DataType:\\n return self._type\",\n \"def data_type(self):\\n return self.unpack_dword(0xC) & DEVPROP_MASK_TYPE\",\n \"def data_type(self):\\n try:\\n return self.attributes.workspace.attributes['library:datatype']['items']\\n except Exception as e:\\n self._logger.debug(f\\\"data_category {e}\\\")\\n return None\",\n \"def getDataType(self):\\n\\n return self._dataType\",\n \"def type(self) -> 'Data_Type':\\n return Data_Type(self._info.htype, self._info.ptype)\",\n \"def datatype(self):\\n return self._datatype\",\n \"def get_band_dtype(self, band_idx=1):\\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\\n raise IndexError(\\\"band index is out of range\\\")\\n return self.dataset.GetRasterBand(band_idx).DataType\",\n \"def data_type(self) -> pulumi.Input['AssetModelDataType']:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"async def get_dtype(self):\\r\\n pass\",\n \"def datatype(parameter):\\n return type(parameter)\",\n \"def data_type_id(self) -> str:\\n return self._data_type_id\",\n \"def dtype(self):\\n return self.data.dtype\",\n \"def dtype(self):\\n return self._data.dtype\",\n \"def dtype(self):\\n return self._data.dtype\",\n \"def sql_type(dtype):\\n if dtype.kind in (\\\"i\\\",\\\"u\\\",\\\"f\\\"):\\n # It's a numeric type\\n if dtype == np.int32:\\n return \\\"integer\\\"\\n elif dtype == np.int64:\\n return \\\"bigint\\\"\\n elif dtype == np.float32:\\n return \\\"real\\\"\\n elif dtype == np.float64:\\n return \\\"float\\\"\\n else:\\n raise ValueError(\\\"Unsupported data type \\\"+str(dtype))\\n elif dtype.kind == \\\"S\\\":\\n # It's a string\\n # Note: this assumes 1 byte = 1 character!\\n return (\\\"char(%d)\\\" % dtype.itemsize)\\n else:\\n # Not numeric or string, don't know what to do with this!\\n raise ValueError(\\\"Unsupported data type \\\"+str(dtype))\",\n \"def _infer_dtype(val):\\n if re.match(r'\\\\d{4}-\\\\d{2}(?:-\\\\d{2})?', val):\\n return 'date'\\n elif re.match(r'[+-]?\\\\d+$', val):\\n return 'int'\\n elif re.match(r'[+-]?\\\\d+%$', val):\\n return 'pct'\\n elif re.match(r'[a-zA-Z ]+', val):\\n return 'text'\\n else:\\n msg = \\\"val={0} dtype not recognized\\\".format(val)\\n raise ValueError(msg)\",\n \"def get_type_name(column_data: Dict) -> Text:\\n column_type = str(column_data[\\\"db_type\\\"]).lower()\\n if \\\"text\\\" in column_type:\\n return \\\"text\\\"\\n elif \\\"json\\\" in column_type:\\n return \\\"json\\\"\\n elif not isinstance(column_data[\\\"type\\\"], tuple):\\n type_ = column_data[\\\"type\\\"].__name__\\n else:\\n type_ = f'array of {column_data[\\\"type\\\"][1]}'\\n return type_\",\n \"def dtype(self):\\n return self.dataset.dtype\",\n \"def _type_data(self, row, index):\\n typed_row = []\\n for i, (entry, entry_type) in enumerate(zip(row, self.column_types)):\\n try:\\n typed_row.append(entry_type(entry))\\n except ValueError:\\n if (entry_type == int or entry_type == float) and self.missing_to_zero:\\n typed_row.append(entry_type(0))\\n else:\\n if [i + 1, index + 2, entry, entry_type] not in self.invalid_typed and self.print_warnings:\\n self.invalid_typed.append([i + 1, index + 2, entry, entry_type])\\n typed_row.append(entry)\\n return typed_row\",\n \"def get_type(self, key):\\n index = self.get_index(key)\\n return self.types[index]\",\n \"def get_field_type(self, table_name, field_name):\\n \\n dtype = self.field_types[(self.field_types.TABNAME == table_name) & (self.field_types.FIELDNAME == field_name)]['DATATYPE'].values[0] \\n return dtype\",\n \"def dtype(self):\\n return self._vars[0].dtype\",\n \"def get_data_type(self, key, interface_type):\\n if interface_type == \\\"input\\\":\\n for param in self.input_params:\\n if param.name_hint == key:\\n return param._checked_type_\\n\\n if interface_type == \\\"output\\\":\\n if isinstance(self.output_type, tvm.ir.type.TupleType):\\n if int(key) < len(self.output_type.fields):\\n return self.output_type.fields[int(key)]\\n elif int(key) == 0:\\n return self.output_type\\n\\n return None\",\n \"def data_types(self):\",\n \"def get_dtype(arr, backend='autograd'):\\n if backend == 'pytorch':\\n return pytorch_dtype_query_mapping_dict[arr.dtype]\\n elif backend == 'autograd':\\n return str(arr.dtype)\",\n \"def dtype(self):\\n return self._channel.datatype\",\n \"def datatype(self) -> MetricTypeEnum:\\n return self._datatype\",\n \"def get_data_type(self) -> Type[ATTRIBUTE_TYPES]:\\n if isinstance(self.value, (list, tuple, set)):\\n value = next(iter(self.value))\\n else:\\n value = self.value\\n value = cast(ATTRIBUTE_TYPES, value)\\n return type(value)\",\n \"def required_data_type(self):\\n return Data\",\n \"def data_types():\\n\\n return ...\",\n \"def test_get_type():\\n formatter = TabularOutputFormatter()\\n\\n tests = (\\n (1, int),\\n (2.0, float),\\n (b\\\"binary\\\", binary_type),\\n (\\\"text\\\", text_type),\\n (None, type(None)),\\n ((), text_type),\\n )\\n\\n for value, data_type in tests:\\n assert data_type is formatter._get_type(value)\",\n \"def getDataType(self, label):\\n\\n try:\\n return self._data[label].dtype\\n except KeyError:\\n return None\",\n \"def _get_data_type_name_by_value(data_type, value, field_name='data_type'):\\n return data_type.DESCRIPTOR.fields_by_name[field_name].enum_type.values_by_number[value].name\",\n \"def type(self):\\n return _python_type_map[self.arrow_dtype.id]\",\n \"def get_data_types(data):\\n\\tdtypes = data.dtypes\\n\\treturn OrderedDict(zip(dtypes.index, dtypes.astype(str)))\",\n \"def GetDataType(self):\\n return _gmat_py.ElementWrapper_GetDataType(self)\",\n \"def getDataSetType(self):\\n return self.__data_set_type__\",\n \"def dtype(self):\\n return self.initial_value.dtype\",\n \"def get_d_value(self, key=\\\"\\\", index=-1):\\n _key = key\\n _index = index\\n if(_index < 0):\\n _index = self.get_index(_key)\\n _value = self.values[_index].strip()\\n _type = self.types[_index].strip().lower()\\n if _type == \\\"string\\\" or _type == \\\"\\\":\\n return _value\\n if _value == \\\"\\\":\\n return 0\\n return eval(_type)(_value)\",\n \"def dtype(self):\\n return self.config[\\\"dtype\\\"]\",\n \"def get_type_string(data):\\r\\n data_type = type(data)\\r\\n\\r\\n if data_type in (int, long):\\r\\n return 'integer'\\r\\n elif data_type == float:\\r\\n return 'float'\\r\\n elif data_type == bool:\\r\\n return 'boolean'\\r\\n elif data_type in (list, tuple):\\r\\n return 'list'\\r\\n elif data_type == dict:\\r\\n return 'hash'\\r\\n elif data is None:\\r\\n return 'null'\\r\\n elif isinstance(data, basestring):\\r\\n return 'string'\",\n \"def test_data_type(self):\\n self.assertTrue(self.tester.data_type(), \\\"18S\\\")\",\n \"def dtype(self):\\n return self._dtype\",\n \"def _map_data_types(dtype):\\n return _data_type_map[dtype]\",\n \"def dtype(self):\\n return self._fl.raw.dtype\",\n \"def _get_type(dtype):\\n if isinstance(dtype, macro):\\n dtype = dtype.as_fn()\\n \\n while isinstance(dtype, jit):\\n dtype = dtype.f \\n \\n while isinstance(dtype, Expr):\\n if isinstance(dtype, UntypedFn):\\n if len(dtype.body) == 1:\\n stmt = dtype.body[0]\\n if stmt.__class__ is Return:\\n expr = stmt.value \\n if expr.__class__ is Cast:\\n dtype = expr.type \\n break\\n assert False, \\\"Don't know how to convert function %s into Parakeet type\\\" % dtype\\n elif isinstance(dtype, TypedFn):\\n dtype = dtype.return_type\\n if isinstance(dtype, TypeValue):\\n dtype = dtype.type_value \\n elif isinstance(dtype.type, TypeValueT):\\n dtype = dtype.type\\n elif isinstance(dtype, Closure):\\n dtype = dtype.fn\\n elif isinstance(dtype.type, (ClosureT, FnT)):\\n dtype = dtype.type.fn\\n else:\\n assert False, \\\"Don't know how to turn %s : %s into Parakeet type\\\" % (dtype, dtype.type) \\n\\n\\n if isinstance(dtype, Type):\\n if isinstance(dtype, TypeValueT):\\n return dtype.type\\n else:\\n return dtype \\n \\n elif isinstance(dtype, (np.dtype, type)):\\n\\n return type_conv.equiv_type(dtype)\\n elif isinstance(dtype, str):\\n return type_conv.equiv_type(np.dtype(dtype))\\n assert False, \\\"Don't know how to turn %s into Parakeet type\\\" % dtype\",\n \"def datafolder_type(datafolderpath):\\n datafolder = os.path.basename(os.path.normpath(datafolderpath))\\n suf = datafolder.split('_')[-1]\\n if suf == 'acc':\\n datatype = 'acc'\\n elif suf == 'bfs':\\n datatype = 'bfs'\\n elif suf == 'bst':\\n datatype = 'bst'\\n elif suf == 'sst':\\n datatype = 'sst'\\n elif suf == 'xst':\\n datatype = 'xst'\\n else:\\n datatype = None\\n return datatype\",\n \"def getReferenceDataType(program: ghidra.program.model.listing.Program, referredToDataType: ghidra.program.model.data.DataType) -> ghidra.program.model.data.DataType:\\n ...\",\n \"def map_dtype(dtype):\\n item_idx = int(math.log(dtype.itemsize, 2))\\n if dtype.kind == 'i':\\n return [int8, int16, int32, int64][item_idx]\\n elif dtype.kind == 'u':\\n return [uint8, uint16, uint32, uint64][item_idx]\\n elif dtype.kind == 'f':\\n if dtype.itemsize == 2:\\n pass # half floats not supported yet\\n elif dtype.itemsize == 4:\\n return float32\\n elif dtype.itemsize == 8:\\n return float64\\n elif dtype.itemsize == 16:\\n return float128\\n elif dtype.kind == 'b':\\n return int8\\n elif dtype.kind == 'c':\\n if dtype.itemsize == 8:\\n return complex64\\n elif dtype.itemsize == 16:\\n return complex128\\n elif dtype.itemsize == 32:\\n return complex256\\n elif dtype.kind == 'O':\\n return object_\",\n \"def typecast(dtype: Any) -> str:\\n if dtype is int:\\n return \\\"Int64\\\"\\n elif dtype is float:\\n return \\\"Float64\\\"\\n elif dtype is bool:\\n return \\\"bool\\\"\\n return \\\"string\\\"\",\n \"def dtype(self) -> DataType:\\n return self._dtype\",\n \"def inspect_dtype(series: pd.Series) -> str:\\n\\n mapping = {pd_types.is_bool_dtype: \\\"bool\\\",\\n pd_types.is_integer_dtype: \\\"int\\\",\\n pd_types.is_float_dtype: \\\"float\\\",\\n pd_types.is_datetime64_any_dtype: \\\"datetime\\\"}\\n\\n for check, result in mapping.items():\\n if check(series):\\n return result\\n\\n raise TypeError(\\\"Type is not understand for column '{}'. Allowed \\\"\\n \\\"types are bool, int, float, str and datetime.\\\"\\n .format(series.name))\",\n \"def basetype(series):\\n if not series.dtype == types.ObjectType:\\n # Type is specific already; return it without further inspection.\\n return series.dtype\\n # Now type is Object (string) See if any more specific type can be deduced:\",\n \"def _spark_data_type(self) -> sql_type.DataType:\\n return self._spark_type_class()\",\n \"def type(self):\\n return struct.unpack('<B', self.raw_data[0])[0]\",\n \"def convert_data_type(self, datatype):\\n converted = Engine.convert_data_type(self, datatype)\\n if \\\"NUMERIC\\\" in converted:\\n converted = \\\"NUMERIC\\\"\\n elif \\\"VARCHAR\\\" in converted:\\n try:\\n length = int(converted.split('(')[1].split(')')[0].split(',')[0])\\n if length > 255:\\n converted = \\\"TEXT\\\"\\n except BaseException:\\n pass\\n return converted\",\n \"def getMatchingDataType(program: ghidra.program.model.listing.Program, comparisonDt: ghidra.program.model.data.DataType) -> ghidra.program.model.data.DataType:\\n ...\",\n \"def type(self):\\n # easy enough\\n return self._dataset._pyre_id.type\",\n \"def _getStatisticType(self, statistic):\\n\\n instructions = simplejson.loads(statistic.instructions_json)\\n return instructions['type']\",\n \"def getEHStateDataType(program: ghidra.program.model.listing.Program) -> ghidra.program.model.data.DataType:\\n ...\",\n \"def get_sensor_type(self):\\n return self.data[1][:-1]\",\n \"def __get_default_dtype(val_type):\\n\\n if val_type == int:\\n dtype = q_consts.int64\\n elif val_type == float:\\n dtype = q_consts.float64\\n else:\\n raise Exception(\\\"input element type %s is not supported\\\" % val_type)\\n return dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def _column_type(t):\\n return 'bigint' if datastore_type[t].numeric else 'text'\",\n \"def numpy_type(sqltype):\\n m = re.match(\\\"char\\\\(([0-9]+)\\\\)\\\", sqltype.strip())\\n if m is not None:\\n # It's a string\\n return np.dtype(\\\"|S\\\"+m.group(1))\\n else:\\n # It's a numeric type\\n if sqltype == \\\"integer\\\" or sqltype == \\\"int\\\":\\n return np.int32\\n elif sqltype == \\\"bigint\\\":\\n return np.int64\\n elif sqltype == \\\"real\\\":\\n return np.float32\\n elif sqltype == \\\"float\\\":\\n return np.float64\\n else:\\n raise ValueError(\\\"Unsupported data type \\\"+sqltype)\",\n \"def type(self):\\n # type: () -> type\\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]\",\n \"def getDataTypes(self, name: unicode) -> List[ghidra.program.model.data.DataType]:\\n ...\",\n \"def dtype_to_type(dtype) -> Type:\\n if dtype == np.object:\\n return str\\n else:\\n return type(np.zeros(1, dtype).item())\",\n \"def get_column_type(type_name: str) -> object:\\n raise NotImplementedError\",\n \"def get_type(self) -> str:\\n return self.row_dict['type']\",\n \"def dtype_offset( dtype, name = None ):\\n if name:\\n # get the dtype for the named value\\n # the offset is the second value\\n return dtype.fields[ name ][ 1 ]\\n else:\\n return 0\",\n \"def dt(self, dtype_key):\\n try:\\n return self.dtypes[dtype_key]\\n except:\\n return None\",\n \"def dtype(self):\\n return self.array.dtype\",\n \"def data_type(arg):\\n if isinstance(arg, str):\\n return len(arg)\\n elif isinstance(arg, bool):\\n return arg\\n elif isinstance(arg, list):\\n return arg[2] if len(arg) >= 3 else None\\n elif isinstance(arg, int):\\n if arg == 100:\\n return \\\"equal to 100\\\"\\n else:\\n if arg < 100:\\n return \\\"less than 100\\\"\\n else:\\n return \\\"greater than 100\\\"\\n else:\\n return \\\"no value\\\"\",\n \"def to_dtype(x, dtype):\\n return x.type(dtype)\",\n \"def infer_dtype(self):\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72349155","0.7209514","0.7037685","0.69469965","0.6839629","0.6827165","0.6827165","0.6827165","0.6680594","0.6609436","0.6554991","0.6540169","0.6508893","0.6493248","0.6450711","0.6441724","0.63943344","0.6353002","0.63170236","0.63162494","0.6316209","0.6304272","0.62776566","0.62629133","0.6216848","0.6192063","0.61802197","0.61366147","0.61300766","0.61212045","0.60986674","0.60815984","0.60618514","0.60582805","0.6031112","0.6031112","0.5997212","0.59964097","0.59884554","0.59849924","0.5975238","0.59740233","0.59584725","0.5956587","0.5941","0.58939976","0.5893874","0.5892974","0.58918357","0.5874683","0.58728176","0.58675635","0.58639705","0.5862044","0.5847815","0.5846682","0.58445686","0.5839734","0.5837202","0.5828208","0.5827193","0.58160686","0.5810116","0.5799439","0.57990736","0.5791041","0.5788004","0.5780957","0.5769986","0.5763336","0.57463485","0.57420605","0.57413846","0.5737375","0.5734095","0.5720776","0.5711165","0.57090586","0.57043314","0.5700789","0.56897235","0.5686845","0.56861126","0.56857264","0.5685535","0.5685535","0.5685535","0.5677529","0.5669671","0.5669017","0.56654","0.56636804","0.56622446","0.566021","0.56495285","0.56448317","0.5640341","0.5630273","0.56282324","0.5622227"],"string":"[\n \"0.72349155\",\n \"0.7209514\",\n \"0.7037685\",\n \"0.69469965\",\n \"0.6839629\",\n \"0.6827165\",\n \"0.6827165\",\n \"0.6827165\",\n \"0.6680594\",\n \"0.6609436\",\n \"0.6554991\",\n \"0.6540169\",\n \"0.6508893\",\n \"0.6493248\",\n \"0.6450711\",\n \"0.6441724\",\n \"0.63943344\",\n \"0.6353002\",\n \"0.63170236\",\n \"0.63162494\",\n \"0.6316209\",\n \"0.6304272\",\n \"0.62776566\",\n \"0.62629133\",\n \"0.6216848\",\n \"0.6192063\",\n \"0.61802197\",\n \"0.61366147\",\n \"0.61300766\",\n \"0.61212045\",\n \"0.60986674\",\n \"0.60815984\",\n \"0.60618514\",\n \"0.60582805\",\n \"0.6031112\",\n \"0.6031112\",\n \"0.5997212\",\n \"0.59964097\",\n \"0.59884554\",\n \"0.59849924\",\n \"0.5975238\",\n \"0.59740233\",\n \"0.59584725\",\n \"0.5956587\",\n \"0.5941\",\n \"0.58939976\",\n \"0.5893874\",\n \"0.5892974\",\n \"0.58918357\",\n \"0.5874683\",\n \"0.58728176\",\n \"0.58675635\",\n \"0.58639705\",\n \"0.5862044\",\n \"0.5847815\",\n \"0.5846682\",\n \"0.58445686\",\n \"0.5839734\",\n \"0.5837202\",\n \"0.5828208\",\n \"0.5827193\",\n \"0.58160686\",\n \"0.5810116\",\n \"0.5799439\",\n \"0.57990736\",\n \"0.5791041\",\n \"0.5788004\",\n \"0.5780957\",\n \"0.5769986\",\n \"0.5763336\",\n \"0.57463485\",\n \"0.57420605\",\n \"0.57413846\",\n \"0.5737375\",\n \"0.5734095\",\n \"0.5720776\",\n \"0.5711165\",\n \"0.57090586\",\n \"0.57043314\",\n \"0.5700789\",\n \"0.56897235\",\n \"0.5686845\",\n \"0.56861126\",\n \"0.56857264\",\n \"0.5685535\",\n \"0.5685535\",\n \"0.5685535\",\n \"0.5677529\",\n \"0.5669671\",\n \"0.5669017\",\n \"0.56654\",\n \"0.56636804\",\n \"0.56622446\",\n \"0.566021\",\n \"0.56495285\",\n \"0.56448317\",\n \"0.5640341\",\n \"0.5630273\",\n \"0.56282324\",\n \"0.5622227\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8298103"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75789,"cells":{"query":{"kind":"string","value":"Returns a dictionary with well parameters"},"document":{"kind":"string","value":"def get_parameters(self):\n return(_deepcopy(self.parameters))"},"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_params(self):\n return {}","def parameters(self):\n return {\"W\": self.W,\n \"T\": self.T,\n \"P\": self.P,\n \"Wo\": self.Wo,\n \"To\": self.To,\n \"Po\": self.Po}","def get_params(self):\n return {\"d\": \"155\"}","def get_params(self, deep=False):\n return {\"alpha\": self.alpha, \"beta\": self.beta, \"gamma\": self.gamma, \"W\": self.W, \"bias\": self.bias, \"add_bias\": self.add_bias, \"opts\": self.opts}","def parameters(self):\n return {}","def parameters(self):\n return {\"W\": self.W,\n \"T\": self.T,\n \"P\": self.P}","def get_params(self):","def parameters(self):\n return {\"P\": self.P,\n \"T\": self.T}","def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }","def get_params(self) -> Dict:\n params: Dict = {}\n params['g_leak'] = self.leak.get_g()\n params['g_kvhh'] = self.kvhh.get_g()\n params['g_cav'] = self.cav.get_g()\n params['g_kca'] = self.kca.get_g()\n params['g_nap'] = self.nap.get_g()\n params['t_ca'] = self.tau_ca\n return params","def parameters_dict(self):\n return","def get_params(self):\n return {'k': self.k, 'q': self.q, 'sigma_s': self.sigma_s, 'm': self.m}","def get_params(self):\n return {\n \"nspecies\": self.nspecies,\n \"lmax\": self.lmax,\n \"nmax\": self.nmax,\n \"rcut\": self.rcut,\n \"sigma\": self.sigma,\n \"trans_width\": self.trans_width\n }","def get_params(self):\n outputs = ['sample',\n 'ratio_params',\n 'despike_params',\n 'autorange_params',\n 'bkgcorrect_params']\n\n out = {}\n for o in outputs:\n out[o] = getattr(self, o)\n\n out['filter_params'] = self.filt.params\n out['filter_sequence'] = self.filt.sequence\n out['filter_used'] = self.filt.make_keydict()\n\n return out","def parameters(self):\n return dict(self._register)","def get_params(self):\n return {'classifier': self.classifier,\n 'grid_param': self.grid_param,\n 'n_param_comb': self.n_param_comb,\n 'top_bagging': self.bagging,\n 'bagging_param': self.bagging_param,\n 'comb_seed': self.comb_seed}","def get_required_params():\n return {}","def good_params():\n from scraper import PARAMS\n params = PARAMS.copy()\n params['Business_Name'] = 'CodeFellows'\n params['City'] = 'Seattle'\n return params","def getParameters( self ):\n parameterDict = {}\n parameterDict['StorageName'] = self.name\n parameterDict['ProtocolName'] = self.protocolName\n parameterDict['Protocol'] = self.protocol\n parameterDict['Host'] = self.host\n parameterDict['Path'] = self.path\n parameterDict['Port'] = self.port\n parameterDict['SpaceToken'] = self.spaceToken\n parameterDict['WSUrl'] = self.wspath\n return S_OK( parameterDict )","def _build_param_dict(self):\n # Add parameter handlers to parameter dict. \n self._param_dict.add(SBE37Parameter.OUTPUTSAL,\n r'(do not )?output salinity with each sample',\n lambda match : False if match.group(1) else True,\n self._true_false_to_string)\n self._param_dict.add(SBE37Parameter.OUTPUTSV,\n r'(do not )?output sound velocity with each sample',\n lambda match : False if match.group(1) else True,\n self._true_false_to_string)\n self._param_dict.add(SBE37Parameter.NAVG,\n r'number of samples to average = (\\d+)',\n lambda match : int(match.group(1)),\n self._int_to_string)\n self._param_dict.add(SBE37Parameter.SAMPLENUM,\n r'samplenumber = (\\d+), free = \\d+',\n lambda match : int(match.group(1)),\n self._int_to_string)\n self._param_dict.add(SBE37Parameter.INTERVAL,\n r'sample interval = (\\d+) seconds',\n lambda match : int(match.group(1)),\n self._int_to_string)\n self._param_dict.add(SBE37Parameter.STORETIME,\n r'(do not )?store time with each sample',\n lambda match : False if match.group(1) else True,\n self._true_false_to_string)\n self._param_dict.add(SBE37Parameter.TXREALTIME,\n r'(do not )?transmit real-time data',\n lambda match : False if match.group(1) else True,\n self._true_false_to_string)\n self._param_dict.add(SBE37Parameter.SYNCMODE,\n r'serial sync mode (enabled|disabled)',\n lambda match : False if (match.group(1)=='disabled') else True,\n self._true_false_to_string)\n self._param_dict.add(SBE37Parameter.SYNCWAIT,\n r'wait time after serial sync sampling = (\\d+) seconds',\n lambda match : int(match.group(1)),\n self._int_to_string)\n self._param_dict.add(SBE37Parameter.TCALDATE,\n r'temperature: +((\\d+)-([a-zA-Z]+)-(\\d+))',\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\n self._date_to_string)\n self._param_dict.add(SBE37Parameter.TA0,\n r' +TA0 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.TA1,\n r' +TA1 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.TA2,\n r' +TA2 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.TA3,\n r' +TA3 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.CCALDATE,\n r'conductivity: +((\\d+)-([a-zA-Z]+)-(\\d+))',\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\n self._date_to_string)\n self._param_dict.add(SBE37Parameter.CG,\n r' +G = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.CH,\n r' +H = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.CI,\n r' +I = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.CJ,\n r' +J = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.WBOTC,\n r' +WBOTC = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.CTCOR,\n r' +CTCOR = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.CPCOR,\n r' +CPCOR = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PCALDATE,\n r'pressure .+ ((\\d+)-([a-zA-Z]+)-(\\d+))',\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\n self._date_to_string)\n self._param_dict.add(SBE37Parameter.PA0,\n r' +PA0 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PA1,\n r' +PA1 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PA2,\n r' +PA2 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PTCA0,\n r' +PTCA0 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PTCA1,\n r' +PTCA1 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PTCA2,\n r' +PTCA2 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PTCB0,\n r' +PTCSB0 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PTCB1,\n r' +PTCSB1 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.PTCB2,\n r' +PTCSB2 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.POFFSET,\n r' +POFFSET = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.RCALDATE,\n r'rtc: +((\\d+)-([a-zA-Z]+)-(\\d+))',\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\n self._date_to_string)\n self._param_dict.add(SBE37Parameter.RTCA0,\n r' +RTCA0 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.RTCA1,\n r' +RTCA1 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)\n self._param_dict.add(SBE37Parameter.RTCA2,\n r' +RTCA2 = (-?\\d.\\d\\d\\d\\d\\d\\de[-+]\\d\\d)',\n lambda match : float(match.group(1)),\n self._float_to_string)","def getParams(self):\n\n\t\tparams = {\"Nparticles\":self.__Nparticles,\"Nkicks\":self.__Nkicks,\"kappa\":self.__kappa, \"eta\":self.__eta,\"gamma\":self.__gamma, \"omega\":self.__omega,\n\t\t\"Kbt\":self.__Kbt, \"tk\":self.__tk}\n\n\t\treturn params","def parameters(self):","def _get_fitted_params(self):\n return {}","def parameters(self):\n res = dict()\n res[\"population_size\"] = self.population_size\n res[\"mutation_prob\"] = self.mutation_prob\n res[\"crossover\"] = self.crossover\n res[\"selection\"] = self.selection\n res[\"sigma\"] = self.sigma\n res[\"crossover_method\"] = self.crossover_method\n res[\"selection_method\"] = self.selection_method\n res[\"best_rate\"] = self.best_rate\n res[\"n_parents\"] = self.n_parents\n res[\"model_parameters\"] = self.model.total_parameters()\n res[\"IDCT_from\"] = self.IDCT_from\n res[\"elitism\"] = self.elitism\n return res","def get_params(self) -> Dict:\n params: Dict = {}\n params['g_leak']: float = self.leak.get_g()\n params['g_nav']: float = self.nav.get_g()\n params['g_kvhh']: float = self.kvhh.get_g()\n params['g_kva']: float = self.kva.get_g()\n params['g_kvsi']: float = self.kvsi.get_g()\n params['g_cav']: float = self.cav.get_g()\n params['g_kca']: float = self.kca.get_g()\n params['g_nap']: float = self.nap.get_g()\n params['g_kir']: float = self.kir.get_g()\n params['g_ampar']: float = self.ampar.get_g()\n params['g.nmdar']: float = self.nmdar.get_g()\n params['g_gabar']: float = self.gabar.get_g()\n params['t_Ca']: float = self.tau_ca\n return params","def params(self):\n return {'cfg': self.cfg,\n 'momentum': self.momentum,\n 'center': self.center,\n 'scale': self.scale,\n 'epsilon': self.epsilon,\n 'act_fn': self.act_fn}","def get_parameters(self):\n params = {\"train_frac\": self.train_frac, \"split_alg\": self.split_alg,\n \"nw_name\": self._nw_name, \"split_id\": self.split_id}\n return params","def get_params(self):\n params = {}\n for step in self.steps:\n params[step[0]] = step[1].get_params()\n return params","def params(self):\n return {'out_dim': self.out_dim,\n 'act_fn': self.act_fn,\n 'use_bias': self.use_bias,\n 'idx': self.idx}","def parameters(self):\n return {\n 'base':self.base.parameters(),\n 'material':[m.parameters() for m in self.material],\n 'fraction':self.fraction,\n }","def parameters(self) -> Dict[str, Any]:\n return self.data[\"args\"].get(\"parameters\", {})","def params(self):\n return {'shape': self.shape,\n 'name': self.name}","def get_parameter_dict(self):\n prm = ModelParameters()\n prm.define(\"a\", self.a)\n return prm","def get_parameters_dictionary(request):\n parameters_dict = {PARAMETER_MESSAGE: request.GET.get('message'),\n PARAMETER_ENTITY_NAME: request.GET.get('entity_name'),\n PARAMETER_STRUCTURED_VALUE: request.GET.get('structured_value'),\n PARAMETER_FALLBACK_VALUE: request.GET.get('fallback_value'),\n PARAMETER_BOT_MESSAGE: request.GET.get('bot_message'),\n PARAMETER_TIMEZONE: request.GET.get('timezone'),\n PARAMETER_LANGUAGE_SCRIPT: request.GET.get('language_script', ENGLISH_LANG),\n PARAMETER_SOURCE_LANGUAGE: request.GET.get('source_language', ENGLISH_LANG),\n PARAMETER_PAST_DATE_REFERENCED: request.GET.get('date_past_reference', 'False'),\n PARAMETER_MIN_DIGITS: request.GET.get('min_number_digits'),\n PARAMETER_MAX_DIGITS: request.GET.get('max_number_digits'),\n PARAMETER_NUMBER_UNIT_TYPE: request.GET.get('unit_type'),\n PARAMETER_LOCALE: request.GET.get('locale'),\n PARAMETER_RANGE_ENABLED: request.GET.get('range_enabled')\n }\n\n return parameters_dict","def parameters(self) -> dict[str, Any]:\n return {\n \"IrrigationEfficiency\": self._get_textvar_value(\"IrrigationEfficiency\"),\n \"MaskMap\": self._get_textvar_value(\"MaskMap\"),\n \"start_time\": get_time(self.forcing.start_time).strftime(ISO_TIMEFMT),\n \"end_time\": get_time(self.forcing.end_time).strftime(ISO_TIMEFMT),\n }","def get_params(self):\n return {'threshold': self.threshold,\n 'subsample': self.subsample,\n 'estimator': self.estimator,\n 'n_folds': self.n_folds,\n 'stratify': self.stratify,\n 'random_state': self.random_state,\n 'n_jobs': self.n_jobs}","def get_params(self):\n pass","def params(self) -> dict:\n\n if not self.exp_metadata.parameters:\n self.exp_metadata.parameters = {}\n return self.exp_metadata.parameters","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 _build_param_dict(self):\n self._build_common_param_dict()\n\n self._param_dict.add(Parameter.NUM_AVG_SAMPLES,\n r'ScansToAverage>([\\d]+)</ScansToAverage>',\n lambda match: int(match.group(1)),\n str,\n type=ParameterDictType.INT,\n display_name=\"Scans to Average\",\n description=\"Number of samples to average (must be even)\",\n range=INT16,\n startup_param=True,\n direct_access=False,\n default_value=4,\n visibility=ParameterDictVisibility.READ_WRITE)\n self._param_dict.add(Parameter.MIN_COND_FREQ,\n r'MinimumCondFreq>([\\d]+)</MinimumCondFreq',\n lambda match: int(match.group(1)),\n str,\n type=ParameterDictType.INT,\n display_name=\"Minimum Conductivity Frequency\",\n range=INT16,\n description=\"Minimum conductivity frequency to enable pump turn-on.\",\n startup_param=True,\n direct_access=False,\n default_value=500,\n units=Units.HERTZ,\n visibility=ParameterDictVisibility.IMMUTABLE)\n self._param_dict.add(Parameter.PUMP_DELAY,\n r'PumpDelay>([\\d]+)</PumpDelay',\n lambda match: int(match.group(1)),\n str,\n type=ParameterDictType.INT,\n display_name=\"Pump Delay\",\n range=INT16,\n description=\"Time to wait after minimum conductivity frequency is reached before turning pump on.\",\n startup_param=True,\n direct_access=False,\n default_value=60,\n units=Units.SECOND,\n visibility=ParameterDictVisibility.READ_WRITE)\n self._param_dict.add(Parameter.AUTO_RUN,\n r'AutoRun>(.*)</AutoRun',\n lambda match: True if match.group(1) == 'yes' else False,\n self._true_false_to_string,\n type=ParameterDictType.BOOL,\n display_name=\"Auto Run\",\n description=\"Enable automatic logging when power is applied: (true | false).\",\n range={'True': True, 'False': False},\n startup_param=True,\n direct_access=True,\n default_value=False,\n visibility=ParameterDictVisibility.IMMUTABLE)\n self._param_dict.add(Parameter.IGNORE_SWITCH,\n r'IgnoreSwitch>(.*)</IgnoreSwitch',\n lambda match: True if match.group(1) == 'yes' else False,\n self._true_false_to_string,\n type=ParameterDictType.BOOL,\n display_name=\"Ignore Switch\",\n description=\"Disable magnetic switch position for starting or stopping logging: (true | false)\",\n range={'True': True, 'False': False},\n startup_param=True,\n direct_access=True,\n default_value=True,\n visibility=ParameterDictVisibility.IMMUTABLE)\n self._param_dict.add(Parameter.OPTODE,\n r'OPTODE>(.*)</OPTODE',\n lambda match: True if match.group(1) == 'yes' else False,\n self._true_false_to_string,\n type=ParameterDictType.BOOL,\n display_name=\"Optode Attached\",\n description=\"Enable optode: (true | false)\",\n range={'True': True, 'False': False},\n startup_param=True,\n direct_access=True,\n default_value=True,\n visibility=ParameterDictVisibility.IMMUTABLE)\n self._param_dict.add(Parameter.VOLT1,\n r'ExtVolt1>(.*)</ExtVolt1',\n lambda match: True if match.group(1) == 'yes' else False,\n self._true_false_to_string,\n type=ParameterDictType.BOOL,\n display_name=\"Volt 1\",\n description=\"Enable external voltage 1: (true | false)\",\n range={'True': True, 'False': False},\n startup_param=True,\n direct_access=True,\n default_value=True,\n visibility=ParameterDictVisibility.IMMUTABLE)\n\n self._build_ctd_specific_params()","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['low'] = self.low\n paramDict['alpha'] = self.alpha\n paramDict['beta'] = self.beta\n return paramDict","def getParameters(self):\n\n current_params = {'taux': self.taux, 'mu': self.mu, 'G': self.G, 'alpha_0': self.alpha_0,\n 'delta': self.delta, 'p': self.p, 'I0': self.I0, 'kparam': self.kparam}\n\n return (current_params)","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['low' ] = self.low\n paramDict['high' ] = self.high\n paramDict['alpha'] = self.alpha\n paramDict['beta' ] = self.beta\n return paramDict","def _get_params(self):\r\n return self.k._get_params()","def attributes(self):\n params = self.model.param_array\n return {'parameters': params}","def params(self):\n return {'kernel_size': self.kernel_size,\n 'stride': self.stride,\n 'n_kernel': self.n_kernel,\n 'padding': self.padding,\n 'act_fn': self.act_fn,\n 'w_init_fn': self.w_init_fn,\n 'resize': self.resize,\n 'use_bias': self.use_bias,\n 'atrous': self.atrous,\n 'idx': self.idx}","def get_params(self):\n return {\n 'dropout': self._dropout,\n 'layer_size': self._layer_size,\n 'num_layers': self._num_layers,\n 'embedding_layer_size': self._embedding_layer_size,\n 'controller_type': self._controller_type\n }","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['method'] = self.method\n paramDict['dimension'] = self.dimension\n paramDict['rank'] = self.rank\n paramDict['mu'] = self.mu\n paramDict['covariance'] = self.covariance\n return paramDict","def params(kernels, time, target, target_frame, observer, corr):\n return {\n 'kernels': kernels,\n 'times': time,\n 'target': target,\n 'target_frame': target_frame,\n 'observer': observer,\n 'aberration_correction': corr,\n }","def _generate_params(self):\n return {\n 'lis_outcome_service_url': self.lis_outcome_service_url,\n 'lis_result_sourcedid': self.lis_result_sourcedid,\n 'oauth_consumer_key': self.key\n }","def base_params(self):\n return {\"key\": self.api_key, \"contentCheckOk\": True, \"racyCheckOk\": True}","def parameters(self) -> Dict[str, Any]:\n return self.trainer_parameters","def parameters(self):\n pass","def get_parameters(self):\n # Get the parameters from the parent class\n params = super(LPEvalSplit, self).get_parameters()\n\n # Add the LP specific parameters\n params.update({\"owa\": self._owa, \"fe_ratio\": self._fe_ratio})\n return params","def testParams ():\n params = {}\n params['MessageID'] = 1\n params['RepeatIndicator'] = 1\n params['UserID'] = 1193046\n params['NavigationStatus'] = 3\n params['ROT'] = -2\n params['SOG'] = Decimal('101.9')\n params['PositionAccuracy'] = 1\n params['longitude'] = Decimal('-122.16328055555556')\n params['latitude'] = Decimal('37.424458333333334')\n params['COG'] = Decimal('34.5')\n params['TrueHeading'] = 41\n params['TimeStamp'] = 35\n params['RegionalReserved'] = 0\n params['Spare'] = 0\n params['RAIM'] = False\n params['state_syncstate'] = 2\n params['state_slottimeout'] = 0\n params['state_slotoffset'] = 1221\n \n return params","def get_params_info(cls):\n return dict(\n config='laygo configuration dictionary.',\n threshold='transistor threshold flavor.',\n draw_boundaries='True to draw boundaries.',\n num_blk='number of driver segments.',\n show_pins='True to draw pin geometries.',\n )","def get_params(self, _deep: bool = True) -> dict:\n return self.params","def getInitParams(self):\n paramDict = {}\n paramDict['upperBoundUsed' ] = self.upperBoundUsed\n paramDict['lowerBoundUsed' ] = self.lowerBoundUsed\n paramDict['hasInfiniteBound'] = self.hasInfiniteBound\n paramDict['upperBound' ] = self.upperBound\n paramDict['lowerBound' ] = self.lowerBound\n paramDict['adjustmentType' ] = self.__adjustmentType\n paramDict['dimensionality' ] = self.dimensionality\n return paramDict","def get_params(self) -> dict:\n # initialize dictionary\n params = dict()\n\n # loop through parameters, adding to parameter dictionary\n for key in self._get_param_names():\n params[key] = getattr(self, key)\n\n return params","def _get_parameters(self, *keys):\n return {k: v for k, v in self.param.items() if k in keys}","def parameters_dict(self):\n return dict(zip(self.parameters_names(), self.parameters_list))","def get_params(self, deep: bool = True) -> Dict[str, Any]:\n return _get_params(self, deep)","def get_params(self):\n return self.params\n\n \"\"\"\n ____________________________________________________________________________\n\n Fields retrieved from search by default\n ---------------------------------------\n 'id': True,\n 'title': True,\n 'agency' : True,\n 'awardeeCity' : True,\n 'awardeeName' : True,\n 'awardeeStateCode' : True,\n 'date' : True,\n 'fundsObligatedAmt' : True,\n 'piFirstName' : True,\n 'piLastName' : True,\n\n Other retrievable fields\n ------------------------\n 'offset' : False\n 'awardeeCountryCode' : False,\n 'awardeeCounty' : False,\n 'awardeeDistrictCode' : False,\n 'awardeeZipCode' : False,\n 'cfdaNumber' : False,\n 'coPDPI' : False,\n 'startDate' : False,\n 'expDate' : False,\n 'estimatedTotalAmt' : False,\n 'fundsObligatedAmt' : True,\n 'dunsNumber' : False,\n 'fundProgramName' : False,\n 'parentDunsNumber' : False,\n 'pdPIName' : False,\n 'perfCity' : False,\n 'perfCountryCode' : False,\n 'perfCounty' : False,\n 'perfDistrictCode' : False,\n 'perfLocation' : False,\n 'perfStateCode' : False,\n 'perfZipCode' : False,\n 'poName' : False,\n 'primaryProgram' : False,\n 'transType' : False,\n 'awardee' : False,\n 'poPhone' : False,\n 'poEmail' : False,\n 'awardeeAddress' : False,\n 'perfAddress' : False,\n 'publicationResearch' : False,\n 'publicationConference' : False,\n 'fundAgencyCode' : False,\n 'awardAgencyCode' : False,\n 'projectOutComesReport' : False,\n 'abstractText' : False,\n 'piMiddeInitial' : False,\n 'piLastName' : True,\n 'piPhone' : False,\n 'piEmail' : False\n \"\"\"","def get_params(self) -> Dict:\n params: Dict = {}\n channel: str\n for channel in list(self.channel.keys()):\n if self.channel_bool[channel]:\n params[channel] = self.channel[channel].get_g()\n if self.tau_ca != float('inf'):\n params['t_ca'] = self.tau_ca\n return params","def params():\n raise NotImplementedError","def parameters(self):\n return {\n 'label': \"undefined\",\n 'name': self.name(),\n 'id': self._id,\n 'created': self.created_to_str(),\n 'stage': self.stage_to_str(),\n 'direction': self.direction_to_str(),\n 'timeframe': self.timeframe_to_str(),\n 'expiry': self.expiry_to_str()\n }","def init_params():\r\n\r\n p = OrderedDict()\r\n p['startYear'] = 1855\r\n p['num5YearAgeClasses'] = 25\r\n p['numCareLevels'] = 5\r\n p['pixelsInPopPyramid'] = 2000\r\n p['pixelsPerTown'] = 16 # 56\r\n p['mapGridXDimension'] = 20\r\n p['mapGridYDimension'] = 25\r\n p['careLevelColour'] = ['deepskyblue','green','yellow','orange','red']\r\n p['careDemandInHours'] = [ 0.0, 12.0, 24.0, 48.0, 96.0 ]\r\n p['unmetNeedColor'] = ['deepskyblue','green','yellow','orange','red', 'mediumorchid']\r\n p['houseSizeColour'] = ['deepskyblue','green','yellow','orange','red', 'mediumorchid']\r\n p['mainFont'] = 'Helvetica 18'\r\n p['fontColour'] = 'white'\r\n p['dateX'] = 70\r\n p['dateY'] = 20\r\n p['popX'] = 70\r\n p['popY'] = 50\r\n p['delayTime'] = 0.0\r\n p['maxTextUpdateList'] = 12\r\n \r\n return p","def params(self):\n return {'kernel_size': self.kernel_size,\n 'stride': self.stride,\n 'n_kernel': self.n_kernel,\n 'padding': self.padding,\n 'act_fn': self.act_fn,\n 'output_shape': self.output_shape,\n 'w_init_fn': self.w_init_fn,\n 'use_bias': self.use_bias,\n 'idx': self.idx}","def get_params(self):\n return []","def getParams(self):\n return self.W, self.b","def get_params(self):\n return self.w, self.b","def parameters(self):\n return self._params","def parameters(self) -> Dict[str, str]:\n return self._parameters","def _get_mechanism_param_values(self):\n from PsyNeuLink.Components.States.ParameterState import ParameterState\n return dict((param, value.value) for param, value in self.paramsCurrent.items()\n if isinstance(value, ParameterState) )","def parameter_type_dict():\n return {'filter' : filters.filter_parameters,\n 'global_options' : global_options.global_options_parameters,\n 'input_device' : input_devices.input_device_parameters,\n 'input_stream' : input_streams.input_stream_parameters,\n 'output_device' : output_devices.output_device_parameters,\n 'output_stream' : output_streams.output_stream_parameters}","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['p'] = self.p\n return paramDict","def get_parameters(self):\n d = Algorithm.get_parameters(self)\n d.update({\n 'M': d.pop('population_size', self.population_size),\n 'num_tests': self.num_tests,\n 'num_searches': self.num_searches,\n 'num_searches_best': self.num_searches_best,\n 'bonus1': self.bonus1,\n 'bonus2': self.bonus2,\n 'num_enabled': self.num_enabled,\n 'local_searches': self.local_searches\n })\n return d","def params(self):\n result = {}\n weight_sets = self._build_weight_definitions(self.n_dim)\n for name in weight_sets.groups:\n result[name] = getattr(self, name)\n for name in weight_sets.singles:\n result[name] = getattr(self, name)\n result['score_net_params'] = self.score_net_params\n result['value_net_params'] = self.value_net_params\n return result","def get_params(self):\n raise NotImplementedError","def buildParamsDict(self):\n self.params_dict = {\n \"img_dir\": self.savePathJoin(\"Images\"),\n \"depth_dir\": self.savePathJoin(\"Depth\"),\n \"back_of_dir\": self.savePathJoin(\"Back_Of\"),\n \"of_dir\": self.savePathJoin(\"Of\"),\n \"save_dir\": self.user[\"Save\"],\n \"high\": self.high,\n \"low\": self.low,\n \"run_dict\": self.run_dict,\n \"of_model\": self.app.get_resource(\n os.path.join(\"of_models\", \"network-default.pytorch\")\n ),\n \"depth_model\": self.app.get_resource(\n os.path.join(\"depth_models\", \"model_city2kitti.meta\")\n ),\n \"yolo_weights\": self.app.get_resource(\n os.path.join(\"yolo\", \"yolov3.weights\")\n ),\n \"yolo_v\": self.app.get_resource(os.path.join(\"yolo\", \"yolov3.cfg\")),\n \"coco_names\": self.app.get_resource(os.path.join(\"yolo\", \"coco.names\")),\n \"object_detection_dir\": self.savePathJoin(\"ObjectDetection\"),\n \"plot_speed_dir\": PLOT_SPEED_DIR,\n \"plot_crash_dir\": PLOT_CRASH_DIR,\n \"numbers_dir\": NP_DIR,\n \"plot_error_dir\": PLOT_ERROR_DIR,\n \"speed_gt\": self.user[\"GT\"],\n \"vid_path\": self.user[\"Video\"],\n \"super_pixel_method\": self.super_pixel_method,\n \"super_pixel_dir\": SUPER_PIXEL_DIR,\n \"send_video_frame\": False,\n \"create_csv\": self.ui.c_csv.isChecked(),\n \"create_draw\": self.ui.c_draw.isChecked(),\n \"create_velocity\": self.ui.c_velocity.isChecked(),\n \"create_video_fps\": int(self.ui.t_fps.text()),\n \"optimize_params\": self.ui.c_optimize.isChecked(),\n \"super_pixel_label_dir\": os.path.join(\n self.savePathJoin(\"Super_Pixel\"), self.super_pixel_method\n ),\n }","def parameters(self):\n return []","def internal_values(self) -> Dict[str, Union[int, float]]:\n return {name: self.hyperparams[name][1] for name in self.names()}","def winglet_parameters():\n\n SPAN = WingletParameters.SPAN.value\n ANGLE_CANT = WingletParameters.ANGLE_CANT.value\n ANGLE_SWEEP = WingletParameters.ANGLE_SWEEP.value\n ANGLE_TWIST_ROOT = WingletParameters.ANGLE_TWIST_ROOT.value\n ANGLE_TWIST_TIP = WingletParameters.ANGLE_TWIST_TIP.value\n TAPER_RATIO = WingletParameters.TAPER_RATIO.value\n CHORD_ROOT = WingletParameters.CHORD_ROOT.value\n W_AIRFOIL = WingletParameters.AIRFOIL.value\n\n _winglet_parameters = {\n SPAN: 0.05,\n TAPER_RATIO: 0.32,\n CHORD_ROOT: 0.65,\n ANGLE_SWEEP: 38,\n ANGLE_CANT: 45,\n ANGLE_TWIST_ROOT: 0.0,\n ANGLE_TWIST_TIP: 0.0,\n W_AIRFOIL: \"naca0012\",\n }\n\n return _winglet_parameters","def parameters(self):\n #print \"in instrument.parameter()\"\n return self._params","def get_params(self, deep=True):\n #params = dict(kernel=self.kernel, dim=self.dim)\n params = dict(columns=self.columns)\n if deep:\n for i, kernel in enumerate(self.kernels):\n print(\"--->\", \"\\ti = \", i, \"\\tkernel = \", kernel)\n deep_items = kernel.get_params().items()\n #params.update((k, val) for k, val in deep_items)\n for k, val in deep_items:\n print(\"\\tkey = \", k, \"\\tvalue = \", val)\n params.update(('k{}__{}'.format(i, k), val) for k, val in deep_items)\n return params","def parameters(self) -> Mapping[str, str]:\n return pulumi.get(self, \"parameters\")","def params(self):\n\t\treturn self.params_","def parameters_config(self) -> dict:\n if not self.params_optional and not self.params_required:\n return {}\n parameters = dict()\n for parameter, parameter_details in self.parameters.items():\n parameters[parameter_details.name] = parameter_details.parameter_config()\n return parameters","def get_params(self):\n\n return None","def get_params(self):\n\n return None","def get_params(self):\n\n return None","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_params(self, deep=True):\n #params = dict(kernel=self.kernel, dim=self.dim)\n params = dict(dim=self.dim)\n if deep:\n deep_items = self.kernel.get_params().items()\n params.update((k, val) for k, val in deep_items)\n return params","def getParameters(self):\n return {'channels':self._channels, 'means':self._means, 'stds':self._stds}","def gen_params(self) -> Dict:\n param_dict: Dict = {}\n\n gX_name: List[str] = ['g_leak', 'g_nav', 'g_kvhh', 'g_kva', 'g_kvsi', \n 'g_cav', 'g_kca', 'g_nap', 'g_kir']\n gX_log: np.ndarray = 4 * np.random.rand(9) - 2 # from -2 to 2\n gX: np.ndarray = (10 * np.ones(9)) ** gX_log # 0.01 ~ 100\n gX_itr: Iterator = zip(gX_name, gX)\n\n gR_name: List[str] = ['g_ampar', 'g_nmdar', 'g_gabar']\n gR_log: np.ndarray = 4 * np.random.rand(3) - 3 # from -3 to 1\n gR: np.ndarray = (10 * np.ones(3)) ** gR_log # 0.001 ~ 10\n gR_itr: Iterator = zip(gR_name, gR)\n\n tCa_log: float = 2 * np.random.rand(1) + 1 # from 1 to 3\n tCa: float = 10 ** tCa_log # 10 ~ 1000\n tCa_dict: Dict = {'t_ca': tCa}\n\n param_dict.update(gX_itr)\n param_dict.update(gR_itr)\n param_dict.update(tCa_dict)\n return param_dict","def get(self):\n return dict(self._params)","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['mean' ] = self.mean\n paramDict['sigma'] = self.sigma\n paramDict['low'] = self.low\n return paramDict","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['n' ] = self.n\n paramDict['p' ] = self.p\n return paramDict","def getDefaultParameterValues(self):\r\n dct = {}\r\n self.initializeRoadRunnerModel()\r\n self.roadrunnerModel.reset()\r\n for parameterName in self.parametersToFit:\r\n dct[parameterName] = self.roadrunnerModel.model[parameterName]\r\n return dct","def getParameters(self):\n\t\td = AdaptiveBatAlgorithm.getParameters(self)\n\t\td.update({\n\t\t\t'A_l': self.A_l,\n\t\t\t'A_u': self.A_u,\n\t\t\t'r_l': self.r_l,\n\t\t\t'r_u': self.r_u,\n\t\t\t'tao_1': self.tao_1,\n\t\t\t'tao_2': self.tao_2\n\t\t})\n\t\treturn d","def _pprint_params(self):\n return {'x_range': self.x_range, 'y_range': self.y_range,\n 'step': self.step, 'shape': self.shape,\n 'type': self.type}"],"string":"[\n \"def get_params(self):\\n return {}\",\n \"def parameters(self):\\n return {\\\"W\\\": self.W,\\n \\\"T\\\": self.T,\\n \\\"P\\\": self.P,\\n \\\"Wo\\\": self.Wo,\\n \\\"To\\\": self.To,\\n \\\"Po\\\": self.Po}\",\n \"def get_params(self):\\n return {\\\"d\\\": \\\"155\\\"}\",\n \"def get_params(self, deep=False):\\n return {\\\"alpha\\\": self.alpha, \\\"beta\\\": self.beta, \\\"gamma\\\": self.gamma, \\\"W\\\": self.W, \\\"bias\\\": self.bias, \\\"add_bias\\\": self.add_bias, \\\"opts\\\": self.opts}\",\n \"def parameters(self):\\n return {}\",\n \"def parameters(self):\\n return {\\\"W\\\": self.W,\\n \\\"T\\\": self.T,\\n \\\"P\\\": self.P}\",\n \"def get_params(self):\",\n \"def parameters(self):\\n return {\\\"P\\\": self.P,\\n \\\"T\\\": self.T}\",\n \"def paramDetails(cls):\\n return {\\n 'dim': (10, 20, 2, 20),\\n 'nIter': (1, 10, 2, 5),\\n 'lamb': (.1, 1., .1, .05),\\n 'alph': (30, 50, 5, 40)\\n }\",\n \"def get_params(self) -> Dict:\\n params: Dict = {}\\n params['g_leak'] = self.leak.get_g()\\n params['g_kvhh'] = self.kvhh.get_g()\\n params['g_cav'] = self.cav.get_g()\\n params['g_kca'] = self.kca.get_g()\\n params['g_nap'] = self.nap.get_g()\\n params['t_ca'] = self.tau_ca\\n return params\",\n \"def parameters_dict(self):\\n return\",\n \"def get_params(self):\\n return {'k': self.k, 'q': self.q, 'sigma_s': self.sigma_s, 'm': self.m}\",\n \"def get_params(self):\\n return {\\n \\\"nspecies\\\": self.nspecies,\\n \\\"lmax\\\": self.lmax,\\n \\\"nmax\\\": self.nmax,\\n \\\"rcut\\\": self.rcut,\\n \\\"sigma\\\": self.sigma,\\n \\\"trans_width\\\": self.trans_width\\n }\",\n \"def get_params(self):\\n outputs = ['sample',\\n 'ratio_params',\\n 'despike_params',\\n 'autorange_params',\\n 'bkgcorrect_params']\\n\\n out = {}\\n for o in outputs:\\n out[o] = getattr(self, o)\\n\\n out['filter_params'] = self.filt.params\\n out['filter_sequence'] = self.filt.sequence\\n out['filter_used'] = self.filt.make_keydict()\\n\\n return out\",\n \"def parameters(self):\\n return dict(self._register)\",\n \"def get_params(self):\\n return {'classifier': self.classifier,\\n 'grid_param': self.grid_param,\\n 'n_param_comb': self.n_param_comb,\\n 'top_bagging': self.bagging,\\n 'bagging_param': self.bagging_param,\\n 'comb_seed': self.comb_seed}\",\n \"def get_required_params():\\n return {}\",\n \"def good_params():\\n from scraper import PARAMS\\n params = PARAMS.copy()\\n params['Business_Name'] = 'CodeFellows'\\n params['City'] = 'Seattle'\\n return params\",\n \"def getParameters( self ):\\n parameterDict = {}\\n parameterDict['StorageName'] = self.name\\n parameterDict['ProtocolName'] = self.protocolName\\n parameterDict['Protocol'] = self.protocol\\n parameterDict['Host'] = self.host\\n parameterDict['Path'] = self.path\\n parameterDict['Port'] = self.port\\n parameterDict['SpaceToken'] = self.spaceToken\\n parameterDict['WSUrl'] = self.wspath\\n return S_OK( parameterDict )\",\n \"def _build_param_dict(self):\\n # Add parameter handlers to parameter dict. \\n self._param_dict.add(SBE37Parameter.OUTPUTSAL,\\n r'(do not )?output salinity with each sample',\\n lambda match : False if match.group(1) else True,\\n self._true_false_to_string)\\n self._param_dict.add(SBE37Parameter.OUTPUTSV,\\n r'(do not )?output sound velocity with each sample',\\n lambda match : False if match.group(1) else True,\\n self._true_false_to_string)\\n self._param_dict.add(SBE37Parameter.NAVG,\\n r'number of samples to average = (\\\\d+)',\\n lambda match : int(match.group(1)),\\n self._int_to_string)\\n self._param_dict.add(SBE37Parameter.SAMPLENUM,\\n r'samplenumber = (\\\\d+), free = \\\\d+',\\n lambda match : int(match.group(1)),\\n self._int_to_string)\\n self._param_dict.add(SBE37Parameter.INTERVAL,\\n r'sample interval = (\\\\d+) seconds',\\n lambda match : int(match.group(1)),\\n self._int_to_string)\\n self._param_dict.add(SBE37Parameter.STORETIME,\\n r'(do not )?store time with each sample',\\n lambda match : False if match.group(1) else True,\\n self._true_false_to_string)\\n self._param_dict.add(SBE37Parameter.TXREALTIME,\\n r'(do not )?transmit real-time data',\\n lambda match : False if match.group(1) else True,\\n self._true_false_to_string)\\n self._param_dict.add(SBE37Parameter.SYNCMODE,\\n r'serial sync mode (enabled|disabled)',\\n lambda match : False if (match.group(1)=='disabled') else True,\\n self._true_false_to_string)\\n self._param_dict.add(SBE37Parameter.SYNCWAIT,\\n r'wait time after serial sync sampling = (\\\\d+) seconds',\\n lambda match : int(match.group(1)),\\n self._int_to_string)\\n self._param_dict.add(SBE37Parameter.TCALDATE,\\n r'temperature: +((\\\\d+)-([a-zA-Z]+)-(\\\\d+))',\\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\\n self._date_to_string)\\n self._param_dict.add(SBE37Parameter.TA0,\\n r' +TA0 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.TA1,\\n r' +TA1 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.TA2,\\n r' +TA2 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.TA3,\\n r' +TA3 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.CCALDATE,\\n r'conductivity: +((\\\\d+)-([a-zA-Z]+)-(\\\\d+))',\\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\\n self._date_to_string)\\n self._param_dict.add(SBE37Parameter.CG,\\n r' +G = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.CH,\\n r' +H = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.CI,\\n r' +I = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.CJ,\\n r' +J = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.WBOTC,\\n r' +WBOTC = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.CTCOR,\\n r' +CTCOR = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.CPCOR,\\n r' +CPCOR = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PCALDATE,\\n r'pressure .