\\ttodo_file = dir_name + '/' + 'TODO.txt'\\n\\n\\t# Create directory if it does not exist yet\\n\\tif not os.path.exists(dir_name):\\n\\t\\tos.mkdir(dir_name)\\n\\t\\tprint(\\\"Directory \\\" , dir_name , \\\" Created \\\")\\n\\n\\t\\t# Create Python file\\n\\t\\tdata = ''\\n\\t\\twith open(template_py, 'r') as file:\\n\\t\\t\\tdata += file.read()\\n\\n\\t\\twith open(py_file, 'w') as f:\\n\\t\\t\\tf.write(data)\\n\\t\\t\\tprint(\\\"Python file created\\\")\\n\\n\\t\\t# Create README file\\n\\t\\tdata = ''\\n\\t\\twith open(template_readme, 'r') as file:\\n\\t\\t\\tdata += file.read()\\n\\n\\t\\twith open(readme_file, 'w') as f:\\n\\t\\t\\tf.write(data)\\n\\t\\t\\tprint(\\\"Readme file created\\\")\\n\\n\\t\\t# Create Todo file\\n\\t\\twith open(todo_file, 'w') as f:\\n\\t\\t\\tprint(\\\"TODO file created\\\")\\n\\n\\t\\t# Create Github repo\\n\\t\\twith open(\\\".env\\\", \\\"r\\\") as f:\\n\\t\\t\\tdata = f.read()\\n\\n\\t\\tindex_1 = data.find('TOKEN=\\\"') + len('TOKEN=\\\"')\\n\\t\\ttoken = data[index_1:-1]\\n\\t\\tg = Github(token)\\n\\t\\tuser = g.get_user()\\n\\t\\trepo = user.create_repo(folder_name)\\n\\t\\tprint(\\\"Succesfully created repository {}\\\".format(folder_name))\\n\\n\\n\\telse: \\n\\t\\tprint(\\\"Directory \\\" , dir_name , \\\" already exists\\\")\",\n \"def create_project(conn, project):\\n sql = ''' INSERT INTO projects(name,begin_date,end_date)\\n VALUES(?,?,?) '''\\n cur = conn.cursor()\\n cur.execute(sql, project)\\n return cur.lastrowid\",\n \"def create():\",\n \"def create():\",\n \"def _create_namespace(self):\\n self.ocp.new_project(self.namespace)\",\n \"def setup_project(client, project_template, do_auth=True):\\n client = deepcopy(client)\\n email = \\\"test@gmail.com\\\"\\n password = \\\"test\\\"\\n urls = URLS()\\n project_config = project_template()\\n\\n # we work in empty database, so let's create business user and login\\n user = User.objects.create(email=email)\\n user.set_password(password) # set password without hash\\n\\n create_business(user)\\n org = Organization.create_organization(created_by=user, title=user.first_name)\\n user.active_organization = org\\n user.save()\\n\\n if do_auth:\\n\\n assert signin(client, email, password).status_code == 302\\n # create project\\n with requests_mock.Mocker() as m:\\n m.register_uri('POST', re.compile(r'ml\\\\.heartex\\\\.net/\\\\d+/validate'), text=json.dumps({'status': 'ok'}))\\n m.register_uri('GET', re.compile(r'ml\\\\.heartex\\\\.net/\\\\d+/health'), text=json.dumps({'status': 'UP'}))\\n r = client.post(urls.project_create, data=project_config)\\n print('Project create with status code:', r.status_code)\\n assert r.status_code == 201, f'Create project result should be redirect to the next page'\\n\\n # get project id and prepare url\\n project = Project.objects.filter(title=project_config['title']).first()\\n urls.set_project(project.pk)\\n print('Project id:', project.id)\\n\\n client.project = project\\n\\n client.user = user\\n client.urls = urls\\n client.project_config = project_config\\n client.org = org\\n return client\",\n \"def add_project(project, network, id):\\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\\n ret = bmi.add_project(project, network, id)\\n if ret[constants.STATUS_CODE_KEY] == 200:\\n click.echo(\\\"Success\\\")\\n else:\\n click.echo(ret[constants.MESSAGE_KEY])\",\n \"def CreateProject(projectName='project'):\\r\\n projectName = input('''The project's name: ''')\\r\\n if not os.path.exists(projectName):\\r\\n os.mkdir(projectName)\\r\\n else:\\r\\n print('There is a file with the same name.')\\r\\n\\r\\n for dir in ['OPT', 'SCF', 'PHO']:\\r\\n if not os.path.exists(projectName + os.sep + dir):\\r\\n os.mkdir(projectName + os.sep + dir)\",\n \"def project(projectname,targetamount):\\n if (validatename(projectname) and validatenum(targetamount)):\\n targetamount=float(targetamount)\\n con = lite.connect(databasefile)\\n with con:\\n cur = con.cursor() \\n cur.execute(\\\"SELECT Id FROM projects where name=?\\\", (projectname,))\\n exists = cur.fetchone()\\n if exists:\\n click.echo(\\\"Project name already exists!\\\")\\n sys.exit()\\n cur.execute(\\\"INSERT INTO projects (Name, Tamount) VALUES (?, ?)\\\", (projectname, targetamount))\\n click.echo(\\\"Added %s project with target of $%-.2f\\\" % (projectname, targetamount))\",\n \"def test_create(self):\\n\\n responses.add(\\n responses.POST,\\n self.host + \\\"/manager\\\",\\n json={\\\"path\\\": \\\"manager?project=ProjectTest\\\", \\\"action\\\": \\\"redirect\\\", \\\"status\\\": \\\"success\\\"},\\n status=200\\n )\\n\\n self.azk.create(self.project, self.description)\",\n \"def add_project(self, project):\\n c = self.conn.cursor()\\n cursor = c.execute(\\\"INSERT INTO projects VALUES (null, ?, ?, ?, ?)\\\", (project['owner'],\\n project['title'],\\n datetime.now(), datetime.now(),))\\n\\n self.conn.commit()\\n project_id = cursor.lastrowid\\n\\n self.conn.cursor().execute(\\\"INSERT INTO users_projects VALUES (?,?)\\\", (project['owner'], project_id),)\\n self.conn.commit()\\n return self.get_project(project_id)\",\n \"async def create_project_flow(\\n create_project_flow_request: CreateProjectFlow,\\n token: str = Depends(oauth2_scheme),\\n):\\n try:\\n logging.info(\\\"Calling /osairis/project_flow/create endpoint\\\")\\n logging.debug(f\\\"Request: {create_project_flow_request}\\\")\\n if decodeJWT(token=token):\\n response = ProjectFlowController().create_project_flow_controller(\\n request=create_project_flow_request\\n )\\n return CreateProjectFlowResponse(**response)\\n else:\\n raise HTTPException(\\n status_code=status.HTTP_401_UNAUTHORIZED,\\n detail=\\\"Invalid access token\\\",\\n headers={\\\"WWW-Authenticate\\\": \\\"Bearer\\\"},\\n )\\n except Exception as error:\\n logging.error(f\\\"Error in /osairis/project_flow/create endpoint: {error}\\\")\\n raise error\",\n \"def create(self, values):\\n TYPE = self.env['anytracker.ticket.type']\\n SEQUENCE = self.env['ir.sequence']\\n types = TYPE.search([('code', '=', 'node')])\\n values.update({\\n 'number': SEQUENCE.sudo().next_by_code('anytracker.ticket')})\\n if values.get('parent_id'):\\n values['project_id'] = self.browse(\\n values['parent_id']).project_id.id\\n\\n # project creation: auto-assign the 'node' type\\n if not values.get('parent_id') and types:\\n values['type'] = types[0].id\\n\\n # add myself to the project at creation\\n if not values.get('parent_id'):\\n p_ids = values.get('participant_ids', [(6, 0, [])])\\n if self.env.uid not in p_ids:\\n values['participant_ids'] = [\\n (6, 0, [self.env.uid] + p_ids[0][2])]\\n\\n # replace ticket numbers with permalinks\\n if 'description' in values:\\n values['description'] = add_permalinks(\\n self.env.cr.dbname, values['description'])\\n\\n ticket = super(Ticket, self).create(values)\\n\\n if not values.get('parent_id'):\\n ticket.write({'project_id': ticket.id})\\n\\n # turn the parent into a node\\n if 'parent_id' in values and values['parent_id'] and types:\\n ticket.browse(values['parent_id']).write({'type': types[0].id})\\n\\n # subscribe the followers of the parent,\\n # or the participants if this is a project\\n # This allows to subscribe or unsubscribe to ticket subtrees\\n if ticket.project_id.participant_ids:\\n if ticket.parent_id:\\n ticket.message_subscribe(\\n ticket.parent_id.message_follower_ids.ids)\\n else:\\n ticket.message_subscribe_users(\\n ticket.participant_ids.ids)\\n\\n return ticket\",\n \"def save(self, project_id=None):\\r\\n if project_id is not None:\\r\\n project = Project.objects.get(pk=int(project_id))\\r\\n else:\\r\\n project = Project()\\r\\n # Fill out the data of the given project and prepare it\\r\\n # for saving into database.\\r\\n project.Name = self.cleaned_data['name']\\r\\n project.ProjectClient = self.cleaned_data['project_client']\\r\\n project.Start = self.cleaned_data['start']\\r\\n project.End = self.cleaned_data['end']\\r\\n project.ProjectManager = self.cleaned_data['project_manager']\\r\\n project.QualityAssurance = self.cleaned_data['quality_assurance']\\r\\n project.Price = self.cleaned_data['price']\\r\\n project.Segment = self.cleaned_data['segment']\\r\\n project.Type = self.cleaned_data['type']\\r\\n project.save()\\r\\n # If the item was just created, set up workflow for it\\r\\n if project_id is None:\\r\\n workflow = Workflow.objects.get(name='Project')\\r\\n utils.set_workflow(project, workflow)\\r\\n state = utils.get_state(project)\\r\\n project.Status = state\\r\\n project.save()\\r\\n return project\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7641362","0.74125963","0.74125963","0.74125963","0.73800826","0.7369805","0.72611123","0.7240642","0.72367054","0.7189181","0.71309054","0.70826024","0.70679694","0.7061639","0.6987262","0.698142","0.696992","0.6924102","0.69188553","0.6910896","0.6910389","0.6896292","0.6886656","0.6839304","0.6798925","0.67972517","0.67882097","0.67828375","0.67761225","0.67730415","0.676983","0.6765078","0.67542094","0.67119503","0.67041045","0.6658832","0.66244453","0.66219175","0.66217315","0.66213596","0.66190153","0.6615325","0.65957326","0.65957326","0.65957326","0.6580955","0.6541764","0.6540238","0.6536616","0.6535735","0.651638","0.6504959","0.6502909","0.64886916","0.64804417","0.64592373","0.64453495","0.6444375","0.64364594","0.6422805","0.6422294","0.64128405","0.64121294","0.6404425","0.6391617","0.63791203","0.6376413","0.636682","0.6366483","0.6353054","0.63263005","0.6308415","0.63074535","0.63015205","0.6300897","0.62934697","0.62706727","0.62626433","0.62578815","0.62483877","0.6242479","0.6237388","0.622969","0.62106353","0.62096643","0.62044406","0.6191682","0.61899376","0.6186025","0.6186025","0.61714125","0.617067","0.61611396","0.61572653","0.61543864","0.615152","0.6142628","0.6130133","0.6122881","0.61210936"],"string":"[\n \"0.7641362\",\n \"0.74125963\",\n \"0.74125963\",\n \"0.74125963\",\n \"0.73800826\",\n \"0.7369805\",\n \"0.72611123\",\n \"0.7240642\",\n \"0.72367054\",\n \"0.7189181\",\n \"0.71309054\",\n \"0.70826024\",\n \"0.70679694\",\n \"0.7061639\",\n \"0.6987262\",\n \"0.698142\",\n \"0.696992\",\n \"0.6924102\",\n \"0.69188553\",\n \"0.6910896\",\n \"0.6910389\",\n \"0.6896292\",\n \"0.6886656\",\n \"0.6839304\",\n \"0.6798925\",\n \"0.67972517\",\n \"0.67882097\",\n \"0.67828375\",\n \"0.67761225\",\n \"0.67730415\",\n \"0.676983\",\n \"0.6765078\",\n \"0.67542094\",\n \"0.67119503\",\n \"0.67041045\",\n \"0.6658832\",\n \"0.66244453\",\n \"0.66219175\",\n \"0.66217315\",\n \"0.66213596\",\n \"0.66190153\",\n \"0.6615325\",\n \"0.65957326\",\n \"0.65957326\",\n \"0.65957326\",\n \"0.6580955\",\n \"0.6541764\",\n \"0.6540238\",\n \"0.6536616\",\n \"0.6535735\",\n \"0.651638\",\n \"0.6504959\",\n \"0.6502909\",\n \"0.64886916\",\n \"0.64804417\",\n \"0.64592373\",\n \"0.64453495\",\n \"0.6444375\",\n \"0.64364594\",\n \"0.6422805\",\n \"0.6422294\",\n \"0.64128405\",\n \"0.64121294\",\n \"0.6404425\",\n \"0.6391617\",\n \"0.63791203\",\n \"0.6376413\",\n \"0.636682\",\n \"0.6366483\",\n \"0.6353054\",\n \"0.63263005\",\n \"0.6308415\",\n \"0.63074535\",\n \"0.63015205\",\n \"0.6300897\",\n \"0.62934697\",\n \"0.62706727\",\n \"0.62626433\",\n \"0.62578815\",\n \"0.62483877\",\n \"0.6242479\",\n \"0.6237388\",\n \"0.622969\",\n \"0.62106353\",\n \"0.62096643\",\n \"0.62044406\",\n \"0.6191682\",\n \"0.61899376\",\n \"0.6186025\",\n \"0.6186025\",\n \"0.61714125\",\n \"0.617067\",\n \"0.61611396\",\n \"0.61572653\",\n \"0.61543864\",\n \"0.615152\",\n \"0.6142628\",\n \"0.6130133\",\n \"0.6122881\",\n \"0.61210936\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75777197"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":691,"cells":{"query":{"kind":"string","value":"Static method for delete a project."},"document":{"kind":"string","value":"def delete_project(project_id):\n client = RequestManager()\n client.set_method(\"DELETE\")\n client.set_endpoint(\"/projects/{0}\".format(project_id))\n client.execute_request()"},"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 delete_project(project):\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.delete_project(project)\n if ret[constants.STATUS_CODE_KEY] == 200:\n click.echo(\"Success\")\n else:\n click.echo(ret[constants.MESSAGE_KEY])","def delete_project(arn=None):\n pass","def do_project_delete(cs, args):\n key = args.project\n if cs.projects.is_id(key):\n id = key\n else:\n id = cs.projects.get_id_by_name(key)\n cs.projects.delete(id)\n print(\"Delete Project '%s' successfully.\" % key)","def delete_stored_project():\n client = RequestManager()\n client.set_method(\"DELETE\")\n client.set_endpoint(\"/projects/{0}\".format(STORED_ID['project_id']))\n client.execute_request()","def delete_project(projectname):\n response = jsonify(admin.delete_project(current_app.scoped_session(), projectname))\n return response","def delete(self):\n _url = f\"{self.connector.base_url}/projects/{self.project_id}\"\n\n self.connector.http_call(\"delete\", _url)\n\n self.project_id = None\n self.name = None","def delete_project(\n name\n):\n\n cmd = dict()\n cmd[\"type_\"] = \"delete_project\"\n cmd[\"name_\"] = name\n\n comm.send(cmd)","def delete_project(request, project_id):\n if not request.user.is_superuser:\n messages.error(request, 'Sorry, only store owners can do that.')\n return redirect(reverse('home'))\n\n project = get_object_or_404(Project, pk=project_id)\n project.delete()\n messages.success(request, 'Project deleted!')\n return redirect(reverse('portfolio'))","def test_delete_project(self):\n pass","def test_delete_project(self):\n pass","def deleteProject(self, projectId):\n uri = \"/v1/projects/\" +str(projectId)\n response = self.client.delete(uri)\n return response","def delete_project(self, project_id):\n self._run(\n url_path=\"projects/delete\",\n id=project_id,\n )\n return True","def delete_project(request, project_id):\n\n profile = get_object_or_404(Profile, user=request.user)\n project = get_object_or_404(GameProject, pk=project_id)\n\n if not profile.is_creator:\n messages.error(request, 'Sorry, only creators can do that.')\n return redirect(reverse('home'))\n if project.owner != profile:\n messages.error(request, 'Sorry, only the project owner can do that.')\n return redirect(reverse('home'))\n\n project = get_object_or_404(GameProject, pk=project_id)\n project.delete()\n messages.success(request, 'Project deleted!')\n return redirect(reverse('all_projects'))","def delete(self, guid):\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\n # search for the Project and delete if found\n key = db.Key.from_path('Project', int(guid))\n project = db.get(key)\n if not project == None:\n project.delete()\n self.response.set_status(204, \"Deleted\")\n else:\n self.response.set_status(404, \"Not Found\")\n else:\n self.response.set_status(401, \"Not Authorized\")","def delete_project(self, project_id):\n return self._delete('/projects/{0}'.format(project_id))","def delete_project(project_id):\n project = Project.query.filter_by(id=project_id).first()\n if not project:\n return {\n 'success': False,\n 'message': f\"No project with the specified id {project_id} found.\",\n }\n\n else:\n if is_project_manager(project, g.user):\n # delete related tasks\n Task.query.filter_by(project=project).delete()\n #delete related invites\n Invitation.query.filter_by(project=project).delete()\n db_session.delete(project)\n db_session.commit()\n return {\n 'success': True,\n 'result': {},\n 'message': \"Project Deleted Successfully.\",\n }","def delete(conn, project):\n with conn:\n c = conn.cursor()\n c.execute(\"DELETE FROM projects WHERE project =?\", (project,))","def delete_project(self, project_name):\n # type(project_name) == unicode\n project = self.db.get_project_by_name(project_name)\n if not project:\n print(u\"*** Error: The project '{}' was not found.\"\n \"\".format(project_name))\n return\n print('Caution! The related tracking will be deleted as well.{eol}'\n 'Do you really want to delete the project? [y/N] '\n ''.format(eol=os.linesep), end='')\n if not helpers.get_yes_no(default='n'):\n return\n self.db.delete_project_by_name(project_name)\n print(u\"The project '%s' has been deleted.\" % project_name)\n self.set_prompt()","def destroy(self, request, pk=None):\n try:\n project = Project.objects.get(pk=pk)\n project.delete()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)\n\n except Project.DoesNotExist as ex:\n return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)\n\n except Exception as ex:\n return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)","def delete_project(self, project_id):\n _url = f\"{self.base_url}/projects/{project_id}\"\n self.http_call(\"delete\", _url)\n return","def delete_project(project_id):\n \n project = mongo.db.projects\n project.delete_one({'_id': ObjectId(project_id)})\n flash('Your project has been deleted.', 'success')\n return redirect(url_for('projects'))","def project_delete(cursor, project):\n haystack = (project['_id'], )\n\n query = \"DELETE FROM projects WHERE _id=?\"\n try:\n cursor.execute(query, haystack)\n except Exception as e:\n on_error(e)\n\n query = \"DELETE FROM namespaces WHERE project_id=?\"\n try:\n cursor.execute(query, haystack)\n except Exception as e:\n on_error(e)\n else:\n cursor.connection.commit()\n raise Return((True, None))","def delete(self, oid):\n path = '/projects/%s' % oid\n res = self.client.call(path, 'DELETE', data='', token=self.manager.identity.token)\n self.logger.debug('Delete openstack project: %s' % truncate(res))\n return True","def test_projects_delete(self):\n project = Project()\n response = self.client.open('/project-tracker/projects',\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def delete_project(proj_id):\n project_obj = Project.objects.get(id=proj_id)\n print('Deleting project the fastq files within the project: ', project_obj.description)\n\n description = project_obj.description.replace(' ', '') # remove any space in the project name\n project_dir = 'documents/%s/%s' % (str(project_obj.date.date()), description)\n shutil.rmtree(project_dir, ignore_errors=True)\n print(\"Files deleted.\")","def delete(self, request, p_name):\n project = Project.objects.get(name=p_name)\n connectors = project.connector_set.all()\n connectors.delete()\n if os.path.isfile(project.project_location):\n os.remove(project.project_location)\n project.delete()\n return HttpResponse(HTTPStatus.OK)","def delete_project(self, project_name, check=True):\n page_projects = self._page_projects()\n\n with page_projects.table_projects.row(\n name=project_name).dropdown_menu as menu:\n menu.button_toggle.click()\n menu.item_delete.click()\n\n page_projects.form_delete_project_confirm.submit()\n\n if check:\n self.close_notification('success')\n page_projects.table_projects.row(\n name=project_name).wait_for_absence()","def delete_project_view(request, id):\n\n # retrieve the project to be deleted through his id. Raise an error if the project does not exist\n project = get_object_or_404(Projet, id=id)\n\n # Check if the logged in user is allowed to delete this project\n if request.user.has_perm('taskmanager.{}_project_permission'.format(project.id)):\n # Eventually delete the project\n project.delete()\n\n return redirect(\"projects\")","def delete_project(request, project_id):\n try:\n project = Project.objects.get(pk=project_id)\n except Project.DoesNotExist:\n raise Http404(\"Project does not exist\")\n # check whether the user is the one who created this project\n if project.user.email != request.session['email']:\n return HttpResponseRedirect('/projects/' + str(project_id))\n else:\n if request.method == \"POST\":\n if project:\n project.delete()\n return HttpResponseRedirect('/projects/')\n else:\n return render(request, 'projects/delete_project.html',\n {'project': project})\n return render(request, 'projects/delete_project.html', {'project': project})","def delete_project(self, name=None, delete_dir=False):\n victim = name or self.current\n if victim not in self:\n raise ValueError(\"{} is not a project\".format(victim))\n\n if len(self) == 1:\n raise ValueError(\"Can't delete only remaining project\")\n\n ProjectDataset.delete().where(ProjectDataset.name == victim).execute()\n\n if delete_dir:\n dir_path = self._base_data_dir / safe_filename(victim)\n assert dir_path.is_dir(), \"Can't find project directory\"\n shutil.rmtree(dir_path)\n\n if name is None or name == self.current:\n if \"default\" in self:\n self.set_current(\"default\")\n else:\n self.set_current(next(iter(self)).name)\n return self.current","def post_project_delete(self, resource_id, resource_dict):\n pass","def delete(self, project_id):\n try:\n authenticated_user_id = token_auth.current_user()\n if not ProjectAdminService.is_user_action_permitted_on_project(\n authenticated_user_id, project_id\n ):\n raise ValueError()\n except ValueError:\n return {\n \"Error\": \"User is not a manager of the project\",\n \"SubCode\": \"UserPermissionError\",\n }, 403\n\n try:\n ProjectAdminService.delete_project(project_id, authenticated_user_id)\n return {\"Success\": \"Project deleted\"}, 200\n except ProjectAdminServiceError as e:\n return {\"Error\": str(e).split(\"-\")[1], \"SubCode\": str(e).split(\"-\")[0]}, 403","def test_projects_id_delete(self):\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\n method='DELETE')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def delete(\n self, url: str\n ) -> pymongo.results.DeleteResult:\n return self._mongo.delete({\n 'url': url\n },\n 'projects'\n )","def delete_project(id):\n result = delete_project_to_db(id)\n flash(result)\n return redirect(url_for(\"portfolio\"))","def test_delete_project(self):\n self.assertEqual(Project.objects.count(), 1)\n self.assertEqual(Group.objects.count(), 2)\n\n delete_project(Project.objects.get(name=\"project A\"))\n\n self.assertEqual(Project.objects.count(), 0)\n self.assertEqual(Group.objects.count(), 0)","def delete_remote_project(profile, project):\n return delete_remote_project_worker.delay(profile_id=profile.id,\n project_id=project.id)","def delete(self, *args, **kwargs):\n if 'user' not in kwargs or not args:\n self.raise401()\n\n user = kwargs['user']\n path = parse_path(args[0])\n project = Project.objects(name=path[0], members__in=[user])\n if not project:\n self.raise401()\n try:\n project.delete()\n self.set_status(204)\n self.finish()\n except Exception as e:\n reason = e.message\n self.raise400(reason=reason)","def delete_keystone_v3_project(self, project_id, domain_id):\n LOG_OBJ.debug(\"Disable the project.\")\n kwargs = {\"project_id\": project_id, \"enabled\": False}\n self.set_keystone_v3_project(**kwargs)\n\n LOG_OBJ.debug(\"Deleting the project.\")\n\n _url = \"http://\" + self.host_ip + \":35357/v3/projects/\" + \\\n str(project_id)\n _headers = {'x-auth-token': self.cloud_admin_info[\"token_domain\"],\n 'content-type': 'application/json'}\n _body = None\n response = self.request(\"DELETE\", _url, _headers, _body)\n\n if response is None:\n LOG_OBJ.error(\"No response from Server while deleting the project\")\n print (\"No response from Server while deleting the project\")\n return response\n\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\" Deleting project Failed with status %s \"\n \"and error : %s\" % (response.status, response.data))\n print (\" Deleting project Failed with status %s and error : %s\" %\n (response.status, response.data))\n return response.status\n\n return True","def es_delete(project=None):\n if project is not None:\n script_indexer.delete_project(project)\n else:\n script_indexer.delete_all()","def test_remove_project(self):\n pass","def destroy(config, args):\n log = logging.getLogger('kraftwerk.destroy')\n if confirm(\"Remove project %s from node %s along with all services and data?\" % \n (args.project.name, args.node.hostname)):\n args.node.ssh(config.template(\"scripts/project_destroy.sh\", project=args.project))\n print \"Project %s removed from node %s\" % \\\n (args.project.name, args.node.hostname )\n for service in args.project.services(args.node):\n args.node.ssh(service.destroy_script)","def delete_project(self):\n tasks = self._get_all_tasks()\n\n task_id = tasks[self.tasks_view.currentRow()].Id\n task_status = tasks[self.tasks_view.currentRow()].Status\n\n if task_status is 0:\n warning = Warning(\n \"<html><head/><body><p align=\\\"center\\\"><span style=\\\" font-weight:600;\\\">\"\n \"Unable delete Task. \"\n \"Make sure the Task is Done\"\n \"</span></p></body></html>\"\n )\n warning.exec_()\n else:\n self.tasks_flow.delete_task(task_id)\n self.write_tasks_table()","def project_delete_event(self, proj_info):\n\n LOG.debug(\"Processing project_delete_event...\")\n proj_id = proj_info.get('resource_info')\n proj_name = self.get_project_name(proj_id)\n if proj_name:\n try:\n self.dcnm_client.delete_project(proj_name,\n self.cfg.dcnm.\n default_partition_name)\n except dexc.DfaClientRequestFailed:\n # Failed to delete project in DCNM.\n # Save the info and mark it as failure and retry it later.\n LOG.error(_LE(\"Failed to create project %s on DCNM.\"),\n proj_name)\n self.update_project_info_cache(proj_id, name=proj_name,\n opcode='delete',\n result=constants.DELETE_FAIL)\n else:\n self.update_project_info_cache(proj_id, opcode='delete')\n LOG.debug('Deleted project:%s', proj_name)\n self.project_delete_notif(proj_id, proj_name)","def pre_project_delete(self, resource_id):\n pass","def delete(self, name, project=None):\n qlist = self._list(project)\n key = self._queue(project, name)\n self._db.delete(key)\n self._db.zremrangebyscore(qlist, -1, 1)","def delete(self, team_id, project_id):\n try:\n if not ProjectAdminService.is_user_action_permitted_on_project(\n token_auth.current_user, project_id\n ):\n raise ValueError()\n TeamService.delete_team_project(team_id, project_id)\n return {\"Success\": True}, 200\n except ValueError:\n return {\n \"Error\": \"User is not a manager of the project\",\n \"SubCode\": \"UserPermissionError\",\n }, 403","def delete_namespaced_project(self, name, **kwargs):\n\n all_params = ['name', 'pretty']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method delete_namespaced_project\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `delete_namespaced_project`\")\n\n resource_path = '/oapi/v1/projects/{name}'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'DELETE',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='UnversionedStatus',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def remove_single_project(project_name):\n p = subprocess.Popen('rm -rf {}/{}'.format(context.__PROJECTS_PATH__, project_name), shell=True)\n p.wait()","def delete(self, guid):\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\n # search for the Project and delete if found\n key = db.Key.from_path('Task', int(guid))\n task = db.get(key)\n wantsNotifications = {\"true\": True, \"false\": False}.get(self.request.params['notify'].lower())\n currentUserId = self.request.params['UUID']\n cukey = db.Key.from_path('User', int(currentUserId))\n user = db.get(cukey)\n if not task == None:\n # cache current values before updates\n taskName = task.name\n taskType = task.type\n taskPriority = task.priority\n taskStatus = task.developmentStatus\n taskValidation = task.validation\n taskSubmitterId = task.submitterId\n taskAssigneeId = task.assigneeId\n taskEffort = task.effort\n taskProjectId = task.projectId\n taskDescription = task.description\n # Push notification email on the queue if we need to notify\n if notification.should_notify(currentUserId,task,\"deleteTask\",wantsNotifications):\n taskqueue.add(url='/mailer', params={'taskId': int(guid), 'currentUUID': self.request.params['UUID'], 'action': \"deleteTask\", 'name': taskName, 'type': taskType, 'priority': taskPriority, 'status': taskStatus, 'validation': taskValidation, 'submitterId': taskSubmitterId, 'assigneeId': taskAssigneeId, 'effort': taskEffort, 'projectId': taskProjectId, 'description': taskDescription})\n task.delete()\n self.response.set_status(204, \"Deleted\")\n else:\n self.response.set_status(404, \"Not Found\")\n else:\n self.response.set_status(401, \"Not Authorized\")","def delete_api(self, project_id, api_id, hbase_manager=None):\n\n self._project_service.get_nondeleted_project(project_id)\n\n proj_api_col = \"apis:{0}\".format(api_id)\n project_id = bytes(project_id, self._charset)\n api_id = bytes(api_id, self._charset)\n\n self.get_nondeleted_api(project_id, api_id)\n\n connection = hbase_manager.connection\n api_tbl = connection.table(\"apis\")\n projects_tbl = connection.table(\"projects\")\n\n projects_tbl.delete(project_id, [bytes(proj_api_col, self._charset)])\n api_tbl.put(api_id, {\n b\"attrs:state\": b\"deleted\"\n })","def delete(self, project_id):\n project_model = ProjectDBModel.query.get(project_id)\n if not project_model:\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\n try:\n data = request.get_json()\n # cambiarlo cuando se vuelva a tener dos PKs\n deleted = FavoritesProjectDBModel.delete(\n data['user_id'], project_id)\n if deleted:\n users = \\\n FavoritesProjectDBModel.get_favorites_of_project_id(\n project_id)\n response_object = {\n \"project_id\": project_id,\n \"users_id\": users,\n }\n return response_object, 200\n else:\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\n except KeyError:\n ns.abort(404, status=MISSING_VALUES_ERROR)","def delete_project_file(self, project=None):\n if type(project) is not Project:\n return False\n\n path = self.data_path + self.project_dir\n\n # generate filenames\n filename = path + '/' + self.us(project.project_id()) + '.flproject'\n\n # check if the file exists and delete it\n if os.path.isfile(filename):\n os.remove(filename)\n return True\n else:\n return False","async def delete(self, ctx, project_name: str) -> None:\n if not ctx.projects.find_project(project_name):\n channel = discord.utils.get(\n ctx.guild.channels, name=f\"{project_name}-project\")\n\n if channel and channel.category.name == \"Flux Projects\":\n if ctx.author.permissions_in(channel).manage_channels:\n message = await ctx.send(\"That project doesn't appear to\"\n \" exist in my database, but the \"\n \"channel still exists. \"\n \"Would you like to delete it?\")\n yes = \"<:greenTick:596576670815879169>\"\n no = \"<:redTick:596576672149667840>\"\n await message.add_reaction(yes)\n await message.add_reaction(no)\n reaction, user = await ctx.bot.wait_for(\n \"reaction_add\",\n check=lambda reaction, user: (user == ctx.author) and\n (str(reaction.emoji) == yes or no) and\n (reaction.message.channel == ctx.channel)\n )\n if reaction.emoji.id == ctx.bot.config.tick_yes:\n await channel.delete(reason=\"Project not found.\")\n await ctx.send(\"The channel was deleted sucessfully.\")\n return\n\n elif reaction.emoji.id == ctx.bot.config.tick_no:\n await ctx.send(\"Not deleting the channel.\")\n return\n\n else: # If author doesn't have access to deleting channels.\n await ctx.send(\"That project does not appear to be in my \"\n \"database, but the channel for it still \"\n \"exists. Please have someone with\"\n \" manage channels run this chommand.\"\n )\n return\n else:\n await ctx.send(\"I could not find this project.\")\n return\n\n if str(ctx.author.id) != ctx.projects.find_project(project_name).get(\n \"owner\"):\n await ctx.send(\"Only the project owner \"\n \"can delete this project.\")\n return\n message = await ctx.send(\"This action __cannot__ be undone. \"\n \"Once you do this, everything is gone. \"\n \"Are you sure you want to continue?\")\n yes = \"<:greenTick:596576670815879169>\"\n no = \"<:redTick:596576672149667840>\"\n await message.add_reaction(yes)\n await message.add_reaction(no)\n reaction, user = await ctx.bot.wait_for(\n \"reaction_add\", check=lambda reaction, user:\n (user == ctx.author) and\n (str(reaction.emoji) == yes or no) and\n (reaction.message.channel == ctx.channel)\n )\n if reaction.emoji.id == ctx.bot.config.tick_yes:\n channel = ctx.projects.find_project(\n project_name).get(\"channel\")\n channel = discord.utils.get(ctx.guild.channels,\n id=int(channel))\n ctx.projects.delete_project(project_name)\n if channel:\n await channel.delete(reason=\"Project deleted.\")\n await ctx.send(\"The project has been deleted.\")\n elif reaction.emoji.id == ctx.bot.config.tick_no:\n await ctx.send(\"Not deleting the project.\")","def delete(self):\n return self.client._perform_empty(\n \"DELETE\", \"/project-folders/%s\" % self.project_folder_id)","def delete(self):\r\n delete_tracks(self.project, [self])","def project_post_delete(sender, instance, **kwargs):\n instance.url.delete(False)","def remove_project(project_id):\n response_object = {'status': 'success'}\n with database.engine.begin() as connection:\n\n stmt = select([models.projects.c.path]).where(\n models.projects.c.project_id == project_id)\n project = connection.execute(stmt).first()\n\n if project:\n app = flask.current_app\n project_path = os.path.join(\n app.root_path, app.config['DATA_DIRECTORY'], project['path'])\n if 'morphocut' in project_path and app.config['DATA_DIRECTORY'] in project_path:\n print('removing project with id {}'.format(project_id))\n if os.path.exists(project_path):\n helpers.remove_directory(project_path)\n\n stmt = models.projects.delete().where( # pylint: disable=no-value-for-parameter\n models.projects.c.project_id == project_id)\n\n connection.execute(stmt)\n\n return jsonify(response_object)","def delete_project_by_name(self, project_name):\n with self._transaction.cursor() as cur:\n # delete associations between this project and any barcodes\n cur.execute(\"DELETE FROM barcodes.project_barcode \"\n \"WHERE project_id in (\"\n \"SELECT project_id FROM barcodes.project \"\n \"WHERE project = %s)\",\n (project_name,))\n\n # now delete the project itself\n cur.execute(\"DELETE FROM barcodes.project WHERE project = %s\",\n (project_name,))\n return cur.rowcount == 1","def _delete_dm_project(self, project_uuid: str) -> None:\n _LOGGER.warning('Deleting DM Project %s...', project_uuid)\n\n dm_rv: DmApiRv = DmApi.delete_project(self.__org_owner_dm_token,\n project_id=project_uuid)\n if not dm_rv.success:\n _LOGGER.error('Failed to delete DM Project %s', project_uuid)\n return\n\n _LOGGER.warning('Deleted DM Project %s', project_uuid)","def delete(project, zone, instance):\n print >>sys.stderr, 'WARNING: duplicated jobs may fail/corrupt results'\n print >>sys.stderr, ('TODO(fejta): See http://stackoverflow.com/'\n 'questions/19645430/changing-jenkins-build-number')\n answer = raw_input('Delete %s [yes/NO]: ')\n if not answer or answer != 'yes':\n print >>sys.stderr, 'aborting'\n sys.exit(1)\n gcloud(\n project,\n 'compute',\n 'instances',\n 'delete',\n '--zone=%s' % zone,\n instance,\n )\n gcloud(\n project,\n 'compute',\n 'disks',\n 'delete',\n '--zone=%s' % zone,\n *get_disks(instance))","def delete_orphan_project(apps, schema_editor):\n Project = apps.get_model('data_manager', 'Project')\n Project.objects.filter(dataset__isnull=True).delete()\n return","def delete(ctx: click.Context, repository_path):\n root_commands.cmd_delete(ctx.obj, repository_path)","def delete_project_quotas(self, project_id, extra_headers=None,\n use_auth=True, user_name=None):\n resp = self.client.delete('project-quotas/' + project_id,\n extra_headers=extra_headers,\n use_auth=use_auth, user_name=user_name)\n return resp","def DelProject(projname):\n\tif projname == \"\" or projname == None:\n\t\tpjnm = raw_input(\"\\nNombre del proyecto: \").lower()\n\t\tif pjnm == \"\" or pjnm == None:\n\t\t\tcancel()\n\telse:\n\t\t# Proceso para borrar todo el proyecto\n\t\tpass\n\n\tpa = open(\"author_name.txt\", \"r\")\t#Abre el archivo con el nombre del autor\n\tpa.read()\n\tpc = open(\"project_code.txt\", \"r\")\t#Abre el archivo con el codigo de proyecto\n\tpc.read()\n\n\tuserpa = raw_input(\"Ingrese el nombre del autor: \").lower()\n\tuserpc = raw_input(\"Ingrese el codigo del proyecto: \").lower()\n\n\tif userpa == pa and userpc == pc:\t#Se verifica que userpa(nombre del autor por el usuario) sea igual a pa(nombre original del autor) y lo mismo con el codigo del proyecto\n\t\tprint \"Iniciando el Borrado del Proyecto...\"\n\t\tpcommands.del_project()\n\t\tprint \"El proyecto se ha borrado con exito!\"\n\telse:\n\t\tprint \"El codigo del proyecto o el nombre del autor no es correcto.\"\n\t\tcancel()","def delete_all_projects():\n client = RequestManager()\n client.set_method(\"GET\")\n client.set_endpoint(\"/projects\")\n response = client.execute_request()\n for project in response.json():\n try:\n ProjectHelper.delete_project(project[\"id\"])\n except TypeError:\n LOGGER.info(project)","def delete(self, endpoint, params=None):\n params = params or dict()\n return self.request(verb=requests.delete, address=self.project_address + endpoint,\n params=params)","def delete(self):\n return self.client._perform_empty(\n \"DELETE\", \"/projects/%s/managedfolders/%s\" % (self.project_key, self.odb_id))","def command_delete(self):\n if self.gcp_env.project not in SUPPORTED_PROJECT_CONFIGS.keys():\n _logger.error(f'Project config not supported {self.gcp_env.project}')\n return 1\n\n if not self.args.bucket and not self.args.id:\n _logger.error(\"--bucket and --id required for delete.\")\n return 1\n\n # Get notification\n client = storage.Client()\n bucket = client.get_bucket(self.args.bucket)\n target = bucket.get_notification(self.args.id, client)\n\n if self.gcp_env.project != target.topic_project:\n _logger.error(\"Notification project and specified project do not match.\")\n return 1\n\n # Delete the notification\n try:\n target.delete(client=client)\n\n except NotFound:\n _logger.error(f\"Notification ID {self.args.id} not found.\")\n return 1\n\n _logger.info(f\"Notification id {self.args.id} has been deleted.\")\n\n _logger.info(\"Removing notification from config...\")\n self.delete_notification_from_config()\n\n return 0","def delete(self, *args, **kwargs):\n self.request(\"delete\", *args, **kwargs)","def _delete(self, *args, **kwargs):\n return self._request('delete', *args, **kwargs)","def test_projects_id_contacts_delete(self):\n project = Contact()\n response = self.client.open('/project-tracker/projects/{id}/contacts'.format(id=56),\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def cmd_apps__destroy(args):\n \n if args.name is None and in_git_repo():\n args.name = _get_current_project_name()\n\n if args.name is None:\n print \"Please provide a project name.\"\n sys.exit(1)\n\n print \"Destroying project %s...\" % args.name\n remote.destroy_project(args.name)\n print \"Project %s destroyed.\" % args.name\n if in_git_repo() and _get_current_project_name() == args.name:\n git(None, 'remote', 'rm', 'tinyserv')\n print \"Removed remote '%s'.\" % args.name","def delete():","def delete(self, application_id):","def _on_del_project(self):\n project = self.ddnCurProject.get()\n# if len(project) > 0:\n if project:\n if '.prj'!= project[-4:]:\n project += '.prj'\n if os.path.exists(self.BibTerm + '/'+ project):\n os.remove(self.BibTerm + '/'+ project)\n self.list_projects = [f.rstrip('.prj') \\\n for f in os.listdir(self.BibTerm) \\\n if f.endswith('.prj')]\n self.ddnCurProject['values'] = self.list_projects\n# if len(self.list_projects) > 0:\n if self.list_projects:\n self.ddnCurProject.set(self.list_projects[0])\n else:\n self.ddnCurProject.set('')\n pass","def delete(self, *args, **kw):\n kw['method'] = 'DELETE'\n return self.open(*args, **kw)","def delete_language_from_project(self, project_id, language_code):\n self._run(\n url_path=\"languages/delete\",\n id=project_id,\n language=language_code\n )\n return True","def record_destroy_for_project(project_id):\n session = get_session()\n with session.begin():\n session.query(models.ProjectAccountRecord).\\\n filter_by(project_id=project_id).\\\n update({'deleted': True,\n 'deleted_at': datetime.datetime.utcnow(),\n 'updated_at': datetime.datetime.utcnow()})","def remove_deleted_project(self, project_id):\n self.db.make_query(\n '''\n DELETE FROM deleted_project WHERE project_id = \"{}\";\n '''.format(project_id)\n )\n\n if self.get_deleted_project(project_id):\n return False\n\n return True","def delete(self):\n\n headers = self._default_headers()\n\n return self._request(self.name,\n ok_status=None,\n data=None,\n headers=headers,\n method=\"DELETE\")","def remove_project(self, project_id):\n project_file_path = '{}/{}'.format(self._storage_location, project_id)\n if os.path.exists(project_file_path):\n os.remove(project_file_path)\n else:\n raise ValueError('The project id {} does not exist!'.format(project_id))","def test_projects_id_comments_delete(self):\n project = Comment()\n response = self.client.open('/project-tracker/projects/{id}/comments'.format(id=56),\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","async def delete_user_byid(*_):\n return web.Response(text=\"PUT project not implemented\", status=501)","def delete(self, *args, **kwargs):\n pass","def delete(self, *args, **kwargs):\n pass","def userproject_post_delete(sender, instance, **kwargs):\n instance.document.delete(False)","def delete(repo):\n print('Repo: %s' % repo)\n print('Deleted')","def delete(project_id, integration_id):\n IntegrationService.delete(project_id, integration_id)\n\n return {\"status\": \"deleted\"}, 200","def delete():\n run('rm -r {}'.format(utils.home('apps', env.PROJECT_NAME)))","def delete_record(request, slug, pk):\n # Try except to make sure the user is a member of this project\n try:\n ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\n except ObjectDoesNotExist:\n # User is not a member\n return HttpResponse(\"You're trying to access a project you're not a member of or a project that does not exist.\")\n else:\n # User is a member\n pm = ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\n # Access control.. if not owner or editor - access denied.\n if pm.is_owner or pm.is_editor:\n # User has access\n record = get_object_or_404(models.Record, pk=pk)\n # Delete record\n models.Record.objects.filter(project=get_object_or_404(models.Project, slug=slug), pk=pk).delete()\n # Send user back to project detail, the overview of all records in the project.\n return redirect('projects:single', slug=slug)\n else:\n # Access denied...\n return HttpResponse(\"You don't have the permission to do this\")","def delete(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"delete\"), kwargs)","def delete(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"delete\"), kwargs)","def delete_bucket_from_project(projectname, bucketname):\n return jsonify(\n admin.delete_bucket_on_project(\n current_app.scoped_session(), projectname, bucketname\n )\n )","def delete(self, *args, **kwargs) -> Any:\n pass","def delete_project_entities(cls, project_id,\n suppress_exception=False,\n session=None):\n cls.db_repo.delete_project_entities(\n project_id, suppress_exception=suppress_exception, session=session)","def delete():\n # Must be logged in to perform any delete commands.\n auth_required()\n pass","def delete(self, api_path, *args, **kwargs):\n\n\t\treturn self._do_operation(u'delete', api_path, *args, **kwargs)","def test_Projects_CanBeCreatedAndDeleted_Successfully(self):\n\n name = \"Test Project \"\n # Test create a new project successfully\n project = self.api.create_project(name, \"Py\")\n self.assertTrue(project['success'])\n self.assertTrue(project['projects'][0]['projectId'] > 0)\n self.assertTrue(project['projects'][0]['name'] == name)\n\n # Delete the project\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\n self.assertTrue(deleteProject['success'])\n\n # Make sure the project is really deleted\n projectList = self.api.list_projects()\n self.assertFalse(any(project['projects'][0]['projectId'] == projectList['projects'][i]['projectId']\n for i in range(len(projectList['projects']))))","def delete(self, *args, **kwargs):\n return self.handle_delete_request()"],"string":"[\n \"def delete_project(project):\\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\\n ret = bmi.delete_project(project)\\n if ret[constants.STATUS_CODE_KEY] == 200:\\n click.echo(\\\"Success\\\")\\n else:\\n click.echo(ret[constants.MESSAGE_KEY])\",\n \"def delete_project(arn=None):\\n pass\",\n \"def do_project_delete(cs, args):\\n key = args.project\\n if cs.projects.is_id(key):\\n id = key\\n else:\\n id = cs.projects.get_id_by_name(key)\\n cs.projects.delete(id)\\n print(\\\"Delete Project '%s' successfully.\\\" % key)\",\n \"def delete_stored_project():\\n client = RequestManager()\\n client.set_method(\\\"DELETE\\\")\\n client.set_endpoint(\\\"/projects/{0}\\\".format(STORED_ID['project_id']))\\n client.execute_request()\",\n \"def delete_project(projectname):\\n response = jsonify(admin.delete_project(current_app.scoped_session(), projectname))\\n return response\",\n \"def delete(self):\\n _url = f\\\"{self.connector.base_url}/projects/{self.project_id}\\\"\\n\\n self.connector.http_call(\\\"delete\\\", _url)\\n\\n self.project_id = None\\n self.name = None\",\n \"def delete_project(\\n name\\n):\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"delete_project\\\"\\n cmd[\\\"name_\\\"] = name\\n\\n comm.send(cmd)\",\n \"def delete_project(request, project_id):\\n if not request.user.is_superuser:\\n messages.error(request, 'Sorry, only store owners can do that.')\\n return redirect(reverse('home'))\\n\\n project = get_object_or_404(Project, pk=project_id)\\n project.delete()\\n messages.success(request, 'Project deleted!')\\n return redirect(reverse('portfolio'))\",\n \"def test_delete_project(self):\\n pass\",\n \"def test_delete_project(self):\\n pass\",\n \"def deleteProject(self, projectId):\\n uri = \\\"/v1/projects/\\\" +str(projectId)\\n response = self.client.delete(uri)\\n return response\",\n \"def delete_project(self, project_id):\\n self._run(\\n url_path=\\\"projects/delete\\\",\\n id=project_id,\\n )\\n return True\",\n \"def delete_project(request, project_id):\\n\\n profile = get_object_or_404(Profile, user=request.user)\\n project = get_object_or_404(GameProject, pk=project_id)\\n\\n if not profile.is_creator:\\n messages.error(request, 'Sorry, only creators can do that.')\\n return redirect(reverse('home'))\\n if project.owner != profile:\\n messages.error(request, 'Sorry, only the project owner can do that.')\\n return redirect(reverse('home'))\\n\\n project = get_object_or_404(GameProject, pk=project_id)\\n project.delete()\\n messages.success(request, 'Project deleted!')\\n return redirect(reverse('all_projects'))\",\n \"def delete(self, guid):\\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\\n # search for the Project and delete if found\\n key = db.Key.from_path('Project', int(guid))\\n project = db.get(key)\\n if not project == None:\\n project.delete()\\n self.response.set_status(204, \\\"Deleted\\\")\\n else:\\n self.response.set_status(404, \\\"Not Found\\\")\\n else:\\n self.response.set_status(401, \\\"Not Authorized\\\")\",\n \"def delete_project(self, project_id):\\n return self._delete('/projects/{0}'.format(project_id))\",\n \"def delete_project(project_id):\\n project = Project.query.filter_by(id=project_id).first()\\n if not project:\\n return {\\n 'success': False,\\n 'message': f\\\"No project with the specified id {project_id} found.\\\",\\n }\\n\\n else:\\n if is_project_manager(project, g.user):\\n # delete related tasks\\n Task.query.filter_by(project=project).delete()\\n #delete related invites\\n Invitation.query.filter_by(project=project).delete()\\n db_session.delete(project)\\n db_session.commit()\\n return {\\n 'success': True,\\n 'result': {},\\n 'message': \\\"Project Deleted Successfully.\\\",\\n }\",\n \"def delete(conn, project):\\n with conn:\\n c = conn.cursor()\\n c.execute(\\\"DELETE FROM projects WHERE project =?\\\", (project,))\",\n \"def delete_project(self, project_name):\\n # type(project_name) == unicode\\n project = self.db.get_project_by_name(project_name)\\n if not project:\\n print(u\\\"*** Error: The project '{}' was not found.\\\"\\n \\\"\\\".format(project_name))\\n return\\n print('Caution! The related tracking will be deleted as well.{eol}'\\n 'Do you really want to delete the project? [y/N] '\\n ''.format(eol=os.linesep), end='')\\n if not helpers.get_yes_no(default='n'):\\n return\\n self.db.delete_project_by_name(project_name)\\n print(u\\\"The project '%s' has been deleted.\\\" % project_name)\\n self.set_prompt()\",\n \"def destroy(self, request, pk=None):\\n try:\\n project = Project.objects.get(pk=pk)\\n project.delete()\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\\n\\n except Project.DoesNotExist as ex:\\n return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)\\n\\n except Exception as ex:\\n return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\",\n \"def delete_project(self, project_id):\\n _url = f\\\"{self.base_url}/projects/{project_id}\\\"\\n self.http_call(\\\"delete\\\", _url)\\n return\",\n \"def delete_project(project_id):\\n \\n project = mongo.db.projects\\n project.delete_one({'_id': ObjectId(project_id)})\\n flash('Your project has been deleted.', 'success')\\n return redirect(url_for('projects'))\",\n \"def project_delete(cursor, project):\\n haystack = (project['_id'], )\\n\\n query = \\\"DELETE FROM projects WHERE _id=?\\\"\\n try:\\n cursor.execute(query, haystack)\\n except Exception as e:\\n on_error(e)\\n\\n query = \\\"DELETE FROM namespaces WHERE project_id=?\\\"\\n try:\\n cursor.execute(query, haystack)\\n except Exception as e:\\n on_error(e)\\n else:\\n cursor.connection.commit()\\n raise Return((True, None))\",\n \"def delete(self, oid):\\n path = '/projects/%s' % oid\\n res = self.client.call(path, 'DELETE', data='', token=self.manager.identity.token)\\n self.logger.debug('Delete openstack project: %s' % truncate(res))\\n return True\",\n \"def test_projects_delete(self):\\n project = Project()\\n response = self.client.open('/project-tracker/projects',\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def delete_project(proj_id):\\n project_obj = Project.objects.get(id=proj_id)\\n print('Deleting project the fastq files within the project: ', project_obj.description)\\n\\n description = project_obj.description.replace(' ', '') # remove any space in the project name\\n project_dir = 'documents/%s/%s' % (str(project_obj.date.date()), description)\\n shutil.rmtree(project_dir, ignore_errors=True)\\n print(\\\"Files deleted.\\\")\",\n \"def delete(self, request, p_name):\\n project = Project.objects.get(name=p_name)\\n connectors = project.connector_set.all()\\n connectors.delete()\\n if os.path.isfile(project.project_location):\\n os.remove(project.project_location)\\n project.delete()\\n return HttpResponse(HTTPStatus.OK)\",\n \"def delete_project(self, project_name, check=True):\\n page_projects = self._page_projects()\\n\\n with page_projects.table_projects.row(\\n name=project_name).dropdown_menu as menu:\\n menu.button_toggle.click()\\n menu.item_delete.click()\\n\\n page_projects.form_delete_project_confirm.submit()\\n\\n if check:\\n self.close_notification('success')\\n page_projects.table_projects.row(\\n name=project_name).wait_for_absence()\",\n \"def delete_project_view(request, id):\\n\\n # retrieve the project to be deleted through his id. Raise an error if the project does not exist\\n project = get_object_or_404(Projet, id=id)\\n\\n # Check if the logged in user is allowed to delete this project\\n if request.user.has_perm('taskmanager.{}_project_permission'.format(project.id)):\\n # Eventually delete the project\\n project.delete()\\n\\n return redirect(\\\"projects\\\")\",\n \"def delete_project(request, project_id):\\n try:\\n project = Project.objects.get(pk=project_id)\\n except Project.DoesNotExist:\\n raise Http404(\\\"Project does not exist\\\")\\n # check whether the user is the one who created this project\\n if project.user.email != request.session['email']:\\n return HttpResponseRedirect('/projects/' + str(project_id))\\n else:\\n if request.method == \\\"POST\\\":\\n if project:\\n project.delete()\\n return HttpResponseRedirect('/projects/')\\n else:\\n return render(request, 'projects/delete_project.html',\\n {'project': project})\\n return render(request, 'projects/delete_project.html', {'project': project})\",\n \"def delete_project(self, name=None, delete_dir=False):\\n victim = name or self.current\\n if victim not in self:\\n raise ValueError(\\\"{} is not a project\\\".format(victim))\\n\\n if len(self) == 1:\\n raise ValueError(\\\"Can't delete only remaining project\\\")\\n\\n ProjectDataset.delete().where(ProjectDataset.name == victim).execute()\\n\\n if delete_dir:\\n dir_path = self._base_data_dir / safe_filename(victim)\\n assert dir_path.is_dir(), \\\"Can't find project directory\\\"\\n shutil.rmtree(dir_path)\\n\\n if name is None or name == self.current:\\n if \\\"default\\\" in self:\\n self.set_current(\\\"default\\\")\\n else:\\n self.set_current(next(iter(self)).name)\\n return self.current\",\n \"def post_project_delete(self, resource_id, resource_dict):\\n pass\",\n \"def delete(self, project_id):\\n try:\\n authenticated_user_id = token_auth.current_user()\\n if not ProjectAdminService.is_user_action_permitted_on_project(\\n authenticated_user_id, project_id\\n ):\\n raise ValueError()\\n except ValueError:\\n return {\\n \\\"Error\\\": \\\"User is not a manager of the project\\\",\\n \\\"SubCode\\\": \\\"UserPermissionError\\\",\\n }, 403\\n\\n try:\\n ProjectAdminService.delete_project(project_id, authenticated_user_id)\\n return {\\\"Success\\\": \\\"Project deleted\\\"}, 200\\n except ProjectAdminServiceError as e:\\n return {\\\"Error\\\": str(e).split(\\\"-\\\")[1], \\\"SubCode\\\": str(e).split(\\\"-\\\")[0]}, 403\",\n \"def test_projects_id_delete(self):\\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\\n method='DELETE')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def delete(\\n self, url: str\\n ) -> pymongo.results.DeleteResult:\\n return self._mongo.delete({\\n 'url': url\\n },\\n 'projects'\\n )\",\n \"def delete_project(id):\\n result = delete_project_to_db(id)\\n flash(result)\\n return redirect(url_for(\\\"portfolio\\\"))\",\n \"def test_delete_project(self):\\n self.assertEqual(Project.objects.count(), 1)\\n self.assertEqual(Group.objects.count(), 2)\\n\\n delete_project(Project.objects.get(name=\\\"project A\\\"))\\n\\n self.assertEqual(Project.objects.count(), 0)\\n self.assertEqual(Group.objects.count(), 0)\",\n \"def delete_remote_project(profile, project):\\n return delete_remote_project_worker.delay(profile_id=profile.id,\\n project_id=project.id)\",\n \"def delete(self, *args, **kwargs):\\n if 'user' not in kwargs or not args:\\n self.raise401()\\n\\n user = kwargs['user']\\n path = parse_path(args[0])\\n project = Project.objects(name=path[0], members__in=[user])\\n if not project:\\n self.raise401()\\n try:\\n project.delete()\\n self.set_status(204)\\n self.finish()\\n except Exception as e:\\n reason = e.message\\n self.raise400(reason=reason)\",\n \"def delete_keystone_v3_project(self, project_id, domain_id):\\n LOG_OBJ.debug(\\\"Disable the project.\\\")\\n kwargs = {\\\"project_id\\\": project_id, \\\"enabled\\\": False}\\n self.set_keystone_v3_project(**kwargs)\\n\\n LOG_OBJ.debug(\\\"Deleting the project.\\\")\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v3/projects/\\\" + \\\\\\n str(project_id)\\n _headers = {'x-auth-token': self.cloud_admin_info[\\\"token_domain\\\"],\\n 'content-type': 'application/json'}\\n _body = None\\n response = self.request(\\\"DELETE\\\", _url, _headers, _body)\\n\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while deleting the project\\\")\\n print (\\\"No response from Server while deleting the project\\\")\\n return response\\n\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\" Deleting project Failed with status %s \\\"\\n \\\"and error : %s\\\" % (response.status, response.data))\\n print (\\\" Deleting project Failed with status %s and error : %s\\\" %\\n (response.status, response.data))\\n return response.status\\n\\n return True\",\n \"def es_delete(project=None):\\n if project is not None:\\n script_indexer.delete_project(project)\\n else:\\n script_indexer.delete_all()\",\n \"def test_remove_project(self):\\n pass\",\n \"def destroy(config, args):\\n log = logging.getLogger('kraftwerk.destroy')\\n if confirm(\\\"Remove project %s from node %s along with all services and data?\\\" % \\n (args.project.name, args.node.hostname)):\\n args.node.ssh(config.template(\\\"scripts/project_destroy.sh\\\", project=args.project))\\n print \\\"Project %s removed from node %s\\\" % \\\\\\n (args.project.name, args.node.hostname )\\n for service in args.project.services(args.node):\\n args.node.ssh(service.destroy_script)\",\n \"def delete_project(self):\\n tasks = self._get_all_tasks()\\n\\n task_id = tasks[self.tasks_view.currentRow()].Id\\n task_status = tasks[self.tasks_view.currentRow()].Status\\n\\n if task_status is 0:\\n warning = Warning(\\n \\\"<html><head/><body><p align=\\\\\\\"center\\\\\\\"><span style=\\\\\\\" font-weight:600;\\\\\\\">\\\"\\n \\\"Unable delete Task. \\\"\\n \\\"Make sure the Task is Done\\\"\\n \\\"</span></p></body></html>\\\"\\n )\\n warning.exec_()\\n else:\\n self.tasks_flow.delete_task(task_id)\\n self.write_tasks_table()\",\n \"def project_delete_event(self, proj_info):\\n\\n LOG.debug(\\\"Processing project_delete_event...\\\")\\n proj_id = proj_info.get('resource_info')\\n proj_name = self.get_project_name(proj_id)\\n if proj_name:\\n try:\\n self.dcnm_client.delete_project(proj_name,\\n self.cfg.dcnm.\\n default_partition_name)\\n except dexc.DfaClientRequestFailed:\\n # Failed to delete project in DCNM.\\n # Save the info and mark it as failure and retry it later.\\n LOG.error(_LE(\\\"Failed to create project %s on DCNM.\\\"),\\n proj_name)\\n self.update_project_info_cache(proj_id, name=proj_name,\\n opcode='delete',\\n result=constants.DELETE_FAIL)\\n else:\\n self.update_project_info_cache(proj_id, opcode='delete')\\n LOG.debug('Deleted project:%s', proj_name)\\n self.project_delete_notif(proj_id, proj_name)\",\n \"def pre_project_delete(self, resource_id):\\n pass\",\n \"def delete(self, name, project=None):\\n qlist = self._list(project)\\n key = self._queue(project, name)\\n self._db.delete(key)\\n self._db.zremrangebyscore(qlist, -1, 1)\",\n \"def delete(self, team_id, project_id):\\n try:\\n if not ProjectAdminService.is_user_action_permitted_on_project(\\n token_auth.current_user, project_id\\n ):\\n raise ValueError()\\n TeamService.delete_team_project(team_id, project_id)\\n return {\\\"Success\\\": True}, 200\\n except ValueError:\\n return {\\n \\\"Error\\\": \\\"User is not a manager of the project\\\",\\n \\\"SubCode\\\": \\\"UserPermissionError\\\",\\n }, 403\",\n \"def delete_namespaced_project(self, name, **kwargs):\\n\\n all_params = ['name', 'pretty']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method delete_namespaced_project\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `delete_namespaced_project`\\\")\\n\\n resource_path = '/oapi/v1/projects/{name}'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'DELETE',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='UnversionedStatus',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def remove_single_project(project_name):\\n p = subprocess.Popen('rm -rf {}/{}'.format(context.__PROJECTS_PATH__, project_name), shell=True)\\n p.wait()\",\n \"def delete(self, guid):\\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\\n # search for the Project and delete if found\\n key = db.Key.from_path('Task', int(guid))\\n task = db.get(key)\\n wantsNotifications = {\\\"true\\\": True, \\\"false\\\": False}.get(self.request.params['notify'].lower())\\n currentUserId = self.request.params['UUID']\\n cukey = db.Key.from_path('User', int(currentUserId))\\n user = db.get(cukey)\\n if not task == None:\\n # cache current values before updates\\n taskName = task.name\\n taskType = task.type\\n taskPriority = task.priority\\n taskStatus = task.developmentStatus\\n taskValidation = task.validation\\n taskSubmitterId = task.submitterId\\n taskAssigneeId = task.assigneeId\\n taskEffort = task.effort\\n taskProjectId = task.projectId\\n taskDescription = task.description\\n # Push notification email on the queue if we need to notify\\n if notification.should_notify(currentUserId,task,\\\"deleteTask\\\",wantsNotifications):\\n taskqueue.add(url='/mailer', params={'taskId': int(guid), 'currentUUID': self.request.params['UUID'], 'action': \\\"deleteTask\\\", 'name': taskName, 'type': taskType, 'priority': taskPriority, 'status': taskStatus, 'validation': taskValidation, 'submitterId': taskSubmitterId, 'assigneeId': taskAssigneeId, 'effort': taskEffort, 'projectId': taskProjectId, 'description': taskDescription})\\n task.delete()\\n self.response.set_status(204, \\\"Deleted\\\")\\n else:\\n self.response.set_status(404, \\\"Not Found\\\")\\n else:\\n self.response.set_status(401, \\\"Not Authorized\\\")\",\n \"def delete_api(self, project_id, api_id, hbase_manager=None):\\n\\n self._project_service.get_nondeleted_project(project_id)\\n\\n proj_api_col = \\\"apis:{0}\\\".format(api_id)\\n project_id = bytes(project_id, self._charset)\\n api_id = bytes(api_id, self._charset)\\n\\n self.get_nondeleted_api(project_id, api_id)\\n\\n connection = hbase_manager.connection\\n api_tbl = connection.table(\\\"apis\\\")\\n projects_tbl = connection.table(\\\"projects\\\")\\n\\n projects_tbl.delete(project_id, [bytes(proj_api_col, self._charset)])\\n api_tbl.put(api_id, {\\n b\\\"attrs:state\\\": b\\\"deleted\\\"\\n })\",\n \"def delete(self, project_id):\\n project_model = ProjectDBModel.query.get(project_id)\\n if not project_model:\\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\\n try:\\n data = request.get_json()\\n # cambiarlo cuando se vuelva a tener dos PKs\\n deleted = FavoritesProjectDBModel.delete(\\n data['user_id'], project_id)\\n if deleted:\\n users = \\\\\\n FavoritesProjectDBModel.get_favorites_of_project_id(\\n project_id)\\n response_object = {\\n \\\"project_id\\\": project_id,\\n \\\"users_id\\\": users,\\n }\\n return response_object, 200\\n else:\\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\\n except KeyError:\\n ns.abort(404, status=MISSING_VALUES_ERROR)\",\n \"def delete_project_file(self, project=None):\\n if type(project) is not Project:\\n return False\\n\\n path = self.data_path + self.project_dir\\n\\n # generate filenames\\n filename = path + '/' + self.us(project.project_id()) + '.flproject'\\n\\n # check if the file exists and delete it\\n if os.path.isfile(filename):\\n os.remove(filename)\\n return True\\n else:\\n return False\",\n \"async def delete(self, ctx, project_name: str) -> None:\\n if not ctx.projects.find_project(project_name):\\n channel = discord.utils.get(\\n ctx.guild.channels, name=f\\\"{project_name}-project\\\")\\n\\n if channel and channel.category.name == \\\"Flux Projects\\\":\\n if ctx.author.permissions_in(channel).manage_channels:\\n message = await ctx.send(\\\"That project doesn't appear to\\\"\\n \\\" exist in my database, but the \\\"\\n \\\"channel still exists. \\\"\\n \\\"Would you like to delete it?\\\")\\n yes = \\\"<:greenTick:596576670815879169>\\\"\\n no = \\\"<:redTick:596576672149667840>\\\"\\n await message.add_reaction(yes)\\n await message.add_reaction(no)\\n reaction, user = await ctx.bot.wait_for(\\n \\\"reaction_add\\\",\\n check=lambda reaction, user: (user == ctx.author) and\\n (str(reaction.emoji) == yes or no) and\\n (reaction.message.channel == ctx.channel)\\n )\\n if reaction.emoji.id == ctx.bot.config.tick_yes:\\n await channel.delete(reason=\\\"Project not found.\\\")\\n await ctx.send(\\\"The channel was deleted sucessfully.\\\")\\n return\\n\\n elif reaction.emoji.id == ctx.bot.config.tick_no:\\n await ctx.send(\\\"Not deleting the channel.\\\")\\n return\\n\\n else: # If author doesn't have access to deleting channels.\\n await ctx.send(\\\"That project does not appear to be in my \\\"\\n \\\"database, but the channel for it still \\\"\\n \\\"exists. Please have someone with\\\"\\n \\\" manage channels run this chommand.\\\"\\n )\\n return\\n else:\\n await ctx.send(\\\"I could not find this project.\\\")\\n return\\n\\n if str(ctx.author.id) != ctx.projects.find_project(project_name).get(\\n \\\"owner\\\"):\\n await ctx.send(\\\"Only the project owner \\\"\\n \\\"can delete this project.\\\")\\n return\\n message = await ctx.send(\\\"This action __cannot__ be undone. \\\"\\n \\\"Once you do this, everything is gone. \\\"\\n \\\"Are you sure you want to continue?\\\")\\n yes = \\\"<:greenTick:596576670815879169>\\\"\\n no = \\\"<:redTick:596576672149667840>\\\"\\n await message.add_reaction(yes)\\n await message.add_reaction(no)\\n reaction, user = await ctx.bot.wait_for(\\n \\\"reaction_add\\\", check=lambda reaction, user:\\n (user == ctx.author) and\\n (str(reaction.emoji) == yes or no) and\\n (reaction.message.channel == ctx.channel)\\n )\\n if reaction.emoji.id == ctx.bot.config.tick_yes:\\n channel = ctx.projects.find_project(\\n project_name).get(\\\"channel\\\")\\n channel = discord.utils.get(ctx.guild.channels,\\n id=int(channel))\\n ctx.projects.delete_project(project_name)\\n if channel:\\n await channel.delete(reason=\\\"Project deleted.\\\")\\n await ctx.send(\\\"The project has been deleted.\\\")\\n elif reaction.emoji.id == ctx.bot.config.tick_no:\\n await ctx.send(\\\"Not deleting the project.\\\")\",\n \"def delete(self):\\n return self.client._perform_empty(\\n \\\"DELETE\\\", \\\"/project-folders/%s\\\" % self.project_folder_id)\",\n \"def delete(self):\\r\\n delete_tracks(self.project, [self])\",\n \"def project_post_delete(sender, instance, **kwargs):\\n instance.url.delete(False)\",\n \"def remove_project(project_id):\\n response_object = {'status': 'success'}\\n with database.engine.begin() as connection:\\n\\n stmt = select([models.projects.c.path]).where(\\n models.projects.c.project_id == project_id)\\n project = connection.execute(stmt).first()\\n\\n if project:\\n app = flask.current_app\\n project_path = os.path.join(\\n app.root_path, app.config['DATA_DIRECTORY'], project['path'])\\n if 'morphocut' in project_path and app.config['DATA_DIRECTORY'] in project_path:\\n print('removing project with id {}'.format(project_id))\\n if os.path.exists(project_path):\\n helpers.remove_directory(project_path)\\n\\n stmt = models.projects.delete().where( # pylint: disable=no-value-for-parameter\\n models.projects.c.project_id == project_id)\\n\\n connection.execute(stmt)\\n\\n return jsonify(response_object)\",\n \"def delete_project_by_name(self, project_name):\\n with self._transaction.cursor() as cur:\\n # delete associations between this project and any barcodes\\n cur.execute(\\\"DELETE FROM barcodes.project_barcode \\\"\\n \\\"WHERE project_id in (\\\"\\n \\\"SELECT project_id FROM barcodes.project \\\"\\n \\\"WHERE project = %s)\\\",\\n (project_name,))\\n\\n # now delete the project itself\\n cur.execute(\\\"DELETE FROM barcodes.project WHERE project = %s\\\",\\n (project_name,))\\n return cur.rowcount == 1\",\n \"def _delete_dm_project(self, project_uuid: str) -> None:\\n _LOGGER.warning('Deleting DM Project %s...', project_uuid)\\n\\n dm_rv: DmApiRv = DmApi.delete_project(self.__org_owner_dm_token,\\n project_id=project_uuid)\\n if not dm_rv.success:\\n _LOGGER.error('Failed to delete DM Project %s', project_uuid)\\n return\\n\\n _LOGGER.warning('Deleted DM Project %s', project_uuid)\",\n \"def delete(project, zone, instance):\\n print >>sys.stderr, 'WARNING: duplicated jobs may fail/corrupt results'\\n print >>sys.stderr, ('TODO(fejta): See http://stackoverflow.com/'\\n 'questions/19645430/changing-jenkins-build-number')\\n answer = raw_input('Delete %s [yes/NO]: ')\\n if not answer or answer != 'yes':\\n print >>sys.stderr, 'aborting'\\n sys.exit(1)\\n gcloud(\\n project,\\n 'compute',\\n 'instances',\\n 'delete',\\n '--zone=%s' % zone,\\n instance,\\n )\\n gcloud(\\n project,\\n 'compute',\\n 'disks',\\n 'delete',\\n '--zone=%s' % zone,\\n *get_disks(instance))\",\n \"def delete_orphan_project(apps, schema_editor):\\n Project = apps.get_model('data_manager', 'Project')\\n Project.objects.filter(dataset__isnull=True).delete()\\n return\",\n \"def delete(ctx: click.Context, repository_path):\\n root_commands.cmd_delete(ctx.obj, repository_path)\",\n \"def delete_project_quotas(self, project_id, extra_headers=None,\\n use_auth=True, user_name=None):\\n resp = self.client.delete('project-quotas/' + project_id,\\n extra_headers=extra_headers,\\n use_auth=use_auth, user_name=user_name)\\n return resp\",\n \"def DelProject(projname):\\n\\tif projname == \\\"\\\" or projname == None:\\n\\t\\tpjnm = raw_input(\\\"\\\\nNombre del proyecto: \\\").lower()\\n\\t\\tif pjnm == \\\"\\\" or pjnm == None:\\n\\t\\t\\tcancel()\\n\\telse:\\n\\t\\t# Proceso para borrar todo el proyecto\\n\\t\\tpass\\n\\n\\tpa = open(\\\"author_name.txt\\\", \\\"r\\\")\\t#Abre el archivo con el nombre del autor\\n\\tpa.read()\\n\\tpc = open(\\\"project_code.txt\\\", \\\"r\\\")\\t#Abre el archivo con el codigo de proyecto\\n\\tpc.read()\\n\\n\\tuserpa = raw_input(\\\"Ingrese el nombre del autor: \\\").lower()\\n\\tuserpc = raw_input(\\\"Ingrese el codigo del proyecto: \\\").lower()\\n\\n\\tif userpa == pa and userpc == pc:\\t#Se verifica que userpa(nombre del autor por el usuario) sea igual a pa(nombre original del autor) y lo mismo con el codigo del proyecto\\n\\t\\tprint \\\"Iniciando el Borrado del Proyecto...\\\"\\n\\t\\tpcommands.del_project()\\n\\t\\tprint \\\"El proyecto se ha borrado con exito!\\\"\\n\\telse:\\n\\t\\tprint \\\"El codigo del proyecto o el nombre del autor no es correcto.\\\"\\n\\t\\tcancel()\",\n \"def delete_all_projects():\\n client = RequestManager()\\n client.set_method(\\\"GET\\\")\\n client.set_endpoint(\\\"/projects\\\")\\n response = client.execute_request()\\n for project in response.json():\\n try:\\n ProjectHelper.delete_project(project[\\\"id\\\"])\\n except TypeError:\\n LOGGER.info(project)\",\n \"def delete(self, endpoint, params=None):\\n params = params or dict()\\n return self.request(verb=requests.delete, address=self.project_address + endpoint,\\n params=params)\",\n \"def delete(self):\\n return self.client._perform_empty(\\n \\\"DELETE\\\", \\\"/projects/%s/managedfolders/%s\\\" % (self.project_key, self.odb_id))\",\n \"def command_delete(self):\\n if self.gcp_env.project not in SUPPORTED_PROJECT_CONFIGS.keys():\\n _logger.error(f'Project config not supported {self.gcp_env.project}')\\n return 1\\n\\n if not self.args.bucket and not self.args.id:\\n _logger.error(\\\"--bucket and --id required for delete.\\\")\\n return 1\\n\\n # Get notification\\n client = storage.Client()\\n bucket = client.get_bucket(self.args.bucket)\\n target = bucket.get_notification(self.args.id, client)\\n\\n if self.gcp_env.project != target.topic_project:\\n _logger.error(\\\"Notification project and specified project do not match.\\\")\\n return 1\\n\\n # Delete the notification\\n try:\\n target.delete(client=client)\\n\\n except NotFound:\\n _logger.error(f\\\"Notification ID {self.args.id} not found.\\\")\\n return 1\\n\\n _logger.info(f\\\"Notification id {self.args.id} has been deleted.\\\")\\n\\n _logger.info(\\\"Removing notification from config...\\\")\\n self.delete_notification_from_config()\\n\\n return 0\",\n \"def delete(self, *args, **kwargs):\\n self.request(\\\"delete\\\", *args, **kwargs)\",\n \"def _delete(self, *args, **kwargs):\\n return self._request('delete', *args, **kwargs)\",\n \"def test_projects_id_contacts_delete(self):\\n project = Contact()\\n response = self.client.open('/project-tracker/projects/{id}/contacts'.format(id=56),\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def cmd_apps__destroy(args):\\n \\n if args.name is None and in_git_repo():\\n args.name = _get_current_project_name()\\n\\n if args.name is None:\\n print \\\"Please provide a project name.\\\"\\n sys.exit(1)\\n\\n print \\\"Destroying project %s...\\\" % args.name\\n remote.destroy_project(args.name)\\n print \\\"Project %s destroyed.\\\" % args.name\\n if in_git_repo() and _get_current_project_name() == args.name:\\n git(None, 'remote', 'rm', 'tinyserv')\\n print \\\"Removed remote '%s'.\\\" % args.name\",\n \"def delete():\",\n \"def delete(self, application_id):\",\n \"def _on_del_project(self):\\n project = self.ddnCurProject.get()\\n# if len(project) > 0:\\n if project:\\n if '.prj'!= project[-4:]:\\n project += '.prj'\\n if os.path.exists(self.BibTerm + '/'+ project):\\n os.remove(self.BibTerm + '/'+ project)\\n self.list_projects = [f.rstrip('.prj') \\\\\\n for f in os.listdir(self.BibTerm) \\\\\\n if f.endswith('.prj')]\\n self.ddnCurProject['values'] = self.list_projects\\n# if len(self.list_projects) > 0:\\n if self.list_projects:\\n self.ddnCurProject.set(self.list_projects[0])\\n else:\\n self.ddnCurProject.set('')\\n pass\",\n \"def delete(self, *args, **kw):\\n kw['method'] = 'DELETE'\\n return self.open(*args, **kw)\",\n \"def delete_language_from_project(self, project_id, language_code):\\n self._run(\\n url_path=\\\"languages/delete\\\",\\n id=project_id,\\n language=language_code\\n )\\n return True\",\n \"def record_destroy_for_project(project_id):\\n session = get_session()\\n with session.begin():\\n session.query(models.ProjectAccountRecord).\\\\\\n filter_by(project_id=project_id).\\\\\\n update({'deleted': True,\\n 'deleted_at': datetime.datetime.utcnow(),\\n 'updated_at': datetime.datetime.utcnow()})\",\n \"def remove_deleted_project(self, project_id):\\n self.db.make_query(\\n '''\\n DELETE FROM deleted_project WHERE project_id = \\\"{}\\\";\\n '''.format(project_id)\\n )\\n\\n if self.get_deleted_project(project_id):\\n return False\\n\\n return True\",\n \"def delete(self):\\n\\n headers = self._default_headers()\\n\\n return self._request(self.name,\\n ok_status=None,\\n data=None,\\n headers=headers,\\n method=\\\"DELETE\\\")\",\n \"def remove_project(self, project_id):\\n project_file_path = '{}/{}'.format(self._storage_location, project_id)\\n if os.path.exists(project_file_path):\\n os.remove(project_file_path)\\n else:\\n raise ValueError('The project id {} does not exist!'.format(project_id))\",\n \"def test_projects_id_comments_delete(self):\\n project = Comment()\\n response = self.client.open('/project-tracker/projects/{id}/comments'.format(id=56),\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"async def delete_user_byid(*_):\\n return web.Response(text=\\\"PUT project not implemented\\\", status=501)\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def userproject_post_delete(sender, instance, **kwargs):\\n instance.document.delete(False)\",\n \"def delete(repo):\\n print('Repo: %s' % repo)\\n print('Deleted')\",\n \"def delete(project_id, integration_id):\\n IntegrationService.delete(project_id, integration_id)\\n\\n return {\\\"status\\\": \\\"deleted\\\"}, 200\",\n \"def delete():\\n run('rm -r {}'.format(utils.home('apps', env.PROJECT_NAME)))\",\n \"def delete_record(request, slug, pk):\\n # Try except to make sure the user is a member of this project\\n try:\\n ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\\n except ObjectDoesNotExist:\\n # User is not a member\\n return HttpResponse(\\\"You're trying to access a project you're not a member of or a project that does not exist.\\\")\\n else:\\n # User is a member\\n pm = ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\\n # Access control.. if not owner or editor - access denied.\\n if pm.is_owner or pm.is_editor:\\n # User has access\\n record = get_object_or_404(models.Record, pk=pk)\\n # Delete record\\n models.Record.objects.filter(project=get_object_or_404(models.Project, slug=slug), pk=pk).delete()\\n # Send user back to project detail, the overview of all records in the project.\\n return redirect('projects:single', slug=slug)\\n else:\\n # Access denied...\\n return HttpResponse(\\\"You don't have the permission to do this\\\")\",\n \"def delete(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"delete\\\"), kwargs)\",\n \"def delete(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"delete\\\"), kwargs)\",\n \"def delete_bucket_from_project(projectname, bucketname):\\n return jsonify(\\n admin.delete_bucket_on_project(\\n current_app.scoped_session(), projectname, bucketname\\n )\\n )\",\n \"def delete(self, *args, **kwargs) -> Any:\\n pass\",\n \"def delete_project_entities(cls, project_id,\\n suppress_exception=False,\\n session=None):\\n cls.db_repo.delete_project_entities(\\n project_id, suppress_exception=suppress_exception, session=session)\",\n \"def delete():\\n # Must be logged in to perform any delete commands.\\n auth_required()\\n pass\",\n \"def delete(self, api_path, *args, **kwargs):\\n\\n\\t\\treturn self._do_operation(u'delete', api_path, *args, **kwargs)\",\n \"def test_Projects_CanBeCreatedAndDeleted_Successfully(self):\\n\\n name = \\\"Test Project \\\"\\n # Test create a new project successfully\\n project = self.api.create_project(name, \\\"Py\\\")\\n self.assertTrue(project['success'])\\n self.assertTrue(project['projects'][0]['projectId'] > 0)\\n self.assertTrue(project['projects'][0]['name'] == name)\\n\\n # Delete the project\\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\\n self.assertTrue(deleteProject['success'])\\n\\n # Make sure the project is really deleted\\n projectList = self.api.list_projects()\\n self.assertFalse(any(project['projects'][0]['projectId'] == projectList['projects'][i]['projectId']\\n for i in range(len(projectList['projects']))))\",\n \"def delete(self, *args, **kwargs):\\n return self.handle_delete_request()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8128038","0.80633974","0.8028948","0.78569174","0.78183025","0.7728668","0.76615155","0.7630471","0.76140267","0.76140267","0.7583802","0.75670856","0.75328547","0.74872017","0.7375651","0.7375104","0.7346846","0.7337656","0.7324851","0.73165905","0.7305317","0.72964025","0.7267763","0.7258648","0.7213935","0.7125384","0.7116282","0.70950204","0.706899","0.7000849","0.69945395","0.69926566","0.6986645","0.69539607","0.6940761","0.6912442","0.6906145","0.68681186","0.6781119","0.6760477","0.672421","0.6641833","0.6641625","0.6600566","0.659823","0.6588102","0.65876395","0.6575108","0.65713507","0.646392","0.6437244","0.6382265","0.6318649","0.6315186","0.6313249","0.6301197","0.6264303","0.6234369","0.6231617","0.6203759","0.6194927","0.6164004","0.61594623","0.6148683","0.6122723","0.60906553","0.6069815","0.6045421","0.6026991","0.6014933","0.59974253","0.59901166","0.5956138","0.5945484","0.5943498","0.59382206","0.5935798","0.590966","0.587238","0.587045","0.58671933","0.5863532","0.58536106","0.58473176","0.5835139","0.5835139","0.58201915","0.58050674","0.5803282","0.5803265","0.5790737","0.57829326","0.57829326","0.5764744","0.57596505","0.5751724","0.5710703","0.5706264","0.5700631","0.56915003"],"string":"[\n \"0.8128038\",\n \"0.80633974\",\n \"0.8028948\",\n \"0.78569174\",\n \"0.78183025\",\n \"0.7728668\",\n \"0.76615155\",\n \"0.7630471\",\n \"0.76140267\",\n \"0.76140267\",\n \"0.7583802\",\n \"0.75670856\",\n \"0.75328547\",\n \"0.74872017\",\n \"0.7375651\",\n \"0.7375104\",\n \"0.7346846\",\n \"0.7337656\",\n \"0.7324851\",\n \"0.73165905\",\n \"0.7305317\",\n \"0.72964025\",\n \"0.7267763\",\n \"0.7258648\",\n \"0.7213935\",\n \"0.7125384\",\n \"0.7116282\",\n \"0.70950204\",\n \"0.706899\",\n \"0.7000849\",\n \"0.69945395\",\n \"0.69926566\",\n \"0.6986645\",\n \"0.69539607\",\n \"0.6940761\",\n \"0.6912442\",\n \"0.6906145\",\n \"0.68681186\",\n \"0.6781119\",\n \"0.6760477\",\n \"0.672421\",\n \"0.6641833\",\n \"0.6641625\",\n \"0.6600566\",\n \"0.659823\",\n \"0.6588102\",\n \"0.65876395\",\n \"0.6575108\",\n \"0.65713507\",\n \"0.646392\",\n \"0.6437244\",\n \"0.6382265\",\n \"0.6318649\",\n \"0.6315186\",\n \"0.6313249\",\n \"0.6301197\",\n \"0.6264303\",\n \"0.6234369\",\n \"0.6231617\",\n \"0.6203759\",\n \"0.6194927\",\n \"0.6164004\",\n \"0.61594623\",\n \"0.6148683\",\n \"0.6122723\",\n \"0.60906553\",\n \"0.6069815\",\n \"0.6045421\",\n \"0.6026991\",\n \"0.6014933\",\n \"0.59974253\",\n \"0.59901166\",\n \"0.5956138\",\n \"0.5945484\",\n \"0.5943498\",\n \"0.59382206\",\n \"0.5935798\",\n \"0.590966\",\n \"0.587238\",\n \"0.587045\",\n \"0.58671933\",\n \"0.5863532\",\n \"0.58536106\",\n \"0.58473176\",\n \"0.5835139\",\n \"0.5835139\",\n \"0.58201915\",\n \"0.58050674\",\n \"0.5803282\",\n \"0.5803265\",\n \"0.5790737\",\n \"0.57829326\",\n \"0.57829326\",\n \"0.5764744\",\n \"0.57596505\",\n \"0.5751724\",\n \"0.5710703\",\n \"0.5706264\",\n \"0.5700631\",\n \"0.56915003\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76985204"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":692,"cells":{"query":{"kind":"string","value":"Static method for delete all projects."},"document":{"kind":"string","value":"def delete_all_projects():\n client = RequestManager()\n client.set_method(\"GET\")\n client.set_endpoint(\"/projects\")\n response = client.execute_request()\n for project in response.json():\n try:\n ProjectHelper.delete_project(project[\"id\"])\n except TypeError:\n LOGGER.info(project)"},"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 clear(self):\n for project in Project.objects:\n project.delete()","def __remove_all_projects__():\n p = subprocess.Popen('rm -rf {}/.wcscanner/*'.format(context.__BASE_PATH__), shell=True)\n p.wait()","def clean_project(self, app_name=None, delete_all=False):\n\n if not app_name and not delete_all:\n ConuException(\"You need to specify either app_name or set delete_all=True\")\n\n if delete_all:\n args = [\"--all\"]\n logger.info('Deleting all objects in current project')\n else:\n args = \"-l app=%s\" % app_name\n logger.info('Deleting all objects with label app=%s', app_name)\n\n try:\n o = run_cmd(self._oc_command([\"delete\", \"all\", args]),\n return_output=True)\n o_lines = o.split('\\n')\n for line in o_lines:\n logger.info(line)\n except subprocess.CalledProcessError as ex:\n raise ConuException(\"Cleanup failed because of exception: %s\" % ex)","def clear_all(delete_id):\n Tasks.query.filter(Tasks.project_id == delete_id).delete()\n Projects.query.filter(Projects.project_id == delete_id).delete()\n db.session.commit()\n\n return redirect('/')","def clean(self):\n\n if not self.__projects:\n return\n\n Console.info(\"Cleaning session...\")\n Console.indent()\n\n for project in self.__projects:\n project.clean()\n\n path = os.path.abspath(os.path.join(\".jasy\", \"locale\"))\n if os.path.exists(path):\n Console.info(\"Cleaning up locale project...\")\n shutil.rmtree(path)\n\n path = os.path.abspath(os.path.join(\".jasy\", \"virtual\"))\n if os.path.exists(path):\n Console.info(\"Cleaning up virtual project...\")\n shutil.rmtree(path)\n\n Console.outdent()","def delete_project(arn=None):\n pass","def delete(self):\n _url = f\"{self.connector.base_url}/projects/{self.project_id}\"\n\n self.connector.http_call(\"delete\", _url)\n\n self.project_id = None\n self.name = None","def test_delete_project(self):\n pass","def test_delete_project(self):\n pass","def project_delete(cursor, project):\n haystack = (project['_id'], )\n\n query = \"DELETE FROM projects WHERE _id=?\"\n try:\n cursor.execute(query, haystack)\n except Exception as e:\n on_error(e)\n\n query = \"DELETE FROM namespaces WHERE project_id=?\"\n try:\n cursor.execute(query, haystack)\n except Exception as e:\n on_error(e)\n else:\n cursor.connection.commit()\n raise Return((True, None))","def tearDown(self):\n Project.objects.all().delete()","def delete_stored_project():\n client = RequestManager()\n client.set_method(\"DELETE\")\n client.set_endpoint(\"/projects/{0}\".format(STORED_ID['project_id']))\n client.execute_request()","def delete_orphan_project(apps, schema_editor):\n Project = apps.get_model('data_manager', 'Project')\n Project.objects.filter(dataset__isnull=True).delete()\n return","def tearDownClass(cls):\n projects = ['arc_project_for_testing_delete_after_usage1', 'arc_project_for_testing_delete_after_usage2',\n 'ar c', 'ar:c', 'ar<c', 'ar%c']\n for project in projects:\n project_directory = os.path.join(arc_path, 'Projects', project)\n shutil.rmtree(project_directory)","def test_projects_delete(self):\n project = Project()\n response = self.client.open('/project-tracker/projects',\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def test_delete_project(self):\n self.assertEqual(Project.objects.count(), 1)\n self.assertEqual(Group.objects.count(), 2)\n\n delete_project(Project.objects.get(name=\"project A\"))\n\n self.assertEqual(Project.objects.count(), 0)\n self.assertEqual(Group.objects.count(), 0)","def es_delete(project=None):\n if project is not None:\n script_indexer.delete_project(project)\n else:\n script_indexer.delete_all()","def do_project_delete(cs, args):\n key = args.project\n if cs.projects.is_id(key):\n id = key\n else:\n id = cs.projects.get_id_by_name(key)\n cs.projects.delete(id)\n print(\"Delete Project '%s' successfully.\" % key)","def delete_project(project):\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.delete_project(project)\n if ret[constants.STATUS_CODE_KEY] == 200:\n click.echo(\"Success\")\n else:\n click.echo(ret[constants.MESSAGE_KEY])","def delete(self, request, p_name):\n project = Project.objects.get(name=p_name)\n connectors = project.connector_set.all()\n connectors.delete()\n if os.path.isfile(project.project_location):\n os.remove(project.project_location)\n project.delete()\n return HttpResponse(HTTPStatus.OK)","def delete(self, oid):\n path = '/projects/%s' % oid\n res = self.client.call(path, 'DELETE', data='', token=self.manager.identity.token)\n self.logger.debug('Delete openstack project: %s' % truncate(res))\n return True","def delete_project(\n name\n):\n\n cmd = dict()\n cmd[\"type_\"] = \"delete_project\"\n cmd[\"name_\"] = name\n\n comm.send(cmd)","def delete_project(projectname):\n response = jsonify(admin.delete_project(current_app.scoped_session(), projectname))\n return response","def delete_project(project_id):\n client = RequestManager()\n client.set_method(\"DELETE\")\n client.set_endpoint(\"/projects/{0}\".format(project_id))\n client.execute_request()","def delete_all(self):\n raise NotImplementedError()","def delete(conn, project):\n with conn:\n c = conn.cursor()\n c.execute(\"DELETE FROM projects WHERE project =?\", (project,))","def remove_single_project(project_name):\n p = subprocess.Popen('rm -rf {}/{}'.format(context.__PROJECTS_PATH__, project_name), shell=True)\n p.wait()","def destroy(config, args):\n log = logging.getLogger('kraftwerk.destroy')\n if confirm(\"Remove project %s from node %s along with all services and data?\" % \n (args.project.name, args.node.hostname)):\n args.node.ssh(config.template(\"scripts/project_destroy.sh\", project=args.project))\n print \"Project %s removed from node %s\" % \\\n (args.project.name, args.node.hostname )\n for service in args.project.services(args.node):\n args.node.ssh(service.destroy_script)","def delete_project_files(self, project, logStat):\n from corrdb.common.models import FileModel\n from corrdb.common.models import EnvironmentModel\n\n for _file in project.resources:\n file_ = FileModel.objects.with_id(_file)\n if file_:\n result = self.storage_delete_file(file_.group, file_.storage)\n if result:\n logStat(deleted=True, file_obj=file_)\n file_.delete()\n\n for record in project.records:\n result = self.delete_record_files(record, logStat)\n if result:\n logStat(deleted=True, record=record)\n record.delete()\n\n for environment_id in project.history:\n _environment = EnvironmentModel.objects.with_id(environment_id)\n if _environment:\n if _environment.bundle and _environment.bundle.scope == \"local\":\n result = self.storage_delete_file('bundle', _environment.bundle.storage)\n if result:\n # logStat(deleted=True, bundle=_environment.bundle)\n # logStat(deleted=True, environment=_environment)\n _environment.bundle.delete()\n # else:\n # logStat(deleted=True, environment=_environment)\n _environment.delete()","def delete_all(self, prog:progress=None): \n\t\tself.__output_status('Delete all files')\n\t\tif (self.__check_terminated()):\n\t\t\treturn;\t\n\t\tdelete_dir(self.root)\n\t\ttime.sleep(0.3)","def delete_project(proj_id):\n project_obj = Project.objects.get(id=proj_id)\n print('Deleting project the fastq files within the project: ', project_obj.description)\n\n description = project_obj.description.replace(' ', '') # remove any space in the project name\n project_dir = 'documents/%s/%s' % (str(project_obj.date.date()), description)\n shutil.rmtree(project_dir, ignore_errors=True)\n print(\"Files deleted.\")","def delete_all(self):\n # delete everything\n shutil.rmtree(self.location)","def destroy(self, request, pk=None):\n try:\n project = Project.objects.get(pk=pk)\n project.delete()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)\n\n except Project.DoesNotExist as ex:\n return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)\n\n except Exception as ex:\n return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)","def delete(\n self, url: str\n ) -> pymongo.results.DeleteResult:\n return self._mongo.delete({\n 'url': url\n },\n 'projects'\n )","def delete(self):\r\n delete_tracks(self.project, [self])","def test_remove_project(self):\n pass","def test_projects_id_delete(self):\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\n method='DELETE')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def delete_project(request, project_id):\n if not request.user.is_superuser:\n messages.error(request, 'Sorry, only store owners can do that.')\n return redirect(reverse('home'))\n\n project = get_object_or_404(Project, pk=project_id)\n project.delete()\n messages.success(request, 'Project deleted!')\n return redirect(reverse('portfolio'))","def delete(self):\n return self.client._perform_empty(\n \"DELETE\", \"/project-folders/%s\" % self.project_folder_id)","def remove_all(lists_id):\n Tasks.query.filter(Tasks.project_id == lists_id).delete()\n db.session.commit()\n\n return redirect('/')","def delete_project(request, project_id):\n\n profile = get_object_or_404(Profile, user=request.user)\n project = get_object_or_404(GameProject, pk=project_id)\n\n if not profile.is_creator:\n messages.error(request, 'Sorry, only creators can do that.')\n return redirect(reverse('home'))\n if project.owner != profile:\n messages.error(request, 'Sorry, only the project owner can do that.')\n return redirect(reverse('home'))\n\n project = get_object_or_404(GameProject, pk=project_id)\n project.delete()\n messages.success(request, 'Project deleted!')\n return redirect(reverse('all_projects'))","def delete_users(project):\n for user_id in project.user_id.all():\n project.user_id.remove(user_id.pk)\n project.save()","def tearDown(self):\n User.objects.all().delete()\n Project.objects.all().delete()","def delete(self, name, project=None):\n qlist = self._list(project)\n key = self._queue(project, name)\n self._db.delete(key)\n self._db.zremrangebyscore(qlist, -1, 1)","def delete_all(self):\n models.CourseLearningOutcome.objects.all().delete()\n #models.CoreLearningOutcome.objects.all().delete()\n #models.CreditType.objects.all().delete()\n models.Course.objects.all().delete()\n models.DegreeProgram.objects.all().delete()\n models.DPCourseSpecific.objects.all().delete()\n models.DPCourseGeneric.objects.all().delete()\n models.DPCourseSubstituteSpecific.objects.all().delete()\n models.DPCourseSubstituteGeneric.objects.all().delete()","def delete_all(cls):\n with sqlite3.connect(cls.dbpath) as connection:\n connection.row_factory = sqlite3.Row\n cursor = connection.cursor()\n SQL = \"DELETE FROM accounts;\"\n cursor.execute(SQL)","def delete_project_entities(cls, project_id,\n suppress_exception=False,\n session=None):\n cls.db_repo.delete_project_entities(\n project_id, suppress_exception=suppress_exception, session=session)","def delete():\n run('rm -r {}'.format(utils.home('apps', env.PROJECT_NAME)))","def deleteAll():\n _table.deleteAll()\n _initialiseGlobals()\n\n return","def close(self):\n\n if not self.