+ ((\\\\d+)-([a-zA-Z]+)-(\\\\d+))',\\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\\n self._date_to_string)\\n self._param_dict.add(SBE37Parameter.PA0,\\n r' +PA0 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PA1,\\n r' +PA1 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PA2,\\n r' +PA2 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PTCA0,\\n r' +PTCA0 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PTCA1,\\n r' +PTCA1 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PTCA2,\\n r' +PTCA2 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PTCB0,\\n r' +PTCSB0 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PTCB1,\\n r' +PTCSB1 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.PTCB2,\\n r' +PTCSB2 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.POFFSET,\\n r' +POFFSET = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.RCALDATE,\\n r'rtc: +((\\\\d+)-([a-zA-Z]+)-(\\\\d+))',\\n lambda match : self._string_to_date(match.group(1), '%d-%b-%y'),\\n self._date_to_string)\\n self._param_dict.add(SBE37Parameter.RTCA0,\\n r' +RTCA0 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.RTCA1,\\n r' +RTCA1 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\\n self._param_dict.add(SBE37Parameter.RTCA2,\\n r' +RTCA2 = (-?\\\\d.\\\\d\\\\d\\\\d\\\\d\\\\d\\\\de[-+]\\\\d\\\\d)',\\n lambda match : float(match.group(1)),\\n self._float_to_string)\",\n \"def getParams(self):\\n\\n\\t\\tparams = {\\\"Nparticles\\\":self.__Nparticles,\\\"Nkicks\\\":self.__Nkicks,\\\"kappa\\\":self.__kappa, \\\"eta\\\":self.__eta,\\\"gamma\\\":self.__gamma, \\\"omega\\\":self.__omega,\\n\\t\\t\\\"Kbt\\\":self.__Kbt, \\\"tk\\\":self.__tk}\\n\\n\\t\\treturn params\",\n \"def parameters(self):\",\n \"def _get_fitted_params(self):\\n return {}\",\n \"def parameters(self):\\n res = dict()\\n res[\\\"population_size\\\"] = self.population_size\\n res[\\\"mutation_prob\\\"] = self.mutation_prob\\n res[\\\"crossover\\\"] = self.crossover\\n res[\\\"selection\\\"] = self.selection\\n res[\\\"sigma\\\"] = self.sigma\\n res[\\\"crossover_method\\\"] = self.crossover_method\\n res[\\\"selection_method\\\"] = self.selection_method\\n res[\\\"best_rate\\\"] = self.best_rate\\n res[\\\"n_parents\\\"] = self.n_parents\\n res[\\\"model_parameters\\\"] = self.model.total_parameters()\\n res[\\\"IDCT_from\\\"] = self.IDCT_from\\n res[\\\"elitism\\\"] = self.elitism\\n return res\",\n \"def get_params(self) -> Dict:\\n params: Dict = {}\\n params['g_leak']: float = self.leak.get_g()\\n params['g_nav']: float = self.nav.get_g()\\n params['g_kvhh']: float = self.kvhh.get_g()\\n params['g_kva']: float = self.kva.get_g()\\n params['g_kvsi']: float = self.kvsi.get_g()\\n params['g_cav']: float = self.cav.get_g()\\n params['g_kca']: float = self.kca.get_g()\\n params['g_nap']: float = self.nap.get_g()\\n params['g_kir']: float = self.kir.get_g()\\n params['g_ampar']: float = self.ampar.get_g()\\n params['g.nmdar']: float = self.nmdar.get_g()\\n params['g_gabar']: float = self.gabar.get_g()\\n params['t_Ca']: float = self.tau_ca\\n return params\",\n \"def params(self):\\n return {'cfg': self.cfg,\\n 'momentum': self.momentum,\\n 'center': self.center,\\n 'scale': self.scale,\\n 'epsilon': self.epsilon,\\n 'act_fn': self.act_fn}\",\n \"def get_parameters(self):\\n params = {\\\"train_frac\\\": self.train_frac, \\\"split_alg\\\": self.split_alg,\\n \\\"nw_name\\\": self._nw_name, \\\"split_id\\\": self.split_id}\\n return params\",\n \"def get_params(self):\\n params = {}\\n for step in self.steps:\\n params[step[0]] = step[1].get_params()\\n return params\",\n \"def params(self):\\n return {'out_dim': self.out_dim,\\n 'act_fn': self.act_fn,\\n 'use_bias': self.use_bias,\\n 'idx': self.idx}\",\n \"def parameters(self):\\n return {\\n 'base':self.base.parameters(),\\n 'material':[m.parameters() for m in self.material],\\n 'fraction':self.fraction,\\n }\",\n \"def parameters(self) -> Dict[str, Any]:\\n return self.data[\\\"args\\\"].get(\\\"parameters\\\", {})\",\n \"def params(self):\\n return {'shape': self.shape,\\n 'name': self.name}\",\n \"def get_parameter_dict(self):\\n prm = ModelParameters()\\n prm.define(\\\"a\\\", self.a)\\n return prm\",\n \"def get_parameters_dictionary(request):\\n parameters_dict = {PARAMETER_MESSAGE: request.GET.get('message'),\\n PARAMETER_ENTITY_NAME: request.GET.get('entity_name'),\\n PARAMETER_STRUCTURED_VALUE: request.GET.get('structured_value'),\\n PARAMETER_FALLBACK_VALUE: request.GET.get('fallback_value'),\\n PARAMETER_BOT_MESSAGE: request.GET.get('bot_message'),\\n PARAMETER_TIMEZONE: request.GET.get('timezone'),\\n PARAMETER_LANGUAGE_SCRIPT: request.GET.get('language_script', ENGLISH_LANG),\\n PARAMETER_SOURCE_LANGUAGE: request.GET.get('source_language', ENGLISH_LANG),\\n PARAMETER_PAST_DATE_REFERENCED: request.GET.get('date_past_reference', 'False'),\\n PARAMETER_MIN_DIGITS: request.GET.get('min_number_digits'),\\n PARAMETER_MAX_DIGITS: request.GET.get('max_number_digits'),\\n PARAMETER_NUMBER_UNIT_TYPE: request.GET.get('unit_type'),\\n PARAMETER_LOCALE: request.GET.get('locale'),\\n PARAMETER_RANGE_ENABLED: request.GET.get('range_enabled')\\n }\\n\\n return parameters_dict\",\n \"def parameters(self) -> dict[str, Any]:\\n return {\\n \\\"IrrigationEfficiency\\\": self._get_textvar_value(\\\"IrrigationEfficiency\\\"),\\n \\\"MaskMap\\\": self._get_textvar_value(\\\"MaskMap\\\"),\\n \\\"start_time\\\": get_time(self.forcing.start_time).strftime(ISO_TIMEFMT),\\n \\\"end_time\\\": get_time(self.forcing.end_time).strftime(ISO_TIMEFMT),\\n }\",\n \"def get_params(self):\\n return {'threshold': self.threshold,\\n 'subsample': self.subsample,\\n 'estimator': self.estimator,\\n 'n_folds': self.n_folds,\\n 'stratify': self.stratify,\\n 'random_state': self.random_state,\\n 'n_jobs': self.n_jobs}\",\n \"def get_params(self):\\n pass\",\n \"def params(self) -> dict:\\n\\n if not self.exp_metadata.parameters:\\n self.exp_metadata.parameters = {}\\n return self.exp_metadata.parameters\",\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 _build_param_dict(self):\\n self._build_common_param_dict()\\n\\n self._param_dict.add(Parameter.NUM_AVG_SAMPLES,\\n r'ScansToAverage>([\\\\d]+)</ScansToAverage>',\\n lambda match: int(match.group(1)),\\n str,\\n type=ParameterDictType.INT,\\n display_name=\\\"Scans to Average\\\",\\n description=\\\"Number of samples to average (must be even)\\\",\\n range=INT16,\\n startup_param=True,\\n direct_access=False,\\n default_value=4,\\n visibility=ParameterDictVisibility.READ_WRITE)\\n self._param_dict.add(Parameter.MIN_COND_FREQ,\\n r'MinimumCondFreq>([\\\\d]+)</MinimumCondFreq',\\n lambda match: int(match.group(1)),\\n str,\\n type=ParameterDictType.INT,\\n display_name=\\\"Minimum Conductivity Frequency\\\",\\n range=INT16,\\n description=\\\"Minimum conductivity frequency to enable pump turn-on.\\\",\\n startup_param=True,\\n direct_access=False,\\n default_value=500,\\n units=Units.HERTZ,\\n visibility=ParameterDictVisibility.IMMUTABLE)\\n self._param_dict.add(Parameter.PUMP_DELAY,\\n r'PumpDelay>([\\\\d]+)</PumpDelay',\\n lambda match: int(match.group(1)),\\n str,\\n type=ParameterDictType.INT,\\n display_name=\\\"Pump Delay\\\",\\n range=INT16,\\n description=\\\"Time to wait after minimum conductivity frequency is reached before turning pump on.\\\",\\n startup_param=True,\\n direct_access=False,\\n default_value=60,\\n units=Units.SECOND,\\n visibility=ParameterDictVisibility.READ_WRITE)\\n self._param_dict.add(Parameter.AUTO_RUN,\\n r'AutoRun>(.*)</AutoRun',\\n lambda match: True if match.group(1) == 'yes' else False,\\n self._true_false_to_string,\\n type=ParameterDictType.BOOL,\\n display_name=\\\"Auto Run\\\",\\n description=\\\"Enable automatic logging when power is applied: (true | false).\\\",\\n range={'True': True, 'False': False},\\n startup_param=True,\\n direct_access=True,\\n default_value=False,\\n visibility=ParameterDictVisibility.IMMUTABLE)\\n self._param_dict.add(Parameter.IGNORE_SWITCH,\\n r'IgnoreSwitch>(.*)</IgnoreSwitch',\\n lambda match: True if match.group(1) == 'yes' else False,\\n self._true_false_to_string,\\n type=ParameterDictType.BOOL,\\n display_name=\\\"Ignore Switch\\\",\\n description=\\\"Disable magnetic switch position for starting or stopping logging: (true | false)\\\",\\n range={'True': True, 'False': False},\\n startup_param=True,\\n direct_access=True,\\n default_value=True,\\n visibility=ParameterDictVisibility.IMMUTABLE)\\n self._param_dict.add(Parameter.OPTODE,\\n r'OPTODE>(.*)</OPTODE',\\n lambda match: True if match.group(1) == 'yes' else False,\\n self._true_false_to_string,\\n type=ParameterDictType.BOOL,\\n display_name=\\\"Optode Attached\\\",\\n description=\\\"Enable optode: (true | false)\\\",\\n range={'True': True, 'False': False},\\n startup_param=True,\\n direct_access=True,\\n default_value=True,\\n visibility=ParameterDictVisibility.IMMUTABLE)\\n self._param_dict.add(Parameter.VOLT1,\\n r'ExtVolt1>(.*)</ExtVolt1',\\n lambda match: True if match.group(1) == 'yes' else False,\\n self._true_false_to_string,\\n type=ParameterDictType.BOOL,\\n display_name=\\\"Volt 1\\\",\\n description=\\\"Enable external voltage 1: (true | false)\\\",\\n range={'True': True, 'False': False},\\n startup_param=True,\\n direct_access=True,\\n default_value=True,\\n visibility=ParameterDictVisibility.IMMUTABLE)\\n\\n self._build_ctd_specific_params()\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['low'] = self.low\\n paramDict['alpha'] = self.alpha\\n paramDict['beta'] = self.beta\\n return paramDict\",\n \"def getParameters(self):\\n\\n current_params = {'taux': self.taux, 'mu': self.mu, 'G': self.G, 'alpha_0': self.alpha_0,\\n 'delta': self.delta, 'p': self.p, 'I0': self.I0, 'kparam': self.kparam}\\n\\n return (current_params)\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['low' ] = self.low\\n paramDict['high' ] = self.high\\n paramDict['alpha'] = self.alpha\\n paramDict['beta' ] = self.beta\\n return paramDict\",\n \"def _get_params(self):\\r\\n return self.k._get_params()\",\n \"def attributes(self):\\n params = self.model.param_array\\n return {'parameters': params}\",\n \"def params(self):\\n return {'kernel_size': self.kernel_size,\\n 'stride': self.stride,\\n 'n_kernel': self.n_kernel,\\n 'padding': self.padding,\\n 'act_fn': self.act_fn,\\n 'w_init_fn': self.w_init_fn,\\n 'resize': self.resize,\\n 'use_bias': self.use_bias,\\n 'atrous': self.atrous,\\n 'idx': self.idx}\",\n \"def get_params(self):\\n return {\\n 'dropout': self._dropout,\\n 'layer_size': self._layer_size,\\n 'num_layers': self._num_layers,\\n 'embedding_layer_size': self._embedding_layer_size,\\n 'controller_type': self._controller_type\\n }\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['method'] = self.method\\n paramDict['dimension'] = self.dimension\\n paramDict['rank'] = self.rank\\n paramDict['mu'] = self.mu\\n paramDict['covariance'] = self.covariance\\n return paramDict\",\n \"def params(kernels, time, target, target_frame, observer, corr):\\n return {\\n 'kernels': kernels,\\n 'times': time,\\n 'target': target,\\n 'target_frame': target_frame,\\n 'observer': observer,\\n 'aberration_correction': corr,\\n }\",\n \"def _generate_params(self):\\n return {\\n 'lis_outcome_service_url': self.lis_outcome_service_url,\\n 'lis_result_sourcedid': self.lis_result_sourcedid,\\n 'oauth_consumer_key': self.key\\n }\",\n \"def base_params(self):\\n return {\\\"key\\\": self.api_key, \\\"contentCheckOk\\\": True, \\\"racyCheckOk\\\": True}\",\n \"def parameters(self) -> Dict[str, Any]:\\n return self.trainer_parameters\",\n \"def parameters(self):\\n pass\",\n \"def get_parameters(self):\\n # Get the parameters from the parent class\\n params = super(LPEvalSplit, self).get_parameters()\\n\\n # Add the LP specific parameters\\n params.update({\\\"owa\\\": self._owa, \\\"fe_ratio\\\": self._fe_ratio})\\n return params\",\n \"def testParams ():\\n params = {}\\n params['MessageID'] = 1\\n params['RepeatIndicator'] = 1\\n params['UserID'] = 1193046\\n params['NavigationStatus'] = 3\\n params['ROT'] = -2\\n params['SOG'] = Decimal('101.9')\\n params['PositionAccuracy'] = 1\\n params['longitude'] = Decimal('-122.16328055555556')\\n params['latitude'] = Decimal('37.424458333333334')\\n params['COG'] = Decimal('34.5')\\n params['TrueHeading'] = 41\\n params['TimeStamp'] = 35\\n params['RegionalReserved'] = 0\\n params['Spare'] = 0\\n params['RAIM'] = False\\n params['state_syncstate'] = 2\\n params['state_slottimeout'] = 0\\n params['state_slotoffset'] = 1221\\n \\n return params\",\n \"def get_params_info(cls):\\n return dict(\\n config='laygo configuration dictionary.',\\n threshold='transistor threshold flavor.',\\n draw_boundaries='True to draw boundaries.',\\n num_blk='number of driver segments.',\\n show_pins='True to draw pin geometries.',\\n )\",\n \"def get_params(self, _deep: bool = True) -> dict:\\n return self.params\",\n \"def getInitParams(self):\\n paramDict = {}\\n paramDict['upperBoundUsed' ] = self.upperBoundUsed\\n paramDict['lowerBoundUsed' ] = self.lowerBoundUsed\\n paramDict['hasInfiniteBound'] = self.hasInfiniteBound\\n paramDict['upperBound' ] = self.upperBound\\n paramDict['lowerBound' ] = self.lowerBound\\n paramDict['adjustmentType' ] = self.__adjustmentType\\n paramDict['dimensionality' ] = self.dimensionality\\n return paramDict\",\n \"def get_params(self) -> dict:\\n # initialize dictionary\\n params = dict()\\n\\n # loop through parameters, adding to parameter dictionary\\n for key in self._get_param_names():\\n params[key] = getattr(self, key)\\n\\n return params\",\n \"def _get_parameters(self, *keys):\\n return {k: v for k, v in self.param.items() if k in keys}\",\n \"def parameters_dict(self):\\n return dict(zip(self.parameters_names(), self.parameters_list))\",\n \"def get_params(self, deep: bool = True) -> Dict[str, Any]:\\n return _get_params(self, deep)\",\n \"def get_params(self):\\n return self.params\\n\\n \\\"\\\"\\\"\\n ____________________________________________________________________________\\n\\n Fields retrieved from search by default\\n ---------------------------------------\\n 'id': True,\\n 'title': True,\\n 'agency' : True,\\n 'awardeeCity' : True,\\n 'awardeeName' : True,\\n 'awardeeStateCode' : True,\\n 'date' : True,\\n 'fundsObligatedAmt' : True,\\n 'piFirstName' : True,\\n 'piLastName' : True,\\n\\n Other retrievable fields\\n ------------------------\\n 'offset' : False\\n 'awardeeCountryCode' : False,\\n 'awardeeCounty' : False,\\n 'awardeeDistrictCode' : False,\\n 'awardeeZipCode' : False,\\n 'cfdaNumber' : False,\\n 'coPDPI' : False,\\n 'startDate' : False,\\n 'expDate' : False,\\n 'estimatedTotalAmt' : False,\\n 'fundsObligatedAmt' : True,\\n 'dunsNumber' : False,\\n 'fundProgramName' : False,\\n 'parentDunsNumber' : False,\\n 'pdPIName' : False,\\n 'perfCity' : False,\\n 'perfCountryCode' : False,\\n 'perfCounty' : False,\\n 'perfDistrictCode' : False,\\n 'perfLocation' : False,\\n 'perfStateCode' : False,\\n 'perfZipCode' : False,\\n 'poName' : False,\\n 'primaryProgram' : False,\\n 'transType' : False,\\n 'awardee' : False,\\n 'poPhone' : False,\\n 'poEmail' : False,\\n 'awardeeAddress' : False,\\n 'perfAddress' : False,\\n 'publicationResearch' : False,\\n 'publicationConference' : False,\\n 'fundAgencyCode' : False,\\n 'awardAgencyCode' : False,\\n 'projectOutComesReport' : False,\\n 'abstractText' : False,\\n 'piMiddeInitial' : False,\\n 'piLastName' : True,\\n 'piPhone' : False,\\n 'piEmail' : False\\n \\\"\\\"\\\"\",\n \"def get_params(self) -> Dict:\\n params: Dict = {}\\n channel: str\\n for channel in list(self.channel.keys()):\\n if self.channel_bool[channel]:\\n params[channel] = self.channel[channel].get_g()\\n if self.tau_ca != float('inf'):\\n params['t_ca'] = self.tau_ca\\n return params\",\n \"def params():\\n raise NotImplementedError\",\n \"def parameters(self):\\n return {\\n 'label': \\\"undefined\\\",\\n 'name': self.name(),\\n 'id': self._id,\\n 'created': self.created_to_str(),\\n 'stage': self.stage_to_str(),\\n 'direction': self.direction_to_str(),\\n 'timeframe': self.timeframe_to_str(),\\n 'expiry': self.expiry_to_str()\\n }\",\n \"def init_params():\\r\\n\\r\\n p = OrderedDict()\\r\\n p['startYear'] = 1855\\r\\n p['num5YearAgeClasses'] = 25\\r\\n p['numCareLevels'] = 5\\r\\n p['pixelsInPopPyramid'] = 2000\\r\\n p['pixelsPerTown'] = 16 # 56\\r\\n p['mapGridXDimension'] = 20\\r\\n p['mapGridYDimension'] = 25\\r\\n p['careLevelColour'] = ['deepskyblue','green','yellow','orange','red']\\r\\n p['careDemandInHours'] = [ 0.0, 12.0, 24.0, 48.0, 96.0 ]\\r\\n p['unmetNeedColor'] = ['deepskyblue','green','yellow','orange','red', 'mediumorchid']\\r\\n p['houseSizeColour'] = ['deepskyblue','green','yellow','orange','red', 'mediumorchid']\\r\\n p['mainFont'] = 'Helvetica 18'\\r\\n p['fontColour'] = 'white'\\r\\n p['dateX'] = 70\\r\\n p['dateY'] = 20\\r\\n p['popX'] = 70\\r\\n p['popY'] = 50\\r\\n p['delayTime'] = 0.0\\r\\n p['maxTextUpdateList'] = 12\\r\\n \\r\\n return p\",\n \"def params(self):\\n return {'kernel_size': self.kernel_size,\\n 'stride': self.stride,\\n 'n_kernel': self.n_kernel,\\n 'padding': self.padding,\\n 'act_fn': self.act_fn,\\n 'output_shape': self.output_shape,\\n 'w_init_fn': self.w_init_fn,\\n 'use_bias': self.use_bias,\\n 'idx': self.idx}\",\n \"def get_params(self):\\n return []\",\n \"def getParams(self):\\n return self.W, self.b\",\n \"def get_params(self):\\n return self.w, self.b\",\n \"def parameters(self):\\n return self._params\",\n \"def parameters(self) -> Dict[str, str]:\\n return self._parameters\",\n \"def _get_mechanism_param_values(self):\\n from PsyNeuLink.Components.States.ParameterState import ParameterState\\n return dict((param, value.value) for param, value in self.paramsCurrent.items()\\n if isinstance(value, ParameterState) )\",\n \"def parameter_type_dict():\\n return {'filter' : filters.filter_parameters,\\n 'global_options' : global_options.global_options_parameters,\\n 'input_device' : input_devices.input_device_parameters,\\n 'input_stream' : input_streams.input_stream_parameters,\\n 'output_device' : output_devices.output_device_parameters,\\n 'output_stream' : output_streams.output_stream_parameters}\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['p'] = self.p\\n return paramDict\",\n \"def get_parameters(self):\\n d = Algorithm.get_parameters(self)\\n d.update({\\n 'M': d.pop('population_size', self.population_size),\\n 'num_tests': self.num_tests,\\n 'num_searches': self.num_searches,\\n 'num_searches_best': self.num_searches_best,\\n 'bonus1': self.bonus1,\\n 'bonus2': self.bonus2,\\n 'num_enabled': self.num_enabled,\\n 'local_searches': self.local_searches\\n })\\n return d\",\n \"def params(self):\\n result = {}\\n weight_sets = self._build_weight_definitions(self.n_dim)\\n for name in weight_sets.groups:\\n result[name] = getattr(self, name)\\n for name in weight_sets.singles:\\n result[name] = getattr(self, name)\\n result['score_net_params'] = self.score_net_params\\n result['value_net_params'] = self.value_net_params\\n return result\",\n \"def get_params(self):\\n raise NotImplementedError\",\n \"def buildParamsDict(self):\\n self.params_dict = {\\n \\\"img_dir\\\": self.savePathJoin(\\\"Images\\\"),\\n \\\"depth_dir\\\": self.savePathJoin(\\\"Depth\\\"),\\n \\\"back_of_dir\\\": self.savePathJoin(\\\"Back_Of\\\"),\\n \\\"of_dir\\\": self.savePathJoin(\\\"Of\\\"),\\n \\\"save_dir\\\": self.user[\\\"Save\\\"],\\n \\\"high\\\": self.high,\\n \\\"low\\\": self.low,\\n \\\"run_dict\\\": self.run_dict,\\n \\\"of_model\\\": self.app.get_resource(\\n os.path.join(\\\"of_models\\\", \\\"network-default.pytorch\\\")\\n ),\\n \\\"depth_model\\\": self.app.get_resource(\\n os.path.join(\\\"depth_models\\\", \\\"model_city2kitti.meta\\\")\\n ),\\n \\\"yolo_weights\\\": self.app.get_resource(\\n os.path.join(\\\"yolo\\\", \\\"yolov3.weights\\\")\\n ),\\n \\\"yolo_v\\\": self.app.get_resource(os.path.join(\\\"yolo\\\", \\\"yolov3.cfg\\\")),\\n \\\"coco_names\\\": self.app.get_resource(os.path.join(\\\"yolo\\\", \\\"coco.names\\\")),\\n \\\"object_detection_dir\\\": self.savePathJoin(\\\"ObjectDetection\\\"),\\n \\\"plot_speed_dir\\\": PLOT_SPEED_DIR,\\n \\\"plot_crash_dir\\\": PLOT_CRASH_DIR,\\n \\\"numbers_dir\\\": NP_DIR,\\n \\\"plot_error_dir\\\": PLOT_ERROR_DIR,\\n \\\"speed_gt\\\": self.user[\\\"GT\\\"],\\n \\\"vid_path\\\": self.user[\\\"Video\\\"],\\n \\\"super_pixel_method\\\": self.super_pixel_method,\\n \\\"super_pixel_dir\\\": SUPER_PIXEL_DIR,\\n \\\"send_video_frame\\\": False,\\n \\\"create_csv\\\": self.ui.c_csv.isChecked(),\\n \\\"create_draw\\\": self.ui.c_draw.isChecked(),\\n \\\"create_velocity\\\": self.ui.c_velocity.isChecked(),\\n \\\"create_video_fps\\\": int(self.ui.t_fps.text()),\\n \\\"optimize_params\\\": self.ui.c_optimize.isChecked(),\\n \\\"super_pixel_label_dir\\\": os.path.join(\\n self.savePathJoin(\\\"Super_Pixel\\\"), self.super_pixel_method\\n ),\\n }\",\n \"def parameters(self):\\n return []\",\n \"def internal_values(self) -> Dict[str, Union[int, float]]:\\n return {name: self.hyperparams[name][1] for name in self.names()}\",\n \"def winglet_parameters():\\n\\n SPAN = WingletParameters.SPAN.value\\n ANGLE_CANT = WingletParameters.ANGLE_CANT.value\\n ANGLE_SWEEP = WingletParameters.ANGLE_SWEEP.value\\n ANGLE_TWIST_ROOT = WingletParameters.ANGLE_TWIST_ROOT.value\\n ANGLE_TWIST_TIP = WingletParameters.ANGLE_TWIST_TIP.value\\n TAPER_RATIO = WingletParameters.TAPER_RATIO.value\\n CHORD_ROOT = WingletParameters.CHORD_ROOT.value\\n W_AIRFOIL = WingletParameters.AIRFOIL.value\\n\\n _winglet_parameters = {\\n SPAN: 0.05,\\n TAPER_RATIO: 0.32,\\n CHORD_ROOT: 0.65,\\n ANGLE_SWEEP: 38,\\n ANGLE_CANT: 45,\\n ANGLE_TWIST_ROOT: 0.0,\\n ANGLE_TWIST_TIP: 0.0,\\n W_AIRFOIL: \\\"naca0012\\\",\\n }\\n\\n return _winglet_parameters\",\n \"def parameters(self):\\n #print \\\"in instrument.parameter()\\\"\\n return self._params\",\n \"def get_params(self, deep=True):\\n #params = dict(kernel=self.kernel, dim=self.dim)\\n params = dict(columns=self.columns)\\n if deep:\\n for i, kernel in enumerate(self.kernels):\\n print(\\\"--->\\\", \\\"\\\\ti = \\\", i, \\\"\\\\tkernel = \\\", kernel)\\n deep_items = kernel.get_params().items()\\n #params.update((k, val) for k, val in deep_items)\\n for k, val in deep_items:\\n print(\\\"\\\\tkey = \\\", k, \\\"\\\\tvalue = \\\", val)\\n params.update(('k{}__{}'.format(i, k), val) for k, val in deep_items)\\n return params\",\n \"def parameters(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"parameters\\\")\",\n \"def params(self):\\n\\t\\treturn self.params_\",\n \"def parameters_config(self) -> dict:\\n if not self.params_optional and not self.params_required:\\n return {}\\n parameters = dict()\\n for parameter, parameter_details in self.parameters.items():\\n parameters[parameter_details.name] = parameter_details.parameter_config()\\n return parameters\",\n \"def get_params(self):\\n\\n return None\",\n \"def get_params(self):\\n\\n return None\",\n \"def get_params(self):\\n\\n return None\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_params(self, deep=True):\\n #params = dict(kernel=self.kernel, dim=self.dim)\\n params = dict(dim=self.dim)\\n if deep:\\n deep_items = self.kernel.get_params().items()\\n params.update((k, val) for k, val in deep_items)\\n return params\",\n \"def getParameters(self):\\n return {'channels':self._channels, 'means':self._means, 'stds':self._stds}\",\n \"def gen_params(self) -> Dict:\\n param_dict: Dict = {}\\n\\n gX_name: List[str] = ['g_leak', 'g_nav', 'g_kvhh', 'g_kva', 'g_kvsi', \\n 'g_cav', 'g_kca', 'g_nap', 'g_kir']\\n gX_log: np.ndarray = 4 * np.random.rand(9) - 2 # from -2 to 2\\n gX: np.ndarray = (10 * np.ones(9)) ** gX_log # 0.01 ~ 100\\n gX_itr: Iterator = zip(gX_name, gX)\\n\\n gR_name: List[str] = ['g_ampar', 'g_nmdar', 'g_gabar']\\n gR_log: np.ndarray = 4 * np.random.rand(3) - 3 # from -3 to 1\\n gR: np.ndarray = (10 * np.ones(3)) ** gR_log # 0.001 ~ 10\\n gR_itr: Iterator = zip(gR_name, gR)\\n\\n tCa_log: float = 2 * np.random.rand(1) + 1 # from 1 to 3\\n tCa: float = 10 ** tCa_log # 10 ~ 1000\\n tCa_dict: Dict = {'t_ca': tCa}\\n\\n param_dict.update(gX_itr)\\n param_dict.update(gR_itr)\\n param_dict.update(tCa_dict)\\n return param_dict\",\n \"def get(self):\\n return dict(self._params)\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['mean' ] = self.mean\\n paramDict['sigma'] = self.sigma\\n paramDict['low'] = self.low\\n return paramDict\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['n' ] = self.n\\n paramDict['p' ] = self.p\\n return paramDict\",\n \"def getDefaultParameterValues(self):\\r\\n dct = {}\\r\\n self.initializeRoadRunnerModel()\\r\\n self.roadrunnerModel.reset()\\r\\n for parameterName in self.parametersToFit:\\r\\n dct[parameterName] = self.roadrunnerModel.model[parameterName]\\r\\n return dct\",\n \"def getParameters(self):\\n\\t\\td = AdaptiveBatAlgorithm.getParameters(self)\\n\\t\\td.update({\\n\\t\\t\\t'A_l': self.A_l,\\n\\t\\t\\t'A_u': self.A_u,\\n\\t\\t\\t'r_l': self.r_l,\\n\\t\\t\\t'r_u': self.r_u,\\n\\t\\t\\t'tao_1': self.tao_1,\\n\\t\\t\\t'tao_2': self.tao_2\\n\\t\\t})\\n\\t\\treturn d\",\n \"def _pprint_params(self):\\n return {'x_range': self.x_range, 'y_range': self.y_range,\\n 'step': self.step, 'shape': self.shape,\\n 'type': self.type}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7157849","0.71448296","0.70548433","0.69977343","0.69741374","0.6912348","0.6773086","0.6764622","0.67219996","0.6715262","0.6695996","0.66917676","0.6640885","0.6640533","0.6637852","0.66346395","0.65994066","0.65687644","0.6556268","0.65546185","0.65205616","0.64955544","0.6484379","0.646304","0.6443311","0.6429313","0.6397637","0.6379275","0.637775","0.63622063","0.63534534","0.6352701","0.6346455","0.63344884","0.63328403","0.63299125","0.6310701","0.6297449","0.62968135","0.6292357","0.6265254","0.6263917","0.62603","0.623613","0.6215013","0.62098986","0.62082165","0.6199888","0.6197377","0.6196924","0.6186873","0.6181027","0.6179919","0.6179632","0.6177151","0.6170113","0.61589676","0.6150353","0.61457026","0.6140505","0.6120643","0.61163193","0.6113621","0.6111402","0.610527","0.61042774","0.6103766","0.6095302","0.60935986","0.6089384","0.60890156","0.6076687","0.607177","0.60674876","0.6053643","0.60516816","0.6051484","0.6048744","0.6041005","0.6040824","0.60224414","0.6022341","0.6006711","0.6003419","0.6003351","0.6000931","0.5998507","0.5998471","0.59942526","0.59942526","0.59942526","0.5992191","0.599042","0.59868264","0.5985406","0.5984318","0.5974728","0.59734577","0.5970803","0.59700465","0.5960014"],"string":"[\n \"0.7157849\",\n \"0.71448296\",\n \"0.70548433\",\n \"0.69977343\",\n \"0.69741374\",\n \"0.6912348\",\n \"0.6773086\",\n \"0.6764622\",\n \"0.67219996\",\n \"0.6715262\",\n \"0.6695996\",\n \"0.66917676\",\n \"0.6640885\",\n \"0.6640533\",\n \"0.6637852\",\n \"0.66346395\",\n \"0.65994066\",\n \"0.65687644\",\n \"0.6556268\",\n \"0.65546185\",\n \"0.65205616\",\n \"0.64955544\",\n \"0.6484379\",\n \"0.646304\",\n \"0.6443311\",\n \"0.6429313\",\n \"0.6397637\",\n \"0.6379275\",\n \"0.637775\",\n \"0.63622063\",\n \"0.63534534\",\n \"0.6352701\",\n \"0.6346455\",\n \"0.63344884\",\n \"0.63328403\",\n \"0.63299125\",\n \"0.6310701\",\n \"0.6297449\",\n \"0.62968135\",\n \"0.6292357\",\n \"0.6265254\",\n \"0.6263917\",\n \"0.62603\",\n \"0.623613\",\n \"0.6215013\",\n \"0.62098986\",\n \"0.62082165\",\n \"0.6199888\",\n \"0.6197377\",\n \"0.6196924\",\n \"0.6186873\",\n \"0.6181027\",\n \"0.6179919\",\n \"0.6179632\",\n \"0.6177151\",\n \"0.6170113\",\n \"0.61589676\",\n \"0.6150353\",\n \"0.61457026\",\n \"0.6140505\",\n \"0.6120643\",\n \"0.61163193\",\n \"0.6113621\",\n \"0.6111402\",\n \"0.610527\",\n \"0.61042774\",\n \"0.6103766\",\n \"0.6095302\",\n \"0.60935986\",\n \"0.6089384\",\n \"0.60890156\",\n \"0.6076687\",\n \"0.607177\",\n \"0.60674876\",\n \"0.6053643\",\n \"0.60516816\",\n \"0.6051484\",\n \"0.6048744\",\n \"0.6041005\",\n \"0.6040824\",\n \"0.60224414\",\n \"0.6022341\",\n \"0.6006711\",\n \"0.6003419\",\n \"0.6003351\",\n \"0.6000931\",\n \"0.5998507\",\n \"0.5998471\",\n \"0.59942526\",\n \"0.59942526\",\n \"0.59942526\",\n \"0.5992191\",\n \"0.599042\",\n \"0.59868264\",\n \"0.5985406\",\n \"0.5984318\",\n \"0.5974728\",\n \"0.59734577\",\n \"0.5970803\",\n \"0.59700465\",\n \"0.5960014\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75790,"cells":{"query":{"kind":"string","value":"Delete all the associate data"},"document":{"kind":"string","value":"def reset_data(self):\n self.data = []"},"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_all(self):\n raise NotImplementedError()","def delete_all(self):\n with self.__lock:\n self.__data = dict()\n self.flush()","def deleteAll(self):\n self.deleteAttributeRange() #Default args = everything","def clear_data():\n logger.info(\"Delete Structure instances\")\n Structure.objects.all().delete()\n logger.info(\"Delete StructureType instances\")\n StructureType.objects.all().delete()\n logger.info(\"Delete Industry instances\")\n Industry.objects.all().delete()\n logger.info(\"Delete Price instances\")\n PriceList.objects.all().delete()\n logger.info(\"Delete Stock instances\")\n Stock.objects.all().delete()\n logger.info(\"Delete News instances\")\n News.objects.all().delete()\n logger.info(\"Delete NewsImages instances\")\n NewsImage.objects.all().delete()\n logger.info(\"Delete News Sections instances\")\n NewsCategorySection.objects.all().delete()\n logger.info(\"Delete Analysis instances\")\n AnalysisOpinion.objects.all().delete()\n logger.info(\"Delete Analysis Images instances\")\n AnalysisImage.objects.all().delete()\n logger.info(\"Delete Analysis Sections instances\")\n AnalysisCategorySection.objects.all().delete()","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 _purge(self):\n for _ in self.all():\n self.delete(_)","def delete_all(self, Model):\n ndb.delete_multi(\n Model.query().fetch(keys_only=True)\n )","def _data_reset(self):\n conn = self.get_connection()\n\n elements = {\n **self.domain.registry.aggregates,\n **self.domain.registry.entities,\n **self.domain.registry.views,\n }\n for _, element_record in elements.items():\n provider = current_domain.providers[element_record.cls.meta_.provider]\n repo = self.domain.repository_for(element_record.cls)\n\n model_cls = repo._model\n if provider.conn_info[\n \"DATABASE\"\n ] == Database.ELASTICSEARCH.value and conn.indices.exists(\n model_cls._index._name\n ):\n conn.delete_by_query(\n refresh=True,\n index=model_cls._index._name,\n body={\"query\": {\"match_all\": {}}},\n )","def delete_all(self):\n if not self.created:\n return\n self.shotgun.batch([dict(\n request_type='delete',\n entity_type=type_,\n entity_id=id_,\n ) for type_, id_ in reversed(self.created)])\n self.created = []","def delete_all(cls):\n cls.dbm().modelclass_deleteall(cls)","def deleteAll():\n _table.deleteAll()\n _initialiseGlobals()\n\n return","def clear(self):\n for key in self.__data.keys():\n del self.__data[key]","def del_all_records():\n delete_alles = Customer.delete().where(Customer.name >= '')\n delete_alles.execute()","def remove_all(self):\n # Post a delete all notice to the manager\n self._remove_all()","def remove_all(self):\n # Post a delete all notice to the manager\n self._remove_all()","def clear(self):\n self.keyvaluepair_set.all().delete()","def test_data_object_del_all(self):\n pass","def delete_all(self):\n self.session.query(TodoItem).delete()\n self.session.query(TodoList).delete()","def delete_all_entities(self):\n self._delete_all_acls()\n self._delete_all_containers()\n self._delete_all_orders()\n self._delete_all_secrets()","def remove_data(self):\n db.session.delete(self)\n db.session.commit( )","def delete(self):\n for obj in self:\n _unset_related_objects_relations(obj)\n\n self.update(deleted=now())","def clear_all():\n bpy.ops.object.select_all(action='SELECT')\n bpy.ops.object.delete()","def delete():","def delete_all(self):\n query = \"\"\"MATCH(n) DETACH DELETE n\"\"\"\n return self.create_tx(query)","def deleteAll(self):\n self.db.execute(\"DELETE FROM MATCH;\", ())","def remove_all():\n storage = FileStorage()\n objects = storage.all()\n objects = list(objects.values())\n\n for element in objects:\n storage.delete(element)\n objects = storage.all()","def delete_data(self):\n conn = self._connect_DB()\n cur = conn.cursor()\n cur.execute(\"DELETE FROM movie_table;\")\n self._close_connection(conn)","def _delete_all(self):\n logging.info(\"Remove all nodes and relations from database.\")\n self.graph.delete_all()\n return","def delete(self):\n pass","def delete(self):\n pass","def delete(self):\n pass","def delete(self):\n pass","def _clean_data_on_db(self):\n self._review_obj.delete_many({\n 'entity_id': self._entity_id,\n })","def clear_data():\n conn = get_connect()\n #conn.execute(\"DELETE from match\")\n #conn.execute(\"DELETE from account\")\n #conn.execute(\"DELETE from championMatchData\")\n conn.execute(\"DELETE from championData\")\n conn.commit()\n conn.close()\n print(\"all data in info.db has been cleared\")\n return","def delete_many(self, keys):\n raise NotImplementedError()","def del_all(self, items):\n for item in items:\n item.key.delete()\n logger.debug(\"Deleted all the items\")","def delete(self):\n self.data = None","def delete(self):\n ...","def clean_exam():\n data = Exam.objects.all()\n data.delete()","def remove_all():\n \"\"\" Removes all from the database \"\"\"\n redis_store.flushall()","def clear(cls):\n\n db = get_db_handle()\n for secret in db.secret_table.select():\n secret.delete_instance(recursive=True)","def delete_all():\n if os.path.exists(DATA_DIR):\n shutil.rmtree(DATA_DIR)","def DeleteAllItems(self):\r\n\r\n self.DeleteRoot()","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(self, data):\r\n pass","def main(self):\n self.delete_details()\n self.delete_cleaned()\n self.vacuum()","def delete(self):\n self._instance.delete()\n self._instance = None\n self._data_defs = []","def clear_db():\n humans = Human4j.nodes.all()\n for h in humans:\n h.delete()\n binomes = Binome4j.nodes.all()\n for b in binomes:\n b.delete()\n projects = Project4j.nodes.all()\n for p in projects:\n p.delete()\n sherpas = Sherpa4j.nodes.all()\n for sh in sherpas:\n sh.delete()\n students = Pioupiou4j.nodes.all()\n for piou in students:\n piou.delete()\n partenaires = Partenaire4j.nodes.all()\n for part in partenaires:\n part.delete()\n ps = Planete_Solidaire.nodes.all()\n for misc in ps:\n misc.delete()","def delete_all_users(self):\n\n User.query.delete()","async def delete_many(self, **query):\n\n try:\n for result in await self.db.get_many(**query):\n await result.delete()\n except IntegrityError:\n raise ConflictException(\n f\"At least one {self.db_model_name} cannot be deleted since it is actively used\"\n )","def clear(self):\r\n for key in self.conn.keys():\r\n self.conn.delete(key)","def clear(self):\r\n self.orderitem_set.all().delete()","def clear_db(self):\n self.cursor.execute(\"DELETE FROM TrackPoint\")\n self.cursor.execute(\"DELETE FROM Activity\")\n self.cursor.execute(\"DELETE FROM User\")\n self.db_connection.commit()","def delete_all(self):\n return self.context.delete(\"/ckks/all\", None,\n \"CKKS:: failed deleting all the CKKS data\"\n )","def bulk_delete(self, data) -> bool:\n # Skip if no identifier is set\n if not self.unique_identifier or not data:\n return False\n\n for obj in data:\n if isinstance(obj, self.model):\n obj.delete()\n\n return True","def tearDown(self):\n for model in MODELS:\n for obj in model.objects.all():\n obj.delete()","def tearDown(self):\n for model in MODELS:\n for obj in model.objects.all():\n obj.delete()","def tearDown(self):\n for model in MODELS:\n for obj in model.objects.all():\n obj.delete()","def remove(self):\n for db in self.values():\n db.remove()","def deleteAllRecord(collection):\n collection_name = collection\n collection = db[collection_name]\n collection.delete_many({})\n\n print(\"Deleting all records from \" + collection_name)\n print(\"Finished operation. Collection cleared.\")\n print(\"--------- \\n\")","def run(self):\n self.db.table('purchases').delete()\n self.db.table('payments').delete()","def reset(self):\n self._execute(\"DELETE FROM collection_table\")\n self._execute(\"DELETE FROM keyword_table\")","def empty_tables():\n Wordform.objects.all().delete()\n Lemma.objects.all().delete()\n Definition.objects.all().delete()\n Language.objects.all().delete()\n ProperName.objects.all().delete()","def clear_all(self):\n raise NotImplementedError","def delete_all(self):\n # delete everything\n shutil.rmtree(self.location)","def full_reset(self):\n for docid in self.iter_docids():\n self.delete(docid)\n self.client.delete(self.dbprefix + 'schema')\n self.client.delete(self.dbprefix + 'docs')\n self.client.delete(self.dbprefix + 'nextid')","def db_delete_user_data(self):\n util.log(\"Clearing all user data\", util.LogLevel.Info)\n self.db.db_clear_data_user()\n util.log(\"Done\", util.LogLevel.Info)","def remove_all_recs(self):\n return self.storage.clear()","def delete(self):\n\n def _delete(result):\n oldid = self.id\n self.id = None\n self._deleted = True\n if self._transaction:\n return self.__class__.deleteAll(where=[\"id = ?\", oldid], transaction=self._transaction)\n else:\n return self.__class__.deleteAll(where=[\"id = ?\", oldid])\n\n def _deleteOnSuccess(result):\n if result == False:\n return defer.succeed(self)\n else:\n ds = []\n for relation in self.HABTM:\n name = relation['name'] if isinstance(relation, dict) else relation\n ds.append(getattr(self, name).clear(transaction=self._transaction))\n return defer.DeferredList(ds).addCallback(_delete)\n\n return defer.maybeDeferred(self.beforeDelete).addCallback(_deleteOnSuccess)","async def clear_all(self) -> None:","def clear(self):\n for key in self.keys():\n del self[key]","def delete_all_reviews(self):\n load.loader.clear_data_for_hotel(self.hotel_name)\n self.data_items = []\n\n self.row_number = str(self.get_row_number())","def delete(self):\n if not hasattr(self, \"id\"):\n return\n key = self.__class__.get_key_prefix()+\"#\"+str(id)\n dal_delete(key)\n logger.debug( \"DELE %s\", str(key) )\n\n for index in self.__class__.get_indexes():\n key = \"_\".join([str(getattr(self, e)) for e in index.cols])\n key = index.name+\"#\"+key\n\n logger.debug( \"DELE %s\", str(key) )\n\n query_list = dal_get(key) or '[]'\n query_list = dal_retrieve(query_list)\n\n if type(query_list)!=type([]):\n query_list = [query_list]\n\n if self.id in query_list:\n query_list.remove(self.id)\n\n dal_set(\n key,\n dal_store(query_list))","def tearDown(self):\n\n User.objects.all().delete()\n Movie.objects.all().delete()\n Vote.objects.all().delete()","def tearDown(self):\n Pics.objects.all().delete()\n Category.objects.all().delete()\n Location.objects.all().delete()","def delete_all(sid):\n Game.objects.all().delete()","def clear_tables(self):\n for name, coin in self.coins.all():\n with self.conflict_resolver.transaction() as session:\n session.query(coin.wallet_model).delete()\n session.query(coin.transaction_model).delete()\n session.query(coin.network_transaction_model).delete()\n session.query(coin.account_model).delete()\n session.query(coin.address_model).delete()","def delete_all_from(self, tablename):\n query = 'delete from ' + tablename\n try:\n self.__cur.execute(query)\n self.__conn.commit()\n except Exception as e:\n self.__conn.rollback()\n raise e","def delete():\n\n # Check the pipe setup.\n check_pipe_setup(sequence=True, j=True)\n\n # The interatomic data.\n for interatom in interatomic_loop():\n # The data.\n if hasattr(interatom, 'j_coupling'):\n del interatom.j_coupling\n\n # The error.\n if hasattr(interatom, 'j_coupling_err'):\n del interatom.j_coupling_err","def removeall(table):\n doall(\"DELETE FROM {table}\".format(table=table))","def delete(self):\n items = ShopcartItem.find_by_shopcartid(self.id)\n\n for item in items:\n item.delete()\n\n db.session.delete(self)\n db.session.commit()","def delete_relatives(self):\n category_ratings = list(self.category_ratings.all())\n self.category_ratings.clear()\n for category_rating in category_ratings:\n if category_rating.isOrphaned():\n category_rating.delete()\n\n word_counts = list(self.word_counts.all())\n self.word_counts.clear()\n for word_count in word_counts:\n if word_count.isOrphaned():\n word_count.delete()","def delete_all_rows(self, table):\n sql = f\"DELETE FROM {table}\"\n connection = self.__create_connection()\n cur = connection.cursor()\n cur.execute(sql)\n cur.close()\n connection.commit()","def flush(self):\n cursor = self.db.cursor()\n cursor.execute(\"DELETE FROM triples\")\n self.db.commit()","def clear_db(db : Session = Depends(get_db)):\n db.query(Acti).delete()\n db.query(Wrist).delete()\n db.commit()","def delete_all_records(db):\n with tables(db.engine) as (connection,):\n metadata = sqlalchemy.MetaData(bind=connection)\n metadata.reflect()\n # We delete the tables in order of dependency, so that foreign-key\n # relationships don't prevent a table from being deleted.\n for tbl in reversed(metadata.sorted_tables):\n tbl.delete().execute()","def cleanAll(self):\n for i in range(len(self.asteroid_type) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_type[i])\n self.del_asteroid(i)\n\n for i in range(len(self.asteroid_id_e) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_id_e[i])\n self.del_asteroid_e(i)","def delete(self):\n if Model.data_connector:\n with Model.data_connector.u_lock:\n Model.data_connector.remove_object(self)","def delete(self):\n\n cursor = self._conn.cursor()\n cursor.execute(\"DELETE FROM saves\")\n self._conn.commit()","def cleanup(self):\n deletes = []\n for item in self._collect.find({'status': 'started'}, {'_id': True}):\n deletes.append(pymongo.DeleteOne(item))\n # Remove them\n if len(deletes):\n print(\"Delete\", self._collect.bulk_write(deletes).deleted_count)","def selenium_teardown():\n families_to_delete, visits_to_delete, responses_to_delete = [], [], []\n\n families_to_delete.extend(Family.objects.filter(study_id_number=59638))\n families_to_delete.extend(Family.objects.filter(study_id_number=83695))\n for f in families_to_delete:\n visits_to_delete.extend(f.visit_set.all())\n for v in visits_to_delete:\n responses_to_delete.extend(v.response_set.all())\n\n for r in responses_to_delete:\n r.delete()\n for v in visits_to_delete:\n v.delete()\n for f in families_to_delete:\n f.delete()","def delete(self):\n raise NotImplementedError","def __del__(self):\r\n train_data_sources = list(self._train_data.values())\r\n test_data_sources = list(self._test_data.values())\r\n all_data_sources = train_data_sources + test_data_sources\r\n for data_source in all_data_sources:\r\n data_source.cleanup()\r\n self._tester.__del__()","def delete_test_data(session_maker):\n\n orm_session = session_maker()\n orm_session.query(USERS).filter(USERS.username.like('%test%')).delete(synchronize_session=False)\n orm_session.query(USER_POSTS).filter(USER_POSTS.username.like('%test%')).delete(synchronize_session=False)\n orm_session.query(COMMENTS).filter(COMMENTS.username.like('%test%')).delete(synchronize_session=False)\n orm_session.query(EMERGENCY_CONTACTS).filter(EMERGENCY_CONTACTS.email.like('%test%')).delete(synchronize_session=False)\n\n orm_session.commit()\n orm_session.close()\n print(\"Test Records deleted\")","def cleanup(self):\n for attribute in self._all_db_field_names:\n delattr(self, attribute)","def delete(self, data):\n self.data.remove(data)","def clear(self):\n try:\n self._load(False)\n except KeyError:\n return\n\n for i in xrange(self.size):\n try:\n del self.db[i]\n except KeyError:\n pass\n del self.db['count']\n del self.db['head']\n del self.db['size']","def delete_task(dataset):\n Observation.delete_all(dataset)\n super(dataset.__class__, dataset).delete({DATASET_ID: dataset.dataset_id})","def delete_all_users():\n\tUser.drop_collection()","def reset(self):\n for _id in self.created_ids:\n review = Review.get_by_id(_id)\n\n if review:\n review.delete()\n\n self.user.delete()\n self.business.delete()","def clear(self, clearall=False):\n def closure(cur):\n cur.execute(\"\"\"\n DELETE FROM events\n \"\"\")\n if clearall:\n cur.execute(\"\"\"\n DELETE FROM slides\n \"\"\")\n cur.execute(\"\"\"\n DELETE FROM rooms\n \"\"\")\n self.execute(closure)"],"string":"[\n \"def delete_all(self):\\n raise NotImplementedError()\",\n \"def delete_all(self):\\n with self.__lock:\\n self.__data = dict()\\n self.flush()\",\n \"def deleteAll(self):\\n self.deleteAttributeRange() #Default args = everything\",\n \"def clear_data():\\n logger.info(\\\"Delete Structure instances\\\")\\n Structure.objects.all().delete()\\n logger.info(\\\"Delete StructureType instances\\\")\\n StructureType.objects.all().delete()\\n logger.info(\\\"Delete Industry instances\\\")\\n Industry.objects.all().delete()\\n logger.info(\\\"Delete Price instances\\\")\\n PriceList.objects.all().delete()\\n logger.info(\\\"Delete Stock instances\\\")\\n Stock.objects.all().delete()\\n logger.info(\\\"Delete News instances\\\")\\n News.objects.all().delete()\\n logger.info(\\\"Delete NewsImages instances\\\")\\n NewsImage.objects.all().delete()\\n logger.info(\\\"Delete News Sections instances\\\")\\n NewsCategorySection.objects.all().delete()\\n logger.info(\\\"Delete Analysis instances\\\")\\n AnalysisOpinion.objects.all().delete()\\n logger.info(\\\"Delete Analysis Images instances\\\")\\n AnalysisImage.objects.all().delete()\\n logger.info(\\\"Delete Analysis Sections instances\\\")\\n AnalysisCategorySection.objects.all().delete()\",\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 _purge(self):\\n for _ in self.all():\\n self.delete(_)\",\n \"def delete_all(self, Model):\\n ndb.delete_multi(\\n Model.query().fetch(keys_only=True)\\n )\",\n \"def _data_reset(self):\\n conn = self.get_connection()\\n\\n elements = {\\n **self.domain.registry.aggregates,\\n **self.domain.registry.entities,\\n **self.domain.registry.views,\\n }\\n for _, element_record in elements.items():\\n provider = current_domain.providers[element_record.cls.meta_.provider]\\n repo = self.domain.repository_for(element_record.cls)\\n\\n model_cls = repo._model\\n if provider.conn_info[\\n \\\"DATABASE\\\"\\n ] == Database.ELASTICSEARCH.value and conn.indices.exists(\\n model_cls._index._name\\n ):\\n conn.delete_by_query(\\n refresh=True,\\n index=model_cls._index._name,\\n body={\\\"query\\\": {\\\"match_all\\\": {}}},\\n )\",\n \"def delete_all(self):\\n if not self.created:\\n return\\n self.shotgun.batch([dict(\\n request_type='delete',\\n entity_type=type_,\\n entity_id=id_,\\n ) for type_, id_ in reversed(self.created)])\\n self.created = []\",\n \"def delete_all(cls):\\n cls.dbm().modelclass_deleteall(cls)\",\n \"def deleteAll():\\n _table.deleteAll()\\n _initialiseGlobals()\\n\\n return\",\n \"def clear(self):\\n for key in self.__data.keys():\\n del self.__data[key]\",\n \"def del_all_records():\\n delete_alles = Customer.delete().where(Customer.name >= '')\\n delete_alles.execute()\",\n \"def remove_all(self):\\n # Post a delete all notice to the manager\\n self._remove_all()\",\n \"def remove_all(self):\\n # Post a delete all notice to the manager\\n self._remove_all()\",\n \"def clear(self):\\n self.keyvaluepair_set.all().delete()\",\n \"def test_data_object_del_all(self):\\n pass\",\n \"def delete_all(self):\\n self.session.query(TodoItem).delete()\\n self.session.query(TodoList).delete()\",\n \"def delete_all_entities(self):\\n self._delete_all_acls()\\n self._delete_all_containers()\\n self._delete_all_orders()\\n self._delete_all_secrets()\",\n \"def remove_data(self):\\n db.session.delete(self)\\n db.session.commit( )\",\n \"def delete(self):\\n for obj in self:\\n _unset_related_objects_relations(obj)\\n\\n self.update(deleted=now())\",\n \"def clear_all():\\n bpy.ops.object.select_all(action='SELECT')\\n bpy.ops.object.delete()\",\n \"def delete():\",\n \"def delete_all(self):\\n query = \\\"\\\"\\\"MATCH(n) DETACH DELETE n\\\"\\\"\\\"\\n return self.create_tx(query)\",\n \"def deleteAll(self):\\n self.db.execute(\\\"DELETE FROM MATCH;\\\", ())\",\n \"def remove_all():\\n storage = FileStorage()\\n objects = storage.all()\\n objects = list(objects.values())\\n\\n for element in objects:\\n storage.delete(element)\\n objects = storage.all()\",\n \"def delete_data(self):\\n conn = self._connect_DB()\\n cur = conn.cursor()\\n cur.execute(\\\"DELETE FROM movie_table;\\\")\\n self._close_connection(conn)\",\n \"def _delete_all(self):\\n logging.info(\\\"Remove all nodes and relations from database.\\\")\\n self.graph.delete_all()\\n return\",\n \"def delete(self):\\n pass\",\n \"def delete(self):\\n pass\",\n \"def delete(self):\\n pass\",\n \"def delete(self):\\n pass\",\n \"def _clean_data_on_db(self):\\n self._review_obj.delete_many({\\n 'entity_id': self._entity_id,\\n })\",\n \"def clear_data():\\n conn = get_connect()\\n #conn.execute(\\\"DELETE from match\\\")\\n #conn.execute(\\\"DELETE from account\\\")\\n #conn.execute(\\\"DELETE from championMatchData\\\")\\n conn.execute(\\\"DELETE from championData\\\")\\n conn.commit()\\n conn.close()\\n print(\\\"all data in info.db has been cleared\\\")\\n return\",\n \"def delete_many(self, keys):\\n raise NotImplementedError()\",\n \"def del_all(self, items):\\n for item in items:\\n item.key.delete()\\n logger.debug(\\\"Deleted all the items\\\")\",\n \"def delete(self):\\n self.data = None\",\n \"def delete(self):\\n ...