__projects:\n return\n\n Console.debug(\"Closing session...\")\n Console.indent()\n\n for project in self.__projects:\n project.close()\n\n self.__projects = None\n\n Console.outdent()","def delete_project(project_id):\n \n project = mongo.db.projects\n project.delete_one({'_id': ObjectId(project_id)})\n flash('Your project has been deleted.', 'success')\n return redirect(url_for('projects'))","def delete_teams_all(self, team_name):\n self.execute(TABELLE['teams']['delete']['all'])","def delete_project(project_id):\n project = Project.query.filter_by(id=project_id).first()\n if not project:\n return {\n 'success': False,\n 'message': f\"No project with the specified id {project_id} found.\",\n }\n\n else:\n if is_project_manager(project, g.user):\n # delete related tasks\n Task.query.filter_by(project=project).delete()\n #delete related invites\n Invitation.query.filter_by(project=project).delete()\n db_session.delete(project)\n db_session.commit()\n return {\n 'success': True,\n 'result': {},\n 'message': \"Project Deleted Successfully.\",\n }","def project_clear_files(request, **kwargs):\n project = kwargs.get(\"project\")\n if request.user.is_authenticated and request.user == project.user:\n project.clear_project_folder()\n return Response(status=status.HTTP_200_OK)\n else:\n raise PermissionDenied","def delete_api(self, project_id, api_id, hbase_manager=None):\n\n self._project_service.get_nondeleted_project(project_id)\n\n proj_api_col = \"apis:{0}\".format(api_id)\n project_id = bytes(project_id, self._charset)\n api_id = bytes(api_id, self._charset)\n\n self.get_nondeleted_api(project_id, api_id)\n\n connection = hbase_manager.connection\n api_tbl = connection.table(\"apis\")\n projects_tbl = connection.table(\"projects\")\n\n projects_tbl.delete(project_id, [bytes(proj_api_col, self._charset)])\n api_tbl.put(api_id, {\n b\"attrs:state\": b\"deleted\"\n })","def cleanup(self):\r\n session = self.get_session()\r\n project = session.create(self._config.name)\r\n \r\n session.home = self._config['dir']\r\n \r\n result = self.__find_project(project)\r\n \r\n path = os.path.join(session.home, project.name)\r\n project.work_area(False, True, True, path=path)\r\n \r\n if (result != None):\r\n _logger.info(\"Project found: '%s'\" % result)\r\n role = session.role\r\n co_role = ccm.get_role_for_purpose(session, str(self._config['purpose']))\r\n session.role = co_role\r\n try:\r\n delResult = result.delete(scope='project_and_subproject_hierarchy')\r\n finally:\r\n session.role = role\r\n ccm.log_result(delResult, ccm.CHECKOUT_LOG_RULES, _logger)","def deleteProject(self, projectId):\n uri = \"/v1/projects/\" +str(projectId)\n response = self.client.delete(uri)\n return response","def reset():\n local('cd {{ project_name }} && \\\n rm -rf static && rm -rf gzip && rm -rf build')","def delete_project(self, name=None, delete_dir=False):\n victim = name or self.current\n if victim not in self:\n raise ValueError(\"{} is not a project\".format(victim))\n\n if len(self) == 1:\n raise ValueError(\"Can't delete only remaining project\")\n\n ProjectDataset.delete().where(ProjectDataset.name == victim).execute()\n\n if delete_dir:\n dir_path = self._base_data_dir / safe_filename(victim)\n assert dir_path.is_dir(), \"Can't find project directory\"\n shutil.rmtree(dir_path)\n\n if name is None or name == self.current:\n if \"default\" in self:\n self.set_current(\"default\")\n else:\n self.set_current(next(iter(self)).name)\n return self.current","def delete_all():\n if os.path.exists(DATA_DIR):\n shutil.rmtree(DATA_DIR)","def unlink(self):\n analytic_accounts_to_delete = self.env['account.analytic.account']\n for project in self:\n if project.analytic_account_id and not project.analytic_account_id.line_ids:\n analytic_accounts_to_delete |= project.analytic_account_id\n result = super(Project, self).unlink()\n analytic_accounts_to_delete.unlink()\n return result","def DeleteAllItems(self):\r\n\r\n self.DeleteRoot()","def delete_project(self, project_name):\n # type(project_name) == unicode\n project = self.db.get_project_by_name(project_name)\n if not project:\n print(u\"*** Error: The project '{}' was not found.\"\n \"\".format(project_name))\n return\n print('Caution! The related tracking will be deleted as well.{eol}'\n 'Do you really want to delete the project? [y/N] '\n ''.format(eol=os.linesep), end='')\n if not helpers.get_yes_no(default='n'):\n return\n self.db.delete_project_by_name(project_name)\n print(u\"The project '%s' has been deleted.\" % project_name)\n self.set_prompt()","def delete_project(self, project_id):\n _url = f\"{self.base_url}/projects/{project_id}\"\n self.http_call(\"delete\", _url)\n return","async def delete_all_games(self):\n all_games = await ex.conn.fetch(\"SELECT gameid FROM blackjack.games\")\n for games in all_games:\n game_id = games[0]\n await self.delete_game(game_id)","def destroy_all(self):\n for name in self.app.config['SIMPLE_DOMAINS']:\n self.connection.delete_domain(name)","def delete_project(self, project_id):\n self._run(\n url_path=\"projects/delete\",\n id=project_id,\n )\n return True","def DeleteContainers(self):\n for container in itertools.chain(*list(self.containers.values())):\n container.Delete()","def destroy_all(self) -> None:\n try:\n containers = self.docker.containers.list(\n all=True,\n filters={\n 'label': LABEL_TASK_ID,\n },\n )\n\n for container in containers:\n container.remove(force=True)\n\n except requests.exceptions.ConnectionError:\n raise ProviderError('Docker engine unavailable')","def delete_all():\n answer = ['YES', 'NO']\n str = rs.GetString(\"Delete all objects?\", 'YES', answer)\n\n if str == 'YES':\n obs = rs.ObjectsByType(0)\n rs.DeleteObjects(obs)\n elif str == 'NO':\n pass\n else:\n sys.exit()","def clean_all_db():\n for model in [\n Component, Arch, AutoCase, AutoCaseFailure, Bug, Linkage, WorkItem,\n Document, Project, Framework]:\n model.objects.all().delete()","def remove_all_users(request):\n id_project = request.POST.get(\"project_id\")\n project = UtilsData.get_object_by_type_and_id(\"project\", id_project)\n if request.user.can_delete(project):\n roles = project.affecteds_set.all()\n for role in roles:\n if role.role not in (Affecteds.ROLE.Manager, Affecteds.ROLE.Admin):\n role.role = Affecteds.ROLE.Nill\n role.save()\n return HttpResponse(json.dumps(\"Ok\"),\n content_type=\"application/json\")\n else:\n logger.error(\"user %s try to remove all users to project %d \" % (request.user.username, id_project))\n return HttpResponse(json.dumps(\"error\"),\n content_type=\"application/json\")","def pre_project_delete(self, resource_id):\n pass","def delete_project(self, project_id):\n return self._delete('/projects/{0}'.format(project_id))","def delete_namespaced_project(self, name, **kwargs):\n\n all_params = ['name', 'pretty']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method delete_namespaced_project\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `delete_namespaced_project`\")\n\n resource_path = '/oapi/v1/projects/{name}'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'DELETE',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='UnversionedStatus',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def delete_all(self):\n self.session.query(TodoItem).delete()\n self.session.query(TodoList).delete()","def delete_keystone_v3_project(self, project_id, domain_id):\n LOG_OBJ.debug(\"Disable the project.\")\n kwargs = {\"project_id\": project_id, \"enabled\": False}\n self.set_keystone_v3_project(**kwargs)\n\n LOG_OBJ.debug(\"Deleting the project.\")\n\n _url = \"http://\" + self.host_ip + \":35357/v3/projects/\" + \\\n str(project_id)\n _headers = {'x-auth-token': self.cloud_admin_info[\"token_domain\"],\n 'content-type': 'application/json'}\n _body = None\n response = self.request(\"DELETE\", _url, _headers, _body)\n\n if response is None:\n LOG_OBJ.error(\"No response from Server while deleting the project\")\n print (\"No response from Server while deleting the project\")\n return response\n\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\" Deleting project Failed with status %s \"\n \"and error : %s\" % (response.status, response.data))\n print (\" Deleting project Failed with status %s and error : %s\" %\n (response.status, response.data))\n return response.status\n\n return True","def delete(self):\n return self.client._perform_empty(\n \"DELETE\", \"/projects/%s/managedfolders/%s\" % (self.project_key, self.odb_id))","def delete(self, guid):\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\n # search for the Project and delete if found\n key = db.Key.from_path('Project', int(guid))\n project = db.get(key)\n if not project == None:\n project.delete()\n self.response.set_status(204, \"Deleted\")\n else:\n self.response.set_status(404, \"Not Found\")\n else:\n self.response.set_status(401, \"Not Authorized\")","def deleteAll(klass, where=None, transaction=None):\n config = Registry.getConfig()\n tablename = klass.tablename()\n return config.delete(tablename, where, transaction)","def delete_all_onprogress_domains():\r\n db = connect()\r\n cursor = db.cursor()\r\n try:\r\n cursor.execute(\"DELETE FROM on_progress_domains\")\r\n db.commit()\r\n except:\r\n cursor.close()\r\n db.close()\r\n raise RuntimeError(\"An Exception happened with the Database, make sure you are connected\")\r\n cursor.close()\r\n db.close()","def delete_project_by_name(self, project_name):\n with self._transaction.cursor() as cur:\n # delete associations between this project and any barcodes\n cur.execute(\"DELETE FROM barcodes.project_barcode \"\n \"WHERE project_id in (\"\n \"SELECT project_id FROM barcodes.project \"\n \"WHERE project = %s)\",\n (project_name,))\n\n # now delete the project itself\n cur.execute(\"DELETE FROM barcodes.project WHERE project = %s\",\n (project_name,))\n return cur.rowcount == 1","def Delete(ids):\n db = catocommon.new_conn()\n\n # we have to check each cloud and see if it's used as the default...\n # if so, you can't delete it without first fixing the account.\n # ACCOUNTS REQUIRE A DEFAULT CLOUD\n existed = False\n for delete_id in ids:\n sql = \"select count(*) from cloud_account where default_cloud_id = %s\" % (delete_id)\n exists = db.select_col_noexcep(sql)\n\n if not exists:\n sql = \"delete from clouds_keypair where cloud_id = %s\" % (delete_id)\n db.tran_exec(sql)\n \n sql = \"delete from clouds where cloud_id = %s\" % (delete_id)\n db.tran_exec(sql)\n \n db.tran_commit()\n else:\n existed = True\n \n db.close()\n\n msg = \"\"\n if existed:\n msg = \"Some of the selected Clouds were not deleted because they are referenced by a Cloud Account. Delete the Account first, or assign it a new Default Cloud.\"\n \n return True, msg","def delete(self):\n return self.client._perform_empty(\"DELETE\", \"/workspaces/%s\" % self.workspace_key)","def delete_project(id):\n result = delete_project_to_db(id)\n flash(result)\n return redirect(url_for(\"portfolio\"))","def delete_contributions(self, project, per_page=100, max_workers=5):\n tic = time.perf_counter()\n\n if max_workers > MAX_WORKERS:\n max_workers = MAX_WORKERS\n print(f\"max_workers reset to max {MAX_WORKERS}\")\n\n cids = self.get_contributions(project)[\"ids\"]\n total = len(cids)\n\n if cids:\n with FuturesSession(max_workers=max_workers) as session:\n while cids:\n futures = [\n session.delete(\n f\"{self.url}/contributions/\",\n headers=self.headers,\n params={\n \"project\": project,\n \"id__in\": \",\".join(chunk),\n \"per_page\": per_page,\n },\n )\n for chunk in chunks(cids, n=per_page)\n ]\n\n self._run_futures(futures, total=len(cids))\n cids = self.get_contributions(project)[\"ids\"]\n\n self.load()\n\n # reset columns to be save (sometimes not all are reset BUGFIX?)\n self.projects.update_entry(pk=project, project={\"columns\": []}).result()\n toc = time.perf_counter()\n dt = (toc - tic) / 60\n print(f\"It took {dt:.1f}min to delete {total} contributions.\")","def update_projects(self, new_projects_list):\n to_stop = [project for project in self if project not in new_projects_list]\n for project_id in to_stop:\n self[project_id].stop()\n del self[project_id]\n\n for project_id in new_projects_list:\n if project_id not in self:\n self[project_id] = ProjectManager(self.zk_client, project_id,\n self.callback)","def purge_project(request):\n data = json.loads(request.body.decode('utf-8'))\n if 'item_id' not in data or not data['item_id']:\n return JsonResponse({'state': 'error', 'error': 'No project specified for reactivating'}, status=400)\n projquery = models.Project.objects.filter(pk=data['item_id'], active=False)\n if not projquery:\n return JsonResponse({'state': 'error', 'error': 'Project does not exist or is still active'}, status=403)\n dsetowners = models.DatasetOwner.objects.filter(dataset__runname__experiment__project_id=data['item_id'], dataset__purged=False).select_related('dataset')\n if not request.user.is_staff:\n return JsonResponse({'state': 'error', 'error': 'User has no permission to purge this project, must be staff'}, status=403)\n result = {'errormsgs': []}\n for dso in dsetowners.distinct('dataset'):\n purged = delete_dataset_from_cold(dso.dataset)\n if purged['state'] == 'error':\n result.update({'state': 'error', 'error': 'Not all project datasets could be purged'})\n result['errormsgs'].append(purged['error'])\n # if any dataset cannot be purged, report it, do not mark proj as purged\n if result['errormsgs']:\n result['error'] = '{} Errors: {}'.format(result['error'], '; '.join(result.pop('errormsgs')))\n return JsonResponse(result, status=500)\n else:\n projquery.update(active=False)\n return JsonResponse({})","def resources(request):\n projects, secrets, pools, storageclasses, pvcs, pods = ([] for i in range(6))\n\n def finalizer():\n \"\"\"\n Delete the resources created during the test\n \"\"\"\n for resource_type in pods, pvcs, storageclasses, secrets:\n for resource in resource_type:\n resource.delete()\n resource.ocp.wait_for_delete(resource.name)\n if pools:\n # Delete only the RBD pool\n pools[0].delete()\n if projects:\n for project in projects:\n project.delete(resource_name=project.namespace)\n project.wait_for_delete(project.namespace)\n\n request.addfinalizer(finalizer)\n\n return projects, secrets, pools, storageclasses, pvcs, pods","def delete_all(submission_client, program, project, batch_size=200, types=['submitted_methylation', 'aliquot', 'sample', 'demographic', 'case', 'experiment']):\n for t in types:\n print('{}-{}.{}'.format(program, project, t))\n try:\n delete_type(submission_client, program, project, batch_size, t)\n except Exception as e:\n print(e)","def delete(self):\n for i in set(self.instances.values()):\n i.delete()\n shutil.rmtree(self.dirpath, True)","def delete_all_domain_pages():\r\n db = connect()\r\n cursor = db.cursor()\r\n try:\r\n cursor.execute(\"DELETE FROM domain_pages\")\r\n db.commit()\r\n except:\r\n cursor.close()\r\n db.close()\r\n raise RuntimeError(\"An Exception happened with the Database, make sure you are connected\")\r\n cursor.close()\r\n db.close()","def DeleteServices(self):\n for service in self.services.values():\n service.Delete()","def delete_project(self, project_name, check=True):\n page_projects = self._page_projects()\n\n with page_projects.table_projects.row(\n name=project_name).dropdown_menu as menu:\n menu.button_toggle.click()\n menu.item_delete.click()\n\n page_projects.form_delete_project_confirm.submit()\n\n if check:\n self.close_notification('success')\n page_projects.table_projects.row(\n name=project_name).wait_for_absence()","def post_project_delete(self, resource_id, resource_dict):\n pass","async def clear_all(self) -> None:","def cmd_apps__destroy(args):\n \n if args.name is None and in_git_repo():\n args.name = _get_current_project_name()\n\n if args.name is None:\n print \"Please provide a project name.\"\n sys.exit(1)\n\n print \"Destroying project %s...\" % args.name\n remote.destroy_project(args.name)\n print \"Project %s destroyed.\" % args.name\n if in_git_repo() and _get_current_project_name() == args.name:\n git(None, 'remote', 'rm', 'tinyserv')\n print \"Removed remote '%s'.\" % args.name","def delete_all(sid):\n Game.objects.all().delete()","def DeleteAll(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def cleanUp(self):\n import evoware.fileutil as F\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)"],"string":"[\n \"def clear(self):\\n for project in Project.objects:\\n project.delete()\",\n \"def __remove_all_projects__():\\n p = subprocess.Popen('rm -rf {}/.wcscanner/*'.format(context.__BASE_PATH__), shell=True)\\n p.wait()\",\n \"def clean_project(self, app_name=None, delete_all=False):\\n\\n if not app_name and not delete_all:\\n ConuException(\\\"You need to specify either app_name or set delete_all=True\\\")\\n\\n if delete_all:\\n args = [\\\"--all\\\"]\\n logger.info('Deleting all objects in current project')\\n else:\\n args = \\\"-l app=%s\\\" % app_name\\n logger.info('Deleting all objects with label app=%s', app_name)\\n\\n try:\\n o = run_cmd(self._oc_command([\\\"delete\\\", \\\"all\\\", args]),\\n return_output=True)\\n o_lines = o.split('\\\\n')\\n for line in o_lines:\\n logger.info(line)\\n except subprocess.CalledProcessError as ex:\\n raise ConuException(\\\"Cleanup failed because of exception: %s\\\" % ex)\",\n \"def clear_all(delete_id):\\n Tasks.query.filter(Tasks.project_id == delete_id).delete()\\n Projects.query.filter(Projects.project_id == delete_id).delete()\\n db.session.commit()\\n\\n return redirect('/')\",\n \"def clean(self):\\n\\n if not self.__projects:\\n return\\n\\n Console.info(\\\"Cleaning session...\\\")\\n Console.indent()\\n\\n for project in self.__projects:\\n project.clean()\\n\\n path = os.path.abspath(os.path.join(\\\".jasy\\\", \\\"locale\\\"))\\n if os.path.exists(path):\\n Console.info(\\\"Cleaning up locale project...\\\")\\n shutil.rmtree(path)\\n\\n path = os.path.abspath(os.path.join(\\\".jasy\\\", \\\"virtual\\\"))\\n if os.path.exists(path):\\n Console.info(\\\"Cleaning up virtual project...\\\")\\n shutil.rmtree(path)\\n\\n Console.outdent()\",\n \"def delete_project(arn=None):\\n pass\",\n \"def delete(self):\\n _url = f\\\"{self.connector.base_url}/projects/{self.project_id}\\\"\\n\\n self.connector.http_call(\\\"delete\\\", _url)\\n\\n self.project_id = None\\n self.name = None\",\n \"def test_delete_project(self):\\n pass\",\n \"def test_delete_project(self):\\n pass\",\n \"def project_delete(cursor, project):\\n haystack = (project['_id'], )\\n\\n query = \\\"DELETE FROM projects WHERE _id=?\\\"\\n try:\\n cursor.execute(query, haystack)\\n except Exception as e:\\n on_error(e)\\n\\n query = \\\"DELETE FROM namespaces WHERE project_id=?\\\"\\n try:\\n cursor.execute(query, haystack)\\n except Exception as e:\\n on_error(e)\\n else:\\n cursor.connection.commit()\\n raise Return((True, None))\",\n \"def tearDown(self):\\n Project.objects.all().delete()\",\n \"def delete_stored_project():\\n client = RequestManager()\\n client.set_method(\\\"DELETE\\\")\\n client.set_endpoint(\\\"/projects/{0}\\\".format(STORED_ID['project_id']))\\n client.execute_request()\",\n \"def delete_orphan_project(apps, schema_editor):\\n Project = apps.get_model('data_manager', 'Project')\\n Project.objects.filter(dataset__isnull=True).delete()\\n return\",\n \"def tearDownClass(cls):\\n projects = ['arc_project_for_testing_delete_after_usage1', 'arc_project_for_testing_delete_after_usage2',\\n 'ar c', 'ar:c', 'ar<c', 'ar%c']\\n for project in projects:\\n project_directory = os.path.join(arc_path, 'Projects', project)\\n shutil.rmtree(project_directory)\",\n \"def test_projects_delete(self):\\n project = Project()\\n response = self.client.open('/project-tracker/projects',\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def test_delete_project(self):\\n self.assertEqual(Project.objects.count(), 1)\\n self.assertEqual(Group.objects.count(), 2)\\n\\n delete_project(Project.objects.get(name=\\\"project A\\\"))\\n\\n self.assertEqual(Project.objects.count(), 0)\\n self.assertEqual(Group.objects.count(), 0)\",\n \"def es_delete(project=None):\\n if project is not None:\\n script_indexer.delete_project(project)\\n else:\\n script_indexer.delete_all()\",\n \"def do_project_delete(cs, args):\\n key = args.project\\n if cs.projects.is_id(key):\\n id = key\\n else:\\n id = cs.projects.get_id_by_name(key)\\n cs.projects.delete(id)\\n print(\\\"Delete Project '%s' successfully.\\\" % key)\",\n \"def delete_project(project):\\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\\n ret = bmi.delete_project(project)\\n if ret[constants.STATUS_CODE_KEY] == 200:\\n click.echo(\\\"Success\\\")\\n else:\\n click.echo(ret[constants.MESSAGE_KEY])\",\n \"def delete(self, request, p_name):\\n project = Project.objects.get(name=p_name)\\n connectors = project.connector_set.all()\\n connectors.delete()\\n if os.path.isfile(project.project_location):\\n os.remove(project.project_location)\\n project.delete()\\n return HttpResponse(HTTPStatus.OK)\",\n \"def delete(self, oid):\\n path = '/projects/%s' % oid\\n res = self.client.call(path, 'DELETE', data='', token=self.manager.identity.token)\\n self.logger.debug('Delete openstack project: %s' % truncate(res))\\n return True\",\n \"def delete_project(\\n name\\n):\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"delete_project\\\"\\n cmd[\\\"name_\\\"] = name\\n\\n comm.send(cmd)\",\n \"def delete_project(projectname):\\n response = jsonify(admin.delete_project(current_app.scoped_session(), projectname))\\n return response\",\n \"def delete_project(project_id):\\n client = RequestManager()\\n client.set_method(\\\"DELETE\\\")\\n client.set_endpoint(\\\"/projects/{0}\\\".format(project_id))\\n client.execute_request()\",\n \"def delete_all(self):\\n raise NotImplementedError()\",\n \"def delete(conn, project):\\n with conn:\\n c = conn.cursor()\\n c.execute(\\\"DELETE FROM projects WHERE project =?\\\", (project,))\",\n \"def remove_single_project(project_name):\\n p = subprocess.Popen('rm -rf {}/{}'.format(context.__PROJECTS_PATH__, project_name), shell=True)\\n p.wait()\",\n \"def destroy(config, args):\\n log = logging.getLogger('kraftwerk.destroy')\\n if confirm(\\\"Remove project %s from node %s along with all services and data?\\\" % \\n (args.project.name, args.node.hostname)):\\n args.node.ssh(config.template(\\\"scripts/project_destroy.sh\\\", project=args.project))\\n print \\\"Project %s removed from node %s\\\" % \\\\\\n (args.project.name, args.node.hostname )\\n for service in args.project.services(args.node):\\n args.node.ssh(service.destroy_script)\",\n \"def delete_project_files(self, project, logStat):\\n from corrdb.common.models import FileModel\\n from corrdb.common.models import EnvironmentModel\\n\\n for _file in project.resources:\\n file_ = FileModel.objects.with_id(_file)\\n if file_:\\n result = self.storage_delete_file(file_.group, file_.storage)\\n if result:\\n logStat(deleted=True, file_obj=file_)\\n file_.delete()\\n\\n for record in project.records:\\n result = self.delete_record_files(record, logStat)\\n if result:\\n logStat(deleted=True, record=record)\\n record.delete()\\n\\n for environment_id in project.history:\\n _environment = EnvironmentModel.objects.with_id(environment_id)\\n if _environment:\\n if _environment.bundle and _environment.bundle.scope == \\\"local\\\":\\n result = self.storage_delete_file('bundle', _environment.bundle.storage)\\n if result:\\n # logStat(deleted=True, bundle=_environment.bundle)\\n # logStat(deleted=True, environment=_environment)\\n _environment.bundle.delete()\\n # else:\\n # logStat(deleted=True, environment=_environment)\\n _environment.delete()\",\n \"def delete_all(self, prog:progress=None): \\n\\t\\tself.__output_status('Delete all files')\\n\\t\\tif (self.__check_terminated()):\\n\\t\\t\\treturn;\\t\\n\\t\\tdelete_dir(self.root)\\n\\t\\ttime.sleep(0.3)\",\n \"def delete_project(proj_id):\\n project_obj = Project.objects.get(id=proj_id)\\n print('Deleting project the fastq files within the project: ', project_obj.description)\\n\\n description = project_obj.description.replace(' ', '') # remove any space in the project name\\n project_dir = 'documents/%s/%s' % (str(project_obj.date.date()), description)\\n shutil.rmtree(project_dir, ignore_errors=True)\\n print(\\\"Files deleted.\\\")\",\n \"def delete_all(self):\\n # delete everything\\n shutil.rmtree(self.location)\",\n \"def destroy(self, request, pk=None):\\n try:\\n project = Project.objects.get(pk=pk)\\n project.delete()\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\\n\\n except Project.DoesNotExist as ex:\\n return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)\\n\\n except Exception as ex:\\n return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\",\n \"def delete(\\n self, url: str\\n ) -> pymongo.results.DeleteResult:\\n return self._mongo.delete({\\n 'url': url\\n },\\n 'projects'\\n )\",\n \"def delete(self):\\r\\n delete_tracks(self.project, [self])\",\n \"def test_remove_project(self):\\n pass\",\n \"def test_projects_id_delete(self):\\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\\n method='DELETE')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def delete_project(request, project_id):\\n if not request.user.is_superuser:\\n messages.error(request, 'Sorry, only store owners can do that.')\\n return redirect(reverse('home'))\\n\\n project = get_object_or_404(Project, pk=project_id)\\n project.delete()\\n messages.success(request, 'Project deleted!')\\n return redirect(reverse('portfolio'))\",\n \"def delete(self):\\n return self.client._perform_empty(\\n \\\"DELETE\\\", \\\"/project-folders/%s\\\" % self.project_folder_id)\",\n \"def remove_all(lists_id):\\n Tasks.query.filter(Tasks.project_id == lists_id).delete()\\n db.session.commit()\\n\\n return redirect('/')\",\n \"def delete_project(request, project_id):\\n\\n profile = get_object_or_404(Profile, user=request.user)\\n project = get_object_or_404(GameProject, pk=project_id)\\n\\n if not profile.is_creator:\\n messages.error(request, 'Sorry, only creators can do that.')\\n return redirect(reverse('home'))\\n if project.owner != profile:\\n messages.error(request, 'Sorry, only the project owner can do that.')\\n return redirect(reverse('home'))\\n\\n project = get_object_or_404(GameProject, pk=project_id)\\n project.delete()\\n messages.success(request, 'Project deleted!')\\n return redirect(reverse('all_projects'))\",\n \"def delete_users(project):\\n for user_id in project.user_id.all():\\n project.user_id.remove(user_id.pk)\\n project.save()\",\n \"def tearDown(self):\\n User.objects.all().delete()\\n Project.objects.all().delete()\",\n \"def delete(self, name, project=None):\\n qlist = self._list(project)\\n key = self._queue(project, name)\\n self._db.delete(key)\\n self._db.zremrangebyscore(qlist, -1, 1)\",\n \"def delete_all(self):\\n models.CourseLearningOutcome.objects.all().delete()\\n #models.CoreLearningOutcome.objects.all().delete()\\n #models.CreditType.objects.all().delete()\\n models.Course.objects.all().delete()\\n models.DegreeProgram.objects.all().delete()\\n models.DPCourseSpecific.objects.all().delete()\\n models.DPCourseGeneric.objects.all().delete()\\n models.DPCourseSubstituteSpecific.objects.all().delete()\\n models.DPCourseSubstituteGeneric.objects.all().delete()\",\n \"def delete_all(cls):\\n with sqlite3.connect(cls.dbpath) as connection:\\n connection.row_factory = sqlite3.Row\\n cursor = connection.cursor()\\n SQL = \\\"DELETE FROM accounts;\\\"\\n cursor.execute(SQL)\",\n \"def delete_project_entities(cls, project_id,\\n suppress_exception=False,\\n session=None):\\n cls.db_repo.delete_project_entities(\\n project_id, suppress_exception=suppress_exception, session=session)\",\n \"def delete():\\n run('rm -r {}'.format(utils.home('apps', env.PROJECT_NAME)))\",\n \"def deleteAll():\\n _table.deleteAll()\\n _initialiseGlobals()\\n\\n return\",\n \"def close(self):\\n\\n if not self.__projects:\\n return\\n\\n Console.debug(\\\"Closing session...\\\")\\n Console.indent()\\n\\n for project in self.__projects:\\n project.close()\\n\\n self.__projects = None\\n\\n Console.outdent()\",\n \"def delete_project(project_id):\\n \\n project = mongo.db.projects\\n project.delete_one({'_id': ObjectId(project_id)})\\n flash('Your project has been deleted.', 'success')\\n return redirect(url_for('projects'))\",\n \"def delete_teams_all(self, team_name):\\n self.execute(TABELLE['teams']['delete']['all'])\",\n \"def delete_project(project_id):\\n project = Project.query.filter_by(id=project_id).first()\\n if not project:\\n return {\\n 'success': False,\\n 'message': f\\\"No project with the specified id {project_id} found.\\\",\\n }\\n\\n else:\\n if is_project_manager(project, g.user):\\n # delete related tasks\\n Task.query.filter_by(project=project).delete()\\n #delete related invites\\n Invitation.query.filter_by(project=project).delete()\\n db_session.delete(project)\\n db_session.commit()\\n return {\\n 'success': True,\\n 'result': {},\\n 'message': \\\"Project Deleted Successfully.\\\",\\n }\",\n \"def project_clear_files(request, **kwargs):\\n project = kwargs.get(\\\"project\\\")\\n if request.user.is_authenticated and request.user == project.user:\\n project.clear_project_folder()\\n return Response(status=status.HTTP_200_OK)\\n else:\\n raise PermissionDenied\",\n \"def delete_api(self, project_id, api_id, hbase_manager=None):\\n\\n self._project_service.get_nondeleted_project(project_id)\\n\\n proj_api_col = \\\"apis:{0}\\\".format(api_id)\\n project_id = bytes(project_id, self._charset)\\n api_id = bytes(api_id, self._charset)\\n\\n self.get_nondeleted_api(project_id, api_id)\\n\\n connection = hbase_manager.connection\\n api_tbl = connection.table(\\\"apis\\\")\\n projects_tbl = connection.table(\\\"projects\\\")\\n\\n projects_tbl.delete(project_id, [bytes(proj_api_col, self._charset)])\\n api_tbl.put(api_id, {\\n b\\\"attrs:state\\\": b\\\"deleted\\\"\\n })\",\n \"def cleanup(self):\\r\\n session = self.get_session()\\r\\n project = session.create(self._config.name)\\r\\n \\r\\n session.home = self._config['dir']\\r\\n \\r\\n result = self.__find_project(project)\\r\\n \\r\\n path = os.path.join(session.home, project.name)\\r\\n project.work_area(False, True, True, path=path)\\r\\n \\r\\n if (result != None):\\r\\n _logger.info(\\\"Project found: '%s'\\\" % result)\\r\\n role = session.role\\r\\n co_role = ccm.get_role_for_purpose(session, str(self._config['purpose']))\\r\\n session.role = co_role\\r\\n try:\\r\\n delResult = result.delete(scope='project_and_subproject_hierarchy')\\r\\n finally:\\r\\n session.role = role\\r\\n ccm.log_result(delResult, ccm.CHECKOUT_LOG_RULES, _logger)\",\n \"def deleteProject(self, projectId):\\n uri = \\\"/v1/projects/\\\" +str(projectId)\\n response = self.client.delete(uri)\\n return response\",\n \"def reset():\\n local('cd {{ project_name }} && \\\\\\n rm -rf static && rm -rf gzip && rm -rf build')\",\n \"def delete_project(self, name=None, delete_dir=False):\\n victim = name or self.current\\n if victim not in self:\\n raise ValueError(\\\"{} is not a project\\\".format(victim))\\n\\n if len(self) == 1:\\n raise ValueError(\\\"Can't delete only remaining project\\\")\\n\\n ProjectDataset.delete().where(ProjectDataset.name == victim).execute()\\n\\n if delete_dir:\\n dir_path = self._base_data_dir / safe_filename(victim)\\n assert dir_path.is_dir(), \\\"Can't find project directory\\\"\\n shutil.rmtree(dir_path)\\n\\n if name is None or name == self.current:\\n if \\\"default\\\" in self:\\n self.set_current(\\\"default\\\")\\n else:\\n self.set_current(next(iter(self)).name)\\n return self.current\",\n \"def delete_all():\\n if os.path.exists(DATA_DIR):\\n shutil.rmtree(DATA_DIR)\",\n \"def unlink(self):\\n analytic_accounts_to_delete = self.env['account.analytic.account']\\n for project in self:\\n if project.analytic_account_id and not project.analytic_account_id.line_ids:\\n analytic_accounts_to_delete |= project.analytic_account_id\\n result = super(Project, self).unlink()\\n analytic_accounts_to_delete.unlink()\\n return result\",\n \"def DeleteAllItems(self):\\r\\n\\r\\n self.DeleteRoot()\",\n \"def delete_project(self, project_name):\\n # type(project_name) == unicode\\n project = self.db.get_project_by_name(project_name)\\n if not project:\\n print(u\\\"*** Error: The project '{}' was not found.\\\"\\n \\\"\\\".format(project_name))\\n return\\n print('Caution! The related tracking will be deleted as well.{eol}'\\n 'Do you really want to delete the project? [y/N] '\\n ''.format(eol=os.linesep), end='')\\n if not helpers.get_yes_no(default='n'):\\n return\\n self.db.delete_project_by_name(project_name)\\n print(u\\\"The project '%s' has been deleted.\\\" % project_name)\\n self.set_prompt()\",\n \"def delete_project(self, project_id):\\n _url = f\\\"{self.base_url}/projects/{project_id}\\\"\\n self.http_call(\\\"delete\\\", _url)\\n return\",\n \"async def delete_all_games(self):\\n all_games = await ex.conn.fetch(\\\"SELECT gameid FROM blackjack.games\\\")\\n for games in all_games:\\n game_id = games[0]\\n await self.delete_game(game_id)\",\n \"def destroy_all(self):\\n for name in self.app.config['SIMPLE_DOMAINS']:\\n self.connection.delete_domain(name)\",\n \"def delete_project(self, project_id):\\n self._run(\\n url_path=\\\"projects/delete\\\",\\n id=project_id,\\n )\\n return True\",\n \"def DeleteContainers(self):\\n for container in itertools.chain(*list(self.containers.values())):\\n container.Delete()\",\n \"def destroy_all(self) -> None:\\n try:\\n containers = self.docker.containers.list(\\n all=True,\\n filters={\\n 'label': LABEL_TASK_ID,\\n },\\n )\\n\\n for container in containers:\\n container.remove(force=True)\\n\\n except requests.exceptions.ConnectionError:\\n raise ProviderError('Docker engine unavailable')\",\n \"def delete_all():\\n answer = ['YES', 'NO']\\n str = rs.GetString(\\\"Delete all objects?\\\", 'YES', answer)\\n\\n if str == 'YES':\\n obs = rs.ObjectsByType(0)\\n rs.DeleteObjects(obs)\\n elif str == 'NO':\\n pass\\n else:\\n sys.exit()\",\n \"def clean_all_db():\\n for model in [\\n Component, Arch, AutoCase, AutoCaseFailure, Bug, Linkage, WorkItem,\\n Document, Project, Framework]:\\n model.objects.all().delete()\",\n \"def remove_all_users(request):\\n id_project = request.POST.get(\\\"project_id\\\")\\n project = UtilsData.get_object_by_type_and_id(\\\"project\\\", id_project)\\n if request.user.can_delete(project):\\n roles = project.affecteds_set.all()\\n for role in roles:\\n if role.role not in (Affecteds.ROLE.Manager, Affecteds.ROLE.Admin):\\n role.role = Affecteds.ROLE.Nill\\n role.save()\\n return HttpResponse(json.dumps(\\\"Ok\\\"),\\n content_type=\\\"application/json\\\")\\n else:\\n logger.error(\\\"user %s try to remove all users to project %d \\\" % (request.user.username, id_project))\\n return HttpResponse(json.dumps(\\\"error\\\"),\\n content_type=\\\"application/json\\\")\",\n \"def pre_project_delete(self, resource_id):\\n pass\",\n \"def delete_project(self, project_id):\\n return self._delete('/projects/{0}'.format(project_id))\",\n \"def delete_namespaced_project(self, name, **kwargs):\\n\\n all_params = ['name', 'pretty']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method delete_namespaced_project\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `delete_namespaced_project`\\\")\\n\\n resource_path = '/oapi/v1/projects/{name}'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'DELETE',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='UnversionedStatus',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def delete_all(self):\\n self.session.query(TodoItem).delete()\\n self.session.query(TodoList).delete()\",\n \"def delete_keystone_v3_project(self, project_id, domain_id):\\n LOG_OBJ.debug(\\\"Disable the project.\\\")\\n kwargs = {\\\"project_id\\\": project_id, \\\"enabled\\\": False}\\n self.set_keystone_v3_project(**kwargs)\\n\\n LOG_OBJ.debug(\\\"Deleting the project.\\\")\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v3/projects/\\\" + \\\\\\n str(project_id)\\n _headers = {'x-auth-token': self.cloud_admin_info[\\\"token_domain\\\"],\\n 'content-type': 'application/json'}\\n _body = None\\n response = self.request(\\\"DELETE\\\", _url, _headers, _body)\\n\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while deleting the project\\\")\\n print (\\\"No response from Server while deleting the project\\\")\\n return response\\n\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\" Deleting project Failed with status %s \\\"\\n \\\"and error : %s\\\" % (response.status, response.data))\\n print (\\\" Deleting project Failed with status %s and error : %s\\\" %\\n (response.status, response.data))\\n return response.status\\n\\n return True\",\n \"def delete(self):\\n return self.client._perform_empty(\\n \\\"DELETE\\\", \\\"/projects/%s/managedfolders/%s\\\" % (self.project_key, self.odb_id))\",\n \"def delete(self, guid):\\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\\n # search for the Project and delete if found\\n key = db.Key.from_path('Project', int(guid))\\n project = db.get(key)\\n if not project == None:\\n project.delete()\\n self.response.set_status(204, \\\"Deleted\\\")\\n else:\\n self.response.set_status(404, \\\"Not Found\\\")\\n else:\\n self.response.set_status(401, \\\"Not Authorized\\\")\",\n \"def deleteAll(klass, where=None, transaction=None):\\n config = Registry.getConfig()\\n tablename = klass.tablename()\\n return config.delete(tablename, where, transaction)\",\n \"def delete_all_onprogress_domains():\\r\\n db = connect()\\r\\n cursor = db.cursor()\\r\\n try:\\r\\n cursor.execute(\\\"DELETE FROM on_progress_domains\\\")\\r\\n db.commit()\\r\\n except:\\r\\n cursor.close()\\r\\n db.close()\\r\\n raise RuntimeError(\\\"An Exception happened with the Database, make sure you are connected\\\")\\r\\n cursor.close()\\r\\n db.close()\",\n \"def delete_project_by_name(self, project_name):\\n with self._transaction.cursor() as cur:\\n # delete associations between this project and any barcodes\\n cur.execute(\\\"DELETE FROM barcodes.project_barcode \\\"\\n \\\"WHERE project_id in (\\\"\\n \\\"SELECT project_id FROM barcodes.project \\\"\\n \\\"WHERE project = %s)\\\",\\n (project_name,))\\n\\n # now delete the project itself\\n cur.execute(\\\"DELETE FROM barcodes.project WHERE project = %s\\\",\\n (project_name,))\\n return cur.rowcount == 1\",\n \"def Delete(ids):\\n db = catocommon.new_conn()\\n\\n # we have to check each cloud and see if it's used as the default...\\n # if so, you can't delete it without first fixing the account.\\n # ACCOUNTS REQUIRE A DEFAULT CLOUD\\n existed = False\\n for delete_id in ids:\\n sql = \\\"select count(*) from cloud_account where default_cloud_id = %s\\\" % (delete_id)\\n exists = db.select_col_noexcep(sql)\\n\\n if not exists:\\n sql = \\\"delete from clouds_keypair where cloud_id = %s\\\" % (delete_id)\\n db.tran_exec(sql)\\n \\n sql = \\\"delete from clouds where cloud_id = %s\\\" % (delete_id)\\n db.tran_exec(sql)\\n \\n db.tran_commit()\\n else:\\n existed = True\\n \\n db.close()\\n\\n msg = \\\"\\\"\\n if existed:\\n msg = \\\"Some of the selected Clouds were not deleted because they are referenced by a Cloud Account. Delete the Account first, or assign it a new Default Cloud.\\\"\\n \\n return True, msg\",\n \"def delete(self):\\n return self.client._perform_empty(\\\"DELETE\\\", \\\"/workspaces/%s\\\" % self.workspace_key)\",\n \"def delete_project(id):\\n result = delete_project_to_db(id)\\n flash(result)\\n return redirect(url_for(\\\"portfolio\\\"))\",\n \"def delete_contributions(self, project, per_page=100, max_workers=5):\\n tic = time.perf_counter()\\n\\n if max_workers > MAX_WORKERS:\\n max_workers = MAX_WORKERS\\n print(f\\\"max_workers reset to max {MAX_WORKERS}\\\")\\n\\n cids = self.get_contributions(project)[\\\"ids\\\"]\\n total = len(cids)\\n\\n if cids:\\n with FuturesSession(max_workers=max_workers) as session:\\n while cids:\\n futures = [\\n session.delete(\\n f\\\"{self.url}/contributions/\\\",\\n headers=self.headers,\\n params={\\n \\\"project\\\": project,\\n \\\"id__in\\\": \\\",\\\".join(chunk),\\n \\\"per_page\\\": per_page,\\n },\\n )\\n for chunk in chunks(cids, n=per_page)\\n ]\\n\\n self._run_futures(futures, total=len(cids))\\n cids = self.get_contributions(project)[\\\"ids\\\"]\\n\\n self.load()\\n\\n # reset columns to be save (sometimes not all are reset BUGFIX?)\\n self.projects.update_entry(pk=project, project={\\\"columns\\\": []}).result()\\n toc = time.perf_counter()\\n dt = (toc - tic) / 60\\n print(f\\\"It took {dt:.1f}min to delete {total} contributions.\\\")\",\n \"def update_projects(self, new_projects_list):\\n to_stop = [project for project in self if project not in new_projects_list]\\n for project_id in to_stop:\\n self[project_id].stop()\\n del self[project_id]\\n\\n for project_id in new_projects_list:\\n if project_id not in self:\\n self[project_id] = ProjectManager(self.zk_client, project_id,\\n self.callback)\",\n \"def purge_project(request):\\n data = json.loads(request.body.decode('utf-8'))\\n if 'item_id' not in data or not data['item_id']:\\n return JsonResponse({'state': 'error', 'error': 'No project specified for reactivating'}, status=400)\\n projquery = models.Project.objects.filter(pk=data['item_id'], active=False)\\n if not projquery:\\n return JsonResponse({'state': 'error', 'error': 'Project does not exist or is still active'}, status=403)\\n dsetowners = models.DatasetOwner.objects.filter(dataset__runname__experiment__project_id=data['item_id'], dataset__purged=False).select_related('dataset')\\n if not request.user.is_staff:\\n return JsonResponse({'state': 'error', 'error': 'User has no permission to purge this project, must be staff'}, status=403)\\n result = {'errormsgs': []}\\n for dso in dsetowners.distinct('dataset'):\\n purged = delete_dataset_from_cold(dso.dataset)\\n if purged['state'] == 'error':\\n result.update({'state': 'error', 'error': 'Not all project datasets could be purged'})\\n result['errormsgs'].append(purged['error'])\\n # if any dataset cannot be purged, report it, do not mark proj as purged\\n if result['errormsgs']:\\n result['error'] = '{} Errors: {}'.format(result['error'], '; '.join(result.pop('errormsgs')))\\n return JsonResponse(result, status=500)\\n else:\\n projquery.update(active=False)\\n return JsonResponse({})\",\n \"def resources(request):\\n projects, secrets, pools, storageclasses, pvcs, pods = ([] for i in range(6))\\n\\n def finalizer():\\n \\\"\\\"\\\"\\n Delete the resources created during the test\\n \\\"\\\"\\\"\\n for resource_type in pods, pvcs, storageclasses, secrets:\\n for resource in resource_type:\\n resource.delete()\\n resource.ocp.wait_for_delete(resource.name)\\n if pools:\\n # Delete only the RBD pool\\n pools[0].delete()\\n if projects:\\n for project in projects:\\n project.delete(resource_name=project.namespace)\\n project.wait_for_delete(project.namespace)\\n\\n request.addfinalizer(finalizer)\\n\\n return projects, secrets, pools, storageclasses, pvcs, pods\",\n \"def delete_all(submission_client, program, project, batch_size=200, types=['submitted_methylation', 'aliquot', 'sample', 'demographic', 'case', 'experiment']):\\n for t in types:\\n print('{}-{}.{}'.format(program, project, t))\\n try:\\n delete_type(submission_client, program, project, batch_size, t)\\n except Exception as e:\\n print(e)\",\n \"def delete(self):\\n for i in set(self.instances.values()):\\n i.delete()\\n shutil.rmtree(self.dirpath, True)\",\n \"def delete_all_domain_pages():\\r\\n db = connect()\\r\\n cursor = db.cursor()\\r\\n try:\\r\\n cursor.execute(\\\"DELETE FROM domain_pages\\\")\\r\\n db.commit()\\r\\n except:\\r\\n cursor.close()\\r\\n db.close()\\r\\n raise RuntimeError(\\\"An Exception happened with the Database, make sure you are connected\\\")\\r\\n cursor.close()\\r\\n db.close()\",\n \"def DeleteServices(self):\\n for service in self.services.values():\\n service.Delete()\",\n \"def delete_project(self, project_name, check=True):\\n page_projects = self._page_projects()\\n\\n with page_projects.table_projects.row(\\n name=project_name).dropdown_menu as menu:\\n menu.button_toggle.click()\\n menu.item_delete.click()\\n\\n page_projects.form_delete_project_confirm.submit()\\n\\n if check:\\n self.close_notification('success')\\n page_projects.table_projects.row(\\n name=project_name).wait_for_absence()\",\n \"def post_project_delete(self, resource_id, resource_dict):\\n pass\",\n \"async def clear_all(self) -> None:\",\n \"def cmd_apps__destroy(args):\\n \\n if args.name is None and in_git_repo():\\n args.name = _get_current_project_name()\\n\\n if args.name is None:\\n print \\\"Please provide a project name.\\\"\\n sys.exit(1)\\n\\n print \\\"Destroying project %s...\\\" % args.name\\n remote.destroy_project(args.name)\\n print \\\"Project %s destroyed.\\\" % args.name\\n if in_git_repo() and _get_current_project_name() == args.name:\\n git(None, 'remote', 'rm', 'tinyserv')\\n print \\\"Removed remote '%s'.\\\" % args.name\",\n \"def delete_all(sid):\\n Game.objects.all().delete()\",\n \"def DeleteAll(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def cleanUp(self):\\n import evoware.fileutil as F\\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7438387","0.74334717","0.7418019","0.6865698","0.6836307","0.6812033","0.6775198","0.6739514","0.6739514","0.6619207","0.6606535","0.65799767","0.65798426","0.65713173","0.6563861","0.6534334","0.64856863","0.6476536","0.6473611","0.6437883","0.6420292","0.6313049","0.62967867","0.62856716","0.62752104","0.6199454","0.6188569","0.61832356","0.6182946","0.61698896","0.6166517","0.61383075","0.6132611","0.61003214","0.60853916","0.6070335","0.6067106","0.6063806","0.6057775","0.60422105","0.60123265","0.5980971","0.5917976","0.5909036","0.58866143","0.58822","0.58793515","0.587845","0.58618027","0.5849153","0.5843611","0.583917","0.58360976","0.5828697","0.581492","0.58137405","0.58069944","0.5786529","0.5766742","0.5762417","0.5739808","0.57331496","0.5728839","0.57157767","0.57091016","0.5707375","0.5704416","0.5701312","0.5699013","0.56961083","0.5686833","0.56839","0.56755704","0.56638396","0.5655906","0.56262493","0.5622429","0.5622028","0.56012005","0.55888313","0.55857366","0.55827016","0.55722743","0.55707765","0.5570158","0.5568325","0.55676097","0.5564349","0.555637","0.5554291","0.5550858","0.5550755","0.5546927","0.55461496","0.5543163","0.5541949","0.55288345","0.5527618","0.5519352","0.5515107"],"string":"[\n \"0.7438387\",\n \"0.74334717\",\n \"0.7418019\",\n \"0.6865698\",\n \"0.6836307\",\n \"0.6812033\",\n \"0.6775198\",\n \"0.6739514\",\n \"0.6739514\",\n \"0.6619207\",\n \"0.6606535\",\n \"0.65799767\",\n \"0.65798426\",\n \"0.65713173\",\n \"0.6563861\",\n \"0.6534334\",\n \"0.64856863\",\n \"0.6476536\",\n \"0.6473611\",\n \"0.6437883\",\n \"0.6420292\",\n \"0.6313049\",\n \"0.62967867\",\n \"0.62856716\",\n \"0.62752104\",\n \"0.6199454\",\n \"0.6188569\",\n \"0.61832356\",\n \"0.6182946\",\n \"0.61698896\",\n \"0.6166517\",\n \"0.61383075\",\n \"0.6132611\",\n \"0.61003214\",\n \"0.60853916\",\n \"0.6070335\",\n \"0.6067106\",\n \"0.6063806\",\n \"0.6057775\",\n \"0.60422105\",\n \"0.60123265\",\n \"0.5980971\",\n \"0.5917976\",\n \"0.5909036\",\n \"0.58866143\",\n \"0.58822\",\n \"0.58793515\",\n \"0.587845\",\n \"0.58618027\",\n \"0.5849153\",\n \"0.5843611\",\n \"0.583917\",\n \"0.58360976\",\n \"0.5828697\",\n \"0.581492\",\n \"0.58137405\",\n \"0.58069944\",\n \"0.5786529\",\n \"0.5766742\",\n \"0.5762417\",\n \"0.5739808\",\n \"0.57331496\",\n \"0.5728839\",\n \"0.57157767\",\n \"0.57091016\",\n \"0.5707375\",\n \"0.5704416\",\n \"0.5701312\",\n \"0.5699013\",\n \"0.56961083\",\n \"0.5686833\",\n \"0.56839\",\n \"0.56755704\",\n \"0.56638396\",\n \"0.5655906\",\n \"0.56262493\",\n \"0.5622429\",\n \"0.5622028\",\n \"0.56012005\",\n \"0.55888313\",\n \"0.55857366\",\n \"0.55827016\",\n \"0.55722743\",\n \"0.55707765\",\n \"0.5570158\",\n \"0.5568325\",\n \"0.55676097\",\n \"0.5564349\",\n \"0.555637\",\n \"0.5554291\",\n \"0.5550858\",\n \"0.5550755\",\n \"0.5546927\",\n \"0.55461496\",\n \"0.5543163\",\n \"0.5541949\",\n \"0.55288345\",\n \"0.5527618\",\n \"0.5519352\",\n \"0.5515107\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8597782"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":693,"cells":{"query":{"kind":"string","value":"Static method who read all projects"},"document":{"kind":"string","value":"def read_project(response):\n STORED_ID['project_id'] = response.json()[\"id\"]"},"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_read_project(self):\n pass","def test_read_project(self):\n pass","def project():","def project():","def project():","def test_get_projects(self):\n pass","def get_projects_data():\n wcscanner_path = context.__BASE_PATH__ + '/.wcscanner'\n\n data = []\n for project in os.listdir(wcscanner_path):\n if (os.path.isdir(os.path.join(wcscanner_path, project))):\n update_project_data(project)\n project_path = '{}/{}'.format(wcscanner_path, project)\n f = open('{}/.project'.format(project_path), 'r')\n data.append(json.load(f))\n f.close()\n return data","def list_projects():\n if '.wcscanner' not in os.listdir(context.__BASE_PATH__):\n return []\n return os.listdir(context.__PROJECTS_PATH__)","def get_projects(self):\n return conf.projects","def test_list_project(self):\n pass","def get_projects(self):\n unaligned_path = self.get_unaligned_path()\n logger.debug(\"collecting list of projects\")\n return [p for p in os.listdir(unaligned_path)\n if len(parsing.get_project_label(p))]","def test_list_projects(self):\n pass","def test_list_projects(self):\n pass","def get_projects(self):\n res = self.conn.cursor().execute(\"SELECT * FROM projects\")\n return res.fetchall()","def get_projects():\n return Project.query.all()","def _get_projects(project_ids):\n if _ALL in project_ids:\n return projects_lib.get_all()\n return projects_lib.get_selective(project_ids)","def test_get_project(self):\n pass","def get_projects(self):\n projects = []\n page = 1\n while not len(projects) % 100:\n projects += self._get('/projects?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\n if not projects:\n break\n page += 1\n return projects","def _page_projects(self):\n return self._open(self.app.page_projects)","def get_all_projects(self, org):\n return [proj for proj in Project.objects.filter(org=org)]","def test_get_projects_expanded(self):\n pass","def get_projects(self):\n return self.http_call(\"get\", url=f\"{self.base_url}/projects\").json()","def project_list(self):\n try:\n ids = self.request[api.DATA][api.DATA][\"ids\"]\n return self._get_keystone_projects(ids)\n except Exception as e:\n LOG.exception(\"Error occurred: %s\" % e)","def projects(self):\r\n return p.Projects(self)","def test_list_project_request(self):\n pass","def get_projects(self):\n if not self.validate():\n raise SettingCustomVisionAccessFailed\n return self.get_trainer_obj().get_projects()","def atlas_projects():\n pass","def list(self):\n\n for name in self.projects:\n self.projects[name].show()\n print(\"\\n\")","def get_projects(self, _is_simple=False):\n req_url = f\"{self.url}/projects\"\n if _is_simple:\n req_url += \"?simple=true\"\n ret = requests.get(req_url, headers = self.req_header)\n return ret.json()","def all(cls):\n projects_url = 'https://www.pivotaltracker.com/services/v5/projects'\n root = _perform_pivotal_get(projects_url)\n if root is not None:\n return [Project.from_json(project_node) for project_node in root]","def get_project_list(config):\n eggs_dir = config.get('eggs_dir', 'eggs')\n if os.path.exists(eggs_dir):\n projects = os.listdir(eggs_dir)\n else:\n projects = []\n try:\n projects += [x[0] for x in config.cp.items('settings')]\n except NoSectionError:\n pass\n return projects","def project(self):\n return read_small_file(self.homeDirectory + \"/.project\")","def db_projects():\n return [{\"name\": \"IT\"}, {\"name\": \"Financial\"}, {\"name\": \"Failed\"}]","def test_projects_get(self):\n response = self.client.open('/project-tracker/projects',\n method='GET')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def get_all_projects():\n return jsonify(admin.get_all_projects(current_app.scoped_session()))","def all(cls):\r\n projects_url = 'https://www.pivotaltracker.com/services/v3/projects'\r\n response = _perform_pivotal_get(projects_url)\r\n\r\n root = ET.fromstring(response.text)\r\n if root is not None:\r\n return [Project.from_node(project_node) for project_node in root]","def get_projects(cls):\n projects = []\n for project in Project.select():\n project_dict = {\n \"id\": project.id,\n \"title\": project.title,\n \"link\": project.link,\n \"description\": project.description\n }\n projects.append(project_dict)\n return projects","def get_projects(self):\n response = self.request(verb=requests.get, address=\"projects\")\n # FIXME: if no results, must we raise an exception?\n return response[\"results\"] if \"results\" in response else response","def _loadProjects(self):\n logger.debug(\"Func: _loadProjects\")\n\n if not os.path.isfile(self._pathsDict[\"projectsFile\"]):\n return\n else:\n projectsData = self._loadJson(self._pathsDict[\"projectsFile\"])\n if projectsData == -2:\n return -2\n return projectsData","def get_projects(self, *criterion):\n from wkcdd.models.helpers import get_project_list\n return get_project_list([self.id], *criterion)","def all_projects(self):\n projects_list = []\n for path in DAVOS_PROJECT_DIR.iterdir():\n if path.is_dir():\n projects_list.append(Project(path.name))\n return projects_list","def list_projects():\n\n cmd = dict()\n cmd[\"type_\"] = \"list_projects\"\n cmd[\"name_\"] = \"\"\n \n s = comm.send_and_receive_socket(cmd)\n\n msg = comm.recv_string(s)\n\n if msg != \"Success!\":\n raise Exception(msg)\n \n json_str = comm.recv_string(s) \n \n s.close() \n\n return json.loads(json_str)[\"projects\"]","def get_project_list():\n return parse_list_output(Popen(\n 'openstack project list'.split(), stdout=STDOUT, stderr=STDERR\n ).communicate()[0])","def project_all(request, format=None):\n if request.method == 'GET':\n projects = Project.objects.all().order_by('key')\n serializer = ProjectSerializer(projects, many=True)\n return Response(serializer.data)","def get_project(self, i):\r\n return self.__projects[i]","def getprojects(self):\n resp = self.conn.request('GET', self.URLS['allprojects'], dict(api_key=self.api_key))\n data = resp.data.decode('utf-8')\n jdata = json.loads(data)['projects']\n # Convert nested JSON documents\n for project_index in range(len(jdata)):\n for field in ('options_json', 'templates_json'):\n jdata[project_index][field] = json.loads(jdata[project_index][field])\n # Pass project details dictionaries to constructors, return array\n return [PhProject(self, project) for project in jdata]","def projects(args):\n _projects = lib.get_projects(\n args.target, username=args.username, password=args.password\n )\n if _projects:\n print(\"\\n\".join(_projects))","def getProjects(self):\n catalog = plone.api.portal.get_tool('portal_catalog')\n path = '{}/projects'.format('/'.join(plone.api.portal.get().getPhysicalPath()))\n query = dict(portal_type='Project', sort_on='sortable_title', path=path)\n result = list()\n for brain in catalog(**query):\n result.append((brain.getId, brain.Title))\n return result","def list_projects(arn=None, nextToken=None):\n pass","def _get_project_tina_entries(self,pool='archive',refresh=False,path_folder=None):\n\t\tif not path_folder: path_folder = self.catalog_path\n\t\tif not refresh:\n\t\t\ttry:\n\t\t\t\treturn self.tina_archive_entries\n\t\t\texcept: pass \n\t\tself.tina_archive_entries = Tina.tina_find(\n\t\t\tpath_folder=path_folder,\n\t\t\tapplication=self.application,\n\t\t\tstrat='A',\n\t\t\tskip_filter=self.skip_filter)\n\t\treturn self.tina_archive_entries","def test_demo_project_call(self):\n resp = DemoAivenStorage(os.environ[\"AIVEN_API_URL\"],\n os.environ[\"AIVEN_TOKEN\"]).get_project_names()\n assert isinstance(resp, list)\n assert len(resp) == 1\n assert 'romainducarrouge-31f2' in resp","def get_project_list(self, dummy_project):\n # TODO: domain scope 403 is probably to do with faulty keystone policy config -- revise?\n if not self._projects:\n self._projects = self._get_keystone_client(dummy_project).projects.list()\n\n return self._projects","async def get_project_info(project_urls):\n project_info = []\n for url in project_urls:\n soup = await get_page(url)\n about = soup.find_all(\"p\")\n title = soup.find(\"h3\").text\n student = about[0].text.splitlines()[2].strip()\n details = about[1].text\n name = about[0].find(\"a\").text\n project_info.append({'Organization': name, 'title': title,\n 'student': student, 'details': details,\n 'link': url})\n\n return project_info","def test_get_projects_filters(fc: fetcher.Fetcher, test_project_name):\n projects = fc.get_projects(test_project_name)\n assert isinstance(projects, list)\n assert len(projects) == 1\n assert projects[0].name == test_project_name","def _project_files(project_name, folder):\n _authenticate()\n if project_name.startswith(\"project-\"):\n project_id = project_name\n else:\n query = dxpy.api.system_find_projects({\"name\": project_name, \"level\": \"VIEW\"})\n if len(query[\"results\"]) == 1:\n project_id = query[\"results\"][0][\"id\"]\n else:\n raise ValueError(\"Did not find DNAnexus project %s: %s\" % (project_name, query))\n dx_proj = dxpy.get_handler(project_id)\n return _recursive_ls(dx_proj, project_name, folder)","def get_projects(self):\n session = self.session_factory()\n results = [row.project for row in session.query(PipelineRun.project.distinct().label('project')).all()]\n session.close()\n return results","def getProjects(self):\n\n return self.__projects","def update_projects(self):\n self._read_directory()\n print(self._filenames)\n for filename in self._filenames:\n project = self._make_project(self._read_project(filename))\n self.projects.append(\n (int(project.get_id()), project)\n )\n self.projects = sorted(self.projects, reverse=True)","def get_projects(self, source=\"all\"):\n self.projects = []\n self._project_indices_by_id = {}\n self._project_indices_by_name = {}\n\n if self.hub_type == self.NAMESPACES[\"a.\"]:\n if not self.auth.three_legged:\n self.logger.warning(\n \"Failed to get projects. '{}' hubs only supports 3-legged access token.\".format( # noqa:E501\n self.NAMESPACES[\"a.\"]\n )\n )\n else:\n for project in self.api.dm.get_projects():\n self.projects.append(\n Project(\n project[\"attributes\"][\"name\"],\n project[\"id\"][2:],\n data=project,\n app=self,\n )\n )\n\n self._project_indices_by_id[project[\"id\"][2:]] = (\n len(self.projects) - 1\n )\n self._project_indices_by_name[\n project[\"attributes\"][\"name\"]\n ] = (len(self.projects) - 1)\n\n elif self.hub_type == self.NAMESPACES[\"b.\"]:\n\n if source.lower() in (\"all\", \"docs\"):\n for project in self.api.dm.get_projects():\n self.projects.append(\n Project(\n project[\"attributes\"][\"name\"],\n project[\"id\"][2:],\n data=project,\n app=self,\n )\n )\n\n self._project_indices_by_id[project[\"id\"][2:]] = (\n len(self.projects) - 1\n )\n self._project_indices_by_name[\n project[\"attributes\"][\"name\"]\n ] = (len(self.projects) - 1)\n\n if (\n source.lower() in (\"all\", \"admin\")\n and not self.auth.three_legged\n ):\n\n for project in self.api.hq.get_projects():\n if project[\"id\"] in self._project_indices_by_id:\n self.projects[\n self._project_indices_by_id[project[\"id\"]]\n ].data = project\n else:\n self.projects.append(\n Project(\n project[\"name\"],\n project[\"id\"],\n data=project,\n app=self,\n )\n )\n self._project_indices_by_id[project[\"id\"]] = (\n len(self.projects) - 1\n )\n\n self._project_indices_by_name[project[\"name\"]] = (\n len(self.projects) - 1\n )\n\n elif source.lower() in (\"all\", \"admin\"):\n self.logger.debug(\n \"Failed to get projects. The BIM 360 API only supports 2-legged access tokens\" # noqa:E501\n )","def list(self, *args, **kwargs):\n projects = Project.objects.all()\n return self.list_by(projects, self.serializer_class)","def get_created_projects(self):\n project_ouessant1 = Project.objects.get(name='Ouessant Tidal Power Phase I')\n project_ouessant2 = Project.objects.get(name='Ouessant Tidal Power Phase II')\n project_liaoning = Project.objects.get(\n name='Liaoning Linghai China Resource Power Wind Power Wind Farm'\n )\n return [project_ouessant1, project_ouessant2, project_liaoning]","def open_projects(request):\n return Project.objects.prefetch_related('task_set').filter(user=request.user, open=True)","def _get_resource_projects(resource):\n resource_type = resource.get('type', '').upper()\n resource_values = resource.get('include', tuple())\n\n projects = tuple()\n if resource_type == _FOLDER:\n projects = _get_folder_projects(resource_values)\n elif resource_type == _PROJECT:\n projects = _get_projects(resource_values)\n elif resource_type == _FILTER:\n projects = _get_filtered_projects(resource_values)\n else:\n logging.info('Projects: No projects for resource %s', resource_type)\n return projects","def get_project_list(token):\n session = requests.Session()\n session.headers.update({'Authorization': f'Token {token}'})\n url = get_project_list_url()\n r = session.get(url=url)\n return r","def test_get_projects(self):\n for project in ['TEST', 'NEWTEST', 'MYPROJECT']:\n self.db.insert_single_result(generate_mock_result(project=project))\n projects = self.db.get_projects()\n self.assertItemsEqual(['MYPROJECT', 'NEWTEST', 'TEST'], projects)","def list_projects(self) -> List['RadsProject']:\n ret = []\n base = self.fspath(\"projects\")\n for name in os.listdir(base):\n if os.path.isdir(f\"{base}/{name}/releases\"):\n ret.append(RadsProject(self, name))\n return ret","def get_projects(self):\n return self.jira.projects()","def load_project_list_from_file(self):\n # cycle through the files and append them converted from json to the list\n out = []\n\n path = self.data_path + self.project_dir\n\n # check if the data_path/clients directory exists and cancel otherwise\n if not os.path.isdir(str(path)):\n return []\n\n for file in sorted(os.listdir(path)):\n if file.endswith('.flproject'):\n # load the file\n f = open(path + '/' + file, 'r')\n load = f.read()\n f.close()\n\n # generate main object\n out.append(Project().from_json(js=load))\n\n return out","def get_all_projects(self, scope):\n url = \"{0}/{1}/{2}\".format(self.keystone_server_url, DEFAULT_KEYSTONE_API_VERSION, \"projects\")\n headers = {'X-Auth-Token': scope.auth_token}\n try:\n r = self._make_request_with_auth_fallback(url, headers)\n return r['projects']\n\n except Exception as e:\n self.warning('Unable to get projects: %s', e)\n raise e\n\n return None","def get_project(self, project):\n return Dict(self.projects.get_entry(pk=project, _fields=[\"_all\"]).result())","def do_projects(self, arg):\n args = shlex.split(arg)\n limit = 10\n from_date = to_date = ''\n if args:\n limit = 0\n try:\n from_date, to_date = helpers.parse_date_parameters(args)\n except ValueError, msg:\n print(msg)\n return\n projects = self.db.get_projects_with_activity_field(\n from_date, to_date, limit=limit)\n refined = map(lambda x: [\n x['pid'], x['name'],\n '[Active]' if x['active'] else '[closed]',\n datetime.datetime.strftime(x['created'], '%c').decode('utf8'),\n x['description']], projects)\n print(tabulate(refined, ['ID', 'Project', 'Activity', 'Created',\n 'Description']))","def scan_all_projects():\n from projectscanner import ProjectScanner\n #ensure that new items get scanned by setting their last scan time to this.\n needs_scan_time = datetime.now() - timedelta(days=30)\n\n s = ProjectScanner()\n for projectinfo in s.scan_all():\n projectinfo.save_receipt(needs_scan_time)","def projects(request):\n return Project.objects.prefetch_related('task_set').filter(user=request.user)","def getAllVcProj (self, inDEV):\n result = []\n def filterVcProj (list, dirname, names):\n for name in names:\n if name [-7:] == '.vcproj':\n fullpath = os.path.join (dirname, name)\n list.append (fullpath)\n os.path.walk (inDEV, filterVcProj, result)\n result = filter (self.isValidPattern, result)\n return result","def list_projects():\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.list_projects()\n if ret[constants.STATUS_CODE_KEY] == 200:\n table = PrettyTable(\n field_names=[\"Id\", \"Name\", \"Provision Network\"])\n projects = ret[constants.RETURN_VALUE_KEY]\n for project in projects:\n table.add_row(project)\n click.echo(table.get_string())\n else:\n click.echo(ret[constants.MESSAGE_KEY])","def get_all_project_records():\r\n records = flask.request.db_api.get_all_project_record()\r\n return flask.jsonify(records=records)","def project_list(cursor):\n query = \"SELECT * FROM projects\"\n try:\n cursor.execute(query, {},)\n except Exception as e:\n on_error(e)\n else:\n projects = cursor.fetchall()\n raise Return((projects, None))","def query_project(self):\n\n # Find stylesheets.\n found = False\n for filename in self.project.namelist():\n if os.path.basename(filename) == 'styles.xml':\n found = True\n print(filename)\n if not found:\n print(\"not found!\")","def find_projects(self):\n\n attrs = ['name', 'remote', 'revision', 'path', 'groups', 'upstream']\n projects = defaultdict(list)\n\n for project in self.tree.findall('project'):\n values = [project.get(attr) for attr in attrs]\n project_dict = dict(zip(attrs, values))\n project_name = project_dict.pop('name')\n\n if project_dict['groups'] is not None:\n project_dict['groups'] = project_dict['groups'].split(',')\n\n if project_name is None:\n if self.fail_on_invalid:\n raise InvalidManifest(\n 'Project entry missing \"name\" attribute'\n )\n else:\n continue\n\n if project_name in projects:\n paths = [\n p_attr.get('path', project_name)\n for name, p_attrs in projects.items()\n for p_attr in p_attrs if name == project_name\n ]\n\n if project_dict['path'] in paths:\n raise InvalidManifest(\n 'Duplicate project entry with matching \"name\" '\n 'and \"path\" attributes'\n )\n\n children = project.getchildren()\n if children:\n for child in children:\n subelement = child.tag\n\n # Only run the following if the element tag\n # is a string (avoids comments)\n if isinstance(subelement, str):\n subdict = getattr(\n self, 'create_{}_dict'.format(subelement)\n )(child)\n\n if subdict is not None:\n project_dict.setdefault(subelement, []).append(\n subdict\n )\n\n projects[project_name].append(\n self.generate_data_dict(project_dict)\n )\n\n self.projects = projects","def getSubProjects(self):\n logger.debug(\"Func: getSubProjects\")\n\n return self._subProjectsList","def index(self):\n return {'projects': [p for p in self.server.projects.values()]}","def project_show(ctx, args):\n for project_id in args:\n data = ctx.obj.get_project_by_project_id(project_id)\n output_json_data(data)","def do_project_list(cs, args):\n _, projects = cs.projects.list()\n fields = [\n 'project_id',\n 'name',\n 'owner_id',\n 'current_user_role_id',\n 'repo_count',\n 'creation_time',\n 'public',\n ]\n utils.print_list(projects, fields, formatters={}, sortby=args.sortby)","def projects_settings():\n return map_settings(settings_repository.settings)","def list_projects(ctx):\n pprint(ctx.obj.groups.get().data)","def test_get_project_list_with_projects(self):\n # Add two test projects.\n projects = [\n add_project(title='1', description='1'),\n add_project(title='2', description='2'),\n ]\n\n result = get_project_list()\n result_projects = result['projects'].object_list\n\n # Make sure two test projects are retrieved.\n for project in projects:\n self.assertTrue(project in result_projects)\n self.assertEqual(len(result_projects), len(projects))\n self.assertIsNone(result['tag'])\n self.assertFalse(result['filtered'])","def test_get_projects_returns_projects(fc: fetcher.Fetcher):\n projects = fc.get_projects()\n assert isinstance(projects, list)\n assert isinstance(projects[0], models.Project)","def get_projects(selection):\n project=[]\n\n # project with keyname\n\n if 'project' in selection.facets:\n project+=selection.facets['project']\n\n\n # project without keyname\n\n # WARNING\n #\n # The code below uses sdinference and query the database to retrieve ESGF parameters.\n # Doing those things here may raise circular dependencies\n # as well as making the whole thing very complex.\n #\n # We do this to make this syntax work (i.e. project value without key)\n # synda search GeoMIP\n #\n # Note that this syntax always works (i.e. load the project level default file), even without this code.\n # synda search project=GeoMIP\n #\n #\n pending_projects=sdearlystreamutils.get_facet_values_early([selection.facets],'project',extract_item=True) # project without keyname or project as part of an identifier.\n\n li=pending_projects+project\n\n li=list(set(li)) # remove duplicate\n\n return li","def _read_directory(self):\n self._filenames = glob.glob(self._directory + \"/*.project\")","def get_projects(self):\n ret = self.v1_projects.get()\n return [each.metadata.name for each in ret.items]","def test_findTwistedStyleProjects(self):\n baseDirectory = self.makeProjects((\"foo\", 2, 3, 0), (\"foo.bar\", 0, 7, 4))\n projects = findTwistedProjects(baseDirectory)\n self.assertProjectsEqual(\n projects,\n [\n Project(baseDirectory.child(\"foo\")),\n Project(baseDirectory.child(\"foo\").child(\"bar\")),\n ],\n )","def project(self, request):\n return self._project(request, 'project')","def projects(self):\n sql = \"\"\"SELECT project\n FROM barcodes.sample\n LEFT JOIN barcodes.project_sample_sets USING (sample_set_id)\n LEFT JOIN barcodes.project USING (project_id)\n WHERE sample_id = %s\n UNION\n SELECT project\n FROM barcodes.project_samples\n LEFT JOIN barcodes.project USING (project_id)\n WHERE sample_id = %s\n \"\"\"\n with pm.sql.TRN:\n pm.sql.TRN.add(sql, [self.id, self.id])\n projects = pm.sql.TRN.execute_fetchflatten()\n return None if not projects else projects","def get_projects():\n data = sql.list_projects()\n names = [(d['id'], d['name']) for d in data]\n return names","def parse_one_project(self, args, project_arg):\n project = self.linguist_worktree.get_linguist_project(project_arg, raises=True)\n return [project]","def filter_projects():\n with open('../results/01.crawling/01.project_ci_services.json', 'r') as infile:\n projects = json.load(infile)\n tr_projects = []\n for project, value in projects.items():\n if \"GitHub\" in value or \"Travis\" in value:\n tr_projects.append(project)\n return tr_projects","def test_findTwistedStyleProjects(self):\n baseDirectory = self.makeProjects(\n Version('foo', 2, 3, 0), Version('foo.bar', 0, 7, 4))\n projects = findTwistedProjects(baseDirectory)\n self.assertProjectsEqual(\n projects,\n [Project(baseDirectory.child('foo')),\n Project(baseDirectory.child('foo').child('bar'))])","def _get_projects(current_project_name):\n projects = []\n\n unique_project_changes = Change.objects.order_by().values(\n 'project_name').distinct()\n for change in unique_project_changes:\n projects.append(change['project_name'])\n\n # sort alphabetically\n projects.sort()\n\n # insert 'all' option as it should be present always\n projects.insert(0, PROJECT_ALL)\n\n # if current_project_name is valid, make it the first element in list so\n # that it shows up as selected in project choice drop down\n if current_project_name != PROJECT_ALL and current_project_name in projects:\n projects.remove(current_project_name)\n projects.insert(0, current_project_name)\n elif current_project_name != PROJECT_ALL:\n logging.error(\"Currently selected project %s not found in any changes.\"\n \" Removing from list.\", current_project_name)\n logging.debug(\"Returning list of projects: %r\", projects)\n return projects","def get_project(arn=None):\n pass","def test_project_list(self):\n rv = self.app.get(\"/\")\n self.assertIn(\"Assignment0\", rv.data)\n self.assertIn(\"Assignment1.0\", rv.data)\n self.assertIn(\"Assignment2.0\", rv.data)","def read_all(self):\r\n pass"],"string":"[\n \"def test_read_project(self):\\n pass\",\n \"def test_read_project(self):\\n pass\",\n \"def project():\",\n \"def project():\",\n \"def project():\",\n \"def test_get_projects(self):\\n pass\",\n \"def get_projects_data():\\n wcscanner_path = context.__BASE_PATH__ + '/.wcscanner'\\n\\n data = []\\n for project in os.listdir(wcscanner_path):\\n if (os.path.isdir(os.path.join(wcscanner_path, project))):\\n update_project_data(project)\\n project_path = '{}/{}'.format(wcscanner_path, project)\\n f = open('{}/.project'.format(project_path), 'r')\\n data.append(json.load(f))\\n f.close()\\n return data\",\n \"def list_projects():\\n if '.wcscanner' not in os.listdir(context.__BASE_PATH__):\\n return []\\n return os.listdir(context.__PROJECTS_PATH__)\",\n \"def get_projects(self):\\n return conf.projects\",\n \"def test_list_project(self):\\n pass\",\n \"def get_projects(self):\\n unaligned_path = self.get_unaligned_path()\\n logger.debug(\\\"collecting list of projects\\\")\\n return [p for p in os.listdir(unaligned_path)\\n if len(parsing.get_project_label(p))]\",\n \"def test_list_projects(self):\\n pass\",\n \"def test_list_projects(self):\\n pass\",\n \"def get_projects(self):\\n res = self.conn.cursor().execute(\\\"SELECT * FROM projects\\\")\\n return res.fetchall()\",\n \"def get_projects():\\n return Project.query.all()\",\n \"def _get_projects(project_ids):\\n if _ALL in project_ids:\\n return projects_lib.get_all()\\n return projects_lib.get_selective(project_ids)\",\n \"def test_get_project(self):\\n pass\",\n \"def get_projects(self):\\n projects = []\\n page = 1\\n while not len(projects) % 100:\\n projects += self._get('/projects?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\\n if not projects:\\n break\\n page += 1\\n return projects\",\n \"def _page_projects(self):\\n return self._open(self.app.page_projects)\",\n \"def get_all_projects(self, org):\\n return [proj for proj in Project.objects.filter(org=org)]\",\n \"def test_get_projects_expanded(self):\\n pass\",\n \"def get_projects(self):\\n return self.http_call(\\\"get\\\", url=f\\\"{self.base_url}/projects\\\").json()\",\n \"def project_list(self):\\n try:\\n ids = self.request[api.DATA][api.DATA][\\\"ids\\\"]\\n return self._get_keystone_projects(ids)\\n except Exception as e:\\n LOG.exception(\\\"Error occurred: %s\\\" % e)\",\n \"def projects(self):\\r\\n return p.Projects(self)\",\n \"def test_list_project_request(self):\\n pass\",\n \"def get_projects(self):\\n if not self.validate():\\n raise SettingCustomVisionAccessFailed\\n return self.get_trainer_obj().get_projects()\",\n \"def atlas_projects():\\n pass\",\n \"def list(self):\\n\\n for name in self.projects:\\n self.projects[name].show()\\n print(\\\"\\\\n\\\")\",\n \"def get_projects(self, _is_simple=False):\\n req_url = f\\\"{self.url}/projects\\\"\\n if _is_simple:\\n req_url += \\\"?simple=true\\\"\\n ret = requests.get(req_url, headers = self.req_header)\\n return ret.json()\",\n \"def all(cls):\\n projects_url = 'https://www.pivotaltracker.com/services/v5/projects'\\n root = _perform_pivotal_get(projects_url)\\n if root is not None:\\n return [Project.from_json(project_node) for project_node in root]\",\n \"def get_project_list(config):\\n eggs_dir = config.get('eggs_dir', 'eggs')\\n if os.path.exists(eggs_dir):\\n projects = os.listdir(eggs_dir)\\n else:\\n projects = []\\n try:\\n projects += [x[0] for x in config.cp.items('settings')]\\n except NoSectionError:\\n pass\\n return projects\",\n \"def project(self):\\n return read_small_file(self.homeDirectory + \\\"/.project\\\")\",\n \"def db_projects():\\n return [{\\\"name\\\": \\\"IT\\\"}, {\\\"name\\\": \\\"Financial\\\"}, {\\\"name\\\": \\\"Failed\\\"}]\",\n \"def test_projects_get(self):\\n response = self.client.open('/project-tracker/projects',\\n method='GET')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def get_all_projects():\\n return jsonify(admin.get_all_projects(current_app.scoped_session()))\",\n \"def all(cls):\\r\\n projects_url = 'https://www.pivotaltracker.com/services/v3/projects'\\r\\n response = _perform_pivotal_get(projects_url)\\r\\n\\r\\n root = ET.fromstring(response.text)\\r\\n if root is not None:\\r\\n return [Project.from_node(project_node) for project_node in root]\",\n \"def get_projects(cls):\\n projects = []\\n for project in Project.select():\\n project_dict = {\\n \\\"id\\\": project.id,\\n \\\"title\\\": project.title,\\n \\\"link\\\": project.link,\\n \\\"description\\\": project.description\\n }\\n projects.append(project_dict)\\n return projects\",\n \"def get_projects(self):\\n response = self.request(verb=requests.get, address=\\\"projects\\\")\\n # FIXME: if no results, must we raise an exception?\\n return response[\\\"results\\\"] if \\\"results\\\" in response else response\",\n \"def _loadProjects(self):\\n logger.debug(\\\"Func: _loadProjects\\\")\\n\\n if not os.path.isfile(self._pathsDict[\\\"projectsFile\\\"]):\\n return\\n else:\\n projectsData = self._loadJson(self._pathsDict[\\\"projectsFile\\\"])\\n if projectsData == -2:\\n return -2\\n return projectsData\",\n \"def get_projects(self, *criterion):\\n from wkcdd.models.helpers import get_project_list\\n return get_project_list([self.id], *criterion)\",\n \"def all_projects(self):\\n projects_list = []\\n for path in DAVOS_PROJECT_DIR.iterdir():\\n if path.is_dir():\\n projects_list.append(Project(path.name))\\n return projects_list\",\n \"def list_projects():\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"list_projects\\\"\\n cmd[\\\"name_\\\"] = \\\"\\\"\\n \\n s = comm.send_and_receive_socket(cmd)\\n\\n msg = comm.recv_string(s)\\n\\n if msg != \\\"Success!\\\":\\n raise Exception(msg)\\n \\n json_str = comm.recv_string(s) \\n \\n s.close() \\n\\n return json.loads(json_str)[\\\"projects\\\"]\",\n \"def get_project_list():\\n return parse_list_output(Popen(\\n 'openstack project list'.split(), stdout=STDOUT, stderr=STDERR\\n ).communicate()[0])\",\n \"def project_all(request, format=None):\\n if request.method == 'GET':\\n projects = Project.objects.all().order_by('key')\\n serializer = ProjectSerializer(projects, many=True)\\n return Response(serializer.data)\",\n \"def get_project(self, i):\\r\\n return self.__projects[i]\",\n \"def getprojects(self):\\n resp = self.conn.request('GET', self.URLS['allprojects'], dict(api_key=self.api_key))\\n data = resp.data.decode('utf-8')\\n jdata = json.loads(data)['projects']\\n # Convert nested JSON documents\\n for project_index in range(len(jdata)):\\n for field in ('options_json', 'templates_json'):\\n jdata[project_index][field] = json.loads(jdata[project_index][field])\\n # Pass project details dictionaries to constructors, return array\\n return [PhProject(self, project) for project in jdata]\",\n \"def projects(args):\\n _projects = lib.get_projects(\\n args.target, username=args.username, password=args.password\\n )\\n if _projects:\\n print(\\\"\\\\n\\\".join(_projects))\",\n \"def getProjects(self):\\n catalog = plone.api.portal.get_tool('portal_catalog')\\n path = '{}/projects'.format('/'.join(plone.api.portal.get().getPhysicalPath()))\\n query = dict(portal_type='Project', sort_on='sortable_title', path=path)\\n result = list()\\n for brain in catalog(**query):\\n result.append((brain.getId, brain.Title))\\n return result\",\n \"def list_projects(arn=None, nextToken=None):\\n pass\",\n \"def _get_project_tina_entries(self,pool='archive',refresh=False,path_folder=None):\\n\\t\\tif not path_folder: path_folder = self.catalog_path\\n\\t\\tif not refresh:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\treturn self.tina_archive_entries\\n\\t\\t\\texcept: pass \\n\\t\\tself.tina_archive_entries = Tina.tina_find(\\n\\t\\t\\tpath_folder=path_folder,\\n\\t\\t\\tapplication=self.application,\\n\\t\\t\\tstrat='A',\\n\\t\\t\\tskip_filter=self.skip_filter)\\n\\t\\treturn self.tina_archive_entries\",\n \"def test_demo_project_call(self):\\n resp = DemoAivenStorage(os.environ[\\\"AIVEN_API_URL\\\"],\\n os.environ[\\\"AIVEN_TOKEN\\\"]).get_project_names()\\n assert isinstance(resp, list)\\n assert len(resp) == 1\\n assert 'romainducarrouge-31f2' in resp\",\n \"def get_project_list(self, dummy_project):\\n # TODO: domain scope 403 is probably to do with faulty keystone policy config -- revise?\\n if not self._projects:\\n self._projects = self._get_keystone_client(dummy_project).projects.list()\\n\\n return self._projects\",\n \"async def get_project_info(project_urls):\\n project_info = []\\n for url in project_urls:\\n soup = await get_page(url)\\n about = soup.find_all(\\\"p\\\")\\n title = soup.find(\\\"h3\\\").text\\n student = about[0].text.splitlines()[2].strip()\\n details = about[1].text\\n name = about[0].find(\\\"a\\\").text\\n project_info.append({'Organization': name, 'title': title,\\n 'student': student, 'details': details,\\n 'link': url})\\n\\n return project_info\",\n \"def test_get_projects_filters(fc: fetcher.Fetcher, test_project_name):\\n projects = fc.get_projects(test_project_name)\\n assert isinstance(projects, list)\\n assert len(projects) == 1\\n assert projects[0].name == test_project_name\",\n \"def _project_files(project_name, folder):\\n _authenticate()\\n if project_name.startswith(\\\"project-\\\"):\\n project_id = project_name\\n else:\\n query = dxpy.api.system_find_projects({\\\"name\\\": project_name, \\\"level\\\": \\\"VIEW\\\"})\\n if len(query[\\\"results\\\"]) == 1:\\n project_id = query[\\\"results\\\"][0][\\\"id\\\"]\\n else:\\n raise ValueError(\\\"Did not find DNAnexus project %s: %s\\\" % (project_name, query))\\n dx_proj = dxpy.get_handler(project_id)\\n return _recursive_ls(dx_proj, project_name, folder)\",\n \"def get_projects(self):\\n session = self.session_factory()\\n results = [row.project for row in session.query(PipelineRun.project.distinct().label('project')).all()]\\n session.close()\\n return results\",\n \"def getProjects(self):\\n\\n return self.__projects\",\n \"def update_projects(self):\\n self._read_directory()\\n print(self._filenames)\\n for filename in self._filenames:\\n project = self._make_project(self._read_project(filename))\\n self.projects.append(\\n (int(project.get_id()), project)\\n )\\n self.projects = sorted(self.projects, reverse=True)\",\n \"def get_projects(self, source=\\\"all\\\"):\\n self.projects = []\\n self._project_indices_by_id = {}\\n self._project_indices_by_name = {}\\n\\n if self.hub_type == self.NAMESPACES[\\\"a.\\\"]:\\n if not self.auth.three_legged:\\n self.logger.warning(\\n \\\"Failed to get projects. '{}' hubs only supports 3-legged access token.\\\".format( # noqa:E501\\n self.NAMESPACES[\\\"a.\\\"]\\n )\\n )\\n else:\\n for project in self.api.dm.get_projects():\\n self.projects.append(\\n Project(\\n project[\\\"attributes\\\"][\\\"name\\\"],\\n project[\\\"id\\\"][2:],\\n data=project,\\n app=self,\\n )\\n )\\n\\n self._project_indices_by_id[project[\\\"id\\\"][2:]] = (\\n len(self.projects) - 1\\n )\\n self._project_indices_by_name[\\n project[\\\"attributes\\\"][\\\"name\\\"]\\n ] = (len(self.projects) - 1)\\n\\n elif self.hub_type == self.NAMESPACES[\\\"b.\\\"]:\\n\\n if source.lower() in (\\\"all\\\", \\\"docs\\\"):\\n for project in self.api.dm.get_projects():\\n self.projects.append(\\n Project(\\n project[\\\"attributes\\\"][\\\"name\\\"],\\n project[\\\"id\\\"][2:],\\n data=project,\\n app=self,\\n )\\n )\\n\\n self._project_indices_by_id[project[\\\"id\\\"][2:]] = (\\n len(self.projects) - 1\\n )\\n self._project_indices_by_name[\\n project[\\\"attributes\\\"][\\\"name\\\"]\\n ] = (len(self.projects) - 1)\\n\\n if (\\n source.lower() in (\\\"all\\\", \\\"admin\\\")\\n and not self.auth.three_legged\\n ):\\n\\n for project in self.api.hq.get_projects():\\n if project[\\\"id\\\"] in self._project_indices_by_id:\\n self.projects[\\n self._project_indices_by_id[project[\\\"id\\\"]]\\n ].data = project\\n else:\\n self.projects.append(\\n Project(\\n project[\\\"name\\\"],\\n project[\\\"id\\\"],\\n data=project,\\n app=self,\\n )\\n )\\n self._project_indices_by_id[project[\\\"id\\\"]] = (\\n len(self.projects) - 1\\n )\\n\\n self._project_indices_by_name[project[\\\"name\\\"]] = (\\n len(self.projects) - 1\\n )\\n\\n elif source.lower() in (\\\"all\\\", \\\"admin\\\"):\\n self.logger.debug(\\n \\\"Failed to get projects. The BIM 360 API only supports 2-legged access tokens\\\" # noqa:E501\\n )\",\n \"def list(self, *args, **kwargs):\\n projects = Project.objects.all()\\n return self.list_by(projects, self.serializer_class)\",\n \"def get_created_projects(self):\\n project_ouessant1 = Project.objects.get(name='Ouessant Tidal Power Phase I')\\n project_ouessant2 = Project.objects.get(name='Ouessant Tidal Power Phase II')\\n project_liaoning = Project.objects.get(\\n name='Liaoning Linghai China Resource Power Wind Power Wind Farm'\\n )\\n return [project_ouessant1, project_ouessant2, project_liaoning]\",\n \"def open_projects(request):\\n return Project.objects.prefetch_related('task_set').filter(user=request.user, open=True)\",\n \"def _get_resource_projects(resource):\\n resource_type = resource.get('type', '').upper()\\n resource_values = resource.get('include', tuple())\\n\\n projects = tuple()\\n if resource_type == _FOLDER:\\n projects = _get_folder_projects(resource_values)\\n elif resource_type == _PROJECT:\\n projects = _get_projects(resource_values)\\n elif resource_type == _FILTER:\\n projects = _get_filtered_projects(resource_values)\\n else:\\n logging.info('Projects: No projects for resource %s', resource_type)\\n return projects\",\n \"def get_project_list(token):\\n session = requests.Session()\\n session.headers.update({'Authorization': f'Token {token}'})\\n url = get_project_list_url()\\n r = session.get(url=url)\\n return r\",\n \"def test_get_projects(self):\\n for project in ['TEST', 'NEWTEST', 'MYPROJECT']:\\n self.db.insert_single_result(generate_mock_result(project=project))\\n projects = self.db.get_projects()\\n self.assertItemsEqual(['MYPROJECT', 'NEWTEST', 'TEST'], projects)\",\n \"def list_projects(self) -> List['RadsProject']:\\n ret = []\\n base = self.fspath(\\\"projects\\\")\\n for name in os.listdir(base):\\n if os.path.isdir(f\\\"{base}/{name}/releases\\\"):\\n ret.append(RadsProject(self, name))\\n return ret\",\n \"def get_projects(self):\\n return self.jira.projects()\",\n \"def load_project_list_from_file(self):\\n # cycle through the files and append them converted from json to the list\\n out = []\\n\\n path = self.data_path + self.project_dir\\n\\n # check if the data_path/clients directory exists and cancel otherwise\\n if not os.path.isdir(str(path)):\\n return []\\n\\n for file in sorted(os.listdir(path)):\\n if file.endswith('.flproject'):\\n # load the file\\n f = open(path + '/' + file, 'r')\\n load = f.read()\\n f.close()\\n\\n # generate main object\\n out.append(Project().from_json(js=load))\\n\\n return out\",\n \"def get_all_projects(self, scope):\\n url = \\\"{0}/{1}/{2}\\\".format(self.keystone_server_url, DEFAULT_KEYSTONE_API_VERSION, \\\"projects\\\")\\n headers = {'X-Auth-Token': scope.auth_token}\\n try:\\n r = self._make_request_with_auth_fallback(url, headers)\\n return r['projects']\\n\\n except Exception as e:\\n self.warning('Unable to get projects: %s', e)\\n raise e\\n\\n return None\",\n \"def get_project(self, project):\\n return Dict(self.projects.get_entry(pk=project, _fields=[\\\"_all\\\"]).result())\",\n \"def do_projects(self, arg):\\n args = shlex.split(arg)\\n limit = 10\\n from_date = to_date = ''\\n if args:\\n limit = 0\\n try:\\n from_date, to_date = helpers.parse_date_parameters(args)\\n except ValueError, msg:\\n print(msg)\\n return\\n projects = self.db.get_projects_with_activity_field(\\n from_date, to_date, limit=limit)\\n refined = map(lambda x: [\\n x['pid'], x['name'],\\n '[Active]' if x['active'] else '[closed]',\\n datetime.datetime.strftime(x['created'], '%c').decode('utf8'),\\n x['description']], projects)\\n print(tabulate(refined, ['ID', 'Project', 'Activity', 'Created',\\n 'Description']))\",\n \"def scan_all_projects():\\n from projectscanner import ProjectScanner\\n #ensure that new items get scanned by setting their last scan time to this.\\n needs_scan_time = datetime.now() - timedelta(days=30)\\n\\n s = ProjectScanner()\\n for projectinfo in s.scan_all():\\n projectinfo.save_receipt(needs_scan_time)\",\n \"def projects(request):\\n return Project.objects.prefetch_related('task_set').filter(user=request.user)\",\n \"def getAllVcProj (self, inDEV):\\n result = []\\n def filterVcProj (list, dirname, names):\\n for name in names:\\n if name [-7:] == '.vcproj':\\n fullpath = os.path.join (dirname, name)\\n list.append (fullpath)\\n os.path.walk (inDEV, filterVcProj, result)\\n result = filter (self.isValidPattern, result)\\n return result\",\n \"def list_projects():\\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\\n ret = bmi.list_projects()\\n if ret[constants.STATUS_CODE_KEY] == 200:\\n table = PrettyTable(\\n field_names=[\\\"Id\\\", \\\"Name\\\", \\\"Provision Network\\\"])\\n projects = ret[constants.RETURN_VALUE_KEY]\\n for project in projects:\\n table.add_row(project)\\n click.echo(table.get_string())\\n else:\\n click.echo(ret[constants.MESSAGE_KEY])\",\n \"def get_all_project_records():\\r\\n records = flask.request.db_api.get_all_project_record()\\r\\n return flask.jsonify(records=records)\",\n \"def project_list(cursor):\\n query = \\\"SELECT * FROM projects\\\"\\n try:\\n cursor.execute(query, {},)\\n except Exception as e:\\n on_error(e)\\n else:\\n projects = cursor.fetchall()\\n raise Return((projects, None))\",\n \"def query_project(self):\\n\\n # Find stylesheets.\\n found = False\\n for filename in self.project.namelist():\\n if os.path.basename(filename) == 'styles.xml':\\n found = True\\n print(filename)\\n if not found:\\n print(\\\"not found!\\\")\",\n \"def find_projects(self):\\n\\n attrs = ['name', 'remote', 'revision', 'path', 'groups', 'upstream']\\n projects = defaultdict(list)\\n\\n for project in self.tree.findall('project'):\\n values = [project.get(attr) for attr in attrs]\\n project_dict = dict(zip(attrs, values))\\n project_name = project_dict.pop('name')\\n\\n if project_dict['groups'] is not None:\\n project_dict['groups'] = project_dict['groups'].split(',')\\n\\n if project_name is None:\\n if self.fail_on_invalid:\\n raise InvalidManifest(\\n 'Project entry missing \\\"name\\\" attribute'\\n )\\n else:\\n continue\\n\\n if project_name in projects:\\n paths = [\\n p_attr.get('path', project_name)\\n for name, p_attrs in projects.items()\\n for p_attr in p_attrs if name == project_name\\n ]\\n\\n if project_dict['path'] in paths:\\n raise InvalidManifest(\\n 'Duplicate project entry with matching \\\"name\\\" '\\n 'and \\\"path\\\" attributes'\\n )\\n\\n children = project.getchildren()\\n if children:\\n for child in children:\\n subelement = child.tag\\n\\n # Only run the following if the element tag\\n # is a string (avoids comments)\\n if isinstance(subelement, str):\\n subdict = getattr(\\n self, 'create_{}_dict'.format(subelement)\\n )(child)\\n\\n if subdict is not None:\\n project_dict.setdefault(subelement, []).append(\\n subdict\\n )\\n\\n projects[project_name].append(\\n self.generate_data_dict(project_dict)\\n )\\n\\n self.projects = projects\",\n \"def getSubProjects(self):\\n logger.debug(\\\"Func: getSubProjects\\\")\\n\\n return self._subProjectsList\",\n \"def index(self):\\n return {'projects': [p for p in self.server.projects.values()]}\",\n \"def project_show(ctx, args):\\n for project_id in args:\\n data = ctx.obj.get_project_by_project_id(project_id)\\n output_json_data(data)\",\n \"def do_project_list(cs, args):\\n _, projects = cs.projects.list()\\n fields = [\\n 'project_id',\\n 'name',\\n 'owner_id',\\n 'current_user_role_id',\\n 'repo_count',\\n 'creation_time',\\n 'public',\\n ]\\n utils.print_list(projects, fields, formatters={}, sortby=args.sortby)\",\n \"def projects_settings():\\n return map_settings(settings_repository.settings)\",\n \"def list_projects(ctx):\\n pprint(ctx.obj.groups.get().data)\",\n \"def test_get_project_list_with_projects(self):\\n # Add two test projects.\\n projects = [\\n add_project(title='1', description='1'),\\n add_project(title='2', description='2'),\\n ]\\n\\n result = get_project_list()\\n result_projects = result['projects'].object_list\\n\\n # Make sure two test projects are retrieved.\\n for project in projects:\\n self.assertTrue(project in result_projects)\\n self.assertEqual(len(result_projects), len(projects))\\n self.assertIsNone(result['tag'])\\n self.assertFalse(result['filtered'])\",\n \"def test_get_projects_returns_projects(fc: fetcher.Fetcher):\\n projects = fc.get_projects()\\n assert isinstance(projects, list)\\n assert isinstance(projects[0], models.Project)\",\n \"def get_projects(selection):\\n project=[]\\n\\n # project with keyname\\n\\n if 'project' in selection.facets:\\n project+=selection.facets['project']\\n\\n\\n # project without keyname\\n\\n # WARNING\\n #\\n # The code below uses sdinference and query the database to retrieve ESGF parameters.\\n # Doing those things here may raise circular dependencies\\n # as well as making the whole thing very complex.\\n #\\n # We do this to make this syntax work (i.e. project value without key)\\n # synda search GeoMIP\\n #\\n # Note that this syntax always works (i.e. load the project level default file), even without this code.\\n # synda search project=GeoMIP\\n #\\n #\\n pending_projects=sdearlystreamutils.get_facet_values_early([selection.facets],'project',extract_item=True) # project without keyname or project as part of an identifier.\\n\\n li=pending_projects+project\\n\\n li=list(set(li)) # remove duplicate\\n\\n return li\",\n \"def _read_directory(self):\\n self._filenames = glob.glob(self._directory + \\\"/*.project\\\")\",\n \"def get_projects(self):\\n ret = self.v1_projects.get()\\n return [each.metadata.name for each in ret.items]\",\n \"def test_findTwistedStyleProjects(self):\\n baseDirectory = self.makeProjects((\\\"foo\\\", 2, 3, 0), (\\\"foo.bar\\\", 0, 7, 4))\\n projects = findTwistedProjects(baseDirectory)\\n self.assertProjectsEqual(\\n projects,\\n [\\n Project(baseDirectory.child(\\\"foo\\\")),\\n Project(baseDirectory.child(\\\"foo\\\").child(\\\"bar\\\")),\\n ],\\n )\",\n \"def project(self, request):\\n return self._project(request, 'project')\",\n \"def projects(self):\\n sql = \\\"\\\"\\\"SELECT project\\n FROM barcodes.sample\\n LEFT JOIN barcodes.project_sample_sets USING (sample_set_id)\\n LEFT JOIN barcodes.project USING (project_id)\\n WHERE sample_id = %s\\n UNION\\n SELECT project\\n FROM barcodes.project_samples\\n LEFT JOIN barcodes.project USING (project_id)\\n WHERE sample_id = %s\\n \\\"\\\"\\\"\\n with pm.sql.TRN:\\n pm.sql.TRN.add(sql, [self.id, self.id])\\n projects = pm.sql.TRN.execute_fetchflatten()\\n return None if not projects else projects\",\n \"def get_projects():\\n data = sql.list_projects()\\n names = [(d['id'], d['name']) for d in data]\\n return names\",\n \"def parse_one_project(self, args, project_arg):\\n project = self.linguist_worktree.get_linguist_project(project_arg, raises=True)\\n return [project]\",\n \"def filter_projects():\\n with open('../results/01.crawling/01.project_ci_services.json', 'r') as infile:\\n projects = json.load(infile)\\n tr_projects = []\\n for project, value in projects.items():\\n if \\\"GitHub\\\" in value or \\\"Travis\\\" in value:\\n tr_projects.append(project)\\n return tr_projects\",\n \"def test_findTwistedStyleProjects(self):\\n baseDirectory = self.makeProjects(\\n Version('foo', 2, 3, 0), Version('foo.bar', 0, 7, 4))\\n projects = findTwistedProjects(baseDirectory)\\n self.assertProjectsEqual(\\n projects,\\n [Project(baseDirectory.child('foo')),\\n Project(baseDirectory.child('foo').child('bar'))])\",\n \"def _get_projects(current_project_name):\\n projects = []\\n\\n unique_project_changes = Change.objects.order_by().values(\\n 'project_name').distinct()\\n for change in unique_project_changes:\\n projects.append(change['project_name'])\\n\\n # sort alphabetically\\n projects.sort()\\n\\n # insert 'all' option as it should be present always\\n projects.insert(0, PROJECT_ALL)\\n\\n # if current_project_name is valid, make it the first element in list so\\n # that it shows up as selected in project choice drop down\\n if current_project_name != PROJECT_ALL and current_project_name in projects:\\n projects.remove(current_project_name)\\n projects.insert(0, current_project_name)\\n elif current_project_name != PROJECT_ALL:\\n logging.error(\\\"Currently selected project %s not found in any changes.\\\"\\n \\\" Removing from list.\\\", current_project_name)\\n logging.debug(\\\"Returning list of projects: %r\\\", projects)\\n return projects\",\n \"def get_project(arn=None):\\n pass\",\n \"def test_project_list(self):\\n rv = self.app.get(\\\"/\\\")\\n self.assertIn(\\\"Assignment0\\\", rv.data)\\n self.assertIn(\\\"Assignment1.0\\\", rv.data)\\n self.assertIn(\\\"Assignment2.0\\\", rv.data)\",\n \"def read_all(self):\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7262337","0.7262337","0.7255745","0.7255745","0.7255745","0.7112538","0.7085046","0.7018626","0.70128983","0.6937012","0.6859167","0.67931646","0.67931646","0.67831695","0.6778349","0.6751696","0.6723736","0.67085075","0.67051655","0.66691184","0.66677755","0.66299576","0.65674734","0.65627205","0.65493214","0.6533076","0.64972013","0.64756566","0.6430738","0.6412717","0.6397353","0.6368578","0.6351308","0.63415426","0.63260525","0.63130385","0.63127124","0.6309737","0.6306162","0.63023627","0.6293193","0.62898564","0.628373","0.6240411","0.6226287","0.6211344","0.6205491","0.6202546","0.61943465","0.6186949","0.6164675","0.6157014","0.61496836","0.61486226","0.6142417","0.6120459","0.61026466","0.6093286","0.607034","0.6055063","0.60542786","0.605276","0.6047426","0.6046009","0.60450107","0.60331094","0.6029376","0.6014959","0.6012668","0.6011489","0.60057104","0.6003364","0.6003353","0.5978157","0.5977512","0.5975524","0.59740865","0.5969523","0.59671205","0.5960949","0.5955031","0.594825","0.5945483","0.59451365","0.5934162","0.59289324","0.5926126","0.5924337","0.59067947","0.5898985","0.58954567","0.5876392","0.58754516","0.5874488","0.58735293","0.58688724","0.58623403","0.58619994","0.5859464","0.58594143","0.58534926"],"string":"[\n \"0.7262337\",\n \"0.7262337\",\n \"0.7255745\",\n \"0.7255745\",\n \"0.7255745\",\n \"0.7112538\",\n \"0.7085046\",\n \"0.7018626\",\n \"0.70128983\",\n \"0.6937012\",\n \"0.6859167\",\n \"0.67931646\",\n \"0.67931646\",\n \"0.67831695\",\n \"0.6778349\",\n \"0.6751696\",\n \"0.6723736\",\n \"0.67085075\",\n \"0.67051655\",\n \"0.66691184\",\n \"0.66677755\",\n \"0.66299576\",\n \"0.65674734\",\n \"0.65627205\",\n \"0.65493214\",\n \"0.6533076\",\n \"0.64972013\",\n \"0.64756566\",\n \"0.6430738\",\n \"0.6412717\",\n \"0.6397353\",\n \"0.6368578\",\n \"0.6351308\",\n \"0.63415426\",\n \"0.63260525\",\n \"0.63130385\",\n \"0.63127124\",\n \"0.6309737\",\n \"0.6306162\",\n \"0.63023627\",\n \"0.6293193\",\n \"0.62898564\",\n \"0.628373\",\n \"0.6240411\",\n \"0.6226287\",\n \"0.6211344\",\n \"0.6205491\",\n \"0.6202546\",\n \"0.61943465\",\n \"0.6186949\",\n \"0.6164675\",\n \"0.6157014\",\n \"0.61496836\",\n \"0.61486226\",\n \"0.6142417\",\n \"0.6120459\",\n \"0.61026466\",\n \"0.6093286\",\n \"0.607034\",\n \"0.6055063\",\n \"0.60542786\",\n \"0.605276\",\n \"0.6047426\",\n \"0.6046009\",\n \"0.60450107\",\n \"0.60331094\",\n \"0.6029376\",\n \"0.6014959\",\n \"0.6012668\",\n \"0.6011489\",\n \"0.60057104\",\n \"0.6003364\",\n \"0.6003353\",\n \"0.5978157\",\n \"0.5977512\",\n \"0.5975524\",\n \"0.59740865\",\n \"0.5969523\",\n \"0.59671205\",\n \"0.5960949\",\n \"0.5955031\",\n \"0.594825\",\n \"0.5945483\",\n \"0.59451365\",\n \"0.5934162\",\n \"0.59289324\",\n \"0.5926126\",\n \"0.5924337\",\n \"0.59067947\",\n \"0.5898985\",\n \"0.58954567\",\n \"0.5876392\",\n \"0.58754516\",\n \"0.5874488\",\n \"0.58735293\",\n \"0.58688724\",\n \"0.58623403\",\n \"0.58619994\",\n \"0.5859464\",\n \"0.58594143\",\n \"0.58534926\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":694,"cells":{"query":{"kind":"string","value":"Static method for delete a project."},"document":{"kind":"string","value":"def delete_stored_project():\n client = RequestManager()\n client.set_method(\"DELETE\")\n client.set_endpoint(\"/projects/{0}\".format(STORED_ID['project_id']))\n client.execute_request()"},"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 delete_project(project):\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.delete_project(project)\n if ret[constants.STATUS_CODE_KEY] == 200:\n click.echo(\"Success\")\n else:\n click.echo(ret[constants.MESSAGE_KEY])","def delete_project(arn=None):\n pass","def do_project_delete(cs, args):\n key = args.project\n if cs.projects.is_id(key):\n id = key\n else:\n id = cs.projects.get_id_by_name(key)\n cs.projects.delete(id)\n print(\"Delete Project '%s' successfully.\" % key)","def delete_project(projectname):\n response = jsonify(admin.delete_project(current_app.scoped_session(), projectname))\n return response","def delete(self):\n _url = f\"{self.connector.base_url}/projects/{self.project_id}\"\n\n self.connector.http_call(\"delete\", _url)\n\n self.project_id = None\n self.name = None","def delete_project(project_id):\n client = RequestManager()\n client.set_method(\"DELETE\")\n client.set_endpoint(\"/projects/{0}\".format(project_id))\n client.execute_request()","def delete_project(\n name\n):\n\n cmd = dict()\n cmd[\"type_\"] = \"delete_project\"\n cmd[\"name_\"] = name\n\n comm.send(cmd)","def delete_project(request, project_id):\n if not request.user.is_superuser:\n messages.error(request, 'Sorry, only store owners can do that.')\n return redirect(reverse('home'))\n\n project = get_object_or_404(Project, pk=project_id)\n project.delete()\n messages.success(request, 'Project deleted!')\n return redirect(reverse('portfolio'))","def test_delete_project(self):\n pass","def test_delete_project(self):\n pass","def deleteProject(self, projectId):\n uri = \"/v1/projects/\" +str(projectId)\n response = self.client.delete(uri)\n return response","def delete_project(self, project_id):\n self._run(\n url_path=\"projects/delete\",\n id=project_id,\n )\n return True","def delete_project(request, project_id):\n\n profile = get_object_or_404(Profile, user=request.user)\n project = get_object_or_404(GameProject, pk=project_id)\n\n if not profile.is_creator:\n messages.error(request, 'Sorry, only creators can do that.')\n return redirect(reverse('home'))\n if project.owner != profile:\n messages.error(request, 'Sorry, only the project owner can do that.')\n return redirect(reverse('home'))\n\n project = get_object_or_404(GameProject, pk=project_id)\n project.delete()\n messages.success(request, 'Project deleted!')\n return redirect(reverse('all_projects'))","def delete(self, guid):\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\n # search for the Project and delete if found\n key = db.Key.from_path('Project', int(guid))\n project = db.get(key)\n if not project == None:\n project.delete()\n self.response.set_status(204, \"Deleted\")\n else:\n self.response.set_status(404, \"Not Found\")\n else:\n self.response.set_status(401, \"Not Authorized\")","def delete_project(self, project_id):\n return self._delete('/projects/{0}'.format(project_id))","def delete_project(project_id):\n project = Project.query.filter_by(id=project_id).first()\n if not project:\n return {\n 'success': False,\n 'message': f\"No project with the specified id {project_id} found.\",\n }\n\n else:\n if is_project_manager(project, g.user):\n # delete related tasks\n Task.query.filter_by(project=project).delete()\n #delete related invites\n Invitation.query.filter_by(project=project).delete()\n db_session.delete(project)\n db_session.commit()\n return {\n 'success': True,\n 'result': {},\n 'message': \"Project Deleted Successfully.\",\n }","def delete(conn, project):\n with conn:\n c = conn.cursor()\n c.execute(\"DELETE FROM projects WHERE project =?\", (project,))","def delete_project(self, project_name):\n # type(project_name) == unicode\n project = self.db.get_project_by_name(project_name)\n if not project:\n print(u\"*** Error: The project '{}' was not found.\"\n \"\".format(project_name))\n return\n print('Caution! The related tracking will be deleted as well.{eol}'\n 'Do you really want to delete the project? [y/N] '\n ''.format(eol=os.linesep), end='')\n if not helpers.get_yes_no(default='n'):\n return\n self.db.delete_project_by_name(project_name)\n print(u\"The project '%s' has been deleted.\" % project_name)\n self.set_prompt()","def destroy(self, request, pk=None):\n try:\n project = Project.objects.get(pk=pk)\n project.delete()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)\n\n except Project.DoesNotExist as ex:\n return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)\n\n except Exception as ex:\n return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)","def delete_project(self, project_id):\n _url = f\"{self.base_url}/projects/{project_id}\"\n self.http_call(\"delete\", _url)\n return","def delete_project(project_id):\n \n project = mongo.db.projects\n project.delete_one({'_id': ObjectId(project_id)})\n flash('Your project has been deleted.', 'success')\n return redirect(url_for('projects'))","def project_delete(cursor, project):\n haystack = (project['_id'], )\n\n query = \"DELETE FROM projects WHERE _id=?\"\n try:\n cursor.execute(query, haystack)\n except Exception as e:\n on_error(e)\n\n query = \"DELETE FROM namespaces WHERE project_id=?\"\n try:\n cursor.execute(query, haystack)\n except Exception as e:\n on_error(e)\n else:\n cursor.connection.commit()\n raise Return((True, None))","def delete(self, oid):\n path = '/projects/%s' % oid\n res = self.client.call(path, 'DELETE', data='', token=self.manager.identity.token)\n self.logger.debug('Delete openstack project: %s' % truncate(res))\n return True","def test_projects_delete(self):\n project = Project()\n response = self.client.open('/project-tracker/projects',\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def delete_project(proj_id):\n project_obj = Project.objects.get(id=proj_id)\n print('Deleting project the fastq files within the project: ', project_obj.description)\n\n description = project_obj.description.replace(' ', '') # remove any space in the project name\n project_dir = 'documents/%s/%s' % (str(project_obj.date.date()), description)\n shutil.rmtree(project_dir, ignore_errors=True)\n print(\"Files deleted.\")","def delete(self, request, p_name):\n project = Project.objects.get(name=p_name)\n connectors = project.connector_set.all()\n connectors.delete()\n if os.path.isfile(project.project_location):\n os.remove(project.project_location)\n project.delete()\n return HttpResponse(HTTPStatus.OK)","def delete_project(self, project_name, check=True):\n page_projects = self._page_projects()\n\n with page_projects.table_projects.row(\n name=project_name).dropdown_menu as menu:\n menu.button_toggle.click()\n menu.item_delete.click()\n\n page_projects.form_delete_project_confirm.submit()\n\n if check:\n self.close_notification('success')\n page_projects.table_projects.row(\n name=project_name).wait_for_absence()","def delete_project_view(request, id):\n\n # retrieve the project to be deleted through his id. Raise an error if the project does not exist\n project = get_object_or_404(Projet, id=id)\n\n # Check if the logged in user is allowed to delete this project\n if request.user.has_perm('taskmanager.{}_project_permission'.format(project.id)):\n # Eventually delete the project\n project.delete()\n\n return redirect(\"projects\")","def delete_project(request, project_id):\n try:\n project = Project.objects.get(pk=project_id)\n except Project.DoesNotExist:\n raise Http404(\"Project does not exist\")\n # check whether the user is the one who created this project\n if project.user.email != request.session['email']:\n return HttpResponseRedirect('/projects/' + str(project_id))\n else:\n if request.method == \"POST\":\n if project:\n project.delete()\n return HttpResponseRedirect('/projects/')\n else:\n return render(request, 'projects/delete_project.html',\n {'project': project})\n return render(request, 'projects/delete_project.html', {'project': project})","def delete_project(self, name=None, delete_dir=False):\n victim = name or self.current\n if victim not in self:\n raise ValueError(\"{} is not a project\".format(victim))\n\n if len(self) == 1:\n raise ValueError(\"Can't delete only remaining project\")\n\n ProjectDataset.delete().where(ProjectDataset.name == victim).execute()\n\n if delete_dir:\n dir_path = self._base_data_dir / safe_filename(victim)\n assert dir_path.is_dir(), \"Can't find project directory\"\n shutil.rmtree(dir_path)\n\n if name is None or name == self.current:\n if \"default\" in self:\n self.set_current(\"default\")\n else:\n self.set_current(next(iter(self)).name)\n return self.current","def post_project_delete(self, resource_id, resource_dict):\n pass","def delete(self, project_id):\n try:\n authenticated_user_id = token_auth.current_user()\n if not ProjectAdminService.is_user_action_permitted_on_project(\n authenticated_user_id, project_id\n ):\n raise ValueError()\n except ValueError:\n return {\n \"Error\": \"User is not a manager of the project\",\n \"SubCode\": \"UserPermissionError\",\n }, 403\n\n try:\n ProjectAdminService.delete_project(project_id, authenticated_user_id)\n return {\"Success\": \"Project deleted\"}, 200\n except ProjectAdminServiceError as e:\n return {\"Error\": str(e).split(\"-\")[1], \"SubCode\": str(e).split(\"-\")[0]}, 403","def test_projects_id_delete(self):\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\n method='DELETE')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def delete(\n self, url: str\n ) -> pymongo.results.DeleteResult:\n return self._mongo.delete({\n 'url': url\n },\n 'projects'\n )","def delete_project(id):\n result = delete_project_to_db(id)\n flash(result)\n return redirect(url_for(\"portfolio\"))","def test_delete_project(self):\n self.assertEqual(Project.objects.count(), 1)\n self.assertEqual(Group.objects.count(), 2)\n\n delete_project(Project.objects.get(name=\"project A\"))\n\n self.assertEqual(Project.objects.count(), 0)\n self.assertEqual(Group.objects.count(), 0)","def delete_remote_project(profile, project):\n return delete_remote_project_worker.delay(profile_id=profile.id,\n project_id=project.id)","def delete(self, *args, **kwargs):\n if 'user' not in kwargs or not args:\n self.raise401()\n\n user = kwargs['user']\n path = parse_path(args[0])\n project = Project.objects(name=path[0], members__in=[user])\n if not project:\n self.raise401()\n try:\n project.delete()\n self.set_status(204)\n self.finish()\n except Exception as e:\n reason = e.message\n self.raise400(reason=reason)","def delete_keystone_v3_project(self, project_id, domain_id):\n LOG_OBJ.debug(\"Disable the project.\")\n kwargs = {\"project_id\": project_id, \"enabled\": False}\n self.set_keystone_v3_project(**kwargs)\n\n LOG_OBJ.debug(\"Deleting the project.\")\n\n _url = \"http://\" + self.host_ip + \":35357/v3/projects/\" + \\\n str(project_id)\n _headers = {'x-auth-token': self.cloud_admin_info[\"token_domain\"],\n 'content-type': 'application/json'}\n _body = None\n response = self.request(\"DELETE\", _url, _headers, _body)\n\n if response is None:\n LOG_OBJ.error(\"No response from Server while deleting the project\")\n print (\"No response from Server while deleting the project\")\n return response\n\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\" Deleting project Failed with status %s \"\n \"and error : %s\" % (response.status, response.data))\n print (\" Deleting project Failed with status %s and error : %s\" %\n (response.status, response.data))\n return response.status\n\n return True","def es_delete(project=None):\n if project is not None:\n script_indexer.delete_project(project)\n else:\n script_indexer.delete_all()","def test_remove_project(self):\n pass","def delete_project(self):\n tasks = self._get_all_tasks()\n\n task_id = tasks[self.tasks_view.currentRow()].Id\n task_status = tasks[self.tasks_view.currentRow()].Status\n\n if task_status is 0:\n warning = Warning(\n \"<html><head/><body><p align=\\\"center\\\"><span style=\\\" font-weight:600;\\\">\"\n \"Unable delete Task. \"\n \"Make sure the Task is Done\"\n \"</span></p></body></html>\"\n )\n warning.exec_()\n else:\n self.tasks_flow.delete_task(task_id)\n self.write_tasks_table()","def destroy(config, args):\n log = logging.getLogger('kraftwerk.destroy')\n if confirm(\"Remove project %s from node %s along with all services and data?\" % \n (args.project.name, args.node.hostname)):\n args.node.ssh(config.template(\"scripts/project_destroy.sh\", project=args.project))\n print \"Project %s removed from node %s\" % \\\n (args.project.name, args.node.hostname )\n for service in args.project.services(args.node):\n args.node.ssh(service.destroy_script)","def project_delete_event(self, proj_info):\n\n LOG.debug(\"Processing project_delete_event...\")\n proj_id = proj_info.get('resource_info')\n proj_name = self.get_project_name(proj_id)\n if proj_name:\n try:\n self.dcnm_client.delete_project(proj_name,\n self.cfg.dcnm.\n default_partition_name)\n except dexc.DfaClientRequestFailed:\n # Failed to delete project in DCNM.\n # Save the info and mark it as failure and retry it later.\n LOG.error(_LE(\"Failed to create project %s on DCNM.\"),\n proj_name)\n self.update_project_info_cache(proj_id, name=proj_name,\n opcode='delete',\n result=constants.DELETE_FAIL)\n else:\n self.update_project_info_cache(proj_id, opcode='delete')\n LOG.debug('Deleted project:%s', proj_name)\n self.project_delete_notif(proj_id, proj_name)","def pre_project_delete(self, resource_id):\n pass","def delete(self, team_id, project_id):\n try:\n if not ProjectAdminService.is_user_action_permitted_on_project(\n token_auth.current_user, project_id\n ):\n raise ValueError()\n TeamService.delete_team_project(team_id, project_id)\n return {\"Success\": True}, 200\n except ValueError:\n return {\n \"Error\": \"User is not a manager of the project\",\n \"SubCode\": \"UserPermissionError\",\n }, 403","def delete(self, name, project=None):\n qlist = self._list(project)\n key = self._queue(project, name)\n self._db.delete(key)\n self._db.zremrangebyscore(qlist, -1, 1)","def delete_namespaced_project(self, name, **kwargs):\n\n all_params = ['name', 'pretty']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method delete_namespaced_project\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `delete_namespaced_project`\")\n\n resource_path = '/oapi/v1/projects/{name}'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'DELETE',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='UnversionedStatus',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def remove_single_project(project_name):\n p = subprocess.Popen('rm -rf {}/{}'.format(context.__PROJECTS_PATH__, project_name), shell=True)\n p.wait()","def delete(self, guid):\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\n # search for the Project and delete if found\n key = db.Key.from_path('Task', int(guid))\n task = db.get(key)\n wantsNotifications = {\"true\": True, \"false\": False}.get(self.request.params['notify'].lower())\n currentUserId = self.request.params['UUID']\n cukey = db.Key.from_path('User', int(currentUserId))\n user = db.get(cukey)\n if not task == None:\n # cache current values before updates\n taskName = task.name\n taskType = task.type\n taskPriority = task.priority\n taskStatus = task.developmentStatus\n taskValidation = task.validation\n taskSubmitterId = task.submitterId\n taskAssigneeId = task.assigneeId\n taskEffort = task.effort\n taskProjectId = task.projectId\n taskDescription = task.description\n # Push notification email on the queue if we need to notify\n if notification.should_notify(currentUserId,task,\"deleteTask\",wantsNotifications):\n taskqueue.add(url='/mailer', params={'taskId': int(guid), 'currentUUID': self.request.params['UUID'], 'action': \"deleteTask\", 'name': taskName, 'type': taskType, 'priority': taskPriority, 'status': taskStatus, 'validation': taskValidation, 'submitterId': taskSubmitterId, 'assigneeId': taskAssigneeId, 'effort': taskEffort, 'projectId': taskProjectId, 'description': taskDescription})\n task.delete()\n self.response.set_status(204, \"Deleted\")\n else:\n self.response.set_status(404, \"Not Found\")\n else:\n self.response.set_status(401, \"Not Authorized\")","def delete_api(self, project_id, api_id, hbase_manager=None):\n\n self._project_service.get_nondeleted_project(project_id)\n\n proj_api_col = \"apis:{0}\".format(api_id)\n project_id = bytes(project_id, self._charset)\n api_id = bytes(api_id, self._charset)\n\n self.get_nondeleted_api(project_id, api_id)\n\n connection = hbase_manager.connection\n api_tbl = connection.table(\"apis\")\n projects_tbl = connection.table(\"projects\")\n\n projects_tbl.delete(project_id, [bytes(proj_api_col, self._charset)])\n api_tbl.put(api_id, {\n b\"attrs:state\": b\"deleted\"\n })","def delete(self, project_id):\n project_model = ProjectDBModel.query.get(project_id)\n if not project_model:\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\n try:\n data = request.get_json()\n # cambiarlo cuando se vuelva a tener dos PKs\n deleted = FavoritesProjectDBModel.delete(\n data['user_id'], project_id)\n if deleted:\n users = \\\n FavoritesProjectDBModel.get_favorites_of_project_id(\n project_id)\n response_object = {\n \"project_id\": project_id,\n \"users_id\": users,\n }\n return response_object, 200\n else:\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\n except KeyError:\n ns.abort(404, status=MISSING_VALUES_ERROR)","def delete_project_file(self, project=None):\n if type(project) is not Project:\n return False\n\n path = self.data_path + self.project_dir\n\n # generate filenames\n filename = path + '/' + self.us(project.project_id()) + '.flproject'\n\n # check if the file exists and delete it\n if os.path.isfile(filename):\n os.remove(filename)\n return True\n else:\n return False","async def delete(self, ctx, project_name: str) -> None:\n if not ctx.projects.find_project(project_name):\n channel = discord.utils.get(\n ctx.guild.channels, name=f\"{project_name}-project\")\n\n if channel and channel.category.name == \"Flux Projects\":\n if ctx.author.permissions_in(channel).manage_channels:\n message = await ctx.send(\"That project doesn't appear to\"\n \" exist in my database, but the \"\n \"channel still exists. \"\n \"Would you like to delete it?\")\n yes = \"<:greenTick:596576670815879169>\"\n no = \"<:redTick:596576672149667840>\"\n await message.add_reaction(yes)\n await message.add_reaction(no)\n reaction, user = await ctx.bot.wait_for(\n \"reaction_add\",\n check=lambda reaction, user: (user == ctx.author) and\n (str(reaction.emoji) == yes or no) and\n (reaction.message.channel == ctx.channel)\n )\n if reaction.emoji.id == ctx.bot.config.tick_yes:\n await channel.delete(reason=\"Project not found.\")\n await ctx.send(\"The channel was deleted sucessfully.\")\n return\n\n elif reaction.emoji.id == ctx.bot.config.tick_no:\n await ctx.send(\"Not deleting the channel.\")\n return\n\n else: # If author doesn't have access to deleting channels.\n await ctx.send(\"That project does not appear to be in my \"\n \"database, but the channel for it still \"\n \"exists. Please have someone with\"\n \" manage channels run this chommand.\"\n )\n return\n else:\n await ctx.send(\"I could not find this project.\")\n return\n\n if str(ctx.author.id) != ctx.projects.find_project(project_name).get(\n \"owner\"):\n await ctx.send(\"Only the project owner \"\n \"can delete this project.\")\n return\n message = await ctx.send(\"This action __cannot__ be undone. \"\n \"Once you do this, everything is gone. \"\n \"Are you sure you want to continue?\")\n yes = \"<:greenTick:596576670815879169>\"\n no = \"<:redTick:596576672149667840>\"\n await message.add_reaction(yes)\n await message.add_reaction(no)\n reaction, user = await ctx.bot.wait_for(\n \"reaction_add\", check=lambda reaction, user:\n (user == ctx.author) and\n (str(reaction.emoji) == yes or no) and\n (reaction.message.channel == ctx.channel)\n )\n if reaction.emoji.id == ctx.bot.config.tick_yes:\n channel = ctx.projects.find_project(\n project_name).get(\"channel\")\n channel = discord.utils.get(ctx.guild.channels,\n id=int(channel))\n ctx.projects.delete_project(project_name)\n if channel:\n await channel.delete(reason=\"Project deleted.\")\n await ctx.send(\"The project has been deleted.\")\n elif reaction.emoji.id == ctx.bot.config.tick_no:\n await ctx.send(\"Not deleting the project.\")","def delete(self):\n return self.client._perform_empty(\n \"DELETE\", \"/project-folders/%s\" % self.project_folder_id)","def delete(self):\r\n delete_tracks(self.project, [self])","def project_post_delete(sender, instance, **kwargs):\n instance.url.delete(False)","def remove_project(project_id):\n response_object = {'status': 'success'}\n with database.engine.begin() as connection:\n\n stmt = select([models.projects.c.path]).where(\n models.projects.c.project_id == project_id)\n project = connection.execute(stmt).first()\n\n if project:\n app = flask.current_app\n project_path = os.path.join(\n app.root_path, app.config['DATA_DIRECTORY'], project['path'])\n if 'morphocut' in project_path and app.config['DATA_DIRECTORY'] in project_path:\n print('removing project with id {}'.format(project_id))\n if os.path.exists(project_path):\n helpers.remove_directory(project_path)\n\n stmt = models.projects.delete().where( # pylint: disable=no-value-for-parameter\n models.projects.c.project_id == project_id)\n\n connection.execute(stmt)\n\n return jsonify(response_object)","def delete_project_by_name(self, project_name):\n with self._transaction.cursor() as cur:\n # delete associations between this project and any barcodes\n cur.execute(\"DELETE FROM barcodes.project_barcode \"\n \"WHERE project_id in (\"\n \"SELECT project_id FROM barcodes.project \"\n \"WHERE project = %s)\",\n (project_name,))\n\n # now delete the project itself\n cur.execute(\"DELETE FROM barcodes.project WHERE project = %s\",\n (project_name,))\n return cur.rowcount == 1","def _delete_dm_project(self, project_uuid: str) -> None:\n _LOGGER.warning('Deleting DM Project %s...', project_uuid)\n\n dm_rv: DmApiRv = DmApi.delete_project(self.__org_owner_dm_token,\n project_id=project_uuid)\n if not dm_rv.success:\n _LOGGER.error('Failed to delete DM Project %s', project_uuid)\n return\n\n _LOGGER.warning('Deleted DM Project %s', project_uuid)","def delete(project, zone, instance):\n print >>sys.stderr, 'WARNING: duplicated jobs may fail/corrupt results'\n print >>sys.stderr, ('TODO(fejta): See http://stackoverflow.com/'\n 'questions/19645430/changing-jenkins-build-number')\n answer = raw_input('Delete %s [yes/NO]: ')\n if not answer or answer != 'yes':\n print >>sys.stderr, 'aborting'\n sys.exit(1)\n gcloud(\n project,\n 'compute',\n 'instances',\n 'delete',\n '--zone=%s' % zone,\n instance,\n )\n gcloud(\n project,\n 'compute',\n 'disks',\n 'delete',\n '--zone=%s' % zone,\n *get_disks(instance))","def delete_orphan_project(apps, schema_editor):\n Project = apps.get_model('data_manager', 'Project')\n Project.objects.filter(dataset__isnull=True).delete()\n return","def delete(ctx: click.Context, repository_path):\n root_commands.cmd_delete(ctx.obj, repository_path)","def delete_project_quotas(self, project_id, extra_headers=None,\n use_auth=True, user_name=None):\n resp = self.client.delete('project-quotas/' + project_id,\n extra_headers=extra_headers,\n use_auth=use_auth, user_name=user_name)\n return resp","def DelProject(projname):\n\tif projname == \"\" or projname == None:\n\t\tpjnm = raw_input(\"\\nNombre del proyecto: \").lower()\n\t\tif pjnm == \"\" or pjnm == None:\n\t\t\tcancel()\n\telse:\n\t\t# Proceso para borrar todo el proyecto\n\t\tpass\n\n\tpa = open(\"author_name.txt\", \"r\")\t#Abre el archivo con el nombre del autor\n\tpa.read()\n\tpc = open(\"project_code.txt\", \"r\")\t#Abre el archivo con el codigo de proyecto\n\tpc.read()\n\n\tuserpa = raw_input(\"Ingrese el nombre del autor: \").lower()\n\tuserpc = raw_input(\"Ingrese el codigo del proyecto: \").lower()\n\n\tif userpa == pa and userpc == pc:\t#Se verifica que userpa(nombre del autor por el usuario) sea igual a pa(nombre original del autor) y lo mismo con el codigo del proyecto\n\t\tprint \"Iniciando el Borrado del Proyecto...\"\n\t\tpcommands.del_project()\n\t\tprint \"El proyecto se ha borrado con exito!\"\n\telse:\n\t\tprint \"El codigo del proyecto o el nombre del autor no es correcto.\"\n\t\tcancel()","def delete_all_projects():\n client = RequestManager()\n client.set_method(\"GET\")\n client.set_endpoint(\"/projects\")\n response = client.execute_request()\n for project in response.json():\n try:\n ProjectHelper.delete_project(project[\"id\"])\n except TypeError:\n LOGGER.info(project)","def delete(self, endpoint, params=None):\n params = params or dict()\n return self.request(verb=requests.delete, address=self.project_address + endpoint,\n params=params)","def delete(self):\n return self.client._perform_empty(\n \"DELETE\", \"/projects/%s/managedfolders/%s\" % (self.project_key, self.odb_id))","def command_delete(self):\n if self.gcp_env.project not in SUPPORTED_PROJECT_CONFIGS.keys():\n _logger.error(f'Project config not supported {self.gcp_env.project}')\n return 1\n\n if not self.args.bucket and not self.args.id:\n _logger.error(\"--bucket and --id required for delete.\")\n return 1\n\n # Get notification\n client = storage.Client()\n bucket = client.get_bucket(self.args.bucket)\n target = bucket.get_notification(self.args.id, client)\n\n if self.gcp_env.project != target.topic_project:\n _logger.error(\"Notification project and specified project do not match.\")\n return 1\n\n # Delete the notification\n try:\n target.delete(client=client)\n\n except NotFound:\n _logger.error(f\"Notification ID {self.args.id} not found.\")\n return 1\n\n _logger.info(f\"Notification id {self.args.id} has been deleted.\")\n\n _logger.info(\"Removing notification from config...\")\n self.delete_notification_from_config()\n\n return 0","def delete(self, *args, **kwargs):\n self.request(\"delete\", *args, **kwargs)","def _delete(self, *args, **kwargs):\n return self._request('delete', *args, **kwargs)","def test_projects_id_contacts_delete(self):\n project = Contact()\n response = self.client.open('/project-tracker/projects/{id}/contacts'.format(id=56),\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def cmd_apps__destroy(args):\n \n if args.name is None and in_git_repo():\n args.name = _get_current_project_name()\n\n if args.name is None:\n print \"Please provide a project name.\"\n sys.exit(1)\n\n print \"Destroying project %s...\" % args.name\n remote.destroy_project(args.name)\n print \"Project %s destroyed.\" % args.name\n if in_git_repo() and _get_current_project_name() == args.name:\n git(None, 'remote', 'rm', 'tinyserv')\n print \"Removed remote '%s'.\" % args.name","def delete():","def delete(self, application_id):","def _on_del_project(self):\n project = self.ddnCurProject.get()\n# if len(project) > 0:\n if project:\n if '.prj'!= project[-4:]:\n project += '.prj'\n if os.path.exists(self.BibTerm + '/'+ project):\n os.remove(self.BibTerm + '/'+ project)\n self.list_projects = [f.rstrip('.prj') \\\n for f in os.listdir(self.BibTerm) \\\n if f.endswith('.prj')]\n self.ddnCurProject['values'] = self.list_projects\n# if len(self.list_projects) > 0:\n if self.list_projects:\n self.ddnCurProject.set(self.list_projects[0])\n else:\n self.ddnCurProject.set('')\n pass","def delete(self, *args, **kw):\n kw['method'] = 'DELETE'\n return self.open(*args, **kw)","def delete_language_from_project(self, project_id, language_code):\n self._run(\n url_path=\"languages/delete\",\n id=project_id,\n language=language_code\n )\n return True","def record_destroy_for_project(project_id):\n session = get_session()\n with session.begin():\n session.query(models.ProjectAccountRecord).\\\n filter_by(project_id=project_id).\\\n update({'deleted': True,\n 'deleted_at': datetime.datetime.utcnow(),\n 'updated_at': datetime.datetime.utcnow()})","def remove_deleted_project(self, project_id):\n self.db.make_query(\n '''\n DELETE FROM deleted_project WHERE project_id = \"{}\";\n '''.format(project_id)\n )\n\n if self.get_deleted_project(project_id):\n return False\n\n return True","def delete(self):\n\n headers = self._default_headers()\n\n return self._request(self.name,\n ok_status=None,\n data=None,\n headers=headers,\n method=\"DELETE\")","def remove_project(self, project_id):\n project_file_path = '{}/{}'.format(self._storage_location, project_id)\n if os.path.exists(project_file_path):\n os.remove(project_file_path)\n else:\n raise ValueError('The project id {} does not exist!'.format(project_id))","def test_projects_id_comments_delete(self):\n project = Comment()\n response = self.client.open('/project-tracker/projects/{id}/comments'.format(id=56),\n method='DELETE',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","async def delete_user_byid(*_):\n return web.Response(text=\"PUT project not implemented\", status=501)","def delete(self, *args, **kwargs):\n pass","def delete(self, *args, **kwargs):\n pass","def userproject_post_delete(sender, instance, **kwargs):\n instance.document.delete(False)","def delete(repo):\n print('Repo: %s' % repo)\n print('Deleted')","def delete(project_id, integration_id):\n IntegrationService.delete(project_id, integration_id)\n\n return {\"status\": \"deleted\"}, 200","def delete():\n run('rm -r {}'.format(utils.home('apps', env.PROJECT_NAME)))","def delete_record(request, slug, pk):\n # Try except to make sure the user is a member of this project\n try:\n ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\n except ObjectDoesNotExist:\n # User is not a member\n return HttpResponse(\"You're trying to access a project you're not a member of or a project that does not exist.\")\n else:\n # User is a member\n pm = ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\n # Access control.. if not owner or editor - access denied.\n if pm.is_owner or pm.is_editor:\n # User has access\n record = get_object_or_404(models.Record, pk=pk)\n # Delete record\n models.Record.objects.filter(project=get_object_or_404(models.Project, slug=slug), pk=pk).delete()\n # Send user back to project detail, the overview of all records in the project.\n return redirect('projects:single', slug=slug)\n else:\n # Access denied...\n return HttpResponse(\"You don't have the permission to do this\")","def delete(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"delete\"), kwargs)","def delete(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"delete\"), kwargs)","def delete_bucket_from_project(projectname, bucketname):\n return jsonify(\n admin.delete_bucket_on_project(\n current_app.scoped_session(), projectname, bucketname\n )\n )","def delete(self, *args, **kwargs) -> Any:\n pass","def delete_project_entities(cls, project_id,\n suppress_exception=False,\n session=None):\n cls.db_repo.delete_project_entities(\n project_id, suppress_exception=suppress_exception, session=session)","def delete():\n # Must be logged in to perform any delete commands.\n auth_required()\n pass","def delete(self, api_path, *args, **kwargs):\n\n\t\treturn self._do_operation(u'delete', api_path, *args, **kwargs)","def test_Projects_CanBeCreatedAndDeleted_Successfully(self):\n\n name = \"Test Project \"\n # Test create a new project successfully\n project = self.api.create_project(name, \"Py\")\n self.assertTrue(project['success'])\n self.assertTrue(project['projects'][0]['projectId'] > 0)\n self.assertTrue(project['projects'][0]['name'] == name)\n\n # Delete the project\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\n self.assertTrue(deleteProject['success'])\n\n # Make sure the project is really deleted\n projectList = self.api.list_projects()\n self.assertFalse(any(project['projects'][0]['projectId'] == projectList['projects'][i]['projectId']\n for i in range(len(projectList['projects']))))","def delete(self, *args, **kwargs):\n return self.handle_delete_request()"],"string":"[\n \"def delete_project(project):\\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\\n ret = bmi.delete_project(project)\\n if ret[constants.STATUS_CODE_KEY] == 200:\\n click.echo(\\\"Success\\\")\\n else:\\n click.echo(ret[constants.MESSAGE_KEY])\",\n \"def delete_project(arn=None):\\n pass\",\n \"def do_project_delete(cs, args):\\n key = args.project\\n if cs.projects.is_id(key):\\n id = key\\n else:\\n id = cs.projects.get_id_by_name(key)\\n cs.projects.delete(id)\\n print(\\\"Delete Project '%s' successfully.\\\" % key)\",\n \"def delete_project(projectname):\\n response = jsonify(admin.delete_project(current_app.scoped_session(), projectname))\\n return response\",\n \"def delete(self):\\n _url = f\\\"{self.connector.base_url}/projects/{self.project_id}\\\"\\n\\n self.connector.http_call(\\\"delete\\\", _url)\\n\\n self.project_id = None\\n self.name = None\",\n \"def delete_project(project_id):\\n client = RequestManager()\\n client.set_method(\\\"DELETE\\\")\\n client.set_endpoint(\\\"/projects/{0}\\\".format(project_id))\\n client.execute_request()\",\n \"def delete_project(\\n name\\n):\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"delete_project\\\"\\n cmd[\\\"name_\\\"] = name\\n\\n comm.send(cmd)\",\n \"def delete_project(request, project_id):\\n if not request.user.is_superuser:\\n messages.error(request, 'Sorry, only store owners can do that.')\\n return redirect(reverse('home'))\\n\\n project = get_object_or_404(Project, pk=project_id)\\n project.delete()\\n messages.success(request, 'Project deleted!')\\n return redirect(reverse('portfolio'))\",\n \"def test_delete_project(self):\\n pass\",\n \"def test_delete_project(self):\\n pass\",\n \"def deleteProject(self, projectId):\\n uri = \\\"/v1/projects/\\\" +str(projectId)\\n response = self.client.delete(uri)\\n return response\",\n \"def delete_project(self, project_id):\\n self._run(\\n url_path=\\\"projects/delete\\\",\\n id=project_id,\\n )\\n return True\",\n \"def delete_project(request, project_id):\\n\\n profile = get_object_or_404(Profile, user=request.user)\\n project = get_object_or_404(GameProject, pk=project_id)\\n\\n if not profile.is_creator:\\n messages.error(request, 'Sorry, only creators can do that.')\\n return redirect(reverse('home'))\\n if project.owner != profile:\\n messages.error(request, 'Sorry, only the project owner can do that.')\\n return redirect(reverse('home'))\\n\\n project = get_object_or_404(GameProject, pk=project_id)\\n project.delete()\\n messages.success(request, 'Project deleted!')\\n return redirect(reverse('all_projects'))\",\n \"def delete(self, guid):\\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\\n # search for the Project and delete if found\\n key = db.Key.from_path('Project', int(guid))\\n project = db.get(key)\\n if not project == None:\\n project.delete()\\n self.response.set_status(204, \\\"Deleted\\\")\\n else:\\n self.response.set_status(404, \\\"Not Found\\\")\\n else:\\n self.response.set_status(401, \\\"Not Authorized\\\")\",\n \"def delete_project(self, project_id):\\n return self._delete('/projects/{0}'.format(project_id))\",\n \"def delete_project(project_id):\\n project = Project.query.filter_by(id=project_id).first()\\n if not project:\\n return {\\n 'success': False,\\n 'message': f\\\"No project with the specified id {project_id} found.\\\",\\n }\\n\\n else:\\n if is_project_manager(project, g.user):\\n # delete related tasks\\n Task.query.filter_by(project=project).delete()\\n #delete related invites\\n Invitation.query.filter_by(project=project).delete()\\n db_session.delete(project)\\n db_session.commit()\\n return {\\n 'success': True,\\n 'result': {},\\n 'message': \\\"Project Deleted Successfully.\\\",\\n }\",\n \"def delete(conn, project):\\n with conn:\\n c = conn.cursor()\\n c.execute(\\\"DELETE FROM projects WHERE project =?\\\", (project,))\",\n \"def delete_project(self, project_name):\\n # type(project_name) == unicode\\n project = self.db.get_project_by_name(project_name)\\n if not project:\\n print(u\\\"*** Error: The project '{}' was not found.\\\"\\n \\\"\\\".format(project_name))\\n return\\n print('Caution! The related tracking will be deleted as well.{eol}'\\n 'Do you really want to delete the project? [y/N] '\\n ''.format(eol=os.linesep), end='')\\n if not helpers.get_yes_no(default='n'):\\n return\\n self.db.delete_project_by_name(project_name)\\n print(u\\\"The project '%s' has been deleted.\\\" % project_name)\\n self.set_prompt()\",\n \"def destroy(self, request, pk=None):\\n try:\\n project = Project.objects.get(pk=pk)\\n project.delete()\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\\n\\n except Project.DoesNotExist as ex:\\n return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)\\n\\n except Exception as ex:\\n return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\",\n \"def delete_project(self, project_id):\\n _url = f\\\"{self.base_url}/projects/{project_id}\\\"\\n self.http_call(\\\"delete\\\", _url)\\n return\",\n \"def delete_project(project_id):\\n \\n project = mongo.db.projects\\n project.delete_one({'_id': ObjectId(project_id)})\\n flash('Your project has been deleted.', 'success')\\n return redirect(url_for('projects'))\",\n \"def project_delete(cursor, project):\\n haystack = (project['_id'], )\\n\\n query = \\\"DELETE FROM projects WHERE _id=?\\\"\\n try:\\n cursor.execute(query, haystack)\\n except Exception as e:\\n on_error(e)\\n\\n query = \\\"DELETE FROM namespaces WHERE project_id=?\\\"\\n try:\\n cursor.execute(query, haystack)\\n except Exception as e:\\n on_error(e)\\n else:\\n cursor.connection.commit()\\n raise Return((True, None))\",\n \"def delete(self, oid):\\n path = '/projects/%s' % oid\\n res = self.client.call(path, 'DELETE', data='', token=self.manager.identity.token)\\n self.logger.debug('Delete openstack project: %s' % truncate(res))\\n return True\",\n \"def test_projects_delete(self):\\n project = Project()\\n response = self.client.open('/project-tracker/projects',\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def delete_project(proj_id):\\n project_obj = Project.objects.get(id=proj_id)\\n print('Deleting project the fastq files within the project: ', project_obj.description)\\n\\n description = project_obj.description.replace(' ', '') # remove any space in the project name\\n project_dir = 'documents/%s/%s' % (str(project_obj.date.date()), description)\\n shutil.rmtree(project_dir, ignore_errors=True)\\n print(\\\"Files deleted.\\\")\",\n \"def delete(self, request, p_name):\\n project = Project.objects.get(name=p_name)\\n connectors = project.connector_set.all()\\n connectors.delete()\\n if os.path.isfile(project.project_location):\\n os.remove(project.project_location)\\n project.delete()\\n return HttpResponse(HTTPStatus.OK)\",\n \"def delete_project(self, project_name, check=True):\\n page_projects = self._page_projects()\\n\\n with page_projects.table_projects.row(\\n name=project_name).dropdown_menu as menu:\\n menu.button_toggle.click()\\n menu.item_delete.click()\\n\\n page_projects.form_delete_project_confirm.submit()\\n\\n if check:\\n self.close_notification('success')\\n page_projects.table_projects.row(\\n name=project_name).wait_for_absence()\",\n \"def delete_project_view(request, id):\\n\\n # retrieve the project to be deleted through his id. Raise an error if the project does not exist\\n project = get_object_or_404(Projet, id=id)\\n\\n # Check if the logged in user is allowed to delete this project\\n if request.user.has_perm('taskmanager.{}_project_permission'.format(project.id)):\\n # Eventually delete the project\\n project.delete()\\n\\n return redirect(\\\"projects\\\")\",\n \"def delete_project(request, project_id):\\n try:\\n project = Project.objects.get(pk=project_id)\\n except Project.DoesNotExist:\\n raise Http404(\\\"Project does not exist\\\")\\n # check whether the user is the one who created this project\\n if project.user.email != request.session['email']:\\n return HttpResponseRedirect('/projects/' + str(project_id))\\n else:\\n if request.method == \\\"POST\\\":\\n if project:\\n project.delete()\\n return HttpResponseRedirect('/projects/')\\n else:\\n return render(request, 'projects/delete_project.html',\\n {'project': project})\\n return render(request, 'projects/delete_project.html', {'project': project})\",\n \"def delete_project(self, name=None, delete_dir=False):\\n victim = name or self.current\\n if victim not in self:\\n raise ValueError(\\\"{} is not a project\\\".format(victim))\\n\\n if len(self) == 1:\\n raise ValueError(\\\"Can't delete only remaining project\\\")\\n\\n ProjectDataset.delete().where(ProjectDataset.name == victim).execute()\\n\\n if delete_dir:\\n dir_path = self._base_data_dir / safe_filename(victim)\\n assert dir_path.is_dir(), \\\"Can't find project directory\\\"\\n shutil.rmtree(dir_path)\\n\\n if name is None or name == self.current:\\n if \\\"default\\\" in self:\\n self.set_current(\\\"default\\\")\\n else:\\n self.set_current(next(iter(self)).name)\\n return self.current\",\n \"def post_project_delete(self, resource_id, resource_dict):\\n pass\",\n \"def delete(self, project_id):\\n try:\\n authenticated_user_id = token_auth.current_user()\\n if not ProjectAdminService.is_user_action_permitted_on_project(\\n authenticated_user_id, project_id\\n ):\\n raise ValueError()\\n except ValueError:\\n return {\\n \\\"Error\\\": \\\"User is not a manager of the project\\\",\\n \\\"SubCode\\\": \\\"UserPermissionError\\\",\\n }, 403\\n\\n try:\\n ProjectAdminService.delete_project(project_id, authenticated_user_id)\\n return {\\\"Success\\\": \\\"Project deleted\\\"}, 200\\n except ProjectAdminServiceError as e:\\n return {\\\"Error\\\": str(e).split(\\\"-\\\")[1], \\\"SubCode\\\": str(e).split(\\\"-\\\")[0]}, 403\",\n \"def test_projects_id_delete(self):\\n response = self.client.open('/project-tracker/projects/{id}'.format(id=3.4),\\n method='DELETE')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def delete(\\n self, url: str\\n ) -> pymongo.results.DeleteResult:\\n return self._mongo.delete({\\n 'url': url\\n },\\n 'projects'\\n )\",\n \"def delete_project(id):\\n result = delete_project_to_db(id)\\n flash(result)\\n return redirect(url_for(\\\"portfolio\\\"))\",\n \"def test_delete_project(self):\\n self.assertEqual(Project.objects.count(), 1)\\n self.assertEqual(Group.objects.count(), 2)\\n\\n delete_project(Project.objects.get(name=\\\"project A\\\"))\\n\\n self.assertEqual(Project.objects.count(), 0)\\n self.assertEqual(Group.objects.count(), 0)\",\n \"def delete_remote_project(profile, project):\\n return delete_remote_project_worker.delay(profile_id=profile.id,\\n project_id=project.id)\",\n \"def delete(self, *args, **kwargs):\\n if 'user' not in kwargs or not args:\\n self.raise401()\\n\\n user = kwargs['user']\\n path = parse_path(args[0])\\n project = Project.objects(name=path[0], members__in=[user])\\n if not project:\\n self.raise401()\\n try:\\n project.delete()\\n self.set_status(204)\\n self.finish()\\n except Exception as e:\\n reason = e.message\\n self.raise400(reason=reason)\",\n \"def delete_keystone_v3_project(self, project_id, domain_id):\\n LOG_OBJ.debug(\\\"Disable the project.\\\")\\n kwargs = {\\\"project_id\\\": project_id, \\\"enabled\\\": False}\\n self.set_keystone_v3_project(**kwargs)\\n\\n LOG_OBJ.debug(\\\"Deleting the project.\\\")\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v3/projects/\\\" + \\\\\\n str(project_id)\\n _headers = {'x-auth-token': self.cloud_admin_info[\\\"token_domain\\\"],\\n 'content-type': 'application/json'}\\n _body = None\\n response = self.request(\\\"DELETE\\\", _url, _headers, _body)\\n\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while deleting the project\\\")\\n print (\\\"No response from Server while deleting the project\\\")\\n return response\\n\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\" Deleting project Failed with status %s \\\"\\n \\\"and error : %s\\\" % (response.status, response.data))\\n print (\\\" Deleting project Failed with status %s and error : %s\\\" %\\n (response.status, response.data))\\n return response.status\\n\\n return True\",\n \"def es_delete(project=None):\\n if project is not None:\\n script_indexer.delete_project(project)\\n else:\\n script_indexer.delete_all()\",\n \"def test_remove_project(self):\\n pass\",\n \"def delete_project(self):\\n tasks = self._get_all_tasks()\\n\\n task_id = tasks[self.tasks_view.currentRow()].Id\\n task_status = tasks[self.tasks_view.currentRow()].Status\\n\\n if task_status is 0:\\n warning = Warning(\\n \\\"<html><head/><body><p align=\\\\\\\"center\\\\\\\"><span style=\\\\\\\" font-weight:600;\\\\\\\">\\\"\\n \\\"Unable delete Task. \\\"\\n \\\"Make sure the Task is Done\\\"\\n \\\"</span></p></body></html>\\\"\\n )\\n warning.exec_()\\n else:\\n self.tasks_flow.delete_task(task_id)\\n self.write_tasks_table()\",\n \"def destroy(config, args):\\n log = logging.getLogger('kraftwerk.destroy')\\n if confirm(\\\"Remove project %s from node %s along with all services and data?\\\" % \\n (args.project.name, args.node.hostname)):\\n args.node.ssh(config.template(\\\"scripts/project_destroy.sh\\\", project=args.project))\\n print \\\"Project %s removed from node %s\\\" % \\\\\\n (args.project.name, args.node.hostname )\\n for service in args.project.services(args.node):\\n args.node.ssh(service.destroy_script)\",\n \"def project_delete_event(self, proj_info):\\n\\n LOG.debug(\\\"Processing project_delete_event...\\\")\\n proj_id = proj_info.get('resource_info')\\n proj_name = self.get_project_name(proj_id)\\n if proj_name:\\n try:\\n self.dcnm_client.delete_project(proj_name,\\n self.cfg.dcnm.\\n default_partition_name)\\n except dexc.DfaClientRequestFailed:\\n # Failed to delete project in DCNM.\\n # Save the info and mark it as failure and retry it later.\\n LOG.error(_LE(\\\"Failed to create project %s on DCNM.\\\"),\\n proj_name)\\n self.update_project_info_cache(proj_id, name=proj_name,\\n opcode='delete',\\n result=constants.DELETE_FAIL)\\n else:\\n self.update_project_info_cache(proj_id, opcode='delete')\\n LOG.debug('Deleted project:%s', proj_name)\\n self.project_delete_notif(proj_id, proj_name)\",\n \"def pre_project_delete(self, resource_id):\\n pass\",\n \"def delete(self, team_id, project_id):\\n try:\\n if not ProjectAdminService.is_user_action_permitted_on_project(\\n token_auth.current_user, project_id\\n ):\\n raise ValueError()\\n TeamService.delete_team_project(team_id, project_id)\\n return {\\\"Success\\\": True}, 200\\n except ValueError:\\n return {\\n \\\"Error\\\": \\\"User is not a manager of the project\\\",\\n \\\"SubCode\\\": \\\"UserPermissionError\\\",\\n }, 403\",\n \"def delete(self, name, project=None):\\n qlist = self._list(project)\\n key = self._queue(project, name)\\n self._db.delete(key)\\n self._db.zremrangebyscore(qlist, -1, 1)\",\n \"def delete_namespaced_project(self, name, **kwargs):\\n\\n all_params = ['name', 'pretty']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method delete_namespaced_project\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `delete_namespaced_project`\\\")\\n\\n resource_path = '/oapi/v1/projects/{name}'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'DELETE',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='UnversionedStatus',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def remove_single_project(project_name):\\n p = subprocess.Popen('rm -rf {}/{}'.format(context.__PROJECTS_PATH__, project_name), shell=True)\\n p.wait()\",\n \"def delete(self, guid):\\n if helpers.authorized(self.request.params['UUID'], self.request.params['ATO'], self.request.params['action']):\\n # search for the Project and delete if found\\n key = db.Key.from_path('Task', int(guid))\\n task = db.get(key)\\n wantsNotifications = {\\\"true\\\": True, \\\"false\\\": False}.get(self.request.params['notify'].lower())\\n currentUserId = self.request.params['UUID']\\n cukey = db.Key.from_path('User', int(currentUserId))\\n user = db.get(cukey)\\n if not task == None:\\n # cache current values before updates\\n taskName = task.name\\n taskType = task.type\\n taskPriority = task.priority\\n taskStatus = task.developmentStatus\\n taskValidation = task.validation\\n taskSubmitterId = task.submitterId\\n taskAssigneeId = task.assigneeId\\n taskEffort = task.effort\\n taskProjectId = task.projectId\\n taskDescription = task.description\\n # Push notification email on the queue if we need to notify\\n if notification.should_notify(currentUserId,task,\\\"deleteTask\\\",wantsNotifications):\\n taskqueue.add(url='/mailer', params={'taskId': int(guid), 'currentUUID': self.request.params['UUID'], 'action': \\\"deleteTask\\\", 'name': taskName, 'type': taskType, 'priority': taskPriority, 'status': taskStatus, 'validation': taskValidation, 'submitterId': taskSubmitterId, 'assigneeId': taskAssigneeId, 'effort': taskEffort, 'projectId': taskProjectId, 'description': taskDescription})\\n task.delete()\\n self.response.set_status(204, \\\"Deleted\\\")\\n else:\\n self.response.set_status(404, \\\"Not Found\\\")\\n else:\\n self.response.set_status(401, \\\"Not Authorized\\\")\",\n \"def delete_api(self, project_id, api_id, hbase_manager=None):\\n\\n self._project_service.get_nondeleted_project(project_id)\\n\\n proj_api_col = \\\"apis:{0}\\\".format(api_id)\\n project_id = bytes(project_id, self._charset)\\n api_id = bytes(api_id, self._charset)\\n\\n self.get_nondeleted_api(project_id, api_id)\\n\\n connection = hbase_manager.connection\\n api_tbl = connection.table(\\\"apis\\\")\\n projects_tbl = connection.table(\\\"projects\\\")\\n\\n projects_tbl.delete(project_id, [bytes(proj_api_col, self._charset)])\\n api_tbl.put(api_id, {\\n b\\\"attrs:state\\\": b\\\"deleted\\\"\\n })\",\n \"def delete(self, project_id):\\n project_model = ProjectDBModel.query.get(project_id)\\n if not project_model:\\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\\n try:\\n data = request.get_json()\\n # cambiarlo cuando se vuelva a tener dos PKs\\n deleted = FavoritesProjectDBModel.delete(\\n data['user_id'], project_id)\\n if deleted:\\n users = \\\\\\n FavoritesProjectDBModel.get_favorites_of_project_id(\\n project_id)\\n response_object = {\\n \\\"project_id\\\": project_id,\\n \\\"users_id\\\": users,\\n }\\n return response_object, 200\\n else:\\n ns.abort(404, status=PROJECT_NOT_FOUND_ERROR)\\n except KeyError:\\n ns.abort(404, status=MISSING_VALUES_ERROR)\",\n \"def delete_project_file(self, project=None):\\n if type(project) is not Project:\\n return False\\n\\n path = self.data_path + self.project_dir\\n\\n # generate filenames\\n filename = path + '/' + self.us(project.project_id()) + '.flproject'\\n\\n # check if the file exists and delete it\\n if os.path.isfile(filename):\\n os.remove(filename)\\n return True\\n else:\\n return False\",\n \"async def delete(self, ctx, project_name: str) -> None:\\n if not ctx.projects.find_project(project_name):\\n channel = discord.utils.get(\\n ctx.guild.channels, name=f\\\"{project_name}-project\\\")\\n\\n if channel and channel.category.name == \\\"Flux Projects\\\":\\n if ctx.author.permissions_in(channel).manage_channels:\\n message = await ctx.send(\\\"That project doesn't appear to\\\"\\n \\\" exist in my database, but the \\\"\\n \\\"channel still exists. \\\"\\n \\\"Would you like to delete it?\\\")\\n yes = \\\"<:greenTick:596576670815879169>\\\"\\n no = \\\"<:redTick:596576672149667840>\\\"\\n await message.add_reaction(yes)\\n await message.add_reaction(no)\\n reaction, user = await ctx.bot.wait_for(\\n \\\"reaction_add\\\",\\n check=lambda reaction, user: (user == ctx.author) and\\n (str(reaction.emoji) == yes or no) and\\n (reaction.message.channel == ctx.channel)\\n )\\n if reaction.emoji.id == ctx.bot.config.tick_yes:\\n await channel.delete(reason=\\\"Project not found.\\\")\\n await ctx.send(\\\"The channel was deleted sucessfully.\\\")\\n return\\n\\n elif reaction.emoji.id == ctx.bot.config.tick_no:\\n await ctx.send(\\\"Not deleting the channel.\\\")\\n return\\n\\n else: # If author doesn't have access to deleting channels.\\n await ctx.send(\\\"That project does not appear to be in my \\\"\\n \\\"database, but the channel for it still \\\"\\n \\\"exists. Please have someone with\\\"\\n \\\" manage channels run this chommand.\\\"\\n )\\n return\\n else:\\n await ctx.send(\\\"I could not find this project.\\\")\\n return\\n\\n if str(ctx.author.id) != ctx.projects.find_project(project_name).get(\\n \\\"owner\\\"):\\n await ctx.send(\\\"Only the project owner \\\"\\n \\\"can delete this project.\\\")\\n return\\n message = await ctx.send(\\\"This action __cannot__ be undone. \\\"\\n \\\"Once you do this, everything is gone. \\\"\\n \\\"Are you sure you want to continue?\\\")\\n yes = \\\"<:greenTick:596576670815879169>\\\"\\n no = \\\"<:redTick:596576672149667840>\\\"\\n await message.add_reaction(yes)\\n await message.add_reaction(no)\\n reaction, user = await ctx.bot.wait_for(\\n \\\"reaction_add\\\", check=lambda reaction, user:\\n (user == ctx.author) and\\n (str(reaction.emoji) == yes or no) and\\n (reaction.message.channel == ctx.channel)\\n )\\n if reaction.emoji.id == ctx.bot.config.tick_yes:\\n channel = ctx.projects.find_project(\\n project_name).get(\\\"channel\\\")\\n channel = discord.utils.get(ctx.guild.channels,\\n id=int(channel))\\n ctx.projects.delete_project(project_name)\\n if channel:\\n await channel.delete(reason=\\\"Project deleted.\\\")\\n await ctx.send(\\\"The project has been deleted.\\\")\\n elif reaction.emoji.id == ctx.bot.config.tick_no:\\n await ctx.send(\\\"Not deleting the project.\\\")\",\n \"def delete(self):\\n return self.client._perform_empty(\\n \\\"DELETE\\\", \\\"/project-folders/%s\\\" % self.project_folder_id)\",\n \"def delete(self):\\r\\n delete_tracks(self.project, [self])\",\n \"def project_post_delete(sender, instance, **kwargs):\\n instance.url.delete(False)\",\n \"def remove_project(project_id):\\n response_object = {'status': 'success'}\\n with database.engine.begin() as connection:\\n\\n stmt = select([models.projects.c.path]).where(\\n models.projects.c.project_id == project_id)\\n project = connection.execute(stmt).first()\\n\\n if project:\\n app = flask.current_app\\n project_path = os.path.join(\\n app.root_path, app.config['DATA_DIRECTORY'], project['path'])\\n if 'morphocut' in project_path and app.config['DATA_DIRECTORY'] in project_path:\\n print('removing project with id {}'.format(project_id))\\n if os.path.exists(project_path):\\n helpers.remove_directory(project_path)\\n\\n stmt = models.projects.delete().where( # pylint: disable=no-value-for-parameter\\n models.projects.c.project_id == project_id)\\n\\n connection.execute(stmt)\\n\\n return jsonify(response_object)\",\n \"def delete_project_by_name(self, project_name):\\n with self._transaction.cursor() as cur:\\n # delete associations between this project and any barcodes\\n cur.execute(\\\"DELETE FROM barcodes.project_barcode \\\"\\n \\\"WHERE project_id in (\\\"\\n \\\"SELECT project_id FROM barcodes.project \\\"\\n \\\"WHERE project = %s)\\\",\\n (project_name,))\\n\\n # now delete the project itself\\n cur.execute(\\\"DELETE FROM barcodes.project WHERE project = %s\\\",\\n (project_name,))\\n return cur.rowcount == 1\",\n \"def _delete_dm_project(self, project_uuid: str) -> None:\\n _LOGGER.warning('Deleting DM Project %s...', project_uuid)\\n\\n dm_rv: DmApiRv = DmApi.delete_project(self.__org_owner_dm_token,\\n project_id=project_uuid)\\n if not dm_rv.success:\\n _LOGGER.error('Failed to delete DM Project %s', project_uuid)\\n return\\n\\n _LOGGER.warning('Deleted DM Project %s', project_uuid)\",\n \"def delete(project, zone, instance):\\n print >>sys.stderr, 'WARNING: duplicated jobs may fail/corrupt results'\\n print >>sys.stderr, ('TODO(fejta): See http://stackoverflow.com/'\\n 'questions/19645430/changing-jenkins-build-number')\\n answer = raw_input('Delete %s [yes/NO]: ')\\n if not answer or answer != 'yes':\\n print >>sys.stderr, 'aborting'\\n sys.exit(1)\\n gcloud(\\n project,\\n 'compute',\\n 'instances',\\n 'delete',\\n '--zone=%s' % zone,\\n instance,\\n )\\n gcloud(\\n project,\\n 'compute',\\n 'disks',\\n 'delete',\\n '--zone=%s' % zone,\\n *get_disks(instance))\",\n \"def delete_orphan_project(apps, schema_editor):\\n Project = apps.get_model('data_manager', 'Project')\\n Project.objects.filter(dataset__isnull=True).delete()\\n return\",\n \"def delete(ctx: click.Context, repository_path):\\n root_commands.cmd_delete(ctx.obj, repository_path)\",\n \"def delete_project_quotas(self, project_id, extra_headers=None,\\n use_auth=True, user_name=None):\\n resp = self.client.delete('project-quotas/' + project_id,\\n extra_headers=extra_headers,\\n use_auth=use_auth, user_name=user_name)\\n return resp\",\n \"def DelProject(projname):\\n\\tif projname == \\\"\\\" or projname == None:\\n\\t\\tpjnm = raw_input(\\\"\\\\nNombre del proyecto: \\\").lower()\\n\\t\\tif pjnm == \\\"\\\" or pjnm == None:\\n\\t\\t\\tcancel()\\n\\telse:\\n\\t\\t# Proceso para borrar todo el proyecto\\n\\t\\tpass\\n\\n\\tpa = open(\\\"author_name.txt\\\", \\\"r\\\")\\t#Abre el archivo con el nombre del autor\\n\\tpa.read()\\n\\tpc = open(\\\"project_code.txt\\\", \\\"r\\\")\\t#Abre el archivo con el codigo de proyecto\\n\\tpc.read()\\n\\n\\tuserpa = raw_input(\\\"Ingrese el nombre del autor: \\\").lower()\\n\\tuserpc = raw_input(\\\"Ingrese el codigo del proyecto: \\\").lower()\\n\\n\\tif userpa == pa and userpc == pc:\\t#Se verifica que userpa(nombre del autor por el usuario) sea igual a pa(nombre original del autor) y lo mismo con el codigo del proyecto\\n\\t\\tprint \\\"Iniciando el Borrado del Proyecto...\\\"\\n\\t\\tpcommands.del_project()\\n\\t\\tprint \\\"El proyecto se ha borrado con exito!\\\"\\n\\telse:\\n\\t\\tprint \\\"El codigo del proyecto o el nombre del autor no es correcto.\\\"\\n\\t\\tcancel()\",\n \"def delete_all_projects():\\n client = RequestManager()\\n client.set_method(\\\"GET\\\")\\n client.set_endpoint(\\\"/projects\\\")\\n response = client.execute_request()\\n for project in response.json():\\n try:\\n ProjectHelper.delete_project(project[\\\"id\\\"])\\n except TypeError:\\n LOGGER.info(project)\",\n \"def delete(self, endpoint, params=None):\\n params = params or dict()\\n return self.request(verb=requests.delete, address=self.project_address + endpoint,\\n params=params)\",\n \"def delete(self):\\n return self.client._perform_empty(\\n \\\"DELETE\\\", \\\"/projects/%s/managedfolders/%s\\\" % (self.project_key, self.odb_id))\",\n \"def command_delete(self):\\n if self.gcp_env.project not in SUPPORTED_PROJECT_CONFIGS.keys():\\n _logger.error(f'Project config not supported {self.gcp_env.project}')\\n return 1\\n\\n if not self.args.bucket and not self.args.id:\\n _logger.error(\\\"--bucket and --id required for delete.\\\")\\n return 1\\n\\n # Get notification\\n client = storage.Client()\\n bucket = client.get_bucket(self.args.bucket)\\n target = bucket.get_notification(self.args.id, client)\\n\\n if self.gcp_env.project != target.topic_project:\\n _logger.error(\\\"Notification project and specified project do not match.\\\")\\n return 1\\n\\n # Delete the notification\\n try:\\n target.delete(client=client)\\n\\n except NotFound:\\n _logger.error(f\\\"Notification ID {self.args.id} not found.\\\")\\n return 1\\n\\n _logger.info(f\\\"Notification id {self.args.id} has been deleted.\\\")\\n\\n _logger.info(\\\"Removing notification from config...\\\")\\n self.delete_notification_from_config()\\n\\n return 0\",\n \"def delete(self, *args, **kwargs):\\n self.request(\\\"delete\\\", *args, **kwargs)\",\n \"def _delete(self, *args, **kwargs):\\n return self._request('delete', *args, **kwargs)\",\n \"def test_projects_id_contacts_delete(self):\\n project = Contact()\\n response = self.client.open('/project-tracker/projects/{id}/contacts'.format(id=56),\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def cmd_apps__destroy(args):\\n \\n if args.name is None and in_git_repo():\\n args.name = _get_current_project_name()\\n\\n if args.name is None:\\n print \\\"Please provide a project name.\\\"\\n sys.exit(1)\\n\\n print \\\"Destroying project %s...\\\" % args.name\\n remote.destroy_project(args.name)\\n print \\\"Project %s destroyed.\\\" % args.name\\n if in_git_repo() and _get_current_project_name() == args.name:\\n git(None, 'remote', 'rm', 'tinyserv')\\n print \\\"Removed remote '%s'.\\\" % args.name\",\n \"def delete():\",\n \"def delete(self, application_id):\",\n \"def _on_del_project(self):\\n project = self.ddnCurProject.get()\\n# if len(project) > 0:\\n if project:\\n if '.prj'!= project[-4:]:\\n project += '.prj'\\n if os.path.exists(self.BibTerm + '/'+ project):\\n os.remove(self.BibTerm + '/'+ project)\\n self.list_projects = [f.rstrip('.prj') \\\\\\n for f in os.listdir(self.BibTerm) \\\\\\n if f.endswith('.prj')]\\n self.ddnCurProject['values'] = self.list_projects\\n# if len(self.list_projects) > 0:\\n if self.list_projects:\\n self.ddnCurProject.set(self.list_projects[0])\\n else:\\n self.ddnCurProject.set('')\\n pass\",\n \"def delete(self, *args, **kw):\\n kw['method'] = 'DELETE'\\n return self.open(*args, **kw)\",\n \"def delete_language_from_project(self, project_id, language_code):\\n self._run(\\n url_path=\\\"languages/delete\\\",\\n id=project_id,\\n language=language_code\\n )\\n return True\",\n \"def record_destroy_for_project(project_id):\\n session = get_session()\\n with session.begin():\\n session.query(models.ProjectAccountRecord).\\\\\\n filter_by(project_id=project_id).\\\\\\n update({'deleted': True,\\n 'deleted_at': datetime.datetime.utcnow(),\\n 'updated_at': datetime.datetime.utcnow()})\",\n \"def remove_deleted_project(self, project_id):\\n self.db.make_query(\\n '''\\n DELETE FROM deleted_project WHERE project_id = \\\"{}\\\";\\n '''.format(project_id)\\n )\\n\\n if self.get_deleted_project(project_id):\\n return False\\n\\n return True\",\n \"def delete(self):\\n\\n headers = self._default_headers()\\n\\n return self._request(self.name,\\n ok_status=None,\\n data=None,\\n headers=headers,\\n method=\\\"DELETE\\\")\",\n \"def remove_project(self, project_id):\\n project_file_path = '{}/{}'.format(self._storage_location, project_id)\\n if os.path.exists(project_file_path):\\n os.remove(project_file_path)\\n else:\\n raise ValueError('The project id {} does not exist!'.format(project_id))\",\n \"def test_projects_id_comments_delete(self):\\n project = Comment()\\n response = self.client.open('/project-tracker/projects/{id}/comments'.format(id=56),\\n method='DELETE',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"async def delete_user_byid(*_):\\n return web.Response(text=\\\"PUT project not implemented\\\", status=501)\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def userproject_post_delete(sender, instance, **kwargs):\\n instance.document.delete(False)\",\n \"def delete(repo):\\n print('Repo: %s' % repo)\\n print('Deleted')\",\n \"def delete(project_id, integration_id):\\n IntegrationService.delete(project_id, integration_id)\\n\\n return {\\\"status\\\": \\\"deleted\\\"}, 200\",\n \"def delete():\\n run('rm -r {}'.format(utils.home('apps', env.PROJECT_NAME)))\",\n \"def delete_record(request, slug, pk):\\n # Try except to make sure the user is a member of this project\\n try:\\n ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\\n except ObjectDoesNotExist:\\n # User is not a member\\n return HttpResponse(\\\"You're trying to access a project you're not a member of or a project that does not exist.\\\")\\n else:\\n # User is a member\\n pm = ProjectMember.objects.get(user=request.user, project=Project.objects.get(slug=slug))\\n # Access control.. if not owner or editor - access denied.\\n if pm.is_owner or pm.is_editor:\\n # User has access\\n record = get_object_or_404(models.Record, pk=pk)\\n # Delete record\\n models.Record.objects.filter(project=get_object_or_404(models.Project, slug=slug), pk=pk).delete()\\n # Send user back to project detail, the overview of all records in the project.\\n return redirect('projects:single', slug=slug)\\n else:\\n # Access denied...\\n return HttpResponse(\\\"You don't have the permission to do this\\\")\",\n \"def delete(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"delete\\\"), kwargs)\",\n \"def delete(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"delete\\\"), kwargs)\",\n \"def delete_bucket_from_project(projectname, bucketname):\\n return jsonify(\\n admin.delete_bucket_on_project(\\n current_app.scoped_session(), projectname, bucketname\\n )\\n )\",\n \"def delete(self, *args, **kwargs) -> Any:\\n pass\",\n \"def delete_project_entities(cls, project_id,\\n suppress_exception=False,\\n session=None):\\n cls.db_repo.delete_project_entities(\\n project_id, suppress_exception=suppress_exception, session=session)\",\n \"def delete():\\n # Must be logged in to perform any delete commands.\\n auth_required()\\n pass\",\n \"def delete(self, api_path, *args, **kwargs):\\n\\n\\t\\treturn self._do_operation(u'delete', api_path, *args, **kwargs)\",\n \"def test_Projects_CanBeCreatedAndDeleted_Successfully(self):\\n\\n name = \\\"Test Project \\\"\\n # Test create a new project successfully\\n project = self.api.create_project(name, \\\"Py\\\")\\n self.assertTrue(project['success'])\\n self.assertTrue(project['projects'][0]['projectId'] > 0)\\n self.assertTrue(project['projects'][0]['name'] == name)\\n\\n # Delete the project\\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\\n self.assertTrue(deleteProject['success'])\\n\\n # Make sure the project is really deleted\\n projectList = self.api.list_projects()\\n self.assertFalse(any(project['projects'][0]['projectId'] == projectList['projects'][i]['projectId']\\n for i in range(len(projectList['projects']))))\",\n \"def delete(self, *args, **kwargs):\\n return self.handle_delete_request()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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redirecting to the LOGIN_URL"},"document":{"kind":"string","value":"def login_required_403(view):\n @wraps(view)\n def dec_view(request, *args, **kwargs):\n if not request.user.is_authenticated():\n return JsonResponse({\"detail\": \"You have to log in\"}, status=403)\n\n return view(request, *args, **kwargs)\n\n return dec_view"},"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 login_required(f):\n @functools.wraps(f)\n def wrap(*args, **kwargs):\n if not user_session.is_auth:\n raise Forbidden()\n return f(*args, **kwargs)\n return wrap","def not_authenticated(func):\n def decorated(request, *args, **kwargs):\n if request.user.is_authenticated():\n return HttpResponseRedirect(get_next_url(request))\n return func(request, *args, **kwargs)\n return decorated","def login_required(func):\n\t@wraps(func)\n\tdef decorated_view(*args, **kwargs):\n\t\tif not users.get_current_user():\n\t\t\treturn redirect(users.create_login_url(request.url))\n\t\treturn func(*args, **kwargs)\n\treturn decorated_view","def require_login(func):\n @wraps(func)\n def wrapper(*args, **kwrds):\n try:\n user = get_user()\n if user:\n setattr(g, 'user', user)\n return func(*args, **kwrds)\n else:\n url = url_for('auth.login', redir=request.url)\n return redirect(url)\n except:\n exc_type, exc_value, exc_traceback = sys.exc_info()\n log = app_logger()\n log.warning(\"Unexpected error: %s\", exc_value)\n log.error(''.join(traceback.format_exception(\n exc_type, exc_value, exc_traceback\n )))\n return abort(500)\n\n return wrapper","def not_logged_in(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if 'idToken' in session:\n return redirect(url_for('index'))\n else:\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if 'user_id' not in login_session:\n flash('User not allowed to access')\n return redirect('/login')\n return f(*args, **kwargs)\n return decorated_function","def login_required(func):\n @wraps(func)\n def decorated_view(*args, **kwargs):\n current = users.get_current_user()\n if not current:\n return redirect(users.create_login_url(request.url))\n elif current.email() == 'akshayubhat@gmail.com':\n return func(*args, **kwargs)\n else:\n return redirect(users.create_logout_url(request.url))\n return decorated_view","def login_required(func):\n @wraps(func)\n def f(*args, **kwargs):\n if g.user is None:\n app.logger.info('redirecting not logged in user')\n return redirect(url_for('index'))\n elif not g.user.initialized and f.__name__ not in ['profile_create','logout']:\n return redirect(url_for('profile_create'))\n else:\n return func(*args, **kwargs)\n return f","def user_login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if not current_user.is_authenticated:\n # Flash warning that user login is required\n flash(\"Logged in user only.\", category=\"error\")\n # Return redirect to login\n return redirect(url_for('main.login'))\n if current_user.banned:\n # Log user out so they can't access their account\n logout_user()\n # Flash warning that user has been banned\n flash(\"You have been banned, please contact an admin.\",\n category=\"error\")\n # Return redirect to index\n return redirect(url_for('main.index'))\n return f(*args, **kwargs)\n return decorated_function","def unauthorized_handler(self):\n return flask.redirect(\"/login\")","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(url_for(\"index\"))\n return f(*args, **kwargs)\n return decorated_function","def require_logged_in():\n def handler(f, *args, **kwargs):\n if args[0].current_user is not None:\n return f(*args, **kwargs)\n else:\n raise HTTPFound(args[0].route_url('user.login', _query={'redirect': encode_route(args[0])}))\n return decorator(handler)","def login_required(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n if g.user is None:\n flash('You have to log in first')\n return redirect(url_for('authentication.login', next=url_for(request.endpoint)))\n return func(*args, **kwargs)\n return wrapper","def admin_required(func):\n @wraps(func)\n def decorated_view(*args, **kwargs):\n if users.get_current_user():\n if not users.is_current_user_admin():\n abort(401) # Unauthorized\n return func(*args, **kwargs)\n return redirect(users.create_login_url(request.url))\n return decorated_view","def login_required(f):\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if g.USER is None:\n return redirect(\n url_for(\"home\", force_login=True, next=request.url)\n )\n\n return f(*args, **kwargs)\n\n return decorated_function","def login_required(func):\n @wraps(func)\n def decorated_view(*args, **kwargs):\n if current_app.login_manager._login_disabled:\n return func(*args, **kwargs)\n elif not current_user.is_authenticated and not current_user.is_active:\n return current_app.login_manager.unauthorized()\n return func(*args, **kwargs)\n\n return decorated_view","def login_required(func):\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n if 'username' not in login_session:\n return redirect('login')\n else:\n return func(*args, **kwargs)\n return wrapper","def anonymous_required(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n if g.user:\n flash('You have been already logged in')\n return redirect(url_for('view.index'))\n return func(*args, **kwargs)\n return wrapper","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n try:\n if session['user_id']:\n pass\n except KeyError:\n return redirect(url_for('users.login', next=request.url))\n return f(*args, **kwargs)\n return decorated_function","def login_required(function):\n\n @wraps(function)\n def wrapper(self, *args, **kw):\n \"\"\"Redirect to main if user doesn't logged in. \"\"\"\n\n if not self.user:\n return self.redirect('/blog/login')\n return function(self, *args, **kw)\n return wrapper","def login_required(func):\n @wraps(func)\n def wrapped(*args, **kwargs):\n if not session.get('logged_in', False):\n abort(401)\n return func(*args, **kwargs)\n return wrapped","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n # checks is user login\n if session.get(\"user_id\") is None:\n return redirect(url_for(\"login\", next=request.url))\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if 'username' in login_session:\n return f(*args, **kwargs)\n else:\n flash(\"You are not allowed to access there\")\n return redirect('/login')\n return decorated_function","def admin_required(func):\n\t@wraps(func)\n\tdef decorated_view(*args, **kwargs):\n\t\tif users.get_current_user():\n\t\t\tif not users.is_current_user_admin():\n\t\t\t\tabort(401) # Unauthorized\n\t\t\treturn func(*args, **kwargs)\n\t\treturn redirect(users.create_login_url(request.url))\n\treturn decorated_view","def decorated_function(*args, **kwargs):\n if not session.get('username'):\n flash('Login to continue', 'warning')\n return redirect(url_for('sign_in', next=request.url))\n return func(*args, **kwargs)","def require_user(func):\n\n @wraps(func)\n def decorator(*args, **kwargs):\n if not g.user:\n # flash('此操作需要登录账户')\n return render_template('account/error.html', error='抱歉，您无权进行该操作！')\n # return redirect(url_for('site.login'))\n return func(*args, **kwargs)\n\n return decorator","def login_required(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n if 'username' in session:\n return func(*args, **kwargs)\n else:\n flash(\"You must be logged in to access that page.\", 'danger')\n return redirect(url_for('login'))\n return wrapper","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\r\n @wraps(f)\r\n def wrap(*args, **kwargs):\r\n if \"logged_in\" in session:\r\n return f(*args, **kwargs)\r\n return redirect(\"/user/login\")\r\n return wrap","def login_required(func):\n @wraps(func)\n def decorated_function(*args, **kwargs):\n \"\"\" Modified descriprition of the decorated function \"\"\"\n if not session.get('username'):\n flash('Login to continue', 'warning')\n return redirect(url_for('sign_in', next=request.url))\n return func(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if 'username' not in login_session:\n flash('You need to be logged in to access here', 'alert-info')\n return redirect(url_for('showLogin'))\n else:\n return f(*args, **kwargs)\n return decorated_function","def account_login_required(fn):\n @wraps(fn)\n def _wrapper(request, *args, **kwargs):\n if request.user is None:\n return HTTPFound(request.route_url(\"login\"))\n\n return fn(request, *args, **kwargs)\n\n return _wrapper","def login_required(func):\n @wraps(func)\n def login(*args, **kwargs):\n # Redirect to login if user not logged in, else execute func.\n if 'username' not in login_session:\n return redirect('/login')\n return func(*args, **kwargs)\n return login","def api_login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return abort(401)\n\n return view(**kwargs)\n\n return wrapped_view","def login_required(f):\r\n @wraps(f)\r\n def decorated_function(*args, **kwargs):\r\n if session.get(\"user_id\") is None:\r\n flash(\"You must log in to view that page.\")\r\n return redirect(url_for(\"login\"))\r\n return f(*args, **kwargs)\r\n return decorated_function","def require_login(f):\n\n @wraps(f)\n def wrapper(*args, **kwds):\n if not api.user.is_logged_in():\n raise PicoException(\"You must be logged in\", 401)\n return f(*args, **kwds)\n\n return wrapper","def login_required(f):\n @wraps(f)\n def login_decorator(*args, **kwargs):\n if not session.get('logged_in'):\n abort(401)\n else:\n return f(*args, **kwargs)\n return login_decorator","def login_required(func):\n @wraps(func)\n def check_login(*args, **kwargs):\n # Redirect to login if user not logged in, else execute func.\n if 'username' not in login_session:\n return redirect('/login')\n else:\n return func(*args, **kwargs)\n return check_login","def login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return redirect(url_for(\"login\"))\n\n return view(**kwargs)\n\n return wrapped_view","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/sign_in\")\n return f(*args, **kwargs)\n return decorated_function","def check_login_required(views_func):\n @wraps(views_func)\n def wrapper(request, *args, **kwargs):\n if request.user.is_authenticated:\n return views_func(request, *args, **kwargs)\n else:\n return HttpResponse(status=401)\n return wrapper","def user_required(handler):\n def check_login(self, *args, **kwargs):\n auth = self.auth\n if not auth.get_user_by_session():\n # If handler has no login_url specified invoke a 403 error\n try:\n self.redirect(self.auth_config['login_url'], abort=True)\n except (AttributeError, KeyError), e:\n self.abort(403)\n else:\n return handler(self, *args, **kwargs)\n\n return check_login","def login_required(func):\n @functools.wraps(func)\n @private_cache_headers\n def is_user_logged_in(*args, **kwargs):\n if not authentication.is_authenticated(flask.session):\n return flask.redirect('login?next=' + flask.request.path)\n\n return func(*args, **kwargs)\n return is_user_logged_in","def login_required(f):\r\n @wraps(f)\r\n def decorated_function(*args, **kwargs):\r\n if session.get(\"user_id\") is None:\r\n return redirect(\"/login\")\r\n return f(*args, **kwargs)\r\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"username\") is None:\n return redirect(\"/signin\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def loginrequired(f):\n def securedf(request, *args, **kwargs):\n if request.user.is_authenticated():\n return f(request, *args, **kwargs)\n else:\n return HttpResponseRedirect(ADMINURL)\n return securedf","def login_required(view):\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return redirect(url_for(\"handlers.login\"))\n\n return view(**kwargs)\n\n return wrapped_view","def login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return redirect(url_for(\"auth.login\"))\n\n return view(**kwargs)\n\n return wrapped_view","def login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return redirect(url_for(\"auth.login\"))\n\n return view(**kwargs)\n\n return wrapped_view","def login_required(f):\n\n @wraps(f)\n def wrapper(*args, **kwargs):\n if \"user_id\" in session:\n return f(*args, **kwargs)\n else:\n flash(\"Please login to access this page.\")\n return redirect('/login')\n\n return wrapper","def auth_required(func):\n @wraps(func)\n def wrapper(request):\n if not request.user:\n return web.json_response({'status': 'error', 'message': 'auth required'}, status=401)\n return func(request)\n return wrapper","def login_required(f):\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"user_id\") is None:\n return redirect(\"/login\")\n return f(*args, **kwargs)\n return decorated_function","def user_required(handler):\n\n def check_login(self, *args, **kwargs):\n auth = self.auth\n if not auth.get_user_by_session():\n # If handler has no login_url specified invoke a 403 error\n try:\n self.redirect(self.auth_config['login_url'], abort=True)\n except (AttributeError, KeyError), e:\n self.abort(403)\n else:\n return handler(self, *args, **kwargs)\n\n return check_login","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if 'user' not in session:\n if len(get_flashed_messages()) == 0:\n flash(\"Please sign in to view this page\", \"error\")\n return redirect(url_for('sign_in', next=request.url))\n else:\n flash(\"Sign Out Successful\", \"success\")\n return redirect(url_for('index'))\n return f(*args, **kwargs)\n return decorated_function","def login_required_for_token(f):\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if g.USER is None:\n return redirect(url_for(\"api_v1_login\", next=request.url))\n return f(*args, **kwargs)\n\n return decorated_function","def unauthorized_only(view_func):\n def is_anonymous(user):\n return user.is_anonymous()\n\n return user_passes_test(is_anonymous, login_url='/', redirect_field_name=None)(view_func)","def authenticated(decoratee):\n\n @wraps(decoratee)\n def wrapper(*args, **kwargs):\n if \"username\" in session.keys() and is_user(session[\"username\"]) and is_enabled(session[\"username\"]):\n return decoratee(*args, **kwargs)\n else:\n session[\"last_error\"] = \"You need to be logged in to view this page.\"\n return redirect(url_for(\"error\"))\n\n return wrapper","def login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return redirect(url_for('auth.login'))\n\n return view(**kwargs)\n\n return wrapped_view","def validate_login_status(func):\n def wrapped_f(request, *args, **kwargs):\n if request.session.get(\"Uid\") is None:\n return HttpResponseRedirect('/')\n return func(request,*args, **kwargs)\n return wrapped_f","def requires_entrepreneur(func):\n def decorator(request, *args, **kwargs):\n if request.user.is_authenticated() and not request.user.is_entrepreneur():\n return redirect('dashboard')\n else:\n return func(request, *args, **kwargs)\n return decorator","def check_logged_in(function):\n @wraps(function)\n def wrapper(*args, **kwargs):\n if 'username' in login_session:\n return function(*args, **kwargs)\n else:\n return redirect(url_for('category_list_handler'))\n return wrapper","def require_admin(func):\n\n @wraps(func)\n def decorator(*args, **kwargs):\n if not g.user:\n # flash('此操作需要登录账户')\n return redirect(url_for('admin.login'))\n if g.user.name != 'admin':\n abort(403)\n return func(*args, **kwargs)\n\n return decorator","def deny_access():\n flash('You must login first.')\n return redirect(url_for('home'))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def anonymous_required(func):\n\n async def wrapped(self, *args, **kwargs):\n if self.request.user is not None:\n print(\"Login please.\")\n # redirect(self.request, 'index')\n\n return await func(self, *args, **kwargs)\n\n return wrapped","def api_login_required(func):\n\n @wraps(func)\n def decorated_view(*args, **kwargs):\n \"\"\"decorator\"\"\"\n if request.method in EXEMPT_METHODS: # pragma: no cover\n return func(*args, **kwargs)\n # 'func' is a Flask.view.MethodView so we have access to some special\n # params\n cls = func.view_class\n login_required = getattr(cls, \"login_required\", True)\n if (\n bui.auth != \"none\"\n and login_required\n and not bui.config.get(\"LOGIN_DISABLED\", False)\n ):\n if not current_user.is_authenticated:\n if request.headers.get(\"X-From-UI\", False):\n abort(403)\n return Response(\n \"Could not verify your access level for that URL.\\n\"\n \"You have to login with proper credentials\",\n 401,\n {\"WWW-Authenticate\": 'Basic realm=\"Login Required\"'},\n )\n return func(*args, **kwargs)\n\n return decorated_view","def requires_company_user(func):\n def decorator(request, *args, **kwargs):\n if request.user.is_authenticated() and not request.user.is_company_user():\n return redirect('dashboard')\n else:\n return func(request, *args, **kwargs)\n return decorator","def require_visitor(func):\n\n @wraps(func)\n def decorator(*args, **kwargs):\n if g.user:\n return redirect(url_for('site.home'))\n return func(*args, **kwargs)\n\n return decorator","def login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if session.get(\"email\") is None:\n return redirect(url_for(\"login\"))\n return f(*args, **kwargs)\n return decorated_function","def require_login_or_401(function):\n def wrap(request, *args, **kwargs):\n if request.user.is_anonymous:\n return Response({\"detail\": \"Must be logged in.\"}, status=401)\n return function(request, *args, **kwargs)\n return wrap","def require_admin(handler_method):\n def Decorate(self):\n if not users.is_current_user_admin():\n self.error(401)\n html = '<html><body><a href=\"%s\">Sign in</a></body></html>'\n self.response.out.write(html % (users.create_login_url(self.request.url)))\n return\n return handler_method(self)\n return Decorate","def anonymous_required(func):\n\n async def wrapped(self, *args, **kwargs):\n if self.request.user is not None:\n add_message(self.request, \"Please log-out to continue.