\",\n \"def clean_exam():\\n data = Exam.objects.all()\\n data.delete()\",\n \"def remove_all():\\n \\\"\\\"\\\" Removes all from the database \\\"\\\"\\\"\\n redis_store.flushall()\",\n \"def clear(cls):\\n\\n db = get_db_handle()\\n for secret in db.secret_table.select():\\n secret.delete_instance(recursive=True)\",\n \"def delete_all():\\n if os.path.exists(DATA_DIR):\\n shutil.rmtree(DATA_DIR)\",\n \"def DeleteAllItems(self):\\r\\n\\r\\n self.DeleteRoot()\",\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(self, data):\\r\\n pass\",\n \"def main(self):\\n self.delete_details()\\n self.delete_cleaned()\\n self.vacuum()\",\n \"def delete(self):\\n self._instance.delete()\\n self._instance = None\\n self._data_defs = []\",\n \"def clear_db():\\n humans = Human4j.nodes.all()\\n for h in humans:\\n h.delete()\\n binomes = Binome4j.nodes.all()\\n for b in binomes:\\n b.delete()\\n projects = Project4j.nodes.all()\\n for p in projects:\\n p.delete()\\n sherpas = Sherpa4j.nodes.all()\\n for sh in sherpas:\\n sh.delete()\\n students = Pioupiou4j.nodes.all()\\n for piou in students:\\n piou.delete()\\n partenaires = Partenaire4j.nodes.all()\\n for part in partenaires:\\n part.delete()\\n ps = Planete_Solidaire.nodes.all()\\n for misc in ps:\\n misc.delete()\",\n \"def delete_all_users(self):\\n\\n User.query.delete()\",\n \"async def delete_many(self, **query):\\n\\n try:\\n for result in await self.db.get_many(**query):\\n await result.delete()\\n except IntegrityError:\\n raise ConflictException(\\n f\\\"At least one {self.db_model_name} cannot be deleted since it is actively used\\\"\\n )\",\n \"def clear(self):\\r\\n for key in self.conn.keys():\\r\\n self.conn.delete(key)\",\n \"def clear(self):\\r\\n self.orderitem_set.all().delete()\",\n \"def clear_db(self):\\n self.cursor.execute(\\\"DELETE FROM TrackPoint\\\")\\n self.cursor.execute(\\\"DELETE FROM Activity\\\")\\n self.cursor.execute(\\\"DELETE FROM User\\\")\\n self.db_connection.commit()\",\n \"def delete_all(self):\\n return self.context.delete(\\\"/ckks/all\\\", None,\\n \\\"CKKS:: failed deleting all the CKKS data\\\"\\n )\",\n \"def bulk_delete(self, data) -> bool:\\n # Skip if no identifier is set\\n if not self.unique_identifier or not data:\\n return False\\n\\n for obj in data:\\n if isinstance(obj, self.model):\\n obj.delete()\\n\\n return True\",\n \"def tearDown(self):\\n for model in MODELS:\\n for obj in model.objects.all():\\n obj.delete()\",\n \"def tearDown(self):\\n for model in MODELS:\\n for obj in model.objects.all():\\n obj.delete()\",\n \"def tearDown(self):\\n for model in MODELS:\\n for obj in model.objects.all():\\n obj.delete()\",\n \"def remove(self):\\n for db in self.values():\\n db.remove()\",\n \"def deleteAllRecord(collection):\\n collection_name = collection\\n collection = db[collection_name]\\n collection.delete_many({})\\n\\n print(\\\"Deleting all records from \\\" + collection_name)\\n print(\\\"Finished operation. Collection cleared.\\\")\\n print(\\\"--------- \\\\n\\\")\",\n \"def run(self):\\n self.db.table('purchases').delete()\\n self.db.table('payments').delete()\",\n \"def reset(self):\\n self._execute(\\\"DELETE FROM collection_table\\\")\\n self._execute(\\\"DELETE FROM keyword_table\\\")\",\n \"def empty_tables():\\n Wordform.objects.all().delete()\\n Lemma.objects.all().delete()\\n Definition.objects.all().delete()\\n Language.objects.all().delete()\\n ProperName.objects.all().delete()\",\n \"def clear_all(self):\\n raise NotImplementedError\",\n \"def delete_all(self):\\n # delete everything\\n shutil.rmtree(self.location)\",\n \"def full_reset(self):\\n for docid in self.iter_docids():\\n self.delete(docid)\\n self.client.delete(self.dbprefix + 'schema')\\n self.client.delete(self.dbprefix + 'docs')\\n self.client.delete(self.dbprefix + 'nextid')\",\n \"def db_delete_user_data(self):\\n util.log(\\\"Clearing all user data\\\", util.LogLevel.Info)\\n self.db.db_clear_data_user()\\n util.log(\\\"Done\\\", util.LogLevel.Info)\",\n \"def remove_all_recs(self):\\n return self.storage.clear()\",\n \"def delete(self):\\n\\n def _delete(result):\\n oldid = self.id\\n self.id = None\\n self._deleted = True\\n if self._transaction:\\n return self.__class__.deleteAll(where=[\\\"id = ?\\\", oldid], transaction=self._transaction)\\n else:\\n return self.__class__.deleteAll(where=[\\\"id = ?\\\", oldid])\\n\\n def _deleteOnSuccess(result):\\n if result == False:\\n return defer.succeed(self)\\n else:\\n ds = []\\n for relation in self.HABTM:\\n name = relation['name'] if isinstance(relation, dict) else relation\\n ds.append(getattr(self, name).clear(transaction=self._transaction))\\n return defer.DeferredList(ds).addCallback(_delete)\\n\\n return defer.maybeDeferred(self.beforeDelete).addCallback(_deleteOnSuccess)\",\n \"async def clear_all(self) -> None:\",\n \"def clear(self):\\n for key in self.keys():\\n del self[key]\",\n \"def delete_all_reviews(self):\\n load.loader.clear_data_for_hotel(self.hotel_name)\\n self.data_items = []\\n\\n self.row_number = str(self.get_row_number())\",\n \"def delete(self):\\n if not hasattr(self, \\\"id\\\"):\\n return\\n key = self.__class__.get_key_prefix()+\\\"#\\\"+str(id)\\n dal_delete(key)\\n logger.debug( \\\"DELE %s\\\", str(key) )\\n\\n for index in self.__class__.get_indexes():\\n key = \\\"_\\\".join([str(getattr(self, e)) for e in index.cols])\\n key = index.name+\\\"#\\\"+key\\n\\n logger.debug( \\\"DELE %s\\\", str(key) )\\n\\n query_list = dal_get(key) or '[]'\\n query_list = dal_retrieve(query_list)\\n\\n if type(query_list)!=type([]):\\n query_list = [query_list]\\n\\n if self.id in query_list:\\n query_list.remove(self.id)\\n\\n dal_set(\\n key,\\n dal_store(query_list))\",\n \"def tearDown(self):\\n\\n User.objects.all().delete()\\n Movie.objects.all().delete()\\n Vote.objects.all().delete()\",\n \"def tearDown(self):\\n Pics.objects.all().delete()\\n Category.objects.all().delete()\\n Location.objects.all().delete()\",\n \"def delete_all(sid):\\n Game.objects.all().delete()\",\n \"def clear_tables(self):\\n for name, coin in self.coins.all():\\n with self.conflict_resolver.transaction() as session:\\n session.query(coin.wallet_model).delete()\\n session.query(coin.transaction_model).delete()\\n session.query(coin.network_transaction_model).delete()\\n session.query(coin.account_model).delete()\\n session.query(coin.address_model).delete()\",\n \"def delete_all_from(self, tablename):\\n query = 'delete from ' + tablename\\n try:\\n self.__cur.execute(query)\\n self.__conn.commit()\\n except Exception as e:\\n self.__conn.rollback()\\n raise e\",\n \"def delete():\\n\\n # Check the pipe setup.\\n check_pipe_setup(sequence=True, j=True)\\n\\n # The interatomic data.\\n for interatom in interatomic_loop():\\n # The data.\\n if hasattr(interatom, 'j_coupling'):\\n del interatom.j_coupling\\n\\n # The error.\\n if hasattr(interatom, 'j_coupling_err'):\\n del interatom.j_coupling_err\",\n \"def removeall(table):\\n doall(\\\"DELETE FROM {table}\\\".format(table=table))\",\n \"def delete(self):\\n items = ShopcartItem.find_by_shopcartid(self.id)\\n\\n for item in items:\\n item.delete()\\n\\n db.session.delete(self)\\n db.session.commit()\",\n \"def delete_relatives(self):\\n category_ratings = list(self.category_ratings.all())\\n self.category_ratings.clear()\\n for category_rating in category_ratings:\\n if category_rating.isOrphaned():\\n category_rating.delete()\\n\\n word_counts = list(self.word_counts.all())\\n self.word_counts.clear()\\n for word_count in word_counts:\\n if word_count.isOrphaned():\\n word_count.delete()\",\n \"def delete_all_rows(self, table):\\n sql = f\\\"DELETE FROM {table}\\\"\\n connection = self.__create_connection()\\n cur = connection.cursor()\\n cur.execute(sql)\\n cur.close()\\n connection.commit()\",\n \"def flush(self):\\n cursor = self.db.cursor()\\n cursor.execute(\\\"DELETE FROM triples\\\")\\n self.db.commit()\",\n \"def clear_db(db : Session = Depends(get_db)):\\n db.query(Acti).delete()\\n db.query(Wrist).delete()\\n db.commit()\",\n \"def delete_all_records(db):\\n with tables(db.engine) as (connection,):\\n metadata = sqlalchemy.MetaData(bind=connection)\\n metadata.reflect()\\n # We delete the tables in order of dependency, so that foreign-key\\n # relationships don't prevent a table from being deleted.\\n for tbl in reversed(metadata.sorted_tables):\\n tbl.delete().execute()\",\n \"def cleanAll(self):\\n for i in range(len(self.asteroid_type) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_type[i])\\n self.del_asteroid(i)\\n\\n for i in range(len(self.asteroid_id_e) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_id_e[i])\\n self.del_asteroid_e(i)\",\n \"def delete(self):\\n if Model.data_connector:\\n with Model.data_connector.u_lock:\\n Model.data_connector.remove_object(self)\",\n \"def delete(self):\\n\\n cursor = self._conn.cursor()\\n cursor.execute(\\\"DELETE FROM saves\\\")\\n self._conn.commit()\",\n \"def cleanup(self):\\n deletes = []\\n for item in self._collect.find({'status': 'started'}, {'_id': True}):\\n deletes.append(pymongo.DeleteOne(item))\\n # Remove them\\n if len(deletes):\\n print(\\\"Delete\\\", self._collect.bulk_write(deletes).deleted_count)\",\n \"def selenium_teardown():\\n families_to_delete, visits_to_delete, responses_to_delete = [], [], []\\n\\n families_to_delete.extend(Family.objects.filter(study_id_number=59638))\\n families_to_delete.extend(Family.objects.filter(study_id_number=83695))\\n for f in families_to_delete:\\n visits_to_delete.extend(f.visit_set.all())\\n for v in visits_to_delete:\\n responses_to_delete.extend(v.response_set.all())\\n\\n for r in responses_to_delete:\\n r.delete()\\n for v in visits_to_delete:\\n v.delete()\\n for f in families_to_delete:\\n f.delete()\",\n \"def delete(self):\\n raise NotImplementedError\",\n \"def __del__(self):\\r\\n train_data_sources = list(self._train_data.values())\\r\\n test_data_sources = list(self._test_data.values())\\r\\n all_data_sources = train_data_sources + test_data_sources\\r\\n for data_source in all_data_sources:\\r\\n data_source.cleanup()\\r\\n self._tester.__del__()\",\n \"def delete_test_data(session_maker):\\n\\n orm_session = session_maker()\\n orm_session.query(USERS).filter(USERS.username.like('%test%')).delete(synchronize_session=False)\\n orm_session.query(USER_POSTS).filter(USER_POSTS.username.like('%test%')).delete(synchronize_session=False)\\n orm_session.query(COMMENTS).filter(COMMENTS.username.like('%test%')).delete(synchronize_session=False)\\n orm_session.query(EMERGENCY_CONTACTS).filter(EMERGENCY_CONTACTS.email.like('%test%')).delete(synchronize_session=False)\\n\\n orm_session.commit()\\n orm_session.close()\\n print(\\\"Test Records deleted\\\")\",\n \"def cleanup(self):\\n for attribute in self._all_db_field_names:\\n delattr(self, attribute)\",\n \"def delete(self, data):\\n self.data.remove(data)\",\n \"def clear(self):\\n try:\\n self._load(False)\\n except KeyError:\\n return\\n\\n for i in xrange(self.size):\\n try:\\n del self.db[i]\\n except KeyError:\\n pass\\n del self.db['count']\\n del self.db['head']\\n del self.db['size']\",\n \"def delete_task(dataset):\\n Observation.delete_all(dataset)\\n super(dataset.__class__, dataset).delete({DATASET_ID: dataset.dataset_id})\",\n \"def delete_all_users():\\n\\tUser.drop_collection()\",\n \"def reset(self):\\n for _id in self.created_ids:\\n review = Review.get_by_id(_id)\\n\\n if review:\\n review.delete()\\n\\n self.user.delete()\\n self.business.delete()\",\n \"def clear(self, clearall=False):\\n def closure(cur):\\n cur.execute(\\\"\\\"\\\"\\n DELETE FROM events\\n \\\"\\\"\\\")\\n if clearall:\\n cur.execute(\\\"\\\"\\\"\\n DELETE FROM slides\\n \\\"\\\"\\\")\\n cur.execute(\\\"\\\"\\\"\\n DELETE FROM rooms\\n \\\"\\\"\\\")\\n self.execute(closure)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78988016","0.77220124","0.770756","0.7586882","0.7411554","0.73536557","0.7343204","0.7230482","0.71741986","0.71688086","0.714042","0.7107411","0.71067667","0.70971084","0.70971084","0.70941174","0.70929646","0.7079958","0.70230395","0.69778144","0.69645554","0.69639754","0.69635177","0.69432706","0.69391584","0.69391346","0.6937656","0.6927491","0.69176036","0.69176036","0.69176036","0.69176036","0.68568087","0.6842889","0.68294954","0.68195325","0.68191314","0.6802903","0.68009627","0.6758612","0.6753877","0.67303205","0.67171407","0.67044246","0.6702497","0.66973954","0.66829187","0.66711277","0.6659697","0.6646827","0.6638766","0.6638385","0.66310877","0.66307664","0.6628569","0.661752","0.661752","0.661752","0.6593738","0.6584472","0.6578224","0.65765214","0.6558441","0.6552694","0.65513855","0.654316","0.6527757","0.6519069","0.6518207","0.65155894","0.6515035","0.6512875","0.65111065","0.6509968","0.6505252","0.6497544","0.6479937","0.6476798","0.64725524","0.6469959","0.64698","0.64504963","0.6449883","0.6435039","0.6432943","0.64259386","0.64250267","0.6414498","0.6408414","0.64038295","0.6403002","0.63968575","0.6394506","0.6386892","0.63778275","0.6375518","0.6372639","0.6371264","0.6370541","0.63593787","0.6354977"],"string":"[\n \"0.78988016\",\n \"0.77220124\",\n \"0.770756\",\n \"0.7586882\",\n \"0.7411554\",\n \"0.73536557\",\n \"0.7343204\",\n \"0.7230482\",\n \"0.71741986\",\n \"0.71688086\",\n \"0.714042\",\n \"0.7107411\",\n \"0.71067667\",\n \"0.70971084\",\n \"0.70971084\",\n \"0.70941174\",\n \"0.70929646\",\n \"0.7079958\",\n \"0.70230395\",\n \"0.69778144\",\n \"0.69645554\",\n \"0.69639754\",\n \"0.69635177\",\n \"0.69432706\",\n \"0.69391584\",\n \"0.69391346\",\n \"0.6937656\",\n \"0.6927491\",\n \"0.69176036\",\n \"0.69176036\",\n \"0.69176036\",\n \"0.69176036\",\n \"0.68568087\",\n \"0.6842889\",\n \"0.68294954\",\n \"0.68195325\",\n \"0.68191314\",\n \"0.6802903\",\n \"0.68009627\",\n \"0.6758612\",\n \"0.6753877\",\n \"0.67303205\",\n \"0.67171407\",\n \"0.67044246\",\n \"0.6702497\",\n \"0.66973954\",\n \"0.66829187\",\n \"0.66711277\",\n \"0.6659697\",\n \"0.6646827\",\n \"0.6638766\",\n \"0.6638385\",\n \"0.66310877\",\n \"0.66307664\",\n \"0.6628569\",\n \"0.661752\",\n \"0.661752\",\n \"0.661752\",\n \"0.6593738\",\n \"0.6584472\",\n \"0.6578224\",\n \"0.65765214\",\n \"0.6558441\",\n \"0.6552694\",\n \"0.65513855\",\n \"0.654316\",\n \"0.6527757\",\n \"0.6519069\",\n \"0.6518207\",\n \"0.65155894\",\n \"0.6515035\",\n \"0.6512875\",\n \"0.65111065\",\n \"0.6509968\",\n \"0.6505252\",\n \"0.6497544\",\n \"0.6479937\",\n \"0.6476798\",\n \"0.64725524\",\n \"0.6469959\",\n \"0.64698\",\n \"0.64504963\",\n \"0.6449883\",\n \"0.6435039\",\n \"0.6432943\",\n \"0.64259386\",\n \"0.64250267\",\n \"0.6414498\",\n \"0.6408414\",\n \"0.64038295\",\n \"0.6403002\",\n \"0.63968575\",\n \"0.6394506\",\n \"0.6386892\",\n \"0.63778275\",\n \"0.6375518\",\n \"0.6372639\",\n \"0.6371264\",\n \"0.6370541\",\n \"0.63593787\",\n \"0.6354977\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75791,"cells":{"query":{"kind":"string","value":"Replaces the well drawdown given the index of a drawdown data in data list"},"document":{"kind":"string","value":"def set_as_drawdown(self, idx):\n dtype = self.get_data_type(idx)\n if dtype == \"Pumping rate\":\n x, y = self.data[idx].get_data()\n self.drawdown.set_data(x, y)\n else:\n raise TypeError('Selected data is not drawdown!')"},"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 remove_from_drawn(section: str, index: int):\r\n del drawn[section][index]","def activateChildWellIndex(self,index,value):\n activeIndices=self.activeChildWellIndices()\n if value and index not in activeIndices:\n activeIndices.append(index)\n activeIndices=sorted(activeIndices)\n elif not value and index in activeIndices:\n activeIndices.remove(index)\n\n self.setActiveChildWellIndices(activeIndices)","def replace_widget(widgets, new_widget):\n\n for pos, current_widget in enumerate(widgets):\n\n if current_widget['widget_id'] == new_widget['widget_id']:\n widgets[pos] = new_widget","def add_to_drawn(section: str, thing: Base, index: int=None):\r\n if index is None:\r\n drawn[section].append(thing)\r\n else:\r\n drawn[section].insert(index, thing)","def draw_walker(indx):\n chart_1.create_oval(hips[indx]-6, hips[indx+1]-6,hips[indx]+6, hips[indx+1]+6, fill= \"magenta\", width = 1, tag = 'line_1') \n chart_1.create_line(hips[indx], hips[indx+1], knee_a[indx], knee_a[indx+1], fill= \"blue\", width = 2, tag = 'line_1') \n chart_1.create_line(hips[indx], hips[indx+1], knee_b[indx], knee_b[indx+1], fill= \"green\", width = 2, tag = 'line_1') \n chart_1.create_line(knee_a[indx], knee_a[indx+1], heel_a[indx], heel_a[indx+1], fill= \"blue\", width = 2, tag = 'line_1') \n chart_1.create_line(knee_b[indx], knee_b[indx+1], heel_b[indx], heel_b[indx+1], fill= \"green\", width = 2, tag = 'line_1')","def update_figure(picks, curve, active_well): \n w = p.get_well(active_well) ##selects the correct welly.Well object\n picks_df = pd.read_json(picks)\n picks_selected = picks_df[picks_df['UWI'] == active_well.replace(' ', '-')]\n \n # regenerate figure with the new horizontal line\n fig = helper.make_log_plot(w=w, ymin=ymin)# , resample=0.1) # resample needs a float to change basis\n fig.update_layout(uirevision=active_well)\n helper.update_picks_on_plot(fig, picks_selected)\n \n return fig","def get_well_name(self, idx):\n name = None\n if type(idx) is int:\n n = self.well_count()\n assert 0 <= idx <= n - 1, \"Bad well index\"\n name = self.wells[idx].drawdown.name\n return(name)","def change_fig(self):\r\n text = self.listWidget.currentItem().text()\r\n alias = self.aliases_dict[text]\r\n figure = self.fig_dict[alias][0]\r\n self.remove_canvas()\r\n self.add_canvas(figure)","def update_cross_section(tops_storage, well_uwi):\n wells_tops = pd.read_json(tops_storage)\n well_tops = wells_tops[wells_tops.UWI == well_uwi]\n csv_txt = df_to_csvtxt(well_tops)\n p.get_well(well_uwi).data['tops'] = Striplog.from_csv(text=csv_txt)\n return encode_xsection(p, legend)","def overlay_down(self, idx):\n if not self.is_bottom_layer(idx):\n self.overlay_list[idx], self.overlay_list[idx-1] = \\\n self.overlay_list[idx-1], self.overlay_list[idx]","def _remove_element(cls, d, idx):\n d[idx, 2] = 0\n return d","def update_well(self, row, col, content):\n self.plate.get_well(row, col).composition.update(content)","def overlay_up(self, idx):\n if not self.is_top_layer(idx):\n self.overlay_list[idx], self.overlay_list[idx+1] = \\\n self.overlay_list[idx+1], self.overlay_list[idx]","def transform(self, y_idx):\n self.y = [row.pop(y_idx) for row in self.data]\n self.X = self.data","def _update_well_meta(self, pos):\n self.well_meta['well']['images'] = [{'path': self.positions[pos]['name']}]\n self.store[self.rows[0]][self.columns[pos]].attrs.put(self.well_meta)","def edit(self):\n wmap = 512\n if not self.mapd:\n data = self.data[24:]\n mapd = self.mapd = []\n for index in xrange(0,3*wmap*wmap,3):\n mapd.append(data[index:index+3])\n self.setChanged()","def replaceItemOfIndex(itemsList, value, index):\n itemsList[index] = value","def switch_points(mutated_genome,index):\n point_index1 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n point_index2 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n temp = mutated_genome[index][2][point_index1]\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][point_index2]\n mutated_genome[index][2][point_index2] = temp","def well_update_changes_curves(well_uwi):\n w = p.get_well(well_uwi) # identifies and gets the correct welly.Well object based on well_uwi\n curve_list = sorted(list(w.data))\n curve = get_first_curve(curve_list)\n curve_dropdown_options = [{'label': k, 'value': k} for k in curve_list] #creates dropdown list\n return curve_dropdown_options, curve # returns the dropdown list options and the initial curve","def correct(self):\n self.parent.copyCurrentWinState(self.pltw)\n self.pltw.blklst[self.blkno][self.ypos] = self.data[1] - self.data[2]\n self.pltw.updatePlot()\n self.pltw.dirty = True\n self.pltw.activecurv = self.cpos\n self.parent.updateUI()\n self.hide()","def replace(self, index, value):\n index += self.n\n self.data[index] = value\n index //= 2\n while index > 0:\n self.data[index] = self.func(self.data[2*index], self.data[2*index+1])\n index //= 2","def edit_the_selected_value_wishlist():\n\n curItem = wishlist_treeview.focus().strip('#')\n\n with open(\"Other_title_categories.json\", \"r\") as other_categories_fo:\n other_categories_foData = json.load(other_categories_fo)\n completeDict = other_categories_foData[\"wish_list\"]\n selectetItemData_complete = completeDict[curItem]\n\n select_values = selectetItemData_complete\n\n def raging_fire():\n \"\"\"call back function for edit button in the edit window\"\"\"\n\n if editspin1.get() != '0': # season\n select_values[0] = int(editspin1.get()) # modify the change\n select_values[3] = \"{}\".format(datetime.datetime.now().date()) # update the modify date\n with open('Other_title_categories.json', 'w') as f:\n completeDict[curItem] = select_values\n json.dump(other_categories_foData, f, indent=2)\n print(\"done writing changes\")\n\n if editspin2.get() != '0': # episode\n select_values[1] = int(editspin2.get())\n select_values[3] = \"{}\".format(datetime.datetime.now().date()) # update the modify date\n with open('Other_title_categories.json', 'w') as f:\n completeDict[curItem] = select_values\n json.dump(other_categories_foData, f, indent=2)\n print(\"done writing changes\")\n\n if editentvar.get() != curItem: # name\n removed_values = completeDict.pop(curItem) # remove the previous title and write the new one\n completeDict[editentvar.get()] = removed_values\n select_values[3] = \"{}\".format(datetime.datetime.now().date())\n with open('Other_title_categories.json', 'w') as f:\n completeDict[editentvar.get()] = select_values # set the values of the edited title\n json.dump(other_categories_foData, f, indent=2)\n print(\"done writing changes\")\n\n edittop.destroy()\n\n if curItem != \"\": # test if an item is highlighted first\n \"\"\"the actual edit window widgets\"\"\"\n edittop = Toplevel()\n\n editlab1 = Label(edittop, text=\"Current Tv-Series title : \")\n editlab1.grid(row=1, column=1, sticky=W, pady=4)\n\n editent = Entry(edittop, textvariable=editentvar, width=30)\n editentvar.set(curItem)\n editent.grid(row=1, column=2, sticky=W, pady=4)\n\n editlab2 = Label(edittop, text=\"Current Season {}, chance to : \".format(select_values[0]))\n editlab2.grid(row=2, column=1, sticky=W, pady=4)\n\n editspin1 = Spinbox(edittop, from_=0, to=1000, width=5)\n editspin1.grid(row=2, column=2, sticky=W, pady=4)\n\n editlab3 = Label(edittop, text=\"Current Episode {}, change to : \".format(select_values[1]))\n editlab3.grid(row=3, column=1, sticky=W, pady=4)\n\n editspin2 = Spinbox(edittop, from_=0, to=1000, width=5)\n editspin2.grid(row=3, column=2, sticky=W, pady=4)\n\n editbut = Button(edittop, text='Edit', command=raging_fire)\n editbut.grid(row=5, column=1, sticky=W, pady=4, padx=20)\n\n edittop.geometry(\"400x200+200+300\")\n edittop.title(\"Edit properties of {} \".format(curItem).upper())","def new_w(w, d):\n\n if w.sum() > 0:\n next_w = w.copy()\n next_w[next_w > 0] -= 1\n return next_w\n else:\n if d[0] == 1:\n return np.array([51,0,0])\n elif d[1] == 1:\n return np.array([0,51,0])\n else:\n return np.array([0,0,51])","def set(self, index, data):\n self.data[index] = data","def set_data(self, data, index, color='black', lw=2., nbins=10,\n symbol='disc', size=10., form='line', th=None, norm=None,\n window=None, overlap=0., baseline=None, clim=None,\n cmap='viridis', interpolation='gaussian', nperseg=256,\n noverlap=128):\n # Update variable :\n self.form = form\n self._index = index\n color = color2vb(color)\n\n # Get data index :\n if data.ndim == 1:\n idx = slice(None)\n elif data.ndim in [2, 3]:\n idx = list(self._navidx[index])\n idx.insert(self._axis, slice(None))\n idx = tuple(idx)\n\n # Convert data to be compatible with VisPy and prepare data :\n data_c = vispy_array(data[idx]).copy()\n _data = self._prep._prepare_data(self._sf, data_c, self._time)\n\n # Set data :\n if form in ['line', 'marker', 'psd', 'butterfly']: # line and marker\n # Get position array :\n pos = np.c_[self._time, _data]\n # Send position :\n if form in ['line', 'psd']:\n if form == 'psd':\n fmax = self._sf / 4.\n f, pxx = welch(_data, self._sf, nperseg=nperseg,\n noverlap=noverlap)\n f_sf4 = abs(f - fmax)\n f_1 = abs(f - 1.)\n fidx_sf4 = np.where(f_sf4 == f_sf4.min())[0][0]\n fidx_1 = np.where(f_1 == f_1.min())[0][0]\n pos = np.c_[f[fidx_1:-fidx_sf4], pxx[fidx_1:-fidx_sf4]]\n # Threshold :\n is_th = isinstance(th, (tuple, list, np.ndarray))\n col = color2vb(color, length=pos.shape[0])\n if is_th:\n # Build threshold segments :\n t_min, t_max = self._time.min(), self._time.max()\n pos_th = np.vstack(([t_min, th[0]], [t_max, th[0]],\n [t_min, th[1]], [t_max, th[1]]))\n self._th.set_data(pos_th, connect='segments',\n color=color2vb('#ab4642'))\n # Build line color :\n col = color2vb(color, length=len(_data))\n cond = np.logical_or(_data < th[0], _data > th[1])\n col[cond, :] = color2vb('#ab4642')\n self._th.visible = is_th\n self._line.set_data(pos, width=lw, color=col)\n self._line.update()\n elif form == 'marker':\n self._mark.set_data(pos, face_color=color, symbol=symbol,\n size=size, edge_width=0.)\n self._mark.update()\n elif form == 'butterfly':\n # Get soe shape related variables :\n n, m = len(self._time), int(np.prod(data.shape))\n n_rep = int(m / n)\n data = vispy_array(data)\n # Build position :\n pos = np.c_[np.tile(self._time.ravel(), n_rep), data.ravel()]\n # Disconnect some points :\n connect = np.c_[np.arange(m - 1), np.arange(1, m)]\n to_delete = np.linspace(n - 1, m - 1, n_rep)\n connect = np.delete(connect, to_delete, axis=0)\n # Build color :\n col = color2vb(color, length=m)\n # singcol = np.random.uniform(size=(n_rep, 3), low=.2,\n # high=.8).astype(np.float32)\n # col = np.repeat(singcol, n, 0)\n # Send data :\n self._line.set_data(pos, width=lw, color=col, connect=connect)\n self._line.update()\n # Get camera rectangle :\n t_min, t_max = pos[:, 0].min(), pos[:, 0].max()\n d_min, d_max = pos[:, 1].min(), pos[:, 1].max()\n off = .05 * (d_max - d_min)\n self.rect = (t_min, d_min - off, t_max - t_min,\n d_max - d_min + 2 * off)\n elif form == 'histogram': # histogram\n # Compute the mesh :\n mesh = scene.visuals.Histogram(_data, nbins)\n # Get the vertices and faces of the mesh :\n vert = mesh.mesh_data.get_vertices()\n faces = mesh.mesh_data.get_faces()\n # Pass vertices and faces to the histogram :\n self._hist.set_data(vert, faces, color=color)\n # Compute the histogram :\n raw, xvec = np.histogram(_data, nbins)\n # Get camera rectangle :\n t_min, t_max = xvec.min(), xvec.max()\n d_min, d_max = 0.9 * raw[np.nonzero(raw)].min(), 1.01 * raw.max()\n self.rect = (t_min, d_min, t_max - t_min, d_max - d_min)\n # Update object :\n self._hist.update()\n elif form == 'tf': # time-frequency map\n self._tf.set_data(_data, self._sf, cmap=cmap, contrast=.5,\n norm=norm, baseline=baseline, n_window=window,\n overlap=overlap, window='hanning', clim=clim)\n self._tf.interpolation = interpolation\n self.rect = self._tf.rect\n\n # Hide non form elements :\n self._visibility()\n\n # Update annotations :\n self.update_annotations(str(self))","def reorder_and_draw_new(final, colours, n_ind, ind_last_now, meas_last, meas_now, df, mot, fr_no, frame_start):\n # Reorder contours based on results of the hungarian algorithm\n equal = np.array_equal(ind_last_now, list(range(len(ind_last_now))))\n meas_now = np.array(meas_now)\n meas_now_tmp = meas_now\n meas_now = np.array(meas_last)\n for i in range(len(meas_now)):\n meas_now[i] = meas_now_tmp[ind_last_now[i]]\n \n # apatch: commented the following for loop because its range is not\n # appropriate for systems where the number of individuals may change\n # frame to frame... for example, due to occlusions when fish cross over\n #for i in range(n_ind):\n meas_now = np.array(meas_now)\n for i in range(len(meas_now)):\n cv2.circle(final, tuple([int(x) for x in meas_now[i,(0,1)]]), 5, colours[i%len(colours)%n_ind], -1, cv2.LINE_AA)\n \n meas_now = list(meas_now)\n # add frame number\n font = cv2.FONT_HERSHEY_SCRIPT_SIMPLEX\n fr_no_print = fr_no - frame_start\n cv2.putText(final, str(int(fr_no_print)), (5,30), font, 1, (255,255,255), 2)\n \n return final, meas_now, df","def __setitem__(self, index, colour):\n self.desk[index] = colour","def extend_wells(self):\n middle_point = (self.nmos_position1.y \n + self.nmos.pwell_position.y \n + self.nmos.well_height \n + (self.pmos_position1.y\n + self.pmos.nwell_position.y \n - self.nmos_position1.y \n - self.nmos.pwell_position.y \n - self.nmos.well_height) / 2 )\n self.nwell_position = vector(0, middle_point)\n self.nwell_height = self.height - middle_point\n self.add_rect(layer=\"nwell\",\n offset=self.nwell_position,\n width=self.well_width,\n height=self.nwell_height)\n self.add_rect(layer=\"vtg\",\n offset=self.nwell_position,\n width=self.well_width,\n height=self.nwell_height)\n\n self.pwell_position = vector(0, 0)\n self.pwell_height = middle_point\n self.add_rect(layer=\"pwell\",\n offset=self.pwell_position,\n width=self.well_width,\n height=self.pwell_height)\n self.add_rect(layer=\"vtg\",\n offset=self.pwell_position, \n width=self.well_width,\n height=self.pwell_height)","def update_pit(self, value, pit_index, index):\n if index == 1:\n self.state[pit_index] = value\n else:\n self.state[pit_index + self.M + 1] = value","def set_custom_data(self, index_from, data):\r\n self.unif[index_from:(index_from + len(data))] = data","def seven_punishment(self):\n if self.current_draw_punishment == 1:\n self.current_draw_punishment = 2\n else:\n self.current_draw_punishment = self.current_draw_punishment + 2","def draw_all(self, im, idx):\n if idx in self.frames_to_boxes_dict:\n self.draw_boxes(im, self.frames_to_boxes_dict[idx])\n\n hud_info = {\"bottles\": 0, \"fragments\":0, \"others\":0}\n if idx in self.frames_to_update_hud:\n hud_info = self.frames_to_update_hud[idx].copy()\n hud_info[\"update\"] = True\n else:\n # find the last hud info\n for x in self.frames_to_update_hud.keys():\n if idx < x:\n break\n hud_info = self.frames_to_update_hud[x]\n self.draw_hud(im, hud_info)","def setDrawIndex(self, idsOfDraws):\n if self.name in idsOfDraws:\n self.drawId = idsOfDraws[self.name]\n else:\n error_msg = (\n f'No id is available for draw {self.name}. '\n f'List of available indices: '\n f'{[n for n, i in idsOfDraws.items()]}'\n )\n raise excep.biogemeError(error_msg)","def FullHouse(handIn, indx):","def single_decoding_weber_law_plot(data_idxs=2):\n\n\tdecode_plot_size = 8\n\tnum_gain_plots = data_idxs/2\n\tgain_plot_size = 4\n\test_plot_upper_pad = 3\n\test_plot_height = 6\n\tnum_est_plots = 2\n\t\n\t\n\tfig = plt.figure()\n\tgrid_width = decode_plot_size + 1 + gain_plot_size*num_gain_plots\n\tgrid_height = decode_plot_size + est_plot_upper_pad + \\\n\t\t\t\t\test_plot_height*num_est_plots\n\tgs = gridspec.GridSpec(grid_height, grid_width)\n\tgs.update(wspace=6.5, hspace=1.0)\n\tfig.set_size_inches(13, 16)\n\t\n\t# Some font and label sizes for later use\n\ttick_label_size_small = 30\n\ttick_label_size_large = 30\n\taxis_label_size = 34\n\tx_axis_label_pad = 15\n\ty_axis_successes_labelpad = 8\n\tx_tick_label_pad = 11\n\t\n\tax = dict()\n\t\n\t# Success error plots\n\tax['successes'] = plt.subplot(gs[0:decode_plot_size, 0:decode_plot_size])\n\tplt.xscale('log')\n\tplt.xticks(10.**sp.arange(-5, 5), fontsize=tick_label_size_large)\n\tplt.yticks([0, 50, 100], fontsize=tick_label_size_large)\n\tax['successes'].tick_params(axis='x', which='major', pad=x_tick_label_pad)\n\tax['successes'].set_xlabel(r'Background signal', fontsize=axis_label_size, \n\t\t\t\t\t\t\t\tlabelpad=x_axis_label_pad)\n\tax['successes'].set_ylabel(r'Correctly decoded (%)', \n\t\t\t\t\t\t\t\tfontsize=axis_label_size, \n\t\t\t\t\t\t\t\tlabelpad=y_axis_successes_labelpad, \n\t\t\t\t\t\t\t\tmultialignment='center')\n\tax['successes'].set_xlim([1e-1, 1.5e2])\n\t\n\t# Gain plots\n\tfor data_idx in sp.arange(0, data_idxs):\n\t\tif data_idx % 2 == 0: \n\t\t\tax['gains_%s' % data_idx] = \\\n\t\t\t\tplt.subplot(gs[0:gain_plot_size, decode_plot_size + \\\n\t\t\t\t\t\t\t1 + (data_idx/2)*gain_plot_size: decode_plot_size + \\\n\t\t\t\t\t\t\t1 + gain_plot_size + (data_idx/2)*gain_plot_size])\n\t\telif data_idx % 2 == 1:\n\t\t\tax['gains_%s' % data_idx] = \\\n\t\t\t\tplt.subplot(gs[gain_plot_size:2*gain_plot_size, decode_plot_size + \\\n\t\t\t\t\t\t\t1 + (data_idx/2)*gain_plot_size: decode_plot_size + \\\n\t\t\t\t\t\t\t1 + gain_plot_size + (data_idx/2)*gain_plot_size])\n\t\n\t\tax['gains_%s' % data_idx].set_xscale('log')\n\t\tax['gains_%s' % data_idx].set_yscale('log')\n\t\tax['gains_%s' % data_idx].set_xlim(1e-2, 1e0)\n\t\tax['gains_%s' % data_idx].set_xticks([])\n\t\tax['gains_%s' % data_idx].set_yticks([])\n\t\t\n\t\t# Gain plots ticks labels and axis ranges\n\t\tif data_idx / 2 == 0:\n\t\t\tax['gains_%s' % data_idx].set_yticks(10.**sp.arange(-5, 0))\n\t\t\tax['gains_%s' % data_idx].tick_params(labelsize=tick_label_size_large)\n\t\tif data_idx % 2 == 0:\n\t\t\tax['gains_%s' % data_idx].set_ylim(2e-3, 4e-2)\n\t\telif data_idx % 2 == 1:\n\t\t\tax['gains_%s' % data_idx].set_ylim(1.1e-2, 2e-1)\n\t\t\tax['gains_%s' % data_idx].set_xticks([1e-2, 1e-1, 1e0])\t\n\t\t\tax['gains_%s' % data_idx].\\\n\t\t\t\t\ttick_params(axis='x', labelsize=tick_label_size_small)\n\t\t\tax['gains_%s' % data_idx].\\\n\t\t\t\t\ttick_params(axis='y', labelsize=tick_label_size_large)\n\t\tax['gains_%s' % data_idx].set_xlim(0.98e-2, 1.4e-1)\n\t\tax['gains_%s' % data_idx].tick_params(axis='x', which='major', \n\t\t\t\t\t\t\t\t\t\t\t\tpad=x_tick_label_pad)\n\t\t\t\n\t\t# Gaain axis label in center of range of plots\n\t\tif num_gain_plots % 2 == 0:\n\t\t\tif data_idx == data_idxs/2 - 1:\n\t\t\t\tax['gains_%s' % data_idx].set_xlabel(r'Background signal', \n\t\t\t\t\t\t\t\t\t\t\tfontsize=axis_label_size, \n\t\t\t\t\t\t\t\t\t\t\tlabelpad=x_axis_label_pad)\n\t\t\t\tax['gains_%s' % data_idx].xaxis.set_label_coords(1.1, -.455)\n\t\t\t\t\n\t\telif num_gain_plots % 2 == 1:\n\t\t\tif data_idx == data_idxs/2:\n\t\t\t\tax['gains_%s' % data_idx].set_xlabel(r'Background signal', \n\t\t\t\t\t\t\t\t\t\t\tfontsize=axis_label_size, \n\t\t\t\t\t\t\t\t\t\t\tlabelpad=x_axis_label_pad)\n\t\tif data_idx == data_idxs - 2:\n\t\t\tax['gains_%s' % data_idx].yaxis.set_label_position('right')\n\t\t\tax['gains_%s' % data_idx].set_ylabel(\n\t\t\t\t\t\tr'Receptor gain', fontsize=axis_label_size, \n\t\t\t\t\t\trotation=270)\n\t\t\tax['gains_%s' % data_idx].yaxis.set_label_coords(1.4, -0.1)\n\t\t\t\t\n\t# Sample estimation plot\n\tfor est_idx in range(num_est_plots):\n\t\tax['est_%s' % est_idx] = \\\n\t\t\tplt.subplot(gs[decode_plot_size + est_plot_upper_pad\n\t\t\t\t\t\t+ est_idx*est_plot_height:\n\t\t\t\t\t\tdecode_plot_size + est_plot_upper_pad \n\t\t\t\t\t\t+ (est_idx + 1)*est_plot_height, 0:grid_width])\n\t\tax['est_%s' % est_idx].set_yticks([])\n\t\t\n\t\t# Sample estimation plots labels only on outside\n\t\tif est_idx == num_est_plots/2:\n\t\t\tax['est_%s' % est_idx].set_xticks(sp.arange(10, 100, 10))\n\t\t\tax['est_%s' % est_idx].yaxis.set_label_coords(-0.03, 1.1)\n\t\t\tax['est_%s' % est_idx].set_xlabel(r'Odorant identity', \n\t\t\t\t\t\t\t\t\t\t\t\tfontsize=axis_label_size,\n\t\t\t\t\t\t\t\t\t\t\t\tlabelpad=x_axis_label_pad)\n\t\t\tax['est_%s' % est_idx].set_ylabel(r'Odorant intensity', \n\t\t\t\t\t\t\t\t\t\t\t\tfontsize=axis_label_size)\n\t\t\tax['est_%s' % est_idx].tick_params(labelsize=tick_label_size_large, \n\t\t\t\t\t\t\t\t\t\t\t\tpad=x_tick_label_pad)\n\t\telse:\n\t\t\tax['est_%s' % est_idx].set_xticks([])\n\t\tax['est_%s' % est_idx].set_xlim(35, 99)\n\t\t\n\treturn fig, ax","def updateDraw(self):\r\n self.delConns()\r\n self.delTags()\r\n self.drawConns()\r\n self.drawTags()","def shift_point_rect(mutated_genome,index):\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\n rectangle = mutated_genome[index][2]\n seed = random.randint(0,1)\n oldpoint = rectangle[seed]\n newpoint = (oldpoint+Xval, newpoint+Yval)\n newrectangle = list(rectangle)\n newrectangle[seed] = newpoint\n mutated_genome[index][2] = tuple(newrectangle)","def reflect_list( wall_1side_list , inout_strands , inout_strands_index , p ):\n if len(wall_1side_list)==((p-1)/2)+1: # base case\n return wall_1side_list #return \n else:\n if inout_strands_index == 0:\n wall_color = wall_1side_list[-1] # bottom universe at crossing\n wall_1side_list.append(reflect(wall_color,inout_strands[inout_strands_index],p))#,inout_strands, 1, p)\n return reflect_list( wall_1side_list, inout_strands , 1, p)\n else:\n wall_color = wall_1side_list[-1]\n wall_1side_list.append(reflect(wall_color,inout_strands[inout_strands_index],p))#,inout_strands, 1, p)\n return reflect_list( wall_1side_list, inout_strands , 0, p)","def draw_stickfigure(data):\n crop = np.array(data['crop']);\n if data['kpss'][data['i']] is not None:\n ut.draw_stick_figure(crop, data['kpss'][data['i']]);\n return crop;","def reformatSagittalView4Needle(self,ID):\n #productive #onButton #report\n profprint()\n for i in range(2): #workaround update problem\n modelNode = slicer.util.getNode('vtkMRMLModelNode'+str(ID))\n polyData = modelNode.GetPolyData()\n nb = polyData.GetNumberOfPoints()\n base = [0,0,0]\n tip = [0,0,0]\n polyData.GetPoint(nb-1,tip)\n polyData.GetPoint(0,base)\n a,b,c = tip[0]-base[0],tip[1]-base[1],tip[2]-base[2]\n #print a,b,c\n sYellow = slicer.mrmlScene.GetNodeByID(\"vtkMRMLSliceNodeYellow\")\n if sYellow ==None :\n sYellow = slicer.mrmlScene.GetNodeByID(\"vtkMRMLSliceNode2\")\n reformatLogic = slicer.vtkSlicerReformatLogic()\n sYellow.SetSliceVisible(1)\n reformatLogic.SetSliceNormal(sYellow,1,-a/b,0)\n m= sYellow.GetSliceToRAS()\n m.SetElement(0,3,base[0])\n m.SetElement(1,3,base[1])\n m.SetElement(2,3,base[2])\n sYellow.Modified()","def updateRow(self, index: int) -> None:\n ...","def test_plot_negative_wiggle_fills(self):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n splt = SEGYPlotter(ax, self.segy)\n # should add a single artist to the active patch dict.\n splt.plot_wiggles(negative_fills=True)\n self.assertEqual(len(splt.ACTIVE_PATCHES['negative_fills']),1)\n # same artist should be in the axes list\n self.assertTrue(splt.ACTIVE_PATCHES['negative_fills'][0] in \\\n splt.ax.patches)\n # should be able to directly remove it\n splt.ax.patches.remove(splt.ACTIVE_PATCHES['negative_fills'][0])\n self.assertEqual(len(splt.ax.patches), 0)\n # and re-add it\n splt.ax.patches.append(splt.ACTIVE_PATCHES['negative_fills'][0])\n self.assertEqual(len(splt.ax.patches), 1)\n # should move artist to the inactive patch dict.\n splt.plot_wiggles(negative_fills=False)\n self.assertEqual(len(splt.INACTIVE_PATCHES['negative_fills']),1)\n self.assertEqual(len(splt.ax.patches), 0)\n # calling again should change nothing\n splt.plot_wiggles(negative_fills=False)\n self.assertEqual(len(splt.INACTIVE_PATCHES['negative_fills']),1)\n self.assertEqual(len(splt.ax.patches), 0)\n # should move artist back to active patch dict.\n splt.plot_wiggles(negative_fills=True)\n self.assertEqual(len(splt.ACTIVE_PATCHES['negative_fills']),1)\n self.assertEqual(len(splt.ax.patches), 1)\n # new plot items should be accessible through the orig. axes\n self.assertEqual(len(splt.ax.patches),\n len(ax.patches))","def blank(self, index=-1):\n self.displays[0].start() # call only once to support shift chain\n if index < 0:\n for d in self.displays:\n d.blank()\n else:\n self.displays[index].blank()\n self.displays[0].latch() # call only once to support shift chain","def swap_layers(self):\n dpg.show_item(f'draw_layer_{self.id}_{self.draw_layer_index^1}')\n dpg.delete_item(f'draw_layer_{self.id}_{self.draw_layer_index}')\n self.draw_layer_index ^= 1","def show2d(self, data):\n self.__plot2d.clear()\n self.__plot2d.addImage(legend=\"data\", data=data)\n self.setCurrentIndex(self.__index2d)","def revise(self,\r\n index,\r\n oldindex=None,\r\n infront=True,\r\n inback=False,\r\n breaker=Note(set(),EMPTYCHAR)):\r\n## self.default_dict['currentindex'] = Index(0)\r\n if str(index) in self.indexes():\r\n\r\n newnote = self.get_note(index)\r\n\r\n if not oldindex or str(oldindex) not in self.indexes():\r\n if infront:\r\n newnote = self.enter(returnnote=True) + breaker + newnote\r\n if inback:\r\n newnote = newnote + breaker + self.enter(returnnote=True)\r\n else:\r\n if infront:\r\n newnote = self.get_note(oldindex) + breaker + newnote\r\n if inback:\r\n newnote = newnote + breaker + self.get_note(oldindex)\r\n\r\n\r\n self.softdelete(index)\r\n self.addnew(newnote.keyset,\r\n newnote.text,\r\n newnote.meta,\r\n right_at=True,\r\n ind=index)\r\n else:\r\n display.noteprint((alerts.ATTENTION,'Fail'))","def updateWindow(gameWindow, figurelist, rounds):\n for item in gameWindow.items[:]:\n item.undraw() #Remove al elements in game window\n\n gameWindow.update() #update to ensure al is gone\n\n for i in range(15): #For loops to draw new figures\n for k in range(25):\n todraw = figurelist[i][k]\n todraw.draw(gameWindow)\n \n scoreText = str(\"Pick number:\" + str(rounds)) #Show how many times a color has been picked(Shows 1 on first round)\n toPrint = g.Text(g.Point(100, 775), scoreText) #Create text to print\n\n toPrint.draw(gameWindow) #Draws text with rounds","def reindex_graphics(self):\n for obj in self.context.static_objects:\n self.canvas.children.remove(obj.widget.canvas)\n # fill _objects_z_index\n _objects_z_index = {}\n for obj in self.context.static_objects:\n y = obj.widget.pos[1]\n if not y in _objects_z_index:\n _objects_z_index[y] = []\n _objects_z_index[y].append(obj)\n _keys = _objects_z_index.keys()\n _keys.sort()\n _keys.reverse()\n for k in _keys:\n objs = _objects_z_index[k]\n for obj in objs:\n self.canvas.add(obj.widget.canvas)","def draw_bars(pencil: turtle.Turtle, data_list):\n # Your code here\n color = [\"red\", \"orange\", \"yellow\", \"green\", \"violet\", \"blue\", \"deeppink\", \"aqua\", \"brown\", \"gold\"]\n for i in data_list:\n t.begin_fill()\n t.color(random.choice(color))\n t.pencolor(\"black\")\n t.left(90)\n t.forward(i)\n t.right(90)\n t.forward(30)\n t.right(90)\n t.forward(i)\n t.right(90)\n t.forward(30)\n t.end_fill()\n t.backward(30)\n t.setheading(0)","def discard_index_from_word(self,word,index):\r\n\r\n # with shelf\r\n if self.using_shelf:\r\n\r\n if word in self.word_dict:\r\n\r\n self.word_dict[word].discard(str(index))\r\n\r\n\r\n #with database\r\n if self.using_database:\r\n\r\n value_tuple = (notebookname,word,str(index),)\r\n db_cursor.execute(\"DELETE FROM word_to_indexes \"\r\n +\"WHERE notebook=? AND word=? \"\r\n +\"AND note_index=?;\",\r\n value_tuple)\r\n\r\n db_cursor.execute(\"SELECT * FROM word_to_indexes\"\r\n +\" WHERE notebook=? and word=?;\",\r\n value_tuple[0:2])\r\n if db_cursor.fetchone():\r\n db_cursor.execute(\"DELETE FROM all_words\"\r\n +\" WHERE notebook=?\"\r\n +\" AND word=?;\",\r\n value_tuple[0:2])","def _update_farness_map(self,ind):","def set_draw_details(self, details):\r\n for x in range(len(self.model)):\r\n self.model[x].set_draw_details(details[x][0], details[x][1], details[x][2], details[x][3], details[x][4], details[x][5])","def recovery():\n # Uncovers the left Leg\n losing_lbl_one.grid(row=0, column=LEFT_COL, rowspan=15, sticky='NESW')\n # Uncovers the Right Leg\n losing_lbl_two.grid(row=0, column=RIGHT_COL, rowspan=15, sticky='NESW')\n # Uncovers the Chest\n losing_lbl_three.grid(row=15, column=LEFT_COL, columnspan=2, rowspan=13, sticky='NESW')\n # Uncovers the Left Arm\n losing_lbl_four.grid(row=28, column=LEFT_COL, rowspan=14, sticky='NESW')\n # Uncovers the Right Arm\n losing_lbl_five.grid(row=28, column=RIGHT_COL, rowspan=14, sticky='NESW')\n # Uncover the Head image\n losing_lbl_six.grid(row=42, column=LEFT_COL, columnspan=2, rowspan=23, sticky='NESW')","def highlight(self, highligtedItem):\n\t\tfor nr, i in enumerate(self.content):\n\t\t\tself.content[nr][1] = self.content[nr][2]\n\t\tself.content[highligtedItem][1] = 16","def mark_bingo_boards(bingo_num):\n global bingoboards\n for board in bingoboards:\n for line in board:\n try:\n line[line.index(bingo_num)] = \"X\"\n except ValueError:\n pass","def replace_season(data,season):\n if not SeasonCharts.matchseason(season):\n raise SeasonCharts.SeasonError\n ## Check data format\n if test_rawdata(data):\n for cat,shows in data.items():\n for show in shows: show['season'] = season\n else:\n for show in data: show['season'] = season","def update_figure(self):\n # if number of kinetics in model did not change\n # update just last lines\n if self.N_lines - 1 == len(self.model.spectra.keys()) * 2:\n self.dataplot.lines[-1].remove()\n self.dataplot.lines[-1].remove()\n self.draw_figure_first()\n # delete all and redraw\n else:\n n = int((self.N_lines - 1) / 2)\n for _ in range(n):\n self.dataplot.lines[-1].remove()\n self.dataplot.lines[-1].remove()\n self.draw_figure_total()\n\n self.dataplot.relim()\n\n self.dataplot.autoscale_view(True, True, True)\n\n self.draw()","def get_index_from_well(self, well):\n pass","def flip_back(self):\n for img in self.image_id:\n self.canvas.delete(img)\n self.image_id.clear()\n for tile in self.canvas.find_withtag(\"match\"):\n self.canvas.itemconfigure(tile, fill=self.color)\n # If item has match tag, removes all the tags\n self.canvas.itemconfigure(tile, tag=\"\")\n for tile in self.canvas.find_withtag(\"selected\"):\n self.canvas.itemconfigure(tile, fill=self.default_color)\n self.canvas.itemconfigure(tile, tag=\"\")\n # check if all tiles are matched, update result label\n if self.num_of_match == 8:\n text_label = f'Game Over!\\n Score: {self.score}\\n ' \\\n f'Number of tries: {self.num_of_tries}'\n self.score_label.config(text=text_label)","def paint_item(self, posx, index):\r\n raise NotImplementedError()","def draw_by_insufficient(self):\n \n if self.cap_counter > 100:\n self.draw_loop(\"draw due to none in 50\")\n \n if self.board.count(self.empty) == 62:\n self.draw_loop(\"draw due to insufficient\")\n\n \n if self.board.count(self.empty) == 61:\n for i in self.board:\n if i != self.empty:\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Bishop] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Bishop]:\n self.draw_loop(\"draw due to insufficient\")\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Knight] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Knight]:\n self.draw_loop(\"draw due to insufficient\")","def test_plot_positive_wiggle_fills(self):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n splt = SEGYPlotter(ax, self.segy)\n # should add a single artist to the active patch dict.\n splt.plot_wiggles(positive_fills=True)\n self.assertEqual(len(splt.ACTIVE_PATCHES['positive_fills']),1)\n # same artist should be in the axes list\n self.assertTrue(splt.ACTIVE_PATCHES['positive_fills'][0] in \\\n splt.ax.patches)\n # should be able to directly remove it\n splt.ax.patches.remove(splt.ACTIVE_PATCHES['positive_fills'][0])\n self.assertEqual(len(splt.ax.patches), 0)\n # and re-add it\n splt.ax.patches.append(splt.ACTIVE_PATCHES['positive_fills'][0])\n self.assertEqual(len(splt.ax.patches), 1)\n # should move artist to the inactive patch dict.\n splt.plot_wiggles(positive_fills=False)\n self.assertEqual(len(splt.INACTIVE_PATCHES['positive_fills']),1)\n self.assertEqual(len(splt.ax.patches), 0)\n # calling again should change nothing\n splt.plot_wiggles(positive_fills=False)\n self.assertEqual(len(splt.INACTIVE_PATCHES['positive_fills']),1)\n self.assertEqual(len(splt.ax.patches), 0)\n # should move artist back to active patch dict.\n splt.plot_wiggles(positive_fills=True)\n self.assertEqual(len(splt.ACTIVE_PATCHES['positive_fills']),1)\n self.assertEqual(len(splt.ax.patches), 1)\n # new plot items should be accessible through the orig. axes\n self.assertEqual(len(splt.ax.patches),\n len(ax.patches))","def add_piece(self, index):\n if isinstance(index, list):\n self.pieces += index\n return\n\n self.pieces.append(index)","def replace_and_make_new_progression(progression, index, fuzzy_harmony):\n new_progression = progression.get_progression()\n progression_list = []\n for i in range(len(new_progression)):\n print(new_progression[i])\n progression_list.append(new_progression[i])\n print(progression_list)\n progression_list[index] = fuzzy_harmony\n new_progression = tuple(progression_list)\n return new_progression","def fill_draw(self):\n self.draw = [x + str(y) for x in COLOR for y in CARD_VALUE]","def decrement_point(mutated_genome,index):\n point_index1 = random.randint(1,max(0,len(mutated_genome[index][2])-1))\n seed = random.randint(0,2)\n if seed == 0:\n point_index2 = point_index1 - 1\n elif seed == 1:\n point_index2 = random.randint(0, point_index1)\n else: #seed == 2:\n point_index2 = 0\n temp = mutated_genome[index][2][point_index1]\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][point_index2]\n mutated_genome[index][2][point_index2] = temp","def radioList(dev, draw, index):\n\n global menuindex\n global listMenuStart, listMenuEnd\n global names\n \n font = ImageFont.load_default()\n draw.rectangle(dev.bounding_box, outline=\"white\", fill=\"black\")\n if index > listMenuEnd:\n listMenuEnd += 1\n listMenuStart += 1\n elif index < listMenuStart:\n listMenuEnd -= 1\n listMenuStart -= 1\n for i in range(6):\n if( i == (index-listMenuStart)):\n menuindex = index\n invert(draw, 4, 4 + (index-listMenuStart)*10, names[listMenuStart + i][0], False)\n else:\n draw.text((4, 4 + i*10), names[listMenuStart + i][0], font = font, fill = 255)","def move_point_rect(mutated_genome,index):\n Xval = random.randint(-int(imagewidth/5.),int(imagewidth*6./5.))\n Yval = random.randint(-int(imageheight/5.),int(imageheight*6./5.))\n point = (Xval,Yval)\n old_points = list(mutated_genome[index][2])\n old_points[random.randint(0,1)] = point\n mutated_genome[index][2] = tuple(old_points)","def redraw(self):\n offset = self.actual_row * self.row_size\n x = 5\n y = 5\n ind = 0\n self.scene.clear()\n for _ in range(self.column_size):\n for _ in range(self.row_size):\n if ind+offset < len(self.cards):\n self.draw_card(x, y, ind+offset)\n x += 90\n ind += 1\n x = 5\n y += 120","def _update_data(self, selected):\n if selected.row() != self.datasets.index:\n self.datasets.index = selected.row()\n self.datasets.update_current()\n self._update_main()","def __draw_unk_style(self, ax, item, p, thumb=False):\n a = True\n for i, d in enumerate(item[\"domains\"]):\n if d[\"name\"] not in self.col_map:\n print(\"Warning: '%s' not found in colour map\" % d[\"name\"])\n else:\n ax.add_patch(Rectangle((d[\"pos\"][0], -0.25), d[\"pos\"][1] - d[\"pos\"][0], 0.5,\n ec=\"none\", fc=self.col_map[d[\"name\"]], lw=0.5))\n\n if a:\n ax.text((d[\"pos\"][0] + d[\"pos\"][1])/2, -0.5, str(d[\"name\"]), ha=\"center\", va=\"center\", fontsize=6, color=\"black\")\n else:\n ax.text((d[\"pos\"][0] + d[\"pos\"][1])/2, -0.8, str(d[\"name\"]), ha=\"center\", va=\"center\", fontsize=6, color=\"black\")\n a = not a\n\n #ax.text((d[\"pos\"][0] + d[\"pos\"][1])/2, -0.7, str(d[\"db\"]), ha=\"center\", va=\"center\", fontsize=6, color=\"black\")\n\n ax.text(0, 0.5, \"%s (%s amino acids)\" % (item[\"name\"], item[\"len\"]), color=\"black\", fontsize=20, ha=\"left\")","def _update_w(self, idx):\n self.w = ((self._w - 0.4) * (self._generations - idx)) /\\\n (self._generations + 0.4)","def refresh(self, data):\n for region in self.loaded_regions:\n self.regions[region].unload()\n super().clear()\n self.data = data\n self.loaded_regions = set()\n self.draw_visible_area()","def updateOldDialogItems(data,newdata):\n warnings.warn(\"warn_widgets_updatedialogitems\")\n if newdata:\n if type(data) is dict:\n for d in data:\n updateOldDialogItems(data[d],newdata)\n else:\n for d in data:\n v = newdata.get(d[0],None)\n if v is not None:\n d[1] = v","def add_point_rate_well(self, injection_rate, cell_index, well_name):\n self.rate_wells.append(cell_index)\n self.rate_wells_rate.append(injection_rate)\n self.rate_wells_name.append(well_name)\n return len(self.rate_wells)-1","def find_index(draw, urn):\n i = 0\n while draw >= 0:\n draw -= urn[i]\n i+=1\n return i-1","def remove_point(mutated_genome,index):\n point_index = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n del mutated_genome[index][2][point_index]","def update_level_name(lumpname, bexdata, bexname):\n\tif bexname not in bexdata:\n\t\traise Exception('Level name %s not defined in '\n\t\t\t\t'DEHACKED lump!' % bexname)\n\t# Strip \"MAP01: \" or \"E1M2: \" etc. from start, if present:\n\tlevelname = re.sub('^\\w*\\d:\\s*', '', bexdata[bexname])\n\twhite_graphics[lumpname] = levelname","def replace_character_at_index(self, index, character):\n self._game_board = self.get_game()[:index] + character + self.get_game()[index + 1:]\n return self._game_board","def __getitem__(self, index):\n ID = self.ID[index]\n turn_id = self.turn_id[index]\n turn_belief = self.turn_belief[index]\n turn_belief_dict = self.turn_belief_dict[index]\n sorted_domainslots = self.sorted_domainslots[index]\n turn_uttr = self.turn_uttr[index]\n context_plain = self.dialog_history[index] \n sorted_lenval = self.sorted_lenval[index]\n sorted_in_domains2 = self.sorted_in_domains2[index]\n sorted_in_slots2 = self.sorted_in_slots2[index]\n sorted_generate_y = self.sorted_generate_y[index]\n c = copy.deepcopy\n context = self.preprocess(context_plain, self.src_word2id)\n delex_context = None\n if self.args['delex_his']:\n temp = self.delex_dialog_history[index].split()\n original = self.dialog_history[index].split()\n if self.split == 'train' and 'p_delex_noise' in self.args and np.random.uniform() < self.args['p_delex_noise']:\n prob = np.random.uniform()\n if prob < 0.5:\n indices = [idx for idx,i in enumerate(temp) if len(i.split('-'))==2]\n if len(indices) > 0:\n random_idx = random.choice(indices)\n temp[random_idx] = original[random_idx] # removal \n else:\n random_token = random.choice(self.all_slots)\n out_words = list(self.mem_word2id.keys())[4:]\n indices = [idx for idx,i in enumerate(original) if i in out_words]\n if len(indices) > 0:\n index = random.choice(indices)\n temp[index] = random_token\n delex_context = ' '.join(temp)\n delex_context = self.preprocess(delex_context, self.src_word2id) \n tag_x, tag_y = None, None\n if not self.args['sep_input_embedding']:\n sorted_in_domains = self.preprocess_seq(self.sorted_in_domains[index], self.src_word2id)\n sorted_in_slots = self.preprocess_seq(self.sorted_in_slots[index], self.src_word2id)\n sorted_in_domains2 = self.preprocess_seq(sorted_in_domains2, self.src_word2id)\n sorted_in_slots2 = self.preprocess_seq(sorted_in_slots2, self.src_word2id)\n else:\n sorted_in_domains = self.preprocess_seq(self.sorted_in_domains[index], self.domain_word2id)\n sorted_in_slots = self.preprocess_seq(self.sorted_in_slots[index], self.slot_word2id)\n sorted_in_domains2 = self.preprocess_seq(sorted_in_domains2, self.domain_word2id)\n sorted_in_slots2 = self.preprocess_seq(sorted_in_slots2, self.slot_word2id)\n sorted_in_domainslots2_idx, y_in, y_out = None, None, None\n if args['auto_regressive']:\n sorted_in_domainslots2_idx = self.sorted_in_domainslots2_idx[index]\n y_in, y_out = self.preprocess_atrg_seq(self.atrg_generate_y[index], self.src_word2id) \n if self.args['pointer_decoder']:\n sorted_generate_y = self.preprocess_seq(sorted_generate_y, self.src_word2id)\n else:\n sorted_generate_y = self.preprocess_seq(sorted_generate_y, self.mem_word2id)\n sorted_gates = None\n if self.sorted_gates[index] is not None:\n sorted_gates = self.sorted_gates[index]\n user_uttr_plain, user_uttr = None, None\n turn_prev_bs_plain, turn_prev_bs = None, None\n \n item_info = {\n \"ID\":ID, \n \"turn_id\":turn_id, \n \"turn_belief\":turn_belief, #?