\")\n redirect(self.request, \"index\")\n return await func(self, *args, **kwargs)\n\n return wrapped","def handle_forbidden_for_homepage(self, request):\n\n login_url = request.link(Auth.from_request_path(request), name='login')\n\n if URL(request.url).path() == '/':\n return morepath.redirect(login_url)\n\n return handle_forbidden(self, request)","def login_required(view_func):\n @wraps(view_func)\n def _checklogin(request, *args, **kwargs):\n if request.user.is_active:\n # The user is valid. Continue to the admin page.\n return view_func(request, *args, **kwargs)\n return site.login(request)\n return _checklogin","def login_required(func):\n def wrapper(self, request, *args, **kwargs):\n if not request.user.is_authenticated():\n raise ApiLoginRequired\n return func(self, request, *args, **kwargs)\n\n wrapper.requires_login = True\n return wraps(func, wrapper)","def login_view(func):\n @wraps(func)\n def decorator(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except MultipassException as e:\n return get_state().multipass.handle_auth_error(e, True)\n\n return decorator","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))"],"string":"[\n \"def login_required(f):\\n @functools.wraps(f)\\n def wrap(*args, **kwargs):\\n if not user_session.is_auth:\\n raise Forbidden()\\n return f(*args, **kwargs)\\n return wrap\",\n \"def not_authenticated(func):\\n def decorated(request, *args, **kwargs):\\n if request.user.is_authenticated():\\n return HttpResponseRedirect(get_next_url(request))\\n return func(request, *args, **kwargs)\\n return decorated\",\n \"def login_required(func):\\n\\t@wraps(func)\\n\\tdef decorated_view(*args, **kwargs):\\n\\t\\tif not users.get_current_user():\\n\\t\\t\\treturn redirect(users.create_login_url(request.url))\\n\\t\\treturn func(*args, **kwargs)\\n\\treturn decorated_view\",\n \"def require_login(func):\\n @wraps(func)\\n def wrapper(*args, **kwrds):\\n try:\\n user = get_user()\\n if user:\\n setattr(g, 'user', user)\\n return func(*args, **kwrds)\\n else:\\n url = url_for('auth.login', redir=request.url)\\n return redirect(url)\\n except:\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n log = app_logger()\\n log.warning(\\\"Unexpected error: %s\\\", exc_value)\\n log.error(''.join(traceback.format_exception(\\n exc_type, exc_value, exc_traceback\\n )))\\n return abort(500)\\n\\n return wrapper\",\n \"def not_logged_in(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if 'idToken' in session:\\n return redirect(url_for('index'))\\n else:\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if 'user_id' not in login_session:\\n flash('User not allowed to access')\\n return redirect('/login')\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(func):\\n @wraps(func)\\n def decorated_view(*args, **kwargs):\\n current = users.get_current_user()\\n if not current:\\n return redirect(users.create_login_url(request.url))\\n elif current.email() == 'akshayubhat@gmail.com':\\n return func(*args, **kwargs)\\n else:\\n return redirect(users.create_logout_url(request.url))\\n return decorated_view\",\n \"def login_required(func):\\n @wraps(func)\\n def f(*args, **kwargs):\\n if g.user is None:\\n app.logger.info('redirecting not logged in user')\\n return redirect(url_for('index'))\\n elif not g.user.initialized and f.__name__ not in ['profile_create','logout']:\\n return redirect(url_for('profile_create'))\\n else:\\n return func(*args, **kwargs)\\n return f\",\n \"def user_login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if not current_user.is_authenticated:\\n # Flash warning that user login is required\\n flash(\\\"Logged in user only.\\\", category=\\\"error\\\")\\n # Return redirect to login\\n return redirect(url_for('main.login'))\\n if current_user.banned:\\n # Log user out so they can't access their account\\n logout_user()\\n # Flash warning that user has been banned\\n flash(\\\"You have been banned, please contact an admin.\\\",\\n category=\\\"error\\\")\\n # Return redirect to index\\n return redirect(url_for('main.index'))\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def unauthorized_handler(self):\\n return flask.redirect(\\\"/login\\\")\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(url_for(\\\"index\\\"))\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def require_logged_in():\\n def handler(f, *args, **kwargs):\\n if args[0].current_user is not None:\\n return f(*args, **kwargs)\\n else:\\n raise HTTPFound(args[0].route_url('user.login', _query={'redirect': encode_route(args[0])}))\\n return decorator(handler)\",\n \"def login_required(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n if g.user is None:\\n flash('You have to log in first')\\n return redirect(url_for('authentication.login', next=url_for(request.endpoint)))\\n return func(*args, **kwargs)\\n return wrapper\",\n \"def admin_required(func):\\n @wraps(func)\\n def decorated_view(*args, **kwargs):\\n if users.get_current_user():\\n if not users.is_current_user_admin():\\n abort(401) # Unauthorized\\n return func(*args, **kwargs)\\n return redirect(users.create_login_url(request.url))\\n return decorated_view\",\n \"def login_required(f):\\n\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if g.USER is None:\\n return redirect(\\n url_for(\\\"home\\\", force_login=True, next=request.url)\\n )\\n\\n return f(*args, **kwargs)\\n\\n return decorated_function\",\n \"def login_required(func):\\n @wraps(func)\\n def decorated_view(*args, **kwargs):\\n if current_app.login_manager._login_disabled:\\n return func(*args, **kwargs)\\n elif not current_user.is_authenticated and not current_user.is_active:\\n return current_app.login_manager.unauthorized()\\n return func(*args, **kwargs)\\n\\n return decorated_view\",\n \"def login_required(func):\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n if 'username' not in login_session:\\n return redirect('login')\\n else:\\n return func(*args, **kwargs)\\n return wrapper\",\n \"def anonymous_required(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n if g.user:\\n flash('You have been already logged in')\\n return redirect(url_for('view.index'))\\n return func(*args, **kwargs)\\n return wrapper\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n try:\\n if session['user_id']:\\n pass\\n except KeyError:\\n return redirect(url_for('users.login', next=request.url))\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(function):\\n\\n @wraps(function)\\n def wrapper(self, *args, **kw):\\n \\\"\\\"\\\"Redirect to main if user doesn't logged in. \\\"\\\"\\\"\\n\\n if not self.user:\\n return self.redirect('/blog/login')\\n return function(self, *args, **kw)\\n return wrapper\",\n \"def login_required(func):\\n @wraps(func)\\n def wrapped(*args, **kwargs):\\n if not session.get('logged_in', False):\\n abort(401)\\n return func(*args, **kwargs)\\n return wrapped\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n # checks is user login\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(url_for(\\\"login\\\", next=request.url))\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if 'username' in login_session:\\n return f(*args, **kwargs)\\n else:\\n flash(\\\"You are not allowed to access there\\\")\\n return redirect('/login')\\n return decorated_function\",\n \"def admin_required(func):\\n\\t@wraps(func)\\n\\tdef decorated_view(*args, **kwargs):\\n\\t\\tif users.get_current_user():\\n\\t\\t\\tif not users.is_current_user_admin():\\n\\t\\t\\t\\tabort(401) # Unauthorized\\n\\t\\t\\treturn func(*args, **kwargs)\\n\\t\\treturn redirect(users.create_login_url(request.url))\\n\\treturn decorated_view\",\n \"def decorated_function(*args, **kwargs):\\n if not session.get('username'):\\n flash('Login to continue', 'warning')\\n return redirect(url_for('sign_in', next=request.url))\\n return func(*args, **kwargs)\",\n \"def require_user(func):\\n\\n @wraps(func)\\n def decorator(*args, **kwargs):\\n if not g.user:\\n # flash('此操作需要登录账户')\\n return render_template('account/error.html', error='抱歉，您无权进行该操作！')\\n # return redirect(url_for('site.login'))\\n return func(*args, **kwargs)\\n\\n return decorator\",\n \"def login_required(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n if 'username' in session:\\n return func(*args, **kwargs)\\n else:\\n flash(\\\"You must be logged in to access that page.\\\", 'danger')\\n return redirect(url_for('login'))\\n return wrapper\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\r\\n @wraps(f)\\r\\n def wrap(*args, **kwargs):\\r\\n if \\\"logged_in\\\" in session:\\r\\n return f(*args, **kwargs)\\r\\n return redirect(\\\"/user/login\\\")\\r\\n return wrap\",\n \"def login_required(func):\\n @wraps(func)\\n def decorated_function(*args, **kwargs):\\n \\\"\\\"\\\" Modified descriprition of the decorated function \\\"\\\"\\\"\\n if not session.get('username'):\\n flash('Login to continue', 'warning')\\n return redirect(url_for('sign_in', next=request.url))\\n return func(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if 'username' not in login_session:\\n flash('You need to be logged in to access here', 'alert-info')\\n return redirect(url_for('showLogin'))\\n else:\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def account_login_required(fn):\\n @wraps(fn)\\n def _wrapper(request, *args, **kwargs):\\n if request.user is None:\\n return HTTPFound(request.route_url(\\\"login\\\"))\\n\\n return fn(request, *args, **kwargs)\\n\\n return _wrapper\",\n \"def login_required(func):\\n @wraps(func)\\n def login(*args, **kwargs):\\n # Redirect to login if user not logged in, else execute func.\\n if 'username' not in login_session:\\n return redirect('/login')\\n return func(*args, **kwargs)\\n return login\",\n \"def api_login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return abort(401)\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def login_required(f):\\r\\n @wraps(f)\\r\\n def decorated_function(*args, **kwargs):\\r\\n if session.get(\\\"user_id\\\") is None:\\r\\n flash(\\\"You must log in to view that page.\\\")\\r\\n return redirect(url_for(\\\"login\\\"))\\r\\n return f(*args, **kwargs)\\r\\n return decorated_function\",\n \"def require_login(f):\\n\\n @wraps(f)\\n def wrapper(*args, **kwds):\\n if not api.user.is_logged_in():\\n raise PicoException(\\\"You must be logged in\\\", 401)\\n return f(*args, **kwds)\\n\\n return wrapper\",\n \"def login_required(f):\\n @wraps(f)\\n def login_decorator(*args, **kwargs):\\n if not session.get('logged_in'):\\n abort(401)\\n else:\\n return f(*args, **kwargs)\\n return login_decorator\",\n \"def login_required(func):\\n @wraps(func)\\n def check_login(*args, **kwargs):\\n # Redirect to login if user not logged in, else execute func.\\n if 'username' not in login_session:\\n return redirect('/login')\\n else:\\n return func(*args, **kwargs)\\n return check_login\",\n \"def login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return redirect(url_for(\\\"login\\\"))\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/sign_in\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def check_login_required(views_func):\\n @wraps(views_func)\\n def wrapper(request, *args, **kwargs):\\n if request.user.is_authenticated:\\n return views_func(request, *args, **kwargs)\\n else:\\n return HttpResponse(status=401)\\n return wrapper\",\n \"def user_required(handler):\\n def check_login(self, *args, **kwargs):\\n auth = self.auth\\n if not auth.get_user_by_session():\\n # If handler has no login_url specified invoke a 403 error\\n try:\\n self.redirect(self.auth_config['login_url'], abort=True)\\n except (AttributeError, KeyError), e:\\n self.abort(403)\\n else:\\n return handler(self, *args, **kwargs)\\n\\n return check_login\",\n \"def login_required(func):\\n @functools.wraps(func)\\n @private_cache_headers\\n def is_user_logged_in(*args, **kwargs):\\n if not authentication.is_authenticated(flask.session):\\n return flask.redirect('login?next=' + flask.request.path)\\n\\n return func(*args, **kwargs)\\n return is_user_logged_in\",\n \"def login_required(f):\\r\\n @wraps(f)\\r\\n def decorated_function(*args, **kwargs):\\r\\n if session.get(\\\"user_id\\\") is None:\\r\\n return redirect(\\\"/login\\\")\\r\\n return f(*args, **kwargs)\\r\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"username\\\") is None:\\n return redirect(\\\"/signin\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def loginrequired(f):\\n def securedf(request, *args, **kwargs):\\n if request.user.is_authenticated():\\n return f(request, *args, **kwargs)\\n else:\\n return HttpResponseRedirect(ADMINURL)\\n return securedf\",\n \"def login_required(view):\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return redirect(url_for(\\\"handlers.login\\\"))\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return redirect(url_for(\\\"auth.login\\\"))\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return redirect(url_for(\\\"auth.login\\\"))\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def login_required(f):\\n\\n @wraps(f)\\n def wrapper(*args, **kwargs):\\n if \\\"user_id\\\" in session:\\n return f(*args, **kwargs)\\n else:\\n flash(\\\"Please login to access this page.\\\")\\n return redirect('/login')\\n\\n return wrapper\",\n \"def auth_required(func):\\n @wraps(func)\\n def wrapper(request):\\n if not request.user:\\n return web.json_response({'status': 'error', 'message': 'auth required'}, status=401)\\n return func(request)\\n return wrapper\",\n \"def login_required(f):\\n\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"user_id\\\") is None:\\n return redirect(\\\"/login\\\")\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def user_required(handler):\\n\\n def check_login(self, *args, **kwargs):\\n auth = self.auth\\n if not auth.get_user_by_session():\\n # If handler has no login_url specified invoke a 403 error\\n try:\\n self.redirect(self.auth_config['login_url'], abort=True)\\n except (AttributeError, KeyError), e:\\n self.abort(403)\\n else:\\n return handler(self, *args, **kwargs)\\n\\n return check_login\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if 'user' not in session:\\n if len(get_flashed_messages()) == 0:\\n flash(\\\"Please sign in to view this page\\\", \\\"error\\\")\\n return redirect(url_for('sign_in', next=request.url))\\n else:\\n flash(\\\"Sign Out Successful\\\", \\\"success\\\")\\n return redirect(url_for('index'))\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def login_required_for_token(f):\\n\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if g.USER is None:\\n return redirect(url_for(\\\"api_v1_login\\\", next=request.url))\\n return f(*args, **kwargs)\\n\\n return decorated_function\",\n \"def unauthorized_only(view_func):\\n def is_anonymous(user):\\n return user.is_anonymous()\\n\\n return user_passes_test(is_anonymous, login_url='/', redirect_field_name=None)(view_func)\",\n \"def authenticated(decoratee):\\n\\n @wraps(decoratee)\\n def wrapper(*args, **kwargs):\\n if \\\"username\\\" in session.keys() and is_user(session[\\\"username\\\"]) and is_enabled(session[\\\"username\\\"]):\\n return decoratee(*args, **kwargs)\\n else:\\n session[\\\"last_error\\\"] = \\\"You need to be logged in to view this page.\\\"\\n return redirect(url_for(\\\"error\\\"))\\n\\n return wrapper\",\n \"def login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return redirect(url_for('auth.login'))\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def validate_login_status(func):\\n def wrapped_f(request, *args, **kwargs):\\n if request.session.get(\\\"Uid\\\") is None:\\n return HttpResponseRedirect('/')\\n return func(request,*args, **kwargs)\\n return wrapped_f\",\n \"def requires_entrepreneur(func):\\n def decorator(request, *args, **kwargs):\\n if request.user.is_authenticated() and not request.user.is_entrepreneur():\\n return redirect('dashboard')\\n else:\\n return func(request, *args, **kwargs)\\n return decorator\",\n \"def check_logged_in(function):\\n @wraps(function)\\n def wrapper(*args, **kwargs):\\n if 'username' in login_session:\\n return function(*args, **kwargs)\\n else:\\n return redirect(url_for('category_list_handler'))\\n return wrapper\",\n \"def require_admin(func):\\n\\n @wraps(func)\\n def decorator(*args, **kwargs):\\n if not g.user:\\n # flash('此操作需要登录账户')\\n return redirect(url_for('admin.login'))\\n if g.user.name != 'admin':\\n abort(403)\\n return func(*args, **kwargs)\\n\\n return decorator\",\n \"def deny_access():\\n flash('You must login first.')\\n return redirect(url_for('home'))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def anonymous_required(func):\\n\\n async def wrapped(self, *args, **kwargs):\\n if self.request.user is not None:\\n print(\\\"Login please.\\\")\\n # redirect(self.request, 'index')\\n\\n return await func(self, *args, **kwargs)\\n\\n return wrapped\",\n \"def api_login_required(func):\\n\\n @wraps(func)\\n def decorated_view(*args, **kwargs):\\n \\\"\\\"\\\"decorator\\\"\\\"\\\"\\n if request.method in EXEMPT_METHODS: # pragma: no cover\\n return func(*args, **kwargs)\\n # 'func' is a Flask.view.MethodView so we have access to some special\\n # params\\n cls = func.view_class\\n login_required = getattr(cls, \\\"login_required\\\", True)\\n if (\\n bui.auth != \\\"none\\\"\\n and login_required\\n and not bui.config.get(\\\"LOGIN_DISABLED\\\", False)\\n ):\\n if not current_user.is_authenticated:\\n if request.headers.get(\\\"X-From-UI\\\", False):\\n abort(403)\\n return Response(\\n \\\"Could not verify your access level for that URL.\\\\n\\\"\\n \\\"You have to login with proper credentials\\\",\\n 401,\\n {\\\"WWW-Authenticate\\\": 'Basic realm=\\\"Login Required\\\"'},\\n )\\n return func(*args, **kwargs)\\n\\n return decorated_view\",\n \"def requires_company_user(func):\\n def decorator(request, *args, **kwargs):\\n if request.user.is_authenticated() and not request.user.is_company_user():\\n return redirect('dashboard')\\n else:\\n return func(request, *args, **kwargs)\\n return decorator\",\n \"def require_visitor(func):\\n\\n @wraps(func)\\n def decorator(*args, **kwargs):\\n if g.user:\\n return redirect(url_for('site.home'))\\n return func(*args, **kwargs)\\n\\n return decorator\",\n \"def login_required(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if session.get(\\\"email\\\") is None:\\n return redirect(url_for(\\\"login\\\"))\\n return f(*args, **kwargs)\\n return decorated_function\",\n \"def require_login_or_401(function):\\n def wrap(request, *args, **kwargs):\\n if request.user.is_anonymous:\\n return Response({\\\"detail\\\": \\\"Must be logged in.\\\"}, status=401)\\n return function(request, *args, **kwargs)\\n return wrap\",\n \"def require_admin(handler_method):\\n def Decorate(self):\\n if not users.is_current_user_admin():\\n self.error(401)\\n html = '<html><body><a href=\\\"%s\\\">Sign in</a></body></html>'\\n self.response.out.write(html % (users.create_login_url(self.request.url)))\\n return\\n return handler_method(self)\\n return Decorate\",\n \"def anonymous_required(func):\\n\\n async def wrapped(self, *args, **kwargs):\\n if self.request.user is not None:\\n add_message(self.request, \\\"Please log-out to continue.\\\")\\n redirect(self.request, \\\"index\\\")\\n return await func(self, *args, **kwargs)\\n\\n return wrapped\",\n \"def handle_forbidden_for_homepage(self, request):\\n\\n login_url = request.link(Auth.from_request_path(request), name='login')\\n\\n if URL(request.url).path() == '/':\\n return morepath.redirect(login_url)\\n\\n return handle_forbidden(self, request)\",\n \"def login_required(view_func):\\n @wraps(view_func)\\n def _checklogin(request, *args, **kwargs):\\n if request.user.is_active:\\n # The user is valid. Continue to the admin page.\\n return view_func(request, *args, **kwargs)\\n return site.login(request)\\n return _checklogin\",\n \"def login_required(func):\\n def wrapper(self, request, *args, **kwargs):\\n if not request.user.is_authenticated():\\n raise ApiLoginRequired\\n return func(self, request, *args, **kwargs)\\n\\n wrapper.requires_login = True\\n return wraps(func, wrapper)\",\n \"def login_view(func):\\n @wraps(func)\\n def decorator(*args, **kwargs):\\n try:\\n return func(*args, **kwargs)\\n except MultipassException as e:\\n return get_state().multipass.handle_auth_error(e, True)\\n\\n return decorator\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', 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not"},"document":{"kind":"string","value":"def start_view(request):\n\n if request.user and Employee.objects.filter(user__pk=request.user.pk).exists():\n if Employee.objects.get(user__pk=request.user.pk).is_manager:\n return HttpResponseRedirect('/dashboard')\n else:\n return HttpResponseRedirect('/employee/show/%d/' % request.user.employee_user.first().pk)\n else:\n return HttpResponseRedirect('/login/')"},"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 login(self):\n identity = request.environ.get('repoze.who.identity')\n came_from = str(request.GET.get('came_from', '')) or \\\n url('/')\n if identity:\n redirect(url(came_from))\n else:\n c.came_from = came_from\n c.login_counter = request.environ['repoze.who.logins'] + 1\n return render('/forms/login.mako')","def ShowLogin():\n current_user = helpers.get_current_user()\n if current_user is None:\n return render_template('login.html')\n else:\n return redirect('/')","def handleLogin(self):\n aVar = self.session.getAttribute(self.settings.authenvar)\n self.loggedin = False\n if not aVar:\n self.currenttemplate = self.settings.logintemplate \n self.logger.debug(\"Not logged in, Login-Mask activated.\")\n return\n\n self.loggedin = True\n self.logger.debug('Loged in as: \"{}\"'.format(aVar))","def GET_login(self):\r\n\r\n # dest is the location to redirect to upon completion\r\n dest = request.get.get('dest','') or request.referer or '/'\r\n return LoginPage(dest = dest).render()","def defaultlanding():\n #send user to description page if not logged in\n if not g.user:\n return redirect(url_for('description'))\n #display leaderboard for competition if logged in\n return redirect(url_for('leaderboard'))","def index(self):\n\n # try and pull the user's data\n user = get_active_user_data()\n\n if not user:\n # they are not logged in give them the login form\n return render('/login_form.html')\n\n # they are logged in, pass them to the home page\n redirect('/')","def login_menu(self):\n print(\"\\nPlease enter your email and password\")\n email = self.validate_email()\n password = self.validate_password()\n self.authenticate_user(email, password)","def login():","def login():","def login():\n login_page = Login()\n login_page.login_main_page()","def goto_login(self):\n self.driver.find_element(*BasePageLocators.MY_ACCOUNT_DROPDOWN).click()\n self.driver.find_element(*BasePageLocators.GO_LOGIN).click()\n return LoginPage(self.driver)","def login(self):\n\t\treturn","def post_login(self, came_from=lurl('/')):\n if not request.identity:\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\n redirect('/login', params=dict(came_from=came_from, __logins=login_counter))\n return\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n redirect(came_from)","def loginView(request):\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n dologin(request, user)\n if isOperator(user): # login as an operator\n return redirect('/operator/map')\n elif isAdmin(user): # login as an admin\n return redirect('/admin/map')\n return HttpResponse('ok')\n else:\n # Return a 'disabled account' error message\n return HttpResponse(\"Disabled account\")\n else:\n # Return an 'invalid login' error message.\n return HttpResponse(\"Invalid login\")","def log_in(self):\n\t\tpass","def post_login(self, came_from='/'):\n if not request.identity:\n login_counter = request.environ['repoze.who.logins'] + 1\n redirect('/login', came_from=came_from, __logins=login_counter)\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n redirect(came_from)","def login(self):\n logging.debug(\"login called\")\n\n # Apply settings\n self.localisationsettings.apply_to_upcoming_session()\n self.admin_setting.apply_to_upcoming_session()\n self.macspoof_setting.apply_to_upcoming_session()\n self.network_setting.apply_to_upcoming_session()\n\n self.mainwindow.hide()\n self.gdmclient.do_login()","def view_login(self):\n with self.client.get(\"/login\", catch_response=True) as response:\n for r_hist in response.history:\n if r_hist.status_code > 200 and r_hist.status_code < 400:\n response.failure(\"Logged on: Got redirect to /home\")","def login(self):","def login_form():\n # if request.method == \"GET\":\n return render_template('login.html')","def do_login(self):\n if self.app.authentication_only:\n self.app.stop()\n else:\n self.set_screen(EXPLORER)","def post_login(self, came_from='/'):\n if not request.identity:\n login_counter = request.environ['repoze.who.logins'] + 1\n redirect('/login', came_from=came_from, __logins=login_counter)\n\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n redirect(came_from)","def login():\n form = LoginForm()\n\n state = process_login(form)\n if state == LoginState.SHOW_LOGIN:\n return render_template('user/login.html', form=form)\n elif state == LoginState.SHOW_LOGIN_LOCKED:\n flash('User is locked, Contact Systems Administrator', 'danger')\n return redirect(url_for('user_view.login'))\n elif state == LoginState.SHOW_LOGIN_INCORRECT_PASSWORD:\n flash('Password is Incorrect', 'danger')\n return redirect(url_for('user_view.login'))\n elif state == LoginState.SHOW_LOGIN_EMAIL_NOT_EXIST:\n flash('Email does not exist', 'warning')\n return redirect(url_for('user_view.login'))\n elif state == LoginState.SHOW_DASHBOARD:\n return redirect(url_for('dashboard_view.home'))","def i_am_in_the_login_page(browser):","def log_in():\n form = LoginForm(request.form)\n if request.method == 'POST' and form.validate():\n if form.username.data != current_app.config['USERNAME']:\n flash('Invalid username.')\n elif form.password.data != current_app.config['PASSWORD']:\n flash('Invalid password.')\n else:\n session['logged_in'] = True\n flash('You were logged in.')\n\n return redirect(url_for('blog.show_posts'))\n\n return render_template('auth/log_in.html', form=form)","def user_login(request):\n\n user = request.user\n if user.is_authenticated():\n status = user.get_profile().application.submitted #Getting the submission status\n if status: #If already submitted, takes to Completion Page\n return redirect('/allotter/complete/')\n else: #Otherwise to Details Submission form \n return redirect('/allotter/details/')\n\n if request.method == \"POST\":\n form = UserLoginForm(request.POST)\n if form.is_valid():\n user = form.cleaned_data\n login(request, user)\n status = user.get_profile().application.submitted #Getting the submission status \n if status:\n return redirect('/allotter/complete/') #Redirect to Completion Page\n else: \n return redirect('/allotter/details/') #Redirect to user details submission \n else:\n context = {\"form\": form}\n return render(request, 'allotter/login.html', context)\n else:\n form = UserLoginForm()\n context = {\"form\": form}\n return render(request, 'allotter/login.html', context)","def login(self):\n\n self.__login_if_required()","def plans_login(self, username='', password=''):\n # the provided username and password ONLY get checked\n # by the plans server if our cookie is expired.\n # hence, if we've logged in recently, this will return True even\n # if un/pw are not provided or are otherwise bad.\n login_info = {'username': username,\n 'password': password,\n 'submit': 'Login'}\n response = self._get_page('index.php', post=login_info)\n # if login is successful, we'll be redirected to home\n success = response.url[-9:] == '/home.php'\n if success:\n self.parser.feed(response.text) # parse out username\n self.username = self.parser.username\n return success","def admin_login_form():\n\n title = 'Login'\n\n return render_template(\"admin_login_page.html\",\n title=title)","def login():\n user_type = get_admin_type_in_session()\n login_form = LoginForm(request.form)\n current_app.logger.info(f'user_type{user_type}')\n if user_type != UserTypeEnum.NOT_LOGIN:\n return redirect(url_for('admin.admin'))\n\n if 'login' in request.form:\n # read form data\n username = request.form.get('username')\n password = request.form.get('password')\n remember = True if request.form.get('remember') else False\n staff_checked = db.session.query(Staff).filter(Staff.username == username).first()\n if not staff_checked or not check_password_hash(staff_checked.password, password):\n return render_template('accounts/login.html', msg='Không Có Tài Khoản hoặc mật khẩu sai, vui lòng kiểm tra',\n form=login_form)\n else:\n session['admin'] = username\n session['admin_type'] = UserTypeEnum.ADMIN_LOGIN\n session['admin_id'] = staff_checked.id\n session['remember'] = remember\n session['admin_avatar'] = staff_checked.avatar_url if staff_checked.avatar_url else ''\n return redirect(url_for('admin.admin'))\n return render_template('accounts/login.html',\n form=login_form)","def login(self, came_from=url('/')):\n login_counter = request.environ['repoze.who.logins']\n if login_counter > 0:\n flash(_('Wrong credentials'), 'warning')\n return dict(page='login', login_counter=str(login_counter),\n came_from=came_from)","def login_view(request):\n\n try:\n # If we were redirected from another page\n next = request.GET['next']\n # Print message\n return render_to_response('login.html', {'next': next})\n except:\n pass\n\n try:\n username = request.POST['login']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n\n if user is not None:\n if user.is_active:\n # All is cool\n login(request, user)\n return redirect('/feeds')\n else:\n # User is disabled\n return render_to_response('login.html', {'disabled': 'true'})\n else:\n # Invalid user\n return render_to_response('login.html', {'invalid': 'true'})\n except KeyError:\n # Or just display the login\n return render_to_response('login.html')","def login_user():\n pass","def log_in(self):\n print('-=' * 12 + \" Log in \" + '-=' * 12)\n mob_num, password = self._input_mob_num('Mobile Number :'), input(\"Password: \")\n self._user = self.auth.log_in(mob_num, password)\n if self._user:\n print(\"you are logged in, Welcome '{}'\".format(self._user.username))\n self.homepage()\n else:\n print(\"Mobile number or/and password is/are Invaild \\n\" + '-=' * 30)\n options = {1: self.log_in, 2: self.logging_page, 3: self.exit}\n print_out = \"(1) Try Again \\n (2) Back to Logging Page \\n (3) Exit\"\n self._take_option(options, print_out)","def do_admin_login():\n if request.form['password'] == 'admin' and request.form['username'] == 'admin':\n teams = get_team()\n if teams:\n return render_template('team-players.html', teams=teams)\n else:\n return render_template('team-players.html')\n else:\n flash('Invalid username or password. Please try again!')\n return render_template('login.html')","def login(self, came_from=lurl('/')):\n login_counter = request.environ.get('repoze.who.logins', 0)\n if login_counter > 0:\n flash(_('Wrong credentials'), 'warning')\n return dict(page='login', login_counter=str(login_counter),\n came_from=came_from)","def login(self, came_from=lurl('/')):\n login_counter = request.environ.get('repoze.who.logins', 0)\n if login_counter > 0:\n flash(_('Wrong credentials'), 'warning')\n return dict(page='login', login_counter=str(login_counter),\n came_from=came_from)","def homepage():\n form = LoginForm()\n return render_template(\"admin/index.html\", title=\"Admin\", form=form)","def login_user(request):\n if request.user.is_authenticated: \n return redirect('dashboard')\n else:\n if request.method == \"POST\":\n login_form = LoginForm(data=request.POST)\n print(login_form)\n try:\n if login_form.is_valid():\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user)\n # Redirect to a success page.\n if request.user.is_authenticated:\n return redirect('index')\n except:\n import traceback\n traceback.print_exc()\n return render(request, \"login.html\", {\"login_form\": login_form})\n\n else:\n login_form = LoginForm()\n return render(request, \"login.html\", {\"login_form\": login_form})","def do_login_login():\n print(inspect.stack()[1][3])\n print(request.form)\n query = select([User]).where(and_(User.columns.email == request.form['email'],User.columns.password==request.form['password'] ))\n ResultProxy = connection.execute(query)\n ResultSet = ResultProxy.fetchone()\n if ResultSet:\n session['logged_in'] = True\n else:\n flash('wrong password!')\n # return str(get_flashed_messages())\n return home(result)","def do_login():\n\n isTeacher = False\n\n # check if this_user is admin or normal user\n this_user = User.query.filter_by(username=request.form['username']).first()\n \n # is this_user is not student or admin then check teacher table\n if this_user is None:\n this_user = Teacher.query.filter_by(username=request.form['username']).first()\n isTeacher = True\n\n # if this_user is still none -> invalid user\n if this_user is not None:\n if this_user.password == request.form[\"password\"]:\n session['authenticated'] = True\n session['username'] = this_user.username\n session['name'] = this_user.name\n session['isTeacher'] = isTeacher\n if session['username'] == \"admin\":\n session['wasAt'] = \"manageusers\"\n try:\n session['cpi'] = this_user.cpi\n session['grp_size'] = this_user.group_size\n except:\n pass\n else:\n flash(\"Incorrect Password, Please Try Again\") \n else:\n flash(\"Invalid Username, Please Try Again\")\n return home()","def dispatch(self, request, *args, **kwargs):\n next_url = request.GET.get('next', 'home')\n if 'username' in request.GET:\n self.login_user()\n return redirect(next_url)\n elif settings.MEMBERS_ONLY_DOMAIN is None:\n superuser, _ = User.objects.get_or_create(\n is_superuser=True,\n defaults={\n 'username': 'member',\n },\n )\n login(self.request, superuser)\n return redirect(next_url)\n else:\n return super().dispatch(request, *args, **kwargs)","def login():\n if current_user.is_authenticated:\n return redirect(url_for('main.home'))\n\n form = LoginForm()\n\n if form.validate_on_submit():\n user = User.query.filter_by(email=form.email.data).first()\n\n if user and bcrypt.check_password_hash(user.password, form.password.data):\n login_user(user, remember=form.remember_me.data)\n next_page = request.args.get('next') # get next url parameter and after login redirect to requested page\n flash(\"Login Successful\", 'success')\n\n # if there was request for specific page that needs authorization, then that argument assigned in\n # variable `next_page` keeps that and after login automatically user is redirect to that page\n return redirect(next_page) if next_page else redirect(url_for('main.home'))\n\n else:\n flash(\"Login Unsuccessful. Please check email and password\", \"danger\")\n\n return redirect(url_for('users.login'))\n\n return render_template('login.html', title='Log In', form=form)","def do_login(request):\n distinct_id = request.session.pop('distinct_id')\n user = User.objects.get(id=distinct_id)\n login(request, user)\n return redirect_to_user_settings()","def login():\n return redirect(build_authorize_url())","def landing():\n if g.user:\n return render_template('landing.html', user=g.user)\n return redirect(url_for('login'))","def post_login(self, came_from=lurl('/')):\n if not request.identity:\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\n redirect('/login',\n params=dict(came_from=came_from, __logins=login_counter))\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n\n # Do not use tg.redirect with tg.url as it will add the mountpoint\n # of the application twice.\n return HTTPFound(location=came_from)","def login_form_valid(self, form):\n self.request.session.update({\n 'user_is_none': None,\n 'user_is_active': None\n })\n\n email = form.cleaned_data['email']\n password = form.cleaned_data['password']\n user = authenticate(email=email, password=password)\n\n if user is None:\n self.request.session['user_is_none'] = True\n return HttpResponseRedirect('/user_account/')\n elif user.active is False:\n self.request.session['user_is_active'] = False\n return HttpResponseRedirect('/user_account/')\n else:\n self.request.session.update({\n 'user_is_none': False,\n 'user_is_active': True\n })\n login(self.request, user)\n return HttpResponseRedirect('/schedule/')","def GET_adminon(self):\r\n #check like this because c.user_is_admin is still false\r\n if not c.user.name in g.admins:\r\n return self.abort404()\r\n self.login(c.user, admin = True)\r\n\r\n dest = request.referer or '/'\r\n return self.redirect(dest)","def custom_login(request, **kwargs):\n if request.user and request.user.is_authenticated():\n return HttpResponseRedirect(settings.LOGIN_REDIRECT_URL)\n else:\n return login(request, **kwargs)","def handle_forbidden_for_homepage(self, request):\n\n login_url = request.link(Auth.from_request_path(request), name='login')\n\n if URL(request.url).path() == '/':\n return morepath.redirect(login_url)\n\n return handle_forbidden(self, request)","def login(self, came_from=url('/')):\n login_counter = request.environ['repoze.who.logins']\n if login_counter > 0:\n flash(_('Wrong credentials'), 'warning')\n\n return dict(page='login', login_counter=str(login_counter),\n came_from=came_from)","def index(request):\n if request.user.is_authenticated():\n return redirect('/matrix/')\n else:\n form = AuthenticationForm(request)\n return render(request, 'registration/login.html', {'form': form})","def process_admin_login():\n\n entered_email = request.form.get(\"email\")\n entered_password = request.form.get(\"password\")\n admin = c.get_admin(entered_email, entered_password)\n\n if admin is False:\n flash('Invalid credentials. Please click on sign up to create an account!')\n return redirect('/')\n session['current_admin'] = entered_email\n ad_id = admin.admin_id\n flash('Logged in as %s' % entered_email)\n if admin.rescue_id is None:\n return redirect('/admin' + '/' + str(ad_id) + '/rescue-info')\n else:\n return redirect('/admin' + '/' + str(ad_id))","def showLogin():\n if(checkLogin()):\n return redirect(url_for('catelog'))\n state = ''.join(random.choice(string.ascii_uppercase + string.digits)\n for x in xrange(32))\n login_session['state'] = state\n return render_template('login.html', STATE=state, isLogin=checkLogin())","def login():\n pass","def login():\n form = LoginForm()\n if not 'username' in session:\n if request.method == 'POST':\n if form.validate_on_submit():\n user = mongo.db.user.find_one({'username':form.username.data})\n if user and bcrypt.checkpw(request.form['password'].encode('utf-8'), user['hashed_password']):\n session['username'] = form.username.data\n current_user = session['username']\n flash(f'Welcome back, {current_user}!', 'success')\n return redirect(url_for('dashboard'))\n \n flash('Please check login details.', 'danger')\n return render_template('pages/login.html', title='Login', form=form)\n flash('You are already logged in. Did you mean to go to your dashboard instead?', 'info')\n return redirect(url_for('dashboard'))","def _login(self, *args, **kwargs):\n pass","def login():\n # Skip login form on forced SSO\n if request.method == \"GET\" and current_app.config[\"OAUTH_SKIP_LOGIN\"]:\n if not request.args.get('local') and oauth_type():\n return redirect(url_for(oauth_type() + '.login'))\n form = LoginForm(request.form)\n # Handle logging in\n if request.method == 'POST':\n if form.is_submitted() and form.validate():\n # Allow login with e-mail address\n if '@' in form.username.data:\n user_by_email = User.query.filter_by(email=form.username.data).first()\n if user_by_email:\n form.username.data = user_by_email.username\n # Validate user account\n login_user(form.user, remember=True)\n if not form.user.active:\n # Note: continue to profile page, where user is warned\n username = current_user.username\n return redirect(url_for('public.user', username=username))\n # Regular user greeting\n flash(\"Time to make something awesome. ≧◡≦\", 'info')\n return redirect_dest(fallback=url_for('public.home'))\n else:\n flash_errors(form)\n logout_user()\n return render_template(\"public/login.html\",\n form=form, oauth_type=oauth_type())","def user_login(request):\n if request.user.is_authenticated():\n try:\n return HttpResponseRedirect(request.GET['next'])\n except:\n return HttpResponseRedirect('%s/newsletter/home/' % (SUBSITE))\n \n if request.POST:\n form = LoginForm(request.POST)\n if form.is_valid():\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n try:\n return HttpResponseRedirect(request.GET['next'])\n except:\n return HttpResponseRedirect('%s/newsletter/home/' % (SUBSITE))\n else:\n return render('inactive user')\n \n form = LoginForm()\n\n args = {\n 'form': form,\n 'SUBSITE': SUBSITE,\n }\n\n return render(request, 'login.html', args, context_instance=RequestContext(request))","def login():\n form = LoginForm()\n if request.method == \"GET\":\n return render_template('login.html', title='Sign In', form=form)\n if request.method == \"POST\":\n if 'loggedin' in session:\n return redirect(url_for('home'))\n if form.validate_on_submit():\n username = form.username.data\n password = form.password.data\n account = db.check_item(\"username\", username)\n if account is None:\n flash('Invalid username or password')\n return redirect(url_for('login'))\n else:\n if check_password_hash(str(account['password_hash']), password):\n session['loggedin'] = True\n session['username'] = account['username']\n session['admin_auth'] = account['admin_auth']\n flash('Login successfully!')\n return redirect(url_for('home'))\n flash('Invalid username or password')\n return redirect(url_for('login'))\n else:\n return redirect(url_for('login'))","def should_be_login_url(self) -> None:\n assert \"login\" in self.browser.current_url, \"URL not login page\"","def login_get():\n next_url = url_for('index.index')\n if g.session:\n flash(gettext('You are already logged in'), 'success')\n return redirect(next_url)\n\n return render_template('sites/auth/login.html', title=gettext('Login'))","def login(self):\n self.button_login.click()\n return dashboard.DashboardPage(self._driver)","def loginFunc(self):\n username = (\n self.lineEdit.text()\n ) # Get the text from the username & password lineedit\n password = self.lineEdit_2.text() #\n # Check if password and username isnt empty, if it is, popup\n if DB.verify_login(username, password) \\\n and not DB.new_customer(username):\n self.customer.budget.set_budget(DB.get_income(self.customer.email),\n DB.get_variable_expenses(self.customer.email),\n DB.get_fixed_expenses(self.customer.email))\n self.customer.budget.set_buffert(DB.get_buffert(username))\n self.displayUi = MenuScreen()\n self.hide()\n self.displayUi.show()\n elif DB.verify_login(username, password) and DB.new_customer(username):\n self.displayUi = FirstLoginScreen()\n self.hide()\n self.displayUi.show()\n else:\n self.popUp.exec_()","def login():\n form = LoginForm()\n if form.validate_on_submit():\n\n # check whether user exists in the database and whether\n # the password entered matches the password in the database\n user = User.query.filter_by(email=form.email.data).first()\n if user is not None and user.verify_password(\n form.password.data):\n # log user in\n login_user(user)\n\n if user.is_admin:\n return redirect(url_for('view.dashboard'))\n else:\n return redirect(url_for('view.dashboard'))\n # when login details are incorrect\n else:\n flash('Invalid email or password.')\n # load login template\n return render_template('index.html', form=form, title='Login')","def test_login_required_dashboard(self):\r\n response = self.client.get(reverse('dashboard'))\r\n self.assertEqual(response.status_code, 302)\r\n self.assertEqual(response['Location'], 'http://testserver/accounts/login?next=/dashboard')","def log_in():\n if request.method == 'POST':\n username = request.form['username']\n password = request.form['password']\n if PLAN.login_user(username, password):\n session['name'] = username\n flash(\"Login success ...\")\n return redirect(url_for('index'))\n flash(\"Login failed ...\")\n return render_template('login.html')\n return render_template('login.html')","def login(self):\r\n\r\n # Open browser with the login URL\r\n self.browser.open(self.config[\"base_url\"] + \"login\")\r\n\r\n # Select the login form\r\n self.browser.select_form('form[action=\"/login/\"]')\r\n\r\n # Fill the login form.\r\n self.browser[\"email\"] = self.config[\"email\"]\r\n self.browser[\"password\"] = self.config[\"password\"]\r\n\r\n # Submit form\r\n self.browser.submit_selected()","def login_required(view_func):\n @wraps(view_func)\n def _checklogin(request, *args, **kwargs):\n if request.user.is_active:\n # The user is valid. Continue to the admin page.\n return view_func(request, *args, **kwargs)\n return site.login(request)\n return _checklogin","def dispatch(request):\n if request.user.is_admin:\n return redirect(reverse(\"admin-dashboard\"))\n else:\n return redirect(reverse(\"trainee-dashboard\"))","def login_page():\n form = loginUser()\n\n if \"user\" in session:\n logged_user = session[\"user\"]\n return redirect(f\"users/{logged_user}\")\n\n if form.validate_on_submit():\n username=form.username.data\n password=form.password.data\n\n user = User.authenticate(username=username, password=password)\n\n if user:\n session[\"user\"] = user.username\n\n return redirect(f'/users/{username}')\n else:\n form.password.errors = ['Unable to log in']\n\n return render_template(\"login_form.html\", form=form)","def adpanel():\n if 'user_id' not in session or session['user_id'] != 'admin':\n return redirect(url_for('login'))\n return render_template('adminpanel.html')","def login_index():\n try:\n err_id = request.args['err_id']\n except:\n err_id = '0'\n if err_id == '1':\n error_message = 'Wrong username! Please make sure your username is right or you have registered before.'\n elif err_id == '2':\n error_message = 'Wrong password! Please type again with correct one!'\n else:\n error_message = ''\n if check_login():\n did_login = True\n else:\n did_login = False\n if not did_login: \n \"\"\"show login page\"\"\"\n return render_template(\"login/login.html\", message=error_message)\n else:\n \"\"\"jump to manage page\"\"\"\n return redirect(url_for('manage.manage_index'))","def login():\n\n if current_user is not None and current_user.is_authenticated():\n return redirect(url_for(\"user.profile\"))\n\n form = LoginForm(request.form)\n if form.validate_on_submit():\n user, authenticated = User.authenticate(form.login.data,\n form.password.data)\n\n if user and authenticated:\n login_user(user, remember=form.remember_me.data)\n return redirect(request.args.get(\"next\") or\n url_for(\"forum.index\"))\n\n flash((\"Wrong username or password\"), \"danger\")\n return render_template(\"auth/login.html\", form=form)","def test_admin_user_login_with_redirect(self):\n self.get_page(\"/\")\n self.at_page(\"/\")\n self.login(\"admin\", \"admin\", \"/admin/\")\n self.at_page(\"/admin/\")\n self.should_see(\"Django administration\")","def login():\n authorized_redirect_URIs = ['localhost:5000', '127.0.0.1:5000']\n\n disabled = False\n if request.host not in authorized_redirect_URIs:\n flash(Markup(\"\"\"Please use\n <a href='http://localhost:5000/login'>http://localhost:5000</a>\n or\n <a href='http://127.0.0.1:5000/login'>http://127.0.0.1:5000</a>\n to access the application otherwise the OAuth will not work\"\"\"),\n category='danger')\n disabled = True\n\n if current_user.is_authenticated:\n return redirect(url_for('url.index'))\n return render_template('login.html', disabled=disabled)","def handle_needs_login():\n flash(\"You must be logged in to access this page.\")\n return redirect(url_for('auth.login', next=request.path))","def login():\n\n return render_template(\"login_form.html\")","def login(self) -> redirect:\n\t\tif self.is_authorized:\n\t\t\tflash(\"You are already logged in.\")\n\t\t\treturn redirect(url_for(\"index\"))\n\t\telif request.method == \"GET\":\n\t\t\treturn render_template(\"login.jinja2\")\n\t\tsession[\"state\"] = str(uuid4())\n\t\treturn self.oauth.authorize(callback=url_for(\"authorize\", _external=True), state=session[\"state\"])","def homepage():\n if g.user:\n return redirect(f\"/user/{g.user.id}\")\n else:\n return redirect(\"/landing\")","def index(request):\n \n user = get_user(request)\n\n # single auth system?\n if len(ENABLED_AUTH_SYSTEMS) == 1 and not user:\n return HttpResponseRedirect(reverse(AUTH_START, args=[ENABLED_AUTH_SYSTEMS[0]])+ '?return_url=' + request.GET.get('return_url', ''))\n\n #if DEFAULT_AUTH_SYSTEM and not user:\n # return HttpResponseRedirect(reverse(start, args=[DEFAULT_AUTH_SYSTEM])+ '?return_url=' + request.GET.get('return_url', ''))\n \n default_auth_system_obj = None\n if DEFAULT_AUTH_SYSTEM:\n default_auth_system_obj = AUTH_SYSTEMS[DEFAULT_AUTH_SYSTEM]\n\n #form = password.LoginForm()\n\n return render_template(request, 'index', {'return_url' : request.GET.get('return_url', '/'),\n 'enabled_auth_systems' : ENABLED_AUTH_SYSTEMS,\n 'default_auth_system': DEFAULT_AUTH_SYSTEM,\n 'default_auth_system_obj': default_auth_system_obj})","def login():\n # Initialise login form\n form = UserLoginForm()\n # Validate and process form data\n if form.validate_on_submit():\n # Get form data\n username = form.username.data\n password = form.password.data\n # Check if username and password is valid\n valid, userID = gdb.verifyuser(username, password)\n if(valid):\n user = gdb.getuserbyid(userID)\n login_user(user)\n return redirect(url_for('main.dashboard'))\n else:\n flash(\"Invalid username or password.\", category=\"error\")\n return redirect(url_for('main.login'))\n # Render template\n return render_template('login.html', form=form)","def login_form(request):\n return {'login_form': LoginForm()}","def login_required(func):\n @wraps(func)\n def f(*args, **kwargs):\n if g.user is None:\n app.logger.info('redirecting not logged in user')\n return redirect(url_for('index'))\n elif not g.user.initialized and f.__name__ not in ['profile_create','logout']:\n return redirect(url_for('profile_create'))\n else:\n return func(*args, **kwargs)\n return f","def logged_in(request):\n return request.current_user is not None","def local_login():\n if not current_app.config[\"USE_LOCAL_AUTH\"]:\n return redirect(url_for('auth.login'))\n login_form = BasicLoginForm()\n if request.method == \"POST\":\n email = request.form[\"email\"]\n\n user = find_user_by_email(email)\n\n if user is not None:\n login_user(user)\n session[\"user_id\"] = current_user.get_id()\n\n create_auth_event(\n auth_event_type=event_type.USER_LOGIN,\n user_guid=session[\"user_id\"],\n new_value={\n 'success': True,\n }\n )\n\n next_url = request.form.get(\"next\")\n if not is_safe_url(next_url):\n return abort(400, UNSAFE_NEXT_URL)\n\n return redirect(next_url or url_for(\"main.index\"))\n else:\n error_message = \"User {email} not found. Please contact your agency FOIL Officer to gain access to the system.\".format(\n email=email)\n flash(error_message, category=\"warning\")\n return render_template(\n \"auth/local_login_form.html\", login_form=login_form\n )\n\n elif request.method == \"GET\":\n return render_template(\n \"auth/local_login_form.html\",\n login_form=login_form,\n next_url=request.args.get(\"next\", \"\"),\n )","def login_to_flask(self, form):\n\n flask_login.login_user(self, remember=form.remember_me.data)\n\n # handle 'next' page if we're stopping them en route\n next_page = flask.request.args.get('next')\n if not next_page or werkzeug.urls.url_parse(next_page).netloc != '':\n next_page = flask.url_for('dashboard')\n\n # create a response, inject the token into the cookie and return\n redirect = flask.redirect(next_page)\n response = app.make_response(redirect)\n response.set_cookie('kdm-manager_token', self.token)\n return response","def do_login(self, backend, user):","def login():\n if current_user.is_authenticated:\n return redirect(url_for('main.index'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(\n username=form.username.data.lower()\n ).first()\n\n if user is None or not user.check_password(form.password.data):\n flash('Invalid username or password', 'error')\n return render_template('login.html',\n title='Sign In', form=form), 401\n\n if not user.email_confirmed:\n flash(\"Please confirm your email \\\n address to activate your Account\", 'error')\n return render_template('login.html',\n title='Sign In', form=form), 401\n\n login_user(user, remember=form.remember_me.data)\n\n next_page = request.args.get('next')\n if not next_page or url_parse(next_page).netloc != '':\n next_page = url_for('main.index')\n return redirect(next_page)\n return render_template('login.html', title='Sign In', form=form), 401","def login_view():\n\n #Bypass if user is logged in\n if current_user.is_authenticated:\n return redirect(url_for(\"profiles.profile_view\",id=current_user.id))\n \n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(email=form.email.data).first()\n\n #Check if the password matched\n if user and user.check_password(form.password.data):\n login_user(user)\n next_page = request.args.get(\"next\")\n return redirect(next_page or url_for(f\"profiles.profile_view\", id=current_user.id))\n flash(\"Invalid email and password.\")\n return redirect(url_for(\"auth.login_view\"))\n return render_template(\"login.html\", form=form)","def adminlogin(request):\n request.GET = request.GET.copy()\n request.GET['redirect'] = '/annaleut/admin/'\n return auth_views.login(request, template_name='admin/login.html',\n redirect_field_name='redirect')","def default():\n\treturn render_template(\"login.html\")","def login_bot(self):\n pass","def login():\n form = LoginForm()\n if form.validate_on_submit():\n\n # check whether employee exists in the database and whether\n # the password entered matches the password in the database\n expert_data = Expert.query.filter_by(email=form.email.data).first()\n if expert_data is not None and expert_data.verify_password(\n form.password.data):\n # log employee in\n login_user(expert_data)\n\n # redirect to the appropriate dashboard page\n if expert_data.is_admin:\n return redirect(url_for('home.admin_dashboard'))\n else:\n return redirect(url_for('home.dashboard'))\n\n # when login details are incorrect\n else:\n flash('Invalid email or password.')\n\n # load login template\n return render_template('auth/login.html', form=form, title='Login')","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return self.login()","def require_login(self):\n\tif users.get_current_user():\n\t return True\n\telse:\n\t self.redirect(users.create_login_url(self.request.uri))\n\t return False","def go_to_dashboard():\n\treturn render_template(\"/dashboard.html\")","def login():\n return render_template('login.html', next=flask.request.args.get(\"next\",\"/sessions\"))","def login(self):\n url = self._root + self._routes[\"login\"]\n self.r = self.reqsession.get(url) \n if self.r.url == 'https://console.zerodha.com/dashboard':\n cookies = self.reqsession.cookies.get_dict('console.zerodha.com')\n self.console_session = cookies['session']\n self.public_token = self.reqsession.cookies['public_token']\n return True\n else:\n raise Exception(\"Login failed or Kite session expired\")","def login():\n return render_template('auth/login.html')"],"string":"[\n \"def login(self):\\n identity = request.environ.get('repoze.who.identity')\\n came_from = str(request.GET.get('came_from', '')) or \\\\\\n url('/')\\n if identity:\\n redirect(url(came_from))\\n else:\\n c.came_from = came_from\\n c.login_counter = request.environ['repoze.who.logins'] + 1\\n return render('/forms/login.mako')\",\n \"def ShowLogin():\\n current_user = helpers.get_current_user()\\n if current_user is None:\\n return render_template('login.html')\\n else:\\n return redirect('/')\",\n \"def handleLogin(self):\\n aVar = self.session.getAttribute(self.settings.authenvar)\\n self.loggedin = False\\n if not aVar:\\n self.currenttemplate = self.settings.logintemplate \\n self.logger.debug(\\\"Not logged in, Login-Mask activated.\\\")\\n return\\n\\n self.loggedin = True\\n self.logger.debug('Loged in as: \\\"{}\\\"'.format(aVar))\",\n \"def GET_login(self):\\r\\n\\r\\n # dest is the location to redirect to upon completion\\r\\n dest = request.get.get('dest','') or request.referer or '/'\\r\\n return LoginPage(dest = dest).render()\",\n \"def defaultlanding():\\n #send user to description page if not logged in\\n if not g.user:\\n return redirect(url_for('description'))\\n #display leaderboard for competition if logged in\\n return redirect(url_for('leaderboard'))\",\n \"def index(self):\\n\\n # try and pull the user's data\\n user = get_active_user_data()\\n\\n if not user:\\n # they are not logged in give them the login form\\n return render('/login_form.html')\\n\\n # they are logged in, pass them to the home page\\n redirect('/')\",\n \"def login_menu(self):\\n print(\\\"\\\\nPlease enter your email and password\\\")\\n email = self.validate_email()\\n password = self.validate_password()\\n self.authenticate_user(email, password)\",\n \"def login():\",\n \"def login():\",\n \"def login():\\n login_page = Login()\\n login_page.login_main_page()\",\n \"def goto_login(self):\\n self.driver.find_element(*BasePageLocators.MY_ACCOUNT_DROPDOWN).click()\\n self.driver.find_element(*BasePageLocators.GO_LOGIN).click()\\n return LoginPage(self.driver)\",\n \"def login(self):\\n\\t\\treturn\",\n \"def post_login(self, came_from=lurl('/')):\\n if not request.identity:\\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\\n redirect('/login', params=dict(came_from=came_from, __logins=login_counter))\\n return\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n redirect(came_from)\",\n \"def loginView(request):\\n username = request.POST.get('username')\\n password = request.POST.get('password')\\n user = authenticate(username=username, password=password)\\n if user is not None:\\n if user.is_active:\\n dologin(request, user)\\n if isOperator(user): # login as an operator\\n return redirect('/operator/map')\\n elif isAdmin(user): # login as an admin\\n return redirect('/admin/map')\\n return HttpResponse('ok')\\n else:\\n # Return a 'disabled account' error message\\n return HttpResponse(\\\"Disabled account\\\")\\n else:\\n # Return an 'invalid login' error message.\\n return HttpResponse(\\\"Invalid login\\\")\",\n \"def log_in(self):\\n\\t\\tpass\",\n \"def post_login(self, came_from='/'):\\n if not request.identity:\\n login_counter = request.environ['repoze.who.logins'] + 1\\n redirect('/login', came_from=came_from, __logins=login_counter)\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n redirect(came_from)\",\n \"def login(self):\\n logging.debug(\\\"login called\\\")\\n\\n # Apply settings\\n self.localisationsettings.apply_to_upcoming_session()\\n self.admin_setting.apply_to_upcoming_session()\\n self.macspoof_setting.apply_to_upcoming_session()\\n self.network_setting.apply_to_upcoming_session()\\n\\n self.mainwindow.hide()\\n self.gdmclient.do_login()\",\n \"def view_login(self):\\n with self.client.get(\\\"/login\\\", catch_response=True) as response:\\n for r_hist in response.history:\\n if r_hist.status_code > 200 and r_hist.status_code < 400:\\n response.failure(\\\"Logged on: Got redirect to /home\\\")\",\n \"def login(self):\",\n \"def login_form():\\n # if request.method == \\\"GET\\\":\\n return render_template('login.html')\",\n \"def do_login(self):\\n if self.app.authentication_only:\\n self.app.stop()\\n else:\\n self.set_screen(EXPLORER)\",\n \"def post_login(self, came_from='/'):\\n if not request.identity:\\n login_counter = request.environ['repoze.who.logins'] + 1\\n redirect('/login', came_from=came_from, __logins=login_counter)\\n\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n redirect(came_from)\",\n \"def login():\\n form = LoginForm()\\n\\n state = process_login(form)\\n if state == LoginState.SHOW_LOGIN:\\n return render_template('user/login.html', form=form)\\n elif state == LoginState.SHOW_LOGIN_LOCKED:\\n flash('User is locked, Contact Systems Administrator', 'danger')\\n return redirect(url_for('user_view.login'))\\n elif state == LoginState.SHOW_LOGIN_INCORRECT_PASSWORD:\\n flash('Password is Incorrect', 'danger')\\n return redirect(url_for('user_view.login'))\\n elif state == LoginState.SHOW_LOGIN_EMAIL_NOT_EXIST:\\n flash('Email does not exist', 'warning')\\n return redirect(url_for('user_view.login'))\\n elif state == LoginState.SHOW_DASHBOARD:\\n return redirect(url_for('dashboard_view.home'))\",\n \"def i_am_in_the_login_page(browser):\",\n \"def log_in():\\n form = LoginForm(request.form)\\n if request.method == 'POST' and form.validate():\\n if form.username.data != current_app.config['USERNAME']:\\n flash('Invalid username.')\\n elif form.password.data != current_app.config['PASSWORD']:\\n flash('Invalid password.')\\n else:\\n session['logged_in'] = True\\n flash('You were logged in.')\\n\\n return redirect(url_for('blog.show_posts'))\\n\\n return render_template('auth/log_in.html', form=form)\",\n \"def user_login(request):\\n\\n user = request.user\\n if user.is_authenticated():\\n status = user.get_profile().application.submitted #Getting the submission status\\n if status: #If already submitted, takes to Completion Page\\n return redirect('/allotter/complete/')\\n else: #Otherwise to Details Submission form \\n return redirect('/allotter/details/')\\n\\n if request.method == \\\"POST\\\":\\n form = UserLoginForm(request.POST)\\n if form.is_valid():\\n user = form.cleaned_data\\n login(request, user)\\n status = user.get_profile().application.submitted #Getting the submission status \\n if status:\\n return redirect('/allotter/complete/') #Redirect to Completion Page\\n else: \\n return redirect('/allotter/details/') #Redirect to user details submission \\n else:\\n context = {\\\"form\\\": form}\\n return render(request, 'allotter/login.html', context)\\n else:\\n form = UserLoginForm()\\n context = {\\\"form\\\": form}\\n return render(request, 'allotter/login.html', context)\",\n \"def login(self):\\n\\n self.__login_if_required()\",\n \"def plans_login(self, username='', password=''):\\n # the provided username and password ONLY get checked\\n # by the plans server if our cookie is expired.\\n # hence, if we've logged in recently, this will return True even\\n # if un/pw are not provided or are otherwise bad.\\n login_info = {'username': username,\\n 'password': password,\\n 'submit': 'Login'}\\n response = self._get_page('index.php', post=login_info)\\n # if login is successful, we'll be redirected to home\\n success = response.url[-9:] == '/home.php'\\n if success:\\n self.parser.feed(response.text) # parse out username\\n self.username = self.parser.username\\n return success\",\n \"def admin_login_form():\\n\\n title = 'Login'\\n\\n return render_template(\\\"admin_login_page.html\\\",\\n title=title)\",\n \"def login():\\n user_type = get_admin_type_in_session()\\n login_form = LoginForm(request.form)\\n current_app.logger.info(f'user_type{user_type}')\\n if user_type != UserTypeEnum.NOT_LOGIN:\\n return redirect(url_for('admin.admin'))\\n\\n if 'login' in request.form:\\n # read form data\\n username = request.form.get('username')\\n password = request.form.get('password')\\n remember = True if request.form.get('remember') else False\\n staff_checked = db.session.query(Staff).filter(Staff.username == username).first()\\n if not staff_checked or not check_password_hash(staff_checked.password, password):\\n return render_template('accounts/login.html', msg='Không Có Tài Khoản hoặc mật khẩu sai, vui lòng kiểm tra',\\n form=login_form)\\n else:\\n session['admin'] = username\\n session['admin_type'] = UserTypeEnum.ADMIN_LOGIN\\n session['admin_id'] = staff_checked.id\\n session['remember'] = remember\\n session['admin_avatar'] = staff_checked.avatar_url if staff_checked.avatar_url else ''\\n return redirect(url_for('admin.admin'))\\n return render_template('accounts/login.html',\\n form=login_form)\",\n \"def login(self, came_from=url('/')):\\n login_counter = request.environ['repoze.who.logins']\\n if login_counter > 0:\\n flash(_('Wrong credentials'), 'warning')\\n return dict(page='login', login_counter=str(login_counter),\\n came_from=came_from)\",\n \"def login_view(request):\\n\\n try:\\n # If we were redirected from another page\\n next = request.GET['next']\\n # Print message\\n return render_to_response('login.html', {'next': next})\\n except:\\n pass\\n\\n try:\\n username = request.POST['login']\\n password = request.POST['password']\\n user = authenticate(username=username, password=password)\\n\\n if user is not None:\\n if user.is_active:\\n # All is cool\\n login(request, user)\\n return redirect('/feeds')\\n else:\\n # User is disabled\\n return render_to_response('login.html', {'disabled': 'true'})\\n else:\\n # Invalid user\\n return render_to_response('login.html', {'invalid': 'true'})\\n except KeyError:\\n # Or just display the login\\n return render_to_response('login.html')\",\n \"def login_user():\\n pass\",\n \"def log_in(self):\\n print('-=' * 12 + \\\" Log in \\\" + '-=' * 12)\\n mob_num, password = self._input_mob_num('Mobile Number :'), input(\\\"Password: \\\")\\n self._user = self.auth.log_in(mob_num, password)\\n if self._user:\\n print(\\\"you are logged in, Welcome '{}'\\\".format(self._user.username))\\n self.homepage()\\n else:\\n print(\\\"Mobile number or/and password is/are Invaild \\\\n\\\" + '-=' * 30)\\n options = {1: self.log_in, 2: self.logging_page, 3: self.exit}\\n print_out = \\\"(1) Try Again \\\\n (2) Back to Logging Page \\\\n (3) Exit\\\"\\n self._take_option(options, print_out)\",\n \"def do_admin_login():\\n if request.form['password'] == 'admin' and request.form['username'] == 'admin':\\n teams = get_team()\\n if teams:\\n return render_template('team-players.html', teams=teams)\\n else:\\n return render_template('team-players.html')\\n else:\\n flash('Invalid username or password. Please try again!')\\n return render_template('login.html')\",\n \"def login(self, came_from=lurl('/')):\\n login_counter = request.environ.get('repoze.who.logins', 0)\\n if login_counter > 0:\\n flash(_('Wrong credentials'), 'warning')\\n return dict(page='login', login_counter=str(login_counter),\\n came_from=came_from)\",\n \"def login(self, came_from=lurl('/')):\\n login_counter = request.environ.get('repoze.who.logins', 0)\\n if login_counter > 0:\\n flash(_('Wrong credentials'), 'warning')\\n return dict(page='login', login_counter=str(login_counter),\\n came_from=came_from)\",\n \"def homepage():\\n form = LoginForm()\\n return render_template(\\\"admin/index.html\\\", title=\\\"Admin\\\", form=form)\",\n \"def login_user(request):\\n if request.user.is_authenticated: \\n return redirect('dashboard')\\n else:\\n if request.method == \\\"POST\\\":\\n login_form = LoginForm(data=request.POST)\\n print(login_form)\\n try:\\n if login_form.is_valid():\\n username = request.POST['username']\\n password = request.POST['password']\\n user = authenticate(request, username=username, password=password)\\n if user is not None:\\n login(request, user)\\n # Redirect to a success page.\\n if request.user.is_authenticated:\\n return redirect('index')\\n except:\\n import traceback\\n traceback.print_exc()\\n return render(request, \\\"login.html\\\", {\\\"login_form\\\": login_form})\\n\\n else:\\n login_form = LoginForm()\\n return render(request, \\\"login.html\\\", {\\\"login_form\\\": login_form})\",\n \"def do_login_login():\\n print(inspect.stack()[1][3])\\n print(request.form)\\n query = select([User]).where(and_(User.columns.email == request.form['email'],User.columns.password==request.form['password'] ))\\n ResultProxy = connection.execute(query)\\n ResultSet = ResultProxy.fetchone()\\n if ResultSet:\\n session['logged_in'] = True\\n else:\\n flash('wrong password!')\\n # return str(get_flashed_messages())\\n return home(result)\",\n \"def do_login():\\n\\n isTeacher = False\\n\\n # check if this_user is admin or normal user\\n this_user = User.query.filter_by(username=request.form['username']).first()\\n \\n # is this_user is not student or admin then check teacher table\\n if this_user is None:\\n this_user = Teacher.query.filter_by(username=request.form['username']).first()\\n isTeacher = True\\n\\n # if this_user is still none -> invalid user\\n if this_user is not None:\\n if this_user.password == request.form[\\\"password\\\"]:\\n session['authenticated'] = True\\n session['username'] = this_user.username\\n session['name'] = this_user.name\\n session['isTeacher'] = isTeacher\\n if session['username'] == \\\"admin\\\":\\n session['wasAt'] = \\\"manageusers\\\"\\n try:\\n session['cpi'] = this_user.cpi\\n session['grp_size'] = this_user.group_size\\n except:\\n pass\\n else:\\n flash(\\\"Incorrect Password, Please Try Again\\\") \\n else:\\n flash(\\\"Invalid Username, Please Try Again\\\")\\n return home()\",\n \"def dispatch(self, request, *args, **kwargs):\\n next_url = request.GET.get('next', 'home')\\n if 'username' in request.GET:\\n self.login_user()\\n return redirect(next_url)\\n elif settings.MEMBERS_ONLY_DOMAIN is None:\\n superuser, _ = User.objects.get_or_create(\\n is_superuser=True,\\n defaults={\\n 'username': 'member',\\n },\\n )\\n login(self.request, superuser)\\n return redirect(next_url)\\n else:\\n return super().dispatch(request, *args, **kwargs)\",\n \"def login():\\n if current_user.is_authenticated:\\n return redirect(url_for('main.home'))\\n\\n form = LoginForm()\\n\\n if form.validate_on_submit():\\n user = User.query.filter_by(email=form.email.data).first()\\n\\n if user and bcrypt.check_password_hash(user.password, form.password.data):\\n login_user(user, remember=form.remember_me.data)\\n next_page = request.args.get('next') # get next url parameter and after login redirect to requested page\\n flash(\\\"Login Successful\\\", 'success')\\n\\n # if there was request for specific page that needs authorization, then that argument assigned in\\n # variable `next_page` keeps that and after login automatically user is redirect to that page\\n return redirect(next_page) if next_page else redirect(url_for('main.home'))\\n\\n else:\\n flash(\\\"Login Unsuccessful. Please check email and password\\\", \\\"danger\\\")\\n\\n return redirect(url_for('users.login'))\\n\\n return render_template('login.html', title='Log In', form=form)\",\n \"def do_login(request):\\n distinct_id = request.session.pop('distinct_id')\\n user = User.objects.get(id=distinct_id)\\n login(request, user)\\n return redirect_to_user_settings()\",\n \"def login():\\n return redirect(build_authorize_url())\",\n \"def landing():\\n if g.user:\\n return render_template('landing.html', user=g.user)\\n return redirect(url_for('login'))\",\n \"def post_login(self, came_from=lurl('/')):\\n if not request.identity:\\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\\n redirect('/login',\\n params=dict(came_from=came_from, __logins=login_counter))\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n\\n # Do not use tg.redirect with tg.url as it will add the mountpoint\\n # of the application twice.\\n return HTTPFound(location=came_from)\",\n \"def login_form_valid(self, form):\\n self.request.session.update({\\n 'user_is_none': None,\\n 'user_is_active': None\\n })\\n\\n email = form.cleaned_data['email']\\n password = form.cleaned_data['password']\\n user = authenticate(email=email, password=password)\\n\\n if user is None:\\n self.request.session['user_is_none'] = True\\n return HttpResponseRedirect('/user_account/')\\n elif user.active is False:\\n self.request.session['user_is_active'] = False\\n return HttpResponseRedirect('/user_account/')\\n else:\\n self.request.session.update({\\n 'user_is_none': False,\\n 'user_is_active': True\\n })\\n login(self.request, user)\\n return HttpResponseRedirect('/schedule/')\",\n \"def GET_adminon(self):\\r\\n #check like this because c.user_is_admin is still false\\r\\n if not c.user.name in g.admins:\\r\\n return self.abort404()\\r\\n self.login(c.user, admin = True)\\r\\n\\r\\n dest = request.referer or '/'\\r\\n return self.redirect(dest)\",\n \"def custom_login(request, **kwargs):\\n if request.user and request.user.is_authenticated():\\n return HttpResponseRedirect(settings.LOGIN_REDIRECT_URL)\\n else:\\n return login(request, **kwargs)\",\n \"def handle_forbidden_for_homepage(self, request):\\n\\n login_url = request.link(Auth.from_request_path(request), name='login')\\n\\n if URL(request.url).path() == '/':\\n return morepath.redirect(login_url)\\n\\n return handle_forbidden(self, request)\",\n \"def login(self, came_from=url('/')):\\n login_counter = request.environ['repoze.who.logins']\\n if login_counter > 0:\\n flash(_('Wrong credentials'), 'warning')\\n\\n return dict(page='login', login_counter=str(login_counter),\\n came_from=came_from)\",\n \"def index(request):\\n if request.user.is_authenticated():\\n return redirect('/matrix/')\\n else:\\n form = AuthenticationForm(request)\\n return render(request, 'registration/login.html', {'form': form})\",\n \"def process_admin_login():\\n\\n entered_email = request.form.get(\\\"email\\\")\\n entered_password = request.form.get(\\\"password\\\")\\n admin = c.get_admin(entered_email, entered_password)\\n\\n if admin is False:\\n flash('Invalid credentials. Please click on sign up to create an account!')\\n return redirect('/')\\n session['current_admin'] = entered_email\\n ad_id = admin.admin_id\\n flash('Logged in as %s' % entered_email)\\n if admin.rescue_id is None:\\n return redirect('/admin' + '/' + str(ad_id) + '/rescue-info')\\n else:\\n return redirect('/admin' + '/' + str(ad_id))\",\n \"def showLogin():\\n if(checkLogin()):\\n return redirect(url_for('catelog'))\\n state = ''.join(random.choice(string.ascii_uppercase + string.digits)\\n for x in xrange(32))\\n login_session['state'] = state\\n return render_template('login.html', STATE=state, isLogin=checkLogin())\",\n \"def login():\\n pass\",\n \"def login():\\n form = LoginForm()\\n if not 'username' in session:\\n if request.method == 'POST':\\n if form.validate_on_submit():\\n user = mongo.db.user.find_one({'username':form.username.data})\\n if user and bcrypt.checkpw(request.form['password'].encode('utf-8'), user['hashed_password']):\\n session['username'] = form.username.data\\n current_user = session['username']\\n flash(f'Welcome back, {current_user}!', 'success')\\n return redirect(url_for('dashboard'))\\n \\n flash('Please check login details.', 'danger')\\n return render_template('pages/login.html', title='Login', form=form)\\n flash('You are already logged in. Did you mean to go to your dashboard instead?', 'info')\\n return redirect(url_for('dashboard'))\",\n \"def _login(self, *args, **kwargs):\\n pass\",\n \"def login():\\n # Skip login form on forced SSO\\n if request.method == \\\"GET\\\" and current_app.config[\\\"OAUTH_SKIP_LOGIN\\\"]:\\n if not request.args.get('local') and oauth_type():\\n return redirect(url_for(oauth_type() + '.login'))\\n form = LoginForm(request.form)\\n # Handle logging in\\n if request.method == 'POST':\\n if form.is_submitted() and form.validate():\\n # Allow login with e-mail address\\n if '@' in form.username.data:\\n user_by_email = User.query.filter_by(email=form.username.data).first()\\n if user_by_email:\\n form.username.data = user_by_email.username\\n # Validate user account\\n login_user(form.user, remember=True)\\n if not form.user.active:\\n # Note: continue to profile page, where user is warned\\n username = current_user.username\\n return redirect(url_for('public.user', username=username))\\n # Regular user greeting\\n flash(\\\"Time to make something awesome. ≧◡≦\\\", 'info')\\n return redirect_dest(fallback=url_for('public.home'))\\n else:\\n flash_errors(form)\\n logout_user()\\n return render_template(\\\"public/login.html\\\",\\n form=form, oauth_type=oauth_type())\",\n \"def user_login(request):\\n if request.user.is_authenticated():\\n try:\\n return HttpResponseRedirect(request.GET['next'])\\n except:\\n return HttpResponseRedirect('%s/newsletter/home/' % (SUBSITE))\\n \\n if request.POST:\\n form = LoginForm(request.POST)\\n if form.is_valid():\\n username = request.POST['username']\\n password = request.POST['password']\\n user = authenticate(username=username, password=password)\\n if user is not None:\\n if user.is_active:\\n login(request, user)\\n try:\\n return HttpResponseRedirect(request.GET['next'])\\n except:\\n return HttpResponseRedirect('%s/newsletter/home/' % (SUBSITE))\\n else:\\n return render('inactive user')\\n \\n form = LoginForm()\\n\\n args = {\\n 'form': form,\\n 'SUBSITE': SUBSITE,\\n }\\n\\n return render(request, 'login.html', args, context_instance=RequestContext(request))\",\n \"def login():\\n form = LoginForm()\\n if request.method == \\\"GET\\\":\\n return render_template('login.html', title='Sign In', form=form)\\n if request.method == \\\"POST\\\":\\n if 'loggedin' in session:\\n return redirect(url_for('home'))\\n if form.validate_on_submit():\\n username = form.username.data\\n password = form.password.data\\n account = db.check_item(\\\"username\\\", username)\\n if account is None:\\n flash('Invalid username or password')\\n return redirect(url_for('login'))\\n else:\\n if check_password_hash(str(account['password_hash']), password):\\n session['loggedin'] = True\\n session['username'] = account['username']\\n session['admin_auth'] = account['admin_auth']\\n flash('Login successfully!')\\n return redirect(url_for('home'))\\n flash('Invalid username or password')\\n return redirect(url_for('login'))\\n else:\\n return redirect(url_for('login'))\",\n \"def should_be_login_url(self) -> None:\\n assert \\\"login\\\" in self.browser.current_url, \\\"URL not login page\\\"\",\n \"def login_get():\\n next_url = url_for('index.index')\\n if g.session:\\n flash(gettext('You are already logged in'), 'success')\\n return redirect(next_url)\\n\\n return render_template('sites/auth/login.html', title=gettext('Login'))\",\n \"def login(self):\\n self.button_login.click()\\n return dashboard.DashboardPage(self._driver)\",\n \"def loginFunc(self):\\n username = (\\n self.lineEdit.text()\\n ) # Get the text from the username & password lineedit\\n password = self.lineEdit_2.text() #\\n # Check if password and username isnt empty, if it is, popup\\n if DB.verify_login(username, password) \\\\\\n and not DB.new_customer(username):\\n self.customer.budget.set_budget(DB.get_income(self.customer.email),\\n DB.get_variable_expenses(self.customer.email),\\n DB.get_fixed_expenses(self.customer.email))\\n self.customer.budget.set_buffert(DB.get_buffert(username))\\n self.displayUi = MenuScreen()\\n self.hide()\\n self.displayUi.show()\\n elif DB.verify_login(username, password) and DB.new_customer(username):\\n self.displayUi = FirstLoginScreen()\\n self.hide()\\n self.displayUi.show()\\n else:\\n self.popUp.exec_()\",\n \"def login():\\n form = LoginForm()\\n if form.validate_on_submit():\\n\\n # check whether user exists in the database and whether\\n # the password entered matches the password in the database\\n user = User.query.filter_by(email=form.email.data).first()\\n if user is not None and user.verify_password(\\n form.password.data):\\n # log user in\\n login_user(user)\\n\\n if user.is_admin:\\n return redirect(url_for('view.dashboard'))\\n else:\\n return redirect(url_for('view.dashboard'))\\n # when login details are incorrect\\n else:\\n flash('Invalid email or password.')\\n # load login template\\n return render_template('index.html', form=form, title='Login')\",\n \"def test_login_required_dashboard(self):\\r\\n response = self.client.get(reverse('dashboard'))\\r\\n self.assertEqual(response.status_code, 302)\\r\\n self.assertEqual(response['Location'], 'http://testserver/accounts/login?next=/dashboard')\",\n \"def log_in():\\n if request.method == 'POST':\\n username = request.form['username']\\n password = request.form['password']\\n if PLAN.login_user(username, password):\\n session['name'] = username\\n flash(\\\"Login success ...\\\")\\n return redirect(url_for('index'))\\n flash(\\\"Login failed ...\\\")\\n return render_template('login.html')\\n return render_template('login.html')\",\n \"def login(self):\\r\\n\\r\\n # Open browser with the login URL\\r\\n self.browser.open(self.config[\\\"base_url\\\"] + \\\"login\\\")\\r\\n\\r\\n # Select the login form\\r\\n self.browser.select_form('form[action=\\\"/login/\\\"]')\\r\\n\\r\\n # Fill the login form.\\r\\n self.browser[\\\"email\\\"] = self.config[\\\"email\\\"]\\r\\n self.browser[\\\"password\\\"] = self.config[\\\"password\\\"]\\r\\n\\r\\n # Submit form\\r\\n self.browser.submit_selected()\",\n \"def login_required(view_func):\\n @wraps(view_func)\\n def _checklogin(request, *args, **kwargs):\\n if request.user.is_active:\\n # The user is valid. Continue to the admin page.\\n return view_func(request, *args, **kwargs)\\n return site.login(request)\\n return _checklogin\",\n \"def dispatch(request):\\n if request.user.is_admin:\\n return redirect(reverse(\\\"admin-dashboard\\\"))\\n else:\\n return redirect(reverse(\\\"trainee-dashboard\\\"))\",\n \"def login_page():\\n form = loginUser()\\n\\n if \\\"user\\\" in session:\\n logged_user = session[\\\"user\\\"]\\n return redirect(f\\\"users/{logged_user}\\\")\\n\\n if form.validate_on_submit():\\n username=form.username.data\\n password=form.password.data\\n\\n user = User.authenticate(username=username, password=password)\\n\\n if user:\\n session[\\\"user\\\"] = user.username\\n\\n return redirect(f'/users/{username}')\\n else:\\n form.password.errors = ['Unable to log in']\\n\\n return render_template(\\\"login_form.html\\\", form=form)\",\n \"def adpanel():\\n if 'user_id' not in session or session['user_id'] != 'admin':\\n return redirect(url_for('login'))\\n return render_template('adminpanel.html')\",\n \"def login_index():\\n try:\\n err_id = request.args['err_id']\\n except:\\n err_id = '0'\\n if err_id == '1':\\n error_message = 'Wrong username! Please make sure your username is right or you have registered before.'\\n elif err_id == '2':\\n error_message = 'Wrong password! Please type again with correct one!'\\n else:\\n error_message = ''\\n if check_login():\\n did_login = True\\n else:\\n did_login = False\\n if not did_login: \\n \\\"\\\"\\\"show login page\\\"\\\"\\\"\\n return render_template(\\\"login/login.html\\\", message=error_message)\\n else:\\n \\\"\\\"\\\"jump to manage page\\\"\\\"\\\"\\n return redirect(url_for('manage.manage_index'))\",\n \"def login():\\n\\n if current_user is not None and current_user.is_authenticated():\\n return redirect(url_for(\\\"user.profile\\\"))\\n\\n form = LoginForm(request.form)\\n if form.validate_on_submit():\\n user, authenticated = User.authenticate(form.login.data,\\n form.password.data)\\n\\n if user and authenticated:\\n login_user(user, remember=form.remember_me.data)\\n return redirect(request.args.get(\\\"next\\\") or\\n url_for(\\\"forum.index\\\"))\\n\\n flash((\\\"Wrong username or password\\\"), \\\"danger\\\")\\n return render_template(\\\"auth/login.html\\\", form=form)\",\n \"def test_admin_user_login_with_redirect(self):\\n self.get_page(\\\"/\\\")\\n self.at_page(\\\"/\\\")\\n self.login(\\\"admin\\\", \\\"admin\\\", \\\"/admin/\\\")\\n self.at_page(\\\"/admin/\\\")\\n self.should_see(\\\"Django administration\\\")\",\n \"def login():\\n authorized_redirect_URIs = ['localhost:5000', '127.0.0.1:5000']\\n\\n disabled = False\\n if request.host not in authorized_redirect_URIs:\\n flash(Markup(\\\"\\\"\\\"Please use\\n <a href='http://localhost:5000/login'>http://localhost:5000</a>\\n or\\n <a href='http://127.0.0.1:5000/login'>http://127.0.0.1:5000</a>\\n to access the application otherwise the OAuth will not work\\\"\\\"\\\"),\\n category='danger')\\n disabled = True\\n\\n if current_user.is_authenticated:\\n return redirect(url_for('url.index'))\\n return render_template('login.html', disabled=disabled)\",\n \"def handle_needs_login():\\n flash(\\\"You must be logged in to access this page.\\\")\\n return redirect(url_for('auth.login', next=request.path))\",\n \"def login():\\n\\n return render_template(\\\"login_form.html\\\")\",\n \"def login(self) -> redirect:\\n\\t\\tif self.is_authorized:\\n\\t\\t\\tflash(\\\"You are already logged in.\\\")\\n\\t\\t\\treturn redirect(url_for(\\\"index\\\"))\\n\\t\\telif request.method == \\\"GET\\\":\\n\\t\\t\\treturn render_template(\\\"login.jinja2\\\")\\n\\t\\tsession[\\\"state\\\"] = str(uuid4())\\n\\t\\treturn self.oauth.authorize(callback=url_for(\\\"authorize\\\", _external=True), state=session[\\\"state\\\"])\",\n \"def homepage():\\n if g.user:\\n return redirect(f\\\"/user/{g.user.id}\\\")\\n else:\\n return redirect(\\\"/landing\\\")\",\n \"def index(request):\\n \\n user = get_user(request)\\n\\n # single auth system?\\n if len(ENABLED_AUTH_SYSTEMS) == 1 and not user:\\n return HttpResponseRedirect(reverse(AUTH_START, args=[ENABLED_AUTH_SYSTEMS[0]])+ '?return_url=' + request.GET.get('return_url', ''))\\n\\n #if DEFAULT_AUTH_SYSTEM and not user:\\n # return HttpResponseRedirect(reverse(start, args=[DEFAULT_AUTH_SYSTEM])+ '?return_url=' + request.GET.get('return_url', ''))\\n \\n default_auth_system_obj = None\\n if DEFAULT_AUTH_SYSTEM:\\n default_auth_system_obj = AUTH_SYSTEMS[DEFAULT_AUTH_SYSTEM]\\n\\n #form = password.LoginForm()\\n\\n return render_template(request, 'index', {'return_url' : request.GET.get('return_url', '/'),\\n 'enabled_auth_systems' : ENABLED_AUTH_SYSTEMS,\\n 'default_auth_system': DEFAULT_AUTH_SYSTEM,\\n 'default_auth_system_obj': default_auth_system_obj})\",\n \"def login():\\n # Initialise login form\\n form = UserLoginForm()\\n # Validate and process form data\\n if form.validate_on_submit():\\n # Get form data\\n username = form.username.data\\n password = form.password.data\\n # Check if username and password is valid\\n valid, userID = gdb.verifyuser(username, password)\\n if(valid):\\n user = gdb.getuserbyid(userID)\\n login_user(user)\\n return redirect(url_for('main.dashboard'))\\n else:\\n flash(\\\"Invalid username or password.\\\", category=\\\"error\\\")\\n return redirect(url_for('main.login'))\\n # Render template\\n return render_template('login.html', form=form)\",\n \"def login_form(request):\\n return {'login_form': LoginForm()}\",\n \"def login_required(func):\\n @wraps(func)\\n def f(*args, **kwargs):\\n if g.user is None:\\n app.logger.info('redirecting not logged in user')\\n return redirect(url_for('index'))\\n elif not g.user.initialized and f.__name__ not in ['profile_create','logout']:\\n return redirect(url_for('profile_create'))\\n else:\\n return func(*args, **kwargs)\\n return f\",\n \"def logged_in(request):\\n return request.current_user is not None\",\n \"def local_login():\\n if not current_app.config[\\\"USE_LOCAL_AUTH\\\"]:\\n return redirect(url_for('auth.login'))\\n login_form = BasicLoginForm()\\n if request.method == \\\"POST\\\":\\n email = request.form[\\\"email\\\"]\\n\\n user = find_user_by_email(email)\\n\\n if user is not None:\\n login_user(user)\\n session[\\\"user_id\\\"] = current_user.get_id()\\n\\n create_auth_event(\\n auth_event_type=event_type.USER_LOGIN,\\n user_guid=session[\\\"user_id\\\"],\\n new_value={\\n 'success': True,\\n }\\n )\\n\\n next_url = request.form.get(\\\"next\\\")\\n if not is_safe_url(next_url):\\n return abort(400, UNSAFE_NEXT_URL)\\n\\n return redirect(next_url or url_for(\\\"main.index\\\"))\\n else:\\n error_message = \\\"User {email} not found. Please contact your agency FOIL Officer to gain access to the system.\\\".format(\\n email=email)\\n flash(error_message, category=\\\"warning\\\")\\n return render_template(\\n \\\"auth/local_login_form.html\\\", login_form=login_form\\n )\\n\\n elif request.method == \\\"GET\\\":\\n return render_template(\\n \\\"auth/local_login_form.html\\\",\\n login_form=login_form,\\n next_url=request.args.get(\\\"next\\\", \\\"\\\"),\\n )\",\n \"def login_to_flask(self, form):\\n\\n flask_login.login_user(self, remember=form.remember_me.data)\\n\\n # handle 'next' page if we're stopping them en route\\n next_page = flask.request.args.get('next')\\n if not next_page or werkzeug.urls.url_parse(next_page).netloc != '':\\n next_page = flask.url_for('dashboard')\\n\\n # create a response, inject the token into the cookie and return\\n redirect = flask.redirect(next_page)\\n response = app.make_response(redirect)\\n response.set_cookie('kdm-manager_token', self.token)\\n return response\",\n \"def do_login(self, backend, user):\",\n \"def login():\\n if current_user.is_authenticated:\\n return redirect(url_for('main.index'))\\n form = LoginForm()\\n if form.validate_on_submit():\\n user = User.query.filter_by(\\n username=form.username.data.lower()\\n ).first()\\n\\n if user is None or not user.check_password(form.password.data):\\n flash('Invalid username or password', 'error')\\n return render_template('login.html',\\n title='Sign In', form=form), 401\\n\\n if not user.email_confirmed:\\n flash(\\\"Please confirm your email \\\\\\n address to activate your Account\\\", 'error')\\n return render_template('login.html',\\n title='Sign In', form=form), 401\\n\\n login_user(user, remember=form.remember_me.data)\\n\\n next_page = request.args.get('next')\\n if not next_page or url_parse(next_page).netloc != '':\\n next_page = url_for('main.index')\\n return redirect(next_page)\\n return render_template('login.html', title='Sign In', form=form), 401\",\n \"def login_view():\\n\\n #Bypass if user is logged in\\n if current_user.is_authenticated:\\n return redirect(url_for(\\\"profiles.profile_view\\\",id=current_user.id))\\n \\n form = LoginForm()\\n if form.validate_on_submit():\\n user = User.query.filter_by(email=form.email.data).first()\\n\\n #Check if the password matched\\n if user and user.check_password(form.password.data):\\n login_user(user)\\n next_page = request.args.get(\\\"next\\\")\\n return redirect(next_page or url_for(f\\\"profiles.profile_view\\\", id=current_user.id))\\n flash(\\\"Invalid email and password.\\\")\\n return redirect(url_for(\\\"auth.login_view\\\"))\\n return render_template(\\\"login.html\\\", form=form)\",\n \"def adminlogin(request):\\n request.GET = request.GET.copy()\\n request.GET['redirect'] = '/annaleut/admin/'\\n return auth_views.login(request, template_name='admin/login.html',\\n redirect_field_name='redirect')\",\n \"def default():\\n\\treturn render_template(\\\"login.html\\\")\",\n \"def login_bot(self):\\n pass\",\n \"def login():\\n form = LoginForm()\\n if form.validate_on_submit():\\n\\n # check whether employee exists in the database and whether\\n # the password entered matches the password in the database\\n expert_data = Expert.query.filter_by(email=form.email.data).first()\\n if expert_data is not None and expert_data.verify_password(\\n form.password.data):\\n # log employee in\\n login_user(expert_data)\\n\\n # redirect to the appropriate dashboard page\\n if expert_data.is_admin:\\n return redirect(url_for('home.admin_dashboard'))\\n else:\\n return redirect(url_for('home.dashboard'))\\n\\n # when login details are incorrect\\n else:\\n flash('Invalid email or password.')