\n \"context\":context,\n \"delex_context_plain\": self.delex_dialog_history[index],\n \"delex_context\": delex_context,\n \"context_plain\":context_plain, \n \"user_uttr\": user_uttr,\n \"user_uttr_plain\": user_uttr_plain, \n \"sorted_in_domains\": sorted_in_domains,\n \"sorted_in_domains2\": sorted_in_domains2,\n \"sorted_in_slots\": sorted_in_slots,\n \"sorted_in_slots2\": sorted_in_slots2,\n \"sorted_in_domainslots2_idx\": sorted_in_domainslots2_idx, \n \"sorted_lenval\": sorted_lenval,\n \"sorted_gates\": sorted_gates,\n \"sorted_generate_y\": sorted_generate_y,\n \"y_in\": y_in,\n \"y_out\": y_out\n }\n return item_info","def InTheWeedsPlot(OutBase,UnfoldObj,RefoldObj=[],Example=None,\n Bins=[50,75,100,150,200,500,1000],**kwargs):\n # get the IWT\n kT = 4.1e-21\n for b in Bins:\n LandscapeObj = InverseWeierstrass.\\\n FreeEnergyAtZeroForce(UnfoldObj,NumBins=b,RefoldingObjs=RefoldObj)\n # make a 2-D histogram of everything\n if (Example is not None):\n fig = PlotUtilities.figure(figsize=(8,8))\n ext_nm = Example.Separation*1e9\n IWT_Util.ForceExtensionHistograms(ext_nm,\n Example.Force*1e12,\n AddAverage=False,\n nBins=b)\n PlotUtilities.savefig(fig,OutBase + \"0_{:d}hist.pdf\".format(b))\n # get the distance to the transition state etc\n print(\"DeltaG_Dagger is {:.1f}kT\".format(Obj.DeltaGDagger))\n fig = PlotUtilities.figure(figsize=(12,12))\n plot_single_landscape(LandscapeObj,add_meta_half=True,\n add_meta_free=True,**kwargs)\n PlotUtilities.savefig(fig,OutBase + \"1_{:d}IWT.pdf\".format(b))","def update(\n self,\n index: int,\n party: PartyEntry,\n sub_scroll_width: float,\n sub_scroll_height: float,\n lineheight: float,\n columnwidget: bui.Widget,\n join_text: bui.Widget,\n filter_text: bui.Widget,\n existing_selection: Selection | None,\n tab: PublicGatherTab,\n ) -> None:\n # pylint: disable=too-many-locals\n\n plus = bui.app.plus\n assert plus is not None\n\n # Quick-out: if we've been marked clean for a certain index and\n # we're still at that index, we're done.\n if party.clean_display_index == index:\n return\n\n ping_good = plus.get_v1_account_misc_read_val('pingGood', 100)\n ping_med = plus.get_v1_account_misc_read_val('pingMed', 500)\n\n self._clear()\n hpos = 20\n vpos = sub_scroll_height - lineheight * index - 50\n self._name_widget = bui.textwidget(\n text=bui.Lstr(value=party.name),\n parent=columnwidget,\n size=(sub_scroll_width * 0.63, 20),\n position=(0 + hpos, 4 + vpos),\n selectable=True,\n on_select_call=bui.WeakCall(\n tab.set_public_party_selection,\n Selection(party.get_key(), SelectionComponent.NAME),\n ),\n on_activate_call=bui.WeakCall(tab.on_public_party_activate, party),\n click_activate=True,\n maxwidth=sub_scroll_width * 0.45,\n corner_scale=1.4,\n autoselect=True,\n color=(1, 1, 1, 0.3 if party.ping is None else 1.0),\n h_align='left',\n v_align='center',\n )\n bui.widget(\n edit=self._name_widget,\n left_widget=join_text,\n show_buffer_top=64.0,\n show_buffer_bottom=64.0,\n )\n if existing_selection == Selection(\n party.get_key(), SelectionComponent.NAME\n ):\n bui.containerwidget(\n edit=columnwidget, selected_child=self._name_widget\n )\n if party.stats_addr:\n url = party.stats_addr.replace(\n '${ACCOUNT}',\n plus.get_v1_account_misc_read_val_2(\n 'resolvedAccountID', 'UNKNOWN'\n ),\n )\n self._stats_button = bui.buttonwidget(\n color=(0.3, 0.6, 0.94),\n textcolor=(1.0, 1.0, 1.0),\n label=bui.Lstr(resource='statsText'),\n parent=columnwidget,\n autoselect=True,\n on_activate_call=bui.Call(bui.open_url, url),\n on_select_call=bui.WeakCall(\n tab.set_public_party_selection,\n Selection(party.get_key(), SelectionComponent.STATS_BUTTON),\n ),\n size=(120, 40),\n position=(sub_scroll_width * 0.66 + hpos, 1 + vpos),\n scale=0.9,\n )\n if existing_selection == Selection(\n party.get_key(), SelectionComponent.STATS_BUTTON\n ):\n bui.containerwidget(\n edit=columnwidget, selected_child=self._stats_button\n )\n\n self._size_widget = bui.textwidget(\n text=str(party.size) + '/' + str(party.size_max),\n parent=columnwidget,\n size=(0, 0),\n position=(sub_scroll_width * 0.86 + hpos, 20 + vpos),\n scale=0.7,\n color=(0.8, 0.8, 0.8),\n h_align='right',\n v_align='center',\n )\n\n if index == 0:\n bui.widget(edit=self._name_widget, up_widget=filter_text)\n if self._stats_button:\n bui.widget(edit=self._stats_button, up_widget=filter_text)\n\n self._ping_widget = bui.textwidget(\n parent=columnwidget,\n size=(0, 0),\n position=(sub_scroll_width * 0.94 + hpos, 20 + vpos),\n scale=0.7,\n h_align='right',\n v_align='center',\n )\n if party.ping is None:\n bui.textwidget(\n edit=self._ping_widget, text='-', color=(0.5, 0.5, 0.5)\n )\n else:\n bui.textwidget(\n edit=self._ping_widget,\n text=str(int(party.ping)),\n color=(0, 1, 0)\n if party.ping <= ping_good\n else (1, 1, 0)\n if party.ping <= ping_med\n else (1, 0, 0),\n )\n\n party.clean_display_index = index","def replace_character_at_index(game, index, character):\n game = game[:index]+character+game[index+1:]\n return game","def updateWHM(self, hardnessLevel):\n\n if hardnessLevel == 1:\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgreen'])\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMgray'])\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMgray'])\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMgray'])\n\n\n elif hardnessLevel == 2:\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMyellow'])\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgray'])\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMgray'])\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMgray'])\n\n elif hardnessLevel == 3:\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMorange'])\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgray'])\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMgray'])\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMgray'])\n\n elif hardnessLevel == 4:\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMred'])\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgray'])\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMgray'])\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMgray'])\n\n self.outcomesWHM = np.append(self.outcomesWHM, hardnessLevel)\n\n if self.outcomesWHM.size > 0:\n elapsed = self.tmr.elapsed() / float(1000)\n self.save_file = open(os.path.join(args.parent_img_path, self.save_file_name), \"a\")\n self.save_file.write(\"Time Elapsed: (\" + str(elapsed) + \" sec) - Hardness Level: \"+str(hardnessLevel)+\",\\n\")\n self.save_file.close()\n self.dataWHM.append({'x': elapsed, 'y': hardnessLevel})\n self.WHMx = [item['x'] for item in self.dataWHM]\n self.WHMy = [item['y'] for item in self.dataWHM]\n self.WHMx = np.asarray(self.WHMx).flatten()\n self.WHMy = np.asarray(self.WHMy).flatten()\n self.curveWHMGraph.setData(x=self.WHMx, y=self.WHMy)\n\n # Stuff for Verbose - Helpful for debugging\n if self.setPointsWHM.verbose > 0:\n if self.setPointsWHM.verbose > 0:\n print 'Receive Time: {:0.6f}ms | Pre-process + SVM Run Time: {:0.6f}ms | Total Run Time: {:0.6f}ms'.format(\n (self.setPointsWHM.algo_time - self.setPointsWHM.receive_time) * 1000.,\n (self.setPointsWHM.final_time - self.setPointsWHM.algo_time) * 1000.,\n (time.time() - self.showtime) * 1000.)\n # reset Showtime\n self.showtime = time.time()\n # print ' Total Showtime: {:0.6f}ms \\n \\n'.format((time.time() - self.showtime) * 1000.)","def shift_point_wline(mutated_genome,index):\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\n point_index = random.randint(1,max(1,len(mutated_genome[index][2])-1))\n point = mutated_genome[index][2][point_index]\n newpoint = (point[0]+Xval,point[1]+Yval)\n mutated_genome[index][2][point_index] = newpoint","def remove_index_from_word(self,word,index):\r\n\r\n # with shelf\r\n if self.using_shelf:\r\n\r\n if word in self.word_dict:\r\n\r\n self.word_dict[word].remove(str(index))\r\n\r\n\r\n #with database\r\n if self.using_database:\r\n\r\n value_tuple = (notebookname,word,str(index),)\r\n db_cursor.execute(\"DELETE FROM\"\r\n +\" word_to_indexes\"\r\n +\" WHERE notebook=?\"\r\n +\" AND word=?\"\r\n +\" AND note_index=?;\",\r\n value_tuple)\r\n\r\n db_cursor.execute(\"SELECT * FROM word_to_indexes\"\r\n +\" WHERE notebook=?\"\r\n +\" and word=?;\",\r\n value_tuple[0:2])\r\n if db_cursor.fetchone():\r\n db_cursor.execute(\"DELETE FROM\"\r\n +\" all_words\"\r\n +\" WHERE notebook=?\"\r\n +\" AND word=?;\",\r\n value_tuple[0:2])","def get_stripe_blanker(feat_idx, stripe_idx):\n from_idx = 0\n for f in range(feat_idx):\n from_idx += features.sizes.get(f, 1)\n for s_idx, (step, start, stop) in enumerate(features.stripes[feat_idx]):\n if step is None:\n width = 1\n else:\n width = int(round((stop-start) / step + 1))\n if s_idx < stripe_idx:\n from_idx += width\n else:\n to_idx = from_idx + width\n break\n\n def _blanker(features):\n# log.info(\"Blanker from={from_idx}, to={to_idx}\".format(from_idx=from_idx, to_idx=to_idx))\n# log.info(\"Before: \" + str(features.nonzero()))\n features[from_idx:to_idx] = 0.0\n# log.info(\"After: \" + str(features.nonzero()))\n return features\n\n return _blanker","def process_non_menu_frame(w_indx: int) -> None:\n for nmi in list(non_menu_frame_widgets.keys()):\n if nmi == w_indx:\n v = non_menu_frame_widgets[nmi]\n for v_wid in v:\n print(c.BRIGHT_WHITE + '· ' + '│ ' * v_wid.get_frame_depth()\n + c.ENDC + widget_terminal_title(v_wid))\n del non_menu_frame_widgets[nmi]","def test_sweat_index():\n pressure = np.array([1008., 1000., 947., 925., 921., 896., 891., 889., 866.,\n 858., 850., 835., 820., 803., 733., 730., 700., 645.,\n 579., 500., 494., 466., 455., 441., 433., 410., 409.,\n 402., 400., 390., 388., 384., 381., 349., 330., 320.,\n 306., 300., 278., 273., 250., 243., 208., 200., 196.,\n 190., 179., 159., 151., 150., 139.]) * units.hPa\n temperature = np.array([27.4, 26.4, 22.9, 21.4, 21.2, 20.7, 20.6, 21.2, 19.4,\n 19.1, 18.8, 17.8, 17.4, 16.3, 11.4, 11.2, 10.2, 6.1,\n 0.6, -4.9, -5.5, -8.5, -9.9, -11.7, -12.3, -13.7, -13.8,\n -14.9, -14.9, -16.1, -16.1, -16.9, -17.3, -21.7, -24.5, -26.1,\n -28.3, -29.5, -33.1, -34.2, -39.3, -41., -50.2, -52.5, -53.5,\n -55.2, -58.6, -65.2, -68.1, -68.5, -72.5]) * units.degC\n dewpoint = np.array([24.9, 24.6, 22., 20.9, 20.7, 14.8, 13.6, 12.2, 16.8,\n 16.6, 16.5, 15.9, 13.6, 13.2, 11.3, 11.2, 8.6, 4.5,\n -0.8, -8.1, -9.5, -12.7, -12.7, -12.8, -13.1, -24.7, -24.4,\n -21.9, -24.9, -36.1, -31.1, -26.9, -27.4, -33., -36.5, -47.1,\n -31.4, -33.5, -40.1, -40.8, -44.1, -45.6, -54., -56.1, -56.9,\n -58.6, -61.9, -68.4, -71.2, -71.6, -77.2]) * units.degC\n speed = np.array([0., 3., 10., 12., 12., 14., 14., 14., 12.,\n 12., 12., 12., 11., 11., 12., 12., 10., 10.,\n 8., 5., 4., 1., 0., 3., 5., 10., 10.,\n 11., 11., 13., 14., 14., 15., 23., 23., 24.,\n 24., 24., 26., 27., 28., 30., 25., 24., 26.,\n 28., 33., 29., 32., 26., 26.]) * units.knot\n direction = np.array([0., 170., 200., 205., 204., 200., 197., 195., 180.,\n 175., 175., 178., 181., 185., 160., 160., 165., 165.,\n 203., 255., 268., 333., 0., 25., 40., 83., 85.,\n 89., 90., 100., 103., 107., 110., 90., 88., 87.,\n 86., 85., 85., 85., 60., 55., 60., 50., 46.,\n 40., 45., 35., 50., 50., 50.]) * units.degree\n\n sweat = sweat_index(pressure, temperature, dewpoint, speed, direction)\n assert_almost_equal(sweat, 227., 2)","def reformatSagittalView4Needle(self, ID):\r\n # productive #onButton #report\r\n profprint()\r\n for i in range(2): # workaround update problem\r\n modelNode = slicer.util.getNode('vtkMRMLModelNode' + str(ID))\r\n polyData = modelNode.GetPolyData()\r\n nb = polyData.GetNumberOfPoints()\r\n base = [0, 0, 0]\r\n tip = [0, 0, 0]\r\n polyData.GetPoint(nb - 1, tip)\r\n polyData.GetPoint(0, base)\r\n a, b, c = tip[0] - base[0], tip[1] - base[1], tip[2] - base[2]\r\n # print a,b,c\r\n sYellow = slicer.mrmlScene.GetNodeByID(\"vtkMRMLSliceNodeYellow\")\r\n if sYellow == None :\r\n sYellow = slicer.mrmlScene.GetNodeByID(\"vtkMRMLSliceNode2\")\r\n reformatLogic = slicer.vtkSlicerReformatLogic()\r\n sYellow.SetSliceVisible(1)\r\n reformatLogic.SetSliceNormal(sYellow, 1, -a / b, 0)\r\n m = sYellow.GetSliceToRAS()\r\n m.SetElement(0, 3, base[0])\r\n m.SetElement(1, 3, base[1])\r\n m.SetElement(2, 3, base[2])\r\n sYellow.Modified()","def test_plot_wiggle_traces(self):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n splt = SEGYPlotter(ax, self.segy)\n # should add a single artist to the active line dict.\n splt.plot_wiggles(wiggle_traces=True)\n self.assertEqual(len(splt.ACTIVE_LINES['wiggle_traces']),1)\n # same artist should be in the axes list\n self.assertTrue(splt.ACTIVE_LINES['wiggle_traces'][0] in \\\n splt.ax.lines)\n # should be able to directly remove it\n splt.ax.lines.remove(splt.ACTIVE_LINES['wiggle_traces'][0])\n self.assertEqual(len(splt.ax.lines), 0)\n # and re-add it\n splt.ax.lines.append(splt.ACTIVE_LINES['wiggle_traces'][0])\n self.assertEqual(len(splt.ax.lines), 1)\n # should move artist to the inactive line dict.\n splt.plot_wiggles(wiggle_traces=False)\n self.assertEqual(len(splt.INACTIVE_LINES['wiggle_traces']),1)\n self.assertEqual(len(splt.ax.lines), 0)\n # calling again should change nothing\n splt.plot_wiggles(wiggle_traces=False)\n self.assertEqual(len(splt.INACTIVE_LINES['wiggle_traces']),1)\n self.assertEqual(len(splt.ax.lines), 0)\n # should move artist back to active line dict and re-add to axes\n splt.plot_wiggles(wiggle_traces=True)\n self.assertEqual(len(splt.ACTIVE_LINES['wiggle_traces']),1)\n self.assertEqual(len(splt.ax.lines), 1)\n # new plot items should be accessible through the orig. axes\n self.assertEqual(len(splt.ax.lines),\n len(ax.lines))","def setBooked(self, data, itemWidget):\n # itemWidget = self.findItemWidgetFromDate(data)\n styleSheet = \"\"\n # try:\n for plat in self.datePrenotazioni['platforms'].keys():\n if data in self.datePrenotazioni['platforms'][plat]['date']:\n color = self.colors[plat]\n r, g, b = self.reformatColor(color)\n styleSheet = f\"background-color: rgba({r},{g},{b},150)\"\n break\n # except KeyError:\n # print('remove try/exc')\n # print('setbooked ' + styleSheet)\n itemWidget.lab_num.setStyleSheet(styleSheet)","def update_which_sde_data(\n current_sde_df,\n latest_esi_df,\n index_key\n):\n pass","def upd_view():\n global state, current_view, show_actives, current_filename\n nonlocal img, listbox\n\n with data_lock:\n if state == 1:\n current_filename = None\n state = 2\n listbox.delete(0, tk.END)\n for item in current_view.actions.by_type(wtl.actions.Click):\n wtl_uid = str(item.target.wtl_uid)\n text = item.target.metadata[\"text\"]\n listbox.insert(tk.END, wtl_uid + f\" ({text})\")\n\n if state == 2:\n filename = \"first\" if show_actives.get() == 0 else \"is_active\"\n if filename != current_filename:\n current_filename = filename\n current_view.snapshot.screenshots[filename].save(Path(\".\"))\n img = tk.PhotoImage(file=f\"{filename}.png\")\n canvas.create_image(5, 5, anchor=tk.NW, image=img)\n\n window.after(250, upd_view)","def resting(self):\n self.image = self.image_list[self.index]\n\n if self.rect.y % 32 != 0:\n self.correct_position(self.rect.y)\n if self.rect.x % 32 != 0:\n self.correct_position(self.rect.x)","def remove_region(data, i):\n x = data.shape[0]\n y = data.shape[1]\n\n data = data.reshape(x * y)\n idx = np.where(data == i)[0]\n data[idx] = 0\n data = data.reshape(x, y)\n\n return data","def reflect_list(wall_1side_list, inout_strands, inout_strands_index, p):\n if len(wall_1side_list) == ((p-1)/2)+1: # base casep\n return wall_1side_list # return\n else:\n if inout_strands_index == 0:\n wall_color = wall_1side_list[-1] # bottom universe at crossing\n # ,inout_strands, 1, p)\n wall_1side_list.append(reflect(wall_color, inout_strands[inout_strands_index], p))\n return reflect_list(wall_1side_list, inout_strands, 1, p)\n else:\n wall_color = wall_1side_list[-1]\n # ,inout_strands, 1, p)\n wall_1side_list.append(reflect(wall_color, inout_strands[inout_strands_index], p))\n return reflect_list(wall_1side_list, inout_strands, 0, p)","def drawScrubber(self):\r\n self.clearScrubber()\r\n x = self.plot(self.progress,0)[0]\r\n self.scrubber = [self.c.create_rectangle(x-3,0,x+3,6,fill=\"#000000\",tags=(\"scrubber\"))]# Scrubber head\r\n self.scrubber.append(self.c.create_line(x,0,x,self.h,fill=\"#000000\",tags=(\"scrubber\")))# Scrubber line\r\n t = \"-\"*(self.progress<0) +str(datetime.timedelta(seconds=abs(round(self.progress))))\r\n self.scrubber.append(self.c.create_text(x+3,0,text=str(t),anchor=tk.NW,tags=(\"scrubber\")))# Timestamp\r\n if not self.sensors or self.sensors == []:\r\n return\r\n if self.progress > 0:\r\n col = self.app.getSensorCol(self.sensors[self.sensor_ids[0]])\r\n self.scrubber.append(self.c.create_text(x+3,10,text=str(self.getData(self.sensor_ids[0],self.progress)),\\\r\n fill=col,anchor=tk.NW,tags=(\"scrubber\")))# Red track value\r\n if len(self.sensor_ids) == 2:# If second track exists\r\n col = self.app.getSensorCol(self.sensors[self.sensor_ids[1]])\r\n self.scrubber.append(self.c.create_text(x+3,20,text=str(self.getData(self.sensor_ids[1],self.progress)),fill=col,anchor=tk.NW,tags=(\"scrubber\")))","def setSpecificIndices(\n self,\n indicesOfFreeBetas,\n indicesOfFixedBetas,\n indicesOfRandomVariables,\n indicesOfDraws,\n ):\n if self.name in indicesOfDraws:\n self.drawId = indicesOfDraws[self.name]\n else:\n error_msg = (\n f'No index is available for draw {self.drawType}.'\n f' Known types of draws: {indicesOfDraws.keys()}'\n )\n raise excep.biogemeError(error_msg)","def updateWidget(self, name, pos, value):\n try:\n self.widgets[\"%s%d.entry\" % (name, pos)].delete(0,'end')\n self.widgets[\"%s%d.entry\" % (name, pos)].insert(0,value)\n except: pass"],"string":"[\n \"def remove_from_drawn(section: str, index: int):\\r\\n del drawn[section][index]\",\n \"def activateChildWellIndex(self,index,value):\\n activeIndices=self.activeChildWellIndices()\\n if value and index not in activeIndices:\\n activeIndices.append(index)\\n activeIndices=sorted(activeIndices)\\n elif not value and index in activeIndices:\\n activeIndices.remove(index)\\n\\n self.setActiveChildWellIndices(activeIndices)\",\n \"def replace_widget(widgets, new_widget):\\n\\n for pos, current_widget in enumerate(widgets):\\n\\n if current_widget['widget_id'] == new_widget['widget_id']:\\n widgets[pos] = new_widget\",\n \"def add_to_drawn(section: str, thing: Base, index: int=None):\\r\\n if index is None:\\r\\n drawn[section].append(thing)\\r\\n else:\\r\\n drawn[section].insert(index, thing)\",\n \"def draw_walker(indx):\\n chart_1.create_oval(hips[indx]-6, hips[indx+1]-6,hips[indx]+6, hips[indx+1]+6, fill= \\\"magenta\\\", width = 1, tag = 'line_1') \\n chart_1.create_line(hips[indx], hips[indx+1], knee_a[indx], knee_a[indx+1], fill= \\\"blue\\\", width = 2, tag = 'line_1') \\n chart_1.create_line(hips[indx], hips[indx+1], knee_b[indx], knee_b[indx+1], fill= \\\"green\\\", width = 2, tag = 'line_1') \\n chart_1.create_line(knee_a[indx], knee_a[indx+1], heel_a[indx], heel_a[indx+1], fill= \\\"blue\\\", width = 2, tag = 'line_1') \\n chart_1.create_line(knee_b[indx], knee_b[indx+1], heel_b[indx], heel_b[indx+1], fill= \\\"green\\\", width = 2, tag = 'line_1')\",\n \"def update_figure(picks, curve, active_well): \\n w = p.get_well(active_well) ##selects the correct welly.Well object\\n picks_df = pd.read_json(picks)\\n picks_selected = picks_df[picks_df['UWI'] == active_well.replace(' ', '-')]\\n \\n # regenerate figure with the new horizontal line\\n fig = helper.make_log_plot(w=w, ymin=ymin)# , resample=0.1) # resample needs a float to change basis\\n fig.update_layout(uirevision=active_well)\\n helper.update_picks_on_plot(fig, picks_selected)\\n \\n return fig\",\n \"def get_well_name(self, idx):\\n name = None\\n if type(idx) is int:\\n n = self.well_count()\\n assert 0 <= idx <= n - 1, \\\"Bad well index\\\"\\n name = self.wells[idx].drawdown.name\\n return(name)\",\n \"def change_fig(self):\\r\\n text = self.listWidget.currentItem().text()\\r\\n alias = self.aliases_dict[text]\\r\\n figure = self.fig_dict[alias][0]\\r\\n self.remove_canvas()\\r\\n self.add_canvas(figure)\",\n \"def update_cross_section(tops_storage, well_uwi):\\n wells_tops = pd.read_json(tops_storage)\\n well_tops = wells_tops[wells_tops.UWI == well_uwi]\\n csv_txt = df_to_csvtxt(well_tops)\\n p.get_well(well_uwi).data['tops'] = Striplog.from_csv(text=csv_txt)\\n return encode_xsection(p, legend)\",\n \"def overlay_down(self, idx):\\n if not self.is_bottom_layer(idx):\\n self.overlay_list[idx], self.overlay_list[idx-1] = \\\\\\n self.overlay_list[idx-1], self.overlay_list[idx]\",\n \"def _remove_element(cls, d, idx):\\n d[idx, 2] = 0\\n return d\",\n \"def update_well(self, row, col, content):\\n self.plate.get_well(row, col).composition.update(content)\",\n \"def overlay_up(self, idx):\\n if not self.is_top_layer(idx):\\n self.overlay_list[idx], self.overlay_list[idx+1] = \\\\\\n self.overlay_list[idx+1], self.overlay_list[idx]\",\n \"def transform(self, y_idx):\\n self.y = [row.pop(y_idx) for row in self.data]\\n self.X = self.data\",\n \"def _update_well_meta(self, pos):\\n self.well_meta['well']['images'] = [{'path': self.positions[pos]['name']}]\\n self.store[self.rows[0]][self.columns[pos]].attrs.put(self.well_meta)\",\n \"def edit(self):\\n wmap = 512\\n if not self.mapd:\\n data = self.data[24:]\\n mapd = self.mapd = []\\n for index in xrange(0,3*wmap*wmap,3):\\n mapd.append(data[index:index+3])\\n self.setChanged()\",\n \"def replaceItemOfIndex(itemsList, value, index):\\n itemsList[index] = value\",\n \"def switch_points(mutated_genome,index):\\n point_index1 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\\n point_index2 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\\n temp = mutated_genome[index][2][point_index1]\\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][point_index2]\\n mutated_genome[index][2][point_index2] = temp\",\n \"def well_update_changes_curves(well_uwi):\\n w = p.get_well(well_uwi) # identifies and gets the correct welly.Well object based on well_uwi\\n curve_list = sorted(list(w.data))\\n curve = get_first_curve(curve_list)\\n curve_dropdown_options = [{'label': k, 'value': k} for k in curve_list] #creates dropdown list\\n return curve_dropdown_options, curve # returns the dropdown list options and the initial curve\",\n \"def correct(self):\\n self.parent.copyCurrentWinState(self.pltw)\\n self.pltw.blklst[self.blkno][self.ypos] = self.data[1] - self.data[2]\\n self.pltw.updatePlot()\\n self.pltw.dirty = True\\n self.pltw.activecurv = self.cpos\\n self.parent.updateUI()\\n self.hide()\",\n \"def replace(self, index, value):\\n index += self.n\\n self.data[index] = value\\n index //= 2\\n while index > 0:\\n self.data[index] = self.func(self.data[2*index], self.data[2*index+1])\\n index //= 2\",\n \"def edit_the_selected_value_wishlist():\\n\\n curItem = wishlist_treeview.focus().strip('#')\\n\\n with open(\\\"Other_title_categories.json\\\", \\\"r\\\") as other_categories_fo:\\n other_categories_foData = json.load(other_categories_fo)\\n completeDict = other_categories_foData[\\\"wish_list\\\"]\\n selectetItemData_complete = completeDict[curItem]\\n\\n select_values = selectetItemData_complete\\n\\n def raging_fire():\\n \\\"\\\"\\\"call back function for edit button in the edit window\\\"\\\"\\\"\\n\\n if editspin1.get() != '0': # season\\n select_values[0] = int(editspin1.get()) # modify the change\\n select_values[3] = \\\"{}\\\".format(datetime.datetime.now().date()) # update the modify date\\n with open('Other_title_categories.json', 'w') as f:\\n completeDict[curItem] = select_values\\n json.dump(other_categories_foData, f, indent=2)\\n print(\\\"done writing changes\\\")\\n\\n if editspin2.get() != '0': # episode\\n select_values[1] = int(editspin2.get())\\n select_values[3] = \\\"{}\\\".format(datetime.datetime.now().date()) # update the modify date\\n with open('Other_title_categories.json', 'w') as f:\\n completeDict[curItem] = select_values\\n json.dump(other_categories_foData, f, indent=2)\\n print(\\\"done writing changes\\\")\\n\\n if editentvar.get() != curItem: # name\\n removed_values = completeDict.pop(curItem) # remove the previous title and write the new one\\n completeDict[editentvar.get()] = removed_values\\n select_values[3] = \\\"{}\\\".format(datetime.datetime.now().date())\\n with open('Other_title_categories.json', 'w') as f:\\n completeDict[editentvar.get()] = select_values # set the values of the edited title\\n json.dump(other_categories_foData, f, indent=2)\\n print(\\\"done writing changes\\\")\\n\\n edittop.destroy()\\n\\n if curItem != \\\"\\\": # test if an item is highlighted first\\n \\\"\\\"\\\"the actual edit window widgets\\\"\\\"\\\"\\n edittop = Toplevel()\\n\\n editlab1 = Label(edittop, text=\\\"Current Tv-Series title : \\\")\\n editlab1.grid(row=1, column=1, sticky=W, pady=4)\\n\\n editent = Entry(edittop, textvariable=editentvar, width=30)\\n editentvar.set(curItem)\\n editent.grid(row=1, column=2, sticky=W, pady=4)\\n\\n editlab2 = Label(edittop, text=\\\"Current Season {}, chance to : \\\".format(select_values[0]))\\n editlab2.grid(row=2, column=1, sticky=W, pady=4)\\n\\n editspin1 = Spinbox(edittop, from_=0, to=1000, width=5)\\n editspin1.grid(row=2, column=2, sticky=W, pady=4)\\n\\n editlab3 = Label(edittop, text=\\\"Current Episode {}, change to : \\\".format(select_values[1]))\\n editlab3.grid(row=3, column=1, sticky=W, pady=4)\\n\\n editspin2 = Spinbox(edittop, from_=0, to=1000, width=5)\\n editspin2.grid(row=3, column=2, sticky=W, pady=4)\\n\\n editbut = Button(edittop, text='Edit', command=raging_fire)\\n editbut.grid(row=5, column=1, sticky=W, pady=4, padx=20)\\n\\n edittop.geometry(\\\"400x200+200+300\\\")\\n edittop.title(\\\"Edit properties of {} \\\".format(curItem).upper())\",\n \"def new_w(w, d):\\n\\n if w.sum() > 0:\\n next_w = w.copy()\\n next_w[next_w > 0] -= 1\\n return next_w\\n else:\\n if d[0] == 1:\\n return np.array([51,0,0])\\n elif d[1] == 1:\\n return np.array([0,51,0])\\n else:\\n return np.array([0,0,51])\",\n \"def set(self, index, data):\\n self.data[index] = data\",\n \"def set_data(self, data, index, color='black', lw=2., nbins=10,\\n symbol='disc', size=10., form='line', th=None, norm=None,\\n window=None, overlap=0., baseline=None, clim=None,\\n cmap='viridis', interpolation='gaussian', nperseg=256,\\n noverlap=128):\\n # Update variable :\\n self.form = form\\n self._index = index\\n color = color2vb(color)\\n\\n # Get data index :\\n if data.ndim == 1:\\n idx = slice(None)\\n elif data.ndim in [2, 3]:\\n idx = list(self._navidx[index])\\n idx.insert(self._axis, slice(None))\\n idx = tuple(idx)\\n\\n # Convert data to be compatible with VisPy and prepare data :\\n data_c = vispy_array(data[idx]).copy()\\n _data = self._prep._prepare_data(self._sf, data_c, self._time)\\n\\n # Set data :\\n if form in ['line', 'marker', 'psd', 'butterfly']: # line and marker\\n # Get position array :\\n pos = np.c_[self._time, _data]\\n # Send position :\\n if form in ['line', 'psd']:\\n if form == 'psd':\\n fmax = self._sf / 4.\\n f, pxx = welch(_data, self._sf, nperseg=nperseg,\\n noverlap=noverlap)\\n f_sf4 = abs(f - fmax)\\n f_1 = abs(f - 1.)\\n fidx_sf4 = np.where(f_sf4 == f_sf4.min())[0][0]\\n fidx_1 = np.where(f_1 == f_1.min())[0][0]\\n pos = np.c_[f[fidx_1:-fidx_sf4], pxx[fidx_1:-fidx_sf4]]\\n # Threshold :\\n is_th = isinstance(th, (tuple, list, np.ndarray))\\n col = color2vb(color, length=pos.shape[0])\\n if is_th:\\n # Build threshold segments :\\n t_min, t_max = self._time.min(), self._time.max()\\n pos_th = np.vstack(([t_min, th[0]], [t_max, th[0]],\\n [t_min, th[1]], [t_max, th[1]]))\\n self._th.set_data(pos_th, connect='segments',\\n color=color2vb('#ab4642'))\\n # Build line color :\\n col = color2vb(color, length=len(_data))\\n cond = np.logical_or(_data < th[0], _data > th[1])\\n col[cond, :] = color2vb('#ab4642')\\n self._th.visible = is_th\\n self._line.set_data(pos, width=lw, color=col)\\n self._line.update()\\n elif form == 'marker':\\n self._mark.set_data(pos, face_color=color, symbol=symbol,\\n size=size, edge_width=0.)\\n self._mark.update()\\n elif form == 'butterfly':\\n # Get soe shape related variables :\\n n, m = len(self._time), int(np.prod(data.shape))\\n n_rep = int(m / n)\\n data = vispy_array(data)\\n # Build position :\\n pos = np.c_[np.tile(self._time.ravel(), n_rep), data.ravel()]\\n # Disconnect some points :\\n connect = np.c_[np.arange(m - 1), np.arange(1, m)]\\n to_delete = np.linspace(n - 1, m - 1, n_rep)\\n connect = np.delete(connect, to_delete, axis=0)\\n # Build color :\\n col = color2vb(color, length=m)\\n # singcol = np.random.uniform(size=(n_rep, 3), low=.2,\\n # high=.8).astype(np.float32)\\n # col = np.repeat(singcol, n, 0)\\n # Send data :\\n self._line.set_data(pos, width=lw, color=col, connect=connect)\\n self._line.update()\\n # Get camera rectangle :\\n t_min, t_max = pos[:, 0].min(), pos[:, 0].max()\\n d_min, d_max = pos[:, 1].min(), pos[:, 1].max()\\n off = .05 * (d_max - d_min)\\n self.rect = (t_min, d_min - off, t_max - t_min,\\n d_max - d_min + 2 * off)\\n elif form == 'histogram': # histogram\\n # Compute the mesh :\\n mesh = scene.visuals.Histogram(_data, nbins)\\n # Get the vertices and faces of the mesh :\\n vert = mesh.mesh_data.get_vertices()\\n faces = mesh.mesh_data.get_faces()\\n # Pass vertices and faces to the histogram :\\n self._hist.set_data(vert, faces, color=color)\\n # Compute the histogram :\\n raw, xvec = np.histogram(_data, nbins)\\n # Get camera rectangle :\\n t_min, t_max = xvec.min(), xvec.max()\\n d_min, d_max = 0.9 * raw[np.nonzero(raw)].min(), 1.01 * raw.max()\\n self.rect = (t_min, d_min, t_max - t_min, d_max - d_min)\\n # Update object :\\n self._hist.update()\\n elif form == 'tf': # time-frequency map\\n self._tf.set_data(_data, self._sf, cmap=cmap, contrast=.5,\\n norm=norm, baseline=baseline, n_window=window,\\n overlap=overlap, window='hanning', clim=clim)\\n self._tf.interpolation = interpolation\\n self.rect = self._tf.rect\\n\\n # Hide non form elements :\\n self._visibility()\\n\\n # Update annotations :\\n self.update_annotations(str(self))\",\n \"def reorder_and_draw_new(final, colours, n_ind, ind_last_now, meas_last, meas_now, df, mot, fr_no, frame_start):\\n # Reorder contours based on results of the hungarian algorithm\\n equal = np.array_equal(ind_last_now, list(range(len(ind_last_now))))\\n meas_now = np.array(meas_now)\\n meas_now_tmp = meas_now\\n meas_now = np.array(meas_last)\\n for i in range(len(meas_now)):\\n meas_now[i] = meas_now_tmp[ind_last_now[i]]\\n \\n # apatch: commented the following for loop because its range is not\\n # appropriate for systems where the number of individuals may change\\n # frame to frame... for example, due to occlusions when fish cross over\\n #for i in range(n_ind):\\n meas_now = np.array(meas_now)\\n for i in range(len(meas_now)):\\n cv2.circle(final, tuple([int(x) for x in meas_now[i,(0,1)]]), 5, colours[i%len(colours)%n_ind], -1, cv2.LINE_AA)\\n \\n meas_now = list(meas_now)\\n # add frame number\\n font = cv2.FONT_HERSHEY_SCRIPT_SIMPLEX\\n fr_no_print = fr_no - frame_start\\n cv2.putText(final, str(int(fr_no_print)), (5,30), font, 1, (255,255,255), 2)\\n \\n return final, meas_now, df\",\n \"def __setitem__(self, index, colour):\\n self.desk[index] = colour\",\n \"def extend_wells(self):\\n middle_point = (self.nmos_position1.y \\n + self.nmos.pwell_position.y \\n + self.nmos.well_height \\n + (self.pmos_position1.y\\n + self.pmos.nwell_position.y \\n - self.nmos_position1.y \\n - self.nmos.pwell_position.y \\n - self.nmos.well_height) / 2 )\\n self.nwell_position = vector(0, middle_point)\\n self.nwell_height = self.height - middle_point\\n self.add_rect(layer=\\\"nwell\\\",\\n offset=self.nwell_position,\\n width=self.well_width,\\n height=self.nwell_height)\\n self.add_rect(layer=\\\"vtg\\\",\\n offset=self.nwell_position,\\n width=self.well_width,\\n height=self.nwell_height)\\n\\n self.pwell_position = vector(0, 0)\\n self.pwell_height = middle_point\\n self.add_rect(layer=\\\"pwell\\\",\\n offset=self.pwell_position,\\n width=self.well_width,\\n height=self.pwell_height)\\n self.add_rect(layer=\\\"vtg\\\",\\n offset=self.pwell_position, \\n width=self.well_width,\\n height=self.pwell_height)\",\n \"def update_pit(self, value, pit_index, index):\\n if index == 1:\\n self.state[pit_index] = value\\n else:\\n self.state[pit_index + self.M + 1] = value\",\n \"def set_custom_data(self, index_from, data):\\r\\n self.unif[index_from:(index_from + len(data))] = data\",\n \"def seven_punishment(self):\\n if self.current_draw_punishment == 1:\\n self.current_draw_punishment = 2\\n else:\\n self.current_draw_punishment = self.current_draw_punishment + 2\",\n \"def draw_all(self, im, idx):\\n if idx in self.frames_to_boxes_dict:\\n self.draw_boxes(im, self.frames_to_boxes_dict[idx])\\n\\n hud_info = {\\\"bottles\\\": 0, \\\"fragments\\\":0, \\\"others\\\":0}\\n if idx in self.frames_to_update_hud:\\n hud_info = self.frames_to_update_hud[idx].copy()\\n hud_info[\\\"update\\\"] = True\\n else:\\n # find the last hud info\\n for x in self.frames_to_update_hud.keys():\\n if idx < x:\\n break\\n hud_info = self.frames_to_update_hud[x]\\n self.draw_hud(im, hud_info)\",\n \"def setDrawIndex(self, idsOfDraws):\\n if self.name in idsOfDraws:\\n self.drawId = idsOfDraws[self.name]\\n else:\\n error_msg = (\\n f'No id is available for draw {self.name}. '\\n f'List of available indices: '\\n f'{[n for n, i in idsOfDraws.items()]}'\\n )\\n raise excep.biogemeError(error_msg)\",\n \"def FullHouse(handIn, indx):\",\n \"def single_decoding_weber_law_plot(data_idxs=2):\\n\\n\\tdecode_plot_size = 8\\n\\tnum_gain_plots = data_idxs/2\\n\\tgain_plot_size = 4\\n\\test_plot_upper_pad = 3\\n\\test_plot_height = 6\\n\\tnum_est_plots = 2\\n\\t\\n\\t\\n\\tfig = plt.figure()\\n\\tgrid_width = decode_plot_size + 1 + gain_plot_size*num_gain_plots\\n\\tgrid_height = decode_plot_size + est_plot_upper_pad + \\\\\\n\\t\\t\\t\\t\\test_plot_height*num_est_plots\\n\\tgs = gridspec.GridSpec(grid_height, grid_width)\\n\\tgs.update(wspace=6.5, hspace=1.0)\\n\\tfig.set_size_inches(13, 16)\\n\\t\\n\\t# Some font and label sizes for later use\\n\\ttick_label_size_small = 30\\n\\ttick_label_size_large = 30\\n\\taxis_label_size = 34\\n\\tx_axis_label_pad = 15\\n\\ty_axis_successes_labelpad = 8\\n\\tx_tick_label_pad = 11\\n\\t\\n\\tax = dict()\\n\\t\\n\\t# Success error plots\\n\\tax['successes'] = plt.subplot(gs[0:decode_plot_size, 0:decode_plot_size])\\n\\tplt.xscale('log')\\n\\tplt.xticks(10.**sp.arange(-5, 5), fontsize=tick_label_size_large)\\n\\tplt.yticks([0, 50, 100], fontsize=tick_label_size_large)\\n\\tax['successes'].tick_params(axis='x', which='major', pad=x_tick_label_pad)\\n\\tax['successes'].set_xlabel(r'Background signal', fontsize=axis_label_size, \\n\\t\\t\\t\\t\\t\\t\\t\\tlabelpad=x_axis_label_pad)\\n\\tax['successes'].set_ylabel(r'Correctly decoded (%)', \\n\\t\\t\\t\\t\\t\\t\\t\\tfontsize=axis_label_size, \\n\\t\\t\\t\\t\\t\\t\\t\\tlabelpad=y_axis_successes_labelpad, \\n\\t\\t\\t\\t\\t\\t\\t\\tmultialignment='center')\\n\\tax['successes'].set_xlim([1e-1, 1.5e2])\\n\\t\\n\\t# Gain plots\\n\\tfor data_idx in sp.arange(0, data_idxs):\\n\\t\\tif data_idx % 2 == 0: \\n\\t\\t\\tax['gains_%s' % data_idx] = \\\\\\n\\t\\t\\t\\tplt.subplot(gs[0:gain_plot_size, decode_plot_size + \\\\\\n\\t\\t\\t\\t\\t\\t\\t1 + (data_idx/2)*gain_plot_size: decode_plot_size + \\\\\\n\\t\\t\\t\\t\\t\\t\\t1 + gain_plot_size + (data_idx/2)*gain_plot_size])\\n\\t\\telif data_idx % 2 == 1:\\n\\t\\t\\tax['gains_%s' % data_idx] = \\\\\\n\\t\\t\\t\\tplt.subplot(gs[gain_plot_size:2*gain_plot_size, decode_plot_size + \\\\\\n\\t\\t\\t\\t\\t\\t\\t1 + (data_idx/2)*gain_plot_size: decode_plot_size + \\\\\\n\\t\\t\\t\\t\\t\\t\\t1 + gain_plot_size + (data_idx/2)*gain_plot_size])\\n\\t\\n\\t\\tax['gains_%s' % data_idx].set_xscale('log')\\n\\t\\tax['gains_%s' % data_idx].set_yscale('log')\\n\\t\\tax['gains_%s' % data_idx].set_xlim(1e-2, 1e0)\\n\\t\\tax['gains_%s' % data_idx].set_xticks([])\\n\\t\\tax['gains_%s' % data_idx].set_yticks([])\\n\\t\\t\\n\\t\\t# Gain plots ticks labels and axis ranges\\n\\t\\tif data_idx / 2 == 0:\\n\\t\\t\\tax['gains_%s' % data_idx].set_yticks(10.**sp.arange(-5, 0))\\n\\t\\t\\tax['gains_%s' % data_idx].tick_params(labelsize=tick_label_size_large)\\n\\t\\tif data_idx % 2 == 0:\\n\\t\\t\\tax['gains_%s' % data_idx].set_ylim(2e-3, 4e-2)\\n\\t\\telif data_idx % 2 == 1:\\n\\t\\t\\tax['gains_%s' % data_idx].set_ylim(1.1e-2, 2e-1)\\n\\t\\t\\tax['gains_%s' % data_idx].set_xticks([1e-2, 1e-1, 1e0])\\t\\n\\t\\t\\tax['gains_%s' % data_idx].\\\\\\n\\t\\t\\t\\t\\ttick_params(axis='x', labelsize=tick_label_size_small)\\n\\t\\t\\tax['gains_%s' % data_idx].\\\\\\n\\t\\t\\t\\t\\ttick_params(axis='y', labelsize=tick_label_size_large)\\n\\t\\tax['gains_%s' % data_idx].set_xlim(0.98e-2, 1.4e-1)\\n\\t\\tax['gains_%s' % data_idx].tick_params(axis='x', which='major', \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tpad=x_tick_label_pad)\\n\\t\\t\\t\\n\\t\\t# Gaain axis label in center of range of plots\\n\\t\\tif num_gain_plots % 2 == 0:\\n\\t\\t\\tif data_idx == data_idxs/2 - 1:\\n\\t\\t\\t\\tax['gains_%s' % data_idx].set_xlabel(r'Background signal', \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfontsize=axis_label_size, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tlabelpad=x_axis_label_pad)\\n\\t\\t\\t\\tax['gains_%s' % data_idx].xaxis.set_label_coords(1.1, -.455)\\n\\t\\t\\t\\t\\n\\t\\telif num_gain_plots % 2 == 1:\\n\\t\\t\\tif data_idx == data_idxs/2:\\n\\t\\t\\t\\tax['gains_%s' % data_idx].set_xlabel(r'Background signal', \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfontsize=axis_label_size, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tlabelpad=x_axis_label_pad)\\n\\t\\tif data_idx == data_idxs - 2:\\n\\t\\t\\tax['gains_%s' % data_idx].yaxis.set_label_position('right')\\n\\t\\t\\tax['gains_%s' % data_idx].set_ylabel(\\n\\t\\t\\t\\t\\t\\tr'Receptor gain', fontsize=axis_label_size, \\n\\t\\t\\t\\t\\t\\trotation=270)\\n\\t\\t\\tax['gains_%s' % data_idx].yaxis.set_label_coords(1.4, -0.1)\\n\\t\\t\\t\\t\\n\\t# Sample estimation plot\\n\\tfor est_idx in range(num_est_plots):\\n\\t\\tax['est_%s' % est_idx] = \\\\\\n\\t\\t\\tplt.subplot(gs[decode_plot_size + est_plot_upper_pad\\n\\t\\t\\t\\t\\t\\t+ est_idx*est_plot_height:\\n\\t\\t\\t\\t\\t\\tdecode_plot_size + est_plot_upper_pad \\n\\t\\t\\t\\t\\t\\t+ (est_idx + 1)*est_plot_height, 0:grid_width])\\n\\t\\tax['est_%s' % est_idx].set_yticks([])\\n\\t\\t\\n\\t\\t# Sample estimation plots labels only on outside\\n\\t\\tif est_idx == num_est_plots/2:\\n\\t\\t\\tax['est_%s' % est_idx].set_xticks(sp.arange(10, 100, 10))\\n\\t\\t\\tax['est_%s' % est_idx].yaxis.set_label_coords(-0.03, 1.1)\\n\\t\\t\\tax['est_%s' % est_idx].set_xlabel(r'Odorant identity', \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfontsize=axis_label_size,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tlabelpad=x_axis_label_pad)\\n\\t\\t\\tax['est_%s' % est_idx].set_ylabel(r'Odorant intensity', \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfontsize=axis_label_size)\\n\\t\\t\\tax['est_%s' % est_idx].tick_params(labelsize=tick_label_size_large, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tpad=x_tick_label_pad)\\n\\t\\telse:\\n\\t\\t\\tax['est_%s' % est_idx].set_xticks([])\\n\\t\\tax['est_%s' % est_idx].set_xlim(35, 99)\\n\\t\\t\\n\\treturn fig, ax\",\n \"def updateDraw(self):\\r\\n self.delConns()\\r\\n self.delTags()\\r\\n self.drawConns()\\r\\n self.drawTags()\",\n \"def shift_point_rect(mutated_genome,index):\\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\\n rectangle = mutated_genome[index][2]\\n seed = random.randint(0,1)\\n oldpoint = rectangle[seed]\\n newpoint = (oldpoint+Xval, newpoint+Yval)\\n newrectangle = list(rectangle)\\n newrectangle[seed] = newpoint\\n mutated_genome[index][2] = tuple(newrectangle)\",\n \"def reflect_list( wall_1side_list , inout_strands , inout_strands_index , p ):\\n if len(wall_1side_list)==((p-1)/2)+1: # base case\\n return wall_1side_list #return \\n else:\\n if inout_strands_index == 0:\\n wall_color = wall_1side_list[-1] # bottom universe at crossing\\n wall_1side_list.append(reflect(wall_color,inout_strands[inout_strands_index],p))#,inout_strands, 1, p)\\n return reflect_list( wall_1side_list, inout_strands , 1, p)\\n else:\\n wall_color = wall_1side_list[-1]\\n wall_1side_list.append(reflect(wall_color,inout_strands[inout_strands_index],p))#,inout_strands, 1, p)\\n return reflect_list( wall_1side_list, inout_strands , 0, p)\",\n \"def draw_stickfigure(data):\\n crop = np.array(data['crop']);\\n if data['kpss'][data['i']] is not None:\\n ut.draw_stick_figure(crop, data['kpss'][data['i']]);\\n return crop;\",\n \"def reformatSagittalView4Needle(self,ID):\\n #productive #onButton #report\\n profprint()\\n for i in range(2): #workaround update problem\\n modelNode = slicer.util.getNode('vtkMRMLModelNode'+str(ID))\\n polyData = modelNode.GetPolyData()\\n nb = polyData.GetNumberOfPoints()\\n base = [0,0,0]\\n tip = [0,0,0]\\n polyData.GetPoint(nb-1,tip)\\n polyData.GetPoint(0,base)\\n a,b,c = tip[0]-base[0],tip[1]-base[1],tip[2]-base[2]\\n #print a,b,c\\n sYellow = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNodeYellow\\\")\\n if sYellow ==None :\\n sYellow = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNode2\\\")\\n reformatLogic = slicer.vtkSlicerReformatLogic()\\n sYellow.SetSliceVisible(1)\\n reformatLogic.SetSliceNormal(sYellow,1,-a/b,0)\\n m= sYellow.GetSliceToRAS()\\n m.SetElement(0,3,base[0])\\n m.SetElement(1,3,base[1])\\n m.SetElement(2,3,base[2])\\n sYellow.Modified()\",\n \"def updateRow(self, index: int) -> None:\\n ...\",\n \"def test_plot_negative_wiggle_fills(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n splt = SEGYPlotter(ax, self.segy)\\n # should add a single artist to the active patch dict.\\n splt.plot_wiggles(negative_fills=True)\\n self.assertEqual(len(splt.ACTIVE_PATCHES['negative_fills']),1)\\n # same artist should be in the axes list\\n self.assertTrue(splt.ACTIVE_PATCHES['negative_fills'][0] in \\\\\\n splt.ax.patches)\\n # should be able to directly remove it\\n splt.ax.patches.remove(splt.ACTIVE_PATCHES['negative_fills'][0])\\n self.assertEqual(len(splt.ax.patches), 0)\\n # and re-add it\\n splt.ax.patches.append(splt.ACTIVE_PATCHES['negative_fills'][0])\\n self.assertEqual(len(splt.ax.patches), 1)\\n # should move artist to the inactive patch dict.\\n splt.plot_wiggles(negative_fills=False)\\n self.assertEqual(len(splt.INACTIVE_PATCHES['negative_fills']),1)\\n self.assertEqual(len(splt.ax.patches), 0)\\n # calling again should change nothing\\n splt.plot_wiggles(negative_fills=False)\\n self.assertEqual(len(splt.INACTIVE_PATCHES['negative_fills']),1)\\n self.assertEqual(len(splt.ax.patches), 0)\\n # should move artist back to active patch dict.\\n splt.plot_wiggles(negative_fills=True)\\n self.assertEqual(len(splt.ACTIVE_PATCHES['negative_fills']),1)\\n self.assertEqual(len(splt.ax.patches), 1)\\n # new plot items should be accessible through the orig. axes\\n self.assertEqual(len(splt.ax.patches),\\n len(ax.patches))\",\n \"def blank(self, index=-1):\\n self.displays[0].start() # call only once to support shift chain\\n if index < 0:\\n for d in self.displays:\\n d.blank()\\n else:\\n self.displays[index].blank()\\n self.displays[0].latch() # call only once to support shift chain\",\n \"def swap_layers(self):\\n dpg.show_item(f'draw_layer_{self.id}_{self.draw_layer_index^1}')\\n dpg.delete_item(f'draw_layer_{self.id}_{self.draw_layer_index}')\\n self.draw_layer_index ^= 1\",\n \"def show2d(self, data):\\n self.__plot2d.clear()\\n self.__plot2d.addImage(legend=\\\"data\\\", data=data)\\n self.setCurrentIndex(self.__index2d)\",\n \"def revise(self,\\r\\n index,\\r\\n oldindex=None,\\r\\n infront=True,\\r\\n inback=False,\\r\\n breaker=Note(set(),EMPTYCHAR)):\\r\\n## self.default_dict['currentindex'] = Index(0)\\r\\n if str(index) in self.indexes():\\r\\n\\r\\n newnote = self.get_note(index)\\r\\n\\r\\n if not oldindex or str(oldindex) not in self.indexes():\\r\\n if infront:\\r\\n newnote = self.enter(returnnote=True) + breaker + newnote\\r\\n if inback:\\r\\n newnote = newnote + breaker + self.enter(returnnote=True)\\r\\n else:\\r\\n if infront:\\r\\n newnote = self.get_note(oldindex) + breaker + newnote\\r\\n if inback:\\r\\n newnote = newnote + breaker + self.get_note(oldindex)\\r\\n\\r\\n\\r\\n self.softdelete(index)\\r\\n self.addnew(newnote.keyset,\\r\\n newnote.text,\\r\\n newnote.meta,\\r\\n right_at=True,\\r\\n ind=index)\\r\\n else:\\r\\n display.noteprint((alerts.ATTENTION,'Fail'))\",\n \"def updateWindow(gameWindow, figurelist, rounds):\\n for item in gameWindow.items[:]:\\n item.undraw() #Remove al elements in game window\\n\\n gameWindow.update() #update to ensure al is gone\\n\\n for i in range(15): #For loops to draw new figures\\n for k in range(25):\\n todraw = figurelist[i][k]\\n todraw.draw(gameWindow)\\n \\n scoreText = str(\\\"Pick number:\\\" + str(rounds)) #Show how many times a color has been picked(Shows 1 on first round)\\n toPrint = g.Text(g.Point(100, 775), scoreText) #Create text to print\\n\\n toPrint.draw(gameWindow) #Draws text with rounds\",\n \"def reindex_graphics(self):\\n for obj in self.context.static_objects:\\n self.canvas.children.remove(obj.widget.canvas)\\n # fill _objects_z_index\\n _objects_z_index = {}\\n for obj in self.context.static_objects:\\n y = obj.widget.pos[1]\\n if not y in _objects_z_index:\\n _objects_z_index[y] = []\\n _objects_z_index[y].append(obj)\\n _keys = _objects_z_index.keys()\\n _keys.sort()\\n _keys.reverse()\\n for k in _keys:\\n objs = _objects_z_index[k]\\n for obj in objs:\\n self.canvas.add(obj.widget.canvas)\",\n \"def draw_bars(pencil: turtle.Turtle, data_list):\\n # Your code here\\n color = [\\\"red\\\", \\\"orange\\\", \\\"yellow\\\", \\\"green\\\", \\\"violet\\\", \\\"blue\\\", \\\"deeppink\\\", \\\"aqua\\\", \\\"brown\\\", \\\"gold\\\"]\\n for i in data_list:\\n t.begin_fill()\\n t.color(random.choice(color))\\n t.pencolor(\\\"black\\\")\\n t.left(90)\\n t.forward(i)\\n t.right(90)\\n t.forward(30)\\n t.right(90)\\n t.forward(i)\\n t.right(90)\\n t.forward(30)\\n t.end_fill()\\n t.backward(30)\\n t.setheading(0)\",\n \"def discard_index_from_word(self,word,index):\\r\\n\\r\\n # with shelf\\r\\n if self.using_shelf:\\r\\n\\r\\n if word in self.word_dict:\\r\\n\\r\\n self.word_dict[word].discard(str(index))\\r\\n\\r\\n\\r\\n #with database\\r\\n if self.using_database:\\r\\n\\r\\n value_tuple = (notebookname,word,str(index),)\\r\\n db_cursor.execute(\\\"DELETE FROM word_to_indexes \\\"\\r\\n +\\\"WHERE notebook=? AND word=? \\\"\\r\\n +\\\"AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n db_cursor.execute(\\\"SELECT * FROM word_to_indexes\\\"\\r\\n +\\\" WHERE notebook=? and word=?;\\\",\\r\\n value_tuple[0:2])\\r\\n if db_cursor.fetchone():\\r\\n db_cursor.execute(\\\"DELETE FROM all_words\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND word=?;\\\",\\r\\n value_tuple[0:2])\",\n \"def _update_farness_map(self,ind):\",\n \"def set_draw_details(self, details):\\r\\n for x in range(len(self.model)):\\r\\n self.model[x].set_draw_details(details[x][0], details[x][1], details[x][2], details[x][3], details[x][4], details[x][5])\",\n \"def recovery():\\n # Uncovers the left Leg\\n losing_lbl_one.grid(row=0, column=LEFT_COL, rowspan=15, sticky='NESW')\\n # Uncovers the Right Leg\\n losing_lbl_two.grid(row=0, column=RIGHT_COL, rowspan=15, sticky='NESW')\\n # Uncovers the Chest\\n losing_lbl_three.grid(row=15, column=LEFT_COL, columnspan=2, rowspan=13, sticky='NESW')\\n # Uncovers the Left Arm\\n losing_lbl_four.grid(row=28, column=LEFT_COL, rowspan=14, sticky='NESW')\\n # Uncovers the Right Arm\\n losing_lbl_five.grid(row=28, column=RIGHT_COL, rowspan=14, sticky='NESW')\\n # Uncover the Head image\\n losing_lbl_six.grid(row=42, column=LEFT_COL, columnspan=2, rowspan=23, sticky='NESW')\",\n \"def highlight(self, highligtedItem):\\n\\t\\tfor nr, i in enumerate(self.content):\\n\\t\\t\\tself.content[nr][1] = self.content[nr][2]\\n\\t\\tself.content[highligtedItem][1] = 16\",\n \"def mark_bingo_boards(bingo_num):\\n global bingoboards\\n for board in bingoboards:\\n for line in board:\\n try:\\n line[line.index(bingo_num)] = \\\"X\\\"\\n except ValueError:\\n pass\",\n \"def replace_season(data,season):\\n if not SeasonCharts.matchseason(season):\\n raise SeasonCharts.SeasonError\\n ## Check data format\\n if test_rawdata(data):\\n for cat,shows in data.items():\\n for show in shows: show['season'] = season\\n else:\\n for show in data: show['season'] = season\",\n \"def update_figure(self):\\n # if number of kinetics in model did not change\\n # update just last lines\\n if self.N_lines - 1 == len(self.model.spectra.keys()) * 2:\\n self.dataplot.lines[-1].remove()\\n self.dataplot.lines[-1].remove()\\n self.draw_figure_first()\\n # delete all and redraw\\n else:\\n n = int((self.N_lines - 1) / 2)\\n for _ in range(n):\\n self.dataplot.lines[-1].remove()\\n self.dataplot.lines[-1].remove()\\n self.draw_figure_total()\\n\\n self.dataplot.relim()\\n\\n self.dataplot.autoscale_view(True, True, True)\\n\\n self.draw()\",\n \"def get_index_from_well(self, well):\\n pass\",\n \"def flip_back(self):\\n for img in self.image_id:\\n self.canvas.delete(img)\\n self.image_id.clear()\\n for tile in self.canvas.find_withtag(\\\"match\\\"):\\n self.canvas.itemconfigure(tile, fill=self.color)\\n # If item has match tag, removes all the tags\\n self.canvas.itemconfigure(tile, tag=\\\"\\\")\\n for tile in self.canvas.find_withtag(\\\"selected\\\"):\\n self.canvas.itemconfigure(tile, fill=self.default_color)\\n self.canvas.itemconfigure(tile, tag=\\\"\\\")\\n # check if all tiles are matched, update result label\\n if self.num_of_match == 8:\\n text_label = f'Game Over!\\\\n Score: {self.score}\\\\n ' \\\\\\n f'Number of tries: {self.num_of_tries}'\\n self.score_label.config(text=text_label)\",\n \"def paint_item(self, posx, index):\\r\\n raise NotImplementedError()\",\n \"def draw_by_insufficient(self):\\n \\n if self.cap_counter > 100:\\n self.draw_loop(\\\"draw due to none in 50\\\")\\n \\n if self.board.count(self.empty) == 62:\\n self.draw_loop(\\\"draw due to insufficient\\\")\\n\\n \\n if self.board.count(self.empty) == 61:\\n for i in self.board:\\n if i != self.empty:\\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Bishop] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Bishop]:\\n self.draw_loop(\\\"draw due to insufficient\\\")\\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Knight] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Knight]:\\n self.draw_loop(\\\"draw due to insufficient\\\")\",\n \"def test_plot_positive_wiggle_fills(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n splt = SEGYPlotter(ax, self.segy)\\n # should add a single artist to the active patch dict.\\n splt.plot_wiggles(positive_fills=True)\\n self.assertEqual(len(splt.ACTIVE_PATCHES['positive_fills']),1)\\n # same artist should be in the axes list\\n self.assertTrue(splt.ACTIVE_PATCHES['positive_fills'][0] in \\\\\\n splt.ax.patches)\\n # should be able to directly remove it\\n splt.ax.patches.remove(splt.ACTIVE_PATCHES['positive_fills'][0])\\n self.assertEqual(len(splt.ax.patches), 0)\\n # and re-add it\\n splt.ax.patches.append(splt.ACTIVE_PATCHES['positive_fills'][0])\\n self.assertEqual(len(splt.ax.patches), 1)\\n # should move artist to the inactive patch dict.\\n splt.plot_wiggles(positive_fills=False)\\n self.assertEqual(len(splt.INACTIVE_PATCHES['positive_fills']),1)\\n self.assertEqual(len(splt.ax.patches), 0)\\n # calling again should change nothing\\n splt.plot_wiggles(positive_fills=False)\\n self.assertEqual(len(splt.INACTIVE_PATCHES['positive_fills']),1)\\n self.assertEqual(len(splt.ax.patches), 0)\\n # should move artist back to active patch dict.\\n splt.plot_wiggles(positive_fills=True)\\n self.assertEqual(len(splt.ACTIVE_PATCHES['positive_fills']),1)\\n self.assertEqual(len(splt.ax.patches), 1)\\n # new plot items should be accessible through the orig. axes\\n self.assertEqual(len(splt.ax.patches),\\n len(ax.patches))\",\n \"def add_piece(self, index):\\n if isinstance(index, list):\\n self.pieces += index\\n return\\n\\n self.pieces.append(index)\",\n \"def replace_and_make_new_progression(progression, index, fuzzy_harmony):\\n new_progression = progression.get_progression()\\n progression_list = []\\n for i in range(len(new_progression)):\\n print(new_progression[i])\\n progression_list.append(new_progression[i])\\n print(progression_list)\\n progression_list[index] = fuzzy_harmony\\n new_progression = tuple(progression_list)\\n return new_progression\",\n \"def fill_draw(self):\\n self.draw = [x + str(y) for x in COLOR for y in CARD_VALUE]\",\n \"def decrement_point(mutated_genome,index):\\n point_index1 = random.randint(1,max(0,len(mutated_genome[index][2])-1))\\n seed = random.randint(0,2)\\n if seed == 0:\\n point_index2 = point_index1 - 1\\n elif seed == 1:\\n point_index2 = random.randint(0, point_index1)\\n else: #seed == 2:\\n point_index2 = 0\\n temp = mutated_genome[index][2][point_index1]\\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][point_index2]\\n mutated_genome[index][2][point_index2] = temp\",\n \"def radioList(dev, draw, index):\\n\\n global menuindex\\n global listMenuStart, listMenuEnd\\n global names\\n \\n font = ImageFont.load_default()\\n draw.rectangle(dev.bounding_box, outline=\\\"white\\\", fill=\\\"black\\\")\\n if index > listMenuEnd:\\n listMenuEnd += 1\\n listMenuStart += 1\\n elif index < listMenuStart:\\n listMenuEnd -= 1\\n listMenuStart -= 1\\n for i in range(6):\\n if( i == (index-listMenuStart)):\\n menuindex = index\\n invert(draw, 4, 4 + (index-listMenuStart)*10, names[listMenuStart + i][0], False)\\n else:\\n draw.text((4, 4 + i*10), names[listMenuStart + i][0], font = font, fill = 255)\",\n \"def move_point_rect(mutated_genome,index):\\n Xval = random.randint(-int(imagewidth/5.),int(imagewidth*6./5.))\\n Yval = random.randint(-int(imageheight/5.),int(imageheight*6./5.))\\n point = (Xval,Yval)\\n old_points = list(mutated_genome[index][2])\\n old_points[random.randint(0,1)] = point\\n mutated_genome[index][2] = tuple(old_points)\",\n \"def redraw(self):\\n offset = self.actual_row * self.row_size\\n x = 5\\n y = 5\\n ind = 0\\n self.scene.clear()\\n for _ in range(self.column_size):\\n for _ in range(self.row_size):\\n if ind+offset < len(self.cards):\\n self.draw_card(x, y, ind+offset)\\n x += 90\\n ind += 1\\n x = 5\\n y += 120\",\n \"def _update_data(self, selected):\\n if selected.row() != self.datasets.index:\\n self.datasets.index = selected.row()\\n self.datasets.update_current()\\n self._update_main()\",\n \"def __draw_unk_style(self, ax, item, p, thumb=False):\\n a = True\\n for i, d in enumerate(item[\\\"domains\\\"]):\\n if d[\\\"name\\\"] not in self.col_map:\\n print(\\\"Warning: '%s' not found in colour map\\\" % d[\\\"name\\\"])\\n else:\\n ax.add_patch(Rectangle((d[\\\"pos\\\"][0], -0.25), d[\\\"pos\\\"][1] - d[\\\"pos\\\"][0], 0.5,\\n ec=\\\"none\\\", fc=self.col_map[d[\\\"name\\\"]], lw=0.5))\\n\\n if a:\\n ax.text((d[\\\"pos\\\"][0] + d[\\\"pos\\\"][1])/2, -0.5, str(d[\\\"name\\\"]), ha=\\\"center\\\", va=\\\"center\\\", fontsize=6, color=\\\"black\\\")\\n else:\\n ax.text((d[\\\"pos\\\"][0] + d[\\\"pos\\\"][1])/2, -0.8, str(d[\\\"name\\\"]), ha=\\\"center\\\", va=\\\"center\\\", fontsize=6, color=\\\"black\\\")\\n a = not a\\n\\n #ax.text((d[\\\"pos\\\"][0] + d[\\\"pos\\\"][1])/2, -0.7, str(d[\\\"db\\\"]), ha=\\\"center\\\", va=\\\"center\\\", fontsize=6, color=\\\"black\\\")\\n\\n ax.text(0, 0.5, \\\"%s (%s amino acids)\\\" % (item[\\\"name\\\"], item[\\\"len\\\"]), color=\\\"black\\\", fontsize=20, ha=\\\"left\\\")\",\n \"def _update_w(self, idx):\\n self.w = ((self._w - 0.4) * (self._generations - idx)) /\\\\\\n (self._generations + 0.4)\",\n \"def refresh(self, data):\\n for region in self.loaded_regions:\\n self.regions[region].unload()\\n super().clear()\\n self.data = data\\n self.loaded_regions = set()\\n self.draw_visible_area()\",\n \"def updateOldDialogItems(data,newdata):\\n warnings.warn(\\\"warn_widgets_updatedialogitems\\\")\\n if newdata:\\n if type(data) is dict:\\n for d in data:\\n updateOldDialogItems(data[d],newdata)\\n else:\\n for d in data:\\n v = newdata.get(d[0],None)\\n if v is not None:\\n d[1] = v\",\n \"def add_point_rate_well(self, injection_rate, cell_index, well_name):\\n self.rate_wells.append(cell_index)\\n self.rate_wells_rate.append(injection_rate)\\n self.rate_wells_name.append(well_name)\\n return len(self.rate_wells)-1\",\n \"def find_index(draw, urn):\\n i = 0\\n while draw >= 0:\\n draw -= urn[i]\\n i+=1\\n return i-1\",\n \"def remove_point(mutated_genome,index):\\n point_index = random.randint(0,max(0,len(mutated_genome[index][2])-1))\\n del mutated_genome[index][2][point_index]\",\n \"def update_level_name(lumpname, bexdata, bexname):\\n\\tif bexname not in bexdata:\\n\\t\\traise Exception('Level name %s not defined in '\\n\\t\\t\\t\\t'DEHACKED lump!' % bexname)\\n\\t# Strip \\\"MAP01: \\\" or \\\"E1M2: \\\" etc. from start, if present:\\n\\tlevelname = re.sub('^\\\\w*\\\\d:\\\\s*', '', bexdata[bexname])\\n\\twhite_graphics[lumpname] = levelname\",\n \"def replace_character_at_index(self, index, character):\\n self._game_board = self.get_game()[:index] + character + self.get_game()[index + 1:]\\n return self._game_board\",\n \"def __getitem__(self, index):\\n ID = self.ID[index]\\n turn_id = self.turn_id[index]\\n turn_belief = self.turn_belief[index]\\n turn_belief_dict = self.turn_belief_dict[index]\\n sorted_domainslots = self.sorted_domainslots[index]\\n turn_uttr = self.turn_uttr[index]\\n context_plain = self.dialog_history[index] \\n sorted_lenval = self.sorted_lenval[index]\\n sorted_in_domains2 = self.sorted_in_domains2[index]\\n sorted_in_slots2 = self.sorted_in_slots2[index]\\n sorted_generate_y = self.sorted_generate_y[index]\\n c = copy.deepcopy\\n context = self.preprocess(context_plain, self.src_word2id)\\n delex_context = None\\n if self.args['delex_his']:\\n temp = self.delex_dialog_history[index].split()\\n original = self.dialog_history[index].split()\\n if self.split == 'train' and 'p_delex_noise' in self.args and np.random.uniform() < self.args['p_delex_noise']:\\n prob = np.random.uniform()\\n if prob < 0.5:\\n indices = [idx for idx,i in enumerate(temp) if len(i.split('-'))==2]\\n if len(indices) > 0:\\n random_idx = random.choice(indices)\\n temp[random_idx] = original[random_idx] # removal \\n else:\\n random_token = random.choice(self.all_slots)\\n out_words = list(self.mem_word2id.keys())[4:]\\n indices = [idx for idx,i in enumerate(original) if i in out_words]\\n if len(indices) > 0:\\n index = random.choice(indices)\\n temp[index] = random_token\\n delex_context = ' '.join(temp)\\n delex_context = self.preprocess(delex_context, self.src_word2id) \\n tag_x, tag_y = None, None\\n if not self.args['sep_input_embedding']:\\n sorted_in_domains = self.preprocess_seq(self.sorted_in_domains[index], self.src_word2id)\\n sorted_in_slots = self.preprocess_seq(self.sorted_in_slots[index], self.src_word2id)\\n sorted_in_domains2 = self.preprocess_seq(sorted_in_domains2, self.src_word2id)\\n sorted_in_slots2 = self.preprocess_seq(sorted_in_slots2, self.src_word2id)\\n else:\\n sorted_in_domains = self.preprocess_seq(self.sorted_in_domains[index], self.domain_word2id)\\n sorted_in_slots = self.preprocess_seq(self.sorted_in_slots[index], self.slot_word2id)\\n sorted_in_domains2 = self.preprocess_seq(sorted_in_domains2, self.domain_word2id)\\n sorted_in_slots2 = self.preprocess_seq(sorted_in_slots2, self.slot_word2id)\\n sorted_in_domainslots2_idx, y_in, y_out = None, None, None\\n if args['auto_regressive']:\\n sorted_in_domainslots2_idx = self.sorted_in_domainslots2_idx[index]\\n y_in, y_out = self.preprocess_atrg_seq(self.atrg_generate_y[index], self.src_word2id) \\n if self.args['pointer_decoder']:\\n sorted_generate_y = self.preprocess_seq(sorted_generate_y, self.src_word2id)\\n else:\\n sorted_generate_y = self.preprocess_seq(sorted_generate_y, self.mem_word2id)\\n sorted_gates = None\\n if self.sorted_gates[index] is not None:\\n sorted_gates = self.sorted_gates[index]\\n user_uttr_plain, user_uttr = None, None\\n turn_prev_bs_plain, turn_prev_bs = None, None\\n \\n item_info = {\\n \\\"ID\\\":ID, \\n \\\"turn_id\\\":turn_id, \\n \\\"turn_belief\\\":turn_belief, #?\\n \\\"context\\\":context,\\n \\\"delex_context_plain\\\": self.delex_dialog_history[index],\\n \\\"delex_context\\\": delex_context,\\n \\\"context_plain\\\":context_plain, \\n \\\"user_uttr\\\": user_uttr,\\n \\\"user_uttr_plain\\\": user_uttr_plain, \\n \\\"sorted_in_domains\\\": sorted_in_domains,\\n \\\"sorted_in_domains2\\\": sorted_in_domains2,\\n \\\"sorted_in_slots\\\": sorted_in_slots,\\n \\\"sorted_in_slots2\\\": sorted_in_slots2,\\n \\\"sorted_in_domainslots2_idx\\\": sorted_in_domainslots2_idx, \\n \\\"sorted_lenval\\\": sorted_lenval,\\n \\\"sorted_gates\\\": sorted_gates,\\n \\\"sorted_generate_y\\\": sorted_generate_y,\\n \\\"y_in\\\": y_in,\\n \\\"y_out\\\": y_out\\n }\\n return item_info\",\n \"def InTheWeedsPlot(OutBase,UnfoldObj,RefoldObj=[],Example=None,\\n Bins=[50,75,100,150,200,500,1000],**kwargs):\\n # get the IWT\\n kT = 4.1e-21\\n for b in Bins:\\n LandscapeObj = InverseWeierstrass.\\\\\\n FreeEnergyAtZeroForce(UnfoldObj,NumBins=b,RefoldingObjs=RefoldObj)\\n # make a 2-D histogram of everything\\n if (Example is not None):\\n fig = PlotUtilities.figure(figsize=(8,8))\\n ext_nm = Example.Separation*1e9\\n IWT_Util.ForceExtensionHistograms(ext_nm,\\n Example.Force*1e12,\\n AddAverage=False,\\n nBins=b)\\n PlotUtilities.savefig(fig,OutBase + \\\"0_{:d}hist.pdf\\\".format(b))\\n # get the distance to the transition state etc\\n print(\\\"DeltaG_Dagger is {:.1f}kT\\\".format(Obj.DeltaGDagger))\\n fig = PlotUtilities.figure(figsize=(12,12))\\n plot_single_landscape(LandscapeObj,add_meta_half=True,\\n add_meta_free=True,**kwargs)\\n PlotUtilities.savefig(fig,OutBase + \\\"1_{:d}IWT.pdf\\\".format(b))\",\n \"def update(\\n self,\\n index: int,\\n party: PartyEntry,\\n sub_scroll_width: float,\\n sub_scroll_height: float,\\n lineheight: float,\\n columnwidget: bui.Widget,\\n join_text: bui.Widget,\\n filter_text: bui.Widget,\\n existing_selection: Selection | None,\\n tab: PublicGatherTab,\\n ) -> None:\\n # pylint: disable=too-many-locals\\n\\n plus = bui.app.plus\\n assert plus is not None\\n\\n # Quick-out: if we've been marked clean for a certain index and\\n # we're still at that index, we're done.\\n if party.clean_display_index == index:\\n return\\n\\n ping_good = plus.get_v1_account_misc_read_val('pingGood', 100)\\n ping_med = plus.get_v1_account_misc_read_val('pingMed', 500)\\n\\n self._clear()\\n hpos = 20\\n vpos = sub_scroll_height - lineheight * index - 50\\n self._name_widget = bui.textwidget(\\n text=bui.Lstr(value=party.name),\\n parent=columnwidget,\\n size=(sub_scroll_width * 0.63, 20),\\n position=(0 + hpos, 4 + vpos),\\n selectable=True,\\n on_select_call=bui.WeakCall(\\n tab.set_public_party_selection,\\n Selection(party.get_key(), SelectionComponent.NAME),\\n ),\\n on_activate_call=bui.WeakCall(tab.on_public_party_activate, party),\\n click_activate=True,\\n maxwidth=sub_scroll_width * 0.45,\\n corner_scale=1.4,\\n autoselect=True,\\n color=(1, 1, 1, 0.3 if party.ping is None else 1.0),\\n h_align='left',\\n v_align='center',\\n )\\n bui.widget(\\n edit=self._name_widget,\\n left_widget=join_text,\\n show_buffer_top=64.0,\\n show_buffer_bottom=64.0,\\n )\\n if existing_selection == Selection(\\n party.get_key(), SelectionComponent.NAME\\n ):\\n bui.containerwidget(\\n edit=columnwidget, selected_child=self._name_widget\\n )\\n if party.stats_addr:\\n url = party.stats_addr.replace(\\n '${ACCOUNT}',\\n plus.get_v1_account_misc_read_val_2(\\n 'resolvedAccountID', 'UNKNOWN'\\n ),\\n )\\n self._stats_button = bui.buttonwidget(\\n color=(0.3, 0.6, 0.94),\\n textcolor=(1.0, 1.0, 1.0),\\n label=bui.Lstr(resource='statsText'),\\n parent=columnwidget,\\n autoselect=True,\\n on_activate_call=bui.Call(bui.open_url, url),\\n on_select_call=bui.WeakCall(\\n tab.set_public_party_selection,\\n Selection(party.get_key(), SelectionComponent.STATS_BUTTON),\\n ),\\n size=(120, 40),\\n position=(sub_scroll_width * 0.66 + hpos, 1 + vpos),\\n scale=0.9,\\n )\\n if existing_selection == Selection(\\n party.get_key(), SelectionComponent.STATS_BUTTON\\n ):\\n bui.containerwidget(\\n edit=columnwidget, selected_child=self._stats_button\\n )\\n\\n self._size_widget = bui.textwidget(\\n text=str(party.size) + '/' + str(party.size_max),\\n parent=columnwidget,\\n size=(0, 0),\\n position=(sub_scroll_width * 0.86 + hpos, 20 + vpos),\\n scale=0.7,\\n color=(0.8, 0.8, 0.8),\\n h_align='right',\\n v_align='center',\\n )\\n\\n if index == 0:\\n bui.widget(edit=self._name_widget, up_widget=filter_text)\\n if self._stats_button:\\n bui.widget(edit=self._stats_button, up_widget=filter_text)\\n\\n self._ping_widget = bui.textwidget(\\n parent=columnwidget,\\n size=(0, 0),\\n position=(sub_scroll_width * 0.94 + hpos, 20 + vpos),\\n scale=0.7,\\n h_align='right',\\n v_align='center',\\n )\\n if party.ping is None:\\n bui.textwidget(\\n edit=self._ping_widget, text='-', color=(0.5, 0.5, 0.5)\\n )\\n else:\\n bui.textwidget(\\n edit=self._ping_widget,\\n text=str(int(party.ping)),\\n color=(0, 1, 0)\\n if party.ping <= ping_good\\n else (1, 1, 0)\\n if party.ping <= ping_med\\n else (1, 0, 0),\\n )\\n\\n party.clean_display_index = index\",\n \"def replace_character_at_index(game, index, character):\\n game = game[:index]+character+game[index+1:]\\n return game\",\n \"def updateWHM(self, hardnessLevel):\\n\\n if hardnessLevel == 1:\\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgreen'])\\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMgray'])\\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMgray'])\\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMgray'])\\n\\n\\n elif hardnessLevel == 2:\\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMyellow'])\\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgray'])\\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMgray'])\\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMgray'])\\n\\n elif hardnessLevel == 3:\\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMorange'])\\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgray'])\\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMgray'])\\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMgray'])\\n\\n elif hardnessLevel == 4:\\n self.labelLevelFourWHM.setPixmap(self.images['imageLevelFourWHMred'])\\n self.labelLevelOneWHM.setPixmap(self.images['imageLevelOneWHMgray'])\\n self.labelLevelTwoWHM.setPixmap(self.images['imageLevelTwoWHMgray'])\\n self.labelLevelThreeWHM.setPixmap(self.images['imageLevelThreeWHMgray'])\\n\\n self.outcomesWHM = np.append(self.outcomesWHM, hardnessLevel)\\n\\n if self.outcomesWHM.size > 0:\\n elapsed = self.tmr.elapsed() / float(1000)\\n self.save_file = open(os.path.join(args.parent_img_path, self.save_file_name), \\\"a\\\")\\n self.save_file.write(\\\"Time Elapsed: (\\\" + str(elapsed) + \\\" sec) - Hardness Level: \\\"+str(hardnessLevel)+\\\",\\\\n\\\")\\n self.save_file.close()\\n self.dataWHM.append({'x': elapsed, 'y': hardnessLevel})\\n self.WHMx = [item['x'] for item in self.dataWHM]\\n self.WHMy = [item['y'] for item in self.dataWHM]\\n self.WHMx = np.asarray(self.WHMx).flatten()\\n self.WHMy = np.asarray(self.WHMy).flatten()\\n self.curveWHMGraph.setData(x=self.WHMx, y=self.WHMy)\\n\\n # Stuff for Verbose - Helpful for debugging\\n if self.setPointsWHM.verbose > 0:\\n if self.setPointsWHM.verbose > 0:\\n print 'Receive Time: {:0.6f}ms | Pre-process + SVM Run Time: {:0.6f}ms | Total Run Time: {:0.6f}ms'.format(\\n (self.setPointsWHM.algo_time - self.setPointsWHM.receive_time) * 1000.,\\n (self.setPointsWHM.final_time - self.setPointsWHM.algo_time) * 1000.,\\n (time.time() - self.showtime) * 1000.)\\n # reset Showtime\\n self.showtime = time.time()\\n # print ' Total Showtime: {:0.6f}ms \\\\n \\\\n'.format((time.time() - self.showtime) * 1000.)\",\n \"def shift_point_wline(mutated_genome,index):\\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\\n point_index = random.randint(1,max(1,len(mutated_genome[index][2])-1))\\n point = mutated_genome[index][2][point_index]\\n newpoint = (point[0]+Xval,point[1]+Yval)\\n mutated_genome[index][2][point_index] = newpoint\",\n \"def remove_index_from_word(self,word,index):\\r\\n\\r\\n # with shelf\\r\\n if self.using_shelf:\\r\\n\\r\\n if word in self.word_dict:\\r\\n\\r\\n self.word_dict[word].remove(str(index))\\r\\n\\r\\n\\r\\n #with database\\r\\n if self.using_database:\\r\\n\\r\\n value_tuple = (notebookname,word,str(index),)\\r\\n db_cursor.execute(\\\"DELETE FROM\\\"\\r\\n +\\\" word_to_indexes\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND word=?\\\"\\r\\n +\\\" AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n db_cursor.execute(\\\"SELECT * FROM word_to_indexes\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" and word=?;\\\",\\r\\n value_tuple[0:2])\\r\\n if db_cursor.fetchone():\\r\\n db_cursor.execute(\\\"DELETE FROM\\\"\\r\\n +\\\" all_words\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND word=?;\\\",\\r\\n value_tuple[0:2])\",\n \"def get_stripe_blanker(feat_idx, stripe_idx):\\n from_idx = 0\\n for f in range(feat_idx):\\n from_idx += features.sizes.get(f, 1)\\n for s_idx, (step, start, stop) in enumerate(features.stripes[feat_idx]):\\n if step is None:\\n width = 1\\n else:\\n width = int(round((stop-start) / step + 1))\\n if s_idx < stripe_idx:\\n from_idx += width\\n else:\\n to_idx = from_idx + width\\n break\\n\\n def _blanker(features):\\n# log.info(\\\"Blanker from={from_idx}, to={to_idx}\\\".format(from_idx=from_idx, to_idx=to_idx))\\n# log.info(\\\"Before: \\\" + str(features.nonzero()))\\n features[from_idx:to_idx] = 0.0\\n# log.info(\\\"After: \\\" + str(features.nonzero()))\\n return features\\n\\n return _blanker\",\n \"def process_non_menu_frame(w_indx: int) -> None:\\n for nmi in list(non_menu_frame_widgets.keys()):\\n if nmi == w_indx:\\n v = non_menu_frame_widgets[nmi]\\n for v_wid in v:\\n print(c.BRIGHT_WHITE + '· ' + '│ ' * v_wid.get_frame_depth()\\n + c.ENDC + widget_terminal_title(v_wid))\\n del non_menu_frame_widgets[nmi]\",\n \"def test_sweat_index():\\n pressure = np.array([1008., 1000., 947., 925., 921., 896., 891., 889., 866.,\\n 858., 850., 835., 820., 803., 733., 730., 700., 645.,\\n 579., 500., 494., 466., 455., 441., 433., 410., 409.,\\n 402., 400., 390., 388., 384., 381., 349., 330., 320.,\\n 306., 300., 278., 273., 250., 243., 208., 200., 196.,\\n 190., 179., 159., 151., 150., 139.]) * units.hPa\\n temperature = np.array([27.4, 26.4, 22.9, 21.4, 21.2, 20.7, 20.6, 21.2, 19.4,\\n 19.1, 18.8, 17.8, 17.4, 16.3, 11.4, 11.2, 10.2, 6.1,\\n 0.6, -4.9, -5.5, -8.5, -9.9, -11.7, -12.3, -13.7, -13.8,\\n -14.9, -14.9, -16.1, -16.1, -16.9, -17.3, -21.7, -24.5, -26.1,\\n -28.3, -29.5, -33.1, -34.2, -39.3, -41., -50.2, -52.5, -53.5,\\n -55.2, -58.6, -65.2, -68.1, -68.5, -72.5]) * units.degC\\n dewpoint = np.array([24.9, 24.6, 22., 20.9, 20.7, 14.8, 13.6, 12.2, 16.8,\\n 16.6, 16.5, 15.9, 13.6, 13.2, 11.3, 11.2, 8.6, 4.5,\\n -0.8, -8.1, -9.5, -12.7, -12.7, -12.8, -13.1, -24.7, -24.4,\\n -21.9, -24.9, -36.1, -31.1, -26.9, -27.4, -33., -36.5, -47.1,\\n -31.4, -33.5, -40.1, -40.8, -44.1, -45.6, -54., -56.1, -56.9,\\n -58.6, -61.9, -68.4, -71.2, -71.6, -77.2]) * units.degC\\n speed = np.array([0., 3., 10., 12., 12., 14., 14., 14., 12.,\\n 12., 12., 12., 11., 11., 12., 12., 10., 10.,\\n 8., 5., 4., 1., 0., 3., 5., 10., 10.,\\n 11., 11., 13., 14., 14., 15., 23., 23., 24.,\\n 24., 24., 26., 27., 28., 30., 25., 24., 26.,\\n 28., 33., 29., 32., 26., 26.]) * units.knot\\n direction = np.array([0., 170., 200., 205., 204., 200., 197., 195., 180.,\\n 175., 175., 178., 181., 185., 160., 160., 165., 165.,\\n 203., 255., 268., 333., 0., 25., 40., 83., 85.,\\n 89., 90., 100., 103., 107., 110., 90., 88., 87.,\\n 86., 85., 85., 85., 60., 55., 60., 50., 46.,\\n 40., 45., 35., 50., 50., 50.]) * units.degree\\n\\n sweat = sweat_index(pressure, temperature, dewpoint, speed, direction)\\n assert_almost_equal(sweat, 227., 2)\",\n \"def reformatSagittalView4Needle(self, ID):\\r\\n # productive #onButton #report\\r\\n profprint()\\r\\n for i in range(2): # workaround update problem\\r\\n modelNode = slicer.util.getNode('vtkMRMLModelNode' + str(ID))\\r\\n polyData = modelNode.GetPolyData()\\r\\n nb = polyData.GetNumberOfPoints()\\r\\n base = [0, 0, 0]\\r\\n tip = [0, 0, 0]\\r\\n polyData.GetPoint(nb - 1, tip)\\r\\n polyData.GetPoint(0, base)\\r\\n a, b, c = tip[0] - base[0], tip[1] - base[1], tip[2] - base[2]\\r\\n # print a,b,c\\r\\n sYellow = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNodeYellow\\\")\\r\\n if sYellow == None :\\r\\n sYellow = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNode2\\\")\\r\\n reformatLogic = slicer.vtkSlicerReformatLogic()\\r\\n sYellow.SetSliceVisible(1)\\r\\n reformatLogic.SetSliceNormal(sYellow, 1, -a / b, 0)\\r\\n m = sYellow.GetSliceToRAS()\\r\\n m.SetElement(0, 3, base[0])\\r\\n m.SetElement(1, 3, base[1])\\r\\n m.SetElement(2, 3, base[2])\\r\\n sYellow.Modified()\",\n \"def test_plot_wiggle_traces(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n splt = SEGYPlotter(ax, self.segy)\\n # should add a single artist to the active line dict.\\n splt.plot_wiggles(wiggle_traces=True)\\n self.assertEqual(len(splt.ACTIVE_LINES['wiggle_traces']),1)\\n # same artist should be in the axes list\\n self.assertTrue(splt.ACTIVE_LINES['wiggle_traces'][0] in \\\\\\n splt.ax.lines)\\n # should be able to directly remove it\\n splt.ax.lines.remove(splt.ACTIVE_LINES['wiggle_traces'][0])\\n self.assertEqual(len(splt.ax.lines), 0)\\n # and re-add it\\n splt.ax.lines.append(splt.ACTIVE_LINES['wiggle_traces'][0])\\n self.assertEqual(len(splt.ax.lines), 1)\\n # should move artist to the inactive line dict.\\n splt.plot_wiggles(wiggle_traces=False)\\n self.assertEqual(len(splt.INACTIVE_LINES['wiggle_traces']),1)\\n self.assertEqual(len(splt.ax.lines), 0)\\n # calling again should change nothing\\n splt.plot_wiggles(wiggle_traces=False)\\n self.assertEqual(len(splt.INACTIVE_LINES['wiggle_traces']),1)\\n self.assertEqual(len(splt.ax.lines), 0)\\n # should move artist back to active line dict and re-add to axes\\n splt.plot_wiggles(wiggle_traces=True)\\n self.assertEqual(len(splt.ACTIVE_LINES['wiggle_traces']),1)\\n self.assertEqual(len(splt.ax.lines), 1)\\n # new plot items should be accessible through the orig. axes\\n self.assertEqual(len(splt.ax.lines),\\n len(ax.lines))\",\n \"def setBooked(self, data, itemWidget):\\n # itemWidget = self.findItemWidgetFromDate(data)\\n styleSheet = \\\"\\\"\\n # try:\\n for plat in self.datePrenotazioni['platforms'].keys():\\n if data in self.datePrenotazioni['platforms'][plat]['date']:\\n color = self.colors[plat]\\n r, g, b = self.reformatColor(color)\\n styleSheet = f\\\"background-color: rgba({r},{g},{b},150)\\\"\\n break\\n # except KeyError:\\n # print('remove try/exc')\\n # print('setbooked ' + styleSheet)\\n itemWidget.lab_num.setStyleSheet(styleSheet)\",\n \"def update_which_sde_data(\\n current_sde_df,\\n latest_esi_df,\\n index_key\\n):\\n pass\",\n \"def upd_view():\\n global state, current_view, show_actives, current_filename\\n nonlocal img, listbox\\n\\n with data_lock:\\n if state == 1:\\n current_filename = None\\n state = 2\\n listbox.delete(0, tk.END)\\n for item in current_view.actions.by_type(wtl.actions.Click):\\n wtl_uid = str(item.target.wtl_uid)\\n text = item.target.metadata[\\\"text\\\"]\\n listbox.insert(tk.END, wtl_uid + f\\\" ({text})\\\")\\n\\n if state == 2:\\n filename = \\\"first\\\" if show_actives.get() == 0 else \\\"is_active\\\"\\n if filename != current_filename:\\n current_filename = filename\\n current_view.snapshot.screenshots[filename].save(Path(\\\".\\\"))\\n img = tk.PhotoImage(file=f\\\"{filename}.png\\\")\\n canvas.create_image(5, 5, anchor=tk.NW, image=img)\\n\\n window.after(250, upd_view)\",\n \"def resting(self):\\n self.image = self.image_list[self.index]\\n\\n if self.rect.y % 32 != 0:\\n self.correct_position(self.rect.y)\\n if self.rect.x % 32 != 0:\\n self.correct_position(self.rect.x)\",\n \"def remove_region(data, i):\\n x = data.shape[0]\\n y = data.shape[1]\\n\\n data = data.reshape(x * y)\\n idx = np.where(data == i)[0]\\n data[idx] = 0\\n data = data.reshape(x, y)\\n\\n return data\",\n \"def reflect_list(wall_1side_list, inout_strands, inout_strands_index, p):\\n if len(wall_1side_list) == ((p-1)/2)+1: # base casep\\n return wall_1side_list # return\\n else:\\n if inout_strands_index == 0:\\n wall_color = wall_1side_list[-1] # bottom universe at crossing\\n # ,inout_strands, 1, p)\\n wall_1side_list.append(reflect(wall_color, inout_strands[inout_strands_index], p))\\n return reflect_list(wall_1side_list, inout_strands, 1, p)\\n else:\\n wall_color = wall_1side_list[-1]\\n # ,inout_strands, 1, p)\\n wall_1side_list.append(reflect(wall_color, inout_strands[inout_strands_index], p))\\n return reflect_list(wall_1side_list, inout_strands, 0, p)\",\n \"def drawScrubber(self):\\r\\n self.clearScrubber()\\r\\n x = self.plot(self.progress,0)[0]\\r\\n self.scrubber = [self.c.create_rectangle(x-3,0,x+3,6,fill=\\\"#000000\\\",tags=(\\\"scrubber\\\"))]# Scrubber head\\r\\n self.scrubber.append(self.c.create_line(x,0,x,self.h,fill=\\\"#000000\\\",tags=(\\\"scrubber\\\")))# Scrubber line\\r\\n t = \\\"-\\\"*(self.progress<0) +str(datetime.timedelta(seconds=abs(round(self.progress))))\\r\\n self.scrubber.append(self.c.create_text(x+3,0,text=str(t),anchor=tk.NW,tags=(\\\"scrubber\\\")))# Timestamp\\r\\n if not self.sensors or self.sensors == []:\\r\\n return\\r\\n if self.progress > 0:\\r\\n col = self.app.getSensorCol(self.sensors[self.sensor_ids[0]])\\r\\n self.scrubber.append(self.c.create_text(x+3,10,text=str(self.getData(self.sensor_ids[0],self.progress)),\\\\\\r\\n fill=col,anchor=tk.NW,tags=(\\\"scrubber\\\")))# Red track value\\r\\n if len(self.sensor_ids) == 2:# If second track exists\\r\\n col = self.app.getSensorCol(self.sensors[self.sensor_ids[1]])\\r\\n self.scrubber.append(self.c.create_text(x+3,20,text=str(self.getData(self.sensor_ids[1],self.progress)),fill=col,anchor=tk.NW,tags=(\\\"scrubber\\\")))\",\n \"def setSpecificIndices(\\n self,\\n indicesOfFreeBetas,\\n indicesOfFixedBetas,\\n indicesOfRandomVariables,\\n indicesOfDraws,\\n ):\\n if self.name in indicesOfDraws:\\n self.drawId = indicesOfDraws[self.name]\\n else:\\n error_msg = (\\n f'No index is available for draw {self.drawType}.'\\n f' Known types of draws: {indicesOfDraws.keys()}'\\n )\\n raise excep.biogemeError(error_msg)\",\n \"def updateWidget(self, name, pos, value):\\n try:\\n self.widgets[\\\"%s%d.entry\\\" % (name, pos)].delete(0,'end')\\n self.widgets[\\\"%s%d.entry\\\" % (name, pos)].insert(0,value)\\n except: pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5802891","0.53335387","0.53131515","0.52770084","0.52692586","0.51205266","0.50659335","0.49722874","0.49578884","0.4950951","0.48292145","0.48174497","0.48127028","0.47716764","0.47540691","0.47284764","0.47264978","0.46799114","0.4676532","0.46628883","0.465863","0.4635999","0.46345836","0.46217406","0.46051663","0.45935473","0.45867634","0.45863822","0.45850134","0.4575866","0.4569088","0.45635","0.45589805","0.4554976","0.4549253","0.45457104","0.4542184","0.45409936","0.45357424","0.45331183","0.45295858","0.45192093","0.45159876","0.45067534","0.45002598","0.4497688","0.4495362","0.44848493","0.44688007","0.44666895","0.4465024","0.44608074","0.44487694","0.44478464","0.44340792","0.44333032","0.4427848","0.44263655","0.44205183","0.44199628","0.44131917","0.4407606","0.44061396","0.44010842","0.43994436","0.4399158","0.43978947","0.4392027","0.43858486","0.43789127","0.43757242","0.43628046","0.4360911","0.4358841","0.43554175","0.43491706","0.43426475","0.43421206","0.43417397","0.43351138","0.43349835","0.43337926","0.43278074","0.43225455","0.43220544","0.43201834","0.43197092","0.43183392","0.43174043","0.43105915","0.43080652","0.43079987","0.43035844","0.4303313","0.43031827","0.42984563","0.42961803","0.42942646","0.42938328","0.4293241"],"string":"[\n \"0.5802891\",\n \"0.53335387\",\n \"0.53131515\",\n \"0.52770084\",\n \"0.52692586\",\n \"0.51205266\",\n \"0.50659335\",\n \"0.49722874\",\n \"0.49578884\",\n \"0.4950951\",\n \"0.48292145\",\n \"0.48174497\",\n \"0.48127028\",\n \"0.47716764\",\n \"0.47540691\",\n \"0.47284764\",\n \"0.47264978\",\n \"0.46799114\",\n \"0.4676532\",\n \"0.46628883\",\n \"0.465863\",\n \"0.4635999\",\n \"0.46345836\",\n \"0.46217406\",\n \"0.46051663\",\n \"0.45935473\",\n \"0.45867634\",\n \"0.45863822\",\n \"0.45850134\",\n \"0.4575866\",\n \"0.4569088\",\n \"0.45635\",\n \"0.45589805\",\n \"0.4554976\",\n \"0.4549253\",\n \"0.45457104\",\n \"0.4542184\",\n \"0.45409936\",\n \"0.45357424\",\n \"0.45331183\",\n \"0.45295858\",\n \"0.45192093\",\n \"0.45159876\",\n \"0.45067534\",\n \"0.45002598\",\n \"0.4497688\",\n \"0.4495362\",\n \"0.44848493\",\n \"0.44688007\",\n \"0.44666895\",\n \"0.4465024\",\n \"0.44608074\",\n \"0.44487694\",\n \"0.44478464\",\n \"0.44340792\",\n \"0.44333032\",\n \"0.4427848\",\n \"0.44263655\",\n \"0.44205183\",\n \"0.44199628\",\n \"0.44131917\",\n \"0.4407606\",\n \"0.44061396\",\n \"0.44010842\",\n \"0.43994436\",\n \"0.4399158\",\n \"0.43978947\",\n \"0.4392027\",\n \"0.43858486\",\n \"0.43789127\",\n \"0.43757242\",\n \"0.43628046\",\n \"0.4360911\",\n \"0.4358841\",\n \"0.43554175\",\n \"0.43491706\",\n \"0.43426475\",\n \"0.43421206\",\n \"0.43417397\",\n \"0.43351138\",\n \"0.43349835\",\n \"0.43337926\",\n \"0.43278074\",\n \"0.43225455\",\n \"0.43220544\",\n \"0.43201834\",\n \"0.43197092\",\n \"0.43183392\",\n \"0.43174043\",\n \"0.43105915\",\n \"0.43080652\",\n \"0.43079987\",\n \"0.43035844\",\n \"0.4303313\",\n \"0.43031827\",\n \"0.42984563\",\n \"0.42961803\",\n \"0.42942646\",\n \"0.42938328\",\n \"0.4293241\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5534069"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":75792,"cells":{"query":{"kind":"string","value":"Set well or piezometer parameters"},"document":{"kind":"string","value":"def set_parameters(self, full=None, r=None, l=None, d=None, z=None):\n\n original = _deepcopy(self.parameters) # save in case of error\n\n if type(full) is bool:\n self.parameters[\"full\"] = full\n if type(r) in [int, float]:\n self.parameters[\"r\"] = float(r)\n if self._type == 2: # observation well\n if type(d) in [int, float]:\n self.parameters[\"d\"] = float(d)\n if type(l) in [int, float]:\n self.parameters[\"l\"] = float(l)\n else: # piezometer\n if type(z) in [int, float]:\n self.parameters[\"z\"] = float(z)\n\n flag, message = self.validate_parameters()\n if not flag:\n print(message)\n self.parameters.update(original)\n # End Function"},"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 setParameters(self):\n\n # Set the parameters\n self.taux = 24.2\n self.mu = 0.23\n self.G = 33.75\n self.alpha_0 = 0.05\n self.delta = 0.0075\n self.p = 0.50\n self.I0 = 9500.0\n self.kparam = 0.55","def set_parameters(self,params):\n K3Supervisor.set_parameters(self,params)\n self.gtg.set_parameters(self.parameters)\n self.avoidobstacles.set_parameters(self.parameters)\n self.wall.set_parameters(self.parameters)","def _set_leg_params(self):\n self.p = 0.01600\n self.q = 0.00000\n self.r = 0.02000\n self.c = 0.01811\n self.u = 0.00000\n self.v = 0.00000\n self.e = -0.06000\n self.h = -0.02820\n self.s = 0.02200\n self.d1 = 0.0\n self.d2 = 0.0\n self.d3 = 0.0\n self.stability = 0.0","def set_parameters(self,params):\n K3Supervisor.set_parameters(self,params)\n self.blending.set_parameters(self.parameters)","def set_param_motor():\n servo.setSpeed(0, 0) # max = 255\n servo.setAccel(0, 0)\n servo.setSpeed(1, 150) # max = 255\n servo.setAccel(1, 150)","def set_params(self, params_):\n x_start, x_end = params_[\"lim_fit\"]\n self.find_idx_of_fit_limit(x_start, x_end)\n self.is_error_bar_for_fit = params_[\"use_error_bar\"]\n self.fitting_method1 = params_[\"method1\"]\n self.fitting_method2 = params_[\"method2\"]\n self.qty_to_min = params_[\"qty_to_min\"]\n\n for i, key in enumerate(self.params):\n # self.params[key].set(value=params_[\"val\"][i], min=params_[\"min\"][i], max=params_[\"max\"][i], vary=bool(params_[\"hold\"][i]), brute_step=params_[\"brute_step\"][i])\n if self.params[key].user_data is not None:\n if \"dontGenerate\" in self.params[key].user_data:\n continue\n self.params[key].set(value=params_[key][\"value\"], min=params_[key][\"min\"], max=params_[key][\"max\"], vary=params_[key][\"vary\"], brute_step=params_[key][\"b_step\"])","def set_physical_params(self, params):\n self.M500 = params[0]\n self.r500 = params[1]\n self.z = params[2]","def set_params(self, params: Dict) -> None:\n self.leak.set_g(params[\"g_leak\"])\n self.kvhh.set_g(params[\"g_kvhh\"])\n self.cav.set_g(params[\"g_cav\"])\n self.kca.set_g(params[\"g_kca\"])\n self.nap.set_g(params[\"g_nap\"])\n self.tau_ca = params[\"t_ca\"]","def set_params(self):\n \n lo, hi = self.R.get((self.h, self.w, self.m), (0.0, 0.0))\n params.update({\n 'gamma' : 1.0, # minesweeper is a finite horizon game\n 'epsilon': 0.0,\n 'K': 16,\n 'R_lo': lo,\n 'R_hi': hi,\n 'max_depth': self.h * self.w / 2,\n 'c':hi-lo\n })","def init_params(self):\n self.params = Parameters()\n self.params.add('qoff', self.qoff, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('yscale', self.yscale, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('int_bg', self.int_bg, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('Rc', self.Rc, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('sur_den', self.sur_den, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('ion_depth', self.ion_depth, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)","def set_params(self, params: Dict) -> None:\n self.leak.set_g(params['g_leak'])\n self.nav.set_g(params['g_nav'])\n self.kvhh.set_g(params['g_kvhh'])\n self.kva.set_g(params['g_kva'])\n self.kvsi.set_g(params['g_kvsi'])\n self.cav.set_g(params['g_cav'])\n self.kca.set_g(params['g_kca'])\n self.nap.set_g(params['g_nap'])\n self.kir.set_g(params['g_kir'])\n self.ampar.set_g(params['g_ampar'])\n self.nmdar.set_g(params['g_nmdar'])\n self.gabar.set_g(params['g_gabar'])\n self.tau_ca = params['t_ca']","def updateParameters(self):\n\n if self.params[1].value:\n if arcpy.Exists(self.params[1].value):\n try:\n min_value = arcpy.GetRasterProperties_management(self.params[1].value, \"MINIMUM\")[0]\n\n if str(self.params[8].value) != str(self.params[1].value):\n self.params[7].value = True\n self.params[8].value = str(self.params[1].value)\n else:\n self.params[7].value = False\n\n if str(min_value) == \"0\":\n if self.params[7].value == True:\n self.params[2].value = True\n self.params[3].enabled = True\n self.params[7].value = False\n else:\n self.params[2].value = False\n self.params[3].enabled = False\n\n except arcpy.ExecuteError:\n pass\n\n if self.params[2].value == True:\n self.params[3].enabled = True\n else:\n self.params[3].enabled = False","def _setup_params(self) -> None:\n self.i = 0 # Year\n self.ela = self.ela_start # Equilibrium line altitude\n self.steady_state = False # Control variable for steady state\n self.fracd8_mode = \"limited\" # Mode of the fracd8 algorithm","def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\"DFTCL_settings\"):\n\n cl_settings = {\"ISPIN\": 2, \"MAGMOM\": magmom, \"SAXIS\": None, \"LSORBIT\": None, \"LNONCOLLINEAR\": None}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIMX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"encut\", encut)","def set_params(self, **params):\n if('threshold' in params.keys()):\n self.threshold = params['threshold']\n if('subsample' in params.keys()):\n self.subsample = params['subsample']\n if('estimator' in params.keys()):\n self.estimator = params['estimator']\n if('n_folds' in params.keys()):\n self.n_folds = params['n_folds']\n if('stratify' in params.keys()):\n self.stratify = params['stratify']\n if('random_state' in params.keys()):\n self.random_state = params['random_state']\n if('n_jobs' in params.keys()):\n self.n_jobs = params['n_jobs']","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rbk[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd_emu[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rbk,Rdy,Rqcd_emu\")","def setParameters(self, sx_sim=None):\n # TODO rething that ..\n #if sx_sim is not None:\n #if ds_model is not None:\n #if di_model is not None:\n self.sx_sim = sx_sim\n p = defaultParams(chord=self._chord, rho=self._rho, sx=self.sx_sim, ds=self.ds_model, di=self.di_model,\n M=self._M33, C=self._C33, K=self._K33)\n p['beta'] = self._beta\n if len(p['Iq'])==0:\n raise Exception('No states are present')\n\n # --- Dynamic inflow / induction\n p['a0'] = self._a0\n p['ap0'] = self._ap0\n p['di_tau1'] = self.di_tau1\n p['di_tau2'] = self.di_tau2\n\n # --- Aerodynamic parameters\n if self._y_AQ>0: \n print('[WARN] y_AQ positive is unconventional')\n p['y_AQ'] = self._y_AQ\n if self._y_AT is None:\n p['y_AT'] = self._y_AQ+self._chord/2 # default is approximatively half a chord behind\n else:\n p['y_AT'] = self._y_AT\n p['x_AQ'] = self._x_AQ\n p['x_AT'] = self._x_AT\n if self._ppol is None:\n raise Exception('Polar parameters need to be set')\n p.update(self._ppol)\n # # p.update({'linModel':False, 'drag':drag})\n\n self.p_sim = p","def init_paramters(self):\r\n carb_bg_ratio = 5.0\r\n time_to_breakdown = 45.0\r\n insulin_bg_ratio = 50.0\r\n time_to_peak = 45.0\r\n basal_rate = 0.0\r\n digestion_speed = 1.0\r\n activation_speed = 1.0\r\n\r\n # set state to initial\r\n self.S = [self.carb_bg_ratio, self.time_to_breakdown,\r\n self.insulin_bg_ratio, self.time_to_peak,\r\n self.basal_rate, self.digestion_speed,\r\n self.activation_speed]","def initialize_dynamic_settings(self):\n self.ship_speed_factor = 1.5\n self.bullet_speed_factor = 3\n self.alien_speed_factor = 1\n self.fleet_direction = 1\n #Puntos\n self.alien_points = 50","def temphum_plot(self, kwargs=None):\n\n def valuechange():\n \"\"\"This is the function which is called, when a value is changed in the spin boxes\"\"\"\n\n tempmin.setMaximum(tempmax.value())\n tempmax.setMinimum(tempmin.value())\n hummin.setMaximum(hummax.value())\n hummax.setMinimum(hummin.value())\n\n self.variables.default_values_dict[\"settings\"][\n \"current_tempmin\"\n ] = tempmin.value()\n self.variables.default_values_dict[\"settings\"][\n \"current_tempmax\"\n ] = tempmax.value()\n self.variables.default_values_dict[\"settings\"][\n \"current_hummin\"\n ] = hummin.value()\n self.variables.default_values_dict[\"settings\"][\n \"current_hummax\"\n ] = hummax.value()\n\n max = build_command(\n self.variables.devices_dict[\"temphum_controller\"],\n (\"set_hummax\", hummax.value()),\n )\n min = build_command(\n self.variables.devices_dict[\"temphum_controller\"],\n (\"set_hummin\", hummin.value()),\n )\n\n self.variables.vcw.write(\n self.variables.devices_dict[\"temphum_controller\"], max\n )\n self.variables.vcw.write(\n self.variables.devices_dict[\"temphum_controller\"], min\n )\n\n def dry_air_action():\n if dry_air_btn.isChecked():\n device_dict = self.variables.devices_dict[\"temphum_controller\"]\n try:\n command = build_command(\n device_dict, (\"set_environement_control\", \"ON\")\n )\n answer = self.variables.vcw.write(device_dict, command)\n if not answer:\n self.log.error(\n \"The environement controller did not responsed accordingly. Answer: \"\n + str(answer).strip()\n )\n return 0\n except:\n self.log.error(\n \"An error occured while changing the environement control\"\n )\n return 0\n dry_air_btn.setText(\"Humidity ctl. on\")\n self.variables.default_values_dict[\"settings\"][\n \"humidity_control\"\n ] = True\n\n else:\n device_dict = self.variables.devices_dict[\"temphum_controller\"]\n try:\n command = build_command(\n device_dict, (\"set_environement_control\", \"OFF\")\n )\n answer = self.variables.vcw.write(device_dict, command)\n if not answer:\n self.log.error(\n \"The environement controller did not responsed accordingly. Answer: \"\n + str(answer).strip()\n )\n\n return 0\n except:\n self.log.error(\n \"An error occured while changing the environement control\"\n )\n return 0\n dry_air_btn.setText(\"Humidity ctl. off\")\n self.variables.default_values_dict[\"settings\"][\n \"humidity_control\"\n ] = False\n\n def light_action():\n \"\"\"This function is debricated\"\"\"\n if light_btn.isChecked():\n self.variables.default_values_dict[\"settings\"][\"external_lights\"] = True\n else:\n self.variables.default_values_dict[\"settings\"][\n \"external_lights\"\n ] = False\n\n def check_light_state():\n if (\n self.variables.default_values_dict[\"settings\"][\"lights\"]\n and not light_btn.text() == \"Lights on\"\n ): # Checks if the lights are on and the button is off\n light_btn.setText(\"Lights on\")\n light_btn.setStyleSheet(\"background : rgb(0,255,0); border-radius: 5px\")\n elif (\n not self.variables.default_values_dict[\"settings\"][\"lights\"]\n and not light_btn.text() == \"Lights off\"\n ):\n light_btn.setText(\"Lights off\")\n light_btn.setStyleSheet(\"background : rgb(255,0,0); border-radius: 5px\")\n\n def config_plot(plot, plot2, pg):\n plot = plot.plotItem\n plot.setLabel(\"right\", \"humidity\", units=\"%\")\n plot.setLabel(\"bottom\", \"time\")\n plot.setLabel(\"left\", \"temperature\", units=\"Celsius\")\n plot.getAxis(\"left\").setPen(pg.mkPen(color=\"#c4380d\", width=3))\n plot.getAxis(\"right\").setPen(pg.mkPen(color=\"#025b94\", width=3))\n plot.showAxis(\"top\", show=True)\n plot.getAxis(\"top\").setTicks([])\n plot.getAxis(\"bottom\").setScale(1e-9)\n # plot.setRange(yRange=[15, 35])\n\n # For second plot\n plot.scene().addItem(\n plot2\n ) # inserts the second plot into the scene of the first\n plot2.setGeometry(plot.vb.sceneBoundingRect())\n plot.getAxis(\"right\").linkToView(\n plot2\n ) # links the second y axis to the second plot\n plot2.setXLink(plot) # sync the x axis of both plots\n # plot2.setRange(yRange=[0, 50])\n\n def __cut_arrays(data_array, maximum_time, arrays_to_cut):\n \"\"\"This function cuts an array to a maximum time difference\n This function is supposed to be used only for temp and humidity shaped arrays\n \"\"\"\n\n try:\n begin_time = data_array[arrays_to_cut[0]][0][0]\n end_time = data_array[arrays_to_cut[0]][0][-1]\n delta_time = (\n data_array[arrays_to_cut[0]][0][1]\n - data_array[arrays_to_cut[0]][0][0]\n )\n total_time = end_time - begin_time\n if total_time > maximum_time:\n over_time = total_time - maximum_time\n array_elm_to_drop = int(over_time / delta_time)\n for arrays in arrays_to_cut:\n data_array[arrays][0] = data_array[arrays][0][\n array_elm_to_drop:\n ]\n data_array[arrays][1] = data_array[arrays][1][\n array_elm_to_drop:\n ]\n except:\n pass\n\n def update_temphum_plots(kwargs=None):\n # for rooms in self.rooms:\n if self.variables.default_values_dict[\"settings\"][\"new_data\"]:\n temphum_plot.clear() # clears the plot and prevents a memory leak\n hum_plot_obj.clear()\n p1 = temphum_plot.plotItem\n\n ax = p1.getAxis(\"bottom\") # This is the trick\n __cut_arrays(\n self.variables.meas_data,\n float(\n self.variables.default_values_dict[\"settings\"].get(\n \"temp_history\", 3600\n )\n ),\n [\"temperature\", \"humidity\"],\n )\n ax.setTicks(\n [\n get_thicks_for_timestamp_plot(\n self.variables.meas_data[\"temperature\"][0],\n 5,\n self.variables.default_values_dict[\"settings\"][\n \"time_format\"\n ],\n )\n ]\n )\n\n try:\n if len(self.variables.meas_data[\"temperature\"][0]) == len(\n self.variables.meas_data[\"humidity\"][1]\n ): # sometimes it happens that the values are not yet ready\n p1.plot(\n self.variables.meas_data[\"temperature\"][0],\n self.variables.meas_data[\"temperature\"][1],\n pen={\"color\": \"r\", \"width\": 2},\n clear=True,\n )\n plot_item = setpg.PlotCurveItem(\n self.variables.meas_data[\"humidity\"][0],\n self.variables.meas_data[\"humidity\"][1],\n pen={\"color\": \"b\", \"width\": 2},\n clear=True,\n )\n hum_plot_obj.addItem(plot_item)\n del plot_item # the plot class needs a plot item which can be rendered, to avoid a mem leak delete the created plot item or 20k ram will be used\n # hum_plot_obj.addItem(setpg.plot(self.variables.meas_data[\"humidity\"][0],self.variables.meas_data[\"humidity\"][1],pen={'color': \"b\", 'width': 2}, clear=True))\n hum_plot_obj.setGeometry(\n p1.vb.sceneBoundingRect()\n ) # resize the second plot!\n except:\n pass\n\n # Create sublayout\n temphum_layout = QGridLayout()\n\n # Frame over the objects\n frame = QLabel()\n frame.setFrameStyle(QFrame.Box | QFrame.Raised)\n frame.setLineWidth(0)\n frame.setMidLineWidth(2)\n\n self.layout.addWidget(\n frame, self.temp_ypos, self.temp_xpos, self.temp_ysize, self.temp_xsize\n )\n\n x = np.zeros(1)\n y = np.zeros(1)\n\n setpg = pq\n # date_axis = CAxisTime(orientation='bottom') # Correctly generates the time axis\n hum_plot_obj = setpg.ViewBox() # generate new plot item\n temphum_plot = pq.PlotWidget()\n config_plot(temphum_plot, hum_plot_obj, setpg) # config the plot items\n\n self.variables.add_update_function(update_temphum_plots)\n\n # Additional Variables will be generated for temp and hum\n # self.variables.default_values_dict[\"settings\"].update({\"lights\": False, \"humidity_control\": True, \"current_tempmin\": 20, \"current_tempmax\": 25, \"current_hummin\": 20,\"current_hummax\": 25})\n\n # Spin Boxes for temp and humidity\n\n tempmin = QSpinBox()\n tempmax = QSpinBox()\n hummin = QSpinBox()\n hummax = QSpinBox()\n\n # Spinbox label\n textbox_temp = QLabel()\n textbox_temp.setText(\"Min temp. Max temp.\")\n textbox_temp.setFont(self.font)\n textbox_hum = QLabel()\n textbox_hum.setText(\"Min hum. Max hum.\")\n textbox_hum.setFont(self.font)\n\n # Config\n\n tempmin.setRange(15, 35)\n tempmin.setValue(\n float(\n self.variables.default_values_dict[\"settings\"].get(\"current_tempmin\", 0)\n )\n )\n tempmax.setRange(15, 35)\n tempmax.setValue(\n float(\n self.variables.default_values_dict[\"settings\"].get(\"current_tempmax\", 0)\n )\n )\n tempmin.valueChanged.connect(valuechange)\n tempmax.valueChanged.connect(valuechange)\n\n hummin.setRange(0, 70)\n hummin.setValue(\n float(\n self.variables.default_values_dict[\"settings\"].get(\"current_hummin\", 0)\n )\n )\n hummax.setRange(0, 70)\n hummax.setValue(\n float(\n self.variables.default_values_dict[\"settings\"].get(\"current_hummax\", 0)\n )\n )\n hummin.valueChanged.connect(valuechange)\n hummax.valueChanged.connect(valuechange)\n\n # Push buttons on the right for humidity control and light control\n\n dry_air_btn = QPushButton(\"Humidity ctl. off\")\n self.variables.default_values_dict[\"settings\"][\"humidity_control\"] = False\n dry_air_btn.setCheckable(True)\n dry_air_btn.toggle()\n dry_air_btn.clicked.connect(dry_air_action)\n dry_air_btn.setChecked(False)\n\n light_btn = QLabel()\n light_btn.setText(\"State not defined\")\n light_btn.setAlignment(QtCore.Qt.AlignVCenter | QtCore.Qt.AlignHCenter)\n light_btn.setStyleSheet(\"background : rgb(255,0,0); border-radius: 5px\")\n\n # light_btn.setCheckable(True)\n # light_btn.clicked.connect(light_action)\n\n # Humidity\n # temphum_plot.plot(x,y, pen=\"b\")\n\n # Widgets add\n temphum_layout.addWidget(textbox_temp, 0, 0, 1, 2)\n temphum_layout.addWidget(tempmin, 1, 0)\n temphum_layout.addWidget(tempmax, 1, 1)\n\n temphum_layout.addWidget(textbox_hum, 2, 0, 1, 2)\n temphum_layout.addWidget(hummin, 3, 0)\n temphum_layout.addWidget(hummax, 3, 1)\n\n temphum_layout.addWidget(dry_air_btn, 4, 0, 1, 2)\n temphum_layout.addWidget(light_btn, 5, 0, 3, 2)\n\n temphum_layout.addWidget(temphum_plot, 0, 3, 10, 2)\n\n temphum_layout.setContentsMargins(8, 8, 0, 8) # Makes a margin to the layout\n\n # Add the layout to the main layout\n self.layout.addLayout(\n temphum_layout,\n self.temp_ypos,\n self.temp_xpos,\n self.temp_ysize,\n self.temp_xsize,\n )\n\n def update():\n pass\n\n self.variables.add_update_function(update)\n self.variables.add_update_function(check_light_state)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rdy,Rqcd\")","def default_pypeit_par(cls):\n par = super().default_pypeit_par()\n\n # Wavelengths\n # 1D wavelength solution with OH lines\n par['calibrations']['wavelengths']['rms_threshold'] = 1.0\n par['calibrations']['wavelengths']['sigdetect']=[5,10,10,10,10,20,30,30,30,30,30,10,30,30,60,30,30,10,20,30,10]\n par['calibrations']['wavelengths']['n_first']=2\n par['calibrations']['wavelengths']['n_final']=[3,3,3,2,4,4,4,3,4,4,4,3,4,4,4,4,4,4,6,6,4]\n par['calibrations']['wavelengths']['lamps'] = ['OH_FIRE_Echelle']\n #par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']\n par['calibrations']['wavelengths']['method'] = 'reidentify'\n par['calibrations']['wavelengths']['cc_thresh'] = 0.35\n par['calibrations']['wavelengths']['reid_arxiv'] = 'magellan_fire_echelle.fits'\n par['calibrations']['wavelengths']['match_toler']=30.0\n\n # Echelle parameters\n par['calibrations']['wavelengths']['echelle'] = True\n# par['calibrations']['wavelengths']['ech_fix_format'] = True\n par['calibrations']['wavelengths']['ech_nspec_coeff'] = 4\n par['calibrations']['wavelengths']['ech_norder_coeff'] = 6\n par['calibrations']['wavelengths']['ech_sigrej'] = 3.0\n\n # Always correct for flexure, starting with default parameters\n par['scienceframe']['process']['sigclip'] = 20.0\n par['scienceframe']['process']['satpix'] ='nothing'\n\n # Set slits and tilts parameters\n par['calibrations']['tilts']['tracethresh'] = 5\n par['calibrations']['slitedges']['edge_thresh'] = 3.