\\n\\n # load login template\\n return render_template('auth/login.html', form=form, title='Login')\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return self.login()\",\n \"def require_login(self):\\n\\tif users.get_current_user():\\n\\t return True\\n\\telse:\\n\\t self.redirect(users.create_login_url(self.request.uri))\\n\\t return False\",\n \"def go_to_dashboard():\\n\\treturn render_template(\\\"/dashboard.html\\\")\",\n \"def login():\\n return render_template('login.html', next=flask.request.args.get(\\\"next\\\",\\\"/sessions\\\"))\",\n \"def login(self):\\n url = self._root + self._routes[\\\"login\\\"]\\n self.r = self.reqsession.get(url) \\n if self.r.url == 'https://console.zerodha.com/dashboard':\\n cookies = self.reqsession.cookies.get_dict('console.zerodha.com')\\n self.console_session = cookies['session']\\n self.public_token = self.reqsession.cookies['public_token']\\n return True\\n else:\\n raise Exception(\\\"Login failed or Kite session expired\\\")\",\n \"def login():\\n return render_template('auth/login.html')\"\n]","isConversational":false},"negative_scores":{"kind":"list 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accesscode"},"document":{"kind":"string","value":"def accesscode(request, code):\n employee = Employee.objects.get(access_code=code)\n user = employee.user\n user.backend = 'django.contrib.auth.backends.ModelBackend'\n login(request, user)\n return HttpResponseRedirect('/')"},"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 login():","def login():","def login(self):\n self.open(self.urls['login'])\n self.select_form(nr=0)\n\n self.form['custno'] = self.username\n self.form['password'] = self.password\n res = self.submit()\n \n return res","def login():\n url = AUTH_URL + '&state=' + str(uuid1())\n try:\n import webbrowser\n webbrowser.open(url)\n except:\n pass\n \n print('Go to the following link in your browser:\\n\\n\\t{}\\n'.format(url))\n\n auth_code = input('Enter verification code: ')\n print('\\nObtaining access token...')\n Spotify.refresh(auth_code)\n print('Credentials saved to {}'.format(CREDS_PATH))\n return","def login(self):","def post_login(self, username, password, code):\n values = {\"ctl00$ContentPlaceHolder1$MemberName\" : username,\n \"ctl00$ContentPlaceHolder1$MemberPass\" : password,\n \"ctl00$ContentPlaceHolder1$CheckCode\" : code,\n \"__EVENTTARGET\" : \"\",\n \"__EVENTARGUMENT\" : \"\",\n \"__VIEWSTATE\" : \"/wEPDwUJNjIyMTU5NjYyZGQ=\",\n \"__EVENTVALIDATION\" : \"/wEWBQLwhorcCAKxtMbHCgKNvavEBwKCi/rDCQKM5+qlBA==\",\n \"ctl00$ContentPlaceHolder1$RegBtn\": \"登 录\",\n }\n result = self.data_post(values, self.login_url, self.login_send_header)\n return result","def login():\n auth_state = str(uuid.uuid4())\n SESSION.auth_state = auth_state\n\n # For this sample, the user selects an account to authenticate. Change\n # this value to 'none' for \"silent SSO\" behavior, and if the user is\n # already authenticated they won't need to re-authenticate.\n prompt_behavior = 'select_account'\n\n params = urllib.parse.urlencode({'response_type': 'code',\n 'client_id': config.CLIENT_ID,\n 'redirect_uri': config.REDIRECT_URI,\n 'state': auth_state,\n 'resource': config.RESOURCE,\n 'prompt': prompt_behavior})\n\n return bottle.redirect(config.AUTHORITY_URL + '/oauth2/authorize?' + params)","def login(self):\n r = self._login_token()","def login(self):\n login_form = {\"kid\": \"\",\n \"uni\": self.server,\n \"login\": self.username,\n \"pass\": self.password}\n url = \"https://%s.ogame.gameforge.com/main/login\" % self.country_code\n result = self.session.post(url, data=login_form)","def login():\n pass","def do_login(self, backend, user):","def login(self):\r\n user_account = db.find_one({\"cpr_number\": request.form.get(\"CPR\")})\r\n if user_account is not None:\r\n if self.verify_password(user_account[\"password\"], request.form.get(\"password\")):\r\n return self.start_session(user_account)\r\n return jsonify({\"error\": \"Invalid login credentials\"}), 401","def login():\n if app.testing:\n callback_url = url_for('user.authorize', _external=True)\n else:\n callback_url = 'https://codegolf.uqcs.org.au/user/authorize'\n return git_auth.authorize(callback=callback_url)","def _login(self, login):\n self._tokens.clear()\n name, password = login\n\n params = {\"action\": \"query\", \"meta\": \"tokens\", \"type\": \"login\"}\n with self._api_lock:\n result = self._api_query(params, no_assert=True)\n try:\n token = result[\"query\"][\"tokens\"][\"logintoken\"]\n except KeyError:\n raise exceptions.LoginError(\"Couldn't get login token\")\n\n params = {\"action\": \"login\", \"lgname\": name, \"lgpassword\": password,\n \"lgtoken\": token}\n with self._api_lock:\n result = self._api_query(params, no_assert=True)\n\n res = result[\"login\"][\"result\"]\n if res == \"Success\":\n self._tokens.clear()\n self._save_cookiejar()\n return\n if res == \"Illegal\":\n e = \"The provided username is illegal.\"\n elif res == \"NotExists\":\n e = \"The provided username does not exist.\"\n elif res == \"EmptyPass\":\n e = \"No password was given.\"\n elif res == \"WrongPass\" or res == \"WrongPluginPass\":\n e = \"The given password is incorrect.\"\n else:\n e = \"Couldn't login; server says '{0}'.\".format(res)\n raise exceptions.LoginError(e)","def mfa_login(self, mfacode):\n\n try:\n\n response = self.post(\"/authentication/login\",\n {\"user\": self.user, \"password\": self.password, \"token\": int(mfacode)})\n if response.status_code == 200:\n print(\"{0}: Orchestrator MFA login success\".format(self.url))\n # get and set X-XSRF-TOKEN\n for cookie in response.cookies:\n if cookie.name == \"orchCsrfToken\":\n self.headers[\"X-XSRF-TOKEN\"] = cookie.value\n return True\n else:\n print(\"{0}: Orchestrator MFA login failed: {1}\".format(self.url, response.text))\n return False\n except:\n print(\"{0}: Exception - unable to connect to Orchestrator\".format(self.url))\n return False","def make_login(your_username, your_password):\r\n instagram.with_credentials(your_username, your_password)\r\n instagram.login()","def login(email, password):\n rino.login.login(email, password)","def login(email, password):\n rino.login.login(email, password)","def login():\n login_hex = request.json.get(\"authentication\")\n if not login_hex:\n return jsonify({\"code\": \"1\", \"type\": \"user\"})\n\n qr_code_password = app.config[\"QRCODE_PASSWORD\"]\n\n if login_hex != qr_code_password:\n return jsonify({\"code\": \"3\"})\n \n jwt_token = generate_token({\"id\": generate_id()})\n\n return jsonify({\"code\": \"0\", \"token\": jwt_token})","def login(self, name, pin):\n self.account = self.bank.get(name, pin)\n if self.account:\n return \"success\"\n else:\n return \"faliure\"","def login(self):\n res = self.sess.get(self._login_url)\n execution = re.search('name=\"execution\" value=\"(.*?)\"', res.text).group(1)\n res = self.sess.get(url=self._pub_key_url).json()\n n, e = res['modulus'], res['exponent']\n encrypt_password = rsa_encrypt(self.password, e, n)\n data = {\n 'username': self.username,\n 'password': encrypt_password,\n 'execution': execution,\n '_eventId': 'submit'\n }\n res = self.sess.post(url=self._login_url, data=data)\n\n # check if login successfully\n if '统一身份认证' in res.content.decode():\n self.status = \"FAILED_LOGIN\"\n raise LoginError('Login failed. Please check your ZJU username and password.')\n logger.info(\"%s Successfully logined.\" % self)\n self.status = \"LOGINED\"\n return self.sess","def login(self, account, password):\n url = 'https://ceq.nkust.edu.tw/Login'\n\n data = {\n '__RequestVerificationToken': self.csrf_key,\n 'UserAccount': account,\n 'Password': password,\n }\n res = self.main_session.post(url=url, data=data, allow_redirects=False)\n if res.status_code == 302:\n soup = BeautifulSoup(res.text, 'html.parser')\n status = soup.find('a')['href']\n if status == '/StuFillIn':\n return True\n return False","def _login(self, *args, **kwargs):\n pass","def login(self):\n \n self.br.open(\"http://kanji.koohii.com/login\")\n self.br.form = list(self.br.forms())[0]\n self.br[\"username\"] = USER\n self.br[\"password\"] = PASSWORD\n my_response = self.br.submit()\n print \"Login successful\"","def do_login(user, password):\n return um.do_login(user, password)","def login(self, response):\n\t\treturn FormRequest.from_response(response,\n\t\t\t formdata={'username': 'scanner1', 'password': 'scanner1'},\n\t\t\t callback=self.check_login_response)","def login(self) -> int:\n r = self.session.post(\n self.api_endpoint,\n data={\n \"action\": \"login\",\n \"lgname\": self.user,\n \"lgpassword\": self.password,\n \"format\": \"json\",\n },\n )\n token = json.loads(r.text)[\"login\"][\"token\"]\n r = self.session.post(\n self.api_endpoint,\n data={\n \"action\": \"login\",\n \"lgname\": self.user,\n \"lgpassword\": self.password,\n \"lgtoken\": token,\n \"format\": \"json\",\n },\n )\n if json.loads(r.text)[\"login\"][\"result\"] != \"Success\":\n return -1\n return 0","def login(self):\n request = self.REQUEST\n response = request['RESPONSE']\n\n login = request.get('__ac_name', '')\n password = request.get('__ac_password', '')\n\n pas_instance = self._getPAS()\n\n if pas_instance is not None:\n pas_instance.updateCredentials(request, response, login, password)","def doLogin(self):\n\t\tlogin_data = urllib.urlencode({\n\t\t\t'operatorName' : self.username,\n\t\t\t'password' : self.password,\n\t\t\t'submit' : 'Iniciar+sesi%C3%B3n',\n\t\t})\n\n\t\tresponse = self.opener.open(\"http://172.16.0.2/tdserver/login_deal.jsp\", login_data)\t\t### deberia devolver verdadero o falso segun se logueo o no","def steam_login(username, password, two_factor_code):\n steam_client = SteamClient() # Make steam client object\n steam_client.login(username, password, two_factor_code=two_factor_code) # Log in\n if not steam_client.logged_on: # Login failed\n raise SteamLoginException('Login failed.')\n return steam_client","def login(self):\n self.open(base_url + '/login')\n self.type(\"#email\", test_user.email)\n self.type(\"#password\", test_user.password)\n self.click('input[type=\"submit\"]')","def login(self, username=None, password=None):\n if not username:\n username = self.email\n if not password:\n password = self.password\n var = {\n 'client': self.client,\n 'accountType': 'HOSTED_OR_GOOGLE',\n 'service': 'reader',\n 'Email': username,\n 'Passwd': password\n }\n # do login\n self.auth_code = \"\"\n resp1 = self.make_request(url_login, var, use_get=False)\n self.auth_code = resp1['Auth']\n self._logger.info(u\"Logged in as {}\".format(username))","def login(self):\n\n # Fetch and parse hidden inputs from login page\n # Use specific CA bundle to fix SSL verify problems if set as env.\n verify = True\n\n override_ca_bundle = os.getenv('OVERRIDE_CA_BUNDLE')\n if override_ca_bundle:\n verify = override_ca_bundle\n\n req = self.session.get(self.BASE_URL + '/im/login/privat',\n verify=verify)\n\n # Get the login form\n soup = BeautifulSoup(req.content, 'html.parser')\n login_form = soup.select('#pPin_form')\n\n # Post login to current URL\n login_post_url = req.url\n\n # Build POST data with login settings and hidden inputs\n data = self._hidden_inputs_as_dict(login_form)\n data['pPin_inp'] = self.personal_identity_number\n data['pPinKod_inp'] = self.pin_code\n\n # Login request\n req = self.session.post(login_post_url, data=data)\n self.last_req_body = req.content\n\n self._parse_tokens(req.text)\n\n return True","def login(self, request):\n request.session['state'] = state = uuid.uuid4().hex\n auth_url = flat_url(\n PROVIDER_AUTH_URL,\n client_id=self.consumer_key,\n response_type='code',\n state=state\n )\n return HTTPFound(location=auth_url)","def login_leetcode(driver):\n login_url = \"https://leetcode.com/accounts/login/\"\n name_field_id = \"id_login\"\n password_field_id = \"id_password\"\n login_button_selector = 'button.btn-primary'\n driver.get(login_url)\n time.sleep(2)\n username_ele = driver.find_element_by_id(name_field_id)\n password_ele = driver.find_element_by_id(password_field_id)\n username_ele.send_keys(username)\n password_ele.send_keys(password)\n driver.find_element_by_css_selector(login_button_selector).click()","def login(self):\n # create auth payload\n payload = '{{\"grant_type\": \"password\", \"username\": \"{}\", \"password\": \"{}\"}}'.format(\n self.username, self.password)\n auth_headers = {**FTDClient.headers}\n r = requests.post(\"https://{}:{}/api/fdm/{}/fdm/token\".format(self.server_address, self.server_port, self.version),\n data=payload, verify=False, headers=auth_headers)\n if r.status_code == 400:\n raise Exception(\"Error logging in: {}\".format(r.content))\n try:\n # This token will act as the\n self.access_token = r.json()['access_token']\n # cache the original token in case we do a custom login\n self.original_access_token = self.access_token\n except:\n logging.error(\n f'Unable to log into server: https://{self.server_address}:{self.server_port}')\n raise","def login(self) -> None:\n\n sObj = Splitwise(self.consumer_key, self.consumer_secret)\n self.url, self.login_secret = sObj.getAuthorizeURL()\n print(self.url)\n self.oauth_token = input('token: ')\n self.oauth_verifier = input('verifier: ')","async def login_access_token(\n form_data: OAuth2PasswordRequestForm = Depends()\n):\n user = await crud.user.authenticate(\n username=form_data.username, password=form_data.password\n )\n if not user:\n raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail=\"Incorrect credentials\")\n elif not user.is_active:\n raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail=\"Inactive user\")\n elif not user.is_email_verified:\n raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail=\"Please verify your account via email\")\n access_token_expires = timedelta(minutes=config.ACCESS_TOKEN_EXPIRE_MINUTES)\n return {\n \"access_token\": create_access_token(\n data={\"user_id\": user.id}, expires_delta=access_token_expires\n ),\n \"token_type\": \"bearer\",\n }","def Login(self, username, password, onSuccess, onFailure):\n pass","def login_access_token(\n db: Session = Depends(get_db),\n form_data: OAuth2PasswordRequestForm = Depends()\n) -> Any:\n user = crud.user.authenticate(\n db, email=form_data.username, password=form_data.password\n )\n if not user:\n raise HTTPException(\n status_code=400, detail=\"Incorrect email or password\")\n elif not crud.user.is_active(user):\n raise HTTPException(status_code=400, detail=\"Inactive user\")\n access_token_expires = timedelta(\n minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES)\n return {\n \"access_token\": security.create_access_token(\n user.id, expires_delta=access_token_expires\n ),\n \"token_type\": \"bearer\",\n }","def log_in(codecool):\n\n login = school_view.get_login()\n password = school_view.get_password()\n\n password = utilities.hash_password(password)\n\n users = codecool.managers_list + codecool.administrators_list + codecool.mentors_list + codecool.students_list\n for user in users:\n if user.login == login and user.password == password:\n return user","def do_login(self, password):\n # Creating JSON string with authentication credentails.\n in_data = ('{{ \"username\":\"{username}\",'\n '\"password\":\"{password}\" }}'\n ).format(\n username=self.pub_user,\n password=password\n )\n\n url = self.base_url + \"/oasis/login\"\n response = self.do_request(url, in_data)\n json_response = json.loads(response.content)\n\n if json_response[\"success\"] == False:\n print(\"Invalid user id or password\")\n else:\n self.cookies = dict(sessionid=response.cookies['sessionid'])\n print(\"You are logged into Mid-tier\")\n\n logger.info( 'Log in response ' + str(response.content))","def authentication_callback(request):\n code = request.GET.get('code')\n user = authenticate(token=code, request=request)\n if user:\n auth_login(request, user)\n set_session_from_user(request, user)\n region = request.user.endpoint\n region_name = dict(Login.get_region_choices()).get(region)\n request.session['region_endpoint'] = region\n request.session['region_name'] = region_name\n url = getattr(settings, \"LOGIN_REDIRECT_URL\", \"/\")\n resp = HttpResponseRedirect(url)\n\n return resp","def login():\n guid = uuid.uuid4() # guid used to only accept initiated logins\n session['state'] = guid\n return msgraphapi.authorize(callback=url_for('authorized', _external=True), state=guid)","def code_login(ui, repo, **opts):\n\tMySend(None)","def auth():\n\tcode = request.query.code\n\tauth = 'https://foursquare.com/oauth2/access_token'\n\tparams = dict(\n\t\tclient_id=CLIENT_ID,\n\t\tclient_secret=CLIENT_SECRET,\n\t\tgrant_type='authorization_code',\n\t\tredirect_uri=REDIRECT_URI,\n\t\tcode=code\n\t)\n\tauth_says = fetch('%s?%s'%(auth, urlencode(params)))\n\tauth_response = json.loads(auth_says.content)\n\tif 'access_token' in auth_response:\n\t\toauth_token=auth_response['access_token']\n\t\tresponse.set_cookie('user', oauth_token, secret=CLIENT_SECRET)\n\t\tlogging.info('new oauth_token:%s'%oauth_token)\n\t\tredirect('/')\n\telse:\n\t\tlogging.error(auth_response)\n\t\tabort()","def login(self):\n\t\treturn","def login(self):\n url = 'https://ngb.to/login.php?do=login'\n\n params = {'do': 'login'}\n payload = {'vb_login_username': self.username,\n 'vb_login_password': self.password,\n 'url': \"index.php\",\n 'do': \"login\",\n 'vb_login_md5password': \"\",\n 'vb_login_md5password_utf': \"\",\n 's': \"\",\n 'securitytoken': \"guest\",\n 'cookieuser': \"1\"}\n\n self.session.post(url, data=payload, params=params)","def einloggen(self):\n \n self.c.login(self.username.text(), self.password.text(), \"1\")","def login():\n req = request.get_json(force=True)\n username = req.get('username', None)\n password = req.get('password', None)\n user = guard.authenticate(username, password)\n ret = {'access_token': guard.encode_jwt_token(user)}\n return ret, 200","def do_login_login():\n print(inspect.stack()[1][3])\n print(request.form)\n query = select([User]).where(and_(User.columns.email == request.form['email'],User.columns.password==request.form['password'] ))\n ResultProxy = connection.execute(query)\n ResultSet = ResultProxy.fetchone()\n if ResultSet:\n session['logged_in'] = True\n else:\n flash('wrong password!')\n # return str(get_flashed_messages())\n return home(result)","def login_access_token(form_data: OAuth2PasswordRequestForm = Depends()):\n user = auth_handler.authenticate_user(\n username=form_data.username, password=form_data.password\n )\n if user is None:\n raise HTTPException(\n detail=\"Incorrect username and/or password\", status_code=400\n )\n\n return APIResponse(\n msg=TokenResponse(\n access_token=auth_handler.encode_token(user.id), token_type=\"bearer\"\n )\n )","def login():\n # if we are already logged in, go back to were we came from\n if auth.is_authenticated():\n return flask.redirect(oid.get_next_url())\n return oid.try_login(\"https://www.google.com/accounts/o8/id\",\n ask_for=['email', 'fullname', 'nickname'])","def login():\n data = request.get_json()\n if 'username' in data and 'password' in data:\n username = data['username']\n password = data['password']\n access_token = authenticate(username, password)\n if access_token is not None:\n print('access token: ' + access_token)\n return jsonify({'access_token': access_token})\n else:\n abort(403)\n else:\n abort(400)","def authenticate_with_github(username=None, password=None, code=None):\n if username is not None and password is not None:\n print(' (auth given as {}:{})'.format(username, '*'*len(password)))\n\n def _2fa_func():\n return code\n\n if code:\n return login(username, password, two_factor_callback=_2fa_func)\n else:\n return GitHub(username, password)","def login(self):\n return self.client.login(username='Georgie', password='12345678')","async def login(form_data: OAuth2PasswordRequestForm = Depends()):\n db = get_database()\n\n user = await crud.user.authenticate(\n db, username=form_data.username, password=form_data.password\n )\n\n if not user:\n raise HTTPException(\n status_code=HTTP_400_BAD_REQUEST, detail=\"Incorrect email or password\"\n )\n elif not crud.user.is_active(user):\n raise HTTPException(\n status_code=HTTP_400_BAD_REQUEST, detail=HTTP_400_BAD_REQUEST_INACTIVE_USER\n )\n\n access_token_expires = timedelta(minutes=config.ACCESS_TOKEN_EXPIRE_MINUTES)\n\n return {\n \"access_token\": create_access_token(\n data={\"username\": user.username}, expires_delta=access_token_expires\n ),\n \"token_type\": \"bearer\",\n }","def login():\n guid = uuid.uuid4() # guid used to only accept initiated logins\n session['state'] = guid\n return msgraphapi.authorize(callback=url_for('authorized', _external=True), state=guid)","def test_auth_code_positive(self, api):\n self.builder.add_user(api.get_user())\n resp = api.login_user(api.get_user().username, api.get_user().password)\n self.builder.del_user(api.get_user())\n assert resp.status_code == 200","def login_user():\n pass","def loginAsManager(self):\n self.browser.open('http://nohost/plone/')\n self.browser.getLink('Log in').click()\n self.browser.getControl('Login Name').value = 'root'\n self.browser.getControl('Password').value = 'secret'\n self.browser.getControl('Log in').click()","async def login(form_data: OAuth2PasswordRequestForm = Depends()):\n user = get_user_info(form_data.username)\n if user == None:\n raise HTTPException(status_code=404, detail=\"Incorrect username or password\")\n hashed_password = simple_hash(form_data.username, form_data.password)\n if not hashed_password == user.password:\n raise HTTPException(status_code=400, detail=\"Incorrect username or password\")\n\n return {\"access_token\": user.name, \"token_type\": \"bearer\"}","def by_code(request):\n\n requestData = {}\n serializer_context = {\n 'request': request,\n }\n requestData['access_code'] = request.data['access_code']\n requestData['prefix'] = request.data['prefix']\n # Notice here that we do not call `serializer.save()` like we did for\n # the registration endpoint. This is because we don't have\n # anything to save. Instead, the `validate` method on our serializer\n # handles everything we need.\n\n serializer = LoginByCodeSerializer(data=requestData,context=serializer_context)\n serializer.is_valid(raise_exception=True) # Retorna error de tipo 400\n # Responde con los datos que ya incluye el token de autenticación que fue agregado x el serializador\n return Response(serializer.validated_data, status=status.HTTP_200_OK)","def login():\n token = generate_token()\n session['token'] = token\n return redirect(esisecurity.get_auth_uri(\n scopes=['esi-wallet.read_character_wallet.v1 esi-characters.read_contacts.v1 esi-characters.write_contacts.v1'],\n state=token,\n ))","def login(userID, robotID, password): #@NoSelf","def login(login_info):\n url = 'http://www.jintiankansha.me/account/signin'\n client = requests.session()\n # Retrieve the CSRF token first\n client.get(url) # sets the cookie\n client.cookies\n xsrf = client.cookies.items()[1]\n login_info[xsrf[0]] = xsrf[1]\n r = client.post(url, data=login_info)\n \n if r.status_code == 200:\n return client\n else:\n return None","async def login_for_access_token(\n form_data: OAuth2PasswordRequestForm = Depends()\n):\n user = authenticate_user(form_data.username, form_data.password)\n if not user:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Incorrect username or password\",\n headers={\"WWW-Authenticate\": \"Bearer\"},\n )\n access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)\n access_token = create_access_token(\n data={\"sub\": user.username}, expires_delta=access_token_expires\n )\n return {\"access_token\": access_token, \"token_type\": \"bearer\"}","def login():\n\n username = str(request.parsed_json['username'])\n password = str(request.parsed_json['password'])\n\n if not auth.check_password(username, password):\n return create_error(401, \"login failed\")\n\n return auth.create_auth_token(username, password), 200","def login():\n domain = parser[\"learningmachine\"][\"domain\"]\n secrets_file = \"{}/{}\".format(dir_path, \"client_secret.json\")\n scope = \"https://www.googleapis.com/auth/userinfo.email\"\n redirect_uri = \"http://{}/login\".format(domain)\n login_handler = LoginHandler(secrets_file, scope, redirect_uri)\n\n if \"code\" in request.args:\n login_handler.setup_user_info(request.args[\"code\"])\n session[\"email\"] = login_handler.email\n session[\"display_name\"] = login_handler.display_name\n\n if not fm.user_exists(login_handler.email):\n msg = \"Adding user: {} with ID of {} to the database.\"\\\n .format(login_handler.email, login_handler.display_name)\n fm.add_user(login_handler.email, login_handler.display_name)\n\n msg = \"Sending user: {} to main page\".format(login_handler.email)\n app.logger.info(msg)\n return redirect(\"/static/main.html\")\n\n else:\n msg = \"No login code yet. Letting Google handle the login process at: {}\"\\\n .format(login_handler.auth_url)\n app.logger.info(msg)\n return redirect(login_handler.auth_url)","def login():\n # Force https redirects when behind a proxy (required on Azure only)\n if 'APPINSIGHTS_INSTRUMENTATIONKEY' in os.environ: # set on Azure\n redirect_uri = url_for('authorize', _external=True, _scheme='https')\n else:\n redirect_uri = url_for('authorize', _external=True)\n\n # As this sample does not have a user session management with loging out, this\n # endpoint always starts a new authorization code flow, without detecting if the user\n # might be already logged in.\n\n return oauth.tapkey.authorize_redirect(redirect_uri)","def attempt_login(self, expected_code, **kwargs):\r\n url = reverse('openid-provider-login')\r\n post_args = {\r\n \"openid.mode\": \"checkid_setup\",\r\n \"openid.return_to\": \"http://testserver/openid/complete/?janrain_nonce=2013-01-23T06%3A20%3A17ZaN7j6H\",\r\n \"openid.assoc_handle\": \"{HMAC-SHA1}{50ff8120}{rh87+Q==}\",\r\n \"openid.claimed_id\": \"http://specs.openid.net/auth/2.0/identifier_select\",\r\n \"openid.ns\": \"http://specs.openid.net/auth/2.0\",\r\n \"openid.realm\": \"http://testserver/\",\r\n \"openid.identity\": \"http://specs.openid.net/auth/2.0/identifier_select\",\r\n \"openid.ns.ax\": \"http://openid.net/srv/ax/1.0\",\r\n \"openid.ax.mode\": \"fetch_request\",\r\n \"openid.ax.required\": \"email,fullname,old_email,firstname,old_nickname,lastname,old_fullname,nickname\",\r\n \"openid.ax.type.fullname\": \"http://axschema.org/namePerson\",\r\n \"openid.ax.type.lastname\": \"http://axschema.org/namePerson/last\",\r\n \"openid.ax.type.firstname\": \"http://axschema.org/namePerson/first\",\r\n \"openid.ax.type.nickname\": \"http://axschema.org/namePerson/friendly\",\r\n \"openid.ax.type.email\": \"http://axschema.org/contact/email\",\r\n \"openid.ax.type.old_email\": \"http://schema.openid.net/contact/email\",\r\n \"openid.ax.type.old_nickname\": \"http://schema.openid.net/namePerson/friendly\",\r\n \"openid.ax.type.old_fullname\": \"http://schema.openid.net/namePerson\",\r\n }\r\n # override the default args with any given arguments\r\n for key in kwargs:\r\n post_args[\"openid.\" + key] = kwargs[key]\r\n\r\n resp = self.client.post(url, post_args)\r\n code = expected_code\r\n self.assertEqual(resp.status_code, code,\r\n \"got code {0} for url '{1}'. Expected code {2}\"\r\n .format(resp.status_code, url, code))","def login():\n data = request.get_json()\n email = data.get('email')\n password = data.get('pwrd')\n user = SQLModel.get_by_attrs(('email', 'pwrd'), 'users', 'email', email)\n try:\n user_pw = user[0][1]\n user_nick = user[0][0]\n if password == user_pw:\n stuff = SQLModel.get_by_attrs(('login', 'pwrdHash', 'type', 'name'), 'users', 'login', login)\n return jsonify(stuff)\n else:\n return 'fail'\n except:\n return 'fail'","def login(self):\n # the login url is just api, not api2\n url = 'https://simple-note.appspot.com/api/login'\n query = {'email': self.email, 'password': self.password}\n data = base64.b64encode(urllib.urlencode(query))\n try:\n fh = urllib2.urlopen(url, data)\n self.authtok = fh.read()\n except urllib2.HTTPError, e:\n # Received a non 2xx status code\n raise SimplenoteError('http error: {}'.format(e.code))\n except urllib2.URLError, e:\n # Non http error, like network issue\n raise SimplenoteError('url error: {}'.format(e.reason))\n fh.close()\n return True","def Login(self):\n self.Send(self.EncryptString('login\\n'))\n\n # 'Please provide your membership number to authenticate:'\n print self.DecryptString(self.Recv(4096))\n\n # Flag 1.\n self.Send(self.EncryptString(self.flag_1))\n\n # 'Ah, I see, sir has a basic account. In that case, your limited cloud\n # execution access has been provisioned.'\n print self.DecryptString(self.Recv(4096))\n\n # 'Will there by anything else today, sir?'\n print self.DecryptString(self.Recv(4096))","def perform_login(self, user_name, user_pass):\n if self.api_type == 'real':\n self.input_user_name(user_name)\n self.input_user_pass(user_pass)\n self.click_login_button()","def login(self, username: str, password: str):\n\n self.driver.get(\"https://myrecsports.usc.edu/booking\")\n self.driver.find_element_by_id(\"loginLink\").click()\n self.driver.find_element_by_id(\"frmExternalLogin\")\n self.driver.execute_script(\"submitExternalLoginForm('Shibboleth')\")\n self.driver.find_element_by_id(\"username\").send_keys(\"noahbkim\")\n self.driver.find_element_by_id(\"password\").send_keys(\"she once was a true love of mine\")\n self.driver.find_element_by_name(\"_eventId_proceed\").click()\n self.driver.find_element_by_id(\"logoutForm\")","def login():\n google = oauth.create_client('google') # create the google oauth client\n redirect_uri = url_for('authorize', _external=True)\n return google.authorize_redirect(redirect_uri)","def login():\n authorization_url, state = facebook.authorization_url(authorization_base_url)\n print 'Please authorize', authorization_url\n\n return redirect(authorization_url, code=302)","def login(self):\n\t\tbot = self.bot\n\t\tbot.get(URL)\n\t\ttime.sleep(2)\n\t\tsign_in = bot.find_element_by_class_name(\"nav__button-secondary\").click()\n\t\ttime.sleep(2)\n\t\temail = bot.find_element_by_id(\"username\")\n\t\temail.send_keys(self.username)\n\t\ttime.sleep(2)\n\t\tpassword = bot.find_element_by_id(\"password\")\n\t\tpassword.send_keys(self.password)\n\t\ttime.sleep(2)\n\t\tsign_in = bot.find_element_by_class_name(\"btn__primary--large.from__button--floating\").click()","def _login_token(self):\n data = {\n 'cmd': 'login',\n 'login': self.username,\n 'password': self.password,\n }\n \n token = self.helper._post_request(\n self.basename,\n self.basic_auth,\n data, \n self.headers)\n\n if token.status_code == 200:\n xml_response = BeautifulSoup(token.content, 'lxml')\n self.token = xml_response.find('token').get_text()\n self.cookies = token.cookies.get_dict()\n else:\n raise Exception('[FAIL] Could not login to OpenVAS')","def login_action(login_page, request, driver):\n login_page.login(request.config.getoption(\"--username\"), request.config.getoption(\"--password\"))","def submit_log_in(self, user_name, password, passcode):\r\n username_txt = user_name.get()\r\n password_txt = password.get()\r\n passcode_txt = passcode.get()\r\n self.my_socket.send(dumps(\"log in\"))\r\n now = datetime.now()\r\n current_time = now.strftime(\"%H:%M:%S\").split(\":\")\r\n time = current_time[0] + current_time[1]\r\n str_log_in = username_txt + \";\" + password_txt + \";\" + passcode_txt + \";\" + time\r\n self.my_socket.send(self.rsa_object.encrypt(str_log_in.encode(), self.server_key))\r\n\r\n response = self.rsa_object.decrypt(self.my_socket.recv(1024)).decode()\r\n if response == \"access granted\":\r\n self.username = username_txt\r\n self.choose_path()\r\n else:\r\n if self.log_in_tries == 2:\r\n self.log_in_tries = 0\r\n self.after_3_wrong_attempts(\"log in\")\r\n else:\r\n self.log_in_tries += 1\r\n lbl_response = Label(self.root, text=response, font=self.title_font,\r\n bg=self.bg_color)\r\n lbl_response.pack(pady=5, padx=10)\r\n lbl_response.after(1000, lbl_response.destroy)\r\n user_name.delete(0, END)\r\n password.delete(0, END)\r\n passcode.delete(0, END)","def do_login(opener, login_info, connection_name, username, password):\n login_payload = {\n \"client_id\": CLIENT_ID,\n \"connection\": connection_name,\n \"password\": password,\n \"popup_options\": \"{}\",\n \"protocol\": \"samlp\",\n \"redirect_uri\": \"https://signin.aws.amazon.com/saml\",\n \"response_type\": \"code\",\n \"scope\": \"openid profile email\",\n \"sso\": True,\n \"state\": login_info[\"state\"],\n \"tenant\": TENANT,\n \"username\": username,\n \"_csrf\": login_info[\"_csrf\"],\n \"_intstate\": \"deprecated\"\n }\n login_payload_json = json.dumps(login_payload).encode()\n # print(login_payload)\n\n headers = {\n \"Content-Type\": \"application/json\",\n \"Origin\": \"https://%s.auth0.com\"%TENANT,\n \"Auth0-Client\": AUTH0_CLIENT_HEADER\n }\n request = urllib.request.Request(\n \"https://%s.auth0.com/usernamepassword/login\"%TENANT,\n data=login_payload_json,\n method=\"POST\",\n headers=headers)\n try:\n response = opener.open(request)\n result = response.read().decode()\n except urllib.error.HTTPError as e:\n error = e.read().decode()\n raise RuntimeError(\"Login error: %s\"%error) from None\n # print(result)\n\n # if success we will get a form in html, post it\n parser = FormParser()\n parser.feed(result)\n callback_params = urllib.parse.urlencode(parser.fields).encode()\n headers = {\n \"Content-Type\": \"application/x-www-form-urlencoded\",\n \"Origin\": \"https://%s.auth0.com\"%TENANT\n }\n request = urllib.request.Request(\n parser.action,\n data=callback_params,\n method=parser.method.upper(), # post => POST\n headers=headers)\n try:\n response = opener.open(request)\n result = response.read().decode()\n except urllib.error.HTTPError as e:\n error = e.read().decode()\n raise RuntimeError(\"Login callback error: %s\"%error) from None\n # print(result)\n\n mfa_info = {\n \"mfaServerUrl\": re.search(\"mfaServerUrl:\\s*?\\\"(.+?)\\\"\", result).group(1),\n \"requestToken\": re.search(\"requestToken:\\s*?\\\"(.+?)\\\"\", result).group(1),\n \"postActionURL\": re.search(\"postActionURL:\\s*?\\\"(.+?)\\\"\", result).group(1),\n \"globalTrackingId\": re.search(\"globalTrackingId:\\s*?\\\"(.+?)\\\"\", result).group(1),\n }\n # print(mfa_info)\n\n return mfa_info","def login(self):\r\n\r\n # Open browser with the login URL\r\n self.browser.open(self.config[\"base_url\"] + \"login\")\r\n\r\n # Select the login form\r\n self.browser.select_form('form[action=\"/login/\"]')\r\n\r\n # Fill the login form.\r\n self.browser[\"email\"] = self.config[\"email\"]\r\n self.browser[\"password\"] = self.config[\"password\"]\r\n\r\n # Submit form\r\n self.browser.submit_selected()","def login(self):\n self.client.login(username=self.user.username, password='test')","def step_impl_1(context, username, pwd):\n\n br = context.browser\n br.get(context.server_url + '/accounts/login/')\n\n user = br.find_element_by_id(\"username\")\n pswd = br.find_element_by_id(\"password\")\n\n user.send_keys(username)\n pswd.send_keys(pwd)\n br.find_element_by_id(\"submit\").click()","def login(self, email, password):\r\n self.provide_info(email, password)\r\n self.submit()","def login(self):\n url = self._root + self._routes[\"login\"]\n self.r = self.reqsession.get(url) \n if self.r.url == 'https://console.zerodha.com/dashboard':\n cookies = self.reqsession.cookies.get_dict('console.zerodha.com')\n self.console_session = cookies['session']\n self.public_token = self.reqsession.cookies['public_token']\n return True\n else:\n raise Exception(\"Login failed or Kite session expired\")","def login(self, returnToURL):\r\n\t\tif not self.request().hasValue('userId') or \\\r\n\t\t\tnot self.request().hasValue('password'):\r\n\t\t\tself.response().sendRedirect('/nova/login.psp?returnToURL=%s' % returnToURL)\r\n\t\t\treturn 0\r\n\t\treturn 1","def registrieren(self):\n self.c.login(self.username.text(), self.password.text(), \"0\")","def _login(self):\n data = self._send(self.nc_request(action=\"login\", parameters={\"apipassword\": self._api_password}))\n\n self._session_id = data[\"apisessionid\"]\n\n logging.info(f\"logged in successfully with session id {self._session_id}\")","def master_login():\n\n master_key = get_master_key().decode()\n login_master = input(\"\\nEnter your master password to begin using Password Manager: \")\n\n if login_master == master_key:\n\n print(\"Access granted!\\n\")\n access_granted = True\n\n return access_granted\n\n else:\n\n print(\"Uh oh, that is not your master password. Try again.\")\n return master_login()","def login(self, username, password):\n\t\turl = \"https://habitica.com/api/v3/user/auth/local/login\"\n\t\tpayload = {\"username\": username, \"password\": password}\n\t\treturn(postUrl(url, self.credentials, payload))","def login():\n token = request.form.get('idtoken')\n if verify_token(token):\n session['logged_in'] = True\n return '', 204\n else:\n return '', 401","def callback():\n # state variable is not used yet but should be used to invalidate the\n # session on incorrect match per client.\n state = request.args.get('state')\n code = request.args.get('code')\n error = request.args.get('error')\n if error:\n return \"Error: \" + error\n headers = {\n 'accept': 'application/json',\n 'cache-control': 'no-cache',\n 'content-type': 'application/x-www-form-urlencoded'\n }\n data = {\n 'grant_type': 'authorization_code',\n 'client_id': CLIENT_ID,\n 'redirect_uri': REDIRECT_URI,\n 'code': code,\n 'code_verifier': CODE_VERIFIER\n }\n client_auth = requests.post(TOKEN_URL, headers=headers, data=data)\n client_json = client_auth.json()\n session['access_token'] = client_json[\"access_token\"]\n return redirect(url_for('.methods'))","def login(self, **kwargs):\n\tusername = kwargs.get('username', self.username)\n\tif not username:\n\t raise RuntimeError, 'no username provided'\n\n\tpassword = kwargs.get('password', self.password)\n\tif not password:\n\t raise RuntimeError, 'no password provided'\n\tself.call('login', username=username, password=password)","def userlogin(self, login, password):\n\n payload = {\n 'PASSWORD': password,\n 'path': \"http://www.gamefaqs.com/\",\n 'EMAILADDR': login,\n }\n\n login_url = 'http://www.gamefaqs.com/user/login'\n\n # Grab key ID\n resp = self.session.get(login_url)\n soup = bs(resp.text, 'html.parser')\n payload['key'] = soup.find('input', class_='hidden')['value']\n\n # Login with user payload\n resp = self.session.post(login_url, data=payload)\n\n soup = bs(resp.text, 'html.parser')\n\n if soup.find_all(string='There was an error while logging you in: '):\n raise Exception('Login Failed!')\n else:\n logging.debug('{} successfully logged in.'.format(self.login))","def login(self):\n \n try:\n # Create socket connection and connect to the server\n self.sck = socket(AF_INET, SOCK_STREAM)\n self.sck.connect((self.host, self.port))\n self.sck.send(self.alchallenge_packet()) # Send Auth Logon Challenge\n srp_rcvd = self.decode_packet(self.sck.recv(1024)) # Read SRP for sending Logon Proof\n csrp = Srp(srp_rcvd['N'], srp_rcvd['g'], self.I, self.p, srp_rcvd['s'], srp_rcvd['B'])\n # Do some math...\n A = csrp.gen_A()\n u = csrp.gen_u()\n S = csrp.gen_S()\n K = csrp.gen_K()\n M = csrp.gen_M()\n # Let's send Auth Logon Proof\n self.sck.send(self.alproof_packet(M.blittle(), A.blittle()))\n return self.decode_packet(self.sck.recv(1024)).get('login') # 1 if no errors\n except ValueError:\n return 0","def login(self):\n headers = {'Content-Type': 'application/x-www-form-urlencoded'}\n token_url = urljoiner(self.baseurl, [\"connect/token\"])\n if(self.debug):\n print(token_url)\n new_auth = dict(self.auth_data)\n new_auth['password'] = \"XXXXX\"\n print(\"Authentication Data (without password):\")\n print(new_auth)\n r = requests.post(token_url, data=self.auth_data, headers=headers)\n # New environments do not redirect /rest/connect/token to\n # /auth/connect/token so lets check this case explicitly\n if(r.status_code > 400):\n new_token_url = self.baseurl.rstrip(\n \"/rest\") + \"/auth/connect/token\"\n if(self.debug):\n print(\"cannot connect to: \" + token_url)\n print(\"trying: \" + new_token_url)\n r = requests.post(\n new_token_url,\n data=self.auth_data,\n headers=headers)\n self.last_login = time.time()\n self.handle_error_message(r)\n self.auth_result = r.json()\n access_token = r.json().get('access_token')\n self.headers = {'Authorization': 'Bearer ' + access_token,\n 'Content-Type': 'application/json'}\n # Always relogin when time remaining on the current token is in between 1 min and 3 min\n self.refresh_window = min(max(60, 0.01 * self.auth_result['expires_in']), 180)","def login(username, password, \n get_steamguard_code = get_steamguard_code_manual, \n solve_captcha = solve_captcha_manual,\n max_tries = 5):\n response_dict = auth.get_rsa_key(username)\n\n # The RSA information is encoded as hex strings.\n # Transform to integers.\n rsa_mod = int(response_dict['publickey_mod'], 16)\n pub_exp = int(response_dict['publickey_exp'], 16)\n\n encrypted_password = auth.get_encrypted_password(password, rsa_mod, pub_exp)\n timestamp = response_dict['timestamp']\n\n gid = ''\n text = ''\n email_auth = ''\n are_we_logged_in = False\n tries = 0\n while not are_we_logged_in and tries < max_tries:\n tries += 1\n response_dict = auth.do_login(username, encrypted_password, timestamp, email_auth, gid, text)\n if 'captcha_needed' in response_dict:\n text = solve_captcha(gid)\n elif 'emailauth_needed' in response_dict:\n email_auth = get_steamguard_code_manual()\n elif 'success' in response_dict and response_dict['success']:\n are_we_logged_in = True\n else:\n print response_dict\n raise Exception(\"I don't understand this state!\")\n\n if tries >= max_tries:\n raise Exception(\"Too many tries!\")\n\n return response_dict"],"string":"[\n \"def login():\",\n \"def login():\",\n \"def login(self):\\n self.open(self.urls['login'])\\n self.select_form(nr=0)\\n\\n self.form['custno'] = self.username\\n self.form['password'] = self.password\\n res = self.submit()\\n \\n return res\",\n \"def login():\\n url = AUTH_URL + '&state=' + str(uuid1())\\n try:\\n import webbrowser\\n webbrowser.open(url)\\n except:\\n pass\\n \\n print('Go to the following link in your browser:\\\\n\\\\n\\\\t{}\\\\n'.format(url))\\n\\n auth_code = input('Enter verification code: ')\\n print('\\\\nObtaining access token...')\\n Spotify.refresh(auth_code)\\n print('Credentials saved to {}'.format(CREDS_PATH))\\n return\",\n \"def login(self):\",\n \"def post_login(self, username, password, code):\\n values = {\\\"ctl00$ContentPlaceHolder1$MemberName\\\" : username,\\n \\\"ctl00$ContentPlaceHolder1$MemberPass\\\" : password,\\n \\\"ctl00$ContentPlaceHolder1$CheckCode\\\" : code,\\n \\\"__EVENTTARGET\\\" : \\\"\\\",\\n \\\"__EVENTARGUMENT\\\" : \\\"\\\",\\n \\\"__VIEWSTATE\\\" : \\\"/wEPDwUJNjIyMTU5NjYyZGQ=\\\",\\n \\\"__EVENTVALIDATION\\\" : \\\"/wEWBQLwhorcCAKxtMbHCgKNvavEBwKCi/rDCQKM5+qlBA==\\\",\\n \\\"ctl00$ContentPlaceHolder1$RegBtn\\\": \\\"登 录\\\",\\n }\\n result = self.data_post(values, self.login_url, self.login_send_header)\\n return result\",\n \"def login():\\n auth_state = str(uuid.uuid4())\\n SESSION.auth_state = auth_state\\n\\n # For this sample, the user selects an account to authenticate. Change\\n # this value to 'none' for \\\"silent SSO\\\" behavior, and if the user is\\n # already authenticated they won't need to re-authenticate.\\n prompt_behavior = 'select_account'\\n\\n params = urllib.parse.urlencode({'response_type': 'code',\\n 'client_id': config.CLIENT_ID,\\n 'redirect_uri': config.REDIRECT_URI,\\n 'state': auth_state,\\n 'resource': config.RESOURCE,\\n 'prompt': prompt_behavior})\\n\\n return bottle.redirect(config.AUTHORITY_URL + '/oauth2/authorize?' + params)\",\n \"def login(self):\\n r = self._login_token()\",\n \"def login(self):\\n login_form = {\\\"kid\\\": \\\"\\\",\\n \\\"uni\\\": self.server,\\n \\\"login\\\": self.username,\\n \\\"pass\\\": self.password}\\n url = \\\"https://%s.ogame.gameforge.com/main/login\\\" % self.country_code\\n result = self.session.post(url, data=login_form)\",\n \"def login():\\n pass\",\n \"def do_login(self, backend, user):\",\n \"def login(self):\\r\\n user_account = db.find_one({\\\"cpr_number\\\": request.form.get(\\\"CPR\\\")})\\r\\n if user_account is not None:\\r\\n if self.verify_password(user_account[\\\"password\\\"], request.form.get(\\\"password\\\")):\\r\\n return self.start_session(user_account)\\r\\n return jsonify({\\\"error\\\": \\\"Invalid login credentials\\\"}), 401\",\n \"def login():\\n if app.testing:\\n callback_url = url_for('user.authorize', _external=True)\\n else:\\n callback_url = 'https://codegolf.uqcs.org.au/user/authorize'\\n return git_auth.authorize(callback=callback_url)\",\n \"def _login(self, login):\\n self._tokens.clear()\\n name, password = login\\n\\n params = {\\\"action\\\": \\\"query\\\", \\\"meta\\\": \\\"tokens\\\", \\\"type\\\": \\\"login\\\"}\\n with self._api_lock:\\n result = self._api_query(params, no_assert=True)\\n try:\\n token = result[\\\"query\\\"][\\\"tokens\\\"][\\\"logintoken\\\"]\\n except KeyError:\\n raise exceptions.LoginError(\\\"Couldn't get login token\\\")\\n\\n params = {\\\"action\\\": \\\"login\\\", \\\"lgname\\\": name, \\\"lgpassword\\\": password,\\n \\\"lgtoken\\\": token}\\n with self._api_lock:\\n result = self._api_query(params, no_assert=True)\\n\\n res = result[\\\"login\\\"][\\\"result\\\"]\\n if res == \\\"Success\\\":\\n self._tokens.clear()\\n self._save_cookiejar()\\n return\\n if res == \\\"Illegal\\\":\\n e = \\\"The provided username is illegal.\\\"\\n elif res == \\\"NotExists\\\":\\n e = \\\"The provided username does not exist.\\\"\\n elif res == \\\"EmptyPass\\\":\\n e = \\\"No password was given.\\\"\\n elif res == \\\"WrongPass\\\" or res == \\\"WrongPluginPass\\\":\\n e = \\\"The given password is incorrect.\\\"\\n else:\\n e = \\\"Couldn't login; server says '{0}'.\\\".format(res)\\n raise exceptions.LoginError(e)\",\n \"def mfa_login(self, mfacode):\\n\\n try:\\n\\n response = self.post(\\\"/authentication/login\\\",\\n {\\\"user\\\": self.user, \\\"password\\\": self.password, \\\"token\\\": int(mfacode)})\\n if response.status_code == 200:\\n print(\\\"{0}: Orchestrator MFA login success\\\".format(self.url))\\n # get and set X-XSRF-TOKEN\\n for cookie in response.cookies:\\n if cookie.name == \\\"orchCsrfToken\\\":\\n self.headers[\\\"X-XSRF-TOKEN\\\"] = cookie.value\\n return True\\n else:\\n print(\\\"{0}: Orchestrator MFA login failed: {1}\\\".format(self.url, response.text))\\n return False\\n except:\\n print(\\\"{0}: Exception - unable to connect to Orchestrator\\\".format(self.url))\\n return False\",\n \"def make_login(your_username, your_password):\\r\\n instagram.with_credentials(your_username, your_password)\\r\\n instagram.login()\",\n \"def login(email, password):\\n rino.login.login(email, password)\",\n \"def login(email, password):\\n rino.login.login(email, password)\",\n \"def login():\\n login_hex = request.json.get(\\\"authentication\\\")\\n if not login_hex:\\n return jsonify({\\\"code\\\": \\\"1\\\", \\\"type\\\": \\\"user\\\"})\\n\\n qr_code_password = app.config[\\\"QRCODE_PASSWORD\\\"]\\n\\n if login_hex != qr_code_password:\\n return jsonify({\\\"code\\\": \\\"3\\\"})\\n \\n jwt_token = generate_token({\\\"id\\\": generate_id()})\\n\\n return jsonify({\\\"code\\\": \\\"0\\\", \\\"token\\\": jwt_token})\",\n \"def login(self, name, pin):\\n self.account = self.bank.get(name, pin)\\n if self.account:\\n return \\\"success\\\"\\n else:\\n return \\\"faliure\\\"\",\n \"def login(self):\\n res = self.sess.get(self._login_url)\\n execution = re.search('name=\\\"execution\\\" value=\\\"(.*?)\\\"', res.text).group(1)\\n res = self.sess.get(url=self._pub_key_url).json()\\n n, e = res['modulus'], res['exponent']\\n encrypt_password = rsa_encrypt(self.password, e, n)\\n data = {\\n 'username': self.username,\\n 'password': encrypt_password,\\n 'execution': execution,\\n '_eventId': 'submit'\\n }\\n res = self.sess.post(url=self._login_url, data=data)\\n\\n # check if login successfully\\n if '统一身份认证' in res.content.decode():\\n self.status = \\\"FAILED_LOGIN\\\"\\n raise LoginError('Login failed. Please check your ZJU username and password.')\\n logger.info(\\\"%s Successfully logined.\\\" % self)\\n self.status = \\\"LOGINED\\\"\\n return self.sess\",\n \"def login(self, account, password):\\n url = 'https://ceq.nkust.edu.tw/Login'\\n\\n data = {\\n '__RequestVerificationToken': self.csrf_key,\\n 'UserAccount': account,\\n 'Password': password,\\n }\\n res = self.main_session.post(url=url, data=data, allow_redirects=False)\\n if res.status_code == 302:\\n soup = BeautifulSoup(res.text, 'html.parser')\\n status = soup.find('a')['href']\\n if status == '/StuFillIn':\\n return True\\n return False\",\n \"def _login(self, *args, **kwargs):\\n pass\",\n \"def login(self):\\n \\n self.br.open(\\\"http://kanji.koohii.com/login\\\")\\n self.br.form = list(self.br.forms())[0]\\n self.br[\\\"username\\\"] = USER\\n self.br[\\\"password\\\"] = PASSWORD\\n my_response = self.br.submit()\\n print \\\"Login successful\\\"\",\n \"def do_login(user, password):\\n return um.do_login(user, password)\",\n \"def login(self, response):\\n\\t\\treturn FormRequest.from_response(response,\\n\\t\\t\\t formdata={'username': 'scanner1', 'password': 'scanner1'},\\n\\t\\t\\t callback=self.check_login_response)\",\n \"def login(self) -> int:\\n r = self.session.post(\\n self.api_endpoint,\\n data={\\n \\\"action\\\": \\\"login\\\",\\n \\\"lgname\\\": self.user,\\n \\\"lgpassword\\\": self.password,\\n \\\"format\\\": \\\"json\\\",\\n },\\n )\\n token = json.loads(r.text)[\\\"login\\\"][\\\"token\\\"]\\n r = self.session.post(\\n self.api_endpoint,\\n data={\\n \\\"action\\\": \\\"login\\\",\\n \\\"lgname\\\": self.user,\\n \\\"lgpassword\\\": self.password,\\n \\\"lgtoken\\\": token,\\n \\\"format\\\": \\\"json\\\",\\n },\\n )\\n if json.loads(r.text)[\\\"login\\\"][\\\"result\\\"] != \\\"Success\\\":\\n return -1\\n return 0\",\n \"def login(self):\\n request = self.REQUEST\\n response = request['RESPONSE']\\n\\n login = request.get('__ac_name', '')\\n password = request.get('__ac_password', '')\\n\\n pas_instance = self._getPAS()\\n\\n if pas_instance is not None:\\n pas_instance.updateCredentials(request, response, login, password)\",\n \"def doLogin(self):\\n\\t\\tlogin_data = urllib.urlencode({\\n\\t\\t\\t'operatorName' : self.username,\\n\\t\\t\\t'password' : self.password,\\n\\t\\t\\t'submit' : 'Iniciar+sesi%C3%B3n',\\n\\t\\t})\\n\\n\\t\\tresponse = self.opener.open(\\\"http://172.16.0.2/tdserver/login_deal.jsp\\\", login_data)\\t\\t### deberia devolver verdadero o falso segun se logueo o no\",\n \"def steam_login(username, password, two_factor_code):\\n steam_client = SteamClient() # Make steam client object\\n steam_client.login(username, password, two_factor_code=two_factor_code) # Log in\\n if not steam_client.logged_on: # Login failed\\n raise SteamLoginException('Login failed.')\\n return steam_client\",\n \"def login(self):\\n self.open(base_url + '/login')\\n self.type(\\\"#email\\\", test_user.email)\\n self.type(\\\"#password\\\", test_user.password)\\n self.click('input[type=\\\"submit\\\"]')\",\n \"def login(self, username=None, password=None):\\n if not username:\\n username = self.email\\n if not password:\\n password = self.password\\n var = {\\n 'client': self.client,\\n 'accountType': 'HOSTED_OR_GOOGLE',\\n 'service': 'reader',\\n 'Email': username,\\n 'Passwd': password\\n }\\n # do login\\n self.auth_code = \\\"\\\"\\n resp1 = self.make_request(url_login, var, use_get=False)\\n self.auth_code = resp1['Auth']\\n self._logger.info(u\\\"Logged in as {}\\\".format(username))\",\n \"def login(self):\\n\\n # Fetch and parse hidden inputs from login page\\n # Use specific CA bundle to fix SSL verify problems if set as env.\\n verify = True\\n\\n override_ca_bundle = os.getenv('OVERRIDE_CA_BUNDLE')\\n if override_ca_bundle:\\n verify = override_ca_bundle\\n\\n req = self.session.get(self.BASE_URL + '/im/login/privat',\\n verify=verify)\\n\\n # Get the login form\\n soup = BeautifulSoup(req.content, 'html.parser')\\n login_form = soup.select('#pPin_form')\\n\\n # Post login to current URL\\n login_post_url = req.url\\n\\n # Build POST data with login settings and hidden inputs\\n data = self._hidden_inputs_as_dict(login_form)\\n data['pPin_inp'] = self.personal_identity_number\\n data['pPinKod_inp'] = self.pin_code\\n\\n # Login request\\n req = self.session.post(login_post_url, data=data)\\n self.last_req_body = req.content\\n\\n self._parse_tokens(req.text)\\n\\n return True\",\n \"def login(self, request):\\n request.session['state'] = state = uuid.uuid4().hex\\n auth_url = flat_url(\\n PROVIDER_AUTH_URL,\\n client_id=self.consumer_key,\\n response_type='code',\\n state=state\\n )\\n return HTTPFound(location=auth_url)\",\n \"def login_leetcode(driver):\\n login_url = \\\"https://leetcode.com/accounts/login/\\\"\\n name_field_id = \\\"id_login\\\"\\n password_field_id = \\\"id_password\\\"\\n login_button_selector = 'button.btn-primary'\\n driver.get(login_url)\\n time.sleep(2)\\n username_ele = driver.find_element_by_id(name_field_id)\\n password_ele = driver.find_element_by_id(password_field_id)\\n username_ele.send_keys(username)\\n password_ele.send_keys(password)\\n driver.find_element_by_css_selector(login_button_selector).click()\",\n \"def login(self):\\n # create auth payload\\n payload = '{{\\\"grant_type\\\": \\\"password\\\", \\\"username\\\": \\\"{}\\\", \\\"password\\\": \\\"{}\\\"}}'.format(\\n self.username, self.password)\\n auth_headers = {**FTDClient.headers}\\n r = requests.post(\\\"https://{}:{}/api/fdm/{}/fdm/token\\\".format(self.server_address, self.server_port, self.version),\\n data=payload, verify=False, headers=auth_headers)\\n if r.status_code == 400:\\n raise Exception(\\\"Error logging in: {}\\\".format(r.content))\\n try:\\n # This token will act as the\\n self.access_token = r.json()['access_token']\\n # cache the original token in case we do a custom login\\n self.original_access_token = self.access_token\\n except:\\n logging.error(\\n f'Unable to log into server: https://{self.server_address}:{self.server_port}')\\n raise\",\n \"def login(self) -> None:\\n\\n sObj = Splitwise(self.consumer_key, self.consumer_secret)\\n self.url, self.login_secret = sObj.getAuthorizeURL()\\n print(self.url)\\n self.oauth_token = input('token: ')\\n self.oauth_verifier = input('verifier: ')\",\n \"async def login_access_token(\\n form_data: OAuth2PasswordRequestForm = Depends()\\n):\\n user = await crud.user.authenticate(\\n username=form_data.username, password=form_data.password\\n )\\n if not user:\\n raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail=\\\"Incorrect credentials\\\")\\n elif not user.is_active:\\n raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail=\\\"Inactive user\\\")\\n elif not user.is_email_verified:\\n raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail=\\\"Please verify your account via email\\\")\\n access_token_expires = timedelta(minutes=config.ACCESS_TOKEN_EXPIRE_MINUTES)\\n return {\\n \\\"access_token\\\": create_access_token(\\n data={\\\"user_id\\\": user.id}, expires_delta=access_token_expires\\n ),\\n \\\"token_type\\\": \\\"bearer\\\",\\n }\",\n \"def Login(self, username, password, onSuccess, onFailure):\\n pass\",\n \"def login_access_token(\\n db: Session = Depends(get_db),\\n form_data: OAuth2PasswordRequestForm = Depends()\\n) -> Any:\\n user = crud.user.authenticate(\\n db, email=form_data.username, password=form_data.password\\n )\\n if not user:\\n raise HTTPException(\\n status_code=400, detail=\\\"Incorrect email or password\\\")\\n elif not crud.user.is_active(user):\\n raise HTTPException(status_code=400, detail=\\\"Inactive user\\\")\\n access_token_expires = timedelta(\\n minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES)\\n return {\\n \\\"access_token\\\": security.create_access_token(\\n user.id, expires_delta=access_token_expires\\n ),\\n \\\"token_type\\\": \\\"bearer\\\",\\n }\",\n \"def log_in(codecool):\\n\\n login = school_view.get_login()\\n password = school_view.get_password()\\n\\n password = utilities.hash_password(password)\\n\\n users = codecool.managers_list + codecool.administrators_list + codecool.mentors_list + codecool.students_list\\n for user in users:\\n if user.login == login and user.password == password:\\n return user\",\n \"def do_login(self, password):\\n # Creating JSON string with authentication credentails.\\n in_data = ('{{ \\\"username\\\":\\\"{username}\\\",'\\n '\\\"password\\\":\\\"{password}\\\" }}'\\n ).format(\\n username=self.pub_user,\\n password=password\\n )\\n\\n url = self.base_url + \\\"/oasis/login\\\"\\n response = self.do_request(url, in_data)\\n json_response = json.loads(response.content)\\n\\n if json_response[\\\"success\\\"] == False:\\n print(\\\"Invalid user id or password\\\")\\n else:\\n self.cookies = dict(sessionid=response.cookies['sessionid'])\\n print(\\\"You are logged into Mid-tier\\\")\\n\\n logger.info( 'Log in response ' + str(response.content))\",\n \"def authentication_callback(request):\\n code = request.GET.get('code')\\n user = authenticate(token=code, request=request)\\n if user:\\n auth_login(request, user)\\n set_session_from_user(request, user)\\n region = request.user.endpoint\\n region_name = dict(Login.get_region_choices()).get(region)\\n request.session['region_endpoint'] = region\\n request.session['region_name'] = region_name\\n url = getattr(settings, \\\"LOGIN_REDIRECT_URL\\\", \\\"/\\\")\\n resp = HttpResponseRedirect(url)\\n\\n return resp\",\n \"def login():\\n guid = uuid.uuid4() # guid used to only accept initiated logins\\n session['state'] = guid\\n return msgraphapi.authorize(callback=url_for('authorized', _external=True), state=guid)\",\n \"def code_login(ui, repo, **opts):\\n\\tMySend(None)\",\n \"def auth():\\n\\tcode = request.query.code\\n\\tauth = 'https://foursquare.com/oauth2/access_token'\\n\\tparams = dict(\\n\\t\\tclient_id=CLIENT_ID,\\n\\t\\tclient_secret=CLIENT_SECRET,\\n\\t\\tgrant_type='authorization_code',\\n\\t\\tredirect_uri=REDIRECT_URI,\\n\\t\\tcode=code\\n\\t)\\n\\tauth_says = fetch('%s?%s'%(auth, urlencode(params)))\\n\\tauth_response = json.loads(auth_says.content)\\n\\tif 'access_token' in auth_response:\\n\\t\\toauth_token=auth_response['access_token']\\n\\t\\tresponse.set_cookie('user', oauth_token, secret=CLIENT_SECRET)\\n\\t\\tlogging.info('new oauth_token:%s'%oauth_token)\\n\\t\\tredirect('/')\\n\\telse:\\n\\t\\tlogging.error(auth_response)\\n\\t\\tabort()\",\n \"def login(self):\\n\\t\\treturn\",\n \"def login(self):\\n url = 'https://ngb.to/login.php?do=login'\\n\\n params = {'do': 'login'}\\n payload = {'vb_login_username': self.username,\\n 'vb_login_password': self.password,\\n 'url': \\\"index.php\\\",\\n 'do': \\\"login\\\",\\n 'vb_login_md5password': \\\"\\\",\\n 'vb_login_md5password_utf': \\\"\\\",\\n 's': \\\"\\\",\\n 'securitytoken': \\\"guest\\\",\\n 'cookieuser': \\\"1\\\"}\\n\\n self.session.post(url, data=payload, params=params)\",\n \"def einloggen(self):\\n \\n self.c.login(self.username.text(), self.password.text(), \\\"1\\\")\",\n \"def login():\\n req = request.get_json(force=True)\\n username = req.get('username', None)\\n password = req.get('password', None)\\n user = guard.authenticate(username, password)\\n ret = {'access_token': guard.encode_jwt_token(user)}\\n return ret, 200\",\n \"def do_login_login():\\n print(inspect.stack()[1][3])\\n print(request.form)\\n query = select([User]).where(and_(User.columns.email == request.form['email'],User.columns.password==request.form['password'] ))\\n ResultProxy = connection.execute(query)\\n ResultSet = ResultProxy.fetchone()\\n if ResultSet:\\n session['logged_in'] = True\\n else:\\n flash('wrong password!')\\n # return str(get_flashed_messages())\\n return home(result)\",\n \"def login_access_token(form_data: OAuth2PasswordRequestForm = Depends()):\\n user = auth_handler.authenticate_user(\\n username=form_data.username, password=form_data.password\\n )\\n if user is None:\\n raise HTTPException(\\n detail=\\\"Incorrect username and/or password\\\", status_code=400\\n )\\n\\n return APIResponse(\\n msg=TokenResponse(\\n access_token=auth_handler.encode_token(user.id), token_type=\\\"bearer\\\"\\n )\\n )\",\n \"def login():\\n # if we are already logged in, go back to were we came from\\n if auth.is_authenticated():\\n return flask.redirect(oid.get_next_url())\\n return oid.try_login(\\\"https://www.google.com/accounts/o8/id\\\",\\n ask_for=['email', 'fullname', 'nickname'])\",\n \"def login():\\n data = request.get_json()\\n if 'username' in data and 'password' in data:\\n username = data['username']\\n password = data['password']\\n access_token = authenticate(username, password)\\n if access_token is not None:\\n print('access token: ' + access_token)\\n return jsonify({'access_token': access_token})\\n else:\\n abort(403)\\n else:\\n abort(400)\",\n \"def authenticate_with_github(username=None, password=None, code=None):\\n if username is not None and password is not None:\\n print(' (auth given as {}:{})'.format(username, '*'*len(password)))\\n\\n def _2fa_func():\\n return code\\n\\n if code:\\n return login(username, password, two_factor_callback=_2fa_func)\\n else:\\n return GitHub(username, password)\",\n \"def login(self):\\n return self.client.login(username='Georgie', password='12345678')\",\n \"async def login(form_data: OAuth2PasswordRequestForm = Depends()):\\n db = get_database()\\n\\n user = await crud.user.authenticate(\\n db, username=form_data.username, password=form_data.password\\n )\\n\\n if not user:\\n raise HTTPException(\\n status_code=HTTP_400_BAD_REQUEST, detail=\\\"Incorrect email or password\\\"\\n )\\n elif not crud.user.is_active(user):\\n raise HTTPException(\\n status_code=HTTP_400_BAD_REQUEST, detail=HTTP_400_BAD_REQUEST_INACTIVE_USER\\n )\\n\\n access_token_expires = timedelta(minutes=config.ACCESS_TOKEN_EXPIRE_MINUTES)\\n\\n return {\\n \\\"access_token\\\": create_access_token(\\n data={\\\"username\\\": user.username}, expires_delta=access_token_expires\\n ),\\n \\\"token_type\\\": \\\"bearer\\\",\\n }\",\n \"def login():\\n guid = uuid.uuid4() # guid used to only accept initiated logins\\n session['state'] = guid\\n return msgraphapi.authorize(callback=url_for('authorized', _external=True), state=guid)\",\n \"def test_auth_code_positive(self, api):\\n self.builder.add_user(api.get_user())\\n resp = api.login_user(api.get_user().username, api.get_user().password)\\n self.builder.del_user(api.get_user())\\n assert resp.status_code == 200\",\n \"def login_user():\\n pass\",\n \"def loginAsManager(self):\\n self.browser.open('http://nohost/plone/')\\n self.browser.getLink('Log in').click()\\n self.browser.getControl('Login Name').value = 'root'\\n self.browser.getControl('Password').value = 'secret'\\n self.browser.getControl('Log in').click()\",\n \"async def login(form_data: OAuth2PasswordRequestForm = Depends()):\\n user = get_user_info(form_data.username)\\n if user == None:\\n raise HTTPException(status_code=404, detail=\\\"Incorrect username or password\\\")\\n hashed_password = simple_hash(form_data.username, form_data.password)\\n if not hashed_password == user.password:\\n raise HTTPException(status_code=400, detail=\\\"Incorrect username or password\\\")\\n\\n return {\\\"access_token\\\": user.name, \\\"token_type\\\": \\\"bearer\\\"}\",\n \"def by_code(request):\\n\\n requestData = {}\\n serializer_context = {\\n 'request': request,\\n }\\n requestData['access_code'] = request.data['access_code']\\n requestData['prefix'] = request.data['prefix']\\n # Notice here that we do not call `serializer.save()` like we did for\\n # the registration endpoint. This is because we don't have\\n # anything to save. Instead, the `validate` method on our serializer\\n # handles everything we need.\\n\\n serializer = LoginByCodeSerializer(data=requestData,context=serializer_context)\\n serializer.is_valid(raise_exception=True) # Retorna error de tipo 400\\n # Responde con los datos que ya incluye el token de autenticación que fue agregado x el serializador\\n return Response(serializer.validated_data, status=status.HTTP_200_OK)\",\n \"def login():\\n token = generate_token()\\n session['token'] = token\\n return redirect(esisecurity.get_auth_uri(\\n scopes=['esi-wallet.read_character_wallet.v1 esi-characters.read_contacts.v1 esi-characters.write_contacts.v1'],\\n state=token,\\n ))\",\n \"def login(userID, robotID, password): #@NoSelf\",\n \"def login(login_info):\\n url = 'http://www.jintiankansha.me/account/signin'\\n client = requests.session()\\n # Retrieve the CSRF token first\\n client.get(url) # sets the cookie\\n client.cookies\\n xsrf = client.cookies.items()[1]\\n login_info[xsrf[0]] = xsrf[1]\\n r = client.post(url, data=login_info)\\n \\n if r.status_code == 200:\\n return client\\n else:\\n return None\",\n \"async def login_for_access_token(\\n form_data: OAuth2PasswordRequestForm = Depends()\\n):\\n user = authenticate_user(form_data.username, form_data.password)\\n if not user:\\n raise HTTPException(\\n status_code=status.HTTP_401_UNAUTHORIZED,\\n detail=\\\"Incorrect username or password\\\",\\n headers={\\\"WWW-Authenticate\\\": \\\"Bearer\\\"},\\n )\\n access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)\\n access_token = create_access_token(\\n data={\\\"sub\\\": user.username}, expires_delta=access_token_expires\\n )\\n return {\\\"access_token\\\": access_token, \\\"token_type\\\": \\\"bearer\\\"}\",\n \"def login():\\n\\n username = str(request.parsed_json['username'])\\n password = str(request.parsed_json['password'])\\n\\n if not auth.check_password(username, password):\\n return create_error(401, \\\"login failed\\\")\\n\\n return auth.create_auth_token(username, password), 200\",\n \"def login():\\n domain = parser[\\\"learningmachine\\\"][\\\"domain\\\"]\\n secrets_file = \\\"{}/{}\\\".format(dir_path, \\\"client_secret.json\\\")\\n scope = \\\"https://www.googleapis.com/auth/userinfo.email\\\"\\n redirect_uri = \\\"http://{}/login\\\".format(domain)\\n login_handler = LoginHandler(secrets_file, scope, redirect_uri)\\n\\n if \\\"code\\\" in request.args:\\n login_handler.setup_user_info(request.args[\\\"code\\\"])\\n session[\\\"email\\\"] = login_handler.email\\n session[\\\"display_name\\\"] = login_handler.display_name\\n\\n if not fm.user_exists(login_handler.email):\\n msg = \\\"Adding user: {} with ID of {} to the database.\\\"\\\\\\n .format(login_handler.email, login_handler.display_name)\\n fm.add_user(login_handler.email, login_handler.display_name)\\n\\n msg = \\\"Sending user: {} to main page\\\".format(login_handler.email)\\n app.logger.info(msg)\\n return redirect(\\\"/static/main.html\\\")\\n\\n else:\\n msg = \\\"No login code yet. Letting Google handle the login process at: {}\\\"\\\\\\n .format(login_handler.auth_url)\\n app.logger.info(msg)\\n return redirect(login_handler.auth_url)\",\n \"def login():\\n # Force https redirects when behind a proxy (required on Azure only)\\n if 'APPINSIGHTS_INSTRUMENTATIONKEY' in os.environ: # set on Azure\\n redirect_uri = url_for('authorize', _external=True, _scheme='https')\\n else:\\n redirect_uri = url_for('authorize', _external=True)\\n\\n # As this sample does not have a user session management with loging out, this\\n # endpoint always starts a new authorization code flow, without detecting if the user\\n # might be already logged in.\\n\\n return oauth.tapkey.authorize_redirect(redirect_uri)\",\n \"def attempt_login(self, expected_code, **kwargs):\\r\\n url = reverse('openid-provider-login')\\r\\n post_args = {\\r\\n \\\"openid.mode\\\": \\\"checkid_setup\\\",\\r\\n \\\"openid.return_to\\\": \\\"http://testserver/openid/complete/?janrain_nonce=2013-01-23T06%3A20%3A17ZaN7j6H\\\",\\r\\n \\\"openid.assoc_handle\\\": \\\"{HMAC-SHA1}{50ff8120}{rh87+Q==}\\\",\\r\\n \\\"openid.claimed_id\\\": \\\"http://specs.openid.net/auth/2.0/identifier_select\\\",\\r\\n \\\"openid.ns\\\": \\\"http://specs.openid.net/auth/2.0\\\",\\r\\n \\\"openid.realm\\\": \\\"http://testserver/\\\",\\r\\n \\\"openid.identity\\\": \\\"http://specs.openid.net/auth/2.0/identifier_select\\\",\\r\\n \\\"openid.ns.ax\\\": \\\"http://openid.net/srv/ax/1.0\\\",\\r\\n \\\"openid.ax.mode\\\": \\\"fetch_request\\\",\\r\\n \\\"openid.ax.required\\\": \\\"email,fullname,old_email,firstname,old_nickname,lastname,old_fullname,nickname\\\",\\r\\n \\\"openid.ax.type.fullname\\\": \\\"http://axschema.org/namePerson\\\",\\r\\n \\\"openid.ax.type.lastname\\\": \\\"http://axschema.org/namePerson/last\\\",\\r\\n \\\"openid.ax.type.firstname\\\": \\\"http://axschema.org/namePerson/first\\\",\\r\\n \\\"openid.ax.type.nickname\\\": \\\"http://axschema.org/namePerson/friendly\\\",\\r\\n \\\"openid.ax.type.email\\\": \\\"http://axschema.org/contact/email\\\",\\r\\n \\\"openid.ax.type.old_email\\\": \\\"http://schema.openid.net/contact/email\\\",\\r\\n \\\"openid.ax.type.old_nickname\\\": \\\"http://schema.openid.net/namePerson/friendly\\\",\\r\\n \\\"openid.ax.type.old_fullname\\\": \\\"http://schema.openid.net/namePerson\\\",\\r\\n }\\r\\n # override the default args with any given arguments\\r\\n for key in kwargs:\\r\\n post_args[\\\"openid.\\\" + key] = kwargs[key]\\r\\n\\r\\n resp = self.client.post(url, post_args)\\r\\n code = expected_code\\r\\n self.assertEqual(resp.status_code, code,\\r\\n \\\"got code {0} for url '{1}'. Expected code {2}\\\"\\r\\n .format(resp.status_code, url, code))\",\n \"def login():\\n data = request.get_json()\\n email = data.get('email')\\n password = data.get('pwrd')\\n user = SQLModel.get_by_attrs(('email', 'pwrd'), 'users', 'email', email)\\n try:\\n user_pw = user[0][1]\\n user_nick = user[0][0]\\n if password == user_pw:\\n stuff = SQLModel.get_by_attrs(('login', 'pwrdHash', 'type', 'name'), 'users', 'login', login)\\n return jsonify(stuff)\\n else:\\n return 'fail'\\n except:\\n return 'fail'\",\n \"def login(self):\\n # the login url is just api, not api2\\n url = 'https://simple-note.appspot.com/api/login'\\n query = {'email': self.email, 'password': self.password}\\n data = base64.b64encode(urllib.urlencode(query))\\n try:\\n fh = urllib2.urlopen(url, data)\\n self.authtok = fh.read()\\n except urllib2.HTTPError, e:\\n # Received a non 2xx status code\\n raise SimplenoteError('http error: {}'.format(e.code))\\n except urllib2.URLError, e:\\n # Non http error, like network issue\\n raise SimplenoteError('url error: {}'.format(e.reason))\\n fh.close()\\n return True\",\n \"def Login(self):\\n self.Send(self.EncryptString('login\\\\n'))\\n\\n # 'Please provide your membership number to authenticate:'\\n print self.DecryptString(self.Recv(4096))\\n\\n # Flag 1.\\n self.Send(self.EncryptString(self.flag_1))\\n\\n # 'Ah, I see, sir has a basic account. In that case, your limited cloud\\n # execution access has been provisioned.'\\n print self.DecryptString(self.Recv(4096))\\n\\n # 'Will there by anything else today, sir?'\\n print self.DecryptString(self.Recv(4096))\",\n \"def perform_login(self, user_name, user_pass):\\n if self.api_type == 'real':\\n self.input_user_name(user_name)\\n self.input_user_pass(user_pass)\\n self.click_login_button()\",\n \"def login(self, username: str, password: str):\\n\\n self.driver.get(\\\"https://myrecsports.usc.edu/booking\\\")\\n self.driver.find_element_by_id(\\\"loginLink\\\").click()\\n self.driver.find_element_by_id(\\\"frmExternalLogin\\\")\\n self.driver.execute_script(\\\"submitExternalLoginForm('Shibboleth')\\\")\\n self.driver.find_element_by_id(\\\"username\\\").send_keys(\\\"noahbkim\\\")\\n self.driver.find_element_by_id(\\\"password\\\").send_keys(\\\"she once was a true love of mine\\\")\\n self.driver.find_element_by_name(\\\"_eventId_proceed\\\").click()\\n self.driver.find_element_by_id(\\\"logoutForm\\\")\",\n \"def login():\\n google = oauth.create_client('google') # create the google oauth client\\n redirect_uri = url_for('authorize', _external=True)\\n return google.authorize_redirect(redirect_uri)\",\n \"def login():\\n authorization_url, state = facebook.authorization_url(authorization_base_url)\\n print 'Please authorize', authorization_url\\n\\n return redirect(authorization_url, code=302)\",\n \"def login(self):\\n\\t\\tbot = self.bot\\n\\t\\tbot.get(URL)\\n\\t\\ttime.sleep(2)\\n\\t\\tsign_in = bot.find_element_by_class_name(\\\"nav__button-secondary\\\").click()\\n\\t\\ttime.sleep(2)\\n\\t\\temail = bot.find_element_by_id(\\\"username\\\")\\n\\t\\temail.send_keys(self.username)\\n\\t\\ttime.sleep(2)\\n\\t\\tpassword = bot.find_element_by_id(\\\"password\\\")\\n\\t\\tpassword.send_keys(self.password)\\n\\t\\ttime.sleep(2)\\n\\t\\tsign_in = bot.find_element_by_class_name(\\\"btn__primary--large.from__button--floating\\\").click()\",\n \"def _login_token(self):\\n data = {\\n 'cmd': 'login',\\n 'login': self.username,\\n 'password': self.password,\\n }\\n \\n token = self.helper._post_request(\\n self.basename,\\n self.basic_auth,\\n data, \\n self.headers)\\n\\n if token.status_code == 200:\\n xml_response = BeautifulSoup(token.content, 'lxml')\\n self.token = xml_response.find('token').get_text()\\n self.cookies = token.cookies.get_dict()\\n else:\\n raise Exception('[FAIL] Could not login to OpenVAS')\",\n \"def login_action(login_page, request, driver):\\n login_page.login(request.config.getoption(\\\"--username\\\"), request.config.getoption(\\\"--password\\\"))\",\n \"def submit_log_in(self, user_name, password, passcode):\\r\\n username_txt = user_name.get()\\r\\n password_txt = password.get()\\r\\n passcode_txt = passcode.get()\\r\\n self.my_socket.send(dumps(\\\"log in\\\"))\\r\\n now = datetime.now()\\r\\n current_time = now.strftime(\\\"%H:%M:%S\\\").split(\\\":\\\")\\r\\n time = current_time[0] + current_time[1]\\r\\n str_log_in = username_txt + \\\";\\\" + password_txt + \\\";\\\" + passcode_txt + \\\";\\\" + time\\r\\n self.my_socket.send(self.rsa_object.encrypt(str_log_in.encode(), self.server_key))\\r\\n\\r\\n response = self.rsa_object.decrypt(self.my_socket.recv(1024)).decode()\\r\\n if response == \\\"access granted\\\":\\r\\n self.username = username_txt\\r\\n self.choose_path()\\r\\n else:\\r\\n if self.log_in_tries == 2:\\r\\n self.log_in_tries = 0\\r\\n self.after_3_wrong_attempts(\\\"log in\\\")\\r\\n else:\\r\\n self.log_in_tries += 1\\r\\n lbl_response = Label(self.root, text=response, font=self.title_font,\\r\\n bg=self.bg_color)\\r\\n lbl_response.pack(pady=5, padx=10)\\r\\n lbl_response.after(1000, lbl_response.destroy)\\r\\n user_name.delete(0, END)\\r\\n password.delete(0, END)\\r\\n passcode.delete(0, END)\",\n \"def do_login(opener, login_info, connection_name, username, password):\\n login_payload = {\\n \\\"client_id\\\": CLIENT_ID,\\n \\\"connection\\\": connection_name,\\n \\\"password\\\": password,\\n \\\"popup_options\\\": \\\"{}\\\",\\n \\\"protocol\\\": \\\"samlp\\\",\\n \\\"redirect_uri\\\": \\\"https://signin.aws.amazon.com/saml\\\",\\n \\\"response_type\\\": \\\"code\\\",\\n \\\"scope\\\": \\\"openid profile email\\\",\\n \\\"sso\\\": True,\\n \\\"state\\\": login_info[\\\"state\\\"],\\n \\\"tenant\\\": TENANT,\\n \\\"username\\\": username,\\n \\\"_csrf\\\": login_info[\\\"_csrf\\\"],\\n \\\"_intstate\\\": \\\"deprecated\\\"\\n }\\n login_payload_json = json.dumps(login_payload).encode()\\n # print(login_payload)\\n\\n headers = {\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Origin\\\": \\\"https://%s.auth0.com\\\"%TENANT,\\n \\\"Auth0-Client\\\": AUTH0_CLIENT_HEADER\\n }\\n request = urllib.request.Request(\\n \\\"https://%s.auth0.com/usernamepassword/login\\\"%TENANT,\\n data=login_payload_json,\\n method=\\\"POST\\\",\\n headers=headers)\\n try:\\n response = opener.open(request)\\n result = response.read().decode()\\n except urllib.error.HTTPError as e:\\n error = e.read().decode()\\n raise RuntimeError(\\\"Login error: %s\\\"%error) from None\\n # print(result)\\n\\n # if success we will get a form in html, post it\\n parser = FormParser()\\n parser.feed(result)\\n callback_params = urllib.parse.urlencode(parser.fields).encode()\\n headers = {\\n \\\"Content-Type\\\": \\\"application/x-www-form-urlencoded\\\",\\n \\\"Origin\\\": \\\"https://%s.auth0.com\\\"%TENANT\\n }\\n request = urllib.request.Request(\\n parser.action,\\n data=callback_params,\\n method=parser.method.upper(), # post => POST\\n headers=headers)\\n try:\\n response = opener.open(request)\\n result = response.read().decode()\\n except urllib.error.HTTPError as e:\\n error = e.read().decode()\\n raise RuntimeError(\\\"Login callback error: %s\\\"%error) from None\\n # print(result)\\n\\n mfa_info = {\\n \\\"mfaServerUrl\\\": re.search(\\\"mfaServerUrl:\\\\s*?\\\\\\\"(.+?)\\\\\\\"\\\", result).group(1),\\n \\\"requestToken\\\": re.search(\\\"requestToken:\\\\s*?\\\\\\\"(.+?)\\\\\\\"\\\", result).group(1),\\n \\\"postActionURL\\\": re.search(\\\"postActionURL:\\\\s*?\\\\\\\"(.+?)\\\\\\\"\\\", result).group(1),\\n \\\"globalTrackingId\\\": re.search(\\\"globalTrackingId:\\\\s*?\\\\\\\"(.+?)\\\\\\\"\\\", result).group(1),\\n }\\n # print(mfa_info)\\n\\n return mfa_info\",\n \"def login(self):\\r\\n\\r\\n # Open browser with the login URL\\r\\n self.browser.open(self.config[\\\"base_url\\\"] + \\\"login\\\")\\r\\n\\r\\n # Select the login form\\r\\n self.browser.select_form('form[action=\\\"/login/\\\"]')\\r\\n\\r\\n # Fill the login form.\\r\\n self.browser[\\\"email\\\"] = self.config[\\\"email\\\"]\\r\\n self.browser[\\\"password\\\"] = self.config[\\\"password\\\"]\\r\\n\\r\\n # Submit form\\r\\n self.browser.submit_selected()\",\n \"def login(self):\\n self.client.login(username=self.user.username, password='test')\",\n \"def step_impl_1(context, username, pwd):\\n\\n br = context.browser\\n br.get(context.server_url + '/accounts/login/')\\n\\n user = br.find_element_by_id(\\\"username\\\")\\n pswd = br.find_element_by_id(\\\"password\\\")\\n\\n user.send_keys(username)\\n pswd.send_keys(pwd)\\n br.find_element_by_id(\\\"submit\\\").click()\",\n \"def login(self, email, password):\\r\\n self.provide_info(email, password)\\r\\n self.submit()\",\n \"def login(self):\\n url = self._root + self._routes[\\\"login\\\"]\\n self.r = self.reqsession.get(url) \\n if self.r.url == 'https://console.zerodha.com/dashboard':\\n cookies = self.reqsession.cookies.get_dict('console.zerodha.com')\\n self.console_session = cookies['session']\\n self.public_token = self.reqsession.cookies['public_token']\\n return True\\n else:\\n raise Exception(\\\"Login failed or Kite session expired\\\")\",\n \"def login(self, returnToURL):\\r\\n\\t\\tif not self.request().hasValue('userId') or \\\\\\r\\n\\t\\t\\tnot self.request().hasValue('password'):\\r\\n\\t\\t\\tself.response().sendRedirect('/nova/login.psp?returnToURL=%s' % returnToURL)\\r\\n\\t\\t\\treturn 0\\r\\n\\t\\treturn 1\",\n \"def registrieren(self):\\n self.c.login(self.username.text(), self.password.text(), \\\"0\\\")\",\n \"def _login(self):\\n data = self._send(self.nc_request(action=\\\"login\\\", parameters={\\\"apipassword\\\": self._api_password}))\\n\\n self._session_id = data[\\\"apisessionid\\\"]\\n\\n logging.info(f\\\"logged in successfully with session id {self._session_id}\\\")\",\n \"def master_login():\\n\\n master_key = get_master_key().decode()\\n login_master = input(\\\"\\\\nEnter your master password to begin using Password Manager: \\\")\\n\\n if login_master == master_key:\\n\\n print(\\\"Access granted!\\\\n\\\")\\n access_granted = True\\n\\n return access_granted\\n\\n else:\\n\\n print(\\\"Uh oh, that is not your master password. Try again.\\\")\\n return master_login()\",\n \"def login(self, username, password):\\n\\t\\turl = \\\"https://habitica.com/api/v3/user/auth/local/login\\\"\\n\\t\\tpayload = {\\\"username\\\": username, \\\"password\\\": password}\\n\\t\\treturn(postUrl(url, self.credentials, payload))\",\n \"def login():\\n token = request.form.get('idtoken')\\n if verify_token(token):\\n session['logged_in'] = True\\n return '', 204\\n else:\\n return '', 401\",\n \"def callback():\\n # state variable is not used yet but should be used to invalidate the\\n # session on incorrect match per client.\\n state = request.args.get('state')\\n code = request.args.get('code')\\n error = request.args.get('error')\\n if error:\\n return \\\"Error: \\\" + error\\n headers = {\\n 'accept': 'application/json',\\n 'cache-control': 'no-cache',\\n 'content-type': 'application/x-www-form-urlencoded'\\n }\\n data = {\\n 'grant_type': 'authorization_code',\\n 'client_id': CLIENT_ID,\\n 'redirect_uri': REDIRECT_URI,\\n 'code': code,\\n 'code_verifier': CODE_VERIFIER\\n }\\n client_auth = requests.post(TOKEN_URL, headers=headers, data=data)\\n client_json = client_auth.json()\\n session['access_token'] = client_json[\\\"access_token\\\"]\\n return redirect(url_for('.methods'))\",\n \"def login(self, **kwargs):\\n\\tusername = kwargs.get('username', self.username)\\n\\tif not username:\\n\\t raise RuntimeError, 'no username provided'\\n\\n\\tpassword = kwargs.get('password', self.password)\\n\\tif not password:\\n\\t raise RuntimeError, 'no password provided'\\n\\tself.call('login', username=username, password=password)\",\n \"def userlogin(self, login, password):\\n\\n payload = {\\n 'PASSWORD': password,\\n 'path': \\\"http://www.gamefaqs.com/\\\",\\n 'EMAILADDR': login,\\n }\\n\\n login_url = 'http://www.gamefaqs.com/user/login'\\n\\n # Grab key ID\\n resp = self.session.get(login_url)\\n soup = bs(resp.text, 'html.parser')\\n payload['key'] = soup.find('input', class_='hidden')['value']\\n\\n # Login with user payload\\n resp = self.session.post(login_url, data=payload)\\n\\n soup = bs(resp.text, 'html.parser')\\n\\n if soup.find_all(string='There was an error while logging you in: '):\\n raise Exception('Login Failed!')\\n else:\\n logging.debug('{} successfully logged in.'.format(self.login))\",\n \"def login(self):\\n \\n try:\\n # Create socket connection and connect to the server\\n self.sck = socket(AF_INET, SOCK_STREAM)\\n self.sck.connect((self.host, self.port))\\n self.sck.send(self.alchallenge_packet()) # Send Auth Logon Challenge\\n srp_rcvd = self.decode_packet(self.sck.recv(1024)) # Read SRP for sending Logon Proof\\n csrp = Srp(srp_rcvd['N'], srp_rcvd['g'], self.I, self.p, srp_rcvd['s'], srp_rcvd['B'])\\n # Do some math...\\n A = csrp.gen_A()\\n u = csrp.gen_u()\\n S = csrp.gen_S()\\n K = csrp.gen_K()\\n M = csrp.gen_M()\\n # Let's send Auth Logon Proof\\n self.sck.send(self.alproof_packet(M.blittle(), A.blittle()))\\n return self.decode_packet(self.sck.recv(1024)).get('login') # 1 if no errors\\n except ValueError:\\n return 0\",\n \"def login(self):\\n headers = {'Content-Type': 'application/x-www-form-urlencoded'}\\n token_url = urljoiner(self.baseurl, [\\\"connect/token\\\"])\\n if(self.debug):\\n print(token_url)\\n new_auth = dict(self.auth_data)\\n new_auth['password'] = \\\"XXXXX\\\"\\n print(\\\"Authentication Data (without password):\\\")\\n print(new_auth)\\n r = requests.post(token_url, data=self.auth_data, headers=headers)\\n # New environments do not redirect /rest/connect/token to\\n # /auth/connect/token so lets check this case explicitly\\n if(r.status_code > 400):\\n new_token_url = self.baseurl.rstrip(\\n \\\"/rest\\\") + \\\"/auth/connect/token\\\"\\n if(self.debug):\\n print(\\\"cannot connect to: \\\" + token_url)\\n print(\\\"trying: \\\" + new_token_url)\\n r = requests.post(\\n new_token_url,\\n data=self.auth_data,\\n headers=headers)\\n self.last_login = time.time()\\n self.handle_error_message(r)\\n self.auth_result = r.json()\\n access_token = r.json().get('access_token')\\n self.headers = {'Authorization': 'Bearer ' + access_token,\\n 'Content-Type': 'application/json'}\\n # Always relogin when time remaining on the current token is in between 1 min and 3 min\\n self.refresh_window = min(max(60, 0.01 * self.auth_result['expires_in']), 180)\",\n \"def login(username, password, \\n get_steamguard_code = get_steamguard_code_manual, \\n solve_captcha = solve_captcha_manual,\\n max_tries = 5):\\n response_dict = auth.get_rsa_key(username)\\n\\n # The RSA information is encoded as hex strings.\\n # Transform to integers.\\n rsa_mod = int(response_dict['publickey_mod'], 16)\\n pub_exp = int(response_dict['publickey_exp'], 16)\\n\\n encrypted_password = auth.get_encrypted_password(password, rsa_mod, pub_exp)\\n timestamp = response_dict['timestamp']\\n\\n gid = ''\\n text = ''\\n email_auth = ''\\n are_we_logged_in = False\\n tries = 0\\n while not are_we_logged_in and tries < max_tries:\\n tries += 1\\n response_dict = auth.do_login(username, encrypted_password, timestamp, email_auth, gid, text)\\n if 'captcha_needed' in response_dict:\\n text = solve_captcha(gid)\\n elif 'emailauth_needed' in response_dict:\\n email_auth = get_steamguard_code_manual()\\n elif 'success' in response_dict and response_dict['success']:\\n are_we_logged_in = True\\n else:\\n print response_dict\\n raise Exception(\\\"I don't understand this state!\\\")\\n\\n if tries >= max_tries:\\n raise Exception(\\\"Too many tries!\\\")\\n\\n return response_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7269786","0.7269786","0.70734197","0.70426136","0.6852028","0.6803463","0.67843354","0.6739689","0.6730675","0.6720502","0.6683724","0.6669751","0.66376764","0.65688664","0.65634507","0.6525562","0.651685","0.651685","0.649879","0.64984405","0.6491229","0.64458877","0.64118016","0.64095795","0.6394115","0.6380953","0.6379928","0.6367268","0.63606215","0.6346407","0.6345381","0.6342872","0.63404155","0.6336025","0.6334202","0.6326229","0.63205093","0.6319115","0.6312457","0.6311932","0.63072616","0.6306049","0.6296511","0.6289371","0.6282049","0.62760425","0.6274943","0.62741256","0.62700075","0.6263721","0.626188","0.6257704","0.6244173","0.62358254","0.6228825","0.62197936","0.62140876","0.621091","0.61955506","0.61881375","0.61851555","0.6184508","0.6176529","0.6174516","0.61707604","0.6165689","0.61651385","0.61583585","0.6150929","0.6150226","0.61473256","0.6139042","0.61357063","0.6134327","0.61292017","0.61273146","0.6126926","0.6126703","0.61242807","0.61187756","0.61114496","0.6111142","0.6107284","0.6107003","0.6106689","0.61054415","0.6098375","0.609553","0.60946375","0.60812914","0.60756177","0.6069772","0.6064025","0.60483456","0.6039952","0.60351694","0.6033701","0.6031333","0.60302496","0.6029842"],"string":"[\n \"0.7269786\",\n \"0.7269786\",\n \"0.70734197\",\n \"0.70426136\",\n \"0.6852028\",\n \"0.6803463\",\n \"0.67843354\",\n \"0.6739689\",\n \"0.6730675\",\n \"0.6720502\",\n \"0.6683724\",\n \"0.6669751\",\n \"0.66376764\",\n \"0.65688664\",\n \"0.65634507\",\n \"0.6525562\",\n \"0.651685\",\n \"0.651685\",\n \"0.649879\",\n \"0.64984405\",\n \"0.6491229\",\n \"0.64458877\",\n \"0.64118016\",\n \"0.64095795\",\n \"0.6394115\",\n \"0.6380953\",\n \"0.6379928\",\n \"0.6367268\",\n \"0.63606215\",\n \"0.6346407\",\n \"0.6345381\",\n \"0.6342872\",\n \"0.63404155\",\n \"0.6336025\",\n \"0.6334202\",\n \"0.6326229\",\n \"0.63205093\",\n \"0.6319115\",\n \"0.6312457\",\n \"0.6311932\",\n \"0.63072616\",\n \"0.6306049\",\n \"0.6296511\",\n \"0.6289371\",\n \"0.6282049\",\n \"0.62760425\",\n \"0.6274943\",\n \"0.62741256\",\n \"0.62700075\",\n \"0.6263721\",\n \"0.626188\",\n \"0.6257704\",\n \"0.6244173\",\n \"0.62358254\",\n \"0.6228825\",\n \"0.62197936\",\n \"0.62140876\",\n \"0.621091\",\n \"0.61955506\",\n \"0.61881375\",\n \"0.61851555\",\n \"0.6184508\",\n \"0.6176529\",\n \"0.6174516\",\n \"0.61707604\",\n \"0.6165689\",\n \"0.61651385\",\n \"0.61583585\",\n \"0.6150929\",\n \"0.6150226\",\n \"0.61473256\",\n \"0.6139042\",\n \"0.61357063\",\n \"0.6134327\",\n \"0.61292017\",\n \"0.61273146\",\n \"0.6126926\",\n \"0.6126703\",\n \"0.61242807\",\n \"0.61187756\",\n \"0.61114496\",\n \"0.6111142\",\n \"0.6107284\",\n \"0.6107003\",\n \"0.6106689\",\n \"0.61054415\",\n \"0.6098375\",\n \"0.609553\",\n \"0.60946375\",\n \"0.60812914\",\n \"0.60756177\",\n \"0.6069772\",\n \"0.6064025\",\n \"0.60483456\",\n \"0.6039952\",\n \"0.60351694\",\n \"0.6033701\",\n \"0.6031333\",\n \"0.60302496\",\n \"0.6029842\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74460435"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":698,"cells":{"query":{"kind":"string","value":"View for all employees (in company) or for current user dependent on employee role"},"document":{"kind":"string","value":"def all_employees(request, company_id=None):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n company_super_user = current_employee.isCompanySuperUserOrHigher()\n if company_id:\n company = Company.objects.get(pk=company_id)\n else:\n company = current_employee.company\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n raise PermissionDenied()\n change_company_form = ChangeCompanyForm(initial=dict(company=company))\n return TemplateResponse(\n request,\n 'all_employees.html',\n {\n 'user': request.user,\n 'company_super_user': company_super_user,\n 'company': company,\n 'change_company_form': change_company_form,\n }\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 show_all_employees():\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employees = Employee.query.all()\n \n \n ## right now this is ALL users... \n \n return render_template(\"employee_display.html\", employees = employees)","def get_manager_employees(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\n if manager_employees:\n emp_list=[]\n for emp in manager_employees:\n emp_data={}\n emp_data[\"id\"] = emp.id\n emp_data[\"username\"] = emp.user.username\n emp_data[\"first_name\"] = emp.user.first_name\n emp_data[\"last_name\"] = emp.user.last_name\n emp_data[\"manager_id\"] = emp.manager.id\n # emp_data[\"status_questions\"] = emp.status_questions\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\n # name_role_list = []\n # for obj in employee_role:\n # name_role_list.append(obj.role.name)\n # emp_data[\"roles\"] = name_role_list\n emp_list.append(emp_data)\n data = {\"employees:\": emp_list}\n return JsonResponse(status=201, data=data)\n else:\n return JsonResponse(\"The user with id={} isn't a manager for any user\".format(current_employee.user.id),\n status=404)","def get_queryset(self, request):\n return models.Employee.objects.exclude(username='root')","def employees():\n # gather data from db about all employees\n return render_template(\"employees.html\")","def show_all_employees(self):\n try:\n employees = self.admin_repository.show_all_employees()\n if employees:\n for employee in employees:\n print(\"Employee Id : {}\".format(employee[0]))\n print(\"Name : {}\".format(employee[1]))\n print(\"Email : {}\".format(employee[2]))\n print(\"----------------------------\")\n return True\n else:\n print(\"No records found.