\n par['calibrations']['slitedges']['trace_thresh'] = 10.\n par['calibrations']['slitedges']['fit_order'] = 5\n par['calibrations']['slitedges']['max_shift_adj'] = 0.5\n par['calibrations']['slitedges']['fit_min_spec_length'] = 0.5\n par['calibrations']['slitedges']['left_right_pca'] = True\n par['calibrations']['slitedges']['pca_order'] = 3\n\n # Model entire slit\n par['reduce']['extraction']['model_full_slit'] = True # local sky subtraction operates on entire slit\n par['reduce']['findobj']['maxnumber_sci'] = 2 # Slit is narrow so allow one object per order\n par['reduce']['findobj']['maxnumber_std'] = 1 # Slit is narrow so allow one object per order\n\n # Processing steps\n turn_off = dict(use_illumflat=False, use_biasimage=False, use_overscan=False,\n use_darkimage=False)\n par.reset_all_processimages_par(**turn_off)\n # Do not correct for flexure\n par['flexure']['spec_method'] = 'skip'\n\n # Set the default exposure time ranges for the frame typing\n par['calibrations']['standardframe']['exprng'] = [None, 60]\n par['calibrations']['arcframe']['exprng'] = [20, None]\n par['calibrations']['darkframe']['exprng'] = [20, None]\n par['scienceframe']['exprng'] = [20, None]\n\n # Sensitivity function parameters\n # Sensitivity function parameters\n par['sensfunc']['algorithm'] = 'IR'\n par['sensfunc']['polyorder'] = 5\n par['sensfunc']['IR']['maxiter'] = 2\n # place holder for telgrid file\n par['sensfunc']['IR']['telgridfile'] = 'TelFit_LasCampanas_3100_26100_R20000.fits'\n\n return par","def setValues(\n self,\n frameRate: int = None,\n timeScale: int = None,\n vpDecorations: Boolean = ON,\n vpBackground: Boolean = OFF,\n compass: Boolean = OFF,\n ):\n pass","def set_parameters(self, par):\n try:\n for l in self.cell.layers:\n r_curve = cmf.VanGenuchtenMualem(\n Ksat=10**par.pKsat, phi=par.porosity, alpha=par.alpha, n=par.n\n )\n r_curve.w0 = r_curve.fit_w0()\n l.soil = r_curve\n self.cell.saturated_depth = 0.5\n self.gw.potential = self.cell.z - 0.5\n except RuntimeError as e:\n sys.stderr.write(\"Set parameters failed with:\\n\" + str(par) + \"\\n\" + str(e))\n raise","def set_parameters(cls):\r\n \"\"\" EXECUTE THIS FUNCTION IN THE FARM CLASS! \"\"\"\r\n cls.TSR, cls.RPM, cls.RAD, cls.BLA, cls.CHR, cls.SEC, cls.NT = \\\r\n np.loadtxt('settings.csv', delimiter=',', skiprows=1, unpack=True)","def __setup_parameters__(self):\r\n self.M=self.N+1\r\n self.u=1+self.pu\r\n self.d=1-self.pd\r\n self.qu=(math.exp((self.r-self.div)*self.dt)-self.d)/(self.u-self.d)\r\n self.qd=1-self.qu","def set_profile_params(self, params):\n self.alpha = params[0]\n self.beta = params[1]\n self.gamma = params[2]\n self.c500 = params[3]\n self.P0 = params[4]\n self.M500 = params[5]\n self.r500 = params[6]\n self.z = params[7]","def set_params(self, **kwargs):\n\n kw_keys = list(kwargs)\n\n if 'alpha' in kw_keys:\n self.alpha = kwargs['alpha']\n\n if 'beta' in kw_keys:\n self.beta = kwargs['beta']\n\n if 'gamma' in kw_keys: \n \tself.gamma = kwargs['gamma']\n\n if 'epsilon' in kw_keys:\n self.epsilon = kwargs['epsilon']\n \n self.nact = self.highbound-self.lowbound\n self.actions = np.arange(self.nact)","def initializeParameters(self):\n\n self.params[2].value = False\n self.params[3].enabled = False\n self.params[7].value = True\n self.params[7].enabled = False\n self.params[8].value = None\n self.params[8].enabled = False","def set_shape_params(self, params):\n self.alpha = params[0]\n self.beta = params[1]\n self.gamma = params[2]\n self.c500 = params[3]\n self.P0 = params[4]","def __init__(self, encut, spinaxis, ldaul, Uparam, Jparam, name='DFTCL_settings'):\n ncl_settings = {\"ISPIN\": 2, \"MAGMOM\": None, \"SAXIS\": spinaxis, \"LSORBIT\": \".TRUE.\", \"LNONCOLLINEAR\": \".TRUE.\"}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=ncl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"ENCUT\", encut)","def initialize_dynamic_settings(self):\n self.ship_speed = 5\n self.bullet_speed = 1.0\n self.alien_speed=1.0\n #fleet direction of 1 represents right -1 represents left\n self.fleet_direction = 1\n #scoring\n self.alien_points=50","def set_params(self, **kargs):\n\n # may further modify parameters manually\n if len(kargs) > 0:\n for key in kargs:\n if key in self.params[self.profile].keys():\n self.params[self.profile][key] = kargs[key]\n else:\n print '%s not a standard key. Will not be used.' % (key)\n \n self.pathout = self.params[self.profile]['pathout']\n self.chans = self.params[self.profile]['chans']\n self.dmarr = self.params[self.profile]['dmarr']\n self.pulsewidth = self.params[self.profile]['pulsewidth'] * n.ones(len(self.chans))\n self.approxuvw = self.params[self.profile]['approxuvw']\n self.beam_params = self.params[self.profile]['beam_params']\n self.long = self.params[self.profile]['long']\n self.lat = self.params[self.profile]['lat']","def initializeParameters(self):\r\n\t\tself.input_raster.enabled = True\r\n\t\tself.approach.enabled = True\r\n\t\tself.predefined_pattern.enabled = False\r\n\t\tself.predefined_pattern.value = 'Mexican Hat wavelet'\r\n\t\tself.pattern_workspace.enabled = False\r\n\t\tself.point_matrix_size.enabled = False\r\n\t\tself.point_matrix_size.value = 3\r\n\t\tself.point_vectors.enabled = False\r\n\t\tself.mapping_field.enabled = False\r\n\t\tself.move_to_max.enabled = False\r\n\t\tself.move_to_max_distance.enabled = False\r\n\t\tself.move_to_max_distance.value = 3\r\n\t\tself.mh_iteration.enabled = False\r\n\t\tself.mh_dil_val.enabled = False\r\n\t\tself.mh_dil_val.value = 1\r\n\t\tself.mh_dil_start.value = 0.01\r\n\t\tself.mh_dil_stop.value = 1\r\n\t\tself.mh_dil_step.value = 0.1\r\n\t\tself.mh_dil_start.enabled = False\r\n\t\tself.mh_dil_stop.enabled = False\r\n\t\tself.mh_dil_step.enabled = False\r\n\t\tself.transform.enabled = False\r\n\t\tself.size_of_the_cell.enabled = False\r\n\t\tself.size_of_the_cell.value = 1\r\n\t\tself.output_sim_matrix.enabled = False\r\n\t\tself.output_table.enabled = False\r\n\t\tself.output_raster_workspace.enabled = False","def _set_parameter(self):\n # Get parameter keys\n self.input_parameter = self.parameter_combobox.currentText()\n self.result_parameter = self.result_parameters[self.input_parameter]\n # Adjust axes labels\n self.ax.set_xlabel('{} steunpunt'.format(self.input_parameter))\n self.ax.set_ylabel('{} uitvoerlocatie'.format(self.input_parameter))\n # Set data\n self._set_data()","def setparams (w=None, delay=None, tau=None, low=None, high=None):\n global firststep\n for n in nclist: \n if w is not None: n.weight[0] = w if useHHCell else -w # neg wts are needed for ArtCell (neg driving force for HHCell)\n if delay is not None: n.delay=delay\n if firststep or (low and high): # only set this up using defaults the 1st time\n firststep = False \n if low is not None: low, high = 10, 11\n for ce,ran,start in zip(cells, np.random.uniform(low, high, ncells), np.random.uniform(0, 30, ncells)): \n if useHHCell:\n ce.stim.amp = ran\n ce.stim.delay = start\n else: \n ce.pp.invl = ran\n if tau is not None: ce.tau = tau","def set_params(self, **kwargs):\n if 'nbins' in kwargs:\n self._nbins = kwargs['nbins']\n if self._nbins != 'auto':\n self._nbins = int(self._nbins)\n if 'symmetric' in kwargs:\n self._symmetric = kwargs['symmetric']\n if 'prune' in kwargs:\n prune = kwargs['prune']\n if prune is not None and prune not in ['upper', 'lower', 'both']:\n raise ValueError(\n \"prune must be 'upper', 'lower', 'both', or None\")\n self._prune = prune\n if 'min_n_ticks' in kwargs:\n self._min_n_ticks = max(1, kwargs['min_n_ticks'])\n if 'steps' in kwargs:\n steps = kwargs['steps']\n if steps is None:\n self._steps = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10]\n else:\n self._steps = self._validate_steps(steps)\n self._extended_steps = self._staircase(self._steps)\n if 'integer' in kwargs:\n self._integer = kwargs['integer']","def set_parameter(self, sensor_name, parameter_name, parameter_value):\n if parameter_name == 'perspective_angle':\n parameter_value = parameter_value / (180 * 2) * math.pi\n if parameter_name in self.params_f:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_f[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n elif parameter_name in self.params_i:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_i[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n else:\n return 'Parameter not found'","def __set_sensor_elements(*args):\n args[0].Data.pt_number = args[1]\n args[0].Data.set_pt_number()\n args[0].Limit.pt_number = args[1]\n args[0].Limit.set_pt_number()\n args[0].Data.tc_number = args[2]\n args[0].Data.set_tc_number()\n args[0].Limit.tc_number = args[2]\n args[0].Limit.set_tc_number()\n args[0].Data.lc_number = args[3]\n args[0].Data.set_lc_number()\n args[0].Limit.lc_number = args[3]\n args[0].Limit.set_lc_number()\n args[0].Data.ls_number = args[4]\n args[0].Data.set_ls_number()\n args[0].Limit.ls_number = args[4]\n args[0].Limit.set_ls_number()","def set_params(**kwargs):\n\t\tif \"study\" in kwargs.keys():\n\t\t\traise TypeError(\"Got an unexpected keyword argument: 'study'\")\n\t\telse:\n\t\t\tfor i in range(len(_RECOGNIZED_ELEMENTS_)):\n\t\t\t\t__settings[_RECOGNIZED_ELEMENTS_[i]] = __fractional(\n\t\t\t\t\t_RECOGNIZED_ELEMENTS_[i], study = \"LC18\", **kwargs)[0]","def initialize_dynamic_settings(self):\n self.ship_speed = 1.5\n self.bullet_speed = 3.0\n self.alien_speed = 0.8 # og speed 1.0\n # Der Wert 1 für fleet_direction bedeutet \"nach rechts\", -1 \"nach link \"\n self.fleet_direction = 1\n # Punktwertung\n self.alien_points = 50","def update_parameters(self):\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\n self.update_gamma()\n self.update_gamma0()\n self.update_lambda()\n self.update_nu()\n if self.sample_xi:\n self.update_xi()","def _set_parameters(self, parameters):\n self.parameters = parameters\n self._set_points_and_weights()","def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\n \n # Gear ratio\n self.R = [np.diag([r1,r1]),np.diag([r1,r2]),np.diag([r2,r1]),np.diag([r2,r2])]\n \n # Inertia\n self.Ia = np.diag([ja,ja])\n \n # Damping (linear)\n self.Da = np.diag([ba,ba])","def setWalabotSettings():\n wlbt.SetProfile(wlbt.PROF_SENSOR)\n wlbt.SetArenaR(R_MIN, R_MAX, R_RES)\n wlbt.SetArenaTheta(THETA_MIN, THETA_MAX, THETA_RES)\n wlbt.SetArenaPhi(PHI_MIN, PHI_MAX, PHI_RES)\n #wlbt.SetThreshold(THRESHOLD)\n wlbt.SetDynamicImageFilter(wlbt.FILTER_TYPE_DERIVATIVE) #Also test with FILTER_TYPE_NONE\n print('- Walabot Configured.')","def set_speed():\n pass","def initialize_dynamic_settings(self):\n self.ship_speed_factor = 1.5\n self.bullet_speed_factor = 3\n self.alien_speed_factor = 1\n self.fleet_direction = 1","def set_parameters(self, *args, **kwargs):\n super(DAEM, self).set_parameters(*args, **kwargs)\n self._Em = self._calc_Em()","def __init__(self, encut, ldaul, Uparam, Jparam, name=\"DFTU_settings\"):\n\n dftu_settings = {\"LDAU\": \".TRUE.\" , \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LADAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIX\": 4}\n InputParameters.__init__(self, name=name, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"ENCUT\", encut)","def __fill_motor_parameters(self, motor_id, model_number):\n # Get the Motor Name\n model_name = self.dynotools.getModelNameByModelNumber(model_number)\n\n # Get Max and Min angles\n angles = self.sdk_io.get_angle_limits(motor_id, model_name)\n self.angles[str(model_name)] = angles \n\n # Get Current, Min, and Max voltages\n voltage = self.sdk_io.get_voltage(motor_id, model_name)\n voltages = self.sdk_io.get_voltage_limits(motor_id, model_name)\n \n # ROS Parameters Setup\n rospy.set_param('dynamixel/%s/%d/model_number' %(self.port_namespace, motor_id), model_number)\n rospy.set_param('dynamixel/%s/%d/model_name' %(self.port_namespace, motor_id), DXL_MODEL_TO_PARAMS[model_number]['name'])\n rospy.set_param('dynamixel/%s/%d/min_angle' %(self.port_namespace, motor_id), angles['min'])\n rospy.set_param('dynamixel/%s/%d/max_angle' %(self.port_namespace, motor_id), angles['max'])\n torque_per_volt = DXL_MODEL_TO_PARAMS[model_number]['torque_per_volt']\n rospy.set_param('dynamixel/%s/%d/torque_per_volt' %(self.port_namespace, motor_id), torque_per_volt)\n rospy.set_param('dynamixel/%s/%d/max_torque' %(self.port_namespace, motor_id), torque_per_volt * voltage)\n \n velocity_per_volt = DXL_MODEL_TO_PARAMS[model_number]['velocity_per_volt']\n rpm_per_tick = DXL_MODEL_TO_PARAMS[model_number]['rpm_per_tick']\n rospy.set_param('dynamixel/%s/%d/velocity_per_volt' %(self.port_namespace, motor_id), velocity_per_volt)\n rospy.set_param('dynamixel/%s/%d/max_velocity' %(self.port_namespace, motor_id), velocity_per_volt * voltage)\n rospy.set_param('dynamixel/%s/%d/radians_second_per_encoder_tick' %(self.port_namespace, motor_id), rpm_per_tick * RPM_TO_RADSEC)\n \n encoder_resolution = DXL_MODEL_TO_PARAMS[model_number]['encoder_resolution']\n range_degrees = DXL_MODEL_TO_PARAMS[model_number]['range_degrees']\n range_radians = math.radians(range_degrees)\n rospy.set_param('dynamixel/%s/%d/encoder_resolution' %(self.port_namespace, motor_id), encoder_resolution)\n rospy.set_param('dynamixel/%s/%d/range_degrees' %(self.port_namespace, motor_id), range_degrees)\n rospy.set_param('dynamixel/%s/%d/range_radians' %(self.port_namespace, motor_id), range_radians)\n rospy.set_param('dynamixel/%s/%d/encoder_ticks_per_degree' %(self.port_namespace, motor_id), encoder_resolution / range_degrees)\n rospy.set_param('dynamixel/%s/%d/encoder_ticks_per_radian' %(self.port_namespace, motor_id), encoder_resolution / range_radians)\n rospy.set_param('dynamixel/%s/%d/degrees_per_encoder_tick' %(self.port_namespace, motor_id), range_degrees / encoder_resolution)\n rospy.set_param('dynamixel/%s/%d/radians_per_encoder_tick' %(self.port_namespace, motor_id), range_radians / encoder_resolution)\n\n # Get Parameters for pos_to_raw\n self.flipped = angles['min'] > angles['max']\n self.encoder_resolution = encoder_resolution\n self.range_degrees = range_degrees\n self.range_radians = range_radians\n self.encoder_ticks_per_degree = encoder_resolution / range_degrees\n self.encoder_ticks_per_radian = encoder_resolution / range_radians\n self.degrees_per_encoder_tick = range_degrees / encoder_resolution\n self.radians_per_encoder_tick = range_radians / encoder_resolution\n self.initial_position_raw = self.sdk_io.get_position(motor_id, model_name)\n\n # Flipped case\n if self.flipped:\n self.min_angle = (self.initial_position_raw - angles['min']) * self.radians_per_encoder_tick\n self.max_angle = (self.initial_position_raw - angles['max']) * self.radians_per_encoder_tick\n else:\n self.min_angle = (angles['min'] - self.initial_position_raw) * self.radians_per_encoder_tick\n self.max_angle = (angles['max']- self.initial_position_raw) * self.radians_per_encoder_tick\n\n # keep some parameters around for diagnostics\n self.motor_static_info[motor_id] = {}\n self.motor_static_info[motor_id]['model'] = DXL_MODEL_TO_PARAMS[model_number]['name']\n self.motor_static_info[motor_id]['min_angle'] = angles['min']\n self.motor_static_info[motor_id]['max_angle'] = angles['max']","def load_params(self, event):\n \n self.robot_type = rospy.get_param(\"robot_type\" , 'pendulum' )\n self.robot_config = rospy.get_param(\"robot_config\", 'wrist-only' )\n self.robot_ctl = rospy.get_param(\"controller\", 'RfixCTC' )\n self.fixed_mode = rospy.get_param(\"fixed_mode\", 1 )\n \n \n ###############################################\n # Load robot model for the right configuration\n if self.robot_config == 'wrist-only':\n self.R = Proto.SingleRevoluteDSDM()\n \n elif self.robot_config == 'dual-plane' :\n self.R = Proto.TwoPlanarSerialDSDM()\n \n else:\n self.R = None\n \n ###############################################\n # Load controller\n if self.robot_ctl == 'RfixCTC' :\n self.Ctl = RminCTC.RfixComputedTorqueController( self.R , self.fixed_mode )\n \n elif self.robot_ctl == 'RminCTC' :\n self.Ctl = RminCTC.RminComputedTorqueController( self.R )\n \n elif self.robot_ctl == 'RfixSLD' :\n self.Ctl = RminCTC.RfixSlidingModeController( self.R , self.fixed_mode )\n \n elif self.robot_ctl == 'RminSLD' :\n self.Ctl = RminCTC.RminSlidingModeController( self.R )\n \n elif self.robot_ctl == 'RollCTC' :\n self.Ctl = RollCTC.RolloutComputedTorqueController( self.R )\n \n elif self.robot_ctl == 'RollSLD' :\n self.Ctl = RollCTC.RolloutSlidingModeController( self.R )\n \n else:\n self.Ctl = None\n \n \n if self.robot_config == 'wrist-only':\n self.Ctl.n_gears = rospy.get_param(\"n_gears\", 2 )\n self.x_d = np.array( rospy.get_param(\"goal\", [0,0] ) )\n \n elif self.robot_config == 'dual-plane' :\n self.Ctl.n_gears = rospy.get_param(\"n_gears\", 4 )\n self.x_d = np.array( rospy.get_param(\"goal\", [0.0,0.0,0.0,0.0] ) )\n #self.x_d = np.array( [-3.14 , 0 , 0 , 0] )\n \n # Gen ctl params\n self.Ctl.hysteresis = rospy.get_param(\"hysteresis\", True )\n self.Ctl.min_delay = rospy.get_param(\"min_delay\", 0.5 )\n \n self.Ctl.w0 = rospy.get_param(\"w0\", 1 )\n self.Ctl.zeta = rospy.get_param(\"zeta\", 0.7 )\n \n self.Ctl.lam = rospy.get_param(\"lam\", 1 )\n self.Ctl.nab = rospy.get_param(\"nab\", 1 )\n self.Ctl.D = rospy.get_param(\"D\", 0 )\n \n self.Ctl.horizon = rospy.get_param(\"horizon\", 0.5 )\n self.Ctl.sim_dt = rospy.get_param(\"sim_dt\", 0.1 )\n \n self.Ctl.domain_check = rospy.get_param(\"domain_check\", False )\n \n # Base policy param for roll \n if self.robot_ctl == 'RollCTC' :\n self.Ctl.FixCtl.lam = self.Ctl.lam\n \n elif self.robot_ctl == 'RollSLD' :\n self.Ctl.FixCtl.lam = self.Ctl.lam \n self.Ctl.FixCtl.nab = self.Ctl.nab \n self.Ctl.FixCtl.D = self.Ctl.D","def _set_parameters(self, parameters, commit=True):\n\n for param in parameters:\n self.opt.setParameter(param[0], param[1])\n\n if commit:\n self.opt.sendConfiguration()\n\n return self.opt.getParameter(\"ReadoutTimeCalculation\")","def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\n \n # Gear ratio\n self.R = [np.diag([r1,r1,r1]),np.diag([r1,r1,r2]),np.diag([r1,r2,r1]), np.diag([r1,r2,r2]),\n np.diag([r2,r1,r1]),np.diag([r2,r1,r2]),np.diag([r2,r2,r1]), np.diag([r2,r2,r2]) ]\n \n # Inertia\n self.Ia = np.diag([ja,ja,ja])\n \n # Damping (linear)\n self.Da = np.diag([ba,ba,ba])","def __init__(self):\n super().__init__()\n self.TERRAIN_VARIANCE = 0.0","def set_motion_params(self, xps_grp, params):\n\n # if (xps_grp not in groupName): raise KeyError(\"KeyError: '{}' is not in \".format(xps_grp))\n if (xps_grp not in groupName): raise KeyError(xps_grp)\n default_velocity = 20\n temp_par = [None]*4\n if (type(params) is int or type(params) is float): temp_par[0] = float(params)\n elif (type(params) is list):\n if (len(params) > 0):\n for i,par in enumerate(params):\n if (i>=4): break\n temp_par[i] = par\n else: temp_par[0] = default_velocity ## Set velocity with default value\n\n else:\n raise TypeError('ERROR: Require a list or float for parameters')\n\n \n self.newportxps.set_velocity(groupName[xps_grp]+'.Pos',\n velo=temp_par[0], accl=temp_par[1],\n min_jerktime=temp_par[2], max_jerktime=temp_par[3])","def assign_model_parameters(self,xmax,zmax,dh,duration):\n self.model_parameters['xmax']=xmax\n self.model_parameters['zmax']=zmax\n self.model_parameters['dh']=dh\n self.model_parameters['duration']=duration","def set_params(self, dic):\n if dic is not None:\n for key, val in zip(dic.keys(), dic.values()):\n if key in self.__dict__.keys():\n self.__dict__[key] = val\n\n if 'scale_params' in self.__dict__.keys():\n self.scale_params.set_params(dic)\n if 'atmospheric_params' in self.__dict__.keys():\n if self.atmospheric_params is not None:\n self.atmospheric_params.set_params(dic)\n\n if 'atemperature_params' in self.__dict__.keys():\n if self.atemperature_params is not None:\n self.atemperature_params.set_params(dic)\n\n if 'oceanic_params' in self.__dict__.keys():\n if self.oceanic_params is not None:\n self.oceanic_params.set_params(dic)\n\n if 'ground_params' in self.__dict__.keys():\n if self.ground_params is not None:\n self.ground_params.set_params(dic)\n\n if 'otemperature_params' in self.__dict__.keys():\n if self.gotemperature_params is not None:\n self.gotemperature_params.set_params(dic)\n\n if 'gtemperature_params' in self.__dict__.keys():\n if self.gotemperature_params is not None:\n self.gotemperature_params.set_params(dic)","def _set_params(self,x):\r\n self.k._set_params(x)","def __init__(self, make, model, year):\r\n super().__init__(make, model, year)\r\n self.battery_size = 70\r\n # self.autopilot = autopilot\r","def set_parameter(self, param, value, stripe = 0, location = 3):\n if param in self.fpga.cabac_top[0].params:\n self.fpga.set_cabac_value(param, value, stripe)\n time.sleep(0.1)\n self.fpga.send_cabac_config(stripe)\n\n elif param in [\"V_SL\", \"V_SH\", \"V_RGL\", \"V_RGH\", \"V_PL\", \"V_PH\"]:\n self.fpga.set_clock_voltages({param: value})\n\n elif param == \"I_OS\":\n self.fpga.set_current_source(value, stripe)\n\n else:\n print(\"Warning: unidentified parameter for the REB: %s\" % param)","def initialize_dynamic_settings(self):\r\n self.alien_speed_factor = 0.1\r\n self.alien_bullet_speed_factor = 0.7","def __init__(self, sensor):\n self.sensor = sensor\n self.temperature = None\n self.humitidy = None\n self.update()","def updateParameters(self, parameters):\r\n #return\r\n parameters[2].enabled = 0\r\n parameters[3].enabled = 0\r\n parameters[4].enabled = 0","def updateParameters(self, parameters):\r\n #return\r\n parameters[2].enabled = 0\r\n parameters[3].enabled = 0\r\n parameters[4].enabled = 0","def update_parameters(self):\n self.alignment_factor = rospy.get_param('/dyn_reconf/alignment_factor')\n self.cohesion_factor = rospy.get_param('/dyn_reconf/cohesion_factor')\n self.separation_factor = rospy.get_param('/dyn_reconf/separation_factor')\n self.avoid_factor = rospy.get_param('/dyn_reconf/avoid_factor')\n self.max_speed = rospy.get_param('/dyn_reconf/max_speed')\n self.max_force = rospy.get_param('/dyn_reconf/max_force')\n self.friction = rospy.get_param('/dyn_reconf/friction')\n self.crowd_radius = rospy.get_param('/dyn_reconf/crowd_radius')\n self.search_radius = rospy.get_param('/dyn_reconf/search_radius')\n\n rospy.loginfo(rospy.get_caller_id() + \" -> Parameters updated\")\n if DEBUG:\n print('alignment_factor: ', self.alignment_factor)\n print('cohesion_factor: ', self.cohesion_factor)\n print('separation_factor: ', self.separation_factor)\n print('avoid_factor: ', self.avoid_factor)\n print('max_speed: ', self.max_speed)\n print('max_force: ', self.max_force)\n print('friction: ', self.friction)\n print('crowd_radius: ', self.crowd_radius)\n print('search_radius: ', self.search_radius)","def update(self, parameters):\n self.set_frequencies(parameters) # f_i\n self.set_coupling_weights(parameters) # w_ij\n self.set_phase_bias(parameters) # theta_i\n self.set_amplitudes_rate(parameters) # a_i\n self.set_nominal_amplitudes(parameters) # R_i","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def teleopInit(self):\n self.gyro.setSensitivity(\n self.voltsPerDegreePerSecond\n ) # calibrates gyro values to equal degrees","def __init__(self, sensor, temperature_resolution, humidity_resolution):\n self.sensor = sensor\n self.sensor.turnHeaterOn() \n time.sleep(1.0) # Burn off condensed stuff.\n self.sensor.turnHeaterOff() \n self.update()\n # Main Program\n #print \"------------\"\n #print \"Manfacturer ID=0x%X\"% self.sensor.readManufacturerID() \n #print \"Device ID=0x%X\"% self.sensor.readDeviceID() \n #print \"Serial Number ID=0x%X\"% self.sensor.readSerialNumber() \n \n # change temperature resolution\n self.sensor.setTemperatureResolution(temperature_resolution)\n self.sensor.setHumidityResolution(humidity_resolution)","def set_parameters(pars):\n y0=[]\n fun=None \n state_evol=None\n if pars[\"state_law\"]==0:\n state_evol=state_evol_d\n elif pars[\"state_law\"]==1:\n state_evol=state_evol_r\n elif pars[\"state_law\"]==2:\n state_evol=state_evol_p\n elif pars[\"state_law\"]==3:\n state_evol=state_evol_n\n \n if pars[\"model\"]==0:\n y0 = [pars[\"Vpl\"]*0.9,0.1,pars[\"sigma1\"]]\n fun = fun_qds\n damping = pars[\"nu\"]\n \n if pars[\"model\"]==1:\n y0 = [pars[\"Vpl\"]*0.9, 0.1,pars[\"sigma1\"],pars[\"sigma1\"]*pars[\"f0\"]]\n fun = fun_fds\n damping = pars[\"m\"]\n\n if pars[\"model\"]==2:\n y0 = [pars[\"Vpl\"]*0.99,pars[\"Vpl\"], pars[\"Vpl\"],0.1,pars[\"sigma1\"],pars[\"sigma2\"]]\n fun= fun_qdc\n damping = pars[\"nu\"]\n\n if pars[\"model\"]==3:\n y0 = [pars[\"Vpl\"]*1.1,pars[\"Vpl\"], pars[\"Vpl\"],0.0,pars[\"sigma1\"],pars[\"sigma2\"],pars[\"sigma1\"]*pars[\"f0\"]]\n fun = fun_fdc\n damping = pars[\"m\"]\n\n return (np.array(y0), state_evol, fun, damping)","def _calculate_parameters(self, thickness: int = 10):\n\n self.thickness = thickness\n\n # set orientation dependent parameters: (different for x, y, z-PML)\n # NOTE: these methods are implemented by the subclasses of PML.\n self._set_locations()\n self._set_shape()\n self._set_sigmaE()\n self._set_sigmaH()\n\n # set the other parameters\n Nx, Ny, Nz = self.shape # is defined by _set_shape()\n self.phi_E = bd.zeros((Nx, Ny, Nz, 3))\n self.phi_H = bd.zeros((Nx, Ny, Nz, 3))\n self.psi_Ex = bd.zeros((Nx, Ny, Nz, 3))\n self.psi_Ey = bd.zeros((Nx, Ny, Nz, 3))\n self.psi_Ez = bd.zeros((Nx, Ny, Nz, 3))\n self.psi_Hx = bd.zeros((Nx, Ny, Nz, 3))\n self.psi_Hy = bd.zeros((Nx, Ny, Nz, 3))\n self.psi_Hz = bd.zeros((Nx, Ny, Nz, 3))\n\n self.bE = bd.exp(-(self.sigmaE / self.k + self.a) * self.grid.courant_number)\n self.cE = (\n (self.bE - 1.0)\n * self.sigmaE # is defined by _set_sigmaE()\n / (self.sigmaE * self.k + self.a * self.k ** 2)\n )\n\n self.bH = bd.exp(-(self.sigmaH / self.k + self.a) * self.grid.courant_number)\n self.cH = (\n (self.bH - 1.0)\n * self.sigmaH # is defined by _set_sigmaH()\n / (self.sigmaH * self.k + self.a * self.k ** 2)\n )","def get_set_potentiometer_value_args(self,comArgs):\n params, flags = self.get_params(comArgs)\n axes = []\n values = []\n for k in params:\n values.append(params[k])\n axes.append(k.lower())\n acodes = {'x':0,'y':1,'z':2,'a':3,'b':4}\n axs = []\n for a in axes:\n axs.append(acodes[a])\n args = [axs, values]\n return args","def iniitialize_dynamic_settings(self):\n\t\tself.speed_factor = 1.5\n\t\tself.bullet_speed_factor = 3\n\t\tself.alien_speed_factor = 1\n\n\t\t# fleet_direction of 1 represents right; -1 represents left. \n\t\tself.fleet_direction = 1","def read_param_phil(self):\n\n if self.params.analysis.run_clustering:\n self.clustering.toggle_boxes()\n self.clustering.threshold.SetValue(\n str(self.params.analysis.cluster_threshold))\n self.clustering.limit.SetValue(\n str(self.params.analysis.cluster_limit))\n self.clustering.n_images.SetValue(str(\n self.params.analysis.cluster_n_images))\n\n viz_idx = self.visualization.ctr.FindString(str(self.params.analysis.viz))\n if str(self.params.analysis.viz).lower() == 'none':\n viz_idx = 0\n self.visualization.ctr.SetSelection(viz_idx)\n\n self.proc_charts.SetValue(self.params.analysis.charts)\n self.summary_graphs.SetValue(self.params.analysis.summary_graphs)","def __init__(self, name=\"\", description=\"\", time_units=\"s\", len_units=\"m\"):\n super(Piezometer, self).__init__()\n\n # Set general info\n self._type = 3 # piezometer id\n self.parameters = {'full': True, # is full penetrating?\n 'r': 1., # distance until pumping well in length units\n 'z': 1.} # piezometer depth in length units\n\n # Set data\n self.name = name\n self.description = description\n\n self.time_units = time_units\n self.len_units = len_units\n\n # Create drawdown data\n self.drawdown = _Data(dtype=1, name=name, description=description)\n self.drawdown.set_units(self.time_units, self.len_units)","def set_params(self):\r\n pass","def set_params(self):\n max_margin = int(self.alpha) + 1\n self.sample_params['add'] = [0, max_margin, max_margin]","def set_params(self, params: Dict) -> None:\n channel_param: str\n for channel_param in list(params.keys())[:-1]:\n channel_name: str = channel_param[2:]\n if self.channel_bool[channel_name]:\n self.channel[channel_name].set_g(params[channel_param])\n else:\n raise AttributeError('Model does not match parameter sets')\n\n if 't_ca' == list(params.keys())[-1]: # or if 't_ca' in params.keys():\n self.tau_ca = params['t_ca']\n else:\n self.tau_ca = float('inf')","def __SetSFParams(self):\n\n # If radial structure functions are in output\n if self.__containsRadial:\n # Defines radial attributes\n self.__nc_RSoft_O.radial_error_tolerance = self.etol_radial\n\n # Defines radial dimensions\n self.__nc_RSoft_O.createDimension('radial_structure_functions',\\\n len(self.mus))\n\n # Defines radial variables\n mus_var_id_O = self.__nc_RSoft_O.createVariable('mus', \\\n 'f4', ('radial_structure_functions'))\n Ls_var_id_O = self.__nc_RSoft_O.createVariable('Ls', \\\n 'f4', ('radial_structure_functions'))\n radial_Xs_var_id_O = self.__nc_RSoft_O.createVariable(\\\n 'radial_Xs', 'i4', ('radial_structure_functions'))\n radial_Ys_var_id_O = self.__nc_RSoft_O.createVariable(\\\n 'radial_Ys', 'i4', ('radial_structure_functions'))\n\n # Sets radial structure function variables\n mus_var_id_O[:] = self.mus\n Ls_var_id_O[:] = self.Ls\n radial_Xs_var_id_O[:] = self.radial_Xs\n radial_Ys_var_id_O[:] = self.radial_Ys\n\n # If angular structure functions are in output\n if self.__containsAngular:\n # Defines angular attributes\n self.__nc_RSoft_O.angular_error_tolerance = self.etol_angular\n\n # Defines angular dimensions\n self.__nc_RSoft_O.createDimension('angular_structure_functions',\\\n len(self.xis))\n\n # Defines angular variables\n xis_var_id_O = self.__nc_RSoft_O.createVariable('xis', \\\n 'f4', ('angular_structure_functions'))\n zetas_var_id_O = self.__nc_RSoft_O.createVariable('zetas', \\\n 'i4', ('angular_structure_functions'))\n lambdas_var_id_O = self.__nc_RSoft_O.createVariable('lambdas', \\\n 'i4', ('angular_structure_functions'))\n angular_Xs_var_id_O = self.__nc_RSoft_O.createVariable(\\\n 'angular_Xs', 'i4', ('angular_structure_functions'))\n angular_Ys_var_id_O = self.__nc_RSoft_O.createVariable(\\\n 'angular_Ys', 'i4', ('angular_structure_functions'))\n angular_Zs_var_id_O = self.__nc_RSoft_O.createVariable(\\\n 'angular_Zs', 'i4', ('angular_structure_functions')) \n\n # Sets angular structure function variables\n xis_var_id_O[:] = self.xis\n zetas_var_id_O[:] = self.zetas\n lambdas_var_id_O[:] = self.lambdas\n angular_Xs_var_id_O[:] = self.angular_Xs\n angular_Ys_var_id_O[:] = self.angular_Ys\n angular_Zs_var_id_O[:] = self.angular_Zs","def set_params(self, **kwargs):\n for param_name, value in kwargs.iteritems():\n # only set parameters that are in the default\n if param_name in self._default_params():\n setattr(self, param_name, value)\n self.params[param_name] = value\n else:\n print('AdjustedStat class does not accept %s as a ' \\\n 'parameter and will be ignored' % param_name)","def set_params(self, rate, radius):\n self.radius = radius\n self.learning_rate = rate\n self.neighborhood.radius = radius","def set_params(self, **kwargs):\n ...","def update(self):\n if self.temperature != None and self.humidity != None:\n self.sensor.set_environmental_data(self.humidity, self.temperature)\n# Trim away error values.\n new_eco2 = self.sensor.eco2\n if new_eco2 < 65535:\n self.eco2 = new_eco2\n self.tvoc = self.sensor.tvoc","def fun_set(self):\n\n self.type.set(self.xtl._scattering_type)\n # self.energy_kev.set(8)\n self.theta_offset.set(self.xtl._scattering_theta_offset)\n self.theta_min.set(self.xtl._scattering_min_theta)\n self.theta_max.set(self.xtl._scattering_max_theta)\n self.twotheta_min.set(self.xtl._scattering_min_two_theta)\n self.twotheta_max.set(self.xtl._scattering_max_two_theta)\n\n if self.orientation.get() == 'Reflection':\n self.direction_h.set(self.xtl._scattering_specular_direction[0])\n self.direction_k.set(self.xtl._scattering_specular_direction[1])\n self.direction_l.set(self.xtl._scattering_specular_direction[2])\n else:\n self.direction_h.set(self.xtl._scattering_parallel_direction[0])\n self.direction_k.set(self.xtl._scattering_parallel_direction[1])\n self.direction_l.set(self.xtl._scattering_parallel_direction[2])","def set_params(self, params):","def load_standard_parameters(self):\n paradic = {'x':'0',\n 'y':'0',\n 'n_oct':'8',\n 'n_spo':'3',\n 'sigma_min':'0.8',\n 'delta_min':'0.5',\n 'sigma_in':'0.5',\n 'C_DoG':'0.015',\n 'C_edge':'10',\n 'n_bins':'36',\n 'lambda_ori':'1.5',\n 't':'0.8',\n 'n_hist':'4',\n 'n_ori':'8',\n 'lambda_descr':'6',\n 'flag_match':'1',\n 'C_match':'0.6'}\n self.cfg['param']['paradic'] = paradic\n self.cfg.save()","def _set_params(self,x):\r\n assert x.size==3\r\n self.variance = x[0]\r\n self.lengthscale = x[1]\r\n self.period = x[2]\r\n\r\n self.a = [1./self.lengthscale, 1.]\r\n self.b = [1]\r\n\r\n self.basis_alpha = np.ones((self.n_basis,))\r\n self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))\r\n self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))\r\n\r\n self.G = self.Gram_matrix()\r\n self.Gi = np.linalg.inv(self.G)","def set_parameters(self, params):\n self.kp = params.pgain","def _set_params(self, x):\r\n assert x.size == self.num_params\r\n self.varianceU = x[0]\r\n self.varianceY = x[1]\r\n self.lengthscaleU = x[2]\r\n self.lengthscaleY = x[3]","def _set_model_parameters(self, verbose=False):\n from scipy.special import gamma\n\n z0 = self.z0\n\n # set parameters that are constants\n p_v, d_v, cs0, sigma, vout0 = (1, 2, 6.7, 0.1, 25.0)\n p_vB, d_vB, Mach0, p_M, d_M = (4, 2, 0.5, 1, 3)\n\n # calculate amplitudes that make the pdf integrate to 1\n A_v = np.log(10)*p_v/gamma(d_v/p_v)\n A_cs = np.log(10)/np.sqrt(2*np.pi)/sigma\n A_vB = np.log(10)*p_vB/gamma(d_vB/p_vB)\n A_M = np.log(10)*p_M/gamma(d_M/p_M)\n\n # store them in dictionaries\n self.cool_params = dict(A_v=A_v, p_v=p_v, d_v=d_v,\n A_cs=A_cs, cs0=cs0, sigma=sigma, vout0=vout0)\n self.hot_params = dict(A_vB=A_vB, p_vB=p_vB, d_vB=d_vB,\n A_M=A_M, Mach0=Mach0,p_M=p_M,d_M=d_M)\n # SN related parameters that set the reference values for loading factors\n self.params = dict(Esn=1.e51*au.erg, mstar=95.5*au.M_sun, vcool=200*au.km/au.s,\n Mej=10.*au.M_sun, ZSN=0.2, ZISM0=0.02)\n self.params['vej'] = np.sqrt(2.0*self.params['Esn']/self.params['Mej']).to('km/s')\n self.ref_params = dict(Mref=self.params['mstar'],\n pref=self.params['Esn']/(2*self.params['vcool']),\n Eref=self.params['Esn'],\n Zref=self.params['Mej']*self.params['ZSN'])\n\n # coefficients used in conversion from mass to other PDFs\n self.vp = (self.ref_params['pref']/self.params['mstar']).to('km/s').value\n self.vE = np.sqrt(self.ref_params['Eref']/self.params['mstar']).to('km/s').value\n self.Ze = (self.ref_params['Zref']/self.params['mstar']).cgs.value\n\n # parameters for scaling relations from Paper~I\n a = np.array(fit_alpha[z0])\n b = np.array(fit_beta[z0])\n\n self.scaling_params = dict(a=a, b=b)\n if z0 == '2H':\n self.cool_params['vout0'] = 45\n self.cool_params['cs0'] = 7.5\n elif z0 == '500':\n self.cool_params['vout0'] = 45\n self.cool_params['cs0'] = 8.5\n elif z0 == '1000':\n self.cool_params['vout0'] = 60\n self.cool_params['cs0'] = 10.0\n self.scaling_params['A'] = np.round(10.**(np.array(self.scaling_params['a'])),2)\n self.scaling_params['p'] = 1.+np.array(self.scaling_params['b'])\n self.enum=dict(M_cool=0, M_int=1, M_hot=2, M_total=3,\n p_cool=4, p_int=5, p_hot=6, p_total=7,\n E_cool=8, E_int=9, E_hot=10, E_total=11,\n Z_cool=12, Z_int=13, Z_hot=14, Z_total=15)\n\n # print parameters\n if verbose:\n self.show_parameters()","def set_params(self):\n raise NotImplementedError","def __init__(self,):\r\n self.g = 9.81\r\n self.l = 0.5\r\n self.m1 = 1.0\r\n self.m2 = 1.0\r\n self.m3 = 1.0\r\n self.r1 = 1.0\r\n self.r2 = 1.0\r\n self.tau = 0.001\r\n self.theta1 = 1.0\r\n self.theta2 = 1.0\r\n self.theta3 = 1.0","def setInternalPulser(self,pulserEnable,pulseHeight):\n pass","def _update_parameter(self, dWxh, dbh, dWhy, dby):\n # Add code to update all the weights and biases here","def set_params(self, **kwargs):\n warnings.warn(\"'set_params()' not defined for locator of type \" +\n str(type(self)))","def default_pypeit_par(cls):\n par = super().default_pypeit_par()\n\n # Wavelengths\n # 1D wavelength solution with arc lines\n par['calibrations']['wavelengths']['rms_threshold'] = 1.0\n par['calibrations']['wavelengths']['sigdetect']=3\n par['calibrations']['wavelengths']['fwhm'] = 20\n par['calibrations']['wavelengths']['n_first']=2\n par['calibrations']['wavelengths']['n_final']=4\n par['calibrations']['wavelengths']['lamps'] = ['ArI', 'ArII', 'ThAr', 'NeI']\n #par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']\n par['calibrations']['wavelengths']['method'] = 'full_template'\n par['calibrations']['wavelengths']['reid_arxiv'] = 'magellan_fire_long.fits'\n par['calibrations']['wavelengths']['match_toler']=5.0\n\n # Set slits and tilts parameters\n par['calibrations']['tilts']['tracethresh'] = 5\n par['calibrations']['slitedges']['trace_thresh'] = 10.\n par['calibrations']['slitedges']['sync_predict'] = 'nearest'\n\n # Processing steps\n turn_off = dict(use_illumflat=False, use_biasimage=False, use_overscan=False,\n use_darkimage=False)\n par.reset_all_processimages_par(**turn_off)\n\n # Scienceimage parameters\n par['reduce']['findobj']['snr_thresh'] = 5\n #par['reduce']['maxnumber'] = 2\n par['reduce']['findobj']['find_trim_edge'] = [50,50]\n par['flexure']['spec_method'] = 'skip'\n\n par['sensfunc']['IR']['telgridfile'] = 'TelFit_LasCampanas_3100_26100_R20000.fits'\n\n # Set the default exposure time ranges for the frame typing\n par['calibrations']['standardframe']['exprng'] = [None, 60]\n par['calibrations']['arcframe']['exprng'] = [1, 50]\n par['calibrations']['darkframe']['exprng'] = [20, None]\n par['scienceframe']['exprng'] = [20, None]\n return par","def _update_params(self):\n _load = not self.san_interface.runmode\n params={}\n if ('iosched' in self._updatedattr or _load) and self.iosched<>IoSchedType.default:\n params['iosched']=str(self.iosched)\n if ('readahead' in self._updatedattr or _load) and self.readahead :\n params['readahead']=self.readahead\n if params:\n for pt in self.paths():\n pt.provider.set_dev_params(pt,params)","def __init__(\n self,\n oxygen: MeasurementStatistics,\n pulse: MeasurementStatistics,\n blood_pressure_systolic: MeasurementStatistics,\n blood_pressure_diastolic: MeasurementStatistics,\n ):\n self.oxygen = oxygen\n self.pulse = pulse\n self.blood_pressure_systolic = blood_pressure_systolic\n self.blood_pressure_diastolic = blood_pressure_diastolic","def __init__(self, envirophat, use_leds):\n self.envirophat = envirophat\n self.use_leds = use_leds\n # sensors readings\n self.light = None\n self.light_red = None\n self.light_green = None\n self.light_blue = None\n self.accelerometer_x = None\n self.accelerometer_y = None\n self.accelerometer_z = None\n self.magnetometer_x = None\n self.magnetometer_y = None\n self.magnetometer_z = None\n self.temperature = None\n self.pressure = None\n self.voltage_0 = None\n self.voltage_1 = None\n self.voltage_2 = None\n self.voltage_3 = None","def _set_params(self,x):\r\n assert x.size == self.num_params\r\n self.variance = x[0]\r\n self.lengthscale = x[1:]"],"string":"[\n \"def setParameters(self):\\n\\n # Set the parameters\\n self.taux = 24.2\\n self.mu = 0.23\\n self.G = 33.75\\n self.alpha_0 = 0.05\\n self.delta = 0.0075\\n self.p = 0.50\\n self.I0 = 9500.0\\n self.kparam = 0.55\",\n \"def set_parameters(self,params):\\n K3Supervisor.set_parameters(self,params)\\n self.gtg.set_parameters(self.parameters)\\n self.avoidobstacles.set_parameters(self.parameters)\\n self.wall.set_parameters(self.parameters)\",\n \"def _set_leg_params(self):\\n self.p = 0.01600\\n self.q = 0.00000\\n self.r = 0.02000\\n self.c = 0.01811\\n self.u = 0.00000\\n self.v = 0.00000\\n self.e = -0.06000\\n self.h = -0.02820\\n self.s = 0.02200\\n self.d1 = 0.0\\n self.d2 = 0.0\\n self.d3 = 0.0\\n self.stability = 0.0\",\n \"def set_parameters(self,params):\\n K3Supervisor.set_parameters(self,params)\\n self.blending.set_parameters(self.parameters)\",\n \"def set_param_motor():\\n servo.setSpeed(0, 0) # max = 255\\n servo.setAccel(0, 0)\\n servo.setSpeed(1, 150) # max = 255\\n servo.setAccel(1, 150)\",\n \"def set_params(self, params_):\\n x_start, x_end = params_[\\\"lim_fit\\\"]\\n self.find_idx_of_fit_limit(x_start, x_end)\\n self.is_error_bar_for_fit = params_[\\\"use_error_bar\\\"]\\n self.fitting_method1 = params_[\\\"method1\\\"]\\n self.fitting_method2 = params_[\\\"method2\\\"]\\n self.qty_to_min = params_[\\\"qty_to_min\\\"]\\n\\n for i, key in enumerate(self.params):\\n # self.params[key].set(value=params_[\\\"val\\\"][i], min=params_[\\\"min\\\"][i], max=params_[\\\"max\\\"][i], vary=bool(params_[\\\"hold\\\"][i]), brute_step=params_[\\\"brute_step\\\"][i])\\n if self.params[key].user_data is not None:\\n if \\\"dontGenerate\\\" in self.params[key].user_data:\\n continue\\n self.params[key].set(value=params_[key][\\\"value\\\"], min=params_[key][\\\"min\\\"], max=params_[key][\\\"max\\\"], vary=params_[key][\\\"vary\\\"], brute_step=params_[key][\\\"b_step\\\"])\",\n \"def set_physical_params(self, params):\\n self.M500 = params[0]\\n self.r500 = params[1]\\n self.z = params[2]\",\n \"def set_params(self, params: Dict) -> None:\\n self.leak.set_g(params[\\\"g_leak\\\"])\\n self.kvhh.set_g(params[\\\"g_kvhh\\\"])\\n self.cav.set_g(params[\\\"g_cav\\\"])\\n self.kca.set_g(params[\\\"g_kca\\\"])\\n self.nap.set_g(params[\\\"g_nap\\\"])\\n self.tau_ca = params[\\\"t_ca\\\"]\",\n \"def set_params(self):\\n \\n lo, hi = self.R.get((self.h, self.w, self.m), (0.0, 0.0))\\n params.update({\\n 'gamma' : 1.0, # minesweeper is a finite horizon game\\n 'epsilon': 0.0,\\n 'K': 16,\\n 'R_lo': lo,\\n 'R_hi': hi,\\n 'max_depth': self.h * self.w / 2,\\n 'c':hi-lo\\n })\",\n \"def init_params(self):\\n self.params = Parameters()\\n self.params.add('qoff', self.qoff, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('yscale', self.yscale, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('int_bg', self.int_bg, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('Rc', self.Rc, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('sur_den', self.sur_den, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('ion_depth', self.ion_depth, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\",\n \"def set_params(self, params: Dict) -> None:\\n self.leak.set_g(params['g_leak'])\\n self.nav.set_g(params['g_nav'])\\n self.kvhh.set_g(params['g_kvhh'])\\n self.kva.set_g(params['g_kva'])\\n self.kvsi.set_g(params['g_kvsi'])\\n self.cav.set_g(params['g_cav'])\\n self.kca.set_g(params['g_kca'])\\n self.nap.set_g(params['g_nap'])\\n self.kir.set_g(params['g_kir'])\\n self.ampar.set_g(params['g_ampar'])\\n self.nmdar.set_g(params['g_nmdar'])\\n self.gabar.set_g(params['g_gabar'])\\n self.tau_ca = params['t_ca']\",\n \"def updateParameters(self):\\n\\n if self.params[1].value:\\n if arcpy.Exists(self.params[1].value):\\n try:\\n min_value = arcpy.GetRasterProperties_management(self.params[1].value, \\\"MINIMUM\\\")[0]\\n\\n if str(self.params[8].value) != str(self.params[1].value):\\n self.params[7].value = True\\n self.params[8].value = str(self.params[1].value)\\n else:\\n self.params[7].value = False\\n\\n if str(min_value) == \\\"0\\\":\\n if self.params[7].value == True:\\n self.params[2].value = True\\n self.params[3].enabled = True\\n self.params[7].value = False\\n else:\\n self.params[2].value = False\\n self.params[3].enabled = False\\n\\n except arcpy.ExecuteError:\\n pass\\n\\n if self.params[2].value == True:\\n self.params[3].enabled = True\\n else:\\n self.params[3].enabled = False\",\n \"def _setup_params(self) -> None:\\n self.i = 0 # Year\\n self.ela = self.ela_start # Equilibrium line altitude\\n self.steady_state = False # Control variable for steady state\\n self.fracd8_mode = \\\"limited\\\" # Mode of the fracd8 algorithm\",\n \"def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\\\"DFTCL_settings\\\"):\\n\\n cl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": magmom, \\\"SAXIS\\\": None, \\\"LSORBIT\\\": None, \\\"LNONCOLLINEAR\\\": None}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIMX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"encut\\\", encut)\",\n \"def set_params(self, **params):\\n if('threshold' in params.keys()):\\n self.threshold = params['threshold']\\n if('subsample' in params.keys()):\\n self.subsample = params['subsample']\\n if('estimator' in params.keys()):\\n self.estimator = params['estimator']\\n if('n_folds' in params.keys()):\\n self.n_folds = params['n_folds']\\n if('stratify' in params.keys()):\\n self.stratify = params['stratify']\\n if('random_state' in params.keys()):\\n self.random_state = params['random_state']\\n if('n_jobs' in params.keys()):\\n self.n_jobs = params['n_jobs']\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rbk[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd_emu[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rbk,Rdy,Rqcd_emu\\\")\",\n \"def setParameters(self, sx_sim=None):\\n # TODO rething that ..\\n #if sx_sim is not None:\\n #if ds_model is not None:\\n #if di_model is not None:\\n self.sx_sim = sx_sim\\n p = defaultParams(chord=self._chord, rho=self._rho, sx=self.sx_sim, ds=self.ds_model, di=self.di_model,\\n M=self._M33, C=self._C33, K=self._K33)\\n p['beta'] = self._beta\\n if len(p['Iq'])==0:\\n raise Exception('No states are present')\\n\\n # --- Dynamic inflow / induction\\n p['a0'] = self._a0\\n p['ap0'] = self._ap0\\n p['di_tau1'] = self.di_tau1\\n p['di_tau2'] = self.di_tau2\\n\\n # --- Aerodynamic parameters\\n if self._y_AQ>0: \\n print('[WARN] y_AQ positive is unconventional')\\n p['y_AQ'] = self._y_AQ\\n if self._y_AT is None:\\n p['y_AT'] = self._y_AQ+self._chord/2 # default is approximatively half a chord behind\\n else:\\n p['y_AT'] = self._y_AT\\n p['x_AQ'] = self._x_AQ\\n p['x_AT'] = self._x_AT\\n if self._ppol is None:\\n raise Exception('Polar parameters need to be set')\\n p.update(self._ppol)\\n # # p.update({'linModel':False, 'drag':drag})\\n\\n self.p_sim = p\",\n \"def init_paramters(self):\\r\\n carb_bg_ratio = 5.0\\r\\n time_to_breakdown = 45.0\\r\\n insulin_bg_ratio = 50.0\\r\\n time_to_peak = 45.0\\r\\n basal_rate = 0.0\\r\\n digestion_speed = 1.0\\r\\n activation_speed = 1.0\\r\\n\\r\\n # set state to initial\\r\\n self.S = [self.carb_bg_ratio, self.time_to_breakdown,\\r\\n self.insulin_bg_ratio, self.time_to_peak,\\r\\n self.basal_rate, self.digestion_speed,\\r\\n self.activation_speed]\",\n \"def initialize_dynamic_settings(self):\\n self.ship_speed_factor = 1.5\\n self.bullet_speed_factor = 3\\n self.alien_speed_factor = 1\\n self.fleet_direction = 1\\n #Puntos\\n self.alien_points = 50\",\n \"def temphum_plot(self, kwargs=None):\\n\\n def valuechange():\\n \\\"\\\"\\\"This is the function which is called, when a value is changed in the spin boxes\\\"\\\"\\\"\\n\\n tempmin.setMaximum(tempmax.value())\\n tempmax.setMinimum(tempmin.value())\\n hummin.setMaximum(hummax.value())\\n hummax.setMinimum(hummin.value())\\n\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"current_tempmin\\\"\\n ] = tempmin.value()\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"current_tempmax\\\"\\n ] = tempmax.value()\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"current_hummin\\\"\\n ] = hummin.value()\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"current_hummax\\\"\\n ] = hummax.value()\\n\\n max = build_command(\\n self.variables.devices_dict[\\\"temphum_controller\\\"],\\n (\\\"set_hummax\\\", hummax.value()),\\n )\\n min = build_command(\\n self.variables.devices_dict[\\\"temphum_controller\\\"],\\n (\\\"set_hummin\\\", hummin.value()),\\n )\\n\\n self.variables.vcw.write(\\n self.variables.devices_dict[\\\"temphum_controller\\\"], max\\n )\\n self.variables.vcw.write(\\n self.variables.devices_dict[\\\"temphum_controller\\\"], min\\n )\\n\\n def dry_air_action():\\n if dry_air_btn.isChecked():\\n device_dict = self.variables.devices_dict[\\\"temphum_controller\\\"]\\n try:\\n command = build_command(\\n device_dict, (\\\"set_environement_control\\\", \\\"ON\\\")\\n )\\n answer = self.variables.vcw.write(device_dict, command)\\n if not answer:\\n self.log.error(\\n \\\"The environement controller did not responsed accordingly. Answer: \\\"\\n + str(answer).strip()\\n )\\n return 0\\n except:\\n self.log.error(\\n \\\"An error occured while changing the environement control\\\"\\n )\\n return 0\\n dry_air_btn.setText(\\\"Humidity ctl. on\\\")\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"humidity_control\\\"\\n ] = True\\n\\n else:\\n device_dict = self.variables.devices_dict[\\\"temphum_controller\\\"]\\n try:\\n command = build_command(\\n device_dict, (\\\"set_environement_control\\\", \\\"OFF\\\")\\n )\\n answer = self.variables.vcw.write(device_dict, command)\\n if not answer:\\n self.log.error(\\n \\\"The environement controller did not responsed accordingly. Answer: \\\"\\n + str(answer).strip()\\n )\\n\\n return 0\\n except:\\n self.log.error(\\n \\\"An error occured while changing the environement control\\\"\\n )\\n return 0\\n dry_air_btn.setText(\\\"Humidity ctl. off\\\")\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"humidity_control\\\"\\n ] = False\\n\\n def light_action():\\n \\\"\\\"\\\"This function is debricated\\\"\\\"\\\"\\n if light_btn.isChecked():\\n self.variables.default_values_dict[\\\"settings\\\"][\\\"external_lights\\\"] = True\\n else:\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"external_lights\\\"\\n ] = False\\n\\n def check_light_state():\\n if (\\n self.variables.default_values_dict[\\\"settings\\\"][\\\"lights\\\"]\\n and not light_btn.text() == \\\"Lights on\\\"\\n ): # Checks if the lights are on and the button is off\\n light_btn.setText(\\\"Lights on\\\")\\n light_btn.setStyleSheet(\\\"background : rgb(0,255,0); border-radius: 5px\\\")\\n elif (\\n not self.variables.default_values_dict[\\\"settings\\\"][\\\"lights\\\"]\\n and not light_btn.text() == \\\"Lights off\\\"\\n ):\\n light_btn.setText(\\\"Lights off\\\")\\n light_btn.setStyleSheet(\\\"background : rgb(255,0,0); border-radius: 5px\\\")\\n\\n def config_plot(plot, plot2, pg):\\n plot = plot.plotItem\\n plot.setLabel(\\\"right\\\", \\\"humidity\\\", units=\\\"%\\\")\\n plot.setLabel(\\\"bottom\\\", \\\"time\\\")\\n plot.setLabel(\\\"left\\\", \\\"temperature\\\", units=\\\"Celsius\\\")\\n plot.getAxis(\\\"left\\\").setPen(pg.mkPen(color=\\\"#c4380d\\\", width=3))\\n plot.getAxis(\\\"right\\\").setPen(pg.mkPen(color=\\\"#025b94\\\", width=3))\\n plot.showAxis(\\\"top\\\", show=True)\\n plot.getAxis(\\\"top\\\").setTicks([])\\n plot.getAxis(\\\"bottom\\\").setScale(1e-9)\\n # plot.setRange(yRange=[15, 35])\\n\\n # For second plot\\n plot.scene().addItem(\\n plot2\\n ) # inserts the second plot into the scene of the first\\n plot2.setGeometry(plot.vb.sceneBoundingRect())\\n plot.getAxis(\\\"right\\\").linkToView(\\n plot2\\n ) # links the second y axis to the second plot\\n plot2.setXLink(plot) # sync the x axis of both plots\\n # plot2.setRange(yRange=[0, 50])\\n\\n def __cut_arrays(data_array, maximum_time, arrays_to_cut):\\n \\\"\\\"\\\"This function cuts an array to a maximum time difference\\n This function is supposed to be used only for temp and humidity shaped arrays\\n \\\"\\\"\\\"\\n\\n try:\\n begin_time = data_array[arrays_to_cut[0]][0][0]\\n end_time = data_array[arrays_to_cut[0]][0][-1]\\n delta_time = (\\n data_array[arrays_to_cut[0]][0][1]\\n - data_array[arrays_to_cut[0]][0][0]\\n )\\n total_time = end_time - begin_time\\n if total_time > maximum_time:\\n over_time = total_time - maximum_time\\n array_elm_to_drop = int(over_time / delta_time)\\n for arrays in arrays_to_cut:\\n data_array[arrays][0] = data_array[arrays][0][\\n array_elm_to_drop:\\n ]\\n data_array[arrays][1] = data_array[arrays][1][\\n array_elm_to_drop:\\n ]\\n except:\\n pass\\n\\n def update_temphum_plots(kwargs=None):\\n # for rooms in self.rooms:\\n if self.variables.default_values_dict[\\\"settings\\\"][\\\"new_data\\\"]:\\n temphum_plot.clear() # clears the plot and prevents a memory leak\\n hum_plot_obj.clear()\\n p1 = temphum_plot.plotItem\\n\\n ax = p1.getAxis(\\\"bottom\\\") # This is the trick\\n __cut_arrays(\\n self.variables.meas_data,\\n float(\\n self.variables.default_values_dict[\\\"settings\\\"].get(\\n \\\"temp_history\\\", 3600\\n )\\n ),\\n [\\\"temperature\\\", \\\"humidity\\\"],\\n )\\n ax.setTicks(\\n [\\n get_thicks_for_timestamp_plot(\\n self.variables.meas_data[\\\"temperature\\\"][0],\\n 5,\\n self.variables.default_values_dict[\\\"settings\\\"][\\n \\\"time_format\\\"\\n ],\\n )\\n ]\\n )\\n\\n try:\\n if len(self.variables.meas_data[\\\"temperature\\\"][0]) == len(\\n self.variables.meas_data[\\\"humidity\\\"][1]\\n ): # sometimes it happens that the values are not yet ready\\n p1.plot(\\n self.variables.meas_data[\\\"temperature\\\"][0],\\n self.variables.meas_data[\\\"temperature\\\"][1],\\n pen={\\\"color\\\": \\\"r\\\", \\\"width\\\": 2},\\n clear=True,\\n )\\n plot_item = setpg.PlotCurveItem(\\n self.variables.meas_data[\\\"humidity\\\"][0],\\n self.variables.meas_data[\\\"humidity\\\"][1],\\n pen={\\\"color\\\": \\\"b\\\", \\\"width\\\": 2},\\n clear=True,\\n )\\n hum_plot_obj.addItem(plot_item)\\n del plot_item # the plot class needs a plot item which can be rendered, to avoid a mem leak delete the created plot item or 20k ram will be used\\n # hum_plot_obj.addItem(setpg.plot(self.variables.meas_data[\\\"humidity\\\"][0],self.variables.meas_data[\\\"humidity\\\"][1],pen={'color': \\\"b\\\", 'width': 2}, clear=True))\\n hum_plot_obj.setGeometry(\\n p1.vb.sceneBoundingRect()\\n ) # resize the second plot!\\n except:\\n pass\\n\\n # Create sublayout\\n temphum_layout = QGridLayout()\\n\\n # Frame over the objects\\n frame = QLabel()\\n frame.setFrameStyle(QFrame.Box | QFrame.Raised)\\n frame.setLineWidth(0)\\n frame.setMidLineWidth(2)\\n\\n self.layout.addWidget(\\n frame, self.temp_ypos, self.temp_xpos, self.temp_ysize, self.temp_xsize\\n )\\n\\n x = np.zeros(1)\\n y = np.zeros(1)\\n\\n setpg = pq\\n # date_axis = CAxisTime(orientation='bottom') # Correctly generates the time axis\\n hum_plot_obj = setpg.ViewBox() # generate new plot item\\n temphum_plot = pq.PlotWidget()\\n config_plot(temphum_plot, hum_plot_obj, setpg) # config the plot items\\n\\n self.variables.add_update_function(update_temphum_plots)\\n\\n # Additional Variables will be generated for temp and hum\\n # self.variables.default_values_dict[\\\"settings\\\"].update({\\\"lights\\\": False, \\\"humidity_control\\\": True, \\\"current_tempmin\\\": 20, \\\"current_tempmax\\\": 25, \\\"current_hummin\\\": 20,\\\"current_hummax\\\": 25})\\n\\n # Spin Boxes for temp and humidity\\n\\n tempmin = QSpinBox()\\n tempmax = QSpinBox()\\n hummin = QSpinBox()\\n hummax = QSpinBox()\\n\\n # Spinbox label\\n textbox_temp = QLabel()\\n textbox_temp.setText(\\\"Min temp. Max temp.\\\")\\n textbox_temp.setFont(self.font)\\n textbox_hum = QLabel()\\n textbox_hum.setText(\\\"Min hum. Max hum.\\\")\\n textbox_hum.setFont(self.font)\\n\\n # Config\\n\\n tempmin.setRange(15, 35)\\n tempmin.setValue(\\n float(\\n self.variables.default_values_dict[\\\"settings\\\"].get(\\\"current_tempmin\\\", 0)\\n )\\n )\\n tempmax.setRange(15, 35)\\n tempmax.setValue(\\n float(\\n self.variables.default_values_dict[\\\"settings\\\"].get(\\\"current_tempmax\\\", 0)\\n )\\n )\\n tempmin.valueChanged.connect(valuechange)\\n tempmax.valueChanged.connect(valuechange)\\n\\n hummin.setRange(0, 70)\\n hummin.setValue(\\n float(\\n self.variables.default_values_dict[\\\"settings\\\"].get(\\\"current_hummin\\\", 0)\\n )\\n )\\n hummax.setRange(0, 70)\\n hummax.setValue(\\n float(\\n self.variables.default_values_dict[\\\"settings\\\"].get(\\\"current_hummax\\\", 0)\\n )\\n )\\n hummin.valueChanged.connect(valuechange)\\n hummax.valueChanged.connect(valuechange)\\n\\n # Push buttons on the right for humidity control and light control\\n\\n dry_air_btn = QPushButton(\\\"Humidity ctl. off\\\")\\n self.variables.default_values_dict[\\\"settings\\\"][\\\"humidity_control\\\"] = False\\n dry_air_btn.setCheckable(True)\\n dry_air_btn.toggle()\\n dry_air_btn.clicked.connect(dry_air_action)\\n dry_air_btn.setChecked(False)\\n\\n light_btn = QLabel()\\n light_btn.setText(\\\"State not defined\\\")\\n light_btn.setAlignment(QtCore.Qt.AlignVCenter | QtCore.Qt.AlignHCenter)\\n light_btn.setStyleSheet(\\\"background : rgb(255,0,0); border-radius: 5px\\\")\\n\\n # light_btn.setCheckable(True)\\n # light_btn.clicked.connect(light_action)\\n\\n # Humidity\\n # temphum_plot.plot(x,y, pen=\\\"b\\\")\\n\\n # Widgets add\\n temphum_layout.addWidget(textbox_temp, 0, 0, 1, 2)\\n temphum_layout.addWidget(tempmin, 1, 0)\\n temphum_layout.addWidget(tempmax, 1, 1)\\n\\n temphum_layout.addWidget(textbox_hum, 2, 0, 1, 2)\\n temphum_layout.addWidget(hummin, 3, 0)\\n temphum_layout.addWidget(hummax, 3, 1)\\n\\n temphum_layout.addWidget(dry_air_btn, 4, 0, 1, 2)\\n temphum_layout.addWidget(light_btn, 5, 0, 3, 2)\\n\\n temphum_layout.addWidget(temphum_plot, 0, 3, 10, 2)\\n\\n temphum_layout.setContentsMargins(8, 8, 0, 8) # Makes a margin to the layout\\n\\n # Add the layout to the main layout\\n self.layout.addLayout(\\n temphum_layout,\\n self.temp_ypos,\\n self.temp_xpos,\\n self.temp_ysize,\\n self.temp_xsize,\\n )\\n\\n def update():\\n pass\\n\\n self.variables.add_update_function(update)\\n self.variables.add_update_function(check_light_state)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rdy,Rqcd\\\")\",\n \"def default_pypeit_par(cls):\\n par = super().default_pypeit_par()\\n\\n # Wavelengths\\n # 1D wavelength solution with OH lines\\n par['calibrations']['wavelengths']['rms_threshold'] = 1.0\\n par['calibrations']['wavelengths']['sigdetect']=[5,10,10,10,10,20,30,30,30,30,30,10,30,30,60,30,30,10,20,30,10]\\n par['calibrations']['wavelengths']['n_first']=2\\n par['calibrations']['wavelengths']['n_final']=[3,3,3,2,4,4,4,3,4,4,4,3,4,4,4,4,4,4,6,6,4]\\n par['calibrations']['wavelengths']['lamps'] = ['OH_FIRE_Echelle']\\n #par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']\\n par['calibrations']['wavelengths']['method'] = 'reidentify'\\n par['calibrations']['wavelengths']['cc_thresh'] = 0.35\\n par['calibrations']['wavelengths']['reid_arxiv'] = 'magellan_fire_echelle.fits'\\n par['calibrations']['wavelengths']['match_toler']=30.0\\n\\n # Echelle parameters\\n par['calibrations']['wavelengths']['echelle'] = True\\n# par['calibrations']['wavelengths']['ech_fix_format'] = True\\n par['calibrations']['wavelengths']['ech_nspec_coeff'] = 4\\n par['calibrations']['wavelengths']['ech_norder_coeff'] = 6\\n par['calibrations']['wavelengths']['ech_sigrej'] = 3.0\\n\\n # Always correct for flexure, starting with default parameters\\n par['scienceframe']['process']['sigclip'] = 20.0\\n par['scienceframe']['process']['satpix'] ='nothing'\\n\\n # Set slits and tilts parameters\\n par['calibrations']['tilts']['tracethresh'] = 5\\n par['calibrations']['slitedges']['edge_thresh'] = 3.\\n par['calibrations']['slitedges']['trace_thresh'] = 10.\\n par['calibrations']['slitedges']['fit_order'] = 5\\n par['calibrations']['slitedges']['max_shift_adj'] = 0.5\\n par['calibrations']['slitedges']['fit_min_spec_length'] = 0.5\\n par['calibrations']['slitedges']['left_right_pca'] = True\\n par['calibrations']['slitedges']['pca_order'] = 3\\n\\n # Model entire slit\\n par['reduce']['extraction']['model_full_slit'] = True # local sky subtraction operates on entire slit\\n par['reduce']['findobj']['maxnumber_sci'] = 2 # Slit is narrow so allow one object per order\\n par['reduce']['findobj']['maxnumber_std'] = 1 # Slit is narrow so allow one object per order\\n\\n # Processing steps\\n turn_off = dict(use_illumflat=False, use_biasimage=False, use_overscan=False,\\n use_darkimage=False)\\n par.reset_all_processimages_par(**turn_off)\\n # Do not correct for flexure\\n par['flexure']['spec_method'] = 'skip'\\n\\n # Set the default exposure time ranges for the frame typing\\n par['calibrations']['standardframe']['exprng'] = [None, 60]\\n par['calibrations']['arcframe']['exprng'] = [20, None]\\n par['calibrations']['darkframe']['exprng'] = [20, None]\\n par['scienceframe']['exprng'] = [20, None]\\n\\n # Sensitivity function parameters\\n # Sensitivity function parameters\\n par['sensfunc']['algorithm'] = 'IR'\\n par['sensfunc']['polyorder'] = 5\\n par['sensfunc']['IR']['maxiter'] = 2\\n # place holder for telgrid file\\n par['sensfunc']['IR']['telgridfile'] = 'TelFit_LasCampanas_3100_26100_R20000.fits'\\n\\n return par\",\n \"def setValues(\\n self,\\n frameRate: int = None,\\n timeScale: int = None,\\n vpDecorations: Boolean = ON,\\n vpBackground: Boolean = OFF,\\n compass: Boolean = OFF,\\n ):\\n pass\",\n \"def set_parameters(self, par):\\n try:\\n for l in self.cell.layers:\\n r_curve = cmf.VanGenuchtenMualem(\\n Ksat=10**par.pKsat, phi=par.porosity, alpha=par.alpha, n=par.n\\n )\\n r_curve.w0 = r_curve.fit_w0()\\n l.soil = r_curve\\n self.cell.saturated_depth = 0.5\\n self.gw.potential = self.cell.z - 0.5\\n except RuntimeError as e:\\n sys.stderr.write(\\\"Set parameters failed with:\\\\n\\\" + str(par) + \\\"\\\\n\\\" + str(e))\\n raise\",\n \"def set_parameters(cls):\\r\\n \\\"\\\"\\\" EXECUTE THIS FUNCTION IN THE FARM CLASS! \\\"\\\"\\\"\\r\\n cls.TSR, cls.RPM, cls.RAD, cls.BLA, cls.CHR, cls.SEC, cls.NT = \\\\\\r\\n np.loadtxt('settings.csv', delimiter=',', skiprows=1, unpack=True)\",\n \"def __setup_parameters__(self):\\r\\n self.M=self.N+1\\r\\n self.u=1+self.pu\\r\\n self.d=1-self.pd\\r\\n self.qu=(math.exp((self.r-self.div)*self.dt)-self.d)/(self.u-self.d)\\r\\n self.qd=1-self.qu\",\n \"def set_profile_params(self, params):\\n self.alpha = params[0]\\n self.beta = params[1]\\n self.gamma = params[2]\\n self.c500 = params[3]\\n self.P0 = params[4]\\n self.M500 = params[5]\\n self.r500 = params[6]\\n self.z = params[7]\",\n \"def set_params(self, **kwargs):\\n\\n kw_keys = list(kwargs)\\n\\n if 'alpha' in kw_keys:\\n self.alpha = kwargs['alpha']\\n\\n if 'beta' in kw_keys:\\n self.beta = kwargs['beta']\\n\\n if 'gamma' in kw_keys: \\n \\tself.gamma = kwargs['gamma']\\n\\n if 'epsilon' in kw_keys:\\n self.epsilon = kwargs['epsilon']\\n \\n self.nact = self.highbound-self.lowbound\\n self.actions = np.arange(self.nact)\",\n \"def initializeParameters(self):\\n\\n self.params[2].value = False\\n self.params[3].enabled = False\\n self.params[7].value = True\\n self.params[7].enabled = False\\n self.params[8].value = None\\n self.params[8].enabled = False\",\n \"def set_shape_params(self, params):\\n self.alpha = params[0]\\n self.beta = params[1]\\n self.gamma = params[2]\\n self.c500 = params[3]\\n self.P0 = params[4]\",\n \"def __init__(self, encut, spinaxis, ldaul, Uparam, Jparam, name='DFTCL_settings'):\\n ncl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": None, \\\"SAXIS\\\": spinaxis, \\\"LSORBIT\\\": \\\".TRUE.\\\", \\\"LNONCOLLINEAR\\\": \\\".TRUE.\\\"}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=ncl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def initialize_dynamic_settings(self):\\n self.ship_speed = 5\\n self.bullet_speed = 1.0\\n self.alien_speed=1.0\\n #fleet direction of 1 represents right -1 represents left\\n self.fleet_direction = 1\\n #scoring\\n self.alien_points=50\",\n \"def set_params(self, **kargs):\\n\\n # may further modify parameters manually\\n if len(kargs) > 0:\\n for key in kargs:\\n if key in self.params[self.profile].keys():\\n self.params[self.profile][key] = kargs[key]\\n else:\\n print '%s not a standard key. Will not be used.' % (key)\\n \\n self.pathout = self.params[self.profile]['pathout']\\n self.chans = self.params[self.profile]['chans']\\n self.dmarr = self.params[self.profile]['dmarr']\\n self.pulsewidth = self.params[self.profile]['pulsewidth'] * n.ones(len(self.chans))\\n self.approxuvw = self.params[self.profile]['approxuvw']\\n self.beam_params = self.params[self.profile]['beam_params']\\n self.long = self.params[self.profile]['long']\\n self.lat = self.params[self.profile]['lat']\",\n \"def initializeParameters(self):\\r\\n\\t\\tself.input_raster.enabled = True\\r\\n\\t\\tself.approach.enabled = True\\r\\n\\t\\tself.predefined_pattern.enabled = False\\r\\n\\t\\tself.predefined_pattern.value = 'Mexican Hat wavelet'\\r\\n\\t\\tself.pattern_workspace.enabled = False\\r\\n\\t\\tself.point_matrix_size.enabled = False\\r\\n\\t\\tself.point_matrix_size.value = 3\\r\\n\\t\\tself.point_vectors.enabled = False\\r\\n\\t\\tself.mapping_field.enabled = False\\r\\n\\t\\tself.move_to_max.enabled = False\\r\\n\\t\\tself.move_to_max_distance.enabled = False\\r\\n\\t\\tself.move_to_max_distance.value = 3\\r\\n\\t\\tself.mh_iteration.enabled = False\\r\\n\\t\\tself.mh_dil_val.enabled = False\\r\\n\\t\\tself.mh_dil_val.value = 1\\r\\n\\t\\tself.mh_dil_start.value = 0.01\\r\\n\\t\\tself.mh_dil_stop.value = 1\\r\\n\\t\\tself.mh_dil_step.value = 0.1\\r\\n\\t\\tself.mh_dil_start.enabled = False\\r\\n\\t\\tself.mh_dil_stop.enabled = False\\r\\n\\t\\tself.mh_dil_step.enabled = False\\r\\n\\t\\tself.transform.enabled = False\\r\\n\\t\\tself.size_of_the_cell.enabled = False\\r\\n\\t\\tself.size_of_the_cell.value = 1\\r\\n\\t\\tself.output_sim_matrix.enabled = False\\r\\n\\t\\tself.output_table.enabled = False\\r\\n\\t\\tself.output_raster_workspace.enabled = False\",\n \"def _set_parameter(self):\\n # Get parameter keys\\n self.input_parameter = self.parameter_combobox.currentText()\\n self.result_parameter = self.result_parameters[self.input_parameter]\\n # Adjust axes labels\\n self.ax.set_xlabel('{} steunpunt'.format(self.input_parameter))\\n self.ax.set_ylabel('{} uitvoerlocatie'.format(self.input_parameter))\\n # Set data\\n self._set_data()\",\n \"def setparams (w=None, delay=None, tau=None, low=None, high=None):\\n global firststep\\n for n in nclist: \\n if w is not None: n.weight[0] = w if useHHCell else -w # neg wts are needed for ArtCell (neg driving force for HHCell)\\n if delay is not None: n.delay=delay\\n if firststep or (low and high): # only set this up using defaults the 1st time\\n firststep = False \\n if low is not None: low, high = 10, 11\\n for ce,ran,start in zip(cells, np.random.uniform(low, high, ncells), np.random.uniform(0, 30, ncells)): \\n if useHHCell:\\n ce.stim.amp = ran\\n ce.stim.delay = start\\n else: \\n ce.pp.invl = ran\\n if tau is not None: ce.tau = tau\",\n \"def set_params(self, **kwargs):\\n if 'nbins' in kwargs:\\n self._nbins = kwargs['nbins']\\n if self._nbins != 'auto':\\n self._nbins = int(self._nbins)\\n if 'symmetric' in kwargs:\\n self._symmetric = kwargs['symmetric']\\n if 'prune' in kwargs:\\n prune = kwargs['prune']\\n if prune is not None and prune not in ['upper', 'lower', 'both']:\\n raise ValueError(\\n \\\"prune must be 'upper', 'lower', 'both', or None\\\")\\n self._prune = prune\\n if 'min_n_ticks' in kwargs:\\n self._min_n_ticks = max(1, kwargs['min_n_ticks'])\\n if 'steps' in kwargs:\\n steps = kwargs['steps']\\n if steps is None:\\n self._steps = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10]\\n else:\\n self._steps = self._validate_steps(steps)\\n self._extended_steps = self._staircase(self._steps)\\n if 'integer' in kwargs:\\n self._integer = kwargs['integer']\",\n \"def set_parameter(self, sensor_name, parameter_name, parameter_value):\\n if parameter_name == 'perspective_angle':\\n parameter_value = parameter_value / (180 * 2) * math.pi\\n if parameter_name in self.params_f:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_f[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n elif parameter_name in self.params_i:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_i[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n else:\\n return 'Parameter not found'\",\n \"def __set_sensor_elements(*args):\\n args[0].Data.pt_number = args[1]\\n args[0].Data.set_pt_number()\\n args[0].Limit.pt_number = args[1]\\n args[0].Limit.set_pt_number()\\n args[0].Data.tc_number = args[2]\\n args[0].Data.set_tc_number()\\n args[0].Limit.tc_number = args[2]\\n args[0].Limit.set_tc_number()\\n args[0].Data.lc_number = args[3]\\n args[0].Data.set_lc_number()\\n args[0].Limit.lc_number = args[3]\\n args[0].Limit.set_lc_number()\\n args[0].Data.ls_number = args[4]\\n args[0].Data.set_ls_number()\\n args[0].Limit.ls_number = args[4]\\n args[0].Limit.set_ls_number()\",\n \"def set_params(**kwargs):\\n\\t\\tif \\\"study\\\" in kwargs.keys():\\n\\t\\t\\traise TypeError(\\\"Got an unexpected keyword argument: 'study'\\\")\\n\\t\\telse:\\n\\t\\t\\tfor i in range(len(_RECOGNIZED_ELEMENTS_)):\\n\\t\\t\\t\\t__settings[_RECOGNIZED_ELEMENTS_[i]] = __fractional(\\n\\t\\t\\t\\t\\t_RECOGNIZED_ELEMENTS_[i], study = \\\"LC18\\\", **kwargs)[0]\",\n \"def initialize_dynamic_settings(self):\\n self.ship_speed = 1.5\\n self.bullet_speed = 3.0\\n self.alien_speed = 0.8 # og speed 1.0\\n # Der Wert 1 für fleet_direction bedeutet \\\"nach rechts\\\", -1 \\\"nach link \\\"\\n self.fleet_direction = 1\\n # Punktwertung\\n self.alien_points = 50\",\n \"def update_parameters(self):\\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\\n self.update_gamma()\\n self.update_gamma0()\\n self.update_lambda()\\n self.update_nu()\\n if self.sample_xi:\\n self.update_xi()\",\n \"def _set_parameters(self, parameters):\\n self.parameters = parameters\\n self._set_points_and_weights()\",\n \"def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\\n \\n # Gear ratio\\n self.R = [np.diag([r1,r1]),np.diag([r1,r2]),np.diag([r2,r1]),np.diag([r2,r2])]\\n \\n # Inertia\\n self.Ia = np.diag([ja,ja])\\n \\n # Damping (linear)\\n self.Da = np.diag([ba,ba])\",\n \"def setWalabotSettings():\\n wlbt.SetProfile(wlbt.PROF_SENSOR)\\n wlbt.SetArenaR(R_MIN, R_MAX, R_RES)\\n wlbt.SetArenaTheta(THETA_MIN, THETA_MAX, THETA_RES)\\n wlbt.SetArenaPhi(PHI_MIN, PHI_MAX, PHI_RES)\\n #wlbt.SetThreshold(THRESHOLD)\\n wlbt.SetDynamicImageFilter(wlbt.FILTER_TYPE_DERIVATIVE) #Also test with FILTER_TYPE_NONE\\n print('- Walabot Configured.')\",\n \"def set_speed():\\n pass\",\n \"def initialize_dynamic_settings(self):\\n self.ship_speed_factor = 1.5\\n self.bullet_speed_factor = 3\\n self.alien_speed_factor = 1\\n self.fleet_direction = 1\",\n \"def set_parameters(self, *args, **kwargs):\\n super(DAEM, self).set_parameters(*args, **kwargs)\\n self._Em = self._calc_Em()\",\n \"def __init__(self, encut, ldaul, Uparam, Jparam, name=\\\"DFTU_settings\\\"):\\n\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\" , \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LADAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIX\\\": 4}\\n InputParameters.__init__(self, name=name, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def __fill_motor_parameters(self, motor_id, model_number):\\n # Get the Motor Name\\n model_name = self.dynotools.getModelNameByModelNumber(model_number)\\n\\n # Get Max and Min angles\\n angles = self.sdk_io.get_angle_limits(motor_id, model_name)\\n self.angles[str(model_name)] = angles \\n\\n # Get Current, Min, and Max voltages\\n voltage = self.sdk_io.get_voltage(motor_id, model_name)\\n voltages = self.sdk_io.get_voltage_limits(motor_id, model_name)\\n \\n # ROS Parameters Setup\\n rospy.set_param('dynamixel/%s/%d/model_number' %(self.port_namespace, motor_id), model_number)\\n rospy.set_param('dynamixel/%s/%d/model_name' %(self.port_namespace, motor_id), DXL_MODEL_TO_PARAMS[model_number]['name'])\\n rospy.set_param('dynamixel/%s/%d/min_angle' %(self.port_namespace, motor_id), angles['min'])\\n rospy.set_param('dynamixel/%s/%d/max_angle' %(self.port_namespace, motor_id), angles['max'])\\n torque_per_volt = DXL_MODEL_TO_PARAMS[model_number]['torque_per_volt']\\n rospy.set_param('dynamixel/%s/%d/torque_per_volt' %(self.port_namespace, motor_id), torque_per_volt)\\n rospy.set_param('dynamixel/%s/%d/max_torque' %(self.port_namespace, motor_id), torque_per_volt * voltage)\\n \\n velocity_per_volt = DXL_MODEL_TO_PARAMS[model_number]['velocity_per_volt']\\n rpm_per_tick = DXL_MODEL_TO_PARAMS[model_number]['rpm_per_tick']\\n rospy.set_param('dynamixel/%s/%d/velocity_per_volt' %(self.port_namespace, motor_id), velocity_per_volt)\\n rospy.set_param('dynamixel/%s/%d/max_velocity' %(self.port_namespace, motor_id), velocity_per_volt * voltage)\\n rospy.set_param('dynamixel/%s/%d/radians_second_per_encoder_tick' %(self.port_namespace, motor_id), rpm_per_tick * RPM_TO_RADSEC)\\n \\n encoder_resolution = DXL_MODEL_TO_PARAMS[model_number]['encoder_resolution']\\n range_degrees = DXL_MODEL_TO_PARAMS[model_number]['range_degrees']\\n range_radians = math.radians(range_degrees)\\n rospy.set_param('dynamixel/%s/%d/encoder_resolution' %(self.port_namespace, motor_id), encoder_resolution)\\n rospy.set_param('dynamixel/%s/%d/range_degrees' %(self.port_namespace, motor_id), range_degrees)\\n rospy.set_param('dynamixel/%s/%d/range_radians' %(self.port_namespace, motor_id), range_radians)\\n rospy.set_param('dynamixel/%s/%d/encoder_ticks_per_degree' %(self.port_namespace, motor_id), encoder_resolution / range_degrees)\\n rospy.set_param('dynamixel/%s/%d/encoder_ticks_per_radian' %(self.port_namespace, motor_id), encoder_resolution / range_radians)\\n rospy.set_param('dynamixel/%s/%d/degrees_per_encoder_tick' %(self.port_namespace, motor_id), range_degrees / encoder_resolution)\\n rospy.set_param('dynamixel/%s/%d/radians_per_encoder_tick' %(self.port_namespace, motor_id), range_radians / encoder_resolution)\\n\\n # Get Parameters for pos_to_raw\\n self.flipped = angles['min'] > angles['max']\\n self.encoder_resolution = encoder_resolution\\n self.range_degrees = range_degrees\\n self.range_radians = range_radians\\n self.encoder_ticks_per_degree = encoder_resolution / range_degrees\\n self.encoder_ticks_per_radian = encoder_resolution / range_radians\\n self.degrees_per_encoder_tick = range_degrees / encoder_resolution\\n self.radians_per_encoder_tick = range_radians / encoder_resolution\\n self.initial_position_raw = self.sdk_io.get_position(motor_id, model_name)\\n\\n # Flipped case\\n if self.flipped:\\n self.min_angle = (self.initial_position_raw - angles['min']) * self.radians_per_encoder_tick\\n self.max_angle = (self.initial_position_raw - angles['max']) * self.radians_per_encoder_tick\\n else:\\n self.min_angle = (angles['min'] - self.initial_position_raw) * self.radians_per_encoder_tick\\n self.max_angle = (angles['max']- self.initial_position_raw) * self.radians_per_encoder_tick\\n\\n # keep some parameters around for diagnostics\\n self.motor_static_info[motor_id] = {}\\n self.motor_static_info[motor_id]['model'] = DXL_MODEL_TO_PARAMS[model_number]['name']\\n self.motor_static_info[motor_id]['min_angle'] = angles['min']\\n self.motor_static_info[motor_id]['max_angle'] = angles['max']\",\n \"def load_params(self, event):\\n \\n self.robot_type = rospy.get_param(\\\"robot_type\\\" , 'pendulum' )\\n self.robot_config = rospy.get_param(\\\"robot_config\\\", 'wrist-only' )\\n self.robot_ctl = rospy.get_param(\\\"controller\\\", 'RfixCTC' )\\n self.fixed_mode = rospy.get_param(\\\"fixed_mode\\\", 1 )\\n \\n \\n ###############################################\\n # Load robot model for the right configuration\\n if self.robot_config == 'wrist-only':\\n self.R = Proto.SingleRevoluteDSDM()\\n \\n elif self.robot_config == 'dual-plane' :\\n self.R = Proto.TwoPlanarSerialDSDM()\\n \\n else:\\n self.R = None\\n \\n ###############################################\\n # Load controller\\n if self.robot_ctl == 'RfixCTC' :\\n self.Ctl = RminCTC.RfixComputedTorqueController( self.R , self.fixed_mode )\\n \\n elif self.robot_ctl == 'RminCTC' :\\n self.Ctl = RminCTC.RminComputedTorqueController( self.R )\\n \\n elif self.robot_ctl == 'RfixSLD' :\\n self.Ctl = RminCTC.RfixSlidingModeController( self.R , self.fixed_mode )\\n \\n elif self.robot_ctl == 'RminSLD' :\\n self.Ctl = RminCTC.RminSlidingModeController( self.R )\\n \\n elif self.robot_ctl == 'RollCTC' :\\n self.Ctl = RollCTC.RolloutComputedTorqueController( self.R )\\n \\n elif self.robot_ctl == 'RollSLD' :\\n self.Ctl = RollCTC.RolloutSlidingModeController( self.R )\\n \\n else:\\n self.Ctl = None\\n \\n \\n if self.robot_config == 'wrist-only':\\n self.Ctl.n_gears = rospy.get_param(\\\"n_gears\\\", 2 )\\n self.x_d = np.array( rospy.get_param(\\\"goal\\\", [0,0] ) )\\n \\n elif self.robot_config == 'dual-plane' :\\n self.Ctl.n_gears = rospy.get_param(\\\"n_gears\\\", 4 )\\n self.x_d = np.array( rospy.get_param(\\\"goal\\\", [0.0,0.0,0.0,0.0] ) )\\n #self.x_d = np.array( [-3.14 , 0 , 0 , 0] )\\n \\n # Gen ctl params\\n self.Ctl.hysteresis = rospy.get_param(\\\"hysteresis\\\", True )\\n self.Ctl.min_delay = rospy.get_param(\\\"min_delay\\\", 0.5 )\\n \\n self.Ctl.w0 = rospy.get_param(\\\"w0\\\", 1 )\\n self.Ctl.zeta = rospy.get_param(\\\"zeta\\\", 0.7 )\\n \\n self.Ctl.lam = rospy.get_param(\\\"lam\\\", 1 )\\n self.Ctl.nab = rospy.get_param(\\\"nab\\\", 1 )\\n self.Ctl.D = rospy.get_param(\\\"D\\\", 0 )\\n \\n self.Ctl.horizon = rospy.get_param(\\\"horizon\\\", 0.5 )\\n self.Ctl.sim_dt = rospy.get_param(\\\"sim_dt\\\", 0.1 )\\n \\n self.Ctl.domain_check = rospy.get_param(\\\"domain_check\\\", False )\\n \\n # Base policy param for roll \\n if self.robot_ctl == 'RollCTC' :\\n self.Ctl.FixCtl.lam = self.Ctl.lam\\n \\n elif self.robot_ctl == 'RollSLD' :\\n self.Ctl.FixCtl.lam = self.Ctl.lam \\n self.Ctl.FixCtl.nab = self.Ctl.nab \\n self.Ctl.FixCtl.D = self.Ctl.D\",\n \"def _set_parameters(self, parameters, commit=True):\\n\\n for param in parameters:\\n self.opt.setParameter(param[0], param[1])\\n\\n if commit:\\n self.opt.sendConfiguration()\\n\\n return self.opt.getParameter(\\\"ReadoutTimeCalculation\\\")\",\n \"def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\\n \\n # Gear ratio\\n self.R = [np.diag([r1,r1,r1]),np.diag([r1,r1,r2]),np.diag([r1,r2,r1]), np.diag([r1,r2,r2]),\\n np.diag([r2,r1,r1]),np.diag([r2,r1,r2]),np.diag([r2,r2,r1]), np.diag([r2,r2,r2]) ]\\n \\n # Inertia\\n self.Ia = np.diag([ja,ja,ja])\\n \\n # Damping (linear)\\n self.Da = np.diag([ba,ba,ba])\",\n \"def __init__(self):\\n super().__init__()\\n self.TERRAIN_VARIANCE = 0.0\",\n \"def set_motion_params(self, xps_grp, params):\\n\\n # if (xps_grp not in groupName): raise KeyError(\\\"KeyError: '{}' is not in \\\".format(xps_grp))\\n if (xps_grp not in groupName): raise KeyError(xps_grp)\\n default_velocity = 20\\n temp_par = [None]*4\\n if (type(params) is int or type(params) is float): temp_par[0] = float(params)\\n elif (type(params) is list):\\n if (len(params) > 0):\\n for i,par in enumerate(params):\\n if (i>=4): break\\n temp_par[i] = par\\n else: temp_par[0] = default_velocity ## Set velocity with default value\\n\\n else:\\n raise TypeError('ERROR: Require a list or float for parameters')\\n\\n \\n self.newportxps.set_velocity(groupName[xps_grp]+'.Pos',\\n velo=temp_par[0], accl=temp_par[1],\\n min_jerktime=temp_par[2], max_jerktime=temp_par[3])\",\n \"def assign_model_parameters(self,xmax,zmax,dh,duration):\\n self.model_parameters['xmax']=xmax\\n self.model_parameters['zmax']=zmax\\n self.model_parameters['dh']=dh\\n self.model_parameters['duration']=duration\",\n \"def set_params(self, dic):\\n if dic is not None:\\n for key, val in zip(dic.keys(), dic.values()):\\n if key in self.__dict__.keys():\\n self.__dict__[key] = val\\n\\n if 'scale_params' in self.__dict__.keys():\\n self.scale_params.set_params(dic)\\n if 'atmospheric_params' in self.__dict__.keys():\\n if self.atmospheric_params is not None:\\n self.atmospheric_params.set_params(dic)\\n\\n if 'atemperature_params' in self.__dict__.keys():\\n if self.atemperature_params is not None:\\n self.atemperature_params.set_params(dic)\\n\\n if 'oceanic_params' in self.__dict__.keys():\\n if self.oceanic_params is not None:\\n self.oceanic_params.set_params(dic)\\n\\n if 'ground_params' in self.__dict__.keys():\\n if self.ground_params is not None:\\n self.ground_params.set_params(dic)\\n\\n if 'otemperature_params' in self.__dict__.keys():\\n if self.gotemperature_params is not None:\\n self.gotemperature_params.set_params(dic)\\n\\n if 'gtemperature_params' in self.__dict__.keys():\\n if self.gotemperature_params is not None:\\n self.gotemperature_params.set_params(dic)\",\n \"def _set_params(self,x):\\r\\n self.k._set_params(x)\",\n \"def __init__(self, make, model, year):\\r\\n super().__init__(make, model, year)\\r\\n self.battery_size = 70\\r\\n # self.autopilot = autopilot\\r\",\n \"def set_parameter(self, param, value, stripe = 0, location = 3):\\n if param in self.fpga.cabac_top[0].params:\\n self.fpga.set_cabac_value(param, value, stripe)\\n time.sleep(0.1)\\n self.fpga.send_cabac_config(stripe)\\n\\n elif param in [\\\"V_SL\\\", \\\"V_SH\\\", \\\"V_RGL\\\", \\\"V_RGH\\\", \\\"V_PL\\\", \\\"V_PH\\\"]:\\n self.fpga.set_clock_voltages({param: value})\\n\\n elif param == \\\"I_OS\\\":\\n self.fpga.set_current_source(value, stripe)\\n\\n else:\\n print(\\\"Warning: unidentified parameter for the REB: %s\\\" % param)\",\n \"def initialize_dynamic_settings(self):\\r\\n self.alien_speed_factor = 0.1\\r\\n self.alien_bullet_speed_factor = 0.7\",\n \"def __init__(self, sensor):\\n self.sensor = sensor\\n self.temperature = None\\n self.humitidy = None\\n self.update()\",\n \"def updateParameters(self, parameters):\\r\\n #return\\r\\n parameters[2].enabled = 0\\r\\n parameters[3].enabled = 0\\r\\n parameters[4].enabled = 0\",\n \"def updateParameters(self, parameters):\\r\\n #return\\r\\n parameters[2].enabled = 0\\r\\n parameters[3].enabled = 0\\r\\n parameters[4].enabled = 0\",\n \"def update_parameters(self):\\n self.alignment_factor = rospy.get_param('/dyn_reconf/alignment_factor')\\n self.cohesion_factor = rospy.get_param('/dyn_reconf/cohesion_factor')\\n self.separation_factor = rospy.get_param('/dyn_reconf/separation_factor')\\n self.avoid_factor = rospy.get_param('/dyn_reconf/avoid_factor')\\n self.max_speed = rospy.get_param('/dyn_reconf/max_speed')\\n self.max_force = rospy.get_param('/dyn_reconf/max_force')\\n self.friction = rospy.get_param('/dyn_reconf/friction')\\n self.crowd_radius = rospy.get_param('/dyn_reconf/crowd_radius')\\n self.search_radius = rospy.get_param('/dyn_reconf/search_radius')\\n\\n rospy.loginfo(rospy.get_caller_id() + \\\" -> Parameters updated\\\")\\n if DEBUG:\\n print('alignment_factor: ', self.alignment_factor)\\n print('cohesion_factor: ', self.cohesion_factor)\\n print('separation_factor: ', self.separation_factor)\\n print('avoid_factor: ', self.avoid_factor)\\n print('max_speed: ', self.max_speed)\\n print('max_force: ', self.max_force)\\n print('friction: ', self.friction)\\n print('crowd_radius: ', self.crowd_radius)\\n print('search_radius: ', self.search_radius)\",\n \"def update(self, parameters):\\n self.set_frequencies(parameters) # f_i\\n self.set_coupling_weights(parameters) # w_ij\\n self.set_phase_bias(parameters) # theta_i\\n self.set_amplitudes_rate(parameters) # a_i\\n self.set_nominal_amplitudes(parameters) # R_i\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def teleopInit(self):\\n self.gyro.setSensitivity(\\n self.voltsPerDegreePerSecond\\n ) # calibrates gyro values to equal degrees\",\n \"def __init__(self, sensor, temperature_resolution, humidity_resolution):\\n self.sensor = sensor\\n self.sensor.turnHeaterOn() \\n time.sleep(1.0) # Burn off condensed stuff.\\n self.sensor.turnHeaterOff() \\n self.update()\\n # Main Program\\n #print \\\"------------\\\"\\n #print \\\"Manfacturer ID=0x%X\\\"% self.sensor.readManufacturerID() \\n #print \\\"Device ID=0x%X\\\"% self.sensor.readDeviceID() \\n #print \\\"Serial Number ID=0x%X\\\"% self.sensor.readSerialNumber() \\n \\n # change temperature resolution\\n self.sensor.setTemperatureResolution(temperature_resolution)\\n self.sensor.setHumidityResolution(humidity_resolution)\",\n \"def set_parameters(pars):\\n y0=[]\\n fun=None \\n state_evol=None\\n if pars[\\\"state_law\\\"]==0:\\n state_evol=state_evol_d\\n elif pars[\\\"state_law\\\"]==1:\\n state_evol=state_evol_r\\n elif pars[\\\"state_law\\\"]==2:\\n state_evol=state_evol_p\\n elif pars[\\\"state_law\\\"]==3:\\n state_evol=state_evol_n\\n \\n if pars[\\\"model\\\"]==0:\\n y0 = [pars[\\\"Vpl\\\"]*0.9,0.1,pars[\\\"sigma1\\\"]]\\n fun = fun_qds\\n damping = pars[\\\"nu\\\"]\\n \\n if pars[\\\"model\\\"]==1:\\n y0 = [pars[\\\"Vpl\\\"]*0.9, 0.1,pars[\\\"sigma1\\\"],pars[\\\"sigma1\\\"]*pars[\\\"f0\\\"]]\\n fun = fun_fds\\n damping = pars[\\\"m\\\"]\\n\\n if pars[\\\"model\\\"]==2:\\n y0 = [pars[\\\"Vpl\\\"]*0.99,pars[\\\"Vpl\\\"], pars[\\\"Vpl\\\"],0.1,pars[\\\"sigma1\\\"],pars[\\\"sigma2\\\"]]\\n fun= fun_qdc\\n damping = pars[\\\"nu\\\"]\\n\\n if pars[\\\"model\\\"]==3:\\n y0 = [pars[\\\"Vpl\\\"]*1.1,pars[\\\"Vpl\\\"], pars[\\\"Vpl\\\"],0.0,pars[\\\"sigma1\\\"],pars[\\\"sigma2\\\"],pars[\\\"sigma1\\\"]*pars[\\\"f0\\\"]]\\n fun = fun_fdc\\n damping = pars[\\\"m\\\"]\\n\\n return (np.array(y0), state_evol, fun, damping)\",\n \"def _calculate_parameters(self, thickness: int = 10):\\n\\n self.thickness = thickness\\n\\n # set orientation dependent parameters: (different for x, y, z-PML)\\n # NOTE: these methods are implemented by the subclasses of PML.\\n self._set_locations()\\n self._set_shape()\\n self._set_sigmaE()\\n self._set_sigmaH()\\n\\n # set the other parameters\\n Nx, Ny, Nz = self.shape # is defined by _set_shape()\\n self.phi_E = bd.zeros((Nx, Ny, Nz, 3))\\n self.phi_H = bd.zeros((Nx, Ny, Nz, 3))\\n self.psi_Ex = bd.zeros((Nx, Ny, Nz, 3))\\n self.psi_Ey = bd.zeros((Nx, Ny, Nz, 3))\\n self.psi_Ez = bd.zeros((Nx, Ny, Nz, 3))\\n self.psi_Hx = bd.zeros((Nx, Ny, Nz, 3))\\n self.psi_Hy = bd.zeros((Nx, Ny, Nz, 3))\\n self.psi_Hz = bd.zeros((Nx, Ny, Nz, 3))\\n\\n self.bE = bd.exp(-(self.sigmaE / self.k + self.a) * self.grid.courant_number)\\n self.cE = (\\n (self.bE - 1.0)\\n * self.sigmaE # is defined by _set_sigmaE()\\n / (self.sigmaE * self.k + self.a * self.k ** 2)\\n )\\n\\n self.bH = bd.exp(-(self.sigmaH / self.k + self.a) * self.grid.courant_number)\\n self.cH = (\\n (self.bH - 1.0)\\n * self.sigmaH # is defined by _set_sigmaH()\\n / (self.sigmaH * self.k + self.a * self.k ** 2)\\n )\",\n \"def get_set_potentiometer_value_args(self,comArgs):\\n params, flags = self.get_params(comArgs)\\n axes = []\\n values = []\\n for k in params:\\n values.append(params[k])\\n axes.append(k.lower())\\n acodes = {'x':0,'y':1,'z':2,'a':3,'b':4}\\n axs = []\\n for a in axes:\\n axs.append(acodes[a])\\n args = [axs, values]\\n return args\",\n \"def iniitialize_dynamic_settings(self):\\n\\t\\tself.speed_factor = 1.5\\n\\t\\tself.bullet_speed_factor = 3\\n\\t\\tself.alien_speed_factor = 1\\n\\n\\t\\t# fleet_direction of 1 represents right; -1 represents left. \\n\\t\\tself.fleet_direction = 1\",\n \"def read_param_phil(self):\\n\\n if self.params.analysis.run_clustering:\\n self.clustering.toggle_boxes()\\n self.clustering.threshold.SetValue(\\n str(self.params.analysis.cluster_threshold))\\n self.clustering.limit.SetValue(\\n str(self.params.analysis.cluster_limit))\\n self.clustering.n_images.SetValue(str(\\n self.params.analysis.cluster_n_images))\\n\\n viz_idx = self.visualization.ctr.FindString(str(self.params.analysis.viz))\\n if str(self.params.analysis.viz).lower() == 'none':\\n viz_idx = 0\\n self.visualization.ctr.SetSelection(viz_idx)\\n\\n self.proc_charts.SetValue(self.params.analysis.charts)\\n self.summary_graphs.SetValue(self.params.analysis.summary_graphs)\",\n \"def __init__(self, name=\\\"\\\", description=\\\"\\\", time_units=\\\"s\\\", len_units=\\\"m\\\"):\\n super(Piezometer, self).__init__()\\n\\n # Set general info\\n self._type = 3 # piezometer id\\n self.parameters = {'full': True, # is full penetrating?\\n 'r': 1., # distance until pumping well in length units\\n 'z': 1.} # piezometer depth in length units\\n\\n # Set data\\n self.name = name\\n self.description = description\\n\\n self.time_units = time_units\\n self.len_units = len_units\\n\\n # Create drawdown data\\n self.drawdown = _Data(dtype=1, name=name, description=description)\\n self.drawdown.set_units(self.time_units, self.len_units)\",\n \"def set_params(self):\\r\\n pass\",\n \"def set_params(self):\\n max_margin = int(self.alpha) + 1\\n self.sample_params['add'] = [0, max_margin, max_margin]\",\n \"def set_params(self, params: Dict) -> None:\\n channel_param: str\\n for channel_param in list(params.keys())[:-1]:\\n channel_name: str = channel_param[2:]\\n if self.channel_bool[channel_name]:\\n self.channel[channel_name].set_g(params[channel_param])\\n else:\\n raise AttributeError('Model does not match parameter sets')\\n\\n if 't_ca' == list(params.keys())[-1]: # or if 't_ca' in params.keys():\\n self.tau_ca = params['t_ca']\\n else:\\n self.tau_ca = float('inf')\",\n \"def __SetSFParams(self):\\n\\n # If radial structure functions are in output\\n if self.__containsRadial:\\n # Defines radial attributes\\n self.__nc_RSoft_O.radial_error_tolerance = self.etol_radial\\n\\n # Defines radial dimensions\\n self.__nc_RSoft_O.createDimension('radial_structure_functions',\\\\\\n len(self.mus))\\n\\n # Defines radial variables\\n mus_var_id_O = self.__nc_RSoft_O.createVariable('mus', \\\\\\n 'f4', ('radial_structure_functions'))\\n Ls_var_id_O = self.__nc_RSoft_O.createVariable('Ls', \\\\\\n 'f4', ('radial_structure_functions'))\\n radial_Xs_var_id_O = self.__nc_RSoft_O.createVariable(\\\\\\n 'radial_Xs', 'i4', ('radial_structure_functions'))\\n radial_Ys_var_id_O = self.__nc_RSoft_O.createVariable(\\\\\\n 'radial_Ys', 'i4', ('radial_structure_functions'))\\n\\n # Sets radial structure function variables\\n mus_var_id_O[:] = self.mus\\n Ls_var_id_O[:] = self.Ls\\n radial_Xs_var_id_O[:] = self.radial_Xs\\n radial_Ys_var_id_O[:] = self.radial_Ys\\n\\n # If angular structure functions are in output\\n if self.__containsAngular:\\n # Defines angular attributes\\n self.__nc_RSoft_O.angular_error_tolerance = self.etol_angular\\n\\n # Defines angular dimensions\\n self.__nc_RSoft_O.createDimension('angular_structure_functions',\\\\\\n len(self.xis))\\n\\n # Defines angular variables\\n xis_var_id_O = self.__nc_RSoft_O.createVariable('xis', \\\\\\n 'f4', ('angular_structure_functions'))\\n zetas_var_id_O = self.__nc_RSoft_O.createVariable('zetas', \\\\\\n 'i4', ('angular_structure_functions'))\\n lambdas_var_id_O = self.__nc_RSoft_O.createVariable('lambdas', \\\\\\n 'i4', ('angular_structure_functions'))\\n angular_Xs_var_id_O = self.__nc_RSoft_O.createVariable(\\\\\\n 'angular_Xs', 'i4', ('angular_structure_functions'))\\n angular_Ys_var_id_O = self.__nc_RSoft_O.createVariable(\\\\\\n 'angular_Ys', 'i4', ('angular_structure_functions'))\\n angular_Zs_var_id_O = self.__nc_RSoft_O.createVariable(\\\\\\n 'angular_Zs', 'i4', ('angular_structure_functions')) \\n\\n # Sets angular structure function variables\\n xis_var_id_O[:] = self.xis\\n zetas_var_id_O[:] = self.zetas\\n lambdas_var_id_O[:] = self.lambdas\\n angular_Xs_var_id_O[:] = self.angular_Xs\\n angular_Ys_var_id_O[:] = self.angular_Ys\\n angular_Zs_var_id_O[:] = self.angular_Zs\",\n \"def set_params(self, **kwargs):\\n for param_name, value in kwargs.iteritems():\\n # only set parameters that are in the default\\n if param_name in self._default_params():\\n setattr(self, param_name, value)\\n self.params[param_name] = value\\n else:\\n print('AdjustedStat class does not accept %s as a ' \\\\\\n 'parameter and will be ignored' % param_name)\",\n \"def set_params(self, rate, radius):\\n self.radius = radius\\n self.learning_rate = rate\\n self.neighborhood.radius = radius\",\n \"def set_params(self, **kwargs):\\n ...\",\n \"def update(self):\\n if self.temperature != None and self.humidity != None:\\n self.sensor.set_environmental_data(self.humidity, self.temperature)\\n# Trim away error values.\\n new_eco2 = self.sensor.eco2\\n if new_eco2 < 65535:\\n self.eco2 = new_eco2\\n self.tvoc = self.sensor.tvoc\",\n \"def fun_set(self):\\n\\n self.type.set(self.xtl._scattering_type)\\n # self.energy_kev.set(8)\\n self.theta_offset.set(self.xtl._scattering_theta_offset)\\n self.theta_min.set(self.xtl._scattering_min_theta)\\n self.theta_max.set(self.xtl._scattering_max_theta)\\n self.twotheta_min.set(self.xtl._scattering_min_two_theta)\\n self.twotheta_max.set(self.xtl._scattering_max_two_theta)\\n\\n if self.orientation.get() == 'Reflection':\\n self.direction_h.set(self.xtl._scattering_specular_direction[0])\\n self.direction_k.set(self.xtl._scattering_specular_direction[1])\\n self.direction_l.set(self.xtl._scattering_specular_direction[2])\\n else:\\n self.direction_h.set(self.xtl._scattering_parallel_direction[0])\\n self.direction_k.set(self.xtl._scattering_parallel_direction[1])\\n self.direction_l.set(self.xtl._scattering_parallel_direction[2])\",\n \"def set_params(self, params):\",\n \"def load_standard_parameters(self):\\n paradic = {'x':'0',\\n 'y':'0',\\n 'n_oct':'8',\\n 'n_spo':'3',\\n 'sigma_min':'0.8',\\n 'delta_min':'0.5',\\n 'sigma_in':'0.5',\\n 'C_DoG':'0.015',\\n 'C_edge':'10',\\n 'n_bins':'36',\\n 'lambda_ori':'1.5',\\n 't':'0.8',\\n 'n_hist':'4',\\n 'n_ori':'8',\\n 'lambda_descr':'6',\\n 'flag_match':'1',\\n 'C_match':'0.6'}\\n self.cfg['param']['paradic'] = paradic\\n self.cfg.save()\",\n \"def _set_params(self,x):\\r\\n assert x.size==3\\r\\n self.variance = x[0]\\r\\n self.lengthscale = x[1]\\r\\n self.period = x[2]\\r\\n\\r\\n self.a = [1./self.lengthscale, 1.]\\r\\n self.b = [1]\\r\\n\\r\\n self.basis_alpha = np.ones((self.n_basis,))\\r\\n self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))\\r\\n self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))\\r\\n\\r\\n self.G = self.Gram_matrix()\\r\\n self.Gi = np.linalg.inv(self.G)\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def _set_params(self, x):\\r\\n assert x.size == self.num_params\\r\\n self.varianceU = x[0]\\r\\n self.varianceY = x[1]\\r\\n self.lengthscaleU = x[2]\\r\\n self.lengthscaleY = x[3]\",\n \"def _set_model_parameters(self, verbose=False):\\n from scipy.special import gamma\\n\\n z0 = self.z0\\n\\n # set parameters that are constants\\n p_v, d_v, cs0, sigma, vout0 = (1, 2, 6.7, 0.1, 25.0)\\n p_vB, d_vB, Mach0, p_M, d_M = (4, 2, 0.5, 1, 3)\\n\\n # calculate amplitudes that make the pdf integrate to 1\\n A_v = np.log(10)*p_v/gamma(d_v/p_v)\\n A_cs = np.log(10)/np.sqrt(2*np.pi)/sigma\\n A_vB = np.log(10)*p_vB/gamma(d_vB/p_vB)\\n A_M = np.log(10)*p_M/gamma(d_M/p_M)\\n\\n # store them in dictionaries\\n self.cool_params = dict(A_v=A_v, p_v=p_v, d_v=d_v,\\n A_cs=A_cs, cs0=cs0, sigma=sigma, vout0=vout0)\\n self.hot_params = dict(A_vB=A_vB, p_vB=p_vB, d_vB=d_vB,\\n A_M=A_M, Mach0=Mach0,p_M=p_M,d_M=d_M)\\n # SN related parameters that set the reference values for loading factors\\n self.params = dict(Esn=1.e51*au.erg, mstar=95.5*au.M_sun, vcool=200*au.km/au.s,\\n Mej=10.*au.M_sun, ZSN=0.2, ZISM0=0.02)\\n self.params['vej'] = np.sqrt(2.0*self.params['Esn']/self.params['Mej']).to('km/s')\\n self.ref_params = dict(Mref=self.params['mstar'],\\n pref=self.params['Esn']/(2*self.params['vcool']),\\n Eref=self.params['Esn'],\\n Zref=self.params['Mej']*self.params['ZSN'])\\n\\n # coefficients used in conversion from mass to other PDFs\\n self.vp = (self.ref_params['pref']/self.params['mstar']).to('km/s').value\\n self.vE = np.sqrt(self.ref_params['Eref']/self.params['mstar']).to('km/s').value\\n self.Ze = (self.ref_params['Zref']/self.params['mstar']).cgs.value\\n\\n # parameters for scaling relations from Paper~I\\n a = np.array(fit_alpha[z0])\\n b = np.array(fit_beta[z0])\\n\\n self.scaling_params = dict(a=a, b=b)\\n if z0 == '2H':\\n self.cool_params['vout0'] = 45\\n self.cool_params['cs0'] = 7.5\\n elif z0 == '500':\\n self.cool_params['vout0'] = 45\\n self.cool_params['cs0'] = 8.5\\n elif z0 == '1000':\\n self.cool_params['vout0'] = 60\\n self.cool_params['cs0'] = 10.0\\n self.scaling_params['A'] = np.round(10.**(np.array(self.scaling_params['a'])),2)\\n self.scaling_params['p'] = 1.+np.array(self.scaling_params['b'])\\n self.enum=dict(M_cool=0, M_int=1, M_hot=2, M_total=3,\\n p_cool=4, p_int=5, p_hot=6, p_total=7,\\n E_cool=8, E_int=9, E_hot=10, E_total=11,\\n Z_cool=12, Z_int=13, Z_hot=14, Z_total=15)\\n\\n # print parameters\\n if verbose:\\n self.show_parameters()\",\n \"def set_params(self):\\n raise NotImplementedError\",\n \"def __init__(self,):\\r\\n self.g = 9.81\\r\\n self.l = 0.5\\r\\n self.m1 = 1.0\\r\\n self.m2 = 1.0\\r\\n self.m3 = 1.0\\r\\n self.r1 = 1.0\\r\\n self.r2 = 1.0\\r\\n self.tau = 0.001\\r\\n self.theta1 = 1.0\\r\\n self.theta2 = 1.0\\r\\n self.theta3 = 1.0\",\n \"def setInternalPulser(self,pulserEnable,pulseHeight):\\n pass\",\n \"def _update_parameter(self, dWxh, dbh, dWhy, dby):\\n # Add code to update all the weights and biases here\",\n \"def set_params(self, **kwargs):\\n warnings.warn(\\\"'set_params()' not defined for locator of type \\\" +\\n str(type(self)))\",\n \"def default_pypeit_par(cls):\\n par = super().default_pypeit_par()\\n\\n # Wavelengths\\n # 1D wavelength solution with arc lines\\n par['calibrations']['wavelengths']['rms_threshold'] = 1.0\\n par['calibrations']['wavelengths']['sigdetect']=3\\n par['calibrations']['wavelengths']['fwhm'] = 20\\n par['calibrations']['wavelengths']['n_first']=2\\n par['calibrations']['wavelengths']['n_final']=4\\n par['calibrations']['wavelengths']['lamps'] = ['ArI', 'ArII', 'ThAr', 'NeI']\\n #par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']\\n par['calibrations']['wavelengths']['method'] = 'full_template'\\n par['calibrations']['wavelengths']['reid_arxiv'] = 'magellan_fire_long.fits'\\n par['calibrations']['wavelengths']['match_toler']=5.0\\n\\n # Set slits and tilts parameters\\n par['calibrations']['tilts']['tracethresh'] = 5\\n par['calibrations']['slitedges']['trace_thresh'] = 10.\\n par['calibrations']['slitedges']['sync_predict'] = 'nearest'\\n\\n # Processing steps\\n turn_off = dict(use_illumflat=False, use_biasimage=False, use_overscan=False,\\n use_darkimage=False)\\n par.reset_all_processimages_par(**turn_off)\\n\\n # Scienceimage parameters\\n par['reduce']['findobj']['snr_thresh'] = 5\\n #par['reduce']['maxnumber'] = 2\\n par['reduce']['findobj']['find_trim_edge'] = [50,50]\\n par['flexure']['spec_method'] = 'skip'\\n\\n par['sensfunc']['IR']['telgridfile'] = 'TelFit_LasCampanas_3100_26100_R20000.fits'\\n\\n # Set the default exposure time ranges for the frame typing\\n par['calibrations']['standardframe']['exprng'] = [None, 60]\\n par['calibrations']['arcframe']['exprng'] = [1, 50]\\n par['calibrations']['darkframe']['exprng'] = [20, None]\\n par['scienceframe']['exprng'] = [20, None]\\n return par\",\n \"def _update_params(self):\\n _load = not self.san_interface.runmode\\n params={}\\n if ('iosched' in self._updatedattr or _load) and self.iosched<>IoSchedType.default:\\n params['iosched']=str(self.iosched)\\n if ('readahead' in self._updatedattr or _load) and self.readahead :\\n params['readahead']=self.readahead\\n if params:\\n for pt in self.paths():\\n pt.provider.set_dev_params(pt,params)\",\n \"def __init__(\\n self,\\n oxygen: MeasurementStatistics,\\n pulse: MeasurementStatistics,\\n blood_pressure_systolic: MeasurementStatistics,\\n blood_pressure_diastolic: MeasurementStatistics,\\n ):\\n self.oxygen = oxygen\\n self.pulse = pulse\\n self.blood_pressure_systolic = blood_pressure_systolic\\n self.blood_pressure_diastolic = blood_pressure_diastolic\",\n \"def __init__(self, envirophat, use_leds):\\n self.envirophat = envirophat\\n self.use_leds = use_leds\\n # sensors readings\\n self.light = None\\n self.light_red = None\\n self.light_green = None\\n self.light_blue = None\\n self.accelerometer_x = None\\n self.accelerometer_y = None\\n self.accelerometer_z = None\\n self.magnetometer_x = None\\n self.magnetometer_y = None\\n self.magnetometer_z = None\\n self.temperature = None\\n self.pressure = None\\n self.voltage_0 = None\\n self.voltage_1 = None\\n self.voltage_2 = None\\n self.voltage_3 = None\",\n \"def _set_params(self,x):\\r\\n assert x.size == self.num_params\\r\\n self.variance = x[0]\\r\\n self.lengthscale = x[1:]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66596335","0.652628","0.6494346","0.6468288","0.6423567","0.640161","0.63715726","0.63502043","0.6313164","0.6293706","0.6262655","0.62223566","0.61831945","0.61662924","0.61540055","0.613113","0.6102963","0.61024153","0.609081","0.60681343","0.60625523","0.60158324","0.6008758","0.59935194","0.598742","0.59829986","0.5972071","0.5949135","0.59246933","0.5916002","0.59003145","0.5896313","0.58826184","0.5859287","0.5852546","0.58437926","0.5838196","0.58254117","0.5816246","0.5801235","0.5800754","0.57989264","0.57891095","0.5784898","0.57821625","0.5780916","0.5775159","0.57748467","0.57732964","0.57658195","0.57652736","0.5764691","0.57633346","0.5758371","0.5755829","0.5744207","0.5740496","0.5736514","0.57319015","0.57224023","0.5721435","0.5697617","0.56959546","0.56959546","0.56943464","0.56896645","0.5687592","0.5686448","0.5680908","0.5670501","0.5666838","0.5657415","0.56489944","0.564733","0.56358796","0.56330323","0.56325865","0.56289303","0.56258035","0.5624028","0.56214494","0.56200397","0.5617389","0.56037253","0.5579215","0.55630225","0.555184","0.5548666","0.5546424","0.5546304","0.55442977","0.5539141","0.55369896","0.5528886","0.55140316","0.551377","0.5513265","0.5506612","0.5506094","0.54925555"],"string":"[\n \"0.66596335\",\n \"0.652628\",\n \"0.6494346\",\n \"0.6468288\",\n \"0.6423567\",\n \"0.640161\",\n \"0.63715726\",\n \"0.63502043\",\n \"0.6313164\",\n \"0.6293706\",\n \"0.6262655\",\n \"0.62223566\",\n \"0.61831945\",\n \"0.61662924\",\n \"0.61540055\",\n \"0.613113\",\n \"0.6102963\",\n \"0.61024153\",\n \"0.609081\",\n \"0.60681343\",\n \"0.60625523\",\n \"0.60158324\",\n \"0.6008758\",\n \"0.59935194\",\n \"0.598742\",\n \"0.59829986\",\n \"0.5972071\",\n \"0.5949135\",\n \"0.59246933\",\n \"0.5916002\",\n \"0.59003145\",\n \"0.5896313\",\n \"0.58826184\",\n \"0.5859287\",\n \"0.5852546\",\n \"0.58437926\",\n \"0.5838196\",\n \"0.58254117\",\n \"0.5816246\",\n \"0.5801235\",\n \"0.5800754\",\n \"0.57989264\",\n \"0.57891095\",\n \"0.5784898\",\n \"0.57821625\",\n \"0.5780916\",\n \"0.5775159\",\n \"0.57748467\",\n \"0.57732964\",\n \"0.57658195\",\n \"0.57652736\",\n \"0.5764691\",\n \"0.57633346\",\n \"0.5758371\",\n \"0.5755829\",\n \"0.5744207\",\n \"0.5740496\",\n \"0.5736514\",\n \"0.57319015\",\n \"0.57224023\",\n \"0.5721435\",\n \"0.5697617\",\n \"0.56959546\",\n \"0.56959546\",\n \"0.56943464\",\n \"0.56896645\",\n \"0.5687592\",\n \"0.5686448\",\n \"0.5680908\",\n \"0.5670501\",\n \"0.5666838\",\n \"0.5657415\",\n \"0.56489944\",\n \"0.564733\",\n \"0.56358796\",\n \"0.56330323\",\n \"0.56325865\",\n \"0.56289303\",\n \"0.56258035\",\n \"0.5624028\",\n \"0.56214494\",\n \"0.56200397\",\n \"0.5617389\",\n \"0.56037253\",\n \"0.5579215\",\n \"0.55630225\",\n \"0.555184\",\n \"0.5548666\",\n \"0.5546424\",\n \"0.5546304\",\n \"0.55442977\",\n \"0.5539141\",\n \"0.55369896\",\n \"0.5528886\",\n \"0.55140316\",\n \"0.551377\",\n \"0.5513265\",\n \"0.5506612\",\n \"0.5506094\",\n \"0.54925555\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6427534"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":75793,"cells":{"query":{"kind":"string","value":"Returns a list of dictionaries containing all the data in the well that can be used to storage the data as json format"},"document":{"kind":"string","value":"def to_dict(self):\n out_dict = _deepcopy(self.