\")\n return False\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def index(request):\n\n context = {'employees': User.objects.select_related('profile').filter(is_staff=True).order_by('first_name')}\n return render(request, 'Employees/index.html', context)","def list(self, request):\n employee = self.controller.retrieve_all_employees()\n serializer = data_serializers.PresentEmployeeDataSerializer(employee, many=True)\n return Response(serializer.data)","def employee(employee_id):\n # gather data from db about all employees\n return render_template(\"employee.html\",\n employee_id=employee_id)","def current_employee(self, request: Request) -> Response:\n serializer = self.get_serializer_class()\n serializer = serializer(request.user, context={'request': request})\n return Response(serializer.data)","def get(self):\n resultado = EmployeeModel.query.all()\n return resultado","def edit_employee(request, employee_id):\n employee = Employee.objects.get(pk=int(employee_id))\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n\n assert isinstance(employee, Employee)\n assert isinstance(current_employee, Employee)\n\n # if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\n # raise PermissionDenied()\n\n if not current_employee.hasAccessTo(employee):\n raise PermissionDenied()\n\n form = EditEmployeeForm(request.user, employee, {\n 'first_name': employee.user.first_name,\n 'last_name': employee.user.last_name,\n 'email': employee.user.email,\n 'manager': employee.manager.id if employee.manager else 0,\n 'language_code': employee.language_code,\n # 'development_plan_type': employee.development_plan_type.id,\n 'is_manager': employee.is_manager\n })\n if 'manager' in form.fields:\n managerQS = Employee.objects.filter(is_manager=True, company__pk=employee.company.pk)\n form.fields['manager'].queryset = managerQS\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company__pk=employee.company.pk) | Q(company__isnull=True)\n # )\n is_me = employee.user.pk == request.user.pk\n return TemplateResponse(\n request,\n 'mus/edit_employee_form.html',\n {\n 'edit_employee_form': form,\n 'employee_id': employee_id,\n 'me': is_me,\n 'name': employee.user.get_full_name()\n }\n )","def get_queryset(self):\n #print(\"request\", self.request)\n user = self.request.user\n return Experience.objects.filter(person=user)","def action_list(request, employee_id=None):\n if employee_id:\n employee = Employee.objects.get(pk=employee_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\n raise PermissionDenied()\n else:\n employee = request.user.employee_user.first()\n actions = employee.action_set.all()\n return TemplateResponse(\n request,\n 'mus/action_list.html',\n dict(\n actions=actions,\n employee=employee\n )\n )","def employee_detail(request, employee_id):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n employee = Employee.objects.get(pk=int(employee_id))\n if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\n raise PermissionDenied()\n actions = employee.action_set.all()\n if not current_employee.pk == int(employee_id):\n if not current_employee.is_manager or not current_employee.company.pk == employee.company.pk:\n if not current_employee.isCompanySuperUserOrHigher():\n return HttpResponse('unauthorized', status=401)\n\n user_files = get_files_for_employee(employee_id)\n\n if request.method == 'POST':\n\n upload_form = UploadFileToEmployeyForm(request.POST, request.FILES)\n form = EmployeeNoteForm(request.POST, instance=employee)\n\n if 'upload' in request.POST:\n if upload_form.is_valid():\n upload_form.handle_upload(employee_id, request.FILES['file'])\n\n return HttpResponseRedirect('/employee/show/{}?upload_status=ok#file-list'.format(employee_id))\n\n else:\n if form.is_valid():\n form.save(request.user, employee)\n return HttpResponseRedirect('/employee/show/%d' % form.instance.pk)\n\n else:\n form = EmployeeNoteForm(instance=employee)\n upload_form = UploadFileToEmployeyForm()\n data = {}\n data[\"first_name\"] = employee.user.first_name\n data[\"last_name\"] = employee.user.last_name\n data[\"email\"] = employee.user.email\n data[\"is_manager\"] = employee.is_manager\n data[\"language_code\"] = employee.language_code\n employee_role = EmployeeRole.objects.filter(employee=employee).all()\n name_role_list = []\n for obj in employee_role:\n name_role_list.append(obj.role.name)\n data[\"roles\"] = name_role_list\n return JsonResponse(status=201, data=data)\n # return TemplateResponse(\n # request,\n # 'mus/detail.html',\n # {\n # 'actions': actions,\n # 'employee': employee,\n # # 'development_plans': development_plans,\n # 'form': form,\n # 'upload_form': upload_form,\n # 'user_files': user_files\n # }\n # )","def get_queryset(self):\n\n user = self.request.user\n\n if user.is_authenticated and user.role == 'LA':\n # check if the user is a landville admin and return all records\n # even soft deleted ones\n return PropertyEnquiry.objects.all()\n\n if user.is_authenticated and user.role == 'CA':\n # if the user is a client admin, return only his records\n employer = user.employer.first()\n return PropertyEnquiry.active_objects.for_client(client=employer)\n\n # if the user is a buyer, return also only his enquiries\n return PropertyEnquiry.active_objects.for_user(user=user)","def show_all_information():\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employees = Employee.query.all()\n all_certs = employee_certification.query.all()\n \n return render_template(\"admin.html\", employees = employees, all_certs = all_certs)","def show_overview_of_all_employees(self):\n\n print(\"OVERVIEW OF EMPLOYEES\\n\")\n\n employees_ob_list = self.llapi.get_employee_overview()\n \n for employee_ob in employees_ob_list:\n print(employee_ob.print_info_in_line(\"*\"))\n \n print(f\"\\nNAN AIR has {len(employees_ob_list)} employees\")\n\n print(\"\\nB Back\\n\")\n\n action_str = self.choose_action([\"b\"])\n while action_str == False:\n action_str = self.choose_action([\"b\"])\n\n if action_str == \"b\":\n return","def employee():\n return Response(render_template('employee/employee.html'))","def get_role_from_rolequeryset(self, role):\n role = super().get_role_from_rolequeryset(role=role)\n\n requesting_appname = self.request.cradmin_app.appname\n if requesting_appname in ['qualifiesforexam', 'overview_all_results']:\n if self.period_admin_access_semi_anonymous_assignments_restricted(period=role):\n raise Http404()\n\n return role","def list(self, request):\n varemployee = employee.objects.all()\n serializer = employeeSerializer(varemployee, many=True)\n return Response(serializer.data)","def get_queryset(self):\n qs = super().get_queryset()\n qs.filter(company=self.request.user.company)\n return qs","def stars_employee_list(request, employee_id):\n if request.method == 'GET':\n employee = get_object_or_404(Employee, pk=employee_id)\n employee_stars = Star.objects.filter(to_user=employee)\n paginator = PageNumberPagination()\n results = paginator.paginate_queryset(employee_stars, request)\n serializer = StarSerializer(results, many=True)\n return paginator.get_paginated_response(serializer.data)","def employees_manager(request):\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)\n employee = Employee.objects.get(pk=request.user.employee_user.id)\n employee_dict = model_to_dict(employee)\n employee_dict['first_name'] = employee.user.first_name\n employee_dict['last_name'] = employee.user.last_name\n employee_dict['photo'] = employee.photo.url if employee.photo else ''\n print employee_dict\n if len(manager_list) > 0:\n result_list = list(manager_list)\n all_managers_list = found_all_managers(manager_list, result_list)\n else:\n data = {\"employee_managers\": employee_dict}\n return JsonResponse(data=data, content_type='application/json', safe=False)\n employees = list()\n for manager in all_managers_list:\n manager_dict = model_to_dict(manager)\n manager_dict['first_name'] = manager.user.first_name\n manager_dict['last_name'] = manager.user.last_name\n manager_dict['photo'] = manager.photo.url if manager.photo else ''\n employees.append(manager_dict)\n employees.append(employee_dict)\n\n data = {\"employee_managers\": employees}\n return JsonResponse(data=data, content_type='application/json', safe=False)","def list(self, request):\n teams = self.controller.retrieve_all_teams_employees()\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(teams, many=True)\n return Response(serializer.data)","def start_view(request):\n\n if request.user and Employee.objects.filter(user__pk=request.user.pk).exists():\n if Employee.objects.get(user__pk=request.user.pk).is_manager:\n return HttpResponseRedirect('/dashboard')\n else:\n return HttpResponseRedirect('/employee/show/%d/' % request.user.employee_user.first().pk)\n else:\n return HttpResponseRedirect('/login/')","def get_employee(self):\n employee_ids = self.env['hr.employee'].search([('user_id', '=', self.env.uid)])\n return employee_ids[0] if employee_ids else False","def get_employee_permissions(user_name: str, store_name: str, employee_name: str):\n user_name = auth.get_username_from_hash(user_name)\n permission_handler.is_permmited_to(user_name, Action.EMPLOYEE_PERMISSIONS, store_name)\n permission_handler.is_working_in_store(employee_name, store_name)\n return user_handler.get_employee_information(\n employee_name) # TODO FOR NOW RETURN INFORMATION MAYBE TO CHANGE TO NEW FUNCTION","def get_queryset(self):\n\n user = self.request.user\n\n if user.role == 'LA':\n return PropertyEnquiry.objects.all()\n\n # check if the user is a client admin\n # and return all enquiries made on his/her property\n if user.role == 'CA':\n return PropertyEnquiry.active_objects.for_client(\n client=user.employer.first())\n\n # else if the user is a buyer return only\n # the records that are associated with him/her\n return PropertyEnquiry.active_objects.for_user(user=user)","def get_employees(self, active_only):\n cursor = self.dbconnect.get_cursor()\n\n if active_only:\n cursor.execute(\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\n 'is_admin, is_active FROM employee WHERE is_active = TRUE')\n else:\n cursor.execute(\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\n 'is_admin, is_active FROM employee')\n\n employees = list()\n for row in cursor:\n obj = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10])\n employees.append(obj)\n return employees","def get_employees(self):\n return self.employees","def test_ReportingPeriodDetailView_current_employee_set_false(self):\n response = self.app.get(\n reverse(\n 'reports:ReportingPeriodDetailView',\n kwargs={'reporting_period': '2015-01-01'},\n )\n )\n self.assertEqual(\n len(response.html.find_all('tr', {'class': 'user'})), 2\n )","def get_queryset(self):\n user = self.request.user\n\n if user.is_authenticated and user.role == 'LA':\n return Property.objects.all()\n\n if user.is_authenticated and user.employer.first():\n client = user.employer.first()\n return Property.active_objects.all_published_and_all_by_client(\n client=client)\n\n return Property.active_objects.all_published()","def get_queryset(self):\n user = self.request.user\n\n if user.is_authenticated and user.role == 'LA':\n return Property.objects.all()\n\n if user.is_authenticated and user.employer.first():\n client = user.employer.first()\n return Property.active_objects.all_published_and_all_by_client(\n client=client)\n\n return Property.active_objects.all_published()","def show_work_role():\n\n work_roles = WorkRole.query.order_by(WorkRole.id.asc())\n return render_template('general/work_role.html', title='Work Roles', work_roles=work_roles)","def myorgs(request):\n context = RequestContext(request)\n \n user = request.user\n orgs = user.orgusers.get_query_set()\n \n context['orgs'] = orgs\n return render_to_response('myorgs.html', context)","def get(self, request):\n varemployee = employee.objects.all()\n serializer = employeeSerializer(varemployee, many=True)\n return Response(serializer.data)","def admin_roles(request):\n user = User.objects.get(username=request.user.username)\n permisos = get_permisos_sistema(user)\n return render_to_response('admin/roles/roles.html',{'user':user,\n 'ver_roles':'Ver roles' in permisos,\n 'crear_rol': 'Crear rol' in permisos,\n 'mod_rol': 'Modificar rol' in permisos,\n 'eliminar_rol': 'Eliminar rol' in permisos},context_instance=RequestContext(request))","def employeeHome(request):\n # assert isinstance(request, HttpRequest)\n \n if userHasBeenCleared(request):\n \n return render(\n request,\n 'app/index_employee.html',\n {\n 'title':'Home Page',\n 'year':datetime.now().year,\n 'email':getEmailSessionVar(request),\n 'isAdmin':isAdmin(request), \n }\n )\n return login(request)","def get_emp_list(self):\n\t\tcondition= ''\n\t\temp_list=[]\n\t\tif self.is_for_all==0:\n\t\t\tif not self.selected_employees:\n\t\t\t\tfrappe.throw(_(\"No employees for the mentioned criteria\"))\n\t\t\t#emp_list = [cstr(d.employee) for d in self.selected_employees]\n\t\t\temp_list = frappe.db.sql_list(\"\"\"\n\t\t\t\tselect\n\t\t\t\t\temployee from `tabAttendance Salary Tool Employee`\n\t\t\t\twhere\n\t\t\t\t\tparent = '%(parent)s' \n\t\t\t\"\"\"%{\"parent\": self.name})\n\t\t\tcondition+= \"\"\" and t1.employee IN %(employees)s \"\"\"\n\t\tif self.is_open_period==0:\n\t\t\tif not self.start_date or not self.end_date:\n\t\t\t\tfrappe.throw(_(\"Satart Date and End Date are Mandatories\"))\n\t\t\tcondition= \"\"\" and attendance_date >= %(start_date)s and attendance_date <= %(end_date)s\"\"\"\n\t\temp_list = frappe.db.sql(\"\"\"\n\t\t\tselect\n\t\t\t\tt1.employee as employee, count(*) as attendance_days\n\t\t\tfrom\n\t\t\t\t`tabAttendance` t1\n\t\t\twhere\n\t\t\t\tt1.attendance_salary_tool is null\n\t\t\t\tand t1.docstatus = 1 and t1.status='Present'\n\t\t\t\t{condition} group by t1.employee order by t1.employee asc\n\t\t\"\"\".format(condition=condition),{\"employees\": tuple(emp_list),\"start_date\": self.start_date,\"end_date\": self.end_date}, as_dict=True)\n\t\treturn emp_list","def event_collaborator_detail(request, event_id, collaborator_id):\n if request.method == 'GET':\n event = get_object_or_404(Event, pk=event_id)\n collaborator = Employee.objects.all().filter(event=event, pk=collaborator_id)\n if collaborator:\n is_registered = True\n else:\n is_registered = False\n serializer = CollaboratorAttendanceSerializer(event, context={'is_registered': is_registered})\n return Response(serializer.data, status=status.HTTP_200_OK)","def home(request):\n\n\tcontext_dict = {}\n\temployee = models.Teacher.objects.filter(\n\t\tuser=request.user\n\t).first()\n\t# context_dict = {\n\t# context_helper.get_emp_info(employee)\n\t# }\n\t# print (context_dict)\n\tcontext_dict.update(context_helper.get_emp_info(employee))\n\treturn render(request, \"home.html\", context_dict)","def get_queryset(self):\r\n username = self.kwargs['username']\r\n return models.Experience.objects.filter(username = username).order_by('-startdate')","def show_employee(emp_id, fields=None):\n ret = {}\n if fields is None:\n fields = \",\".join(\n (\n \"canUploadPhoto\",\n \"department\",\n \"displayName\",\n \"firstName\",\n \"id\",\n \"jobTitle\",\n \"lastName\",\n \"location\",\n \"mobilePhone\",\n \"nickname\",\n \"photoUploaded\",\n \"photoUrl\",\n \"workEmail\",\n \"workPhone\",\n \"workPhoneExtension\",\n )\n )\n\n status, result = _query(action=\"employees\", command=emp_id, args={\"fields\": fields})\n\n root = ET.fromstring(result)\n\n ret = {\"id\": emp_id}\n for item in root:\n ret[next(iter(item.values()))] = item.text\n return ret","def is_employee():\n return _is_member('uw_employee')","def getEmployees(self):\n return self.employees","def get_queryset(self, request):\n qs = super(EventAdmin, self).get_queryset(request)\n if request.user.is_superuser:\n return qs\n return qs.filter(dep=request.user.profile.department)","def get(self):\n employees = self.service.get_employees(strategy=selectinload)\n return self.schema.dump(employees, many=True), 200","def get_admins(self):\n from Employee import Employee\n admins = list()\n cursorRoles = self.dbconnect.get_cursor()\n cursorRoles.execute('select * from employeeRoles where role=\\'admin\\'')\n for row in cursorRoles:\n admins.append(self.get_employee(row[0]))\n return admins","def profile_detail(request, employee_id):\n current_employee = Employee.objects.filter(user__pk=request.user.pk).first()\n employee = Employee.objects.get(pk=int(employee_id))\n\n if not current_employee:\n raise PermissionDenied(\"You don't have any employee assigned to you.\", 401)\n\n if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\n raise PermissionDenied()\n actions = employee.action_set.all()\n if not current_employee.pk == int(employee_id):\n if not current_employee.is_manager or not current_employee.company.pk == employee.company.pk:\n if not current_employee.isCompanySuperUserOrHigher():\n return HttpResponse('unauthorized', status=401)\n\n user_files = get_files_for_employee(employee_id)\n\n if request.method == 'POST':\n\n upload_form = UploadFileToEmployeyForm(request.POST, request.FILES)\n form = EmployeeNoteForm(request.POST, instance=employee)\n\n if 'upload' in request.POST:\n if upload_form.is_valid():\n upload_form.handle_upload(employee_id, request.FILES['file'])\n\n return HttpResponseRedirect('/employee/show/{}?upload_status=ok#file-list'.format(employee_id))\n\n else:\n if form.is_valid():\n form.save(request.user, employee)\n return HttpResponseRedirect('/employee/show/%d' % form.instance.pk)\n\n else:\n form = EmployeeNoteForm(instance=employee)\n upload_form = UploadFileToEmployeyForm()\n data = {}\n data[\"user\"] = employee.user.first_name + \" \" + employee.user.last_name\n data[\"id\"] = str(employee.user.pk)\n data[\"title\"] = employee.title\n data[\"email\"] = employee.user.email\n data[\"phone\"] = employee.phone\n company_dict = {}\n company_dict[\"name\"] = employee.company.name\n company_dict[\"id\"] = str(employee.company.pk)\n\n data[\"company\"] = company_dict\n employee_username = \"\"\n emp = Employee.objects.filter(manager=employee.manager).all()\n for obj in emp:\n employee_username = obj.manager.user.username if obj.manager else \"\"\n employee_first = obj.manager.user.first_name if obj.manager else \"\"\n employee_last = obj.manager.user.last_name if obj.manager else \"\"\n manager_dict = {}\n manager_dict[\"name\"] = employee_username\n manager_dict[\"id\"] = employee_id\n manager_dict[\"first_last_name\"] = employee_first + \" \" + employee_last\n data[\"manager\"] = manager_dict\n data[\"date_of_birth\"] = employee.date_of_birth\n data[\"status_questions\"] = employee.status_questions\n data[\"notes\"] = employee.notes\n employee_role = EmployeeRole.objects.filter(employee=employee).all()\n name_role_list = []\n for obj in employee_role:\n name_role_list.append(obj.role.name)\n data[\"roles\"] = name_role_list\n data[\"potenciale\"] = employee.potenciale\n data[\"date_start\"] = employee.created_at\n data[\"is_manager\"] = employee.is_manager\n data[\"date_finish\"] = \"\"\n data['photo'] = employee.photo.url if employee.photo else ''\n\n return JsonResponse(status=200, data=data)\n # return TemplateResponse(\n # request,\n # 'mus/detail.html',\n # {\n # 'actions': actions,\n # 'employee': employee,\n # # 'development_plans': development_plans,\n # 'form': form,\n # 'upload_form': upload_form,\n # 'user_files': user_files\n # }\n # )","def test_ReportingPeriodDetailView_current_employee_toggle(self):\n self.former_employee.user_data.current_employee = True\n self.former_employee.user_data.save()\n response = self.app.get(\n reverse(\n 'reports:ReportingPeriodDetailView',\n kwargs={'reporting_period': '2015-01-01'},\n )\n )\n self.assertEqual(\n len(response.html.find_all('tr', {'class': 'user'})), 3\n )\n self.former_employee","def collection_get(request):\n\n # Our account parameter\n account = request.matchdict['id_account']\n\n # Our admin object\n admin = _get_admin(request)\n\n # Check if the account exists\n if account not in admin.list_accounts():\n request.response.status_int = 404\n return\n\n # Get the roles\n list_roles = admin.list_roles(account)\n\n # Return appropriately\n request.response.status_int = 200\n return {\n 'roles':\n list_roles\n }","def filter_queryset(self, request, queryset, view):\n if view.action == \"retrieve\" and request.method == \"GET\":\n return queryset.model.objects.all()\n\n filtered_queryset = super().filter_queryset(request, queryset, view)\n org_users = set(\n [group.team.organization for group in request.user.groups.all()] +\n [o.user for o in filtered_queryset]\n )\n\n return queryset.model.objects.filter(user__in=org_users, user__is_active=True)","def employee_required(fn):\n @wraps(fn)\n def wrapper(*args, **kwargs):\n verify_jwt_in_request()\n user_id = get_jwt_identity()\n target_user = User.query.filter_by(id=user_id).first()\n\n if target_user is None:\n return redirect('admin/login.html', code=403)\n\n if target_user.role != RoleType.ADMINISTRATOR and target_user.role != RoleType.EMPLOYEE:\n return redirect('admin/login.html', code=403)\n return fn(*args, **kwargs)\n return wrapper","def get_queryset(self):\n user = self.request.user\n expenses = Expense.objects.filter(\n Q(userexpense__in=user.userexpense_set.all())\n | Q(group__in=user.group_set.all()))\n\n if self.request.query_params.get('q', None) is not None:\n expenses = expenses.filter(\n description__icontains=self.request.query_params.get(\n 'q', None))\n return expenses","def do_employee_login():\n user_requested = request.form['email'].lower()\n password_requested = request.form['password']\n\n target_user = User.query.filter_by(mail=user_requested).first()\n if target_user is None:\n return Response(render_template('admin/login.html',\n message=\"Unknown Credentials\"))\n\n if not target_user.check_password(password_requested):\n return Response(render_template('admin/login.html',\n message=\"Unknown Credentials\"))\n\n if not target_user.state == StateType.ACTIVE:\n return Response(render_template('admin/login.html',\n message=\"User account deactivated. Cannot login.\"))\n\n resp = Response(render_template('employee/employee.html', user=target_user.name,\n message=\"Login succeeded\"))\n set_access_cookies(resp, create_access_token(identity=target_user.id))\n return resp","def employee_login():\n return Response(render_template('admin/login.html'))","def index(request):\n users = User.objects.filter(is_staff=False, is_active=True).order_by('username')\n return render(request, 'users/view_all_users.html',\n { 'users': users })","def get_employee_information(user_name: str, employee_name: str, store_name: str):\n\n user_name = auth.get_username_from_hash(user_name)\n permission_handler.is_permmited_to(user_name=user_name, action=Action.EMPLOYEE_INFO.value,\n store_name=store_name)\n permission_handler.is_working_in_store(employee_name, store_name)\n return user_handler.get_employee_information(employee_name)","def name_search(self, cr, uid, name, args=None, operator='ilike', context=None, limit=100):\n if context is None:\n context = {}\n if 'emp_hours' in context:\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('emp.luggage_transfer.hours'),\n context.get('emp_hours'), [\"employee\"], context)\n args.append(('id', 'not in', [isinstance(d['employee'], tuple) and d['employee'][0] or d['employee'] for d in emp_ids]))\n if 'mission_line' in context:\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('hr.employee.mission.line'),\n context.get('mission_line'), [\"employee_id\"], context)\n args.append(('id', 'not in', [isinstance(d['employee_id'], tuple) and d['employee_id'][0] or d['employee_id'] for d in emp_ids]))\n \n if 'illness' in context:\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('hr.employee.illness'),\n context.get('illness'), [\"employee_id\"], context)\n args.append(('id', 'not in', [isinstance(d['employee_id'], tuple) and d['employee_id'][0] or d['employee_id'] for d in emp_ids]))\n \n\n if 'same' in context:\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('hr.employee.mission.line'),\n context.get('same'), [\"employee_id\"], context)\n args.append(('id', 'in', [isinstance(d['employee_id'], tuple) and d['employee_id'][0] or d['employee_id'] for d in emp_ids]))\n \n \n if 'alternative_setting_id' in context:\n old_ids = super(hr_employee, self).name_search(cr, uid, name, args=args, operator=operator, context={}, limit=limit)\n\n alternative_setting_id = context.get('alternative_setting_id')\n setting_obj = self.pool.get('hr.alternative.setting')\n alternative_setting_id = setting_obj.browse(cr, uid, alternative_setting_id)\n degrees_ids = [\n x.id for x in alternative_setting_id.degrees_ids]\n degrees_ids += degrees_ids\n degrees_ids = tuple(degrees_ids)\n\n departments_ids = [\n x.id for x in alternative_setting_id.departments_ids]\n departments_ids += departments_ids\n departments_ids = tuple(departments_ids)\n\n ex_employees_ids = [\n x.id for x in alternative_setting_id.employees_ids]\n ex_employees_ids += ex_employees_ids\n ex_employees_ids = tuple(ex_employees_ids)\n\n\n old_ids_tuple = [x[0] for x in old_ids] + [x[0] for x in old_ids]\n old_ids_tuple = tuple(old_ids_tuple)\n\n accessed_ids = self.search(cr, uid, [])\n accessed_ids += accessed_ids\n accessed_ids = tuple(accessed_ids)\n\n if not old_ids_tuple:\n old_ids_tuple = (0,0)\n \n if not departments_ids:\n departments_ids = (0,0)\n cr.execute(\n ''' Select emp.id,(SELECT MAX(date) as max_date\n FROM hr_alternative_process_line\n WHERE employee_id=emp.id and state='confirmed')date\n from hr_employee emp\n where emp.degree_id in %s \n and emp.department_id not in %s \n and emp.state = 'approved' \n and emp.payroll_state = 'khartoum' \n and emp.id in %s \n and emp.gender='male' \n and emp.id in %s \n and emp.id not in %s \n order by date NULLS LAST''', (degrees_ids,departments_ids,old_ids_tuple,accessed_ids,ex_employees_ids))\n history = cr.dictfetchall()\n new_ids = []\n while True:\n try:\n new_ids.append( history.pop()['id'] )\n except:\n break\n\n temp = dict(old_ids)\n old_ids = [x for x in old_ids if x[0] in new_ids]\n #new_ids = [x for x in new_ids if x in accessed_ids]\n #print \"..........................temp\",new_ids\n #print \"......................\",[(x, temp.get(x,False) ) for x in new_ids]\n #print \"......................\",sorted(old_ids, key=lambda x :new_ids.index(x[0]))\n return sorted(old_ids, key=lambda x :new_ids.index(x[0]))\n\n return super(hr_employee, self).name_search(cr, uid, name, args=args, operator=operator, context=context, limit=limit)","def search_encomendas_admin(request, name):\n token = getToken(request)\n superUser = isSuperUser(token)\n\n if superUser == True:\n try:\n encomendas = Encomenda.objects.filter(produtos__titulo__contains=name).order_by(\"-id\")\n except Encomenda.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n serializer = EncomendaReadSerializer(encomendas, many=True)\n return Response(serializer.data)\n else:\n return Response(status=status.HTTP_401_UNAUTHORIZED)","def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employee\", request)\n raise PermissionDenied()\n form = EmployeeForm(request, initial=dict(company=company))\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company=company) | Q(company__isnull=True))\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n\n return TemplateResponse(\n request,\n 'mus/create_employee_form.html',\n {\n 'employee_form': form,\n }\n )\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n # return JsonResponse(status=200, data=data)","def filter_queryset(self, queryset):\n user = self.request.user\n if user.is_superuser:\n return super().filter_queryset(queryset)\n return queryset.filter(collaborators=user)","def employee_list(request):\n response_data = []\n for emp in Employee.objects.all().values(\n 'id', 'first_name', 'last_name', 'age', 'address', 'city',\n 'state', 'country'):\n response_data.append(emp)\n return JsonResponse(response_data, safe=False)","def test_access_employee(self):\n # Employee can't see any SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).read()\n # Employee can't edit the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\n # Employee can't create the SO\n with self.assertRaises(AccessError):\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\n 'partner_id': self.partner_a.id,\n })\n # Employee can't delete the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).unlink()","def employees_json(request):\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\n employee_list = Employee.objects.filter(manager=request.user.employee_user)\n employees = list()\n for employee in employee_list:\n manager_dict = model_to_dict(employee)\n manager_dict['first_name'] = employee.user.first_name\n manager_dict['last_name'] = employee.user.last_name\n employees.append(manager_dict)\n data = {\"employees\": employees}\n return JsonResponse(data=data, content_type='application/json', safe=False)","def retrieve(self, request, pk=None):\n employee = self.get_employee_object(pk)\n print(F\"Employee: {employee}\")\n serializer = data_serializers.PresentEmployeeDataSerializer(employee)\n return Response(serializer.data)","def default_get(self, cr, uid, fields, context=None): \n \n \n res = super(granted_rights_order, self).default_get(cr, uid, fields, context=context)\n \n employee_obj = self.pool.get('hr.employee')\n department_obj = self.pool.get('hr.department')\n manager = False\n donor_emp_id = []\n \n if uid != 1 :\n\n donor_emp_id = employee_obj.search(cr ,uid, [('user_id' , '=' , uid )])\n deparment_id = employee_obj.browse(cr,uid,donor_emp_id[0]).department_id.id\n \n if donor_emp_id[0] == department_obj.browse(cr,uid,deparment_id).manager_id.id :\n manager = True\n \n \n \n \n \n \n \n \n \n if donor_emp_id :\n res.update({ 'employee_donor': donor_emp_id[0], \n 'department_id' : deparment_id,\n 'is_a_amanger' : manager,\n })\n return res","def list(self, **kwargs):\n # TODO(adriant): Look up user by name/id\n url = '/openstack/users/%s/roles' % kwargs['user']\n return self._list(url, 'roles')","def get_queryset(self):\n user = self.request.user\n\n if user.is_authenticated and user.role == 'LA':\n # admins view all property, no filtering\n return Property.objects.all()\n\n if user.is_authenticated and user.employer.first():\n # if the user is a client_admin, they see all published property\n # and also their client's published and unpublished property.\n client = user.employer.first()\n return Property.active_objects.all_published_and_all_by_client(\n client=client)\n\n # other users only see published property\n return Property.active_objects.all_published()","def _current_login_employee(self):\n hr_employee = self.env[\"hr.employee\"].search(\n [(\"user_id\", \"=\", self._current_login_user())], limit=1\n )\n return hr_employee.id","def get_queryset(self):\r\n username = self.kwargs['username']\r\n return models.Education.objects.filter(username = username) .order_by('-startdate')","def get_employee(self, name):\n name = name.upper()\n if name in EMPLOYEE_MAP:\n name = EMPLOYEE_MAP[name]\n try:\n int(name)\n emps = Employee.objects.filter(id=name)\n except ValueError:\n if name == 'NN':\n emps = Employee.objects.filter(user__first_name='Nieznany')\n elif Employee.objects.filter(user__username__iexact=name).exists():\n emps = Employee.objects.filter(user__username__iexact=name)\n elif len(name) == 3:\n emps = Employee.objects.filter(user__first_name__istartswith=name[0],\n user__last_name__istartswith=name[1:3],\n status=0)\n else:\n emps = Employee.objects.filter(user__first_name__istartswith=name[0],\n user__last_name__istartswith=name[1:],\n status=0)\n if not emps:\n emps = Employee.objects.filter(user__username__istartswith=name)\n if len(emps) == 1:\n return emps[0]\n elif len(emps) > 1:\n self.stdout.write(self.style.ERROR('Multiple employee matches for {}. Choices are:'\n .format(name)))\n for e in emps:\n self.stdout.write(self.style.ERROR(' -{}'.format(e.user.get_full_name())))\n else:\n raise CommandError('Employee {} does not exists! Fix your input file.'.format(name))\n\n return None","def get_queryset(self):\n return AutomaticEmail.objects.filter(staff_user=self.request.user)","def get_all_user_development_plans_for_manager(request, employee_id):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n user_development_plans = DevelopmentPlan.objects.filter(employee_relation=current_employee).all()\n employee = Employee.objects.filter(pk=int(employee_id)).first()\n\n if not current_employee:\n raise PermissionDenied(\"You don't have any employee assigned to you.\", 401)\n\n if not current_employee.isEnsoUser() and current_employee.is_manager:\n raise PermissionDenied()\n actions = employee.action_set.all()\n if not int(employee_id) in [obj.id for obj in Employee.objects.filter(manager=current_employee).all()]:\n raise PermissionDenied(\"Employee with id={} is not assigned to you.\".format(employee_id), 401)\n\n if user_development_plans:\n data={}\n user_development_plans_list = []\n for plan in user_development_plans:\n\n development_plan_object_list=[]\n dev_plan = {}\n\n dev_plan[\"id\"] = plan.id\n dev_plan[\"deleted\"] = plan.deleted\n if plan.type:\n dev_plan[\"type\"] = plan.type.name\n dev_plan[\"finished_at\"] = DevelopmentPlanToEmployeeRelation.objects\\\n .get(employee=current_employee, development_plan = plan).finished_at\n dev_plan[\"created_at\"] = plan.created_at\n dev_plan[\"created_by\"] = plan.created_by.username\n\n development_plan_object_list.append({\"dev_plan_details\":dev_plan})\n\n manager_data = {}\n manager_data[\"manager_username\"] = plan.manager_relation.user.username\n manager_data[\"id\"] = plan.manager_relation.user.id\n\n development_plan_object_list.append({\"manager_data\":manager_data})\n user_development_plans_list.append(development_plan_object_list)\n\n else:\n return JsonResponse(data={\"details\":\"Employee with id={} doesn't have any Development Plan\"\n .format(request.user.pk)}, status=404)\n\n data = {\"user_development_plans:\": user_development_plans_list}\n return JsonResponse(status=201, data=data)","def explore_view(request):\r\n # explore items\r\n user = request.user.userprofile\r\n items = Item.objects.explore(user)\r\n context = {'items':items}\r\n return render(request, 'explore/explore.html', context)","def get_employees(self):\n from Employee import Employee\n cursor = self.dbconnect.get_cursor()\n cursor.execute('select * from employee')\n\n employees = list()\n for row in cursor:\n employee = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8])\n employees.append(employee)\n return employees","def users_with_role(self):\r\n entries = User.objects.filter(\r\n courseaccessrole__role=self._role_name,\r\n courseaccessrole__org=self.org,\r\n courseaccessrole__course_id=self.course_key\r\n )\r\n return entries","def badges_employee_list(request, employee_id):\n if request.method == 'GET':\n employee = get_object_or_404(Employee, pk=employee_id)\n employee_bages = EmployeeBadge.objects.filter(to_user=employee)\n paginator = PageNumberPagination()\n results = paginator.paginate_queryset(employee_bages, request)\n serializer = EmployeeBadgeSerializer(results, many=True)\n return paginator.get_paginated_response(serializer.data)","def query_employee(self, employee_inputs):\n\n query = \"select * from employee where \"\n row_names = [\n \"emp_ID\", \"Region_ID\", \"Emp_Lname\", \"Emp_Mi\", \"Emp_Fname\",\n \"Emp_Hiredate\"\n ]\n filled_attributes = []\n\n row_index = 0\n row_options = []\n for item in employee_inputs:\n if item is not None:\n row_options.append(row_index)\n filled_attributes.append(item)\n row_index += 1\n\n j = 0\n for i in row_options:\n if j == 0:\n query += \"{}='{}' \".format(row_names[i], filled_attributes[j])\n else:\n query += \"and {}='{}' \".format(row_names[i],\n filled_attributes[j])\n j += 1\n\n try:\n self.dbCursor.execute(query)\n return self.dbCursor.fetchall()\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)","def user_view(request):\n user = User.objects.all()\n return render(request, template_name, {'data_set':data_set})","def list(self , request,*args,**kwargs):\n return super(UsersViewset,self).list(request,args,kwargs)","def employee_profile_page(cls, employee_id):\n return cls.__profile_page(employee_id, cls._logger)","def get(self, request):\r\n\r\n if not request.user.is_staff:\r\n raise Http404\r\n data = []\r\n\r\n for course in self.get_courses(): # pylint: disable=unused-variable\r\n datum = [course.display_name, course.id]\r\n datum += [CourseEnrollment.objects.filter(\r\n course_id=course.id).count()]\r\n datum += [CourseStaffRole(course.id).users_with_role().count()]\r\n datum += [','.join([x.username for x in CourseInstructorRole(\r\n course.id).users_with_role()])]\r\n data.append(datum)\r\n\r\n datatable = dict(header=[_('Course Name'), _('course_id'),\r\n _('# enrolled'), _('# staff'),\r\n _('instructors')],\r\n title=_('Enrollment information for all courses'),\r\n data=data)\r\n context = {\r\n 'datatable': datatable,\r\n 'msg': self.msg,\r\n 'djangopid': os.getpid(),\r\n 'modeflag': {'staffing': 'active-section'},\r\n 'edx_platform_version': getattr(settings, 'EDX_PLATFORM_VERSION_STRING', ''),\r\n }\r\n return render_to_response(self.template_name, context)","def showORGusers(**kwargs):\n sessiontoken = kwargs['sessiontoken']\n ORG_ID = kwargs['ORG_ID']\n strCSPProdURL = kwargs['strCSPProdURL']\n jsonResponse = get_csp_users_json(strCSPProdURL, ORG_ID, sessiontoken)\n if jsonResponse == None:\n print(\"API Error\")\n sys.exit(1)\n\n users = jsonResponse['results']\n table = PrettyTable(['First Name', 'Last Name', 'User Name'])\n for i in users:\n table.add_row([i['user']['firstName'],i['user']['lastName'],i['user']['username']])\n print (table.get_string(sortby=\"Last Name\"))","def get_companies(request):\n companies = Company.objects.all()\n context={'user_id': request.user.id}\n serializer = CompanySerializers(companies, context=context)\n return Response(serializer.data)","def display_certs(employee_id):\n\n if not g.user:\n flash(\"Please Login to continue.\", \"danger\")\n return redirect(\"/\")\n\n employee = Employee.query.get_or_404(employee_id)\n \n certs = employee_certification.query.filter_by(employee_id = employee_id).all()\n \n all_certs = Cert.query.all()\n \n return render_template(\"users/display_cert.html\", employee = employee, certs = certs, all_certs = all_certs)","def get_queryset(self):\r\n name = self.kwargs['name']\r\n course_id_string = self.request.QUERY_PARAMS.get('course_id')\r\n if not course_id_string:\r\n raise ParseError('course_id must be specified')\r\n course_id = SlashSeparatedCourseKey.from_deprecated_string(course_id_string)\r\n role = Role.objects.get_or_create(course_id=course_id, name=name)[0]\r\n users = role.users.all()\r\n return users","def get_queryset(self, *args, **kwargs):\n qs = super().get_queryset(*args, **kwargs)\n user = self.request.user\n if not user.is_authenticated:\n return qs.none()\n if self.is_get and not user.has_perm(\"users.view_user\"):\n return qs.filter(pk=user.pk)\n if self.is_update and not user.has_perm(\"users.change_user\"):\n return qs.filter(pk=user.pk)\n return qs","def get_queryset(self):\n user = self.request.user\n collabLists = ListObject.objects.filter(collaborators__id=user.id)\n return collabLists","def employees_json_id(request, employee_id):\n curent_employee = Employee.objects.get(pk=int(employee_id))\n if curent_employee.is_manager:\n employee_list = Employee.objects.filter(manager=curent_employee)\n employees = list()\n for employee in employee_list:\n manager_dict = model_to_dict(employee)\n manager_dict['first_name'] = employee.user.first_name\n manager_dict['last_name'] = employee.user.last_name\n manager_dict['photo'] = employee.photo.url if employee.photo else ''\n employees.append(manager_dict)\n data = {\"employees\": employees}\n else:\n return JsonResponse(status=400, data={\"error\": \"Employee with id={} not is_manager\".format(int(employee_id))})\n return JsonResponse(data=data, content_type='application/json', safe=False)","def get_job_query(self):\n context = aq_inner(self.context)\n catalog = getToolByName(context, 'portal_catalog')\n mt = getToolByName(self, 'portal_membership') \n currentUser = mt.getAuthenticatedMember() \n \n if \"Site Administrators\" not in currentUser.getGroups():\n\treturn catalog.searchResults(portal_type= 'SeniorProject.PloneAddOn.job', \t\t\t\t Creator = currentUser.getUserName())\n else: \n return catalog.searchResults(portal_type= 'SeniorProject.PloneAddOn.job')","def test_api_can_get_all_employees(self):\n res = self.client().get(service_url_emp)\n self.assertEqual(res.status_code, 200)\n self.assertIn('name1', str(res.data))\n self.assertIn('name2', str(res.data))","def gather_employee_entries(self):\n user_inputs = [\n self.emp_lname.get(), self.emp_mi.get(), self.emp_fname.get(),\n self.emp_hiredate.get()\n ]\n\n return self.check_input_empty(user_inputs)","def get_list_filter(self, request):\n if request.user.is_superuser:\n return self.list_filter\n return self.list_filter_companies","def list(self, request, *args, **kwargs):\n return super(UserViewSet, self).list(request, *args, **kwargs)","def getELUsers(**kwargs):\n \n for key in kwargs:\n if type(kwargs[key]) == list:\n kwargs[key] = kwargs[key][0]\n \n allELUsers = ELUser.ELUser.all(**kwargs)\n allELUsersDictionaries = [dict(eluser) for eluser in allELUsers if dict(eluser)]\n \n return flask.Response(\n response = json.dumps(allELUsersDictionaries),\n status = 200,\n content_type = 'application/json'\n )","def get_queryset(self):\n return self.request.user.setting_set.get().companies","def get_queryset(self):\n return self.request.user.setting_set.get().companies","def get_queryset(self):\n return self.request.user.setting_set.get().companies","def get_queryset(self):\n return self.request.user.setting_set.get().companies"],"string":"[\n \"def show_all_employees():\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n employees = Employee.query.all()\\n \\n \\n ## right now this is ALL users... \\n \\n return render_template(\\\"employee_display.html\\\", employees = employees)\",\n \"def get_manager_employees(request):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\\n if manager_employees:\\n emp_list=[]\\n for emp in manager_employees:\\n emp_data={}\\n emp_data[\\\"id\\\"] = emp.id\\n emp_data[\\\"username\\\"] = emp.user.username\\n emp_data[\\\"first_name\\\"] = emp.user.first_name\\n emp_data[\\\"last_name\\\"] = emp.user.last_name\\n emp_data[\\\"manager_id\\\"] = emp.manager.id\\n # emp_data[\\\"status_questions\\\"] = emp.status_questions\\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\\n # name_role_list = []\\n # for obj in employee_role:\\n # name_role_list.append(obj.role.name)\\n # emp_data[\\\"roles\\\"] = name_role_list\\n emp_list.append(emp_data)\\n data = {\\\"employees:\\\": emp_list}\\n return JsonResponse(status=201, data=data)\\n else:\\n return JsonResponse(\\\"The user with id={} isn't a manager for any user\\\".format(current_employee.user.id),\\n status=404)\",\n \"def get_queryset(self, request):\\n return models.Employee.objects.exclude(username='root')\",\n \"def employees():\\n # gather data from db about all employees\\n return render_template(\\\"employees.html\\\")\",\n \"def show_all_employees(self):\\n try:\\n employees = self.admin_repository.show_all_employees()\\n if employees:\\n for employee in employees:\\n print(\\\"Employee Id : {}\\\".format(employee[0]))\\n print(\\\"Name : {}\\\".format(employee[1]))\\n print(\\\"Email : {}\\\".format(employee[2]))\\n print(\\\"----------------------------\\\")\\n return True\\n else:\\n print(\\\"No records found.\\\")\\n return False\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def index(request):\\n\\n context = {'employees': User.objects.select_related('profile').filter(is_staff=True).order_by('first_name')}\\n return render(request, 'Employees/index.html', context)\",\n \"def list(self, request):\\n employee = self.controller.retrieve_all_employees()\\n serializer = data_serializers.PresentEmployeeDataSerializer(employee, many=True)\\n return Response(serializer.data)\",\n \"def employee(employee_id):\\n # gather data from db about all employees\\n return render_template(\\\"employee.html\\\",\\n employee_id=employee_id)\",\n \"def current_employee(self, request: Request) -> Response:\\n serializer = self.get_serializer_class()\\n serializer = serializer(request.user, context={'request': request})\\n return Response(serializer.data)\",\n \"def get(self):\\n resultado = EmployeeModel.query.all()\\n return resultado\",\n \"def edit_employee(request, employee_id):\\n employee = Employee.objects.get(pk=int(employee_id))\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n\\n assert isinstance(employee, Employee)\\n assert isinstance(current_employee, Employee)\\n\\n # if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\\n # raise PermissionDenied()\\n\\n if not current_employee.hasAccessTo(employee):\\n raise PermissionDenied()\\n\\n form = EditEmployeeForm(request.user, employee, {\\n 'first_name': employee.user.first_name,\\n 'last_name': employee.user.last_name,\\n 'email': employee.user.email,\\n 'manager': employee.manager.id if employee.manager else 0,\\n 'language_code': employee.language_code,\\n # 'development_plan_type': employee.development_plan_type.id,\\n 'is_manager': employee.is_manager\\n })\\n if 'manager' in form.fields:\\n managerQS = Employee.objects.filter(is_manager=True, company__pk=employee.company.pk)\\n form.fields['manager'].queryset = managerQS\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company__pk=employee.company.pk) | Q(company__isnull=True)\\n # )\\n is_me = employee.user.pk == request.user.pk\\n return TemplateResponse(\\n request,\\n 'mus/edit_employee_form.html',\\n {\\n 'edit_employee_form': form,\\n 'employee_id': employee_id,\\n 'me': is_me,\\n 'name': employee.user.get_full_name()\\n }\\n )\",\n \"def get_queryset(self):\\n #print(\\\"request\\\", self.request)\\n user = self.request.user\\n return Experience.objects.filter(person=user)\",\n \"def action_list(request, employee_id=None):\\n if employee_id:\\n employee = Employee.objects.get(pk=employee_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\\n raise PermissionDenied()\\n else:\\n employee = request.user.employee_user.first()\\n actions = employee.action_set.all()\\n return TemplateResponse(\\n request,\\n 'mus/action_list.html',\\n dict(\\n actions=actions,\\n employee=employee\\n )\\n )\",\n \"def employee_detail(request, employee_id):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n employee = Employee.objects.get(pk=int(employee_id))\\n if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\\n raise PermissionDenied()\\n actions = employee.action_set.all()\\n if not current_employee.pk == int(employee_id):\\n if not current_employee.is_manager or not current_employee.company.pk == employee.company.pk:\\n if not current_employee.isCompanySuperUserOrHigher():\\n return HttpResponse('unauthorized', status=401)\\n\\n user_files = get_files_for_employee(employee_id)\\n\\n if request.method == 'POST':\\n\\n upload_form = UploadFileToEmployeyForm(request.POST, request.FILES)\\n form = EmployeeNoteForm(request.POST, instance=employee)\\n\\n if 'upload' in request.POST:\\n if upload_form.is_valid():\\n upload_form.handle_upload(employee_id, request.FILES['file'])\\n\\n return HttpResponseRedirect('/employee/show/{}?upload_status=ok#file-list'.format(employee_id))\\n\\n else:\\n if form.is_valid():\\n form.save(request.user, employee)\\n return HttpResponseRedirect('/employee/show/%d' % form.instance.pk)\\n\\n else:\\n form = EmployeeNoteForm(instance=employee)\\n upload_form = UploadFileToEmployeyForm()\\n data = {}\\n data[\\\"first_name\\\"] = employee.user.first_name\\n data[\\\"last_name\\\"] = employee.user.last_name\\n data[\\\"email\\\"] = employee.user.email\\n data[\\\"is_manager\\\"] = employee.is_manager\\n data[\\\"language_code\\\"] = employee.language_code\\n employee_role = EmployeeRole.objects.filter(employee=employee).all()\\n name_role_list = []\\n for obj in employee_role:\\n name_role_list.append(obj.role.name)\\n data[\\\"roles\\\"] = name_role_list\\n return JsonResponse(status=201, data=data)\\n # return TemplateResponse(\\n # request,\\n # 'mus/detail.html',\\n # {\\n # 'actions': actions,\\n # 'employee': employee,\\n # # 'development_plans': development_plans,\\n # 'form': form,\\n # 'upload_form': upload_form,\\n # 'user_files': user_files\\n # }\\n # )\",\n \"def get_queryset(self):\\n\\n user = self.request.user\\n\\n if user.is_authenticated and user.role == 'LA':\\n # check if the user is a landville admin and return all records\\n # even soft deleted ones\\n return PropertyEnquiry.objects.all()\\n\\n if user.is_authenticated and user.role == 'CA':\\n # if the user is a client admin, return only his records\\n employer = user.employer.first()\\n return PropertyEnquiry.active_objects.for_client(client=employer)\\n\\n # if the user is a buyer, return also only his enquiries\\n return PropertyEnquiry.active_objects.for_user(user=user)\",\n \"def show_all_information():\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n employees = Employee.query.all()\\n all_certs = employee_certification.query.all()\\n \\n return render_template(\\\"admin.html\\\", employees = employees, all_certs = all_certs)\",\n \"def show_overview_of_all_employees(self):\\n\\n print(\\\"OVERVIEW OF EMPLOYEES\\\\n\\\")\\n\\n employees_ob_list = self.llapi.get_employee_overview()\\n \\n for employee_ob in employees_ob_list:\\n print(employee_ob.print_info_in_line(\\\"*\\\"))\\n \\n print(f\\\"\\\\nNAN AIR has {len(employees_ob_list)} employees\\\")\\n\\n print(\\\"\\\\nB Back\\\\n\\\")\\n\\n action_str = self.choose_action([\\\"b\\\"])\\n while action_str == False:\\n action_str = self.choose_action([\\\"b\\\"])\\n\\n if action_str == \\\"b\\\":\\n return\",\n \"def employee():\\n return Response(render_template('employee/employee.html'))\",\n \"def get_role_from_rolequeryset(self, role):\\n role = super().get_role_from_rolequeryset(role=role)\\n\\n requesting_appname = self.request.cradmin_app.appname\\n if requesting_appname in ['qualifiesforexam', 'overview_all_results']:\\n if self.period_admin_access_semi_anonymous_assignments_restricted(period=role):\\n raise Http404()\\n\\n return role\",\n \"def list(self, request):\\n varemployee = employee.objects.all()\\n serializer = employeeSerializer(varemployee, many=True)\\n return Response(serializer.data)\",\n \"def get_queryset(self):\\n qs = super().get_queryset()\\n qs.filter(company=self.request.user.company)\\n return qs\",\n \"def stars_employee_list(request, employee_id):\\n if request.method == 'GET':\\n employee = get_object_or_404(Employee, pk=employee_id)\\n employee_stars = Star.objects.filter(to_user=employee)\\n paginator = PageNumberPagination()\\n results = paginator.paginate_queryset(employee_stars, request)\\n serializer = StarSerializer(results, many=True)\\n return paginator.get_paginated_response(serializer.data)\",\n \"def employees_manager(request):\\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)\\n employee = Employee.objects.get(pk=request.user.employee_user.id)\\n employee_dict = model_to_dict(employee)\\n employee_dict['first_name'] = employee.user.first_name\\n employee_dict['last_name'] = employee.user.last_name\\n employee_dict['photo'] = employee.photo.url if employee.photo else ''\\n print employee_dict\\n if len(manager_list) > 0:\\n result_list = list(manager_list)\\n all_managers_list = found_all_managers(manager_list, result_list)\\n else:\\n data = {\\\"employee_managers\\\": employee_dict}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\\n employees = list()\\n for manager in all_managers_list:\\n manager_dict = model_to_dict(manager)\\n manager_dict['first_name'] = manager.user.first_name\\n manager_dict['last_name'] = manager.user.last_name\\n manager_dict['photo'] = manager.photo.url if manager.photo else ''\\n employees.append(manager_dict)\\n employees.append(employee_dict)\\n\\n data = {\\\"employee_managers\\\": employees}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def list(self, request):\\n teams = self.controller.retrieve_all_teams_employees()\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(teams, many=True)\\n return Response(serializer.data)\",\n \"def start_view(request):\\n\\n if request.user and Employee.objects.filter(user__pk=request.user.pk).exists():\\n if Employee.objects.get(user__pk=request.user.pk).is_manager:\\n return HttpResponseRedirect('/dashboard')\\n else:\\n return HttpResponseRedirect('/employee/show/%d/' % request.user.employee_user.first().pk)\\n else:\\n return HttpResponseRedirect('/login/')\",\n \"def get_employee(self):\\n employee_ids = self.env['hr.employee'].search([('user_id', '=', self.env.uid)])\\n return employee_ids[0] if employee_ids else False\",\n \"def get_employee_permissions(user_name: str, store_name: str, employee_name: str):\\n user_name = auth.get_username_from_hash(user_name)\\n permission_handler.is_permmited_to(user_name, Action.EMPLOYEE_PERMISSIONS, store_name)\\n permission_handler.is_working_in_store(employee_name, store_name)\\n return user_handler.get_employee_information(\\n employee_name) # TODO FOR NOW RETURN INFORMATION MAYBE TO CHANGE TO NEW FUNCTION\",\n \"def get_queryset(self):\\n\\n user = self.request.user\\n\\n if user.role == 'LA':\\n return PropertyEnquiry.objects.all()\\n\\n # check if the user is a client admin\\n # and return all enquiries made on his/her property\\n if user.role == 'CA':\\n return PropertyEnquiry.active_objects.for_client(\\n client=user.employer.first())\\n\\n # else if the user is a buyer return only\\n # the records that are associated with him/her\\n return PropertyEnquiry.active_objects.for_user(user=user)\",\n \"def get_employees(self, active_only):\\n cursor = self.dbconnect.get_cursor()\\n\\n if active_only:\\n cursor.execute(\\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\\n 'is_admin, is_active FROM employee WHERE is_active = TRUE')\\n else:\\n cursor.execute(\\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\\n 'is_admin, is_active FROM employee')\\n\\n employees = list()\\n for row in cursor:\\n obj = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10])\\n employees.append(obj)\\n return employees\",\n \"def get_employees(self):\\n return self.employees\",\n \"def test_ReportingPeriodDetailView_current_employee_set_false(self):\\n response = self.app.get(\\n reverse(\\n 'reports:ReportingPeriodDetailView',\\n kwargs={'reporting_period': '2015-01-01'},\\n )\\n )\\n self.assertEqual(\\n len(response.html.find_all('tr', {'class': 'user'})), 2\\n )\",\n \"def get_queryset(self):\\n user = self.request.user\\n\\n if user.is_authenticated and user.role == 'LA':\\n return Property.objects.all()\\n\\n if user.is_authenticated and user.employer.first():\\n client = user.employer.first()\\n return Property.active_objects.all_published_and_all_by_client(\\n client=client)\\n\\n return Property.active_objects.all_published()\",\n \"def get_queryset(self):\\n user = self.request.user\\n\\n if user.is_authenticated and user.role == 'LA':\\n return Property.objects.all()\\n\\n if user.is_authenticated and user.employer.first():\\n client = user.employer.first()\\n return Property.active_objects.all_published_and_all_by_client(\\n client=client)\\n\\n return Property.active_objects.all_published()\",\n \"def show_work_role():\\n\\n work_roles = WorkRole.query.order_by(WorkRole.id.asc())\\n return render_template('general/work_role.html', title='Work Roles', work_roles=work_roles)\",\n \"def myorgs(request):\\n context = RequestContext(request)\\n \\n user = request.user\\n orgs = user.orgusers.get_query_set()\\n \\n context['orgs'] = orgs\\n return render_to_response('myorgs.html', context)\",\n \"def get(self, request):\\n varemployee = employee.objects.all()\\n serializer = employeeSerializer(varemployee, many=True)\\n return Response(serializer.data)\",\n \"def admin_roles(request):\\n user = User.objects.get(username=request.user.username)\\n permisos = get_permisos_sistema(user)\\n return render_to_response('admin/roles/roles.html',{'user':user,\\n 'ver_roles':'Ver roles' in permisos,\\n 'crear_rol': 'Crear rol' in permisos,\\n 'mod_rol': 'Modificar rol' in permisos,\\n 'eliminar_rol': 'Eliminar rol' in permisos},context_instance=RequestContext(request))\",\n \"def employeeHome(request):\\n # assert isinstance(request, HttpRequest)\\n \\n if userHasBeenCleared(request):\\n \\n return render(\\n request,\\n 'app/index_employee.html',\\n {\\n 'title':'Home Page',\\n 'year':datetime.now().year,\\n 'email':getEmailSessionVar(request),\\n 'isAdmin':isAdmin(request), \\n }\\n )\\n return login(request)\",\n \"def get_emp_list(self):\\n\\t\\tcondition= ''\\n\\t\\temp_list=[]\\n\\t\\tif self.is_for_all==0:\\n\\t\\t\\tif not self.selected_employees:\\n\\t\\t\\t\\tfrappe.throw(_(\\\"No employees for the mentioned criteria\\\"))\\n\\t\\t\\t#emp_list = [cstr(d.employee) for d in self.selected_employees]\\n\\t\\t\\temp_list = frappe.db.sql_list(\\\"\\\"\\\"\\n\\t\\t\\t\\tselect\\n\\t\\t\\t\\t\\temployee from `tabAttendance Salary Tool Employee`\\n\\t\\t\\t\\twhere\\n\\t\\t\\t\\t\\tparent = '%(parent)s' \\n\\t\\t\\t\\\"\\\"\\\"%{\\\"parent\\\": self.name})\\n\\t\\t\\tcondition+= \\\"\\\"\\\" and t1.employee IN %(employees)s \\\"\\\"\\\"\\n\\t\\tif self.is_open_period==0:\\n\\t\\t\\tif not self.start_date or not self.end_date:\\n\\t\\t\\t\\tfrappe.throw(_(\\\"Satart Date and End Date are Mandatories\\\"))\\n\\t\\t\\tcondition= \\\"\\\"\\\" and attendance_date >= %(start_date)s and attendance_date <= %(end_date)s\\\"\\\"\\\"\\n\\t\\temp_list = frappe.db.sql(\\\"\\\"\\\"\\n\\t\\t\\tselect\\n\\t\\t\\t\\tt1.employee as employee, count(*) as attendance_days\\n\\t\\t\\tfrom\\n\\t\\t\\t\\t`tabAttendance` t1\\n\\t\\t\\twhere\\n\\t\\t\\t\\tt1.attendance_salary_tool is null\\n\\t\\t\\t\\tand t1.docstatus = 1 and t1.status='Present'\\n\\t\\t\\t\\t{condition} group by t1.employee order by t1.employee asc\\n\\t\\t\\\"\\\"\\\".format(condition=condition),{\\\"employees\\\": tuple(emp_list),\\\"start_date\\\": self.start_date,\\\"end_date\\\": self.end_date}, as_dict=True)\\n\\t\\treturn emp_list\",\n \"def event_collaborator_detail(request, event_id, collaborator_id):\\n if request.method == 'GET':\\n event = get_object_or_404(Event, pk=event_id)\\n collaborator = Employee.objects.all().filter(event=event, pk=collaborator_id)\\n if collaborator:\\n is_registered = True\\n else:\\n is_registered = False\\n serializer = CollaboratorAttendanceSerializer(event, context={'is_registered': is_registered})\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def home(request):\\n\\n\\tcontext_dict = {}\\n\\temployee = models.Teacher.objects.filter(\\n\\t\\tuser=request.user\\n\\t).first()\\n\\t# context_dict = {\\n\\t# context_helper.get_emp_info(employee)\\n\\t# }\\n\\t# print (context_dict)\\n\\tcontext_dict.update(context_helper.get_emp_info(employee))\\n\\treturn render(request, \\\"home.html\\\", context_dict)\",\n \"def get_queryset(self):\\r\\n username = self.kwargs['username']\\r\\n return models.Experience.objects.filter(username = username).order_by('-startdate')\",\n \"def show_employee(emp_id, fields=None):\\n ret = {}\\n if fields is None:\\n fields = \\\",\\\".join(\\n (\\n \\\"canUploadPhoto\\\",\\n \\\"department\\\",\\n \\\"displayName\\\",\\n \\\"firstName\\\",\\n \\\"id\\\",\\n \\\"jobTitle\\\",\\n \\\"lastName\\\",\\n \\\"location\\\",\\n \\\"mobilePhone\\\",\\n \\\"nickname\\\",\\n \\\"photoUploaded\\\",\\n \\\"photoUrl\\\",\\n \\\"workEmail\\\",\\n \\\"workPhone\\\",\\n \\\"workPhoneExtension\\\",\\n )\\n )\\n\\n status, result = _query(action=\\\"employees\\\", command=emp_id, args={\\\"fields\\\": fields})\\n\\n root = ET.fromstring(result)\\n\\n ret = {\\\"id\\\": emp_id}\\n for item in root:\\n ret[next(iter(item.values()))] = item.text\\n return ret\",\n \"def is_employee():\\n return _is_member('uw_employee')\",\n \"def getEmployees(self):\\n return self.employees\",\n \"def get_queryset(self, request):\\n qs = super(EventAdmin, self).get_queryset(request)\\n if request.user.is_superuser:\\n return qs\\n return qs.filter(dep=request.user.profile.department)\",\n \"def get(self):\\n employees = self.service.get_employees(strategy=selectinload)\\n return self.schema.dump(employees, many=True), 200\",\n \"def get_admins(self):\\n from Employee import Employee\\n admins = list()\\n cursorRoles = self.dbconnect.get_cursor()\\n cursorRoles.execute('select * from employeeRoles where role=\\\\'admin\\\\'')\\n for row in cursorRoles:\\n admins.append(self.get_employee(row[0]))\\n return admins\",\n \"def profile_detail(request, employee_id):\\n current_employee = Employee.objects.filter(user__pk=request.user.pk).first()\\n employee = Employee.objects.get(pk=int(employee_id))\\n\\n if not current_employee:\\n raise PermissionDenied(\\\"You don't have any employee assigned to you.\\\", 401)\\n\\n if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:\\n raise PermissionDenied()\\n actions = employee.action_set.all()\\n if not current_employee.pk == int(employee_id):\\n if not current_employee.is_manager or not current_employee.company.pk == employee.company.pk:\\n if not current_employee.isCompanySuperUserOrHigher():\\n return HttpResponse('unauthorized', status=401)\\n\\n user_files = get_files_for_employee(employee_id)\\n\\n if request.method == 'POST':\\n\\n upload_form = UploadFileToEmployeyForm(request.POST, request.FILES)\\n form = EmployeeNoteForm(request.POST, instance=employee)\\n\\n if 'upload' in request.POST:\\n if upload_form.is_valid():\\n upload_form.handle_upload(employee_id, request.FILES['file'])\\n\\n return HttpResponseRedirect('/employee/show/{}?upload_status=ok#file-list'.format(employee_id))\\n\\n else:\\n if form.is_valid():\\n form.save(request.user, employee)\\n return HttpResponseRedirect('/employee/show/%d' % form.instance.pk)\\n\\n else:\\n form = EmployeeNoteForm(instance=employee)\\n upload_form = UploadFileToEmployeyForm()\\n data = {}\\n data[\\\"user\\\"] = employee.user.first_name + \\\" \\\" + employee.user.last_name\\n data[\\\"id\\\"] = str(employee.user.pk)\\n data[\\\"title\\\"] = employee.title\\n data[\\\"email\\\"] = employee.user.email\\n data[\\\"phone\\\"] = employee.phone\\n company_dict = {}\\n company_dict[\\\"name\\\"] = employee.company.name\\n company_dict[\\\"id\\\"] = str(employee.company.pk)\\n\\n data[\\\"company\\\"] = company_dict\\n employee_username = \\\"\\\"\\n emp = Employee.objects.filter(manager=employee.manager).all()\\n for obj in emp:\\n employee_username = obj.manager.user.username if obj.manager else \\\"\\\"\\n employee_first = obj.manager.user.first_name if obj.manager else \\\"\\\"\\n employee_last = obj.manager.user.last_name if obj.manager else \\\"\\\"\\n manager_dict = {}\\n manager_dict[\\\"name\\\"] = employee_username\\n manager_dict[\\\"id\\\"] = employee_id\\n manager_dict[\\\"first_last_name\\\"] = employee_first + \\\" \\\" + employee_last\\n data[\\\"manager\\\"] = manager_dict\\n data[\\\"date_of_birth\\\"] = employee.date_of_birth\\n data[\\\"status_questions\\\"] = employee.status_questions\\n data[\\\"notes\\\"] = employee.notes\\n employee_role = EmployeeRole.objects.filter(employee=employee).all()\\n name_role_list = []\\n for obj in employee_role:\\n name_role_list.append(obj.role.name)\\n data[\\\"roles\\\"] = name_role_list\\n data[\\\"potenciale\\\"] = employee.potenciale\\n data[\\\"date_start\\\"] = employee.created_at\\n data[\\\"is_manager\\\"] = employee.is_manager\\n data[\\\"date_finish\\\"] = \\\"\\\"\\n data['photo'] = employee.photo.url if employee.photo else ''\\n\\n return JsonResponse(status=200, data=data)\\n # return TemplateResponse(\\n # request,\\n # 'mus/detail.html',\\n # {\\n # 'actions': actions,\\n # 'employee': employee,\\n # # 'development_plans': development_plans,\\n # 'form': form,\\n # 'upload_form': upload_form,\\n # 'user_files': user_files\\n # }\\n # )\",\n \"def test_ReportingPeriodDetailView_current_employee_toggle(self):\\n self.former_employee.user_data.current_employee = True\\n self.former_employee.user_data.save()\\n response = self.app.get(\\n reverse(\\n 'reports:ReportingPeriodDetailView',\\n kwargs={'reporting_period': '2015-01-01'},\\n )\\n )\\n self.assertEqual(\\n len(response.html.find_all('tr', {'class': 'user'})), 3\\n )\\n self.former_employee\",\n \"def collection_get(request):\\n\\n # Our account parameter\\n account = request.matchdict['id_account']\\n\\n # Our admin object\\n admin = _get_admin(request)\\n\\n # Check if the account exists\\n if account not in admin.list_accounts():\\n request.response.status_int = 404\\n return\\n\\n # Get the roles\\n list_roles = admin.list_roles(account)\\n\\n # Return appropriately\\n request.response.status_int = 200\\n return {\\n 'roles':\\n list_roles\\n }\",\n \"def filter_queryset(self, request, queryset, view):\\n if view.action == \\\"retrieve\\\" and request.method == \\\"GET\\\":\\n return queryset.model.objects.all()\\n\\n filtered_queryset = super().filter_queryset(request, queryset, view)\\n org_users = set(\\n [group.team.organization for group in request.user.groups.all()] +\\n [o.user for o in filtered_queryset]\\n )\\n\\n return queryset.model.objects.filter(user__in=org_users, user__is_active=True)\",\n \"def employee_required(fn):\\n @wraps(fn)\\n def wrapper(*args, **kwargs):\\n verify_jwt_in_request()\\n user_id = get_jwt_identity()\\n target_user = User.query.filter_by(id=user_id).first()\\n\\n if target_user is None:\\n return redirect('admin/login.html', code=403)\\n\\n if target_user.role != RoleType.ADMINISTRATOR and target_user.role != RoleType.EMPLOYEE:\\n return redirect('admin/login.html', code=403)\\n return fn(*args, **kwargs)\\n return wrapper\",\n \"def get_queryset(self):\\n user = self.request.user\\n expenses = Expense.objects.filter(\\n Q(userexpense__in=user.userexpense_set.all())\\n | Q(group__in=user.group_set.all()))\\n\\n if self.request.query_params.get('q', None) is not None:\\n expenses = expenses.filter(\\n description__icontains=self.request.query_params.get(\\n 'q', None))\\n return expenses\",\n \"def do_employee_login():\\n user_requested = request.form['email'].lower()\\n password_requested = request.form['password']\\n\\n target_user = User.query.filter_by(mail=user_requested).first()\\n if target_user is None:\\n return Response(render_template('admin/login.html',\\n message=\\\"Unknown Credentials\\\"))\\n\\n if not target_user.check_password(password_requested):\\n return Response(render_template('admin/login.html',\\n message=\\\"Unknown Credentials\\\"))\\n\\n if not target_user.state == StateType.ACTIVE:\\n return Response(render_template('admin/login.html',\\n message=\\\"User account deactivated. Cannot login.\\\"))\\n\\n resp = Response(render_template('employee/employee.html', user=target_user.name,\\n message=\\\"Login succeeded\\\"))\\n set_access_cookies(resp, create_access_token(identity=target_user.id))\\n return resp\",\n \"def employee_login():\\n return Response(render_template('admin/login.html'))\",\n \"def index(request):\\n users = User.objects.filter(is_staff=False, is_active=True).order_by('username')\\n return render(request, 'users/view_all_users.html',\\n { 'users': users })\",\n \"def get_employee_information(user_name: str, employee_name: str, store_name: str):\\n\\n user_name = auth.get_username_from_hash(user_name)\\n permission_handler.is_permmited_to(user_name=user_name, action=Action.EMPLOYEE_INFO.value,\\n store_name=store_name)\\n permission_handler.is_working_in_store(employee_name, store_name)\\n return user_handler.get_employee_information(employee_name)\",\n \"def name_search(self, cr, uid, name, args=None, operator='ilike', context=None, limit=100):\\n if context is None:\\n context = {}\\n if 'emp_hours' in context:\\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('emp.luggage_transfer.hours'),\\n context.get('emp_hours'), [\\\"employee\\\"], context)\\n args.append(('id', 'not in', [isinstance(d['employee'], tuple) and d['employee'][0] or d['employee'] for d in emp_ids]))\\n if 'mission_line' in context:\\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('hr.employee.mission.line'),\\n context.get('mission_line'), [\\\"employee_id\\\"], context)\\n args.append(('id', 'not in', [isinstance(d['employee_id'], tuple) and d['employee_id'][0] or d['employee_id'] for d in emp_ids]))\\n \\n if 'illness' in context:\\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('hr.employee.illness'),\\n context.get('illness'), [\\\"employee_id\\\"], context)\\n args.append(('id', 'not in', [isinstance(d['employee_id'], tuple) and d['employee_id'][0] or d['employee_id'] for d in emp_ids]))\\n \\n\\n if 'same' in context:\\n emp_ids = resolve_o2m_operations(cr, uid, self.pool.get('hr.employee.mission.line'),\\n context.get('same'), [\\\"employee_id\\\"], context)\\n args.append(('id', 'in', [isinstance(d['employee_id'], tuple) and d['employee_id'][0] or d['employee_id'] for d in emp_ids]))\\n \\n \\n if 'alternative_setting_id' in context:\\n old_ids = super(hr_employee, self).name_search(cr, uid, name, args=args, operator=operator, context={}, limit=limit)\\n\\n alternative_setting_id = context.get('alternative_setting_id')\\n setting_obj = self.pool.get('hr.alternative.setting')\\n alternative_setting_id = setting_obj.browse(cr, uid, alternative_setting_id)\\n degrees_ids = [\\n x.id for x in alternative_setting_id.degrees_ids]\\n degrees_ids += degrees_ids\\n degrees_ids = tuple(degrees_ids)\\n\\n departments_ids = [\\n x.id for x in alternative_setting_id.departments_ids]\\n departments_ids += departments_ids\\n departments_ids = tuple(departments_ids)\\n\\n ex_employees_ids = [\\n x.id for x in alternative_setting_id.employees_ids]\\n ex_employees_ids += ex_employees_ids\\n ex_employees_ids = tuple(ex_employees_ids)\\n\\n\\n old_ids_tuple = [x[0] for x in old_ids] + [x[0] for x in old_ids]\\n old_ids_tuple = tuple(old_ids_tuple)\\n\\n accessed_ids = self.search(cr, uid, [])\\n accessed_ids += accessed_ids\\n accessed_ids = tuple(accessed_ids)\\n\\n if not old_ids_tuple:\\n old_ids_tuple = (0,0)\\n \\n if not departments_ids:\\n departments_ids = (0,0)\\n cr.execute(\\n ''' Select emp.id,(SELECT MAX(date) as max_date\\n FROM hr_alternative_process_line\\n WHERE employee_id=emp.id and state='confirmed')date\\n from hr_employee emp\\n where emp.degree_id in %s \\n and emp.department_id not in %s \\n and emp.state = 'approved' \\n and emp.payroll_state = 'khartoum' \\n and emp.id in %s \\n and emp.gender='male' \\n and emp.id in %s \\n and emp.id not in %s \\n order by date NULLS LAST''', (degrees_ids,departments_ids,old_ids_tuple,accessed_ids,ex_employees_ids))\\n history = cr.dictfetchall()\\n new_ids = []\\n while True:\\n try:\\n new_ids.append( history.pop()['id'] )\\n except:\\n break\\n\\n temp = dict(old_ids)\\n old_ids = [x for x in old_ids if x[0] in new_ids]\\n #new_ids = [x for x in new_ids if x in accessed_ids]\\n #print \\\"..........................temp\\\",new_ids\\n #print \\\"......................\\\",[(x, temp.get(x,False) ) for x in new_ids]\\n #print \\\"......................\\\",sorted(old_ids, key=lambda x :new_ids.index(x[0]))\\n return sorted(old_ids, key=lambda x :new_ids.index(x[0]))\\n\\n return super(hr_employee, self).name_search(cr, uid, name, args=args, operator=operator, context=context, limit=limit)\",\n \"def search_encomendas_admin(request, name):\\n token = getToken(request)\\n superUser = isSuperUser(token)\\n\\n if superUser == True:\\n try:\\n encomendas = Encomenda.objects.filter(produtos__titulo__contains=name).order_by(\\\"-id\\\")\\n except Encomenda.DoesNotExist:\\n return Response(status=status.HTTP_404_NOT_FOUND)\\n serializer = EncomendaReadSerializer(encomendas, many=True)\\n return Response(serializer.data)\\n else:\\n return Response(status=status.HTTP_401_UNAUTHORIZED)\",\n \"def create_employee(request, company_id):\\n\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n logUnauthorizedAccess(\\\"User tried to create_employee\\\", request)\\n raise PermissionDenied()\\n form = EmployeeForm(request, initial=dict(company=company))\\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company=company) | Q(company__isnull=True))\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_employee_form.html',\\n {\\n 'employee_form': form,\\n }\\n )\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n # return JsonResponse(status=200, data=data)\",\n \"def filter_queryset(self, queryset):\\n user = self.request.user\\n if user.is_superuser:\\n return super().filter_queryset(queryset)\\n return queryset.filter(collaborators=user)\",\n \"def employee_list(request):\\n response_data = []\\n for emp in Employee.objects.all().values(\\n 'id', 'first_name', 'last_name', 'age', 'address', 'city',\\n 'state', 'country'):\\n response_data.append(emp)\\n return JsonResponse(response_data, safe=False)\",\n \"def test_access_employee(self):\\n # Employee can't see any SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).read()\\n # Employee can't edit the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\\n # Employee can't create the SO\\n with self.assertRaises(AccessError):\\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\\n 'partner_id': self.partner_a.id,\\n })\\n # Employee can't delete the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).unlink()\",\n \"def employees_json(request):\\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\\n employee_list = Employee.objects.filter(manager=request.user.employee_user)\\n employees = list()\\n for employee in employee_list:\\n manager_dict = model_to_dict(employee)\\n manager_dict['first_name'] = employee.user.first_name\\n manager_dict['last_name'] = employee.user.last_name\\n employees.append(manager_dict)\\n data = {\\\"employees\\\": employees}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def retrieve(self, request, pk=None):\\n employee = self.get_employee_object(pk)\\n print(F\\\"Employee: {employee}\\\")\\n serializer = data_serializers.PresentEmployeeDataSerializer(employee)\\n return Response(serializer.data)\",\n \"def default_get(self, cr, uid, fields, context=None): \\n \\n \\n res = super(granted_rights_order, self).default_get(cr, uid, fields, context=context)\\n \\n employee_obj = self.pool.get('hr.employee')\\n department_obj = self.pool.get('hr.department')\\n manager = False\\n donor_emp_id = []\\n \\n if uid != 1 :\\n\\n donor_emp_id = employee_obj.search(cr ,uid, [('user_id' , '=' , uid )])\\n deparment_id = employee_obj.browse(cr,uid,donor_emp_id[0]).department_id.id\\n \\n if donor_emp_id[0] == department_obj.browse(cr,uid,deparment_id).manager_id.id :\\n manager = True\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n if donor_emp_id :\\n res.update({ 'employee_donor': donor_emp_id[0], \\n 'department_id' : deparment_id,\\n 'is_a_amanger' : manager,\\n })\\n return res\",\n \"def list(self, **kwargs):\\n # TODO(adriant): Look up user by name/id\\n url = '/openstack/users/%s/roles' % kwargs['user']\\n return self._list(url, 'roles')\",\n \"def get_queryset(self):\\n user = self.request.user\\n\\n if user.is_authenticated and user.role == 'LA':\\n # admins view all property, no filtering\\n return Property.objects.all()\\n\\n if user.is_authenticated and user.employer.first():\\n # if the user is a client_admin, they see all published property\\n # and also their client's published and unpublished property.\\n client = user.employer.first()\\n return Property.active_objects.all_published_and_all_by_client(\\n client=client)\\n\\n # other users only see published property\\n return Property.active_objects.all_published()\",\n \"def _current_login_employee(self):\\n hr_employee = self.env[\\\"hr.employee\\\"].search(\\n [(\\\"user_id\\\", \\\"=\\\", self._current_login_user())], limit=1\\n )\\n return hr_employee.id\",\n \"def get_queryset(self):\\r\\n username = self.kwargs['username']\\r\\n return models.Education.objects.filter(username = username) .order_by('-startdate')\",\n \"def get_employee(self, name):\\n name = name.upper()\\n if name in EMPLOYEE_MAP:\\n name = EMPLOYEE_MAP[name]\\n try:\\n int(name)\\n emps = Employee.objects.filter(id=name)\\n except ValueError:\\n if name == 'NN':\\n emps = Employee.objects.filter(user__first_name='Nieznany')\\n elif Employee.objects.filter(user__username__iexact=name).exists():\\n emps = Employee.objects.filter(user__username__iexact=name)\\n elif len(name) == 3:\\n emps = Employee.objects.filter(user__first_name__istartswith=name[0],\\n user__last_name__istartswith=name[1:3],\\n status=0)\\n else:\\n emps = Employee.objects.filter(user__first_name__istartswith=name[0],\\n user__last_name__istartswith=name[1:],\\n status=0)\\n if not emps:\\n emps = Employee.objects.filter(user__username__istartswith=name)\\n if len(emps) == 1:\\n return emps[0]\\n elif len(emps) > 1:\\n self.stdout.write(self.style.ERROR('Multiple employee matches for {}. Choices are:'\\n .format(name)))\\n for e in emps:\\n self.stdout.write(self.style.ERROR(' -{}'.format(e.user.get_full_name())))\\n else:\\n raise CommandError('Employee {} does not exists! Fix your input file.'.format(name))\\n\\n return None\",\n \"def get_queryset(self):\\n return AutomaticEmail.objects.filter(staff_user=self.request.user)\",\n \"def get_all_user_development_plans_for_manager(request, employee_id):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n user_development_plans = DevelopmentPlan.objects.filter(employee_relation=current_employee).all()\\n employee = Employee.objects.filter(pk=int(employee_id)).first()\\n\\n if not current_employee:\\n raise PermissionDenied(\\\"You don't have any employee assigned to you.\\\", 401)\\n\\n if not current_employee.isEnsoUser() and current_employee.is_manager:\\n raise PermissionDenied()\\n actions = employee.action_set.all()\\n if not int(employee_id) in [obj.id for obj in Employee.objects.filter(manager=current_employee).all()]:\\n raise PermissionDenied(\\\"Employee with id={} is not assigned to you.\\\".format(employee_id), 401)\\n\\n if user_development_plans:\\n data={}\\n user_development_plans_list = []\\n for plan in user_development_plans:\\n\\n development_plan_object_list=[]\\n dev_plan = {}\\n\\n dev_plan[\\\"id\\\"] = plan.id\\n dev_plan[\\\"deleted\\\"] = plan.deleted\\n if plan.type:\\n dev_plan[\\\"type\\\"] = plan.type.name\\n dev_plan[\\\"finished_at\\\"] = DevelopmentPlanToEmployeeRelation.objects\\\\\\n .get(employee=current_employee, development_plan = plan).finished_at\\n dev_plan[\\\"created_at\\\"] = plan.created_at\\n dev_plan[\\\"created_by\\\"] = plan.created_by.username\\n\\n development_plan_object_list.append({\\\"dev_plan_details\\\":dev_plan})\\n\\n manager_data = {}\\n manager_data[\\\"manager_username\\\"] = plan.manager_relation.user.username\\n manager_data[\\\"id\\\"] = plan.manager_relation.user.id\\n\\n development_plan_object_list.append({\\\"manager_data\\\":manager_data})\\n user_development_plans_list.append(development_plan_object_list)\\n\\n else:\\n return JsonResponse(data={\\\"details\\\":\\\"Employee with id={} doesn't have any Development Plan\\\"\\n .format(request.user.pk)}, status=404)\\n\\n data = {\\\"user_development_plans:\\\": user_development_plans_list}\\n return JsonResponse(status=201, data=data)\",\n \"def explore_view(request):\\r\\n # explore items\\r\\n user = request.user.userprofile\\r\\n items = Item.objects.explore(user)\\r\\n context = {'items':items}\\r\\n return render(request, 'explore/explore.html', context)\",\n \"def get_employees(self):\\n from Employee import Employee\\n cursor = self.dbconnect.get_cursor()\\n cursor.execute('select * from employee')\\n\\n employees = list()\\n for row in cursor:\\n employee = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8])\\n employees.append(employee)\\n return employees\",\n \"def users_with_role(self):\\r\\n entries = User.objects.filter(\\r\\n courseaccessrole__role=self._role_name,\\r\\n courseaccessrole__org=self.org,\\r\\n courseaccessrole__course_id=self.course_key\\r\\n )\\r\\n return entries\",\n \"def badges_employee_list(request, employee_id):\\n if request.method == 'GET':\\n employee = get_object_or_404(Employee, pk=employee_id)\\n employee_bages = EmployeeBadge.objects.filter(to_user=employee)\\n paginator = PageNumberPagination()\\n results = paginator.paginate_queryset(employee_bages, request)\\n serializer = EmployeeBadgeSerializer(results, many=True)\\n return paginator.get_paginated_response(serializer.data)\",\n \"def query_employee(self, employee_inputs):\\n\\n query = \\\"select * from employee where \\\"\\n row_names = [\\n \\\"emp_ID\\\", \\\"Region_ID\\\", \\\"Emp_Lname\\\", \\\"Emp_Mi\\\", \\\"Emp_Fname\\\",\\n \\\"Emp_Hiredate\\\"\\n ]\\n filled_attributes = []\\n\\n row_index = 0\\n row_options = []\\n for item in employee_inputs:\\n if item is not None:\\n row_options.append(row_index)\\n filled_attributes.append(item)\\n row_index += 1\\n\\n j = 0\\n for i in row_options:\\n if j == 0:\\n query += \\\"{}='{}' \\\".format(row_names[i], filled_attributes[j])\\n else:\\n query += \\\"and {}='{}' \\\".format(row_names[i],\\n filled_attributes[j])\\n j += 1\\n\\n try:\\n self.dbCursor.execute(query)\\n return self.dbCursor.fetchall()\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\",\n \"def user_view(request):\\n user = User.objects.all()\\n return render(request, template_name, {'data_set':data_set})\",\n \"def list(self , request,*args,**kwargs):\\n return super(UsersViewset,self).list(request,args,kwargs)\",\n \"def employee_profile_page(cls, employee_id):\\n return cls.__profile_page(employee_id, cls._logger)\",\n \"def get(self, request):\\r\\n\\r\\n if not request.user.is_staff:\\r\\n raise Http404\\r\\n data = []\\r\\n\\r\\n for course in self.get_courses(): # pylint: disable=unused-variable\\r\\n datum = [course.display_name, course.id]\\r\\n datum += [CourseEnrollment.objects.filter(\\r\\n course_id=course.id).count()]\\r\\n datum += [CourseStaffRole(course.id).users_with_role().count()]\\r\\n datum += [','.join([x.username for x in CourseInstructorRole(\\r\\n course.id).users_with_role()])]\\r\\n data.append(datum)\\r\\n\\r\\n datatable = dict(header=[_('Course Name'), _('course_id'),\\r\\n _('# enrolled'), _('# staff'),\\r\\n _('instructors')],\\r\\n title=_('Enrollment information for all courses'),\\r\\n data=data)\\r\\n context = {\\r\\n 'datatable': datatable,\\r\\n 'msg': self.msg,\\r\\n 'djangopid': os.getpid(),\\r\\n 'modeflag': {'staffing': 'active-section'},\\r\\n 'edx_platform_version': getattr(settings, 'EDX_PLATFORM_VERSION_STRING', ''),\\r\\n }\\r\\n return render_to_response(self.template_name, context)\",\n \"def showORGusers(**kwargs):\\n sessiontoken = kwargs['sessiontoken']\\n ORG_ID = kwargs['ORG_ID']\\n strCSPProdURL = kwargs['strCSPProdURL']\\n jsonResponse = get_csp_users_json(strCSPProdURL, ORG_ID, sessiontoken)\\n if jsonResponse == None:\\n print(\\\"API Error\\\")\\n sys.exit(1)\\n\\n users = jsonResponse['results']\\n table = PrettyTable(['First Name', 'Last Name', 'User Name'])\\n for i in users:\\n table.add_row([i['user']['firstName'],i['user']['lastName'],i['user']['username']])\\n print (table.get_string(sortby=\\\"Last Name\\\"))\",\n \"def get_companies(request):\\n companies = Company.objects.all()\\n context={'user_id': request.user.id}\\n serializer = CompanySerializers(companies, context=context)\\n return Response(serializer.data)\",\n \"def display_certs(employee_id):\\n\\n if not g.user:\\n flash(\\\"Please Login to continue.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n employee = Employee.query.get_or_404(employee_id)\\n \\n certs = employee_certification.query.filter_by(employee_id = employee_id).all()\\n \\n all_certs = Cert.query.all()\\n \\n return render_template(\\\"users/display_cert.html\\\", employee = employee, certs = certs, all_certs = all_certs)\",\n \"def get_queryset(self):\\r\\n name = self.kwargs['name']\\r\\n course_id_string = self.request.QUERY_PARAMS.get('course_id')\\r\\n if not course_id_string:\\r\\n raise ParseError('course_id must be specified')\\r\\n course_id = SlashSeparatedCourseKey.from_deprecated_string(course_id_string)\\r\\n role = Role.objects.get_or_create(course_id=course_id, name=name)[0]\\r\\n users = role.users.all()\\r\\n return users\",\n \"def get_queryset(self, *args, **kwargs):\\n qs = super().get_queryset(*args, **kwargs)\\n user = self.request.user\\n if not user.is_authenticated:\\n return qs.none()\\n if self.is_get and not user.has_perm(\\\"users.view_user\\\"):\\n return qs.filter(pk=user.pk)\\n if self.is_update and not user.has_perm(\\\"users.change_user\\\"):\\n return qs.filter(pk=user.pk)\\n return qs\",\n \"def get_queryset(self):\\n user = self.request.user\\n collabLists = ListObject.objects.filter(collaborators__id=user.id)\\n return collabLists\",\n \"def employees_json_id(request, employee_id):\\n curent_employee = Employee.objects.get(pk=int(employee_id))\\n if curent_employee.is_manager:\\n employee_list = Employee.objects.filter(manager=curent_employee)\\n employees = list()\\n for employee in employee_list:\\n manager_dict = model_to_dict(employee)\\n manager_dict['first_name'] = employee.user.first_name\\n manager_dict['last_name'] = employee.user.last_name\\n manager_dict['photo'] = employee.photo.url if employee.photo else ''\\n employees.append(manager_dict)\\n data = {\\\"employees\\\": employees}\\n else:\\n return JsonResponse(status=400, data={\\\"error\\\": \\\"Employee with id={} not is_manager\\\".format(int(employee_id))})\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def get_job_query(self):\\n context = aq_inner(self.context)\\n catalog = getToolByName(context, 'portal_catalog')\\n mt = getToolByName(self, 'portal_membership') \\n currentUser = mt.getAuthenticatedMember() \\n \\n if \\\"Site Administrators\\\" not in currentUser.getGroups():\\n\\treturn catalog.searchResults(portal_type= 'SeniorProject.PloneAddOn.job', \\t\\t\\t\\t Creator = currentUser.getUserName())\\n else: \\n return catalog.searchResults(portal_type= 'SeniorProject.PloneAddOn.job')\",\n \"def test_api_can_get_all_employees(self):\\n res = self.client().get(service_url_emp)\\n self.assertEqual(res.status_code, 200)\\n self.assertIn('name1', str(res.data))\\n self.assertIn('name2', str(res.data))\",\n \"def gather_employee_entries(self):\\n user_inputs = [\\n self.emp_lname.get(), self.emp_mi.get(), self.emp_fname.get(),\\n self.emp_hiredate.get()\\n ]\\n\\n return self.check_input_empty(user_inputs)\",\n \"def get_list_filter(self, request):\\n if request.user.is_superuser:\\n return self.list_filter\\n return self.list_filter_companies\",\n \"def list(self, request, *args, **kwargs):\\n return super(UserViewSet, self).list(request, *args, **kwargs)\",\n \"def getELUsers(**kwargs):\\n \\n for key in kwargs:\\n if type(kwargs[key]) == list:\\n kwargs[key] = kwargs[key][0]\\n \\n allELUsers = ELUser.ELUser.all(**kwargs)\\n allELUsersDictionaries = [dict(eluser) for eluser in allELUsers if dict(eluser)]\\n \\n return flask.Response(\\n response = json.dumps(allELUsersDictionaries),\\n status = 200,\\n content_type = 'application/json'\\n )\",\n \"def get_queryset(self):\\n return self.request.user.setting_set.get().companies\",\n \"def get_queryset(self):\\n return self.request.user.setting_set.get().companies\",\n \"def get_queryset(self):\\n return self.request.user.setting_set.get().companies\",\n \"def get_queryset(self):\\n return self.request.user.setting_set.get().companies\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.5349817\",\n \"0.53454113\",\n \"0.53447485\",\n \"0.53430444\",\n \"0.53365314\",\n \"0.5319792\",\n \"0.5317902\",\n \"0.5302403\",\n \"0.5300769\",\n \"0.52874345\",\n \"0.5241847\",\n \"0.52348393\",\n \"0.52252674\",\n \"0.5214821\",\n \"0.51963586\",\n \"0.5192847\",\n \"0.519159\",\n \"0.5189371\",\n \"0.5187232\",\n \"0.5176226\",\n \"0.5162283\",\n \"0.5159732\",\n \"0.51566255\",\n \"0.51546204\",\n \"0.514097\",\n \"0.5127809\",\n \"0.51253223\",\n \"0.5124406\",\n \"0.5120045\",\n \"0.51059484\",\n \"0.5098111\",\n \"0.5094945\",\n \"0.50798523\",\n \"0.5076737\",\n \"0.50758624\",\n \"0.5072332\",\n \"0.50717163\",\n \"0.50656736\",\n \"0.5062178\",\n \"0.5060405\",\n \"0.5060405\",\n \"0.5060405\",\n \"0.5060405\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7280209"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":699,"cells":{"query":{"kind":"string","value":"Get all employees as json"},"document":{"kind":"string","value":"def employees_json(request):\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\n employee_list = Employee.objects.filter(manager=request.user.employee_user)\n employees = list()\n for employee in employee_list:\n manager_dict = model_to_dict(employee)\n manager_dict['first_name'] = employee.user.first_name\n manager_dict['last_name'] = employee.user.last_name\n employees.append(manager_dict)\n data = {\"employees\": employees}\n return JsonResponse(data=data, content_type='application/json', safe=False)"},"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(self):\n employees = self.service.get_employees(strategy=selectinload)\n return self.schema.dump(employees, many=True), 200","def employees(employee_id=None):\n\tif not employee_id:\n\t\temployee_data = _serialize_list(Employee.query.all())\n\telse:\n\t\temployee_data = _serialize_model(Employee.query.filter_by(id=employee_id).first())\n\n\tresp = jsonify(employee_data)\n\treturn resp","def employee_list(request):\n response_data = []\n for emp in Employee.objects.all().values(\n 'id', 'first_name', 'last_name', 'age', 'address', 'city',\n 'state', 'country'):\n response_data.append(emp)\n return JsonResponse(response_data, safe=False)","def get(self):\n resultado = EmployeeModel.query.all()\n return resultado","def list(self, request):\n employee = self.controller.retrieve_all_employees()\n serializer = data_serializers.PresentEmployeeDataSerializer(employee, many=True)\n return Response(serializer.data)","def getEmployees(self):\n return self.employees","def list(self, request):\n varemployee = employee.objects.all()\n serializer = employeeSerializer(varemployee, many=True)\n return Response(serializer.data)","def employees_json_id(request, employee_id):\n curent_employee = Employee.objects.get(pk=int(employee_id))\n if curent_employee.is_manager:\n employee_list = Employee.objects.filter(manager=curent_employee)\n employees = list()\n for employee in employee_list:\n manager_dict = model_to_dict(employee)\n manager_dict['first_name'] = employee.user.first_name\n manager_dict['last_name'] = employee.user.last_name\n manager_dict['photo'] = employee.photo.url if employee.photo else ''\n employees.append(manager_dict)\n data = {\"employees\": employees}\n else:\n return JsonResponse(status=400, data={\"error\": \"Employee with id={} not is_manager\".format(int(employee_id))})\n return JsonResponse(data=data, content_type='application/json', safe=False)","def get_employees(self):\n return self.employees","def get(self, request):\n varemployee = employee.objects.all()\n serializer = employeeSerializer(varemployee, many=True)\n return Response(serializer.data)","def get_employees_directory(self):\n response = requests.get(self._base_url + \"employees/directory\",\n auth=(self._api_key, \"pass\"),\n headers={'Accept': 'application/json'})\n if response.status_code != 200:\n response.raise_for_status()\n emps_json = json.loads(response.text)['employees']\n return {int(e['id']): Employee(e['displayName'],\n e['firstName'],\n e['lastName'],\n e['nickname']) for e in emps_json}","def get_employees(self):\n from Employee import Employee\n cursor = self.dbconnect.get_cursor()\n cursor.execute('select * from employee')\n\n employees = list()\n for row in cursor:\n employee = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8])\n employees.append(employee)\n return employees","def get_employee(employee_id):\n\n employee = Employee.objects.get_or_404(id=employee_id)\n\n return jsonify({\n 'employee': employee\n })","def employees():\n # gather data from db about all employees\n return render_template(\"employees.html\")","def list_employees(order_by=\"id\"):\n ret = {}\n status, result = _query(action=\"employees\", command=\"directory\")\n root = ET.fromstring(result)\n for cat in root:\n if cat.tag != \"employees\":\n continue\n for item in cat:\n emp_id = next(iter(item.values()))\n emp_ret = {\"id\": emp_id}\n for details in item:\n emp_ret[next(iter(details.values()))] = details.text\n ret[emp_ret[order_by]] = emp_ret\n return ret","def get_manager_employees(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\n if manager_employees:\n emp_list=[]\n for emp in manager_employees:\n emp_data={}\n emp_data[\"id\"] = emp.id\n emp_data[\"username\"] = emp.user.username\n emp_data[\"first_name\"] = emp.user.first_name\n emp_data[\"last_name\"] = emp.user.last_name\n emp_data[\"manager_id\"] = emp.manager.id\n # emp_data[\"status_questions\"] = emp.status_questions\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\n # name_role_list = []\n # for obj in employee_role:\n # name_role_list.append(obj.role.name)\n # emp_data[\"roles\"] = name_role_list\n emp_list.append(emp_data)\n data = {\"employees:\": emp_list}\n return JsonResponse(status=201, data=data)\n else:\n return JsonResponse(\"The user with id={} isn't a manager for any user\".format(current_employee.user.id),\n status=404)","def employees(self) -> object:\n return self._employees","def get_employees(self, active_only):\n cursor = self.dbconnect.get_cursor()\n\n if active_only:\n cursor.execute(\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\n 'is_admin, is_active FROM employee WHERE is_active = TRUE')\n else:\n cursor.execute(\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\n 'is_admin, is_active FROM employee')\n\n employees = list()\n for row in cursor:\n obj = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10])\n employees.append(obj)\n return employees","def employees_manager(request):\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)\n employee = Employee.objects.get(pk=request.user.employee_user.id)\n employee_dict = model_to_dict(employee)\n employee_dict['first_name'] = employee.user.first_name\n employee_dict['last_name'] = employee.user.last_name\n employee_dict['photo'] = employee.photo.url if employee.photo else ''\n print employee_dict\n if len(manager_list) > 0:\n result_list = list(manager_list)\n all_managers_list = found_all_managers(manager_list, result_list)\n else:\n data = {\"employee_managers\": employee_dict}\n return JsonResponse(data=data, content_type='application/json', safe=False)\n employees = list()\n for manager in all_managers_list:\n manager_dict = model_to_dict(manager)\n manager_dict['first_name'] = manager.user.first_name\n manager_dict['last_name'] = manager.user.last_name\n manager_dict['photo'] = manager.photo.url if manager.photo else ''\n employees.append(manager_dict)\n employees.append(employee_dict)\n\n data = {\"employee_managers\": employees}\n return JsonResponse(data=data, content_type='application/json', safe=False)","def get_all_data():\n return jsonify(service.get_all_data())","def show_all_employees(self):\n try:\n employees = self.admin_repository.show_all_employees()\n if employees:\n for employee in employees:\n print(\"Employee Id : {}\".format(employee[0]))\n print(\"Name : {}\".format(employee[1]))\n print(\"Email : {}\".format(employee[2]))\n print(\"----------------------------\")\n return True\n else:\n print(\"No records found.\")\n return False\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def get_companies():\n all_companies = storage.all(Company).values()\n list_companies = []\n for company in all_companies:\n list_companies.append(company.to_dict())\n return jsonify(list_companies)","def test_api_can_get_all_employees(self):\n res = self.client().get(service_url_emp)\n self.assertEqual(res.status_code, 200)\n self.assertIn('name1', str(res.data))\n self.assertIn('name2', str(res.data))","def getELUsers(**kwargs):\n \n for key in kwargs:\n if type(kwargs[key]) == list:\n kwargs[key] = kwargs[key][0]\n \n allELUsers = ELUser.ELUser.all(**kwargs)\n allELUsersDictionaries = [dict(eluser) for eluser in allELUsers if dict(eluser)]\n \n return flask.Response(\n response = json.dumps(allELUsersDictionaries),\n status = 200,\n content_type = 'application/json'\n )","def jsonify_all(cls):\n return jsonify(accounts=[account.as_dict() for account in cls.query.all()])","def get_persons(self):\n response = self.do_request('/management/persons/export/json/')\n if response:\n return response.json()","def userJSON():\n user = session.query(User).all()\n result = []\n\n for i in user:\n result += [i.serialize]\n\n return jsonify(Users=result)","def get_users():\n return jsonify([\n users.to_dict()\n for users in models.storage.all('User').values()\n ])","def get_persons(self):\n response = self.do_request('/misc/user/export/json')\n if response:\n return response.json()","def employee_get(emp_id):\n try:\n emp = Employee.objects.get(id=emp_id)\n except Employee.DoesNotExist:\n return JsonResponse({\n 'status': False,\n 'message': 'Employee does not exists in database'\n }, status=404)\n _data = {\n 'id': emp.id,\n 'first_name': emp.first_name,\n 'last_name': emp.last_name,\n 'age': emp.age,\n 'city': emp.city.name,\n 'state': emp.state.name,\n 'country': emp.country.name\n }\n return JsonResponse(_data, safe=False)","def get_emp_list(self):\n\t\tcondition= ''\n\t\temp_list=[]\n\t\tif self.is_for_all==0:\n\t\t\tif not self.selected_employees:\n\t\t\t\tfrappe.throw(_(\"No employees for the mentioned criteria\"))\n\t\t\t#emp_list = [cstr(d.employee) for d in self.selected_employees]\n\t\t\temp_list = frappe.db.sql_list(\"\"\"\n\t\t\t\tselect\n\t\t\t\t\temployee from `tabAttendance Salary Tool Employee`\n\t\t\t\twhere\n\t\t\t\t\tparent = '%(parent)s' \n\t\t\t\"\"\"%{\"parent\": self.name})\n\t\t\tcondition+= \"\"\" and t1.employee IN %(employees)s \"\"\"\n\t\tif self.is_open_period==0:\n\t\t\tif not self.start_date or not self.end_date:\n\t\t\t\tfrappe.throw(_(\"Satart Date and End Date are Mandatories\"))\n\t\t\tcondition= \"\"\" and attendance_date >= %(start_date)s and attendance_date <= %(end_date)s\"\"\"\n\t\temp_list = frappe.db.sql(\"\"\"\n\t\t\tselect\n\t\t\t\tt1.employee as employee, count(*) as attendance_days\n\t\t\tfrom\n\t\t\t\t`tabAttendance` t1\n\t\t\twhere\n\t\t\t\tt1.attendance_salary_tool is null\n\t\t\t\tand t1.docstatus = 1 and t1.status='Present'\n\t\t\t\t{condition} group by t1.employee order by t1.employee asc\n\t\t\"\"\".format(condition=condition),{\"employees\": tuple(emp_list),\"start_date\": self.start_date,\"end_date\": self.end_date}, as_dict=True)\n\t\treturn emp_list","def get_amenities():\n list_amenities = []\n for amenity in storage.all('Amenity').values():\n list_amenities.append(amenity.to_dict())\n return jsonify(list_amenities)","def show_employee(emp_id, fields=None):\n ret = {}\n if fields is None:\n fields = \",\".join(\n (\n \"canUploadPhoto\",\n \"department\",\n \"displayName\",\n \"firstName\",\n \"id\",\n \"jobTitle\",\n \"lastName\",\n \"location\",\n \"mobilePhone\",\n \"nickname\",\n \"photoUploaded\",\n \"photoUrl\",\n \"workEmail\",\n \"workPhone\",\n \"workPhoneExtension\",\n )\n )\n\n status, result = _query(action=\"employees\", command=emp_id, args={\"fields\": fields})\n\n root = ET.fromstring(result)\n\n ret = {\"id\": emp_id}\n for item in root:\n ret[next(iter(item.values()))] = item.text\n return ret","def data():\n empo = get(empo_url).json()\n dict_new = {}\n for u in empo:\n empo_id = str(u.get(\"id\"))\n tasks = get(tasks_url + \"?userId=\" + empo_id).json()\n task_list = []\n for i in tasks:\n if i[\"userId\"] != empo_id:\n task_list.append({\"username\": u[\"username\"],\n \"task\": i[\"title\"],\n \"completed\": i[\"completed\"]})\n dict_new[empo_id] = task_list\n with open(\"todo_all_employees.json\", \"w\") as file:\n dump(dict_new, file)","def show_all_amenities():\n\n amenities = storage.all(Amenity).values()\n new_list = []\n for amenity in amenities:\n new_list.append(amenity.to_dict())\n return jsonify(new_list)","def show_all_employees():\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employees = Employee.query.all()\n \n \n ## right now this is ALL users... \n \n return render_template(\"employee_display.html\", employees = employees)","def all_amenities():\n amenities_list = []\n for amenity in storage.all(Amenity).values():\n amenities_list.append(amenity.to_dict())\n return jsonify(amenities_list)","def get(self):\n args = self.parser.parse_args()\n date = get_date_or_none(args['date'])\n start_date = get_date_or_none(args['start_date'])\n end_date = get_date_or_none(args['end_date'])\n\n if date:\n employees = self.service.get_employees_by_date_of_birth(\n date, strategy=selectinload\n )\n elif start_date and end_date:\n employees = self.service.get_employees_born_in_period(\n start_date, end_date, strategy=selectinload\n )\n else:\n return self.BAD_DATE_MESSAGE, 400\n\n return self.schema.dump(employees, many=True), 200","def return_amenities():\n amenities = list(storage.all(Amenity).values())\n amenity_list = []\n for amenity in amenities:\n amenity_list.append(amenity.to_dict())\n return jsonify(amenity_list)","def get_users():\n users = User.query # no need to order\n users_data = [user.to_dict() for user in users.all()]\n return jsonify(users=users_data)","def get_all_user():\n user = UserModel.objects()\n return jsonify(user), 200","def get_articles():\n _, articles = base_query(db_session)\n return jsonify([p.serialize for p in articles])","def list(self, request):\n teams = self.controller.retrieve_all_teams_employees()\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(teams, many=True)\n return Response(serializer.data)","def amenity_get_all():\n am_list = []\n am_obj = storage.all(\"Amenity\")\n for obj in am_obj.values():\n am_list.append(obj.to_json())\n\n return jsonify(am_list)","def search_and_export_json():\n\n url = 'https://jsonplaceholder.typicode.com/'\n\n users_dict = requests.get(\"{}users\".format(url)).json()\n tasks_dict = requests.get(\"{}todos\".format(url)).json()\n\n my_dict = {}\n usernames_dict = {}\n\n for info in users_dict:\n user_id = info.get('id')\n my_dict[user_id] = []\n usernames_dict[user_id] = info.get('username')\n\n for info in tasks_dict:\n tasks_all_emp = {}\n user_id = info.get('userId')\n tasks_all_emp[\"task\"] = info.get('title')\n tasks_all_emp[\"completed\"] = info.get('completed')\n tasks_all_emp[\"username\"] = usernames_dict.get(user_id)\n my_dict.get(user_id).append(tasks_all_emp)\n\n file_name = \"todo_all_employees.json\"\n with open(file_name, mode='w') as json_file:\n json.dump(my_dict, json_file)","def get_amenities():\n amenities = []\n for amenity in storage.all(Amenity).values():\n amenities.append(amenity.to_dict())\n return jsonify(amenities)","def get(self, request):\n employee = EmployeeDetail.objects.all()\n response = {\n 'payment_methods': EmployeeSerializer(\n employee,\n many=True\n ).data\n }\n return Response(response)","def _get_employee_info() -> List[List[str]]:\n return [\n ['100', 'Dave', 'Team Leader'],\n ['101', 'Ram', 'Developer'],\n ['102', 'Raj', 'Developer'],\n ['103', 'Rahul', 'Tester'],\n ]","def return_expenses():\r\n g.db.execute(\"SELECT * FROM monthly_data ORDER BY Sr\")\r\n rows = g.db.fetchall()\r\n data = []\r\n for x in rows:\r\n data.append({'sr':x[0],'name':x[1], 'id':x[2], 'item':x[3], 'price':x[5], 'date':x[4]})\r\n return jsonify(data)","def employee(employee_id):\n # gather data from db about all employees\n return render_template(\"employee.html\",\n employee_id=employee_id)","def amenity_ret():\n ame_list = []\n all_objs = storage.all(\"Amenity\")\n for obj in all_objs.values():\n ame_list.append(obj.to_dict())\n return jsonify(ame_list)","def get_users():\n selection = []\n try:\n selection = [{'id':usr.id, 'username':usr.username, 'email':usr.email} \n for usr in User.query.all()]\n except:\n selection = {'error':True}\n return json.dumps(selection)","def companies():\n res = requests.get('http://0.0.0.0:5002/companies')\n return jsonify(res.json())","def user_ret():\n user_list = []\n all_objs = storage.all(\"User\")\n for obj in all_objs.values():\n user_list.append(obj.to_dict())\n return jsonify(user_list)","def all_Users():\n new_dict = []\n for usr in storage.all('User').values():\n new_dict.append(usr.to_dict())\n return jsonify(new_dict)","def get_amenities():\n amenities_dict_list = [amenity.to_dict() for amenity in\n storage.all(\"Amenity\").values()]\n return jsonify(amenities_dict_list)","def sportsJSON():\n\n sports = session.query(Sport).all()\n return jsonify(sports=[r.serialize for r in sports])","def restaurants_all() -> str:\n restaurant_objects = restaurants.load_restaurants()\n return jsonify(restaurant_objects)","def all_items_handler():\n items = getAllItems()\n return jsonify(items=[i.serialize for i in items])","def departments(department_name=None):\n\tif not department_name:\n\t\tdepartment_data = _serialize_list(Department.query.all(), backrefs=[\"employees\"])\n\t\tdepartment_data = {'departments': department_data, 'total': len(department_data)}\n\telse:\n\t\tdepartment_data = _serialize_model(Department.query.filter_by(name=department_name).first(), backrefs=[\"employees\"])\n\n\treturn jsonify(department_data)","def get_all_users():\n users = []\n for mv in storage.all(\"User\").values():\n users.append(mv.to_dict())\n return jsonify(users)","def return_artistnames(): \n\n names = [] #list for artist names\n rows = db.session.query(Artist.name).all()\n for row in rows: \n names.append(row[0])\n\n return jsonify(names)","def get_users():\n users = User.query.all()\n users_schema = UserSchema()\n result = users_schema.dump(users, many=True)\n return jsonify({'users': result.data})","def get_employees(cls, strategy=lazyload):\n cls._check_strategy(strategy)\n\n return db.session.query(Employee).options(\n strategy(Employee.department)\n ).all()","def list_all_amenities():\n data = storage.all('Amenity')\n amenities = [v.to_dict() for k, v in data.items()]\n return jsonify(amenities)","def jugadores():\n\tjugadores = Jugador.query.order_by(Jugador.id.desc()).filter_by(activo=True)\n\treturn jsonify([jugador.to_dict()\n\t\t for jugador in jugadores])","def get_all_by_name():\n name = request.args['name']\n return jsonify(service.get_all_data_by_name(name))","def all_sales(self, username):\n con = dbcon()\n cur = con.cursor()\n cur.execute(\"SELECT * FROM sales;\")\n res = cur.fetchall()\n sales_records=[]\n for a_sale in res:\n record = {\n 'sales_id':a_sale[0],\n 'attendant':a_sale[1],\n 'product_name':a_sale[2],\n 'price':a_sale[3],\n 'quantity':a_sale[4]\n }\n sales_records.append(record)\n return jsonify({\"Records\": sales_records}), 200","def get_companies(self):\n response = self.do_request('/management/companies/export/json')\n if response:\n return response.json()","def json_view(self, recursive=False):\n\n context = self.context.aq_inner\n data = self.export(context, recursive=recursive)\n pretty = json.dumps(data, sort_keys=True, indent=4)\n self.request.response.setHeader(\"Content-type\", \"application/json\")\n return pretty","def allCategoriesJSON():\n categories = db_session.query(Category).all()\n return jsonify(categories=[c.serialize for c in categories])","def all_employees(request, company_id=None):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n company_super_user = current_employee.isCompanySuperUserOrHigher()\n if company_id:\n company = Company.objects.get(pk=company_id)\n else:\n company = current_employee.company\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n raise PermissionDenied()\n change_company_form = ChangeCompanyForm(initial=dict(company=company))\n return TemplateResponse(\n request,\n 'all_employees.html',\n {\n 'user': request.user,\n 'company_super_user': company_super_user,\n 'company': company,\n 'change_company_form': change_company_form,\n }\n )","def json_get_all_names_and_emails(request):\n\n # Check out HVZ/main/models.py for helper functions relating to Players.\n # Player.current_players() returns all Players in the current Game.\n emails = [(str(p.user.first_name) + \" \" + str(p.user.last_name), p.mailbox) for p in Player.current_players()]\n\n # json.dumps creates a string from a Python object. You can then\n # read the string and convert it into an Objective-C data\n # structure using NSJSONSerialization in Objective-C.\n json_data = json.dumps(emails)\n\n return HttpResponse(\n json_data,\n content_type=\"application/json\"\n )","def get_stores():\n stores = Store.query # no need to order\n stores_data = [store.to_dict() for store in stores.all()]\n return jsonify(stores=stores_data)","def read_all():\n # Create the list of users from our data\n users = User.query.order_by(User.first_name).all()\n\n # Serialize the data for the response\n user_schema = UserSchema(many=True)\n data = user_schema.dump(users)\n return data","def list_users():\n return jsonify(user=\"joe\")","def names():\n\n df = pd.read_sql_query(f\"SELECT * FROM olympics_raw\", con = engine)\n print(df.head())\n \n\n # return jsonify(all_olympians)\n return jsonify(df.to_dict(orient='records'))","def get(cls, employee_id):\n employee = EmployeeModel.find_by_id(employee_id)\n if not employee:\n return {'message': 'Employee not found, or you do not have the access'}, 404\n\n return employee.json()","def get_mentee_list():\n # Get db object and users table\n db = get_db()\n users = db.users\n \n # Search database for mentees\n cursor = users.find({\"role\": \"Mentee\"})\n \n context = {'mentees': []}\n \n for document in cursor:\n temp = document\n del temp['_id']\n context['mentees'].append(temp)\n \n context['url'] = \"/api/v1/mentees/\"\n return flask.jsonify(**context)","def get(self):\n\n users = UserModel.get_top_earners()\n users_json = [user.json() for user in users]\n return {\"users\": users_json}","def get_exercises():\n email = session.get(\"email\")\n tag_arg = request.args.get(\"tag\")\n exercises = fm.get_all_exercises(email, tag_arg)\n msg = \"Found {} exercises for {}\".format(len(exercises), email)\n app.logger.info(msg)\n return jsonify(dict(exercises=exercises))","def list():\n trucks = Foodtruck.query.all()\n return jsonify(foodtrucks=[truck.to_dict() for truck in trucks])","def get_users():\n users = storage.all('User')\n users_list = []\n for user in users.values():\n users_list.append(user.to_dict())\n return jsonify(users_list), 200","def get(self):\n users = User.query.all()\n usersJSON = []\n for u in users:\n usersJSON.append({'id': u.id, 'admin': u.admin})\n return {'users': usersJSON}","def all_entries(cls):\n info = Diary.entries\n response = jsonify({\"data\": info})\n response.status_code = 200\n return response","def employers_get(label=None, page=None, per_page=None): # noqa: E501\n\n\n return query_manager.get_resource(\n label=label,\n page=page,\n per_page=per_page,\n rdf_type_uri=EMPLOYER_TYPE_URI,\n rdf_type_name=EMPLOYER_TYPE_NAME, \n kls=Employer)","def get_countries():\n countries = Country.query # no need to order\n countries_data = [country.to_dict() for country in countries.all()]\n return jsonify(countries=countries_data)","def getall():\n elements = Advertisements().get_all_elements()\n data = jsonify(elements)\n data.statut_code = 200\n return data","def amenities_all():\n return jsonify(list(map(lambda x: x.to_dict(),\n list(storage.all(Amenity).values()))))","def get_all_project_records():\r\n records = flask.request.db_api.get_all_project_record()\r\n return flask.jsonify(records=records)","def happy():\n # Query all venues\n results = session.query(VP.name, VP.latitude, VP.longitude).all()\n \n # Create a dictionary from the row data and append to a list of all_venue\n all_venues = []\n for name, lat, lon in results:\n venue_dict = {}\n venue_dict[\"name\"] = name\n venue_dict[\"latitude\"] = lat\n venue_dict[\"longitude\"] = lon\n all_venues.append(venue_dict)\n \n return jsonify(all_venues)","def list(self):\n return JSONResponse(self.request).data(items=self._get_agenda_items()).dump()","def get(cls):\n return {'products': [product.to_json() for product in ProductModel.find_all()]}","def get(self):\n users = User.query.all()\n usersJSON = []\n for u in users:\n usersJSON.append({'id':u.id, 'admin':u.admin})\n return { 'users' : usersJSON }","def current_employee(self, request: Request) -> Response:\n serializer = self.get_serializer_class()\n serializer = serializer(request.user, context={'request': request})\n return Response(serializer.data)","def get_employee():\n\n employee_id = get_employee_input_int('Enter employee ID to get the data ')\n employee = db.get_employee(employee_id)\n if not employee:\n print(\"No employee found with id \", employee_id)\n else:\n payscale = db.get_payScale(employee.grade)\n print('DATA:-> {} {} has grade = {} which gives {} per hours\\n'\n .format(employee.first_name, employee.last_name, employee.grade, payscale.salary))","def get_books():\n return jsonify({'books': Book.get_all_books()})","def get_books():\n return jsonify({'books': Book.get_all_books()})","def retrieve(self, request, pk=None):\n employee = self.get_employee_object(pk)\n print(F\"Employee: {employee}\")\n serializer = data_serializers.PresentEmployeeDataSerializer(employee)\n return Response(serializer.data)","def get(self):\n all_patients = model_patient.query.all()\n return jsonify(all_patients)"],"string":"[\n \"def get(self):\\n employees = self.service.get_employees(strategy=selectinload)\\n return self.schema.dump(employees, many=True), 200\",\n \"def employees(employee_id=None):\\n\\tif not employee_id:\\n\\t\\temployee_data = _serialize_list(Employee.query.all())\\n\\telse:\\n\\t\\temployee_data = _serialize_model(Employee.query.filter_by(id=employee_id).first())\\n\\n\\tresp = jsonify(employee_data)\\n\\treturn resp\",\n \"def employee_list(request):\\n response_data = []\\n for emp in Employee.objects.all().values(\\n 'id', 'first_name', 'last_name', 'age', 'address', 'city',\\n 'state', 'country'):\\n response_data.append(emp)\\n return JsonResponse(response_data, safe=False)\",\n \"def get(self):\\n resultado = EmployeeModel.query.all()\\n return resultado\",\n \"def list(self, request):\\n employee = self.controller.retrieve_all_employees()\\n serializer = data_serializers.PresentEmployeeDataSerializer(employee, many=True)\\n return Response(serializer.data)\",\n \"def getEmployees(self):\\n return self.employees\",\n \"def list(self, request):\\n varemployee = employee.objects.all()\\n serializer = employeeSerializer(varemployee, many=True)\\n return Response(serializer.data)\",\n \"def employees_json_id(request, employee_id):\\n curent_employee = Employee.objects.get(pk=int(employee_id))\\n if curent_employee.is_manager:\\n employee_list = Employee.objects.filter(manager=curent_employee)\\n employees = list()\\n for employee in employee_list:\\n manager_dict = model_to_dict(employee)\\n manager_dict['first_name'] = employee.user.first_name\\n manager_dict['last_name'] = employee.user.last_name\\n manager_dict['photo'] = employee.photo.url if employee.photo else ''\\n employees.append(manager_dict)\\n data = {\\\"employees\\\": employees}\\n else:\\n return JsonResponse(status=400, data={\\\"error\\\": \\\"Employee with id={} not is_manager\\\".format(int(employee_id))})\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def get_employees(self):\\n return self.employees\",\n \"def get(self, request):\\n varemployee = employee.objects.all()\\n serializer = employeeSerializer(varemployee, many=True)\\n return Response(serializer.data)\",\n \"def get_employees_directory(self):\\n response = requests.get(self._base_url + \\\"employees/directory\\\",\\n auth=(self._api_key, \\\"pass\\\"),\\n headers={'Accept': 'application/json'})\\n if response.status_code != 200:\\n response.raise_for_status()\\n emps_json = json.loads(response.text)['employees']\\n return {int(e['id']): Employee(e['displayName'],\\n e['firstName'],\\n e['lastName'],\\n e['nickname']) for e in emps_json}\",\n \"def get_employees(self):\\n from Employee import Employee\\n cursor = self.dbconnect.get_cursor()\\n cursor.execute('select * from employee')\\n\\n employees = list()\\n for row in cursor:\\n employee = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8])\\n employees.append(employee)\\n return employees\",\n \"def get_employee(employee_id):\\n\\n employee = Employee.objects.get_or_404(id=employee_id)\\n\\n return jsonify({\\n 'employee': employee\\n })\",\n \"def employees():\\n # gather data from db about all employees\\n return render_template(\\\"employees.html\\\")\",\n \"def list_employees(order_by=\\\"id\\\"):\\n ret = {}\\n status, result = _query(action=\\\"employees\\\", command=\\\"directory\\\")\\n root = ET.fromstring(result)\\n for cat in root:\\n if cat.tag != \\\"employees\\\":\\n continue\\n for item in cat:\\n emp_id = next(iter(item.values()))\\n emp_ret = {\\\"id\\\": emp_id}\\n for details in item:\\n emp_ret[next(iter(details.values()))] = details.text\\n ret[emp_ret[order_by]] = emp_ret\\n return ret\",\n \"def get_manager_employees(request):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\\n if manager_employees:\\n emp_list=[]\\n for emp in manager_employees:\\n emp_data={}\\n emp_data[\\\"id\\\"] = emp.id\\n emp_data[\\\"username\\\"] = emp.user.username\\n emp_data[\\\"first_name\\\"] = emp.user.first_name\\n emp_data[\\\"last_name\\\"] = emp.user.last_name\\n emp_data[\\\"manager_id\\\"] = emp.manager.id\\n # emp_data[\\\"status_questions\\\"] = emp.status_questions\\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\\n # name_role_list = []\\n # for obj in employee_role:\\n # name_role_list.append(obj.role.name)\\n # emp_data[\\\"roles\\\"] = name_role_list\\n emp_list.append(emp_data)\\n data = {\\\"employees:\\\": emp_list}\\n return JsonResponse(status=201, data=data)\\n else:\\n return JsonResponse(\\\"The user with id={} isn't a manager for any user\\\".format(current_employee.user.id),\\n status=404)\",\n \"def employees(self) -> object:\\n return self._employees\",\n \"def get_employees(self, active_only):\\n cursor = self.dbconnect.get_cursor()\\n\\n if active_only:\\n cursor.execute(\\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\\n 'is_admin, is_active FROM employee WHERE is_active = TRUE')\\n else:\\n cursor.execute(\\n 'SELECT id, name, email, office, extra_info, picture_location, research_group, title, is_external, '\\n 'is_admin, is_active FROM employee')\\n\\n employees = list()\\n for row in cursor:\\n obj = Employee(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10])\\n employees.append(obj)\\n return employees\",\n \"def employees_manager(request):\\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)\\n employee = Employee.objects.get(pk=request.user.employee_user.id)\\n employee_dict = model_to_dict(employee)\\n employee_dict['first_name'] = employee.user.first_name\\n employee_dict['last_name'] = employee.user.last_name\\n employee_dict['photo'] = employee.photo.url if employee.photo else ''\\n print employee_dict\\n if len(manager_list) > 0:\\n result_list = list(manager_list)\\n all_managers_list = found_all_managers(manager_list, result_list)\\n else:\\n data = {\\\"employee_managers\\\": employee_dict}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\\n employees = list()\\n for manager in all_managers_list:\\n manager_dict = model_to_dict(manager)\\n manager_dict['first_name'] = manager.user.first_name\\n manager_dict['last_name'] = manager.user.last_name\\n manager_dict['photo'] = manager.photo.url if manager.photo else ''\\n employees.append(manager_dict)\\n employees.append(employee_dict)\\n\\n data = {\\\"employee_managers\\\": employees}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def get_all_data():\\n return jsonify(service.get_all_data())\",\n \"def show_all_employees(self):\\n try:\\n employees = self.admin_repository.show_all_employees()\\n if employees:\\n for employee in employees:\\n print(\\\"Employee Id : {}\\\".format(employee[0]))\\n print(\\\"Name : {}\\\".format(employee[1]))\\n print(\\\"Email : {}\\\".format(employee[2]))\\n print(\\\"----------------------------\\\")\\n return True\\n else:\\n print(\\\"No records found.\\\")\\n return False\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def get_companies():\\n all_companies = storage.all(Company).values()\\n list_companies = []\\n for company in all_companies:\\n list_companies.append(company.to_dict())\\n return jsonify(list_companies)\",\n \"def test_api_can_get_all_employees(self):\\n res = self.client().get(service_url_emp)\\n self.assertEqual(res.status_code, 200)\\n self.assertIn('name1', str(res.data))\\n self.assertIn('name2', str(res.data))\",\n \"def getELUsers(**kwargs):\\n \\n for key in kwargs:\\n if type(kwargs[key]) == list:\\n kwargs[key] = kwargs[key][0]\\n \\n allELUsers = ELUser.ELUser.all(**kwargs)\\n allELUsersDictionaries = [dict(eluser) for eluser in allELUsers if dict(eluser)]\\n \\n return flask.Response(\\n response = json.dumps(allELUsersDictionaries),\\n status = 200,\\n content_type = 'application/json'\\n )\",\n \"def jsonify_all(cls):\\n return jsonify(accounts=[account.as_dict() for account in cls.query.all()])\",\n \"def get_persons(self):\\n response = self.do_request('/management/persons/export/json/')\\n if response:\\n return response.json()\",\n \"def userJSON():\\n user = session.query(User).all()\\n result = []\\n\\n for i in user:\\n result += [i.serialize]\\n\\n return jsonify(Users=result)\",\n \"def get_users():\\n return jsonify([\\n users.to_dict()\\n for users in models.storage.all('User').values()\\n ])\",\n \"def get_persons(self):\\n response = self.do_request('/misc/user/export/json')\\n if response:\\n return response.json()\",\n \"def employee_get(emp_id):\\n try:\\n emp = Employee.objects.get(id=emp_id)\\n except Employee.DoesNotExist:\\n return JsonResponse({\\n 'status': False,\\n 'message': 'Employee does not exists in database'\\n }, status=404)\\n _data = {\\n 'id': emp.id,\\n 'first_name': emp.first_name,\\n 'last_name': emp.last_name,\\n 'age': emp.age,\\n 'city': emp.city.name,\\n 'state': emp.state.name,\\n 'country': emp.country.name\\n }\\n return JsonResponse(_data, safe=False)\",\n \"def get_emp_list(self):\\n\\t\\tcondition= ''\\n\\t\\temp_list=[]\\n\\t\\tif self.is_for_all==0:\\n\\t\\t\\tif not self.selected_employees:\\n\\t\\t\\t\\tfrappe.throw(_(\\\"No employees for the mentioned criteria\\\"))\\n\\t\\t\\t#emp_list = [cstr(d.employee) for d in self.selected_employees]\\n\\t\\t\\temp_list = frappe.db.sql_list(\\\"\\\"\\\"\\n\\t\\t\\t\\tselect\\n\\t\\t\\t\\t\\temployee from `tabAttendance Salary Tool Employee`\\n\\t\\t\\t\\twhere\\n\\t\\t\\t\\t\\tparent = '%(parent)s' \\n\\t\\t\\t\\\"\\\"\\\"%{\\\"parent\\\": self.name})\\n\\t\\t\\tcondition+= \\\"\\\"\\\" and t1.employee IN %(employees)s \\\"\\\"\\\"\\n\\t\\tif self.is_open_period==0:\\n\\t\\t\\tif not self.start_date or not self.end_date:\\n\\t\\t\\t\\tfrappe.throw(_(\\\"Satart Date and End Date are Mandatories\\\"))\\n\\t\\t\\tcondition= \\\"\\\"\\\" and attendance_date >= %(start_date)s and attendance_date <= %(end_date)s\\\"\\\"\\\"\\n\\t\\temp_list = frappe.db.sql(\\\"\\\"\\\"\\n\\t\\t\\tselect\\n\\t\\t\\t\\tt1.employee as employee, count(*) as attendance_days\\n\\t\\t\\tfrom\\n\\t\\t\\t\\t`tabAttendance` t1\\n\\t\\t\\twhere\\n\\t\\t\\t\\tt1.attendance_salary_tool is null\\n\\t\\t\\t\\tand t1.docstatus = 1 and t1.status='Present'\\n\\t\\t\\t\\t{condition} group by t1.employee order by t1.employee asc\\n\\t\\t\\\"\\\"\\\".format(condition=condition),{\\\"employees\\\": tuple(emp_list),\\\"start_date\\\": self.start_date,\\\"end_date\\\": self.end_date}, as_dict=True)\\n\\t\\treturn emp_list\",\n \"def get_amenities():\\n list_amenities = []\\n for amenity in storage.all('Amenity').values():\\n list_amenities.append(amenity.to_dict())\\n return jsonify(list_amenities)\",\n \"def show_employee(emp_id, fields=None):\\n ret = {}\\n if fields is None:\\n fields = \\\",\\\".join(\\n (\\n \\\"canUploadPhoto\\\",\\n \\\"department\\\",\\n \\\"displayName\\\",\\n \\\"firstName\\\",\\n \\\"id\\\",\\n \\\"jobTitle\\\",\\n \\\"lastName\\\",\\n \\\"location\\\",\\n \\\"mobilePhone\\\",\\n \\\"nickname\\\",\\n \\\"photoUploaded\\\",\\n \\\"photoUrl\\\",\\n \\\"workEmail\\\",\\n \\\"workPhone\\\",\\n \\\"workPhoneExtension\\\",\\n )\\n )\\n\\n status, result = _query(action=\\\"employees\\\", command=emp_id, args={\\\"fields\\\": fields})\\n\\n root = ET.fromstring(result)\\n\\n ret = {\\\"id\\\": emp_id}\\n for item in root:\\n ret[next(iter(item.values()))] = item.text\\n return ret\",\n \"def data():\\n empo = get(empo_url).json()\\n dict_new = {}\\n for u in empo:\\n empo_id = str(u.get(\\\"id\\\"))\\n tasks = get(tasks_url + \\\"?userId=\\\" + empo_id).json()\\n task_list = []\\n for i in tasks:\\n if i[\\\"userId\\\"] != empo_id:\\n task_list.append({\\\"username\\\": u[\\\"username\\\"],\\n \\\"task\\\": i[\\\"title\\\"],\\n \\\"completed\\\": i[\\\"completed\\\"]})\\n dict_new[empo_id] = task_list\\n with open(\\\"todo_all_employees.json\\\", \\\"w\\\") as file:\\n dump(dict_new, file)\",\n \"def show_all_amenities():\\n\\n amenities = storage.all(Amenity).values()\\n new_list = []\\n for amenity in amenities:\\n new_list.append(amenity.to_dict())\\n return jsonify(new_list)\",\n \"def show_all_employees():\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n employees = Employee.query.all()\\n \\n \\n ## right now this is ALL users... \\n \\n return render_template(\\\"employee_display.html\\\", employees = employees)\",\n \"def all_amenities():\\n amenities_list = []\\n for amenity in storage.all(Amenity).values():\\n amenities_list.append(amenity.to_dict())\\n return jsonify(amenities_list)\",\n \"def get(self):\\n args = self.parser.parse_args()\\n date = get_date_or_none(args['date'])\\n start_date = get_date_or_none(args['start_date'])\\n end_date = get_date_or_none(args['end_date'])\\n\\n if date:\\n employees = self.service.get_employees_by_date_of_birth(\\n date, strategy=selectinload\\n )\\n elif start_date and end_date:\\n employees = self.service.get_employees_born_in_period(\\n start_date, end_date, strategy=selectinload\\n )\\n else:\\n return self.BAD_DATE_MESSAGE, 400\\n\\n return self.schema.dump(employees, many=True), 200\",\n \"def return_amenities():\\n amenities = list(storage.all(Amenity).values())\\n amenity_list = []\\n for amenity in amenities:\\n amenity_list.append(amenity.to_dict())\\n return jsonify(amenity_list)\",\n \"def get_users():\\n users = User.query # no need to order\\n users_data = [user.to_dict() for user in users.all()]\\n return jsonify(users=users_data)\",\n \"def get_all_user():\\n user = UserModel.objects()\\n return jsonify(user), 200\",\n \"def get_articles():\\n _, articles = base_query(db_session)\\n return jsonify([p.serialize for p in articles])\",\n \"def list(self, request):\\n teams = self.controller.retrieve_all_teams_employees()\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(teams, many=True)\\n return Response(serializer.data)\",\n \"def amenity_get_all():\\n am_list = []\\n am_obj = storage.all(\\\"Amenity\\\")\\n for obj in am_obj.values():\\n am_list.append(obj.to_json())\\n\\n return jsonify(am_list)\",\n \"def search_and_export_json():\\n\\n url = 'https://jsonplaceholder.typicode.com/'\\n\\n users_dict = requests.get(\\\"{}users\\\".format(url)).json()\\n tasks_dict = requests.get(\\\"{}todos\\\".format(url)).json()\\n\\n my_dict = {}\\n usernames_dict = {}\\n\\n for info in users_dict:\\n user_id = info.get('id')\\n my_dict[user_id] = []\\n usernames_dict[user_id] = info.get('username')\\n\\n for info in tasks_dict:\\n tasks_all_emp = {}\\n user_id = info.get('userId')\\n tasks_all_emp[\\\"task\\\"] = info.get('title')\\n tasks_all_emp[\\\"completed\\\"] = info.get('completed')\\n tasks_all_emp[\\\"username\\\"] = usernames_dict.get(user_id)\\n my_dict.get(user_id).append(tasks_all_emp)\\n\\n file_name = \\\"todo_all_employees.json\\\"\\n with open(file_name, mode='w') as json_file:\\n json.dump(my_dict, json_file)\",\n \"def get_amenities():\\n amenities = []\\n for amenity in storage.all(Amenity).values():\\n amenities.append(amenity.to_dict())\\n return jsonify(amenities)\",\n \"def get(self, request):\\n employee = EmployeeDetail.objects.all()\\n response = {\\n 'payment_methods': EmployeeSerializer(\\n employee,\\n many=True\\n ).data\\n }\\n return Response(response)\",\n \"def _get_employee_info() -> List[List[str]]:\\n return [\\n ['100', 'Dave', 'Team Leader'],\\n ['101', 'Ram', 'Developer'],\\n ['102', 'Raj', 'Developer'],\\n ['103', 'Rahul', 'Tester'],\\n ]\",\n \"def return_expenses():\\r\\n g.db.execute(\\\"SELECT * FROM monthly_data ORDER BY Sr\\\")\\r\\n rows = g.db.fetchall()\\r\\n data = []\\r\\n for x in rows:\\r\\n data.append({'sr':x[0],'name':x[1], 'id':x[2], 'item':x[3], 'price':x[5], 'date':x[4]})\\r\\n return jsonify(data)\",\n \"def employee(employee_id):\\n # gather data from db about all employees\\n return render_template(\\\"employee.html\\\",\\n employee_id=employee_id)\",\n \"def amenity_ret():\\n ame_list = []\\n all_objs = storage.all(\\\"Amenity\\\")\\n for obj in all_objs.values():\\n ame_list.append(obj.to_dict())\\n return jsonify(ame_list)\",\n \"def get_users():\\n selection = []\\n try:\\n selection = [{'id':usr.id, 'username':usr.username, 'email':usr.email} \\n for usr in User.query.all()]\\n except:\\n selection = {'error':True}\\n return json.dumps(selection)\",\n \"def companies():\\n res = requests.get('http://0.0.0.0:5002/companies')\\n return jsonify(res.json())\",\n \"def user_ret():\\n user_list = []\\n all_objs = storage.all(\\\"User\\\")\\n for obj in all_objs.values():\\n user_list.append(obj.to_dict())\\n return jsonify(user_list)\",\n \"def all_Users():\\n new_dict = []\\n for usr in storage.all('User').values():\\n new_dict.append(usr.to_dict())\\n return jsonify(new_dict)\",\n \"def get_amenities():\\n amenities_dict_list = [amenity.to_dict() for amenity in\\n storage.all(\\\"Amenity\\\").values()]\\n return jsonify(amenities_dict_list)\",\n \"def sportsJSON():\\n\\n sports = session.query(Sport).all()\\n return jsonify(sports=[r.serialize for r in sports])\",\n \"def restaurants_all() -> str:\\n restaurant_objects = restaurants.load_restaurants()\\n return jsonify(restaurant_objects)\",\n \"def all_items_handler():\\n items = getAllItems()\\n return jsonify(items=[i.serialize for i in items])\",\n \"def departments(department_name=None):\\n\\tif not department_name:\\n\\t\\tdepartment_data = _serialize_list(Department.query.all(), backrefs=[\\\"employees\\\"])\\n\\t\\tdepartment_data = {'departments': department_data, 'total': len(department_data)}\\n\\telse:\\n\\t\\tdepartment_data = _serialize_model(Department.query.filter_by(name=department_name).first(), backrefs=[\\\"employees\\\"])\\n\\n\\treturn jsonify(department_data)\",\n \"def get_all_users():\\n users = []\\n for mv in storage.all(\\\"User\\\").values():\\n users.append(mv.to_dict())\\n return jsonify(users)\",\n \"def return_artistnames(): \\n\\n names = [] #list for artist names\\n rows = db.session.query(Artist.name).all()\\n for row in rows: \\n names.append(row[0])\\n\\n return jsonify(names)\",\n \"def get_users():\\n users = User.query.all()\\n users_schema = UserSchema()\\n result = users_schema.dump(users, many=True)\\n return jsonify({'users': result.data})\",\n \"def get_employees(cls, strategy=lazyload):\\n cls._check_strategy(strategy)\\n\\n return db.session.query(Employee).options(\\n strategy(Employee.department)\\n ).all()\",\n \"def list_all_amenities():\\n data = storage.all('Amenity')\\n amenities = [v.to_dict() for k, v in data.items()]\\n return jsonify(amenities)\",\n \"def jugadores():\\n\\tjugadores = Jugador.query.order_by(Jugador.id.desc()).filter_by(activo=True)\\n\\treturn jsonify([jugador.to_dict()\\n\\t\\t for jugador in jugadores])\",\n \"def get_all_by_name():\\n name = request.args['name']\\n return jsonify(service.get_all_data_by_name(name))\",\n \"def all_sales(self, username):\\n con = dbcon()\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM sales;\\\")\\n res = cur.fetchall()\\n sales_records=[]\\n for a_sale in res:\\n record = {\\n 'sales_id':a_sale[0],\\n 'attendant':a_sale[1],\\n 'product_name':a_sale[2],\\n 'price':a_sale[3],\\n 'quantity':a_sale[4]\\n }\\n sales_records.append(record)\\n return jsonify({\\\"Records\\\": sales_records}), 200\",\n \"def get_companies(self):\\n response = self.do_request('/management/companies/export/json')\\n if response:\\n return response.json()\",\n \"def json_view(self, recursive=False):\\n\\n context = self.context.aq_inner\\n data = self.export(context, recursive=recursive)\\n pretty = json.dumps(data, sort_keys=True, indent=4)\\n self.request.response.setHeader(\\\"Content-type\\\", \\\"application/json\\\")\\n return pretty\",\n \"def allCategoriesJSON():\\n categories = db_session.query(Category).all()\\n return jsonify(categories=[c.serialize for c in categories])\",\n \"def all_employees(request, company_id=None):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n company_super_user = current_employee.isCompanySuperUserOrHigher()\\n if company_id:\\n company = Company.objects.get(pk=company_id)\\n else:\\n company = current_employee.company\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n raise PermissionDenied()\\n change_company_form = ChangeCompanyForm(initial=dict(company=company))\\n return TemplateResponse(\\n request,\\n 'all_employees.html',\\n {\\n 'user': request.user,\\n 'company_super_user': company_super_user,\\n 'company': company,\\n 'change_company_form': change_company_form,\\n }\\n )\",\n \"def json_get_all_names_and_emails(request):\\n\\n # Check out HVZ/main/models.py for helper functions relating to Players.\\n # Player.current_players() returns all Players in the current Game.\\n emails = [(str(p.user.first_name) + \\\" \\\" + str(p.user.last_name), p.mailbox) for p in Player.current_players()]\\n\\n # json.dumps creates a string from a Python object. You can then\\n # read the string and convert it into an Objective-C data\\n # structure using NSJSONSerialization in Objective-C.\\n json_data = json.dumps(emails)\\n\\n return HttpResponse(\\n json_data,\\n content_type=\\\"application/json\\\"\\n )\",\n \"def get_stores():\\n stores = Store.query # no need to order\\n stores_data = [store.to_dict() for store in stores.all()]\\n return jsonify(stores=stores_data)\",\n \"def read_all():\\n # Create the list of users from our data\\n users = User.query.order_by(User.first_name).all()\\n\\n # Serialize the data for the response\\n user_schema = UserSchema(many=True)\\n data = user_schema.dump(users)\\n return data\",\n \"def list_users():\\n return jsonify(user=\\\"joe\\\")\",\n \"def names():\\n\\n df = pd.read_sql_query(f\\\"SELECT * FROM olympics_raw\\\", con = engine)\\n print(df.head())\\n \\n\\n # return jsonify(all_olympians)\\n return jsonify(df.to_dict(orient='records'))\",\n \"def get(cls, employee_id):\\n employee = EmployeeModel.find_by_id(employee_id)\\n if not employee:\\n return {'message': 'Employee not found, or you do not have the access'}, 404\\n\\n return employee.json()\",\n \"def get_mentee_list():\\n # Get db object and users table\\n db = get_db()\\n users = db.users\\n \\n # Search database for mentees\\n cursor = users.find({\\\"role\\\": \\\"Mentee\\\"})\\n \\n context = {'mentees': []}\\n \\n for document in cursor:\\n temp = document\\n del temp['_id']\\n context['mentees'].append(temp)\\n \\n context['url'] = \\\"/api/v1/mentees/\\\"\\n return flask.jsonify(**context)\",\n \"def get(self):\\n\\n users = UserModel.get_top_earners()\\n users_json = [user.json() for user in users]\\n return {\\\"users\\\": users_json}\",\n \"def get_exercises():\\n email = session.get(\\\"email\\\")\\n tag_arg = request.args.get(\\\"tag\\\")\\n exercises = fm.get_all_exercises(email, tag_arg)\\n msg = \\\"Found {} exercises for {}\\\".format(len(exercises), email)\\n app.logger.info(msg)\\n return jsonify(dict(exercises=exercises))\",\n \"def list():\\n trucks = Foodtruck.query.all()\\n return jsonify(foodtrucks=[truck.to_dict() for truck in trucks])\",\n \"def get_users():\\n users = storage.all('User')\\n users_list = []\\n for user in users.values():\\n users_list.append(user.to_dict())\\n return jsonify(users_list), 200\",\n \"def get(self):\\n users = User.query.all()\\n usersJSON = []\\n for u in users:\\n usersJSON.append({'id': u.id, 'admin': u.admin})\\n return {'users': usersJSON}\",\n \"def all_entries(cls):\\n info = Diary.entries\\n response = jsonify({\\\"data\\\": info})\\n response.status_code = 200\\n return response\",\n \"def employers_get(label=None, page=None, per_page=None): # noqa: E501\\n\\n\\n return query_manager.get_resource(\\n label=label,\\n page=page,\\n per_page=per_page,\\n rdf_type_uri=EMPLOYER_TYPE_URI,\\n rdf_type_name=EMPLOYER_TYPE_NAME, \\n kls=Employer)\",\n \"def get_countries():\\n countries = Country.query # no need to order\\n countries_data = [country.to_dict() for country in countries.all()]\\n return jsonify(countries=countries_data)\",\n \"def getall():\\n elements = Advertisements().get_all_elements()\\n data = jsonify(elements)\\n data.statut_code = 200\\n return data\",\n \"def amenities_all():\\n return jsonify(list(map(lambda x: x.to_dict(),\\n list(storage.all(Amenity).values()))))\",\n \"def get_all_project_records():\\r\\n records = flask.request.db_api.get_all_project_record()\\r\\n return flask.jsonify(records=records)\",\n \"def happy():\\n # Query all venues\\n results = session.query(VP.name, VP.latitude, VP.longitude).all()\\n \\n # Create a dictionary from the row data and append to a list of all_venue\\n all_venues = []\\n for name, lat, lon in results:\\n venue_dict = {}\\n venue_dict[\\\"name\\\"] = name\\n venue_dict[\\\"latitude\\\"] = lat\\n venue_dict[\\\"longitude\\\"] = lon\\n all_venues.append(venue_dict)\\n \\n return jsonify(all_venues)\",\n \"def list(self):\\n return JSONResponse(self.request).data(items=self._get_agenda_items()).dump()\",\n \"def get(cls):\\n return {'products': [product.to_json() for product in ProductModel.find_all()]}\",\n \"def get(self):\\n users = User.query.all()\\n usersJSON = []\\n for u in users:\\n usersJSON.append({'id':u.id, 'admin':u.admin})\\n return { 'users' : usersJSON }\",\n \"def current_employee(self, request: Request) -> Response:\\n serializer = self.get_serializer_class()\\n serializer = serializer(request.user, context={'request': request})\\n return Response(serializer.data)\",\n \"def get_employee():\\n\\n employee_id = get_employee_input_int('Enter employee ID to get the data ')\\n employee = db.get_employee(employee_id)\\n if not employee:\\n print(\\\"No employee found with id \\\", employee_id)\\n else:\\n payscale = db.get_payScale(employee.grade)\\n print('DATA:-> {} {} has grade = {} which gives {} per hours\\\\n'\\n .format(employee.first_name, employee.last_name, employee.grade, payscale.salary))\",\n \"def get_books():\\n return jsonify({'books': Book.get_all_books()})\",\n \"def get_books():\\n return jsonify({'books': Book.get_all_books()})\",\n \"def retrieve(self, request, pk=None):\\n employee = self.get_employee_object(pk)\\n print(F\\\"Employee: {employee}\\\")\\n serializer = data_serializers.PresentEmployeeDataSerializer(employee)\\n return Response(serializer.data)\",\n \"def get(self):\\n all_patients = model_patient.query.all()\\n return jsonify(all_patients)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80177975","0.79526293","0.78115326","0.72639924","0.7209743","0.71376187","0.71241915","0.7055127","0.7054203","0.7002898","0.68735474","0.6857624","0.68054706","0.67973894","0.6706132","0.66616935","0.6618934","0.6581776","0.6578094","0.65394276","0.64805084","0.64098907","0.6351272","0.6322406","0.6278039","0.6247345","0.62471193","0.6222043","0.6201518","0.6161673","0.61550957","0.6143976","0.61379087","0.6111975","0.6108984","0.6103555","0.61003774","0.60972583","0.6095555","0.60948074","0.6083758","0.60582894","0.60456747","0.6030361","0.60233027","0.6018328","0.6002323","0.59965116","0.5982986","0.59603274","0.5948698","0.592496","0.59212166","0.5877531","0.5869281","0.5860559","0.5858288","0.5849215","0.58439463","0.5843509","0.5842587","0.5833205","0.58226055","0.5817468","0.5799135","0.5798358","0.5779523","0.57713354","0.5763276","0.57588625","0.57579094","0.5754775","0.5749727","0.5748318","0.5737075","0.57322073","0.57300824","0.5725105","0.5708375","0.5696335","0.5691881","0.56893724","0.56828105","0.56800884","0.56727463","0.5669546","0.56485367","0.5644668","0.56350523","0.5633134","0.562746","0.562347","0.5621738","0.5618645","0.5605881","0.5605512","0.55904907","0.55904907","0.5587724","0.5585772"],"string":"[\n \"0.80177975\",\n \"0.79526293\",\n \"0.78115326\",\n \"0.72639924\",\n \"0.7209743\",\n \"0.71376187\",\n \"0.71241915\",\n \"0.7055127\",\n \"0.7054203\",\n \"0.7002898\",\n \"0.68735474\",\n \"0.6857624\",\n \"0.68054706\",\n \"0.67973894\",\n \"0.6706132\",\n \"0.66616935\",\n \"0.6618934\",\n \"0.6581776\",\n \"0.6578094\",\n \"0.65394276\",\n \"0.64805084\",\n \"0.64098907\",\n \"0.6351272\",\n \"0.6322406\",\n \"0.6278039\",\n \"0.6247345\",\n \"0.62471193\",\n \"0.6222043\",\n \"0.6201518\",\n \"0.6161673\",\n \"0.61550957\",\n \"0.6143976\",\n \"0.61379087\",\n \"0.6111975\",\n \"0.6108984\",\n \"0.6103555\",\n \"0.61003774\",\n \"0.60972583\",\n \"0.6095555\",\n \"0.60948074\",\n \"0.6083758\",\n \"0.60582894\",\n \"0.60456747\",\n \"0.6030361\",\n \"0.60233027\",\n \"0.6018328\",\n \"0.6002323\",\n \"0.59965116\",\n \"0.5982986\",\n \"0.59603274\",\n \"0.5948698\",\n \"0.592496\",\n \"0.59212166\",\n \"0.5877531\",\n \"0.5869281\",\n \"0.5860559\",\n \"0.5858288\",\n \"0.5849215\",\n \"0.58439463\",\n \"0.5843509\",\n \"0.5842587\",\n \"0.5833205\",\n \"0.58226055\",\n \"0.5817468\",\n \"0.5799135\",\n \"0.5798358\",\n \"0.5779523\",\n \"0.57713354\",\n \"0.5763276\",\n \"0.57588625\",\n \"0.57579094\",\n \"0.5754775\",\n \"0.5749727\",\n \"0.5748318\",\n \"0.5737075\",\n \"0.57322073\",\n \"0.57300824\",\n \"0.5725105\",\n \"0.5708375\",\n \"0.5696335\",\n \"0.5691881\",\n \"0.56893724\",\n \"0.56828105\",\n \"0.56800884\",\n \"0.56727463\",\n \"0.5669546\",\n \"0.56485367\",\n \"0.5644668\",\n \"0.56350523\",\n \"0.5633134\",\n \"0.562746\",\n \"0.562347\",\n \"0.5621738\",\n \"0.5618645\",\n \"0.5605881\",\n \"0.5605512\",\n \"0.55904907\",\n \"0.55904907\",\n \"0.5587724\",\n \"0.5585772\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.760211"},"document_rank":{"kind":"string","value":"3"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":6,"numItemsPerPage":100,"numTotalItems":95772,"offset":600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzU1NDU2MCwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM1NTgxNjAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.GdqSdxuOUfhrH_p0OmvfBHCfHg30KkxqCAyHunsXygZj-cDJu7yONW3GcEtLb0TPNyUTXRLXrvQUcd2T9AJbCg","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 |
|---|---|---|---|---|---|---|
Returns the number of milliseconds for the given number of jiffies (a weird timing unit used the kernel). | def calculate_time_ms(self, jiffies):
return int((jiffies * 1000.0) / self._jiffies_per_sec) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __calculate_time_cs(self, jiffies):\n\n return int((jiffies * 100.0) / self._jiffies_per_sec)",
"def jiffies(_load_time=time.time()):\n return int(100*(time.time()-_load_time))",
"def millis(): \n return int(round(monotonic.monotonic() * C.MILLISECONDS))",
"def millis(): \r\n re... | [
"0.73772615",
"0.7025039",
"0.6870207",
"0.6851476",
"0.6639381",
"0.6579558",
"0.6533067",
"0.63947713",
"0.6242466",
"0.61780435",
"0.6164305",
"0.6149434",
"0.61466753",
"0.6125577",
"0.61053866",
"0.6076043",
"0.6049589",
"0.60407495",
"0.60054356",
"0.59866726",
"0.59761... | 0.82893556 | 0 |
Returns the number of milliseconds the system has been up. | def __get_uptime_ms(self):
if self._boot_time_ms is None:
# We read /proc/uptime once to get the current boot time.
uptime_file = None
try:
uptime_file = open("/proc/uptime", "r")
# The first number in the file is the number of seconds since
# boot time. So, we just use that to calculate the milliseconds
# past epoch.
self._boot_time_ms = int(time.time()) * 1000 - int(
float(uptime_file.readline().split()[0]) * 1000.0
)
finally:
if uptime_file is not None:
uptime_file.close()
# Calculate the uptime by just taking current time and subtracting out
# the boot time.
return int(time.time()) * 1000 - self._boot_time_ms | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sys_up_time():\n\n with open('/proc/uptime', 'r') as f:\n uptime_seconds = float(f.readline().split()[0])\n return int(uptime_seconds)",
"def getUpTime(self):\n return self.__upTime + time() - self.__fingerTime",
"def uptime(self):\n return self._call_txtrader_api('up... | [
"0.76271576",
"0.74804974",
"0.7351499",
"0.7336892",
"0.7335807",
"0.7160131",
"0.6963318",
"0.69100386",
"0.68876153",
"0.6848875",
"0.67855483",
"0.67283034",
"0.67062175",
"0.6680035",
"0.6660014",
"0.66218805",
"0.66216844",
"0.6590366",
"0.6590068",
"0.6582871",
"0.6569... | 0.74130756 | 2 |
Gathers the metrics from the stat file. | def gather_sample(self, stat_file, collector=None):
if not collector:
collector = {}
# The file format is just a single line of all the fields.
line = stat_file.readlines()[0]
# Chop off first part which is the pid and executable file. The
# executable file is terminated with a paren so just search for that.
line = line[(line.find(") ") + 2) :]
fields = line.split()
# Then the fields we want are just at fixed field positions in the
# string. Just grab them.
# See http://man7.org/linux/man-pages/man5/proc.5.html for reference on field numbers
# Keep in mind that we chop first 3 values away (pid, command line, state), so you need to
# subtract 3 from the field numbers from the man page (e.g. on the man page nice is number
# 19, but in our case it's 16 aka 19 - 3)
process_uptime = self.__get_uptime_ms() - self.calculate_time_ms(
int(fields[19])
)
collector.update(
{
Metric("app.cpu", "user"): self.__calculate_time_cs(int(fields[11])),
Metric("app.cpu", "system"): self.__calculate_time_cs(int(fields[12])),
Metric("app.uptime", None): process_uptime,
Metric("app.nice", None): float(fields[16]),
Metric("app.threads", None): int(fields[17]),
Metric("app.mem.majflt", None): int(fields[9]),
Metric("app.io.wait", None): int(fields[39])
if len(fields) >= 39
else 0,
}
)
return collector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gather_sample(self, stat_file, collector=None):\n\n if not collector:\n collector = {}\n\n for line in stat_file:\n # Each line has a format of:\n # Tag: Value\n #\n # We parse out all lines looking like that and match the stats we care about... | [
"0.6764025",
"0.67190164",
"0.6688609",
"0.65135384",
"0.6446264",
"0.6374985",
"0.63547957",
"0.6333468",
"0.63260734",
"0.6261242",
"0.6237093",
"0.6232534",
"0.61950576",
"0.618388",
"0.6182165",
"0.6152383",
"0.6111384",
"0.6086403",
"0.6057407",
"0.6038634",
"0.60113156"... | 0.7137697 | 0 |
Gathers the metrics from the status file. | def gather_sample(self, stat_file, collector=None):
if not collector:
collector = {}
for line in stat_file:
# Each line has a format of:
# Tag: Value
#
# We parse out all lines looking like that and match the stats we care about.
m = re.search(r"^(\w+):\s*(\d+)", line)
if m is None:
continue
field_name = m.group(1)
int_value = int(m.group(2))
# FDSize is not the same as the number of open file descriptors. Disable
# for now.
# if field_name == "FDSize":
# self.print_sample("app.fd", int_value)
if field_name == "VmSize":
collector.update({Metric("app.mem.bytes", "vmsize"): int_value * 1024})
elif field_name == "VmPeak":
collector.update(
{Metric("app.mem.bytes", "peak_vmsize"): int_value * 1024}
)
elif field_name == "VmRSS":
collector.update(
{Metric("app.mem.bytes", "resident"): int_value * 1024}
)
elif field_name == "VmHWM":
collector.update(
{Metric("app.mem.bytes", "peak_resident"): int_value * 1024}
)
return collector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_status(self):\n\n # Memory information can be found in status and statm /proc/PID files\n # status file VmRSS equivalent to top's RES column\n # statm disagrees with status VmRSS, I think it may not include\n # sub-processes\n # From: man proc\n # * VmPeak... | [
"0.6695974",
"0.6339612",
"0.6311073",
"0.62527573",
"0.6087654",
"0.60828906",
"0.60479605",
"0.5964161",
"0.5951921",
"0.5916135",
"0.5879056",
"0.5801343",
"0.5773241",
"0.57671124",
"0.57634276",
"0.5756819",
"0.5740762",
"0.5738426",
"0.5729482",
"0.57033396",
"0.5692026... | 0.5690548 | 21 |
Gathers the metrics from the io file. | def gather_sample(self, stat_file, collector=None):
if not collector:
collector = {}
# File format is single value per line with "fieldname:" prefix.
for x in stat_file:
fields = x.split()
if len(fields) == 0:
continue
if not collector:
collector = {}
if fields[0] == "rchar:":
collector.update({Metric("app.disk.bytes", "read"): int(fields[1])})
elif fields[0] == "syscr:":
collector.update({Metric("app.disk.requests", "read"): int(fields[1])})
elif fields[0] == "wchar:":
collector.update({Metric("app.disk.bytes", "write"): int(fields[1])})
elif fields[0] == "syscw:":
collector.update({Metric("app.disk.requests", "write"): int(fields[1])})
return collector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_metrics_file(self):\n\n with open(self.metrics_path, \"w+\") as f_metrics:\n\n f_metrics.write(get_metrics_file_form())",
"def read_metrics(self):\n raise NotImplementedError()",
"def emit_metrics(self):\n parse_time = time.perf_counter() - self._parsing_start_time\n ... | [
"0.69621795",
"0.6846196",
"0.5868116",
"0.5818642",
"0.58145773",
"0.569007",
"0.553364",
"0.55294347",
"0.5510199",
"0.5496143",
"0.5489615",
"0.54809064",
"0.5451518",
"0.54069734",
"0.54033786",
"0.5399427",
"0.5393153",
"0.538728",
"0.5384428",
"0.5374179",
"0.53578675",... | 0.5644732 | 6 |
Gathers the metrics from the netstate file. | def gather_sample(self, stat_file, collector=None):
# This file format is weird. Each set of stats is outputted in two
# lines. First, a header line that list the field names. Then a
# a value line where each value is specified in the appropriate column.
# You have to match the column name from the header line to determine
# what that column's value is. Also, each pair of lines is prefixed
# with the same name to make it clear they are tied together.
all_lines = stat_file.readlines()
# We will create an array of all of the column names in field_names
# and all of the corresponding values in field_values.
field_names = []
field_values = []
# To simplify the stats, we add together the two forms of retransmit
# I could find in the netstats. Those to fast retransmit Reno and those
# to selective Ack.
retransmits = 0
found_retransmit_metric = False
# Read over lines, looking at adjacent lines. If their row names match,
# then append their column names and values to field_names
# and field_values. This will break if the two rows are not adjacent
# but I do not think that happens in practice. If it does, we just
# won't report the stats.
for i in range(0, len(all_lines) - 1):
names_split = all_lines[i].split()
values_split = all_lines[i + 1].split()
# Check the row names are the same.
if names_split[0] == values_split[0] and len(names_split) == len(
values_split
):
field_names.extend(names_split)
field_values.extend(values_split)
if not collector:
collector = {}
# Now go back and look for the actual stats we care about.
for i in range(0, len(field_names)):
if field_names[i] == "InOctets":
collector.update({Metric("app.net.bytes", "in"): field_values[i]})
elif field_names[i] == "OutOctets":
collector.update({Metric("app.net.bytes", "out"): field_values[i]})
elif field_names[i] == "TCPRenoRecovery":
retransmits += int(field_values[i])
found_retransmit_metric = True
elif field_names[i] == "TCPSackRecovery":
retransmits += int(field_values[i])
found_retransmit_metric = True
# If we found both forms of retransmit, add them up.
if found_retransmit_metric:
collector.update({Metric("app.net.tcp_retransmits", None): retransmits})
return collector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_metrics(self):\n raise NotImplementedError()",
"def gather_sample(self, stat_file, collector=None):\n\n if not collector:\n collector = {}\n\n for line in stat_file:\n # We just look for the different \"inuse\" lines and output their\n # socket type ... | [
"0.59211487",
"0.5842457",
"0.5809869",
"0.5712683",
"0.5633532",
"0.56009007",
"0.55989665",
"0.5477207",
"0.54482716",
"0.5303792",
"0.5302852",
"0.5279958",
"0.52649426",
"0.5263945",
"0.52634865",
"0.5221303",
"0.5212169",
"0.52069163",
"0.5195175",
"0.5184402",
"0.518232... | 0.56973577 | 4 |
Gathers the metrics from the sockstat file. | def gather_sample(self, stat_file, collector=None):
if not collector:
collector = {}
for line in stat_file:
# We just look for the different "inuse" lines and output their
# socket type along with the count.
m = re.search(r"(\w+): inuse (\d+)", line)
if m is not None:
collector.update(
{
Metric("app.net.sockets_in_use", m.group(1).lower()): int(
m.group(2)
)
}
)
return collector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gather_sample(self, stat_file, collector=None):\n\n # This file format is weird. Each set of stats is outputted in two\n # lines. First, a header line that list the field names. Then a\n # a value line where each value is specified in the appropriate column.\n # You have to match... | [
"0.6393384",
"0.6306739",
"0.60562605",
"0.6037875",
"0.597491",
"0.59416795",
"0.5933875",
"0.5916901",
"0.57845867",
"0.577276",
"0.5749845",
"0.571024",
"0.5700701",
"0.56980515",
"0.5637241",
"0.5629849",
"0.56119055",
"0.55269897",
"0.5518267",
"0.55144465",
"0.5510193",... | 0.71067053 | 0 |
Collects the metrics from the gathers | def collect(self):
collector = {}
for gather in self.gathers:
try:
stats = gather.run_single_cycle(collector=collector)
if stats:
collector.update(stats)
except Exception as ex:
self._logger.exception(
"Exception while collecting metrics for PID: %s of type: %s. Details: %s",
self.pid,
type(gather),
repr(ex),
)
return collector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect(self): # pylint: disable=no-self-use\n start = time.time()\n for metric in metric_rq():\n yield metric\n\n gauge = GaugeMetricFamily(\n \"nautobot_rq_metrics_processing_ms\", \"Time in ms to generate the app metrics endpoint\"\n )\n duration = t... | [
"0.6958763",
"0.69119155",
"0.68189895",
"0.66842306",
"0.66356134",
"0.6568736",
"0.6555862",
"0.6482938",
"0.64655095",
"0.64260054",
"0.641891",
"0.63836133",
"0.63763535",
"0.62167543",
"0.62135196",
"0.62097865",
"0.6177793",
"0.61742324",
"0.61667204",
"0.61239713",
"0.... | 0.7913655 | 0 |
Return the process of the agent. | def current_process(self):
return self._current_process | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _launchAgentProcess( self ):\n return subprocess.Popen( [ sys.executable, os.path.join( sys.path[0], 'agentProcess.py' ), str( _processPid ) ], stdin=subprocess.PIPE, stdout=subprocess.PIPE )",
"def get_process(self, pid):\n return self.processes.get(pid, None)",
"def get_my_process():\n r... | [
"0.69227177",
"0.68076235",
"0.66409147",
"0.65917194",
"0.6577045",
"0.65301675",
"0.6481512",
"0.6463723",
"0.6425003",
"0.639765",
"0.6345163",
"0.6329305",
"0.63107604",
"0.63107604",
"0.6309831",
"0.6220328",
"0.6186098",
"0.61813504",
"0.6084275",
"0.6083225",
"0.605136... | 0.69763553 | 0 |
Given a string, match the processes on the name | def get_matches_commandline(self, match_pattern):
matches = []
for _process in self.processes:
if re.search(match_pattern, _process["cmd"]):
matches.append(_process["pid"])
return matches | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ps_find(name):\n for proc in psutil.process_iter():\n if proc.name() == name:\n return True\n return False",
"def findPIDs(name, user = os.getpid()):\n\n pids = []\n\n ps = subprocess.Popen(['ps', '-u', user, 'w'], stdout=subprocess.PIPE).communicate()[0]\n processes = ps.spl... | [
"0.67669374",
"0.6710715",
"0.66695976",
"0.64684063",
"0.64647824",
"0.6255509",
"0.60690784",
"0.6056705",
"0.60546",
"0.595144",
"0.59447294",
"0.5906793",
"0.5887537",
"0.58319217",
"0.58199763",
"0.57570976",
"0.5753294",
"0.572602",
"0.57223743",
"0.5722138",
"0.5618653... | 0.611642 | 6 |
Given a process id, return all children processes (recursively) | def get_child_processes(self, ppid):
all_children = []
children_to_explore = set()
for _pid in self.parent_to_children_map[ppid]:
all_children.append(_pid)
children_to_explore.add(_pid)
# get the children 'recursively'
while children_to_explore: # the invariant
child_to_explore = children_to_explore.pop()
if not self.parent_to_children_map.get(child_to_explore):
continue
unvisited = self.parent_to_children_map[child_to_explore]
for node in unvisited:
if node not in all_children:
children_to_explore.add(node)
all_children.append(node)
return list(set(all_children)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_children(ppid):\n\n pid_dct = {}\n for proc in build_process_list():\n proc[\"_children\"] = []\n pid_dct[proc[\"pid\"]] = proc\n\n # fill in children array\n for pid in list(pid_dct.keys()):\n parent_pid = pid_dct[pid][\"parent_pid\"]\n\n if parent_pid in pid_dct:\n... | [
"0.8440308",
"0.78711176",
"0.7738845",
"0.73896194",
"0.73563373",
"0.7110651",
"0.6810038",
"0.66680056",
"0.651955",
"0.6309715",
"0.62305254",
"0.603989",
"0.59731925",
"0.5933038",
"0.5905366",
"0.5880942",
"0.5819915",
"0.57845265",
"0.5777258",
"0.5750268",
"0.574295",... | 0.7898279 | 1 |
Returns a list of all running process ids | def get_running_processes(self):
all_processes = []
for _process in self.processes:
all_processes.append(_process["pid"])
return all_processes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_pid_list():\r\n pids = [int(x) for x in os.listdir('/proc') if x.isdigit()]\r\n return pids",
"def pids(self):\n return self._pidToProcess.iterkeys()",
"def running_procs(self) -> List[int]:\n return [p.model_id for p in self.primary_scheduler.queue_nodes.run_q]",
"def getActivePr... | [
"0.7804924",
"0.7730347",
"0.7697995",
"0.7590113",
"0.7520795",
"0.7483522",
"0.7411062",
"0.7385953",
"0.7380598",
"0.73299384",
"0.73251057",
"0.7316895",
"0.7233392",
"0.7152342",
"0.71504444",
"0.71448386",
"0.7116604",
"0.7027047",
"0.69536257",
"0.6934355",
"0.6899609"... | 0.813076 | 0 |
Like get_matches_commandline method, given a string, match the processes on the name but also returns the matched processes' children | def get_matches_commandline_with_children(self, match_pattern):
matched_pids = self.get_matches_commandline(match_pattern)
for matched_pid in matched_pids:
matched_pids.extend(self.get_child_processes(matched_pid))
return list(set(matched_pids)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_matches_commandline(self, match_pattern):\n\n matches = []\n for _process in self.processes:\n if re.search(match_pattern, _process[\"cmd\"]):\n matches.append(_process[\"pid\"])\n return matches",
"def find(name, arg=None):\r\n for p in get_processes():\... | [
"0.69588715",
"0.63922125",
"0.61809975",
"0.6156374",
"0.6085242",
"0.602419",
"0.59251225",
"0.5850459",
"0.58301437",
"0.58174837",
"0.58165544",
"0.5804579",
"0.58037686",
"0.57153285",
"0.56987065",
"0.5696199",
"0.5649648",
"0.5642261",
"0.55726624",
"0.5563265",
"0.555... | 0.75858635 | 0 |
For a process, record the metrics in a historical metrics collector Collects the historical result of each metric per process in __metrics_history | def record_metrics(self, pid, metrics):
for _metric, _metric_value in metrics.items():
if not self.__metrics_history[pid].get(_metric):
self.__metrics_history[pid][_metric] = []
self.__metrics_history[pid][_metric].append(_metric_value)
# only keep the last 2 running history for any metric
self.__metrics_history[pid][_metric] = self.__metrics_history[pid][_metric][
-2:
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _calculate_aggregated_metrics(self):\n\n # using the historical values, calculate the aggregate\n # there are two kinds of metrics:\n # a) cumulative metrics - only the delta of the last 2 recorded values is used (eg cpu cycles)\n # b) absolute metrics - the last absolute value is u... | [
"0.6638309",
"0.61203325",
"0.6019831",
"0.59060955",
"0.59058285",
"0.55844504",
"0.54852",
"0.54657155",
"0.53771794",
"0.53647846",
"0.5356617",
"0.5345713",
"0.5341496",
"0.53098404",
"0.52967745",
"0.5279151",
"0.5278557",
"0.52188367",
"0.52059686",
"0.51821625",
"0.517... | 0.7013191 | 0 |
At the beginning of each process metric calculation, the absolute (noncumulative) metrics need to be overwritten to the combined process(es) result. Only the cumulative metrics need the previous value to calculate delta. We should set the absolute metric to 0 in the beginning of this "epoch" | def _reset_absolute_metrics(self):
for pid, process_metrics in self.__metrics_history.items():
for _metric, _metric_values in process_metrics.items():
if not _metric.is_cumulative:
self.__aggregated_metrics[_metric] = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _calculate_aggregated_metrics(self):\n\n # using the historical values, calculate the aggregate\n # there are two kinds of metrics:\n # a) cumulative metrics - only the delta of the last 2 recorded values is used (eg cpu cycles)\n # b) absolute metrics - the last absolute value is u... | [
"0.66141224",
"0.5771144",
"0.57508063",
"0.5575068",
"0.5574594",
"0.55501413",
"0.5535069",
"0.54281497",
"0.53845894",
"0.535291",
"0.5337103",
"0.5333742",
"0.5300862",
"0.5274768",
"0.5269434",
"0.52690667",
"0.52612674",
"0.5259409",
"0.52576226",
"0.5246559",
"0.524457... | 0.75371194 | 0 |
Calculates the aggregated metric values based on the current running processes and the historical metric record | def _calculate_aggregated_metrics(self):
# using the historical values, calculate the aggregate
# there are two kinds of metrics:
# a) cumulative metrics - only the delta of the last 2 recorded values is used (eg cpu cycles)
# b) absolute metrics - the last absolute value is used
running_pids_set = set(self.__pids)
for pid, process_metrics in self.__metrics_history.items():
for _metric, _metric_values in process_metrics.items():
if not self.__aggregated_metrics.get(_metric):
self.__aggregated_metrics[_metric] = 0
if _metric.is_cumulative:
if pid in running_pids_set:
if len(_metric_values) > 1:
# only report the cumulative metrics for more than one sample
self.__aggregated_metrics[_metric] += (
_metric_values[-1] - _metric_values[-2]
)
else:
if pid in running_pids_set:
# absolute metric - accumulate the last reported value
self.__aggregated_metrics[_metric] += _metric_values[-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_metrics(self):\n pass",
"def _reset_absolute_metrics(self):\n\n for pid, process_metrics in self.__metrics_history.items():\n for _metric, _metric_values in process_metrics.items():\n if not _metric.is_cumulative:\n self.__aggregated_metrics[... | [
"0.65272045",
"0.6503945",
"0.6044549",
"0.5962655",
"0.5960727",
"0.5932751",
"0.58754987",
"0.5782237",
"0.5775919",
"0.5757956",
"0.57359225",
"0.57347035",
"0.5726887",
"0.57170856",
"0.57170856",
"0.56863284",
"0.56776404",
"0.56517655",
"0.56427175",
"0.5625753",
"0.561... | 0.8260474 | 0 |
Collect the perprocess tracker for the monitored process(es). | def gather_sample(self):
for _pid in self._select_processes():
if not self.__trackers.get(_pid):
self.__trackers[_pid] = ProcessTracker(_pid, self._logger, self.__id)
self._reset_absolute_metrics()
for _tracker in self.__trackers.values():
_metrics = _tracker.collect()
self.record_metrics(_tracker.pid, _metrics)
self._calculate_aggregated_metrics()
self._remove_dead_processes()
self.print_metrics() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _proc_collect(self) -> None:\n while True:\n self.process_num_threads.set(self._process.num_threads())\n self.process_memory_bytes.set(self._process.memory_info().rss)\n self.process_cpu_percent.set(self._process.cpu_percent())\n\n sleep(self.process_scrape_in... | [
"0.70074445",
"0.70008403",
"0.65227914",
"0.6477468",
"0.64733964",
"0.6447299",
"0.64127636",
"0.6285023",
"0.6207518",
"0.6167097",
"0.6145146",
"0.61055756",
"0.6044723",
"0.6031808",
"0.5893498",
"0.58866787",
"0.58456194",
"0.5834667",
"0.5807094",
"0.5745234",
"0.57324... | 0.7125841 | 0 |
Returns true if the current process is still running. | def __is_running(pid):
try:
# signal flag 0 does not actually try to kill the process but does an error
# check that is useful to see if a process is still running.
os.kill(pid, 0)
return True
except OSError as e:
# Errno #3 corresponds to the process not running. We could get
# other errors like this process does not have permission to send
# a signal to self.pid. But, if that error is returned to us, we
# know the process is running at least, so we ignore the error.
return e.errno != errno.ESRCH | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_running(self):\r\n if self._gone:\r\n return False\r\n try:\r\n # Checking if pid is alive is not enough as the pid might\r\n # have been reused by another process.\r\n # pid + creation time, on the other hand, is supposed to\r\n # identif... | [
"0.8082826",
"0.8080181",
"0.8026835",
"0.8013894",
"0.8012157",
"0.7952827",
"0.78069186",
"0.77576995",
"0.7746866",
"0.77379435",
"0.7681154",
"0.76794994",
"0.7676883",
"0.76404685",
"0.7629628",
"0.76252586",
"0.75991505",
"0.7584161",
"0.75792927",
"0.75562996",
"0.7544... | 0.7129584 | 59 |
Returns a list of the process ids of processes that fulfills the match criteria. This will either use the commandline matcher or the target pid to find the process. If no process is matched, an empty list is returned. | def _select_processes(self):
# check if at least one process is running
is_running = False
for pid in self.__pids:
if ProcessMonitor.__is_running(pid):
is_running = True
break # at least one process is running
if is_running:
if not self.__aggregate_multiple_processes:
return self.__pids
# aggregate metrics, check the last discovered time
if (
self.__last_discovered
and time.time() * 1000 - self.__last_discovered
< self.__process_discovery_interval * 1000
):
return self.__pids
ps = ProcessList()
if self.__commandline_matcher:
self.__last_discovered = time.time() * 1000
if self.__include_child_processes:
matched_processes = ps.get_matches_commandline_with_children(
self.__commandline_matcher
)
else:
matched_processes = ps.get_matches_commandline(
self.__commandline_matcher
)
self.__pids = matched_processes
if not self.__aggregate_multiple_processes and len(self.__pids) > 1:
# old behaviour where multiple processes were not supported for aggregation
self._logger.warning(
"Multiple processes match the command '%s'. Returning existing pid. "
"You can turn on the multi process aggregation support by adding the "
"aggregate_multiple_processes configuration to true"
% self.__commandline_matcher,
limit_once_per_x_secs=300,
limit_key="linux-process-monitor-existing-pid",
)
self.__pids = [self.__pids[0]]
else:
# See if the specified target pid is running. If so, then return it.
# Special cases:
# '$$' mean this process.
# '$$TBD' mean that the PID of the target process has not been determined yet and it will be set later.
pids = []
if self.__target_pids:
for t_pid in self.__target_pids:
if t_pid == "$$":
t_pid = int(os.getpid())
# skip this until it will be replaced with a real PID.
elif t_pid == "$$TBD":
continue
else:
t_pid = int(t_pid)
pids.append(t_pid)
self.__pids = pids
return self.__pids | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_matching_pids(pattern):\n cmd = [\"pgrep\", \"-f\", pattern]\n rc, output, err = run_cmd_output(cmd)\n if rc == 0:\n # One or more processes matched\n pids = [int(p) for p in output.split('\\n') if p != \"\"]\n elif rc == 1:\n # No processes matched\n pids = []\n ... | [
"0.7879332",
"0.7805906",
"0.7679771",
"0.7294594",
"0.70915145",
"0.67127335",
"0.6696709",
"0.6683365",
"0.6582418",
"0.65806013",
"0.64939845",
"0.6404458",
"0.63337654",
"0.6324033",
"0.6305915",
"0.63057446",
"0.6257686",
"0.62433344",
"0.6234016",
"0.612377",
"0.6083050... | 0.72393227 | 4 |
Set the PID of the process that was marked as $$TBD. | def set_pid(self, pid): # type: (int) -> None
for i in range(len(self.__target_pids)):
if self.__target_pids[i] == "$$TBD":
self.__target_pids[i] = pid
break | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def def_pid(self,pid):\n self.pid=int(pid)",
"def pid(self, pid):\n\n self._pid = pid",
"def pid(self, pid):\n\n self._pid = pid",
"def _update_PID(self):\n self.pid = PID(p=self.paramP, i=self.paramI, d=self.paramD, setpoint=self.voltageSetpoint, memory=self.paramMemory)",
"def... | [
"0.6045698",
"0.6005458",
"0.6005458",
"0.5414311",
"0.5351061",
"0.5235025",
"0.5235025",
"0.52202576",
"0.52202576",
"0.51928914",
"0.51871926",
"0.50754386",
"0.5040368",
"0.5033574",
"0.49666995",
"0.49178597",
"0.49152836",
"0.49008197",
"0.4899533",
"0.48833144",
"0.488... | 0.79088074 | 0 |
Infer class scores from the input image. This function defines the networks architecture. | def inference(self, image_rgb):
s = image_rgb.get_shape().as_list()
with tf.name_scope('image-preprocessing'):
MEAN = [103.939, 116.779, 123.68]
assert s[1:] == [227, 227, 3]
red, green, blue = tf.split(image_rgb, 3, 3)
bgr = tf.concat([
blue - MEAN[0],
green - MEAN[1],
red - MEAN[2],
], 3)
# 1st Layer: Conv (w ReLu) -> Pool -> Lrn
conv1 = conv(bgr, 11, 11, 96, 4, 4, padding='VALID', name='conv1')
pool1 = max_pool(conv1, 3, 3, 2, 2, padding='VALID', name='pool1')
norm1 = lrn(pool1, 2, 2e-05, 0.75, name='norm1')
# 2nd Layer: Conv (w ReLu) -> Pool -> Lrn
conv2 = conv(norm1, 5, 5, 256, 1, 1, name='conv2', groups=2)
pool2 = max_pool(conv2, 3, 3, 2, 2, padding='VALID', name='pool2')
norm2 = lrn(pool2, 2, 2e-05, 0.75, name='norm2')
# 3rd Layer: Conv (w ReLu)
conv3 = conv(norm2, 3, 3, 384, 1, 1, name='conv3')
# 4th Layer: Conv (w ReLu)
conv4 = conv(conv3, 3, 3, 384, 1, 1, name='conv4', groups=2)
# 5th Layer: Conv (w ReLu) -> Pool
conv5 = conv(conv4, 3, 3, 256, 1, 1, name='conv5', groups=2)
pool5 = max_pool(conv5, 3, 3, 2, 2, padding='VALID', name='pool5')
# 6th Layer: Flatten -> FC (w ReLu) -> Dropout
flattened = tf.reshape(pool5, [-1, 6*6*256])
fc6 = fc(flattened, 6*6*256, 4096, name='fc6')
dropout6 = dropout(fc6, self.KEEP_PROB)
# 7th Layer: FC (w ReLu) -> Dropout
fc7 = fc(dropout6, 4096, 4096, name='fc7')
dropout7 = dropout(fc7, self.KEEP_PROB)
# 8th Layer: FC and return unscaled activations (for tf.nn.softmax_cross_entropy_with_logits)
score_imagenet_classes = fc(dropout7, 4096, 1000, relu=False, name='fc8')
# New score layer for the new task (network is shared up to this point)
score_retrained = fc(dropout7, 4096, self.NUM_CLASSES, relu=False, name='fc8_new')
return score_imagenet_classes, score_retrained | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def classify_images():\n\n # Load the desired image\n img_path = 'dataset/colorize_images/n02085782_919.jpg'\n img = image.load_img(img_path, target_size=(299, 299))\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n\n model = InceptionV3(weights=\"imagen... | [
"0.6846357",
"0.658142",
"0.6509911",
"0.64659",
"0.6432261",
"0.6414406",
"0.6399443",
"0.6369863",
"0.63570625",
"0.6340332",
"0.6332008",
"0.6302619",
"0.6292333",
"0.6278609",
"0.62669504",
"0.6261403",
"0.62539554",
"0.62080777",
"0.6207685",
"0.61810887",
"0.6176677",
... | 0.0 | -1 |
Return the count of the highest order model used. | def order(self):
return self.n | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_last_order_number_used():\n return Order.__last_order_number_used",
"def _get_top_models_to_improve(self):\n self._validate_top_models_to_improve()\n if self.top_models_to_improve == \"auto\":\n if self._get_mode() == \"Explain\":\n return 0\n if ... | [
"0.6919758",
"0.64155287",
"0.63922703",
"0.6360874",
"0.62786955",
"0.622468",
"0.61927",
"0.6129268",
"0.6083907",
"0.60793793",
"0.6077859",
"0.5981426",
"0.5938052",
"0.57794553",
"0.5749997",
"0.57499874",
"0.57459366",
"0.573959",
"0.5728474",
"0.57043415",
"0.57005787"... | 0.0 | -1 |
Get the probability of a word following a context. i.e. The conditional probability P(word|context) | def prob(self, word, context=None):
if not context:
context = ()
else:
context = tuple(context)
prob = 0
for i in range(len(context) + 1):
prob += self.weights[i] * self.ngram_cpd[context[i:]][word]
return prob | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prob(self, word, context):\n\n context = tuple(context)\n \n context_lenth = len(context) \n if context_lenth == 0:\n line = ''\n elif context_lenth == 1:\n line = context[0]\n elif context_lenth >= 2:\n line = context[0]\n f... | [
"0.8106743",
"0.75592023",
"0.74971807",
"0.7463757",
"0.7168955",
"0.7168955",
"0.7081482",
"0.707541",
"0.69993883",
"0.69632804",
"0.6952478",
"0.6929444",
"0.6909919",
"0.67921746",
"0.6687317",
"0.6680557",
"0.66741383",
"0.66102266",
"0.6583568",
"0.6526557",
"0.6460864... | 0.8385041 | 0 |
Number of (nonbackground) categories. Returns int Number of (nonbackground) categories. | def num_class(self):
return self._num_class | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_number_of_categories() -> List[int]:\n\n return [len(cats) for cats in wiki_data[\"categories\"]]",
"def category_count(self, cat):\n res = self.con.execute('select count from cc where category=\"%s\"'\n %(cat)).fetchone()\n if res == None:\n return 0\n e... | [
"0.7708821",
"0.7072953",
"0.6978198",
"0.64453894",
"0.64185804",
"0.6267535",
"0.62063515",
"0.61772865",
"0.6093896",
"0.60898095",
"0.6053971",
"0.6049354",
"0.6049354",
"0.60457516",
"0.6007069",
"0.59920245",
"0.59676576",
"0.5959858",
"0.58949566",
"0.58928066",
"0.588... | 0.0 | -1 |
Return names of (nonbackground) categories. Returns iterable of str Names of (nonbackground) categories. | def classes(self):
return self._classes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def category_names(self):\n return list(self.categories.keys())",
"def category_names(self):\n return self._category_names",
"def CategoryNames(self):\r\n return sorted(self.getSampleMetadata(self.SampleIds[0]).keys()) \\\r\n if len(self.SampleIds) > 0 else []",
"def categorie... | [
"0.79573333",
"0.7846303",
"0.73073953",
"0.7111364",
"0.7110502",
"0.70963407",
"0.70941347",
"0.7074836",
"0.6971838",
"0.6940362",
"0.6827828",
"0.68070495",
"0.67111456",
"0.66973966",
"0.6610056",
"0.6607805",
"0.65921",
"0.65612626",
"0.6545443",
"0.653917",
"0.65071493... | 0.0 | -1 |
YOLOV3 network hybrid forward. | def hybrid_forward(self, F, x, *args):
all_box_centers = []
all_box_scales = []
all_objectness = []
all_class_pred = []
all_anchors = []
all_offsets = []
all_feat_maps = []
all_detections = []
routes = []
for stage, block, output in zip(self.stages, self.yolo_blocks, self.yolo_outputs):
x = stage(x)
routes.append(x)
# the YOLO output layers are used in reverse order, i.e., from very deep layers to shallow
for i, block, output in zip(range(len(routes)), self.yolo_blocks, self.yolo_outputs):
x, tip = block(x)
if autograd.is_training():
dets, box_centers, box_scales, objness, class_pred, anchors, offsets = output(tip)
all_box_centers.append(box_centers.reshape((0, -3, -1)))
all_box_scales.append(box_scales.reshape((0, -3, -1)))
all_objectness.append(objness.reshape((0, -3, -1)))
all_class_pred.append(class_pred.reshape((0, -3, -1)))
all_anchors.append(anchors)
all_offsets.append(offsets)
# here we use fake featmap to reduce memory consuption, only shape[2, 3] is used
fake_featmap = F.zeros_like(tip.slice_axis(
axis=0, begin=0, end=1).slice_axis(axis=1, begin=0, end=1))
all_feat_maps.append(fake_featmap)
else:
dets = output(tip)
all_detections.append(dets)
if i >= len(routes) - 1:
break
# add transition layers
x = self.transitions[i](x)
# upsample feature map reverse to shallow layers
upsample = _upsample(x, stride=2)
route_now = routes[::-1][i + 1]
x = F.concat(F.slice_like(upsample, route_now * 0, axes=(2, 3)), route_now, dim=1)
if autograd.is_training():
# during training, the network behaves differently since we don't need detection results
if autograd.is_recording():
# generate losses and return them directly
box_preds = F.concat(*all_detections, dim=1)
all_preds = [F.concat(*p, dim=1) for p in [
all_objectness, all_box_centers, all_box_scales, all_class_pred]]
all_targets = self._target_generator(box_preds, *args)
return self._loss(*(all_preds + all_targets))
# return raw predictions, this is only used in DataLoader transform function.
return (F.concat(*all_detections, dim=1), all_anchors, all_offsets, all_feat_maps,
F.concat(*all_box_centers, dim=1), F.concat(*all_box_scales, dim=1),
F.concat(*all_objectness, dim=1), F.concat(*all_class_pred, dim=1))
# concat all detection results from different stages
result = F.concat(*all_detections, dim=1)
# apply nms per class
if self.nms_thresh > 0 and self.nms_thresh < 1:
result = F.contrib.box_nms(
result, overlap_thresh=self.nms_thresh, valid_thresh=0.01,
topk=self.nms_topk, id_index=0, score_index=1, coord_start=2, force_suppress=False)
if self.post_nms > 0:
result = result.slice_axis(axis=1, begin=0, end=self.post_nms)
ids = result.slice_axis(axis=-1, begin=0, end=1)
scores = result.slice_axis(axis=-1, begin=1, end=2)
bboxes = result.slice_axis(axis=-1, begin=2, end=None)
return ids, scores, bboxes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, x):\n for name, module in self.base._modules.items():\n if name == 'avgpool':\n break\n\n if name == 'layer3':\n l2 = Variable(x)\n\n x = Variable(module(x))\n l4 = Variable(x)\n\n \"\"\"for name, param in sel... | [
"0.65314513",
"0.6292418",
"0.6263829",
"0.6261159",
"0.62541807",
"0.6251553",
"0.6219152",
"0.62162906",
"0.61927277",
"0.6178997",
"0.6141839",
"0.6136382",
"0.6127676",
"0.6119838",
"0.6111306",
"0.61022437",
"0.6099669",
"0.60930973",
"0.60845137",
"0.60679704",
"0.60615... | 0.7524717 | 0 |
Set nonmaximum suppression parameters. | def set_nms(self, nms_thresh=0.45, nms_topk=400, post_nms=100):
self._clear_cached_op()
self.nms_thresh = nms_thresh
self.nms_topk = nms_topk
self.post_nms = post_nms | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_suppress_flow(self):\n self.suppressed = self.packet_count\n self.fcip.update_one({'hash': self.fcip_hash},\n {'$set': {'suppressed': self.suppressed},})",
"def non_maxima_suppression(boxes, probs, classes_num, thr=0.2):\n for i, box in enumerate(boxes):\n ... | [
"0.5966529",
"0.55709445",
"0.55309284",
"0.5446378",
"0.5345884",
"0.5345884",
"0.5295788",
"0.52417874",
"0.5239064",
"0.52170867",
"0.52112365",
"0.520788",
"0.5186509",
"0.51748765",
"0.51727885",
"0.5166071",
"0.510891",
"0.50601727",
"0.5058307",
"0.50549585",
"0.504755... | 0.59055877 | 1 |
Reset class categories and class predictors. | def reset_class(self, classes):
self._clear_cached_op()
self._classes = classes
if self._pos_iou_thresh >= 1:
self._target_generator = YOLOV3TargetMerger(len(classes), self._ignore_iou_thresh)
for outputs in self.yolo_outputs:
outputs.reset_class(classes) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n self.pred_classes.clear()\n self.gold_classes.clear()\n self.pred_probas.clear()\n self.gold_probas.clear()\n self.loss = 0\n self.nb_batches = 0\n self.prec_rec_f1 = None\n self.acc = None\n self.mcc = None",
"def reset(self):\n ... | [
"0.72172534",
"0.6899433",
"0.6748963",
"0.6726632",
"0.66773576",
"0.6629043",
"0.6529936",
"0.6484326",
"0.6478405",
"0.6478405",
"0.6471619",
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"0.62709737",
"0.6202787",
"0.61770207",
"0.6149169",
"0.612528",
"0.60959744",
"0.6091237",
"0.6087531",... | 0.70744306 | 1 |
YOLO3 multiscale with darknet53 base network on VOC dataset. | def yolo3_darknet53_voc(pretrained_base=True, pretrained=False, num_sync_bn_devices=-1, **kwargs):
from ...data import VOCDetection
pretrained_base = False if pretrained else pretrained_base
base_net = darknet53(
pretrained=pretrained_base, num_sync_bn_devices=num_sync_bn_devices, **kwargs)
stages = [base_net.features[:15], base_net.features[15:24], base_net.features[24:]]
anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
strides = [8, 16, 32]
classes = VOCDetection.CLASSES
return get_yolov3(
'darknet53', stages, [512, 256, 128], anchors, strides, classes, 'voc',
pretrained=pretrained, num_sync_bn_devices=num_sync_bn_devices, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n # TODO\n self.confThreshold = 0.6\n self.nmsThreshold = 0.5\n self.inpWidth = 320\n self.inpHeight = 320\n classesFile = \"/content/drive/My Drive/tracking_course/Detection/yolo_workshop/coco.names\"\n self.classes = None\n with open(cla... | [
"0.6369418",
"0.6306215",
"0.6232091",
"0.6182974",
"0.60732025",
"0.5921903",
"0.5916691",
"0.5855269",
"0.5853199",
"0.5839653",
"0.57973075",
"0.5778595",
"0.574271",
"0.573733",
"0.57194585",
"0.5616862",
"0.55473757",
"0.5541811",
"0.554155",
"0.5500795",
"0.5457074",
... | 0.6497924 | 0 |
Update information about a host | def update(self, compute_node=None, service=None):
@utils.synchronized((self.hostname, compute_node))
def _locked_update(self, compute_node, service):
if compute_node is not None:
LOG.debug('Update host state from compute node: %s',
compute_node)
self._update_from_compute_node(compute_node)
if service is not None:
LOG.debug('Update host state with service: %s', service)
self.service = service
return _locked_update(self, compute_node, service) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update(self, host):\n pass",
"def update_host(self, conf, tenant_id, network_id, host_id, body):\n\t\tpass",
"def update(self, host, values):\n body = dict(host=values)\n return self._update(\"/os-hosts/%s\" % host, body, response_key='host')",
"def update(self, **kwargs):\n\n ... | [
"0.837656",
"0.7654533",
"0.71974707",
"0.7100948",
"0.69615793",
"0.68412817",
"0.67987186",
"0.674337",
"0.6699968",
"0.6577405",
"0.6523611",
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"0.6492715",
"0.63358724",
"0.63345975",
"0.62820405",
"0.62123007",
"0.62056744",
"0.617595",
"0.61610746",
"0.616107... | 0.0 | -1 |
Update information about a host from a Compute object | def _update_from_compute_node(self, compute_node):
if (self.updated and compute_node.updated_at and
self.updated > compute_node.updated_at):
return
self.uuid = compute_node.rp_uuid
self.mem_available = compute_node.mem_available
self.mem_total = compute_node.mem_total
self.mem_free = compute_node.mem_free
self.mem_used = compute_node.mem_used
self.cpus = compute_node.cpus
self.cpu_used = compute_node.cpu_used
self.disk_total = compute_node.disk_total
self.disk_used = compute_node.disk_used
self.numa_topology = compute_node.numa_topology
self.labels = compute_node.labels
self.pci_stats = pci_stats.PciDeviceStats(
stats=compute_node.pci_device_pools)
self.disk_quota_supported = compute_node.disk_quota_supported
self.runtimes = compute_node.runtimes
self.enable_cpu_pinning = compute_node.enable_cpu_pinning
self.updated = compute_node.updated_at | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update(self, host):\n pass",
"def update_host(self, conf, tenant_id, network_id, host_id, body):\n\t\tpass",
"def update(self, **kwargs):\n\n host = self.get()\n if not host:\n self.raiseNotFoundError()\n return host.update(**kwargs)",
"def update(self, host, value... | [
"0.7401245",
"0.7244819",
"0.67560756",
"0.6739064",
"0.63846487",
"0.63500065",
"0.6329535",
"0.62974095",
"0.61992675",
"0.6124308",
"0.60235953",
"0.6017592",
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"0.5938988",
"0.59209824",
"0.58763146",
"0.58476484",
"0.5826684",
"0.5826684",
"0.582578",
"0.573376... | 0.5865002 | 16 |
Incrementally update host state from a Container object. | def consume_from_request(self, container):
@utils.synchronized(self._lock_name)
@set_update_time_on_success
def _locked(self, container):
# Scheduler API is inherently multi-threaded as every incoming RPC
# message will be dispatched in its own green thread. So the
# shared host state should be consumed in a consistent way to make
# sure its data is valid under concurrent write operations.
self._locked_consume_from_request(container)
return _locked(self, container) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _increment(state: Dict, host: str = None) -> Dict:\n logger.info(f'view-count@{host}: {state[host] + 1}')\n\n return {\n **state,\n host: state[host] + 1\n }",
"def _inc_counter(self) -> None:\n self._state_storage.increment_counter()",
"def _update_container(self):\n c... | [
"0.6660293",
"0.6045445",
"0.561173",
"0.5611655",
"0.5515509",
"0.54336005",
"0.5417022",
"0.5400421",
"0.53642195",
"0.53447664",
"0.5336348",
"0.5238454",
"0.5179859",
"0.5147485",
"0.5103677",
"0.5096942",
"0.50933737",
"0.50852305",
"0.50512236",
"0.50192106",
"0.4984766... | 0.0 | -1 |
Set updated time of HostState when consuming succeed. | def set_update_time_on_success(function):
@functools.wraps(function)
def decorated_function(self, container):
return_value = None
try:
return_value = function(self, container)
except Exception as e:
# Ignores exception raised from consume_from_request() so that
# booting container would fail in the resource claim of compute
# node, other suitable node may be chosen during scheduling retry.
LOG.warning("Selected host: %(host)s failed to consume from "
"container. Error: %(error)s",
{'host': self.hostname, 'error': e})
else:
self.updated = timeutils.utcnow()
return return_value
return decorated_function | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def host_up(self, host):\n with self.cond:\n if self.state is not None:\n LOG.warning(\"host_up called, but we think host is already up\")\n self._host_down()\n\n # Wait until all operations using a previous state generation are\n # complete bef... | [
"0.6223716",
"0.61008537",
"0.6037427",
"0.5854355",
"0.568859",
"0.5629717",
"0.56151396",
"0.5608436",
"0.5571809",
"0.5568824",
"0.55633",
"0.5542896",
"0.5542896",
"0.5508925",
"0.549049",
"0.5477234",
"0.5477234",
"0.54645914",
"0.54598725",
"0.5455025",
"0.54405177",
... | 0.5197365 | 64 |
The uri returned from request.uri is not properly urlencoded (sometimes it's partially urldecoded) This is a weird hack to get werkzeug to return the proper urlencoded string uri | def _get_uri_from_request(request):
uri = request.base_url
if request.query_string:
uri += '?' + request.query_string.decode('utf-8')
return uri | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _urlnorm(self, uri):\r\n (scheme, authority, path, query, fragment) = parse_uri(uri)\r\n if not scheme or not authority:\r\n raise Exception(\"Only absolute URIs are allowed. uri = %s\" % uri)\r\n authority = authority.lower()\r\n scheme = scheme.lower()\r\n if not... | [
"0.67314285",
"0.65002495",
"0.6443756",
"0.6264452",
"0.6258882",
"0.6235578",
"0.6230169",
"0.6225036",
"0.61642367",
"0.6159104",
"0.61166435",
"0.6091703",
"0.60887986",
"0.6040494",
"0.602881",
"0.6023126",
"0.60162497",
"0.6009078",
"0.6009078",
"0.5996785",
"0.5991015"... | 0.68531525 | 0 |
Defines, parses, checks, and returns command line arguments | def parse_arguments():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--adj', help=""".adj file regulon""",
type=argparse.FileType(mode='r'), required=True)
parser.add_argument('--expr_genes', help="""list of gene IDs in expression
matrix (first column of expr matrix)""",
type=argparse.FileType(mode='r'), required=True)
# parser.add_argument('--cutoff_percent', help="""remove entire row (regulator plus genes)
# if percent (out of 100) of genes remaining is below this value AND # genes remaining is below
# the cutoff_number argument""", type=int, required=False, default=30)
parser.add_argument('--cutoff_number', help=""""remove entire row (regulator plus regulon genes)
if number of genes remaining is below this value, defaults to 25""", type=int, required=False, default=25)
args = parser.parse_args()
return args | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_arguments(args):",
"def command_line_arguments():\n\n try:\n parser = argparse.ArgumentParser(description='Log Handler/Cleaner/Copier for Idemia DocAuth')\n\n # Add required arguments.\n parser.add_argument('action', choices=['clean', 'download'], type=str, help='clean or downlo... | [
"0.76032376",
"0.7410202",
"0.7344581",
"0.7313167",
"0.7288181",
"0.7207524",
"0.7184706",
"0.71445954",
"0.71318215",
"0.7104654",
"0.70891064",
"0.70799434",
"0.7070761",
"0.7066492",
"0.704149",
"0.70361483",
"0.7026928",
"0.7025998",
"0.70166886",
"0.7002696",
"0.7000962... | 0.0 | -1 |
Parse arguments, filter regulon, and print to stdout. | def main():
# get commmand line args
args = parse_arguments()
adj_file = args.adj # open("UCSC_VIPER/pathways/extended_pathways_transcriptional.adj", "r")
# this set isn't actually used in the script, but I was curious...
adj_gene_set = set()
cutoff_number = args.cutoff_number
#cutoff_percent = args.cutoff_percent
expr_gene_file = args.expr_genes #open("stanford_batchK1-12.HUGO_only_genes.lst", 'r')
expr_genes = [line.strip() for line in expr_gene_file]
# for each line, check that the regulator and other genes are in the
# expression matrix gene set. if not, remove them, or remove the whole
# line if the regulator isn't in the set or if too few genes remain
for line in adj_file:
line_list = line.strip().split()
regulator_plus_gene_list = [x for x in line_list if x !="1.0"]
regulator = regulator_plus_gene_list[0]
if regulator not in expr_genes:
# remove the whole regulator + regulon
print("Skipped a line (regulator not in expr genes): ",
line_list[0], file=sys.stderr)
continue
gene_list = regulator_plus_gene_list[1:]
list_size = len(gene_list)
adj_gene_set.update(gene_list)
how_many_to_remove= 0
good_genes = []
for gene in gene_list:
if gene not in expr_genes:
how_many_to_remove += 1
else:
good_genes.append(gene)
#print("\n")
#pdb.set_trace()
#if (100-how_many_to_remove/list_size*100 < cutoff_percent) and (list_size-how_many_to_remove < cutoff_number):
if (list_size-how_many_to_remove < cutoff_number):
print("Skipped a line (too many removed): ", line_list[0], file=sys.stderr)
else:
# re-generate the new line of the .adj file with kept genes
#genes_to_print = good_genes.insert(0, regulator)
regulated_genes = "\t1.0\t".join(good_genes)
print(regulator+"\t"+regulated_genes+"\t1.0") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')",
"def main():\n filter_freq = 1.e4\n re_sample_freq = 1.e5\n glob_search = '*.log'\n\n # parse the command line arguments\n parser = argparse.ArgumentParser(description=\"Filters files in a directory based on a file e... | [
"0.6211614",
"0.6186331",
"0.6099759",
"0.60936326",
"0.60703206",
"0.5910186",
"0.5906957",
"0.5856465",
"0.5850204",
"0.58114266",
"0.577527",
"0.5722053",
"0.57207435",
"0.56683946",
"0.56594044",
"0.5633427",
"0.55837566",
"0.5580852",
"0.55734587",
"0.5561661",
"0.555925... | 0.0 | -1 |
Converts the Csv file into required data format for Time Series prediction | def preprocess(dataframe_csvpath, cols_x, cols_y, window_in, window_out, data_div_frac, popu_size):
#Loading .CSV file and creating dataframe
df = pd.read_csv(dataframe_csvpath)
len_ser = len(df[df['Series_No'] == 1])
#randomly shuffle different series
permute = np.random.permutation(range(1, len(set(df['Series_No']))))
train_series_seq = permute[: int(len(set(df['Series_No'])) * data_div_frac)]
test_series_seq = permute[int( len(set(df['Series_No'])) * data_div_frac):]
#taking relevent columns from dataframe
df_x = df[cols_x]
df_y = df[cols_y]
#Innitialize empty lists which are later to be appended
train_seq, test_seq = [], []
x_test = []
y_true =[]
#Creating time series data
for series_no in train_series_seq:
#new dataframe variable assignment for particular series drom df_x, df_y
series_df_x = df_x[df_x['Series_No'] == series_no]
series_df_y = df_x[df_y['Series_No'] == series_no]
#converting into numpy arrays
array_x = np.array(series_df_x)
array_y = np.array(series_df_y)
#for loop to append to x_train y_train arrays according to window_in, window_out
for idx in range(len(series_df_x) - window_in - window_out + 1): #'len(series_df_x) - window_in - window_out + 1' needs to be checked
arrayx = array_x.copy()
x = arrayx [idx:idx + window_in, : len(cols_x) - 1]
#print(x)
x[:,0:3] = x[:,0:3] / popu_size
#print(x)
arrayy = array_y.copy()
y = arrayy[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1]
y = y / popu_size
train_seq.append((x, y)) #out col_x and col_y has last item 'Series number' so to remove that [, : len(cols_x)]
#y_train.append(array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])
#print(train_seq)
#repeat for test sequence
for series_no in test_series_seq:
#new dataframe variable assignment for particular series drom df_x, df_y
series_df_x = df_x[df_x['Series_No'] == series_no]
series_df_y = df_x[df_y['Series_No'] == series_no]
#converting into numpy arrays
array_x = np.array(series_df_x)
array_y = np.array(series_df_y)
#for loop to append to x_train y_train arrays according to window_in, window_out
for idx in range(len(series_df_x) - window_in - window_out + 1): #'len(series_df_x) - window_in - window_out + 1' needs to be checked
arrayx = array_x.copy()
x = arrayx[idx:idx + window_in, : len(cols_x) - 1]
x[:,0:3] = x[:,0:3] / popu_size
x_test.append(x)
arrayy = array_y.copy()
y = arrayy[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1]
y = y / popu_size
y_true.append(y)
test_seq.append((x, y))
#test_seq.append((array_x[idx:idx + window_in, : len(cols_x) - 1], array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])) #out col_x and col_y has last item 'Series number' so to remove that [, : len(cols_x)]
#y_test.append(array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])
win_len_per_ser = len_ser - window_in - window_out + 1
return np.array(train_seq), np.array(test_seq), len_ser, win_len_per_ser, np.array(x_test), np.array(y_true) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_training_data(self, csv_path):\n data = pd.read_csv(csv_path)\n data[['Hashrate', 'Addresses', 'Supply', 'Trx_Fee', 'Daily_Trx']] = data[['Hashrate', 'Addresses', 'Supply', 'Trx_Fee', 'Daily_Trx']].apply(pd.to_numeric)\n data[['Timestamp']] = data[['Timestamp']].apply(pd.to_datetime)\n... | [
"0.6448471",
"0.64109224",
"0.63845986",
"0.6249481",
"0.6204732",
"0.6172775",
"0.6129716",
"0.61131644",
"0.6100166",
"0.6092652",
"0.6074208",
"0.60713947",
"0.6030789",
"0.6018793",
"0.60126376",
"0.59962356",
"0.59944284",
"0.5981371",
"0.5959057",
"0.5953626",
"0.593562... | 0.0 | -1 |
Visualize a particular column of Y_pred anf Y_test for a particular series | def visualize_data(y_test, x_test, window_out, num_plots, num_win_ser, cols_y, col_idx):
ser_idx = [i for i in range(0, len(y_test), num_win_ser)]
if num_plots > len(ser_idx):
print("Too many plots, reduce the mumber")
else:
indx = ser_idx[0:num_plots]
days = range(num_win_ser)
for idx in indx:
CR = x_test[idx][0][3]
#pred = y_pred[idx : idx+num_win_ser, window_out -1, col_idx]
true = y_test[idx : idx+num_win_ser, window_out -1, col_idx]
plt.title("Y_True, CR: "+ str(CR))
plt.xlabel('Days')
plt.ylabel(cols_y[col_idx])
#plt.plot(days, pred, label = 'Pred')
plt.plot(days, true, label = 'True')
plt.legend()
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_pred(y_test, y_pred, test_seq, window_out, num_plots, num_win_ser, cols_y, col_idx):\n \n \n ser_idx = [i for i in range(0, len(y_test), num_win_ser)]\n if num_plots > len(ser_idx):\n print(\"Too many plots, reduce the mumber\")\n else:\n indx = ser_idx[0:num_plots]\n ... | [
"0.6890531",
"0.65764153",
"0.6521914",
"0.6249297",
"0.61620384",
"0.61183745",
"0.60831374",
"0.60805416",
"0.60410786",
"0.6039407",
"0.60239905",
"0.6023398",
"0.6006061",
"0.5991418",
"0.5943231",
"0.593769",
"0.59314346",
"0.59314346",
"0.5888226",
"0.58814496",
"0.5877... | 0.6406863 | 3 |
Makes Predictions for time series data using the model trained. | def predictions(loader, model, win_len_per_ser, criterion, device, window_out = 1 ):
model.eval()
num_win_per_ser = win_len_per_ser #num windows
#print(num_win_per_ser)
y_pred = []
y_true = []
with torch.no_grad():
for idx, (x, y) in enumerate(loader): #for i in range(0, len(y_test), num_win_per_ser): # i takes index values of first windows of different series
win_start = torch.tensor(x[0]).float().to(device) # saving the first window of each series
#print('win_start:', win_start)
CR = win_start[0][3] # saving the CR value for particular series -> to be used for prediction
#print('CR:', CR)
win = win_start # window variable which will be updated for new windows, takes first value as the starting window
#print(win)
win = win.reshape((1, win.shape[0], win.shape[1]))
#print(win.shape)
for j in range(num_win_per_ser): # prediction loop
y_hat = model(win) # predicting values wrt win variable
#print('y_hat ', y_hat)
y_pred.append(y_hat[0].cpu().detach().numpy()) # add the value to y_pred
#print('y_pred:', y_pred)
y_true.append(y[j].cpu().detach().numpy()) # add the value to y_pred
#print('y_true:', y_true)
cr_dummy = torch.empty((1, window_out, 1), dtype=torch.float32).to(device)
y_hat = torch.cat((y_hat, cr_dummy.fill_(float(CR))), 2).float()
#y_hat = tf.concat([y_hat, tf.fill(dims = (1, window_out, 1), value = CR)], axis = 2) # adding CR value y_hat for furter predictions
#print('cr added to y_hat', y_hat)
win = torch.cat((win, y_hat), 1)
#win = tf.concat([win, y_hat], axis = 1) # adding our prediction to win
#print('win', win)
win = win[:,window_out:,:] # updating win by removing the starting elements
#print('new_win for next iter', win)
y_pred = torch.tensor(y_pred).to(device)
y_true = torch.tensor(y_true).to(device)
assert (y_pred.shape == y_true.shape)
mae = criterion(y_pred, y_true)
#mae = tf.reduce_sum(tf.keras.metrics.mean_absolute_error(y_pred, y_test))
print(f'The error is: " {mae: .5f}')
model.train()
return y_pred.cpu().detach().numpy(), y_true.cpu().detach().numpy(), mae.cpu().detach().numpy() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_predict(train_file_arg):\n\n # Load command line arguments\n train_file = train_file_arg\n parameter_file = \"prophet/lstm/trainingConfig.json\"\n\n # Load training parameters\n params = json.loads(open(parameter_file).read())\n\n # Load time series dataset, and split it into train and ... | [
"0.6817948",
"0.66349447",
"0.6473964",
"0.6437722",
"0.6398785",
"0.63897544",
"0.6377936",
"0.6365075",
"0.6336138",
"0.62935156",
"0.62524164",
"0.6220017",
"0.6206941",
"0.6185454",
"0.61810833",
"0.6175819",
"0.6175819",
"0.6175819",
"0.6175819",
"0.6175819",
"0.61737525... | 0.0 | -1 |
Visualize a particular column of Y_pred anf Y_test for a particular series | def visualize_pred(y_test, y_pred, test_seq, window_out, num_plots, num_win_ser, cols_y, col_idx):
ser_idx = [i for i in range(0, len(y_test), num_win_ser)]
if num_plots > len(ser_idx):
print("Too many plots, reduce the mumber")
else:
indx = ser_idx[0:num_plots]
days = range(num_win_ser)
for idx in indx:
CR = test_seq[idx][0][0][3]
pred = y_pred[idx : idx+num_win_ser, window_out -1, col_idx]
true = y_test[idx : idx+num_win_ser, window_out -1, col_idx]
plt.title("Y_True V/S Y_Pred, CR: "+ str(CR))
plt.xlabel('Days')
plt.ylabel(cols_y[col_idx])
plt.plot(days, pred, label = 'Pred')
plt.plot(days, true, label = 'True')
plt.legend()
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def actual_pred_plot(preds):\r\n actual_pred = pd.DataFrame(columns=['Cost', 'prediction'])\r\n actual_pred['Cost'] = all_data['2020':].iloc[:, -1][1:len(preds) + 1]\r\n actual_pred['prediction'] = preds[:, -1]\r\n\r\n from keras.metrics import MeanSquaredError\r\n m = MeanSquaredError()\r\n m.up... | [
"0.6575989",
"0.65215456",
"0.6404881",
"0.62492937",
"0.6163107",
"0.61196136",
"0.60816854",
"0.6080583",
"0.6039545",
"0.6038659",
"0.6025292",
"0.60227543",
"0.6006382",
"0.59930116",
"0.5945069",
"0.5939615",
"0.5928846",
"0.5928846",
"0.5887865",
"0.5881625",
"0.5878290... | 0.6889194 | 0 |
Mock out the tkinter canvas and all graphics | def setUp(self):
self.screentype_patcher = mock.patch(
'turtle._Screen',
new=mock.Mock
)
self.mock_screentype = self.screentype_patcher.start()
self.screen_patcher = mock.patch('turtle.Turtle._screen')
self.mock_screen = self.screen_patcher.start()
self.mock_screen.xscale = 1.0
self.mock_screen.yscale = 1.0
self.mock_screen.mode.return_value = 'standard'
self.update_patcher = mock.patch(
'aioturtle.aioturtle.AioBaseTurtle._update_graphics'
)
self.mock_update = self.update_patcher.start() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_canvas(self):\n # create frame to contain canvas\n self.world_container = tk.Frame(self,\n width = self.world_size[1],\n height = self.world_size[0])\n self.world_container.grid(row = 1, column = 0, sticky ... | [
"0.6882413",
"0.67479193",
"0.6690179",
"0.64564663",
"0.64564663",
"0.64564663",
"0.64194477",
"0.6331927",
"0.626784",
"0.62565655",
"0.61583567",
"0.6147961",
"0.6143228",
"0.6132862",
"0.6108119",
"0.609335",
"0.603408",
"0.6019643",
"0.59988374",
"0.59641194",
"0.5958032... | 0.5550649 | 59 |
Test the AioBaseTurtle._calc_move function | def test_calc_move(self):
t = AioBaseTurtle()
t.speed(speed=5)
steps, delta = t._calc_move(Vec2D(0, 100))
self.assertEqual(steps, 20)
self.assertAlmostEqual(delta[0], 0.0)
self.assertAlmostEqual(delta[1], 5.0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move_step(self):\n t = AioBaseTurtle()\n t._move_step(Vec2D(-100, 0), 20, Vec2D(10,5))\n self.assertAlmostEqual(t._position[0], 100)\n self.assertAlmostEqual(t._position[1], 100)\n t.screen._drawline.assert_called_once_with(\n t.currentLineItem,\n (... | [
"0.77467954",
"0.67991954",
"0.67526376",
"0.6682421",
"0.6611246",
"0.66024905",
"0.65450394",
"0.65094215",
"0.6458481",
"0.6436995",
"0.6424645",
"0.64221406",
"0.6376048",
"0.63608104",
"0.6327564",
"0.63082105",
"0.6292444",
"0.62719584",
"0.62709236",
"0.62575185",
"0.6... | 0.88943046 | 0 |
Test the AioBaseTurtle._calc_rotation function | def test_calc_rotation(self):
t = AioBaseTurtle()
t.speed(speed=2)
orient, steps, delta = t._calc_rotation(120)
self.assertEqual(steps, 21)
self.assertAlmostEqual(delta, 120.0 / 21.0)
self.assertAlmostEqual(orient[0], math.cos(math.radians(120)))
self.assertAlmostEqual(orient[1], math.sin(math.radians(120))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_rotation_angle(self):\n\n self.test_shape.azimuth_placement_angle = [45, 135, 225, 315]\n test_volume = self.test_shape.volume()\n self.test_shape.rotation_angle = 180\n assert self.test_shape.volume() == pytest.approx(test_volume * 0.5)",
"def test_rotation(self, tol):\n ... | [
"0.72649866",
"0.71997005",
"0.7070056",
"0.6858857",
"0.67564845",
"0.6559771",
"0.65583205",
"0.6543984",
"0.65229213",
"0.6519182",
"0.64923644",
"0.64011544",
"0.63578784",
"0.632081",
"0.62979436",
"0.62914723",
"0.62909883",
"0.62908113",
"0.6243588",
"0.62338036",
"0.6... | 0.91761756 | 0 |
Test the AioBaseTurtle._calc_circle function | def test_calc_circle(self):
t = AioBaseTurtle()
steps, step_len, rot_step = t._calc_circle(100, extent=180)
self.assertEqual(steps, 14)
self.assertAlmostEqual(rot_step, 180.0 / 14.0)
self.assertAlmostEqual(step_len, 22.3928952207) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_circle(c):\n turtle.circle(c.radius)",
"def draw_circle(c):\n turtle.circle(c.radius)",
"def GetCircle(circle):\r\n pass",
"def test_get_radius():\n center = Coordinates(7, 3)\n radius = 12\n\n returned_rad = get_radius(center, radius, 30)\n\n assert returned_rad == radius\n... | [
"0.734142",
"0.734142",
"0.72636837",
"0.703413",
"0.6813236",
"0.6676832",
"0.6654256",
"0.66349846",
"0.6573545",
"0.65500736",
"0.6521824",
"0.6442423",
"0.64218247",
"0.6412573",
"0.63668287",
"0.63393223",
"0.6279476",
"0.6262953",
"0.6237789",
"0.6233661",
"0.6233039",
... | 0.8547868 | 0 |
Test the AioBaseTurtle._move_step function | def test_move_step(self):
t = AioBaseTurtle()
t._move_step(Vec2D(-100, 0), 20, Vec2D(10,5))
self.assertAlmostEqual(t._position[0], 100)
self.assertAlmostEqual(t._position[1], 100)
t.screen._drawline.assert_called_once_with(
t.currentLineItem,
((-100.0, 0.0), (100.0, 100.0)), # called with mutable _position
"black",
1,
False
)
self.mock_update.assert_called_once_with() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def step(self, move):",
"def test_calc_move(self):\n t = AioBaseTurtle()\n t.speed(speed=5)\n steps, delta = t._calc_move(Vec2D(0, 100))\n self.assertEqual(steps, 20)\n self.assertAlmostEqual(delta[0], 0.0)\n self.assertAlmostEqual(delta[1], 5.0)",
"def move(self, dire... | [
"0.7820133",
"0.7763472",
"0.6788755",
"0.6762144",
"0.6700953",
"0.66414285",
"0.6528104",
"0.651918",
"0.6467042",
"0.644896",
"0.6419937",
"0.639203",
"0.637763",
"0.63649815",
"0.6354341",
"0.63443154",
"0.63338375",
"0.63002443",
"0.6289629",
"0.62895745",
"0.62881094",
... | 0.8622489 | 0 |
run the tesselation on a empty image | def test_on_map_of_0s(synthetic_checkerboard):
img = synthetic_checkerboard['img']
di = np.zeros_like(img)
cpp_vorimg = tess.tessellate_labimg(img,di)
py_vorimg = pytess.tessellate_labimg(img,di)
assert np.alltrue(py_vorimg[:4,:4] == 1)
printers.store_ndarray("py_voronoi_on_map_of_0s_output.txt",py_vorimg)
assert cpp_vorimg.size > 0
assert cpp_vorimg.shape == synthetic_checkerboard['img'].shape
assert np.alltrue(synthetic_checkerboard['img'][1:3,1:3] == 1)
printers.store_ndarray("cpp_voronoi_input.txt",img)
printers.store_ndarray("cpp_voronoi_on_map_of_0s_output.txt",cpp_vorimg)
assert np.alltrue(cpp_vorimg[:4,:4] == 1)
assert np.alltrue(cpp_vorimg == py_vorimg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_stuff(self):\n self.create_tourism_raster()",
"def write_stitched_image(self):\r\n\r\n self.write_debug(\"End of train detected. Writing stitched image.\")\r\n cv2.imwrite(os.path.join(self.output_dir_stitched, 'stitched.jpg'), self.stitched_image)",
"def final_plain():\n\n\tconfig ... | [
"0.61748016",
"0.5833619",
"0.57900006",
"0.57824117",
"0.5640208",
"0.5631252",
"0.56215304",
"0.56194323",
"0.5608988",
"0.56024307",
"0.5585959",
"0.55422133",
"0.5494821",
"0.54943854",
"0.54853487",
"0.5449842",
"0.54339325",
"0.54300725",
"0.54207504",
"0.5395208",
"0.5... | 0.0 | -1 |
run the tesselation on a empty image | def test_on_map_of_constants(synthetic_checkerboard):
img = synthetic_checkerboard['img']
di = synthetic_checkerboard['cdi']
cpp_vorimg = tess.tessellate_labimg(img,di)
py_vorimg = pytess.tessellate_labimg(img,di)
assert np.alltrue(py_vorimg[:4,:4] == 1)
printers.store_ndarray("py_voronoi_on_map_of_constants_output.txt",py_vorimg)
assert cpp_vorimg.size > 0
assert cpp_vorimg.shape == synthetic_checkerboard['img'].shape
assert np.alltrue(synthetic_checkerboard['img'][1:3,1:3] == 1)
printers.store_ndarray("cpp_voronoi_input.txt",img)
printers.store_ndarray("cpp_voronoi_on_map_of_constants_output.txt",cpp_vorimg)
assert np.alltrue(cpp_vorimg[:4,:4] == 1)
assert np.alltrue(cpp_vorimg == py_vorimg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_stuff(self):\n self.create_tourism_raster()",
"def write_stitched_image(self):\r\n\r\n self.write_debug(\"End of train detected. Writing stitched image.\")\r\n cv2.imwrite(os.path.join(self.output_dir_stitched, 'stitched.jpg'), self.stitched_image)",
"def final_plain():\n\n\tconfig ... | [
"0.6173433",
"0.5833571",
"0.5788354",
"0.5786499",
"0.56434995",
"0.56299114",
"0.56189775",
"0.5616973",
"0.5609669",
"0.5601143",
"0.5585079",
"0.55399793",
"0.549523",
"0.5494799",
"0.5486404",
"0.5449044",
"0.5434429",
"0.543015",
"0.5419234",
"0.53946966",
"0.5386449",
... | 0.0 | -1 |
run the tesselation on a empty image | def test_on_map_of_sinus(synthetic_checkerboard):
img = synthetic_checkerboard['img']
di = synthetic_checkerboard['sindi']
cpp_vorimg = tess.tessellate_labimg(img,di)
py_vorimg = pytess.tessellate_labimg(img,di)
assert np.alltrue(py_vorimg[:4,:4] == 1)
printers.store_ndarray("py_voronoi_on_map_of_sinus_output.txt",py_vorimg)
assert cpp_vorimg.size > 0
assert cpp_vorimg.shape == synthetic_checkerboard['img'].shape
assert np.alltrue(synthetic_checkerboard['img'][1:3,1:3] == 1)
printers.store_ndarray("cpp_voronoi_input.txt",img)
printers.store_ndarray("cpp_voronoi_on_map_of_sinus_output.txt",cpp_vorimg)
assert np.alltrue(cpp_vorimg[:4,:4] == 1)
assert np.alltrue(cpp_vorimg == py_vorimg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_stuff(self):\n self.create_tourism_raster()",
"def write_stitched_image(self):\r\n\r\n self.write_debug(\"End of train detected. Writing stitched image.\")\r\n cv2.imwrite(os.path.join(self.output_dir_stitched, 'stitched.jpg'), self.stitched_image)",
"def final_plain():\n\n\tconfig ... | [
"0.6175",
"0.5833953",
"0.5790842",
"0.5783519",
"0.56402856",
"0.5631712",
"0.56218964",
"0.56209666",
"0.56089985",
"0.5602803",
"0.5586337",
"0.55410194",
"0.54952884",
"0.54952645",
"0.5484749",
"0.545018",
"0.54336417",
"0.54323924",
"0.5420694",
"0.5396064",
"0.53868186... | 0.0 | -1 |
run the tesselation on a empty image | def test_on_map_of_noise(synthetic_checkerboard):
img = synthetic_checkerboard['img']
di = synthetic_checkerboard['distimg']
cpp_vorimg = tess.tessellate_labimg(img,di)
py_vorimg = pytess.tessellate_labimg(img,di)
printers.store_ndarray("py_voronoi_on_map_of_noise_output.txt",py_vorimg)
assert cpp_vorimg.size > 0
assert cpp_vorimg.shape == synthetic_checkerboard['img'].shape
assert np.alltrue(synthetic_checkerboard['img'][1:3,1:3] == 1)
printers.store_ndarray("cpp_voronoi_input.txt",img)
printers.store_ndarray("cpp_voronoi_on_map_of_noise_output.txt",cpp_vorimg)
# assert np.alltrue(cpp_vorimg[:4,:4] == 1)
assert np.alltrue(cpp_vorimg == py_vorimg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_stuff(self):\n self.create_tourism_raster()",
"def write_stitched_image(self):\r\n\r\n self.write_debug(\"End of train detected. Writing stitched image.\")\r\n cv2.imwrite(os.path.join(self.output_dir_stitched, 'stitched.jpg'), self.stitched_image)",
"def final_plain():\n\n\tconfig ... | [
"0.6175",
"0.5833953",
"0.5790842",
"0.5783519",
"0.56402856",
"0.5631712",
"0.56218964",
"0.56209666",
"0.56089985",
"0.5602803",
"0.5586337",
"0.55410194",
"0.54952884",
"0.54952645",
"0.5484749",
"0.545018",
"0.54336417",
"0.54323924",
"0.5420694",
"0.5396064",
"0.53868186... | 0.0 | -1 |
Temporarily overwrite the settings with test settings. This allows to use test datasets for testing. | def generate_test_settings(tmpdir, dataset):
# When `tmpdir` is a path convert it to a string
if isinstance(tmpdir, py._path.local.LocalPath):
tmpdir = str(tmpdir)
test_settings = {
'datasets': {
'mnist': {
'train': {
'images': "file://" + tmpdir + "/" + dataset + "/server/train-images-idx3-ubyte.gz",
'labels': "file://" + tmpdir + "/" + dataset + "/server/train-labels-idx1-ubyte.gz"
},
'test': {
'images': "file://" + tmpdir + "/" + dataset + "/server/t10k-images-idx3-ubyte.gz",
'labels': "file://" + tmpdir + "/" + dataset + "/server/t10k-labels-idx1-ubyte.gz"
},
},
},
'data-dir': tmpdir + "/" + dataset + "/data"
}
overwrite_settings(test_settings) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def force_test_setting(dm, tsm, output_path):\n if dm is not None:\n data_json_path = os.path.join(output_path, 'cur_data_setting.json')\n dm.data_par['datapro']['dataset']['prepare_data'] = False\n dm.data_par['datapro']['reg']['max_num_for_loading'] = [1, 1, -1, 1]\n dm.save(data_j... | [
"0.69389164",
"0.6621101",
"0.6452525",
"0.64237857",
"0.64121604",
"0.6407746",
"0.6387266",
"0.6371625",
"0.6295202",
"0.6287137",
"0.6241228",
"0.6236511",
"0.62239265",
"0.6192211",
"0.6178279",
"0.61352205",
"0.6134868",
"0.6040336",
"0.6021929",
"0.6021816",
"0.6011449"... | 0.6982843 | 0 |
Generate archive files for the given test dataset in tmpdir | def generate_test_dataset_archive(filepath, dataset):
# 'file:///some/path' to '/some/path'
if filepath[:7] == 'file://':
filepath = filepath[7:]
# Check if the dataset exists.
# When not been generate it.
if not os.path.isfile(filepath):
print("Generating", filepath)
data = get_test_dataset(dataset)
ensure_dir(os.path.dirname(filepath))
idxgz.save(filepath, data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_test_environment(tmpdir, dataset):\n\n # Overwrite settings with test settings\n generate_test_settings(tmpdir, dataset)\n\n # Generate the archive files\n for usage in ['train', 'test']:\n \n for dstype in ['images', 'labels']:\n \n dataset_type = usage... | [
"0.7358341",
"0.6791461",
"0.6418583",
"0.63508826",
"0.63468665",
"0.6301004",
"0.6291766",
"0.61660707",
"0.6096657",
"0.6091523",
"0.60853094",
"0.6035293",
"0.602757",
"0.59755576",
"0.595753",
"0.59322333",
"0.5913556",
"0.5871974",
"0.58634",
"0.5859327",
"0.58383375",
... | 0.75224614 | 0 |
Generate a test environment using the given dataset. The settings are temporarily overwritten to use the test data. | def generate_test_environment(tmpdir, dataset):
# Overwrite settings with test settings
generate_test_settings(tmpdir, dataset)
# Generate the archive files
for usage in ['train', 'test']:
for dstype in ['images', 'labels']:
dataset_type = usage + '.' + dstype
mnist_dataset = 'datasets.mnist.' + dataset_type
filepath = get_setting(mnist_dataset)
test_dataset = dataset + '.' + dataset_type
generate_test_dataset_archive(filepath, test_dataset) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_environment(dataset, tmpdir):\n\n print(\">>> Test environment:\")\n print(\"dataset:\", dataset)\n print(\"tmpdir:\", tmpdir)\n\n generate_test_environment(tmpdir, dataset)\n\n return { 'dataset': dataset, 'tmpdir': tmpdir }",
"def test_generate_test_environment(dataset):\n\n prin... | [
"0.7599391",
"0.72667193",
"0.69813424",
"0.66441184",
"0.64025056",
"0.6268748",
"0.625678",
"0.6244715",
"0.61173195",
"0.60907793",
"0.60787404",
"0.60093623",
"0.5926749",
"0.5911557",
"0.5894079",
"0.58914727",
"0.58914727",
"0.58914727",
"0.58914727",
"0.58877015",
"0.5... | 0.7804764 | 0 |
For debugging test environments | def test_generate_test_environment(dataset):
print("## =========================================================")
print("## Dataset:", dataset)
print("## ---------------------------------------------------------")
print("")
tmpdir = "/tmp/collagen"
generate_test_environment(tmpdir, dataset)
# Generate the archive files
for usage in ['train', 'test']:
for dstype in ['images', 'labels']:
dataset_type = usage + '.' + dstype
mnist_dataset = 'datasets.mnist.' + dataset_type
filepath = get_setting(mnist_dataset)
# 'file:///some/path' to '/some/path'
if filepath[:7] == 'file://':
filepath = filepath[7:]
# Unpack
print("")
print("{}: {}".format(mnist_dataset, filepath))
print("")
data = idxgz.load(filepath)
print("data:", data)
print("type:", type(data))
print("dtype:", data.dtype)
print("shape:", data.shape)
print("") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def debug():",
"def debugging_tests():\n logging.warning(\"Running debugging tests...\")\n pass",
"def debug() -> bool:",
"def test_debug(self) -> Debug:\n return self._test_debug",
"def test_debug_info():\n # Just check a sample we control.\n assert version.__version__ in _env_info",
... | [
"0.76501894",
"0.74378276",
"0.7319192",
"0.7256087",
"0.7246349",
"0.7120246",
"0.7063667",
"0.69600326",
"0.6871428",
"0.67112607",
"0.6628233",
"0.66059375",
"0.65979975",
"0.65336007",
"0.6533267",
"0.6531516",
"0.6531516",
"0.6531516",
"0.65074044",
"0.64774877",
"0.6432... | 0.0 | -1 |
Extracts (typically) overlapping regular patches from a grayscale image Changing the offset and stride parameters will result in images reconstructed by reconstruct_from_grayscale_patches having different dimensions! Callers should pad and unpad as necessary! | def extract_grayscale_patches( img, shape, offset=(0,0), stride=(1,1) ):
px, py = np.meshgrid( np.arange(shape[1]),np.arange(shape[0]))
l, t = np.meshgrid(
np.arange(offset[1],img.shape[1]-shape[1]+1,stride[1]),
np.arange(offset[0],img.shape[0]-shape[0]+1,stride[0]) )
l = l.ravel()
t = t.ravel()
x = np.tile( px[None,:,:], (t.size,1,1)) + np.tile( l[:,None,None], (1,shape[0],shape[1]))
y = np.tile( py[None,:,:], (t.size,1,1)) + np.tile( t[:,None,None], (1,shape[0],shape[1]))
return img[y.ravel(),x.ravel()].reshape((t.size,shape[0],shape[1])), (t,l) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_patches_and_positions_from_image(\n image, patch_size, patch_stride, hse_grid_size,\n n_crops, h, w,\n c, scale_id, max_seq_len):\n p = tf.image.extract_patches(\n image, [1, patch_size, patch_size, 1], [1, patch_stride, patch_stride, 1],\n [1, 1, 1, 1],\n padding='SAME')\n\n ... | [
"0.69615763",
"0.6910621",
"0.6902872",
"0.6756077",
"0.6684931",
"0.6669656",
"0.66367173",
"0.66181797",
"0.6613878",
"0.6597331",
"0.65837264",
"0.65633184",
"0.65623057",
"0.652115",
"0.6456871",
"0.6398756",
"0.6369198",
"0.6359843",
"0.63577175",
"0.6351932",
"0.6345157... | 0.7973168 | 0 |
Rebuild an image from a set of patches by averaging The reconstructed image will have different dimensions than the original image if the strides and offsets of the patches were changed from the defaults! | def reconstruct_from_grayscale_patches( patches, origin, epsilon=1e-12 ):
patch_width = patches.shape[2]
patch_height = patches.shape[1]
img_width = np.max( origin[1] ) + patch_width
img_height = np.max( origin[0] ) + patch_height
out = np.zeros( (img_height,img_width) )
wgt = np.zeros( (img_height,img_width) )
for i in range(patch_height):
for j in range(patch_width):
out[origin[0]+i,origin[1]+j] += patches[:,i,j]
wgt[origin[0]+i,origin[1]+j] += 1.0
return out/np.maximum( wgt, epsilon ), wgt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reconstruct_avg(img, nnf, patch_size=5):\r\n\r\n final = np.zeros_like(img)\r\n for i in range(img.shape[0]):\r\n for j in range(img.shape[1]):\r\n\r\n dx0 = dy0 = patch_size // 2\r\n dx1 = dy1 = patch_size // 2 + 1\r\n dx0 = min(j, dx0)\r\n dx1 = min(im... | [
"0.75404114",
"0.6949689",
"0.6838472",
"0.68046606",
"0.67437595",
"0.66327584",
"0.6593758",
"0.6556427",
"0.6351604",
"0.6174992",
"0.6122615",
"0.60943955",
"0.6007776",
"0.5980942",
"0.59666556",
"0.59625",
"0.5957962",
"0.5952175",
"0.59199303",
"0.58981425",
"0.5874043... | 0.64738786 | 8 |
This is the base "correct" case (they have enough money, are bidders, haven't passed, etc). | def testInitializeMove(self):
bid_move = self._move()
context = self._context()
bfpc = BiddingForPrivateCompany()
self.assertTrue(bfpc.run(bid_move, context), bfpc.errors()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_amount_not_enough(self):\n item, change, _ = give_item_and_change('coke', .50)\n self.assertIsNone(item)\n self.assertEqual(change, 0.5)",
"def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\n if bid:\n if len(self.trades[\"buys\"][commodity... | [
"0.67128986",
"0.67048556",
"0.66379863",
"0.6527508",
"0.6469274",
"0.64126986",
"0.63823014",
"0.63767564",
"0.63319266",
"0.6226689",
"0.62261546",
"0.61649483",
"0.6154119",
"0.61476374",
"0.6144717",
"0.613952",
"0.6133715",
"0.61022395",
"0.6094778",
"0.60594314",
"0.60... | 0.0 | -1 |
Make sure they have enough money. | def testInsufficientCash(self):
bid_move = self._move()
context = self._context()
context.players[0].cash = 200
bfpc = BiddingForPrivateCompany()
self.assertFalse(bfpc.run(bid_move, context), bfpc.errors()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_amount_not_enough(self):\n item, change, _ = give_item_and_change('coke', .50)\n self.assertIsNone(item)\n self.assertEqual(change, 0.5)",
"def check_costs(self):\r\n if self.cost > self.owner.player.char_ob.currency:\r\n self.add_error(\r\n \"celebr... | [
"0.73113734",
"0.7142943",
"0.6958009",
"0.683706",
"0.6807364",
"0.6756622",
"0.6750081",
"0.6689734",
"0.6550323",
"0.65317523",
"0.6529866",
"0.65116155",
"0.6492988",
"0.6469569",
"0.64487016",
"0.64354503",
"0.6415727",
"0.6402978",
"0.6386353",
"0.637824",
"0.6367256",
... | 0.6613819 | 8 |
Make sure they have enough money. | def testPassedAlready(self):
_pass_move = self._pass_move()
bid_move = self._move()
context = self._context()
bfpc = BiddingForPrivateCompany()
self.assertTrue(bfpc.run(_pass_move, context), bfpc.errors())
self.assertEqual(_pass_move.move_type, BidType.PASS)
self.assertEqual(len(context.private_companies[1].passed_by), 1)
self.assertFalse(bfpc.run(bid_move, context), bfpc.errors())
self.assertIn("You can only keep bidding until you've passed once.", bfpc.errors()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_amount_not_enough(self):\n item, change, _ = give_item_and_change('coke', .50)\n self.assertIsNone(item)\n self.assertEqual(change, 0.5)",
"def check_costs(self):\r\n if self.cost > self.owner.player.char_ob.currency:\r\n self.add_error(\r\n \"celebr... | [
"0.7311504",
"0.71432513",
"0.6958015",
"0.6837109",
"0.68072706",
"0.6756763",
"0.6750037",
"0.6689844",
"0.66139734",
"0.6550322",
"0.6531817",
"0.65297884",
"0.65116364",
"0.6493205",
"0.64698845",
"0.6448786",
"0.6435723",
"0.64158696",
"0.6403044",
"0.6386391",
"0.637806... | 0.0 | -1 |
If the last person passes, you should assign the new owner asap. | def testAutopurchaseOnLastPass(self):
_pass_move = self._pass_move()
context = self._context()
bfpc = BiddingForPrivateCompany()
self.assertTrue(bfpc.run(_pass_move, context), bfpc.errors())
self.assertEqual(_pass_move.move_type, BidType.PASS)
self.assertEqual(len(context.private_companies[1].passed_by), 1)
self.assertTrue(context.private_companies[1].hasOwner())
self.assertNotEqual(context.private_companies[1].belongs_to,
_pass_move.player,
context.private_companies[1].belongs_to.id
)
self.assertEqual(
context.private_companies[1].belongs_to,
context.players[1],
context.private_companies[1].belongs_to.id
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testOwnershipAfterCreate(self):\n self.simulateATGUIInteraction(task='create')\n self.failUnlessEqual(self.person.getOwnerTuple()[1], 'abc123')",
"def add_to(self, newowner):\n self.prevai = newowner.ai\n newowner.ai = self",
"def possessed_by(self, other):\r\n self.owner... | [
"0.677503",
"0.64477986",
"0.6446304",
"0.63864005",
"0.6358",
"0.62971616",
"0.62954915",
"0.6290303",
"0.6241448",
"0.6205976",
"0.6128244",
"0.6090834",
"0.6000378",
"0.5993534",
"0.5971924",
"0.594644",
"0.5895443",
"0.5857133",
"0.5824147",
"0.58180535",
"0.58094245",
... | 0.0 | -1 |
Get the identifier of person | def identifier(self):
return self._identifier | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_identifier(self):",
"def getIdent (self) :\n return self.id",
"def get_identifier(self) -> str:\n return self.identifier",
"def get_person_id(person_data):\n person_ref = person_data['Casualty_Reference']\n veh_ref = person_data['Vehicle_Reference']\n acc_id = get_acc_id_from_d... | [
"0.75057507",
"0.7319578",
"0.7309798",
"0.7292267",
"0.71864873",
"0.7161316",
"0.70634836",
"0.6956172",
"0.6956172",
"0.6955756",
"0.6924296",
"0.6895861",
"0.68554884",
"0.68335015",
"0.6826491",
"0.6826491",
"0.68212664",
"0.68004686",
"0.67956805",
"0.67936885",
"0.6743... | 0.68210834 | 21 |
Get the name of person | def name(self):
return self._name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def person_name(self):\n return self._person_name",
"def persona_name(self) -> str:\n return self._persona_name",
"def get_name() -> str:",
"def get_person_name(self, person_id):\n res = requests.get(url=\"https://api.ciscospark.com/v1/people/{}\".format(person_id),\n ... | [
"0.8474906",
"0.8018867",
"0.78476787",
"0.77369803",
"0.76065636",
"0.75772613",
"0.7530738",
"0.750647",
"0.74815065",
"0.7397122",
"0.736096",
"0.7346389",
"0.73411",
"0.73411",
"0.73411",
"0.73353255",
"0.7317605",
"0.7317605",
"0.7317605",
"0.7317226",
"0.73119605",
"0... | 0.0 | -1 |
Get the surname of person | def surname(self):
return self._surname | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def surname(self):\n return self.__surname",
"def surname(self):\n if \"surname\" in self._prop_dict:\n return self._prop_dict[\"surname\"]\n else:\n return None",
"def surname(self):\n if \"surname\" in self._prop_dict:\n return self._prop_dict[\"su... | [
"0.84594584",
"0.8456766",
"0.8456766",
"0.7657546",
"0.75671947",
"0.7503995",
"0.71551466",
"0.7144786",
"0.6926863",
"0.68105274",
"0.67284966",
"0.66660845",
"0.6651198",
"0.6651198",
"0.6651198",
"0.6651198",
"0.6651198",
"0.66335547",
"0.6632711",
"0.65882415",
"0.65831... | 0.8435841 | 4 |
Get the phone of person | def phone(self):
return self._phone | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPhone(self):\n return self.phone",
"def phone(self) -> str:\n return pulumi.get(self, \"phone\")",
"def phone(self):\n return self._phone",
"def phone(self):\n return self._phone",
"def phone(self):\n return self._phone",
"def personal_phone(self):\n retur... | [
"0.7979565",
"0.7934985",
"0.7780116",
"0.7780116",
"0.7780116",
"0.7700906",
"0.76863",
"0.75520843",
"0.7512064",
"0.7512064",
"0.7222818",
"0.7215174",
"0.7091844",
"0.70915896",
"0.7069842",
"0.69753623",
"0.6879566",
"0.6879566",
"0.6869756",
"0.681976",
"0.6818524",
"... | 0.7533967 | 8 |
Get the address of person | def address(self):
return self._address | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAddress(user):",
"def get_address(self):\n if self.get_entity: # needs an entity to work\n if self.building:\n address = self.get_entity.get_institutional_address()\n address.extend(self.building.get_postal_address())\n return address\n ... | [
"0.7992872",
"0.7607708",
"0.75525093",
"0.73847014",
"0.7379411",
"0.737529",
"0.73570526",
"0.7323264",
"0.722714",
"0.71870494",
"0.7097504",
"0.7097504",
"0.7092015",
"0.7092015",
"0.7092015",
"0.7066334",
"0.7032395",
"0.69323504",
"0.69010234",
"0.69005394",
"0.6900194"... | 0.6411183 | 70 |
Get the mail of person | def mail(self):
return self._mail | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recipient(self, recipient, model):\n if recipient == \"hod\":\n workmails = model.address_id, model.work_email\n workmail = {workmail for workmail in workmails if workmail}\n workmail = workmail.pop() if workmail else model.work_email\n if not isinstance(workm... | [
"0.7303463",
"0.7285791",
"0.72157747",
"0.72157747",
"0.7144919",
"0.70124036",
"0.6947377",
"0.6876924",
"0.6824107",
"0.6776341",
"0.67702425",
"0.67517656",
"0.6741758",
"0.6727407",
"0.67177004",
"0.6696528",
"0.6696528",
"0.6696528",
"0.6696325",
"0.6682962",
"0.6642721... | 0.6388883 | 45 |
Get the url of person | def url(self):
return self._url | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getURLForThing(thing):",
"def get_absolute_url(self):\n if self.kind == \"persona_profile\":\n p = Persona.query.filter(Persona.profile_id == self.id).first()\n return url_for(\"persona\", id=p.id)\n elif self.kind == \"group_profile\":\n g = Group.query.filter(... | [
"0.72001034",
"0.7117052",
"0.71160406",
"0.7037918",
"0.7014563",
"0.7014563",
"0.6958404",
"0.6889335",
"0.6881808",
"0.68336815",
"0.68336815",
"0.67946136",
"0.67801034",
"0.677251",
"0.6765108",
"0.6752407",
"0.6691526",
"0.6678413",
"0.6663654",
"0.6660274",
"0.6658939"... | 0.6515759 | 40 |
Model with no features.Always predicts a passenger did not survive. | def predictions_0(data):
predictions=[]
for _, passenger in data.iterrows():
#Predict the survival of 'passenger'
predictions.append(0)
#Return our predictions
return pd.Series(predictions)
#make the predictions
predictions=predictions_0(data)
'''
Question 1
Using the RMS Titanic data,how accurate would a prediction be that none of the passengers survived?
Hint:Run the code cell below to see the accuracy of this prediction.
'''
print accuracy_score(outcomes,predictions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_only(self):",
"def nonlearning():\n\taT.featureAndTrain(['../../AudioData/chunked_data_sorted/pos', '../../AudioData/chunked_data_sorted/neg'], \n\t\t\t\t\t\t1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, \n \"svm\", \"emotion_classifier\", True)",
"def train_naive(): # add argu... | [
"0.7398218",
"0.6274821",
"0.60658264",
"0.6061463",
"0.6021806",
"0.5954597",
"0.5936909",
"0.5917042",
"0.59065545",
"0.58827984",
"0.5858222",
"0.5814856",
"0.5814105",
"0.57477957",
"0.5726169",
"0.57161635",
"0.5714358",
"0.57077813",
"0.568969",
"0.56806886",
"0.5669478... | 0.5549601 | 38 |
Model with multiple features.Makes a prediction with an accuracy of at least 90% | def predictions_3(data):
predictions=[]
for _,passenger in data.iterrows():
if passenger['Sex']=='female' or passenger['Sex']=='male' and passenger['Age']<16 and passenger['SibSp']<2:
predictions.append(1)
else:
predictions.append(0)
#Return our predictions
return pd.Series(predictions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_only(self):",
"def trainModel( self, featureTrain, classTrain):",
"def predict(self, model, arg):\n prediction = model.predict(arg)\n\n return prediction\n\n #def getAccuracyScore(self, n_splits):\n \"\"\"\n Gives an cross-validated accuracy score for the new model.\n... | [
"0.69236994",
"0.6804752",
"0.66647077",
"0.6594891",
"0.6577277",
"0.6555848",
"0.6513031",
"0.65010685",
"0.64928365",
"0.6475141",
"0.6472414",
"0.64383626",
"0.6421993",
"0.6405277",
"0.6402431",
"0.63951135",
"0.63951135",
"0.638529",
"0.63590425",
"0.63344693",
"0.63329... | 0.0 | -1 |
Initialize monitor with name and units. | def __init__(self, pvname: str, units: str, controller: Controller) -> None:
self.units = units.split(":")
self.pvname = pvname
self.controller = controller | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __initializeMonitor( self ):\n if self.__moduleProperties[ 'standalone' ]:\n self.monitor = gMonitor\n else:\n self.monitor = MonitoringClient()\n self.monitor.setComponentType( self.monitor.COMPONENT_AGENT )\n self.monitor.setComponentName( self.__moduleProperties[ 'fullName' ] )\n se... | [
"0.69244856",
"0.5961898",
"0.5762785",
"0.5757067",
"0.5748094",
"0.5743669",
"0.5689868",
"0.5632864",
"0.56286836",
"0.5607507",
"0.55625767",
"0.5536116",
"0.5534481",
"0.5490145",
"0.5483726",
"0.5446595",
"0.54104185",
"0.54083985",
"0.5397087",
"0.539698",
"0.53613365"... | 0.53771675 | 20 |
Collects image data via appropriate protocol and builds image data dictionary. Returns dict Dictionary mapping image components to values. | def poll(self) -> Dict[str, list]:
try:
value = self.controller.get_image(self.pvname)
except TimeoutError:
print(f"No process variable found for {self.pvname}")
return DEFAULT_IMAGE_DATA
# now prepare the value using method defined by the model
return value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_imagenet_as_dict(self):\n real_file_path = os.path.realpath(self.map_file)\n if not os.path.exists(real_file_path):\n raise IOError(\"map file {} not exists\".format(self.map_file))\n\n label_dict = {}\n with open(real_file_path) as fp:\n line = fp.readlin... | [
"0.65801144",
"0.6529789",
"0.6423225",
"0.63869387",
"0.62811023",
"0.6264642",
"0.62389845",
"0.6179889",
"0.61555016",
"0.61398166",
"0.6100159",
"0.6036023",
"0.59768283",
"0.59654045",
"0.59536004",
"0.59490883",
"0.593953",
"0.5929546",
"0.59160763",
"0.5884778",
"0.583... | 0.0 | -1 |
Collects image data via appropriate protocol and returns time and data. | def poll(self) -> Tuple[np.ndarray]:
t = time.time()
try:
v = self.controller.get(self.pvname)
except TimeoutError:
print(f"No process variable found for {self.pvname}")
v = DEFAULT_SCALAR_VALUE[self.pvname]
self.time = np.append(self.time, t)
self.data = np.append(self.data, v)
return self.time - self.tstart, self.data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self):\n global CAM\n while CAM.isOpened():\n _, frame = CAM.read()\n _, jpeg = cv2.imencode('.jpg', frame)\n encoded_img = \"data:image/jpg;base64,\" + str(base64.b64encode(jpeg.tobytes()).decode())\n SIO.emit('video_frame',\n ... | [
"0.58509904",
"0.5848849",
"0.5797124",
"0.5720454",
"0.5719993",
"0.5676541",
"0.5621858",
"0.5572144",
"0.55561996",
"0.55333614",
"0.5499547",
"0.54932874",
"0.5475753",
"0.54749036",
"0.5426874",
"0.5406819",
"0.54067355",
"0.53759944",
"0.5365759",
"0.53495574",
"0.53261... | 0.0 | -1 |
Collects image data via appropriate protocol and returns time and data. | def poll(self) -> Tuple[np.ndarray]:
try:
v = self.controller.get(self.pvname)
except TimeoutError:
print(f"No process variable found for {self.pvname}")
v = DEFAULT_SCALAR_VALUE
return v | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self):\n global CAM\n while CAM.isOpened():\n _, frame = CAM.read()\n _, jpeg = cv2.imencode('.jpg', frame)\n encoded_img = \"data:image/jpg;base64,\" + str(base64.b64encode(jpeg.tobytes()).decode())\n SIO.emit('video_frame',\n ... | [
"0.5853295",
"0.5850195",
"0.5799188",
"0.5721936",
"0.57214046",
"0.56789905",
"0.562323",
"0.5574254",
"0.55553085",
"0.5534616",
"0.54999393",
"0.5493341",
"0.5476053",
"0.5475871",
"0.54282975",
"0.5408226",
"0.54078424",
"0.53759134",
"0.53672683",
"0.534906",
"0.5328712... | 0.0 | -1 |
Encode image for Dash Application | def encodedImage(imageFile):
imageFile = "".join([METRICS_PATH, imageFile])
encoded = base64.b64encode(open(imageFile, 'rb').read())
return 'data:image/jpg;base64,{}'.format(encoded.decode()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(image):\n from encoder import launch\n launch(image)",
"def encode(self, image) -> bytes:\n raise NotImplementedError()",
"def prepare_output(image: np.ndarray) -> str:\n response_image = Image.fromarray(np.uint8(image * 255))\n buffer = BytesIO()\n response_image.save(buffer, ... | [
"0.7395458",
"0.71863705",
"0.6997544",
"0.69900215",
"0.6834343",
"0.67835957",
"0.67522126",
"0.6698545",
"0.6664718",
"0.6626647",
"0.65300465",
"0.643985",
"0.64295864",
"0.6397522",
"0.63932425",
"0.6282071",
"0.62708914",
"0.6267106",
"0.625478",
"0.62303776",
"0.617828... | 0.62895167 | 15 |
assert json schema for requests from api.openweathermap.org | def validate_schema_openweathermap(self, actual, schema):
resources_dir = os.path.abspath(os.getcwd())
relative_schema_path = valid_json_schema if schema == 'Valid' else error_json_schema
schema_data = open(os.path.join(resources_dir, relative_schema_path))
self.validate_schema(actual, json.load(schema_data))
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_complete_data_schema(self):\n response = self.client.get(self.url)\n data = response.data\n self.assertIn('id', data)\n self.assertIn('title', data)\n self.assertIn('release_year', data)\n self.assertIn('casting', data)\n self.assertIn('directors', data)\n ... | [
"0.62621653",
"0.6240114",
"0.6214563",
"0.6125648",
"0.5942485",
"0.5931073",
"0.575438",
"0.57441986",
"0.57291126",
"0.57290787",
"0.57119447",
"0.56926596",
"0.5668845",
"0.5663258",
"0.5649895",
"0.5649895",
"0.5649045",
"0.56264323",
"0.5599427",
"0.5597548",
"0.5595037... | 0.6302012 | 0 |
Count the number of nonempty dicts/lists or other objects | def recursive_count(o):
if isinstance(o, dict):
c = 0
for v in o.values():
c += recursive_count(v)
return c
elif isinstance(o, list):
c = 0
for v in o:
c += recursive_count(v)
return c
else:
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_empty(self):\n count = 0\n for i in self.__buckets:\n if i.size() == 0:\n count += 1\n return count",
"def __len__(self):\n total_objs = 0\n\n if self._shelve is not None:\n total_objs += len(self._shelve)\n\n if self._dict is... | [
"0.71943367",
"0.71715474",
"0.69646627",
"0.6960094",
"0.6950782",
"0.6901174",
"0.6860939",
"0.6854527",
"0.68178976",
"0.6807361",
"0.6695624",
"0.6688045",
"0.6622141",
"0.6599806",
"0.6558737",
"0.6528312",
"0.6516975",
"0.64872235",
"0.6447478",
"0.6436087",
"0.641087",... | 0.7272351 | 0 |
Reorganize xarray object a bit for netcdf files | def process_lidar(radial_file, scan_file, wind_file, site, period, netcdf_path):
lidar = rasp.lidar_from_csv(radial_file, scan_file, wind=wind_file)
# remove status==0 data (if we have the whole data)
if 'Status' in lidar.data_vars:
lidar['CNR'] = lidar['CNR'].where(lidar['Status'])
lidar['DRWS'] = lidar['DRWS'].where(lidar['Status'])
# remove unneeded variables if they exist
to_drop = list(set(lidar.data_vars) & set(['Status', 'Error', 'Confidence', 'RWS']))
lidar = lidar.drop(to_drop)
lidar = lidar.rasp.cf_compliant()
lidar.to_netcdf(netcdf_path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_netCDF_to_memory(self):\n f = cfdm.example_field(4)\n f.data.to_memory() # on non-compressed array\n f.compress(\"indexed_contiguous\", inplace=True)\n f.data.to_memory() # on compressed array",
"def __init__(self, xarray_obj):\n super(RasterDataArray, self).__init__... | [
"0.57716465",
"0.5675053",
"0.5598693",
"0.5529777",
"0.5516983",
"0.54658604",
"0.54497313",
"0.54397637",
"0.53918636",
"0.5390577",
"0.5377511",
"0.5367062",
"0.5363264",
"0.5362354",
"0.53438973",
"0.5343177",
"0.5296879",
"0.5289519",
"0.5279383",
"0.5264651",
"0.5245457... | 0.0 | -1 |
Taken a word, if it is an abbreviation it returns the meaning | def get_abbr(self, word):
assert (self.collection is not None)
for conv in self.collection:
ln = conv.split("*")
if word == ln[0]:
return ln[1]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_abbrev(word):\r\n return abbreviations[word.lower()] if word.lower() in abbreviations.keys() else word",
"def aux_lemma(word):\n if re.match(r\"(does|did|doing)\", word):\n return (\"do\")\n elif re.match(r\"(had|has|'ve|having)\", word):\n return (\"have\")\n elif re.match(... | [
"0.6904979",
"0.68378323",
"0.6644737",
"0.6541667",
"0.6537515",
"0.6440209",
"0.6420517",
"0.6390089",
"0.63880235",
"0.6369538",
"0.63481516",
"0.63338417",
"0.6322253",
"0.6277778",
"0.62644935",
"0.6260187",
"0.62546563",
"0.62457216",
"0.6238056",
"0.6238056",
"0.619860... | 0.66452616 | 2 |
Create an instance of all possible analysis. If you're not able to create it... you're not able to use it. | def build_all_analysis(self, matrix_handler, trajectory_handler):
distance_matrix = matrix_handler.distance_matrix
self.all_possible_analysis = {}
# Pure queries
self.all_possible_analysis["Details"] = Analysis("Details", self.analysis_function_details)
self.all_possible_analysis["NumClusters"] = Analysis("Number of clusters", self.analysis_function_num_clusters)
self.all_possible_analysis["NumClusteredElems"] = Analysis("Number of clustered elements", self.analysis_function_total_elements)
self.all_possible_analysis["MeanClusterSize"] = Analysis("Mean cluster size", self.analysis_function_mean_cluster_size)
self.all_possible_analysis["PercentInTop4"] = Analysis("Percent in top 4 clusters", self.analysis_function_top_4)
self.all_possible_analysis["PercentInTop"] = Analysis("Percent in top cluster", self.analysis_function_top_percent)
self.all_possible_analysis["ClustersTo90"] = Analysis("Clusters to 90", self.analysis_function_num_clusters_to_percent, 90)
self.all_possible_analysis["NoiseLevel"] = Analysis("Noise level", self.analysis_function_noise_level, distance_matrix.row_length)
# Evaluators
self.all_possible_analysis["MirrorCohesion"] = Analysis("MirrorCohesion", self.evaluate_with_calculator,
{"class":MirrorCohesionCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Cohesion"] = Analysis("Cohesion", self.evaluate_with_calculator,
{"class":CohesionCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Separation"] = Analysis("Separation", self.evaluate_with_calculator,
{"class":SeparationCalculator,"matrix":distance_matrix})
self.all_possible_analysis["MinimumMeanSeparation"] = Analysis("MinimumMeanSeparation", self.evaluate_with_calculator,
{"class":MeanMinimumDistanceCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Silhouette"] = Analysis("Silhouette", self.evaluate_with_calculator,
{"class":SilhouetteCoefficientCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Calinski-Harabasz"] = Analysis("Calinski-Harabasz", self.evaluate_with_calculator,
{"class":CalinskiHarabaszCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Dunn"] = Analysis("Dunn", self.evaluate_with_calculator,
{"class":DunnCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Davies-Bouldin"] = Analysis("Davies-Bouldin", self.evaluate_with_calculator,
{"class":DaviesBouldinCalculator,"matrix":distance_matrix})
self.all_possible_analysis["GaussianSeparation"] = Analysis("GaussianSeparation", self.evaluate_with_calculator,
{"class":GaussianSeparationCalculator,"matrix":distance_matrix})
self.all_possible_analysis["Compactness"] = Analysis("Compactness", self.evaluate_with_calculator,
{"class":CompactnessCalculator,"matrix":distance_matrix})
# Cython
self.all_possible_analysis["CythonMirrorCohesion"] = Analysis("CythonMirrorCohesion", self.evaluate_with_calculator,
{"class":CythonMirrorCohesionCalculator,"matrix":distance_matrix})
self.all_possible_analysis["CythonMinimumMeanSeparation"] = Analysis("CythonMinimumMeanSeparation", self.evaluate_with_calculator,
{"class":CythonMeanMinimumDistanceCalculator,"matrix":distance_matrix})
self.all_possible_analysis["CythonSilhouette"] = Analysis("CythonSilhouette", self.evaluate_with_calculator,
{"class":CythonSilhouetteCoefficientCalculator,"matrix":distance_matrix})
# Graph
self.all_possible_analysis["RatioCut"] = Analysis("RatioCut", self.evaluate_with_calculator,
{"class":RatioCut,"matrix":distance_matrix})
self.all_possible_analysis["NCut"] = Analysis("NCut", self.evaluate_with_calculator,
{"class":NCut,"matrix":distance_matrix})
self.all_possible_analysis["NormNCut"] = Analysis("NormNCut", self.analysis_function_norm_n_cut,distance_matrix)
self.all_possible_analysis["MinMaxCut"] = Analysis("MinMaxCut", self.evaluate_with_calculator,
{"class":MinMaxCut,"matrix":distance_matrix})
# Cython & Graph
self.all_possible_analysis["CythonNormNCut"] = Analysis("CythonNormNCut", self.analysis_function_cython_norm_n_cut,distance_matrix)
# PCA
self.all_possible_analysis["PCAanalysis"] = Analysis("PCAanalysis", self.analysis_function_pca, trajectory_handler) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_analysis_tools(self):\r\n raise NotImplementedError()",
"def analysis_setup(self):\n pass",
"def init():\n # analyzer es utilizado para interactuar con el modelo\n analyzer = model.newAnalyzer()\n return analyzer",
"def analysis(self, checkid):\r\n return analysis.Analysi... | [
"0.6476997",
"0.64702034",
"0.63428855",
"0.62444305",
"0.623065",
"0.5992507",
"0.59902406",
"0.59565115",
"0.5947113",
"0.5947113",
"0.59458697",
"0.5939853",
"0.59148765",
"0.58706826",
"0.5858152",
"0.58355874",
"0.58180684",
"0.5758296",
"0.5750707",
"0.57501674",
"0.574... | 0.60086685 | 5 |
Generates the list of required analyzers. | def get_analysis_list(self):
analysys_list = []
analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)
for analysis_type in analysis_types:
if analysis_type in self.all_possible_analysis:
analysys_list.append(self.all_possible_analysis[analysis_type])
else:
print "[WARNING]", analysis_type, "is not an allowed analysis type"
return analysys_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_analyzers(args: argparse.Namespace):\n first = True\n queue = [tuple(c) + (\"lookout.\",) for c in pkgutil.iter_modules(lookout.__path__)]\n while queue:\n importer, name, ispkg, prefix = queue.pop(0)\n\n if not ispkg or name == \"core\":\n continue\n\n m = importe... | [
"0.69321376",
"0.6514944",
"0.57968",
"0.5703347",
"0.5684623",
"0.5570689",
"0.54497045",
"0.53985465",
"0.5392003",
"0.5353222",
"0.53327596",
"0.52788645",
"0.525477",
"0.52528656",
"0.5236998",
"0.51388603",
"0.49939647",
"0.49732646",
"0.4960888",
"0.49380273",
"0.487532... | 0.46405792 | 47 |
Returns a list formed by all the analysis we need to calculate, without repetition, from the data in parameters. | def get_query_and_evaluation_analysis_types(self, parameters):
queries = parameters["clustering"]["evaluation"]["query_types"]
queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))
return list(set(queries)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_analysis_list(self):\n analysys_list = []\n\n analysis_types = AnalysisPopulator.get_query_and_evaluation_analysis_types(self.parameters)\n\n for analysis_type in analysis_types:\n if analysis_type in self.all_possible_analysis:\n analysys_list.append(self.all... | [
"0.62758255",
"0.6011133",
"0.5986083",
"0.59858525",
"0.5896374",
"0.58353883",
"0.58151305",
"0.57914615",
"0.57429975",
"0.5728949",
"0.571652",
"0.57030505",
"0.5637215",
"0.5610428",
"0.55925506",
"0.559063",
"0.5552268",
"0.5549528",
"0.5538644",
"0.5536552",
"0.5535558... | 0.5309142 | 65 |
Returns a list formed by the evaluation types present in criteria. | def get_evaluation_analysis_types(self, parameters):
eval_types =[]
for evaluation_criteria_id in parameters["clustering"]["evaluation"]["evaluation_criteria"]:
# for subcriteria in parameters["clustering"]["evaluation"]["evaluation_criteria"][evaluation_criteria_id]:
# eval_types.append(subcriteria)
eval_types.extend(parameters["clustering"]["evaluation"]["evaluation_criteria"][evaluation_criteria_id].keys())
return list(set(eval_types)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_query_and_evaluation_analysis_types(self, parameters):\n queries = parameters[\"clustering\"][\"evaluation\"][\"query_types\"]\n queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))\n return list(set(queries))",
"def getResultDefs(self, type=None):\n res... | [
"0.67036015",
"0.615326",
"0.59587413",
"0.5896585",
"0.5799256",
"0.5777026",
"0.5747461",
"0.5745",
"0.5661408",
"0.5641381",
"0.5631738",
"0.551762",
"0.551464",
"0.5496893",
"0.5494026",
"0.54865164",
"0.54589295",
"0.5457875",
"0.5433231",
"0.54234606",
"0.5407233",
"0... | 0.76133484 | 0 |
Returns the 'details' field of a clustering. | def analysis_function_details(self,clustering):
return clustering.details | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_details(self):\n return self.details",
"def get_details(self):\n return self.details",
"def get_details(self):\n return self.details",
"def details(self) -> \"dict\":\n return self._attrs.get(\"details\")",
"def details(self):\n return self._details",
"def detai... | [
"0.6636939",
"0.6636939",
"0.6636939",
"0.6543469",
"0.64725894",
"0.63629097",
"0.62274104",
"0.6106484",
"0.60739094",
"0.6035391",
"0.6035391",
"0.60135996",
"0.6000487",
"0.5963064",
"0.59448",
"0.5934605",
"0.5902071",
"0.5895396",
"0.58460885",
"0.5807865",
"0.57522964"... | 0.7595597 | 0 |
Returns the number of cluster a clustering has. | def analysis_function_num_clusters(self,clustering):
return len(clustering.clusters) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())",
"def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)",
"def n_clusters(self):\n ... | [
"0.8762908",
"0.84918106",
"0.80784905",
"0.77352",
"0.71900326",
"0.7170602",
"0.7130262",
"0.70022637",
"0.6933635",
"0.68955094",
"0.6862841",
"0.6851633",
"0.68233424",
"0.68233424",
"0.6755365",
"0.6752718",
"0.66931707",
"0.66912186",
"0.6680638",
"0.66151196",
"0.66151... | 0.81173706 | 2 |
Returns the number of elements that are clusterized in this clustering (which may not be the total number of elements of the dataset if there were noisy elements) | def analysis_function_total_elements(self,clustering):
return clustering.total_number_of_elements | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def n_clusters(self):\n return len(self.clusters)",
"def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())",
"def count_elements_in_dataset(dataset):\n return dataset.count()",
"def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.pu... | [
"0.7475261",
"0.73660713",
"0.7281601",
"0.7209829",
"0.7184535",
"0.71797055",
"0.71275187",
"0.7005667",
"0.6999668",
"0.69551873",
"0.68624055",
"0.68511283",
"0.67983764",
"0.6792057",
"0.6780994",
"0.6774845",
"0.6771099",
"0.66886616",
"0.66632557",
"0.66320395",
"0.661... | 0.74841356 | 0 |
Returns the percentage of elements of the clustering that are in the 4 bigger clusters. | def analysis_function_top_4(self,clustering):
clustering.sort_clusters_by_size()
total = 0
percents = clustering.get_population_percent_of_n_bigger_clusters(4)
for p in percents:
total = total+p
return total | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dissimilarity(clusters):\n totDist = 0\n for c in clusters:\n totDist += c.variability()\n return totDist",
"def purity_score(clusters, classes):\n clusters = np.array(clusters)\n classes = np.array(classes)\n A = np.c_[(clusters,classes)]\n\n n_accurate = 0.\n\n ... | [
"0.68239075",
"0.66668284",
"0.6481002",
"0.6474694",
"0.6268896",
"0.6198322",
"0.61978954",
"0.6153407",
"0.6145863",
"0.61173046",
"0.6088561",
"0.6067123",
"0.60514313",
"0.605035",
"0.60220045",
"0.6007003",
"0.59786797",
"0.5954374",
"0.5944898",
"0.591898",
"0.5904012"... | 0.7220413 | 0 |
Returns the maximum number of clusters needed to have a percent of the total number of clustered elements. | def analysis_function_num_clusters_to_percent(self,clustering,percent):
return clustering.number_of_clusters_to_get_percent(percent) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)",
"def get_clust_num_perc(model, vis_perc=0.9):\n\tnc = len(np.where(model.allocmodel.Nk > 0)[0])\n\tidx = np.argsort... | [
"0.73421067",
"0.72231865",
"0.6814632",
"0.6761012",
"0.67437637",
"0.6737779",
"0.6622332",
"0.66150904",
"0.6535396",
"0.649745",
"0.64956445",
"0.6430533",
"0.64143646",
"0.6354163",
"0.63429755",
"0.63404",
"0.6339005",
"0.632302",
"0.6261867",
"0.62327045",
"0.62271553"... | 0.6772024 | 3 |
Returns the percent of elements over the total number of elements of the clustering, that have been clustered into the bigger cluster. | def analysis_function_top_percent(self,clustering):
clustering.sort_clusters_by_size()
return clustering.get_population_percent_of_cluster(0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analysis_function_num_clusters_to_percent(self,clustering,percent):\n return clustering.number_of_clusters_to_get_percent(percent)",
"def analysis_function_top_4(self,clustering):\n clustering.sort_clusters_by_size()\n total = 0\n percents = clustering.get_population_percent_of_n_... | [
"0.7153438",
"0.704848",
"0.6598043",
"0.65899014",
"0.65830696",
"0.6483606",
"0.6449112",
"0.6421873",
"0.63327456",
"0.62887275",
"0.6227568",
"0.6170871",
"0.61646694",
"0.6152447",
"0.6065469",
"0.6052555",
"0.6034332",
"0.60112214",
"0.6000157",
"0.5957048",
"0.5954154"... | 0.6579574 | 5 |
Returns the percent of noise elements in the dataset. | def analysis_function_noise_level(self, clustering, total_elements):
return 100.-(clustering.total_number_of_elements/float(total_elements))*100. | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_noise_level(self, data):\n noise = max(data)\n noise_min = 2600\n noise_max = 4095\n ratio = (noise - noise_min)/(noise_max - noise_min)\n return int(ratio*100)",
"def getNoiseVar(img,fraction=0.95):\n last_val = np.percentile(img,fraction)\n #si(img<last_val... | [
"0.68183047",
"0.67847115",
"0.6347133",
"0.62229025",
"0.61716366",
"0.61085075",
"0.6024723",
"0.59983605",
"0.59494674",
"0.5946805",
"0.5945206",
"0.5942025",
"0.5883616",
"0.58745587",
"0.58745587",
"0.5860042",
"0.5856353",
"0.5841199",
"0.5834648",
"0.57891965",
"0.578... | 0.70178664 | 0 |
Returns the mean cluster size. | def analysis_function_mean_cluster_size(self,clustering):
sizes = get_cluster_sizes(clustering.clusters)[1]
return numpy.mean(sizes) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mean_cluster(self, labelled_cluster):\n sum_of_points = self.sum_cluster(labelled_cluster)\n size_cluster = len(labelled_cluster)\n if self.sigma_cl1:\n size_cluster += np.sqrt(2)*self.sigma_cl1*np.random.randn()\n mean_of_points = sum_of_points * (1.0 / size_cluster)\n ... | [
"0.7243587",
"0.7179832",
"0.7015998",
"0.7009933",
"0.68922645",
"0.68876517",
"0.684724",
"0.6808377",
"0.669405",
"0.6671428",
"0.6667033",
"0.6651806",
"0.65882844",
"0.6482646",
"0.63869536",
"0.63827366",
"0.63742137",
"0.63045746",
"0.6303817",
"0.62123394",
"0.6193753... | 0.8215371 | 0 |
Creates a calculator using a class. | def evaluate_with_calculator(self, clustering, key_args):
calculator = key_args['class']()
return calculator.evaluate(clustering, key_args['matrix']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n model = Calculator()",
"def __init__(self, mode=\"Normal\"):\n\n self.mode = mode\n # minor optimization\n # apparently [] is 5x faster than list()\n # https://youtu.be/YjHsOrOOSuI?t=19m30s\n self.button = []\n \"\"\"button: List of buttons.\"\"\"\n\n ... | [
"0.6303939",
"0.5606582",
"0.5468204",
"0.5322988",
"0.53078365",
"0.529308",
"0.5283356",
"0.5271009",
"0.52677727",
"0.5266936",
"0.5252997",
"0.5238419",
"0.5233305",
"0.5231769",
"0.5197301",
"0.51828855",
"0.5182773",
"0.5162779",
"0.51625335",
"0.51475465",
"0.51475465"... | 0.0 | -1 |
This method create a project in pivotal tracker | def create_project():
client = RequestManager()
project_name = "".join(choices(string.ascii_letters + string.digits, k=10))
client.set_method("POST")
client.set_endpoint("/projects")
body = {"name": project_name}
client.set_body(json.dumps(body))
response = client.execute_request()
STORED_ID['project_id'] = response.json()['id'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def project_create(project):\n client.project.create(project)",
"def test_create_project(self):\n pass",
"def test_create_project(self):\n pass",
"def test_create_project(self):\n pass",
"def create_project(self, **kwargs):\n save = kwargs.get('save', True) \n if kwarg... | [
"0.7641362",
"0.74125963",
"0.74125963",
"0.74125963",
"0.73800826",
"0.7369805",
"0.72611123",
"0.7240642",
"0.72367054",
"0.7189181",
"0.71309054",
"0.70826024",
"0.70679694",
"0.7061639",
"0.6987262",
"0.698142",
"0.696992",
"0.6924102",
"0.69188553",
"0.6910896",
"0.69103... | 0.75777197 | 1 |
Static method for delete a project. | def delete_project(project_id):
client = RequestManager()
client.set_method("DELETE")
client.set_endpoint("/projects/{0}".format(project_id))
client.execute_request() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_project(project):\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.delete_project(project)\n if ret[constants.STATUS_CODE_KEY] == 200:\n click.echo(\"Success\")\n else:\n click.echo(ret[constants.MESSAGE_KEY])",
"def delete... | [
"0.8128038",
"0.80633974",
"0.8028948",
"0.78569174",
"0.78183025",
"0.7728668",
"0.76615155",
"0.7630471",
"0.76140267",
"0.76140267",
"0.7583802",
"0.75670856",
"0.75328547",
"0.74872017",
"0.7375651",
"0.7375104",
"0.7346846",
"0.7337656",
"0.7324851",
"0.73165905",
"0.730... | 0.76985204 | 6 |
Static method for delete all projects. | def delete_all_projects():
client = RequestManager()
client.set_method("GET")
client.set_endpoint("/projects")
response = client.execute_request()
for project in response.json():
try:
ProjectHelper.delete_project(project["id"])
except TypeError:
LOGGER.info(project) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear(self):\n for project in Project.objects:\n project.delete()",
"def __remove_all_projects__():\n p = subprocess.Popen('rm -rf {}/.wcscanner/*'.format(context.__BASE_PATH__), shell=True)\n p.wait()",
"def clean_project(self, app_name=None, delete_all=False):\n\n if not ap... | [
"0.7438387",
"0.74334717",
"0.7418019",
"0.6865698",
"0.6836307",
"0.6812033",
"0.6775198",
"0.6739514",
"0.6739514",
"0.6619207",
"0.6606535",
"0.65799767",
"0.65798426",
"0.65713173",
"0.6563861",
"0.6534334",
"0.64856863",
"0.6476536",
"0.6473611",
"0.6437883",
"0.6420292"... | 0.8597782 | 0 |
Static method who read all projects | def read_project(response):
STORED_ID['project_id'] = response.json()["id"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_read_project(self):\n pass",
"def test_read_project(self):\n pass",
"def project():",
"def project():",
"def project():",
"def test_get_projects(self):\n pass",
"def get_projects_data():\n wcscanner_path = context.__BASE_PATH__ + '/.wcscanner'\n\n data = []\n for ... | [
"0.7262337",
"0.7262337",
"0.7255745",
"0.7255745",
"0.7255745",
"0.7112538",
"0.7085046",
"0.7018626",
"0.70128983",
"0.6937012",
"0.6859167",
"0.67931646",
"0.67931646",
"0.67831695",
"0.6778349",
"0.6751696",
"0.6723736",
"0.67085075",
"0.67051655",
"0.66691184",
"0.666777... | 0.0 | -1 |
Static method for delete a project. | def delete_stored_project():
client = RequestManager()
client.set_method("DELETE")
client.set_endpoint("/projects/{0}".format(STORED_ID['project_id']))
client.execute_request() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_project(project):\n with BMI(_username, _password, constants.BMI_ADMIN_PROJECT) as bmi:\n ret = bmi.delete_project(project)\n if ret[constants.STATUS_CODE_KEY] == 200:\n click.echo(\"Success\")\n else:\n click.echo(ret[constants.MESSAGE_KEY])",
"def delete... | [
"0.8127805",
"0.8062877",
"0.802956",
"0.7817713",
"0.7729044",
"0.7698603",
"0.766216",
"0.7631897",
"0.7615524",
"0.7615524",
"0.7582872",
"0.7567336",
"0.7534077",
"0.7488375",
"0.73751706",
"0.73750573",
"0.7347409",
"0.7338061",
"0.73248214",
"0.7316373",
"0.73060185",
... | 0.7856347 | 3 |
Decorator that returns 403 status if user isn't logged in instead of redirecting to the LOGIN_URL | def login_required_403(view):
@wraps(view)
def dec_view(request, *args, **kwargs):
if not request.user.is_authenticated():
return JsonResponse({"detail": "You have to log in"}, status=403)
return view(request, *args, **kwargs)
return dec_view | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login_required(f):\n @functools.wraps(f)\n def wrap(*args, **kwargs):\n if not user_session.is_auth:\n raise Forbidden()\n return f(*args, **kwargs)\n return wrap",
"def not_authenticated(func):\n def decorated(request, *args, **kwargs):\n if request.user.is_authen... | [
"0.7650609",
"0.7616408",
"0.7589018",
"0.7559412",
"0.7558609",
"0.7510281",
"0.7494149",
"0.7491331",
"0.7440667",
"0.74237275",
"0.74189824",
"0.74179274",
"0.7398192",
"0.73772556",
"0.7373833",
"0.73519063",
"0.7316154",
"0.7309339",
"0.7299229",
"0.7293337",
"0.7288219"... | 0.79399925 | 0 |
Decide where to go, dashboard if logged in, login form if not | def start_view(request):
if request.user and Employee.objects.filter(user__pk=request.user.pk).exists():
if Employee.objects.get(user__pk=request.user.pk).is_manager:
return HttpResponseRedirect('/dashboard')
else:
return HttpResponseRedirect('/employee/show/%d/' % request.user.employee_user.first().pk)
else:
return HttpResponseRedirect('/login/') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login(self):\n identity = request.environ.get('repoze.who.identity')\n came_from = str(request.GET.get('came_from', '')) or \\\n url('/')\n if identity:\n redirect(url(came_from))\n else:\n c.came_from = came_from\n c.login_counter... | [
"0.7260029",
"0.7014417",
"0.69468075",
"0.6839301",
"0.6821525",
"0.67518455",
"0.67216855",
"0.6685984",
"0.6685984",
"0.65857905",
"0.6580526",
"0.657079",
"0.65570045",
"0.6549986",
"0.65449136",
"0.65154564",
"0.6505277",
"0.65018094",
"0.64914584",
"0.6470852",
"0.64603... | 0.0 | -1 |
Login with an accesscode | def accesscode(request, code):
employee = Employee.objects.get(access_code=code)
user = employee.user
user.backend = 'django.contrib.auth.backends.ModelBackend'
login(request, user)
return HttpResponseRedirect('/') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login():",
"def login():",
"def login(self):\n self.open(self.urls['login'])\n self.select_form(nr=0)\n\n self.form['custno'] = self.username\n self.form['password'] = self.password\n res = self.submit()\n \n return res",
"def login():\n url = AUTH_URL ... | [
"0.7269786",
"0.7269786",
"0.70734197",
"0.70426136",
"0.6852028",
"0.6803463",
"0.67843354",
"0.6739689",
"0.6730675",
"0.6720502",
"0.6683724",
"0.6669751",
"0.66376764",
"0.65688664",
"0.65634507",
"0.6525562",
"0.651685",
"0.651685",
"0.649879",
"0.64984405",
"0.6491229",... | 0.74460435 | 0 |
View for all employees (in company) or for current user dependent on employee role | def all_employees(request, company_id=None):
current_employee = Employee.objects.get(user__pk=request.user.pk)
company_super_user = current_employee.isCompanySuperUserOrHigher()
if company_id:
company = Company.objects.get(pk=company_id)
else:
company = current_employee.company
if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:
raise PermissionDenied()
change_company_form = ChangeCompanyForm(initial=dict(company=company))
return TemplateResponse(
request,
'all_employees.html',
{
'user': request.user,
'company_super_user': company_super_user,
'company': company,
'change_company_form': change_company_form,
}
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_all_employees():\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employees = Employee.query.all()\n \n \n ## rig... | [
"0.75012994",
"0.676221",
"0.675863",
"0.66391176",
"0.659517",
"0.65067995",
"0.63182765",
"0.6227569",
"0.62267536",
"0.6176886",
"0.61206603",
"0.6090563",
"0.6077315",
"0.60686785",
"0.6056699",
"0.59416133",
"0.5920941",
"0.5915269",
"0.5865308",
"0.5852588",
"0.5848536"... | 0.7280209 | 1 |
Get all employees as json | def employees_json(request):
# current_employee = Employee.objects.get(user__pk=request.user.pk)
employee_list = Employee.objects.filter(manager=request.user.employee_user)
employees = list()
for employee in employee_list:
manager_dict = model_to_dict(employee)
manager_dict['first_name'] = employee.user.first_name
manager_dict['last_name'] = employee.user.last_name
employees.append(manager_dict)
data = {"employees": employees}
return JsonResponse(data=data, content_type='application/json', safe=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self):\n employees = self.service.get_employees(strategy=selectinload)\n return self.schema.dump(employees, many=True), 200",
"def employees(employee_id=None):\n\tif not employee_id:\n\t\temployee_data = _serialize_list(Employee.query.all())\n\telse:\n\t\temployee_data = _serialize_model(Em... | [
"0.80177975",
"0.79526293",
"0.78115326",
"0.72639924",
"0.7209743",
"0.71376187",
"0.71241915",
"0.7055127",
"0.7054203",
"0.7002898",
"0.68735474",
"0.6857624",
"0.68054706",
"0.67973894",
"0.6706132",
"0.66616935",
"0.6618934",
"0.6581776",
"0.6578094",
"0.65394276",
"0.64... | 0.760211 | 3 |
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