__dict__)\n out_dict[\"drawdown\"] = self.drawdown.to_dict()\n out_data = []\n for i in range(self.data_count()):\n out_data.append(self.data[i].to_dict())\n out_dict[\"data\"] = out_data\n return(out_dict)"},"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_datalist_fr_json(self):\n raw_data = json.load(open(self.saved_json_file, 'r'))\n for indivdual_set in raw_data['query']['results']['stats']:\n temp_dict_data = {}\n if type(indivdual_set) == str:\n #for single data\n continue # temp do not use\n for parameters in indivdual_set.keys():\n if type(indivdual_set[parameters]) == str:\n temp_dict_data[parameters] = indivdual_set[parameters]#for symbol\n elif type(indivdual_set[parameters]) == dict:\n if indivdual_set[parameters].has_key('content'):\n temp_dict_data[parameters] = indivdual_set[parameters]['content']\n\n ## append to list\n self.com_data_allstock_list.append(temp_dict_data)","def json_data(self):\n return_data = []\n\n # Loop over num files\n for ifile in range(len(self.data)):\n\n # Loop over steps\n file_return_data = {}\n for istep in range(len(self.data[ifile])):\n file_return_data[istep] = {}\n for key in self.data[ifile][istep]:\n file_return_data[istep][key] = self.data[ifile][istep][key].tolist()\n\n return_data.append(file_return_data)\n\n return return_data","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def get_data(self):\n return {\n \"fd\": self.get_fd_j(self.id),\n \"fd_part\": self.get_fd_part_j(self.id),\n \"config\": self.config,\n # \"config\": self.get_config_j(self.id),\n \"prp\": self.get_prp_j(self.id),\n \"alll\": self.my_alll(self.id)\n }","def read_all(self):\n def is_data(i):\n \"\"\"\n It checks if given key is different than added by system\n \"\"\"\n keys = ['_id', '_time']\n return all(i != k for k in keys)\n\n self.logger.log_reading()\n return simplejson.dumps([{i: x[i] for i in x if is_data(i)} for x in self.json_collection.find()])","def getData(self):\n return dict(self._dump_data)","def setup_data( self ):\n with open( f'{project_dir}/data/05_source_key_data.json', 'r', encoding='utf-8' ) as f:\n dct = json.loads( f.read() )\n with open( f'{project_dir}/data/04_snapshot_open_textbook.json', 'r', encoding='utf-8' ) as f:\n lst = json.loads( f.read() )\n return ( dct, lst )","def to_dict(self):\n return self.data.to_dict('list')","def into_data(self) -> List[Dict[str, Any]]:\n if self.previous:\n data = self.previous.into_data()\n else:\n data = []\n\n datum: Dict[str, Any] = dict(\n required={name: up_to_date.into_data() for name, up_to_date in self.required.items()}\n )\n\n if self.command is None:\n assert self.start is None\n assert self.end is None\n else:\n assert self.start is not None\n assert self.end is not None\n datum[\"command\"] = self.command\n datum[\"start\"] = str(self.start)\n datum[\"end\"] = str(self.end)\n\n data.append(datum)\n return data","def codebook_json_data_factory() -> List[Dict[str, Any]]:\n codebook_data = [\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_A\"\n },\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_B\"\n },\n ]\n return codebook_data","def __json_data__(self):\n if schema.is_dense(self.namespace):\n dense_records = dict()\n for field in Observation._fields:\n dense_records[field] = []\n\n for obs in self.data:\n for key, val in six.iteritems(obs._asdict()):\n dense_records[key].append(serialize_obj(val))\n\n return dense_records\n\n else:\n return [serialize_obj(_) for _ in self.data]","def dict(self):\n return self.data","def __convert_data_to_list_of_dict__(self, data):\n jsons = list()\n for row in data:\n json_for_row = dict(zip(self.__fieldnames__, row))\n jsons += [json_for_row]\n return jsons","def get_data(self):\n return [{'title': th['title'], 'img_link': th['img_link'], 'id': j, 'url': th['url'], 'text': th.get('text', '0')}\n for j, th in enumerate(self)]","def get_data(self):\n self.data = dict()\n # list to save all the attributes we are going to create\n self.attr = []\n # list to save all the groups available in the incomming input\n self.groups.extend(self.values.keys())\n # Grouping\n self.parse_data()","def json_data(self):\n self.check_proof()\n return {\n \"vars\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\n \"proof\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\n for item in self.prf.items], []),\n \"report\": self.rpt.json_data(),\n \"method_sig\": self.get_method_sig()\n }","def to_dict(self):\n out_dict = _deepcopy(self.__dict__)\n out_dict[\"pumprate\"] = self.pumprate.to_dict()\n out_wells = []\n for i in range(self.well_count()):\n out_wells.append(self.wells[i].to_dict())\n out_dict[\"wells\"] = out_wells\n return(out_dict)","def extract_data(self):\n values = {}\n for injkey in self.data_sets.keys():\n values[injkey] = {}\n alldata = self.data_sets[injkey]\n paramkeys = alldata['params'].keys()\n for datakey in alldata.keys():\n if not datakey == 'params':\n values[injkey][datakey] = {}\n values[injkey][datakey]['metric_val'] = {}\n values[injkey][datakey]['metric_val']['vals'] = []\n for paramkey in paramkeys:\n values[injkey][datakey][paramkey] = {}\n values[injkey][datakey][paramkey]['vals'] = []\n trials = alldata[datakey]\n for trial_num in trials.keys():\n trial = trials[trial_num]\n values[injkey][datakey]['metric_val']['vals'] \\\n .append(trial['metric_val'])\n values[injkey][datakey]['metric_val']['type'] \\\n = trial['metric']\n values[injkey][datakey]['metric_val']['units'] \\\n = 'dimensionless'\n param_vals = trial['params']\n for param_name in param_vals.keys():\n val, units = self.parse_pint_string(\n pint_string=param_vals[param_name]\n )\n values[injkey][datakey][param_name]['vals'] \\\n .append(float(val))\n values[injkey][datakey][param_name]['units'] \\\n = units\n self.values = values","def get_objects_data(self):\n return dict(result=self.objects)","def data():\n result = {}\n for thread in DATA.threads:\n result[thread] = [formatNode(node) for node in DATA.threads[thread].tree]\n return json.dumps({\n 'checkpoints': DATA.checkpoints,\n 'threads': result\n })","def dict(self):\n\t\treturn self.json","def get_data(self):\n return self.data.to_json()","def get_dicts(self, clean=False):\n return list(self.iter_dicts(clean=clean))","def get_objects_data(self):\n return dict(items=self.objects)","def get_objects_data(self):\n return dict(objects=self.objects)","def json_data(self):\n json_providers = getAdapters((self.context, self.request, self.view), IJSONDataProvider)\n results = []\n for name, provider in json_providers:\n results.append({'name': name or None,\n 'data': json.dumps(provider())})\n return results","def data(self):\n\n for i in json_parsed:\n name = i['name']\n number = i['number']\n address=i['address']\n banking=i['banking']\n position=i['position']\n latitude=position['lat']\n longitude=position['lng']\n bike_stands=i['bike_stands']\n status=i['status']\n insert_data(name, number, address, banking, latitude, longitude, bike_stands, status)","def get_data(self):\n data = {}\n _priv = self.get('_private', [])\n\n def check_data(v):\n if isinstance(v, Yaco):\n v = v.get_data()\n elif isinstance(v, list):\n v = [check_data(x) for x in v]\n return v\n\n for k in list(self.keys()):\n if k in _priv:\n continue\n if isinstance(k, (str)) and k and k[0] == '_':\n continue\n # print self.keys()\n # print k, 'x' * 30\n data[k] = check_data(self[k])\n return data","def get_json(self):\r\n self.get_recordrange()\r\n [\r\n self.json_data.setter(n, self.get_data(\"json\", x))\r\n for n, x in enumerate(tqdm(self.iterlist))\r\n ]","def dict(self):\n return {\"data\": self.data.dict(), \"inventory\": self.inventory.dict()}","def generate_data_list(self, data_list):\n sol = []\n for i in data_list:\n sol.append({\n \"data_sig\": i[0],\n \"data\": pickle.loads(i[1]),\n \"attachment\": i[2],\n \"likes\":pickle.loads(i[3]),\n \"dislikes\":pickle.loads(i[4]),\n \"owner\": i[5]\n })\n return sol","def get_keys_from_list():\n json_data = request.get_json()\n\n d = dict()\n d['elements'] = list()\n settings.setOptionsFile(get_info('uid'))\n fn = settings.getHistoROOTFileName()\n rfn = settings.getReferenceROOTFileName()\n# open root file stored in the root database\n f = ROOT.TFile(fn)\n# open reference root file stored in the root database\n rf = ROOT.TFile(rfn)\n\n for values in json_data.itervalues():\n for k in values:\n subd = dict()\n subd[\"index\"] = k[\"index\"]\n if fn != k[\"file\"]: \n fn = k[\"file\"]\n settings.setHistoROOTFileName(fn)\n f = ROOT.TFile(fn)\n print \"histogram :>>>>>: \",k[\"histogram\"]\n subd[\"data\"] = eval(cppyy.gbl.getDictionary(f,k[\"histogram\"]))\n if rfn != k[\"referenceFile\"]: \n rfn = k[\"referenceFile\"]\n settings.setReferenceROOTFileName(rfn)\n rf = ROOT.TFile(rfn)\n subd[\"refdata\"] = eval(cppyy.gbl.getDictionary(rf,k[\"reference\"]))\n d['elements'].append(subd)\n\n f.Close()\n rf.Close()\n\n return jsonify(d)","def getDictWells(self):\n #Method begins here\n #nx=self.__grid['nx'] #From the geometry in grid\n ny=self.__grid['ny']\n nz=self.__grid['nz']\n minx=self.__grid['ox']\n miny=self.__grid['oy']\n minz=self.__grid['oz']\n rx=self.__grid['dx']\n ry=self.__grid['dy']\n rz=self.__grid['dz']\n \n # well package\n # Remember to use zero-based layer, row, column indices!\n lcoordw=np.zeros((self.__nwells,3),dtype=np.int32)\n for i in range (self.__nwells):\n lcoordw[i,0]=floor((self.__dflst.iloc[i,3]-minx)/rx)\n #In MODFLOW y ans z coordinates are inverted\n lcoordw[i,1]=floor((miny+ry*ny-self.__dflst.iloc[i,4])/ry)\n lcoordw[i,2]=floor((minz+rz*nz-self.__dflst.iloc[i,5])/rz)\n \n nper=self.__df.getForcPer()\n wel_sp = {} \n for i in range(nper):\n lst=[]\n for j in range(self.__nwells):\n pumping_rate=self.__dfwells.iloc[i+1,j+1]\n lst.append( [lcoordw[j,2], lcoordw[j,1], lcoordw[j,0], pumping_rate] )\n wel_sp[i]=lst\n print(wel_sp)\n \n print('*--- Succesfull reading of wells ---*')\n \n return wel_sp","def get_all_data():\n return jsonify(service.get_all_data())","def to_dict(self, complete=False):\n if complete:\n data = self.complete_data.to_dict('list')\n else:\n data = self.data.to_dict('list')\n # Get metadata.\n data.update(wildtype=self.wildtype, mutations=self.mutations)\n return data","def metadata(self):\n self.data_as_dict = {}\n for ele in self.data:\n self.data_as_dict[ele.name] = ele.value\n return self.data_as_dict","def json(self) -> List[Dict[str, Union[str, List[str]]]]:\n model_json: List[Dict[str, Union[str, List[str]]]] = []\n for phrase in self.phrase_index:\n entry = {'phrase': phrase}\n if phrase in self.has_variants:\n entry['variants'] = list(self.has_variants[phrase])\n if phrase in self.has_labels:\n entry['label'] = list(self.has_labels[phrase])\n if phrase in self.custom:\n entry['custom'] = self.custom[phrase]\n model_json += [entry]\n return model_json","def data_list(self):\n # return [data_object, data_object, ...]\n return self._data_list","def get_all(self):\n # read log\n d = {}\n log = self.get_logfile()\n if not os.path.isfile(log):\n return d\n f = open(log, \"r\")\n if f.mode == 'r':\n lines = f.readlines()\n for line in lines:\n dline = json.loads(line)\n d.update(dline)\n f.close()\n return d","def _data_list(json: 'a json'):\n data = json['Time Series (Daily)']\n return list(data.items())","def get_data(data_type):\n json_list = list()\n for source in data_type.value:\n json_list += open_json_file(source)\n return json_list","def get_data(self):\n if not hasattr(self, 'json_data'):\n self.json_data = collections.OrderedDict()\n return self.json_data","def data_json(request):\n json_data = []\n for resource in Resource.objects.all():\n record = {} \n record['title'] = resource.name\n record['description'] = resource.description\n record['keyword'] = resource.csw_keywords.split(',')\n record['modified'] = resource.last_updated\n record['publisher'] = resource.organization\n record['contactPoint'] = resource.metadata_contact\n record['mbox'] = resource.contact_email\n record['identifier'] = resource.csw_identifier\n if resource.is_published:\n record['accessLevel'] = 'public'\n else:\n record['accessLevel'] = 'non-public'\n\n json_data.append(record)\n\n return HttpResponse(json.dumps(json_data), 'application/json')","def get_json(self):\n return {\n \"power\": self.get_power(), \n \"timestamp\": self.get_timestamp(), \n \"shortage\": self.get_shortage()\n }","def data():\n return volumes_fetchers.get_json_data()","def json(self):\n json_data = {}\n\n for item in self.iteritems():\n try:\n value = item.get_value(json_serializable=True)\n except TypeError:\n value = \"NOT_SERIALIZABLE\"\n item_data = {\n \"tags\": list(item.tags),\n \"value\": value,\n }\n if item.name in json_data:\n json_data[item.name].append(item_data)\n else:\n json_data[item.name] = [item_data]\n\n return json_data","def raw_data() -> Dict:\n return {\"neighbourhood\":\"Buttes-Montmartre\",\"room_type\":\"Entire home/apt\",\"minimum_nights\":1.555,\"mois\":2,\"voyageurs\":2.5,\"chambres\":1,\"lits\":1,\"salle_de_bains\":1}","def get_json(ticker_symbol):\n s_data = Stock.objects.filter(ticker=ticker_symbol).order_by('-date')\n json_file = []\n for data in s_data:\n json_obj = StockSerializer(data).data\n json_file.append(json_obj)\n return json_file","def to_dict(self):\n# \"\"\" The JSON model used is like:\n# <code>\n#{\n# \"duration\": 15,\n# \"url\": \"url1\",\n# \"selections\": [{\n# \"annotations\": [{\n# \"author\": \"\",\n# \"description\": \"speaker\",\n# \"keyword\": \"john\",\n# \"lang\": \"EN\"\n# },\n# {\n# \"author\": \"\",\n# \"description\": \"speakerLabel\",\n# \"keyword\": \"S0\",\n# \"lang\": \"EN\"\n# }\n# , {\n# \"author\": \"\",\n# \"description\": \"gender\",\n# \"keyword\": \"F\",\n# \"lang\": \"EN\" \n# }],\n# \"resolution\": \"0x0\",\n# \"selW\": 20,\n# \"selH\": 15,\n# \"selY\": 10,\n# \"selX\": 10,\n# \"startTime\" : 0,\n# \"endTime\" : 10\n# \n# }]\n#}\n# </code>\n# \n# \"\"\"\n\n dic = {\"duration\": self.get_duration(),\n \"url\": self._filename,\n \"db\":self.get_db().get_path(),\n \"selections\": [] }\n for seg in self.get_time_slices():\n dic['selections'].append({\n \"startTime\": float(seg[0]) / 100.0,\n \"endTime\": float(seg[1]) / 100.0,\n 'speaker': seg[-2],\n 'speakerLabel': seg[-1],\n 'gender': seg[2],\n 'speakers': seg[3]\n })\n return dic","def get_structure(self):\n main = {}\n for line in self.load():\n match = re.match('^\\s*([A-Za-z0-9_]+)(\\((\\d+)\\))?=(.*)$', line)\n if match:\n key = match.group(1)\n index = match.group(3)\n value = match.group(4)\n if index is None:\n main[key] = self.parse_data_value(value)\n else:\n if key not in main:\n main[key] = []\n main[key].append(self.parse_data_value(value))\n #else:\n # print(line)\n return main","def data():\n print (\"&\")\n res = {}\n\t\n # Load Data\n with open(DATA_PATH_TRAIN, 'rb') as f:\n data = pickle.load(f)\n\t\t\n for d in data:\n for j in range(len(d)):\n if not d[j][\"addinfo\"][\"path\"] in res:\n res[d[j][\"addinfo\"][\"path\"]] = {}\n d[j][\"environment\"][\"text\"] = d[j][\"addinfo\"][\"text\"]\n res[d[j][\"addinfo\"][\"path\"]][d[j][\"addinfo\"][\"line\"]] = d[j][\"environment\"]\n\t \t\n with open(DATA_PATH_TEST, 'rb') as f:\n data = pickle.load(f)\n\t\t\n for d in data:\n for j in range(len(d)):\n if not d[j][\"addinfo\"][\"path\"] in res:\n res[d[j][\"addinfo\"][\"path\"]] = {}\n d[j][\"environment\"][\"text\"] = d[j][\"addinfo\"][\"text\"]\n res[d[j][\"addinfo\"][\"path\"]][d[j][\"addinfo\"][\"line\"]] = d[j][\"environment\"]\n\t\t\t\n with open('tasks/env/data/data.json', 'w') as outfile:\n json.dump(res, outfile)","def getAllData(self):\r\n return self.data","def set_data(self):\n data = [\n {\"ASN1P-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"ASN1P\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n {\"asn1rt-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"asn1rt\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n {\"cadessignature-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"cadessignature\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n { \"JCP-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"JCP\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n { \"JCPRequest-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"JCPRequest\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }}\n ]\n return data","def list(self):\n return {\n k: json.loads(v)\n for k, v in iteritems(self._db.hgetall(self.index))\n }","def jsons(self):\n if self._jsons is None:\n self.make_jsons()\n return self._jsons","def json(self):\n return {\"name\": self.name,\n \"items\": [item.json() for item in self.item]} # self.name==storeObj.name and self.item==storeObj.item","def _get_data(self):\n data = {}\n\n for name, field in self._get_fields().items():\n if isinstance(field, fields.Factory):\n # skip for factories for now\n continue\n value = getattr(self, name)\n raw_value = field.to_raw(value)\n if isinstance(field, fields.Secret):\n data[f\"__{name}\"] = raw_value\n else:\n data[name] = raw_value\n\n return data","def wem_market_value_all():\n __query = \"\"\"\n select\n date_trunc('month', wfs.trading_interval) AS trading_day,\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\n wf.fueltech_id\n from wem_facility_scada wfs\n left join wem_facility wf on wfs.facility_id = wf.code\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\n where\n wf.fueltech_id is not null\n group by 1, wf.fueltech_id\n order by 1 desc, 2 asc\n \"\"\"\n\n query = __query.format()\n\n json_envelope = {}\n\n with engine.connect() as c:\n rows = c.execute(query)\n\n current_tech = None\n\n for row in rows:\n\n current_tech = row[2]\n\n if current_tech not in json_envelope.keys():\n json_envelope[current_tech] = {\n \"id\": f\"wem.fuel_tech.{current_tech}.market_value\",\n \"fuel_tech\": current_tech,\n \"region\": \"wa\",\n \"type\": \"market_value\",\n \"units\": \"AUD\",\n \"history\": {\n \"interval\": \"1M\",\n \"start\": None,\n \"last\": None,\n \"data\": [],\n },\n }\n\n if (\n json_envelope[current_tech][\"history\"][\"start\"] == None\n or row[0] < json_envelope[current_tech][\"history\"][\"start\"]\n ):\n json_envelope[current_tech][\"history\"][\"start\"] = row[0]\n\n if (\n json_envelope[current_tech][\"history\"][\"last\"] == None\n or row[0] > json_envelope[current_tech][\"history\"][\"last\"]\n ):\n json_envelope[current_tech][\"history\"][\"last\"] = row[0]\n\n json_envelope[current_tech][\"history\"][\"data\"].append(row[1])\n\n return [json_envelope[i] for i in json_envelope.keys()]","def get_info(self) -> List[Dict]:\n return [{} for _ in range(self.num)]","def parse_source_json(data):\n data_dict=[]\n for item in data:\n tmp_dict ={}\n tmp_dict[\"_id\"]=item[\"POIID\"]\n tmp_dict[\"POIName\"]=item[\"POIName\"]\n tmp_dict[\"Address\"]=item[\"Address\"]\n try:\n if item[\"isDining\"]=='Y':\n tmp_dict[\"isDining\"] = True\n else:\n tmp_dict[\"isDining\"] = False\n except:\n tmp_dict[\"isDining\"] = None\n tmp_dict[\"location\"] = {\"type\": \"Point\", \"coordinates\": [item[\"X\"],item[\"Y\"]]}\n \n data_dict.append(tmp_dict)\n return data_dict","def get_json_ldata( location_list ):\n\tdata = []\n\tfor idx,location in enumerate( location_list ):\n\t\ttemp = {\n\t\t\t'name' \t\t\t: location.get_name(),\n\t\t\t'url' \t\t\t: location.get_url(),\n\t\t\t'population' \t: location.get_population(),\n\t\t\t'type'\t\t\t: location.get_location_type(),\n\t\t\t'tweet_count'\t: location.tweet_count\n\t\t}\n\t\t# data[ str( idx ) ] = temp\n\t\tdata.append(temp)\n\treturn data","def export_data(self):\n\n # We need to remove spaces for TW-1504, use custom separators\n data_tuples = ((key, self._serialize(key, value))\n for key, value in six.iteritems(self._data))\n\n # Empty string denotes empty serialized value, we do not want\n # to pass that to TaskWarrior.\n data_tuples = filter(lambda t: t[1] is not '', data_tuples)\n data = dict(data_tuples)\n return json.dumps(data, separators=(',',':'))","def dict(self):\n return self.__data_dict","def get_all(self):\n self.data = list(self.data)\n return self.data","def _collect_data(self):\n data = {\n \"K\": self.K,\n \"root\": self.root\n }\n return data","def get_json(self):\n data = {}\n data['ip'] = self.ip\n\n try:\n data['country'] = self.processedvtdata[\"country\"]\n except KeyError:\n data['country'] = 'None'\n try:\n data['as'] = self.processedvtdata[\"as_owner\"]\n except KeyError:\n data['as'] = 'None'\n try:\n data['rdns'] = self.processedvtdata[\"self.reversedns\"]\n except KeyError:\n data['rdns'] = 'None'\n try:\n data['label'] = self.expertlabel\n except AttributeError:\n data['label'] = ''\n\n # geodata\n #{\"status\":\"success\",\"country\":\"Yemen\",\"countryCode\":\"YE\",\"region\":\"SA\",\"regionName\":\"Amanat Alasimah\",\"city\":\"Sanaa\",\"zip\":\"\",\"lat\":15.3522,\"lon\":44.2095,\"timezone\":\"Asia/Aden\",\"isp\":\"Public Telecommunication Corporation\",\"org\":\"YemenNet\",\"as\":\"AS30873 Public Telecommunication Corporation\",\"query\":\"134.35.218.63\"}\n if self.geodata:\n data['geodata'] = self.geodata\n \n # vt resolutions. Is a list\n data['vt'] = {}\n try:\n if self.processedvtdata['resolutions'] != 'None':\n data['vt']['resolutions'] = []\n for count, resolution_tuple in enumerate(self.processedvtdata['resolutions']):\n if count >= self.amount_to_print:\n break\n temp = {}\n temp['date'] = resolution_tuple[0]\n temp['domain'] = resolution_tuple[1]\n data['vt']['resolutions'].append(temp)\n except KeyError:\n pass\n\n # vt urls. Is a list\n try:\n if self.processedvtdata['detected_urls'] != 'None':\n data['vt']['detected_urls'] = []\n for count, url_tuple in enumerate(self.processedvtdata['detected_urls']):\n if count >= self.amount_to_print:\n break\n temp = {}\n temp['date'] = url_tuple[0]\n temp['url'] = url_tuple[1][0]\n temp['detections'] = str(url_tuple[1][1]) + '/' + str(url_tuple[1][2])\n data['vt']['detected_urls'].append(temp)\n except KeyError:\n pass\n\n\n # vt detected communicating samples. Is a list\n try:\n if self.processedvtdata['detected_communicating_samples'] != 'None':\n data['vt']['detected_communicating_samples'] = []\n for count, communcating_tuple in enumerate(self.processedvtdata['detected_communicating_samples']):\n if count >= self.amount_to_print:\n break\n temp = {}\n temp['date'] = communcating_tuple[0]\n temp['detections'] = str(communcating_tuple[1][0]) + '/' + str(communcating_tuple[1][1])\n temp['sha256'] = communcating_tuple[1][2]\n data['vt']['detected_communicating_samples'].append(temp)\n except AttributeError:\n pass\n\n # vt detected downloaded samples. Is a list\n try:\n if self.processedvtdata['detected_downloaded_samples'] != 'None':\n data['vt']['detected_downloaded_samples'] = []\n for count, detected_tuple in enumerate(self.processedvtdata['detected_downloaded_samples']):\n if count >= self.amount_to_print:\n break\n temp = {}\n temp['date'] = detected_tuple[0]\n temp['detections'] = str(detected_tuple[1][0]) + '/' + str(detected_tuple[1][1])\n temp['sha256'] = detected_tuple[1][2]\n data['vt']['detected_downloaded_samples'].append(temp)\n except AttributeError:\n pass\n\n # vt referrer downloaded samples. Is a list\n try:\n if self.processedvtdata['detected_referrer_samples'] != 'None':\n data['vt']['detected_referrer_samples'] = []\n for count, referrer_tuple in enumerate(self.processedvtdata['detected_referrer_samples']):\n if count >= self.amount_to_print:\n break\n temp = {}\n temp['sha256'] = referrer_tuple[0]\n temp['detections'] = str(referrer_tuple[1][0]) + '/' + str(referrer_tuple[1][1])\n data['vt']['detected_referrer_samples'].append(temp)\n except AttributeError:\n pass\n\n # pt data\n data['pt'] = {}\n if self.processedptdata:\n count = 0\n data['pt']['passive_dns'] = []\n for result in self.processedptdata_results:\n if count >= self.amount_to_print:\n break\n temp = {}\n temp['lastseen'] = result[0]\n temp['firstseen'] = result[1][0]\n temp['hostname'] = result[1][1]\n data['pt']['passive_dns'].append(temp)\n count += 1\n\n # shodan data\n try:\n if self.shodandata:\n data['shodan'] = self.shodandata\n except AttributeError:\n pass\n\n data = json.dumps(data)\n return data","def get_all(self):\n return {\"parcels\": self.db}, 200","def json(self):\n return [t.json for t in self.values()]","def to_list_of_dicts(self) -> List[Dict[str, Any]]:\n return [this.to_dict() for this in self._elements]","def logs(self):\n dataTuples = list()\n try:\n with sqlite3.connect(self.db_file) as conn:\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM curves\")\n dataTuples = cur.fetchall()\n return [{\"id\": a[0], \"name\": a[1], \"units\": a[2],\n \"descr\": a[3]} for a in dataTuples]\n except:\n return dataTuples","def to_dict(self):\n print(\"\\n\\nSTARTING...\")\n ea = db.session.query(entity_assets).filter(entity_assets.c.entity_id == self.id).all()\n print(\"\\n\\nmade it\", ea)\n em = db.session.query(entity_meters).filter(entity_meters.c.entity_id == self.id).all()\n est = db.session.query(entity_statuses).filter(entity_statuses.c.entity_id == self.id).all()\n \n return {\n \"id\": self.id,\n \"user_id\": self.user_id,\n \"creator\": self.user.username,\n \"type\": self.type,\n \"category\": self.category,\n \"title\": self.title,\n \"description\": self.description,\n \"color\": self.color,\n \"icon\": self.icon,\n \"image\": self.image,\n \"created_at\": self.created_at,\n\n \"location_id\": self.location_id,\n \"generated_id\": self.generated_id,\n \n \"assets\": [(a.asset_id, a.quantity) for a in ea],\n \"statuses\": [(s.status_id, s.expiry) for s in est],\n \"meters\": [(m.meter_id, m.points) for m in em],\n \"slots\": [(slot.slot_id, slot.filler_id) for slot in self.entity_slots],\n }","def dicts(self):\n if self._dicts is None:\n self.make_dicts()\n return self._dicts","def data_dict0():\n\n # 0- Sample from detectron2 -> 5 different sections.\n info_val0 = [{\"date_created\": \"2020-03-15 04:59:45.442988\",\n \"description\": \"Automatically generated COCO json file for Detectron2.\"}]\n images0 = [{\"id\": \"image\", \"width\": 100,\n \"height\": 100, \"file_name\": \"image.png\"}]\n annotations0 = [{\"id\": 1, \"image_id\": \"image\", \"bbox\": [70.0, 30.0, 30.0, 40.0],\n \"area\": 1200.0, \"iscrowd\": 0, \"category_id\": 0}]\n categories0 = [{\"id\": 0, \"name\": \"first\"}]\n licence0 = 'null'\n\n return [{\"info\": info_val0,\n \"images\": images0,\n \"annotations\": annotations0,\n \"categories\": categories0,\n \"licenses\": licence0}]","def get_json(self):\n json_item = {\"id: \": self.id,\n \"question: \": self.question,\n \"documents: \": self.documents,\n \"document_ids: \": self.document_ids,\n \"gold answers: \": self.gold}\n return json_item","def get_dict(self):\n return self._data","def get_list():\n hash_map_list = model.hash_table.find()\n data = dict(success=True, hash_map_list=hash_map_list)\n return data","def get_full_data(self):\n to_return = {}\n keywords = Keywords()\n for word in self.keywords:\n to_return[word] = keywords[word].get_info()\n return to_return","def get_all_items():\n try:\n return get_json(get_data())\n except ValueError as e:\n print(e)\n except TypeError as e:\n print(e)","def Mydata():\n\n stmt = db.session.query(Appsdata).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n \n return jsonify(df.to_dict())","def bridge_all_json():\n return {\n \"base_stations\": [\n {\n \"id\": 12345,\n \"name\": None,\n \"mode\": \"home\",\n \"hardware_id\": \"0x1234567890abcdef\",\n \"hardware_revision\": 4,\n \"firmware_version\": {\n \"wifi\": \"0.121.0\",\n \"wifi_app\": \"3.3.0\",\n \"silabs\": \"1.0.1\",\n },\n \"missing_at\": None,\n \"created_at\": \"2019-04-30T01:43:50.497Z\",\n \"updated_at\": \"2019-04-30T01:44:43.749Z\",\n \"system_id\": 12345,\n \"firmware\": {\"wifi\": \"0.121.0\", \"wifi_app\": \"3.3.0\", \"silabs\": \"1.0.1\"},\n \"links\": {\"system\": 12345},\n }\n ]\n }","def to_dict(self) -> List[dict]:\n return asyncio.run(self.parse(key=\"series\"))","def toJSON(self):\r\n\r\n jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n if len(self.slctData) > 100:\r\n self.getSimMatSummary(100)\r\n jsonToRet.append(self.summaryOrdering)\r\n for i in range(0,len(self.simMatSmm)):\r\n for n in self.simMatSmm[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n\r\n jsonToRet.append(self.patchOrdering)\r\n # jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n for i in range(0,len(self.simMat)):\r\n for n in self.simMat[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n return jsonToRet","def _get_data_as_json(self, data):\n data = self._get_data_as_df(data)\n data = data.to_json(orient=\"records\")\n char_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)\n return list(map(''.join, zip(*[iter(data)] * char_per_line)))","def GetDict(self):\n # CL actions are be stored in self._cl_action_list instead of\n # in self._metadata_dict['cl_actions'], because _cl_action_list\n # is potentially a multiprocess.lis. So, _cl_action_list needs to\n # be copied into a normal list.\n temp = self._metadata_dict.copy()\n temp['cl_actions'] = list(self._cl_action_list)\n\n # Similarly, the per-board dicts are stored in a flattened form in\n # _per_board_dict. Un-flatten into nested dict.\n per_board_dict = {}\n for k, v in self._per_board_dict.items():\n board, key = k.split(':')\n board_dict = per_board_dict.setdefault(board, {})\n if key:\n board_dict[key] = v\n\n temp['board-metadata'] = per_board_dict\n return temp","def to_listing_dict(self) -> dict:\n data = super().to_listing_dict()\n return data","def get_dict(self):\n return","def list_items(self) -> List[Dict[str, Any]]:\n return [c.to_dict() for c in self._objects.values()]","def listings_data():\n\n stmt = db.session.query(nyc).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n df[\"latitude\"] = pd.to_numeric(df[\"latitude\"])\n df[\"longitude\"] = pd.to_numeric(df[\"longitude\"])\n df[\"accommodates\"] = pd.to_numeric(df[\"accommodates\"])\n\n data = df.to_dict(orient='index')\n # Create a dictionary entry for each row of metadata information\n # data = {}\n # for result in results:\n #\n # data[\"ID\"] = result[0]\n # data[\"LISTING_URL\"] = result[1]\n # data[\"NAME\"] = result[2]\n # data[\"HOST_ID\"] = result[3]\n # data[\"NEIGHBORHOOD\"] = result[4]\n # data[\"NEIGHBORHOOD_GROUP\"] = result[5]\n # data[\"CITY\"] = result[6]\n # data[\"ZIPCODE\"] = result[7]\n # data[\"LAT\"] = float(result[8])\n # data[\"LON\"] = float(result[9])\n #\n # print(data)\n\n return jsonify(data)","def to_dict(self) -> List[Dict[str, Any]]:\n return [x.to_dict() for x in self.params]","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 book_list_manipulation():\n global book_list\n book_json = {}\n for book in book_list:\n book_container = {'title': book.title,\n 'author': book.author,\n 'read': str(book.read),\n 'rating': book.rating,\n 'review': book.review,\n 'date read': book.date_read,\n 'id': str(book.id)}\n book_json[str(book.id)] = book_container\n\n return book_json","def get_json():\n data_list = []\n\n for page in range(1,13):\n url = BASE_URL + STYLE_URL + \"&\" + PAGINATION_URL + str(page)\n print(page, \"pages processed\")\n try:\n response = requests.get(url, timeout=METADATA_REQUEST_TIMEOUT)\n data = response.json()['Paintings']\n parse_data(data_list, data)\n except requests.exceptions.RequestException as e:\n print(e)\n sys.exit(1)\n\n return data_list","def upload_initial_data():\n data = {\"updated\":1512866292573,\"data\":[[[\"DAY %\",\"PPS (CAD)\",\"TKR\",\"NAME\",\"SECTOR\",\"EX.\",\"ALT. TKR\",\"VOL\",\"VOL @ CRNT $\",\"MC ($MM)\"],[\"5.84%\",\"1.45\",\"ABCN\",\"ABcann Medicinals\",\"BioTech\",\"CVE\",\"ABCCF\",\"901,940\",\"1,307,813\",\"78.49\"],[\"6.14%\",\"7.09\",\"ACB\",\"Aurora Cannabis\",\"Cultivation & Retail\",\"TSE\",\"ACBFF\",\"13,927,205\",\"98,743,883.45\",\"2,861.53\"],[\"9.09%\",\"0.24\",\"ACG\",\"Alliance Growers \",\"AgTech\",\"CNSX\",\"--\",\"1,189,385\",\"285,452.4\",\"\"],[\"0.00%\",\"0.10\",\"AFI\",\"Affinor Growers\",\"AgTech\",\"CNSX\",\"RSSFF\",\"210,310\",\"21,031\",\"\"],[\"3.22%\",\"13.47\",\"APH\",\"Aphria\",\"Cultivation & Retail\",\"TSE\",\"APHQF\",\"2,663,133\",\"35,872,401.51\",\"2,042.08\"],[\"13.95%\",\"0.25\",\"ATT\",\"Abattis Bio\",\"BioTech\",\"CNSX\",\"ATTBF\",\"2,706,357\",\"676,589.25\",\"39.86\"],[\"0.00%\",\"2.03\",\"BE\",\"Beleave\",\"Cultivation & Retail\",\"CNSX\",\"BLEVF\",\"597,909\",\"1,213,755.27\",\"\"],[\"1.28%\",\"1.58\",\"BLO\",\"Cannabix Tech\",\"LawTech\",\"CNSX\",\"BLOZF\",\"465,869\",\"736,073.02\",\"136.61\"],[\"-2.20%\",\"0.89\",\"CBW\",\"Cannabis Wheaton \",\"Investing & Finance\",\"CVE\",\"KWFLF\",\"815,477\",\"725,774.53\",\"234.57\"],[\"-0.65%\",\"19.93\",\"CMED\",\"Cannimed\",\"Cultivation & Retail\",\"TSE\",\"CMMDF\",\"130,722\",\"2,605,289.46\",\"457.69\"],[\"12.73%\",\"0.62\",\"CMM\",\"Canabo Medical\",\"MedCare\",\"CVE\",\"CAMDF\",\"330,404\",\"204,850.48\",\"23.54\"],[\"-2.87%\",\"2.71\",\"CRZ\",\"Cannaroyalty\",\"Investing & Finance\",\"CNSX\",\"CNNRF\",\"961,449\",\"2,605,526.79\",\"115.09\"],[\"-6.67%\",\"0.28\",\"CYX\",\"Calyx Bio\",\"AgTech\",\"CVE\",\"CLYXF\",\"2,120,562\",\"593,757.36\",\"24.23\"],[\"0.00%\",\"1.23\",\"DOJA\",\"DOJA Cannabis\",\"Cultivation & Retail\",\"CNSX\",\"DJACF\",\"206,635\",\"254,161.05\",\"72.27\"],[\"-4.40%\",\"0.44\",\"DVA\",\"Delivra\",\"BioTech\",\"CVE\",\"--\",\"89,485\",\"39,373.4\",\"19.55\"],[\"6.52%\",\"0.25\",\"EAT\",\"Nutritional High\",\"Marijuana Edibles & Extracts\",\"CNSX\",\"SPLIF\",\"3,067,636\",\"766,909\",\"61.54\"],[\"-1.20%\",\"1.64\",\"EMC\",\"Emblem\",\"Cultivation & Retail\",\"CVE\",\"EMMBF\",\"411,764\",\"675,292.96\",\"130.60\"],[\"2.05%\",\"3.98\",\"EMH\",\"Emerald\",\"Cultivation & Retail\",\"CVE\",\"TBQBF\",\"1,430,067\",\"5,691,666.66\",\"374.34\"],[\"-5.88%\",\"0.48\",\"FFT\",\"Future Farm Tech\",\"AgTech\",\"CNSX\",\"AGSTF\",\"1,291,240\",\"619,795.2\",\"0.61\"],[\"1.06%\",\"1.90\",\"FIRE\",\"Supreme Pharma\",\"Cultivation & Retail\",\"CVE\",\"SPRWF\",\"1,275,906\",\"2,424,221.4\",\"391.96\"],[\"5.26%\",\"0.10\",\"GHG\",\"Global Hemp\",\"Cultivation & Retail\",\"CNSX\",\"GBHPF\",\"764,350\",\"76,435\",\"\"],[\"3.28%\",\"0.31\",\"GLH\",\"Golden Leaf\",\"Marijuana Products\",\"CNSX\",\"GLDFF\",\"4,298,567\",\"1,332,555.77\",\"116.96\"],[\"-1.96%\",\"0.50\",\"HC\",\"High Hampton Holdings\",\"Investing & Finance\",\"CNSX\",\"--\",\"727,116\",\"363,558\",\"\"],[\"1.89%\",\"0.54\",\"HIP\",\"Newstirke Resources \",\"Cultivation & Retail\",\"CVE\",\"NWKRF\",\"431,875\",\"233,212.5\",\"210.35\"],[\"8.91%\",\"1.10\",\"HVST\",\"Harvest One Cannabis\",\"Cultivation & Retail\",\"CVE\",\"HRVOF\",\"2,192,877\",\"2,412,164.7\",\"98.10\"],[\"8.89%\",\"0.98\",\"ICC\",\"International Cannabis\",\"Cultivation & Retail\",\"CVE\",\"ICCLF\",\"123,538\",\"121,067.24\",\"110.84\"],[\"0.00%\",\"1.62\",\"IMH\",\"Invictus MD\",\"Investing & Finance\",\"CVE\",\"IVITF\",\"781,924\",\"1,266,716.88\",\"129.87\"],[\"12.50%\",\"0.90\",\"IN\",\"Inmed Pharma\",\"BioTech\",\"CNSX\",\"IMLFF\",\"3,846,586\",\"3,461,927.4\",\"\"],[\"2.27%\",\"1.80\",\"ISOL\",\"Isodiol International \",\"Hemp Products\",\"CNSX\",\"LAGBF\",\"8,514,952\",\"15,326,913.6\",\"\"],[\"7.84%\",\"0.28\",\"KALY\",\"Kalytera Therapeutics\",\"BioTech\",\"CVE\",\"QUEZD\",\"5,634,186\",\"1,577,572.08\",\"34.74\"],[\"-1.72%\",\"0.57\",\"LDS\",\"Lifestyle Delivery Systems\",\"BioTech\",\"CNSX\",\"LDSYF\",\"685,628\",\"390,807.96\",\"51.44\"],[\"0.19%\",\"15.50\",\"LEAF\",\"MedReleaf Corp\",\"Cultivation & Retail\",\"TSE\",\"MEDFF\",\"229,190\",\"3,552,445\",\"1,459.18\"],[\"2.33%\",\"0.44\",\"LIB\",\"Liberty Leaf Holdings\",\"Investing & Finance\",\"CNSX\",\"LIBFF\",\"4,555,082\",\"2,004,236.08\",\"\"],[\"10.42%\",\"1.59\",\"LXX\",\"Lexaria Bio\",\"Hemp Products\",\"CNSX\",\"LXRP\",\"1,523,338\",\"2,422,107.42\",\"\"],[\"-1.38%\",\"2.14\",\"MARI\",\"Maricann Group\",\"Cultivation & Retail\",\"CNSX\",\"MRRCF\",\"678,106\",\"1,451,146.84\",\"157.10\"],[\"3.26%\",\"0.95\",\"MDM\",\"Marapharm\",\"Cultivation & Retail\",\"CNSX\",\"MRPHF\",\"209,019\",\"198,568.05\",\"\"],[\"0.00%\",\"0.57\",\"MGW\",\"Maple Leaf Green World\",\"Cultivation & Retail\",\"CVE\",\"MGWFF\",\"367,479\",\"209,463.03\",\"83.83\"],[\"7.37%\",\"1.02\",\"MJ\",\"True Leaf\",\"Hemp Pet Chews\",\"CNSX\",\"TLFMF\",\"164,101\",\"167,383.02\",\"\"],[\"2.27%\",\"4.50\",\"MJN\",\"Pharmacan /Cronos\",\"Investing & Finance\",\"CVE\",\"PRMCF\",\"419,922\",\"1,889,649\",\"675.43\"],[\"4.23%\",\"2.71\",\"MYM\",\"My Marijuana\",\"Cultivation & Retail\",\"CNSX\",\"--\",\"1,066,122\",\"2,889,190.62\",\"\"],[\"4.40%\",\"0.95\",\"N\",\"Namaste Tech\",\"Consumption Devices\",\"CNSX\",\"NXTTF\",\"5,714,764\",\"5,429,025.8\",\"192.50\"],[\"0.00%\",\"0.10\",\"NF\",\"New Age Farm\",\"Hemp Products\",\"CNSX\",\"NWGFF\",\"3,938,476\",\"393,847.6\",\"\"],[\"-7.27%\",\"0.25\",\"NSP\",\"Naturally Splendid\",\"Hemp Products\",\"CVE\",\"NSPDF\",\"484,812\",\"121,203\",\"24.42\"],[\"4.99%\",\"3.79\",\"OGI\",\"Organigram\",\"Cultivation & Retail\",\"CVE\",\"OGRMF\",\"3,654,843\",\"13,851,854.97\",\"375.89\"],[\"1.15%\",\"0.88\",\"PUF\",\"PUF Ventures\",\"Consumption Devices\",\"CNSX\",\"PUFXF\",\"719,534\",\"633,189.92\",\"45.85\"],[\"10.68%\",\"1.14\",\"RHT\",\"Reliq Health Tech\",\"Mobile Software\",\"CVE\",\"RQHTF\",\"1,564,567\",\"1,783,606.38\",\"98.74\"],[\"4.05%\",\"1.80\",\"RTI\",\"Radient Technologies\",\"Extraction\",\"CVE\",\"RDDTF\",\"2,181,473\",\"3,926,651.4\",\"345.53\"],[\"3.64%\",\"0.28\",\"RVV\",\"Revive Therapeutics\",\"Medication\",\"CVE\",\"RVVTF\",\"399,705\",\"111,917.4\",\"15.50\"],[\"-2.90%\",\"0.67\",\"SUN\",\"Wildflower\",\"Hemp Products\",\"CNSX\",\"WLDFF\",\"87,197\",\"58,421.99\",\"29.48\"],[\"-0.67%\",\"4.45\",\"SXP\",\"Supremex\",\"Packaging\",\"TSE\",\"SUMXF\",\"27,015\",\"120,216.75\",\"126.40\"],[\"0.00%\",\"0.76\",\"TBP\",\"Tetra Bio-Pharma\",\"BioTech\",\"CVE\",\"GRPOF\",\"497,745\",\"378,286.2\",\"88.67\"],[\"2.44%\",\"2.10\",\"TER\",\"TerrAscend Corp\",\"Cultivation & Retail\",\"CNSX\",\"--\",\"270,176\",\"567,369.6\",\"\"],[\"4.29%\",\"0.73\",\"THC\",\"THC Biomed\",\"BioTech\",\"CNSX\",\"THCBF\",\"818,162\",\"597,258.26\",\"81.29\"],[\"3.55%\",\"3.21\",\"THCX\",\"Hydropothecary Corp\",\"Cultivation & Retail\",\"CVE\",\"HYYDF\",\"1,581,640\",\"5,077,064.4\",\"282.37\"],[\"8.22%\",\"0.79\",\"TNY\",\"Tinley Beverage Co\",\"Beverage\",\"CNSX\",\"QRSRF\",\"945,154\",\"746,671.66\",\"57.81\"],[\"3.49%\",\"7.70\",\"TRST\",\"CannTrust\",\"Cultivation & Biotech\",\"CNSX\",\"CNTTF\",\"368,892\",\"2,840,468.4\",\"699.98\"],[\"-8.04%\",\"1.03\",\"VGW\",\"Valens Groworks\",\"BioTech\",\"CNSX\",\"MYMSF\",\"23,285\",\"23,983.55\",\"62.77\"],[\"0.00%\",\"0.52\",\"VIN\",\"Vinergy Resources\",\"Investing & Finance\",\"CNSX\",\"VNNYF\",\"0\",\"\",\"\"],[\"-2.50%\",\"0.39\",\"VP\",\"Vodis Pharma\",\"Cultivation & Retail\",\"CNSX\",\"VDQSF\",\"52,661\",\"20,537.79\",\"\"],[\"6.67%\",\"0.80\",\"VRT\",\"Veritas Pharma\",\"BioTech\",\"CNSX\",\"VRTHF\",\"377,901\",\"302,320.8\",\"\"],[\"6.41%\",\"19.42\",\"WEED\",\"Canopy Growth\",\"Cultivation & Retail\",\"TSE\",\"TWMJF\",\"4,940,034\",\"95,935,460.28\",\"3,706.63\"],[\"6.25%\",\"2.38\",\"WMD\",\"WeedMD\",\"Cultivation & Retail\",\"CVE\",\"WDDMF\",\"1,174,148\",\"2,794,472.24\",\"124.71\"],[\"3.36%\",\"14.75\",\"HMMJ\",\"Horizons Marijuana Life Sciences\",\"Canadian Marijuana ETF\",\"TSE\",\"HMLSF\",\"336,579\",\"4,964,540.25\",\"197.64\"]]],\"sheetnames\":[\"ALLSHOW\"]}\n \n exchange_suffixes = {'TSE': 'TO', 'CVE': 'V'}\n\n # create sector\n sector, _ = Sector.objects.get_or_create(name=\"Cannabis\", slug='cannabis')\n\n # create currency\n currency, _ = Currency.objects.get_or_create(symbol='CAD', defaults={'character':'$', 'name':'Canadian Dollar'})\n us_currency, _ = Currency.objects.get_or_create(symbol='USD', defaults={'character':'$', 'name':'US Dollar'})\n\n # OTC exchange\n otc, _ = Exchange.objects.get_or_create(symbol='OTC', defaults={'name':'OTC', 'currency': us_currency})\n\n # iterate over each item in our table, make the items\n for row in data[\"data\"][0][1:]:\n # percent = float(row[0].replace(\"%\",\"\"))\n suffix = exchange_suffixes[row[5]] if row[5] in exchange_suffixes else ''\n exchange, _ = Exchange.objects.get_or_create(symbol=row[5], defaults={'name':row[5], 'currency':currency, 'ticker_suffix': suffix})\n company, _ = Company.objects.get_or_create(name=row[3], defaults={'sector':sector})\n stock, _ = Stock.objects.get_or_create(ticker=row[2], defaults={\n 'company': company,\n 'exchange': exchange,\n 'market_cap': float(row[9].replace(\",\",\"\")) * 1000000 if row[9] else 0.0,\n # 'previous_close': float(row[1]) - float(row[1]) * percent / 100,\n # 'open': float(row[1]),\n # 'current': float(row[1]),\n # 'volume': float(row[8].replace(\",\",\"\")) if row[8] else 0.0,\n })\n stock.save()\n\n if row[4]:\n tag, _ = Tag.objects.get_or_create(name=row[4])\n company.tags.add(tag)\n\n if row[6] and not row[6] == \"--\":\n stock, _ = Stock.objects.get_or_create(ticker=row[6], defaults={'company':company, 'exchange':otc})\n\n\n print data","def get_from_file() -> List[Dict]:\n with open(get_file_path('all'), 'r', encoding='utf-8') as file:\n return json.load(file)","def record_data(self) -> dict:\n data = {}\n for provider in self.providers.values():\n data.update(provider.record_data())\n\n for obs in self.observations.values():\n data.update(obs.record_data())\n\n return data","def serialise(self):\n return {\n 'id': self.id,\n 'name': self.name,\n 'items': [i.serialise for i in self.items]\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)","def get_json_adata( activity_list ):\n\tdata = []\n\tfor idx,activity in enumerate( activity_list ):\n\t\ttemp = {\n\t\t\t'name' \t\t\t: activity.get_name(),\n\t\t\t'url' \t\t\t: activity.get_url(),\n\t\t\t'type'\t\t\t: activity.get_activity_type(),\n\t\t\t'hour'\t\t\t: activity.get_hours(),\n\t\t\t'locations'\t\t: activity.get_location_list(),\n\t\t\t'tweet_count'\t: activity.tweet_count\n\t\t}\n\t\t# data[ str( idx ) ] = temp\n\t\tdata.append(temp)\n\treturn data","def fullLoad(f_list):\n\n t_1 = datetime.now()\n\n id_list = []\n data_dict = {}\n\n for i, f in enumerate(f_list):\n \n i_t_1 = datetime.now()\n\n r_f = open(f, \"rb\")\n r_data = r_f.read().split(\"\\n\")\n\n ln_r_data = len(r_data)\n\n for j, r in enumerate(r_data[:-1]):\n j_r = json.loads(r)\n if j_r[\"_id\"] not in id_list:\n id_list.append(j_r[\"_id\"])\n date = datetime.strptime(j_r[\"pub_date\"][0:10], \"%Y-%m-%d\")\n if date not in data_dict:\n data_dict[date] = \"\"\n data_dict[date] += \" %s\" % j_r[\"lead_paragraph\"]\n print (j * 100.) / ln_r_data, datetime.now() - t_1, datetime.now() - i_t_1, i \n return data_dict","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)"],"string":"[\n \"def get_datalist_fr_json(self):\\n raw_data = json.load(open(self.saved_json_file, 'r'))\\n for indivdual_set in raw_data['query']['results']['stats']:\\n temp_dict_data = {}\\n if type(indivdual_set) == str:\\n #for single data\\n continue # temp do not use\\n for parameters in indivdual_set.keys():\\n if type(indivdual_set[parameters]) == str:\\n temp_dict_data[parameters] = indivdual_set[parameters]#for symbol\\n elif type(indivdual_set[parameters]) == dict:\\n if indivdual_set[parameters].has_key('content'):\\n temp_dict_data[parameters] = indivdual_set[parameters]['content']\\n\\n ## append to list\\n self.com_data_allstock_list.append(temp_dict_data)\",\n \"def json_data(self):\\n return_data = []\\n\\n # Loop over num files\\n for ifile in range(len(self.data)):\\n\\n # Loop over steps\\n file_return_data = {}\\n for istep in range(len(self.data[ifile])):\\n file_return_data[istep] = {}\\n for key in self.data[ifile][istep]:\\n file_return_data[istep][key] = self.data[ifile][istep][key].tolist()\\n\\n return_data.append(file_return_data)\\n\\n return return_data\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def get_data(self):\\n return {\\n \\\"fd\\\": self.get_fd_j(self.id),\\n \\\"fd_part\\\": self.get_fd_part_j(self.id),\\n \\\"config\\\": self.config,\\n # \\\"config\\\": self.get_config_j(self.id),\\n \\\"prp\\\": self.get_prp_j(self.id),\\n \\\"alll\\\": self.my_alll(self.id)\\n }\",\n \"def read_all(self):\\n def is_data(i):\\n \\\"\\\"\\\"\\n It checks if given key is different than added by system\\n \\\"\\\"\\\"\\n keys = ['_id', '_time']\\n return all(i != k for k in keys)\\n\\n self.logger.log_reading()\\n return simplejson.dumps([{i: x[i] for i in x if is_data(i)} for x in self.json_collection.find()])\",\n \"def getData(self):\\n return dict(self._dump_data)\",\n \"def setup_data( self ):\\n with open( f'{project_dir}/data/05_source_key_data.json', 'r', encoding='utf-8' ) as f:\\n dct = json.loads( f.read() )\\n with open( f'{project_dir}/data/04_snapshot_open_textbook.json', 'r', encoding='utf-8' ) as f:\\n lst = json.loads( f.read() )\\n return ( dct, lst )\",\n \"def to_dict(self):\\n return self.data.to_dict('list')\",\n \"def into_data(self) -> List[Dict[str, Any]]:\\n if self.previous:\\n data = self.previous.into_data()\\n else:\\n data = []\\n\\n datum: Dict[str, Any] = dict(\\n required={name: up_to_date.into_data() for name, up_to_date in self.required.items()}\\n )\\n\\n if self.command is None:\\n assert self.start is None\\n assert self.end is None\\n else:\\n assert self.start is not None\\n assert self.end is not None\\n datum[\\\"command\\\"] = self.command\\n datum[\\\"start\\\"] = str(self.start)\\n datum[\\\"end\\\"] = str(self.end)\\n\\n data.append(datum)\\n return data\",\n \"def codebook_json_data_factory() -> List[Dict[str, Any]]:\\n codebook_data = [\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_A\\\"\\n },\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_B\\\"\\n },\\n ]\\n return codebook_data\",\n \"def __json_data__(self):\\n if schema.is_dense(self.namespace):\\n dense_records = dict()\\n for field in Observation._fields:\\n dense_records[field] = []\\n\\n for obs in self.data:\\n for key, val in six.iteritems(obs._asdict()):\\n dense_records[key].append(serialize_obj(val))\\n\\n return dense_records\\n\\n else:\\n return [serialize_obj(_) for _ in self.data]\",\n \"def dict(self):\\n return self.data\",\n \"def __convert_data_to_list_of_dict__(self, data):\\n jsons = list()\\n for row in data:\\n json_for_row = dict(zip(self.__fieldnames__, row))\\n jsons += [json_for_row]\\n return jsons\",\n \"def get_data(self):\\n return [{'title': th['title'], 'img_link': th['img_link'], 'id': j, 'url': th['url'], 'text': th.get('text', '0')}\\n for j, th in enumerate(self)]\",\n \"def get_data(self):\\n self.data = dict()\\n # list to save all the attributes we are going to create\\n self.attr = []\\n # list to save all the groups available in the incomming input\\n self.groups.extend(self.values.keys())\\n # Grouping\\n self.parse_data()\",\n \"def json_data(self):\\n self.check_proof()\\n return {\\n \\\"vars\\\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\\n \\\"proof\\\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\\n for item in self.prf.items], []),\\n \\\"report\\\": self.rpt.json_data(),\\n \\\"method_sig\\\": self.get_method_sig()\\n }\",\n \"def to_dict(self):\\n out_dict = _deepcopy(self.__dict__)\\n out_dict[\\\"pumprate\\\"] = self.pumprate.to_dict()\\n out_wells = []\\n for i in range(self.well_count()):\\n out_wells.append(self.wells[i].to_dict())\\n out_dict[\\\"wells\\\"] = out_wells\\n return(out_dict)\",\n \"def extract_data(self):\\n values = {}\\n for injkey in self.data_sets.keys():\\n values[injkey] = {}\\n alldata = self.data_sets[injkey]\\n paramkeys = alldata['params'].keys()\\n for datakey in alldata.keys():\\n if not datakey == 'params':\\n values[injkey][datakey] = {}\\n values[injkey][datakey]['metric_val'] = {}\\n values[injkey][datakey]['metric_val']['vals'] = []\\n for paramkey in paramkeys:\\n values[injkey][datakey][paramkey] = {}\\n values[injkey][datakey][paramkey]['vals'] = []\\n trials = alldata[datakey]\\n for trial_num in trials.keys():\\n trial = trials[trial_num]\\n values[injkey][datakey]['metric_val']['vals'] \\\\\\n .append(trial['metric_val'])\\n values[injkey][datakey]['metric_val']['type'] \\\\\\n = trial['metric']\\n values[injkey][datakey]['metric_val']['units'] \\\\\\n = 'dimensionless'\\n param_vals = trial['params']\\n for param_name in param_vals.keys():\\n val, units = self.parse_pint_string(\\n pint_string=param_vals[param_name]\\n )\\n values[injkey][datakey][param_name]['vals'] \\\\\\n .append(float(val))\\n values[injkey][datakey][param_name]['units'] \\\\\\n = units\\n self.values = values\",\n \"def get_objects_data(self):\\n return dict(result=self.objects)\",\n \"def data():\\n result = {}\\n for thread in DATA.threads:\\n result[thread] = [formatNode(node) for node in DATA.threads[thread].tree]\\n return json.dumps({\\n 'checkpoints': DATA.checkpoints,\\n 'threads': result\\n })\",\n \"def dict(self):\\n\\t\\treturn self.json\",\n \"def get_data(self):\\n return self.data.to_json()\",\n \"def get_dicts(self, clean=False):\\n return list(self.iter_dicts(clean=clean))\",\n \"def get_objects_data(self):\\n return dict(items=self.objects)\",\n \"def get_objects_data(self):\\n return dict(objects=self.objects)\",\n \"def json_data(self):\\n json_providers = getAdapters((self.context, self.request, self.view), IJSONDataProvider)\\n results = []\\n for name, provider in json_providers:\\n results.append({'name': name or None,\\n 'data': json.dumps(provider())})\\n return results\",\n \"def data(self):\\n\\n for i in json_parsed:\\n name = i['name']\\n number = i['number']\\n address=i['address']\\n banking=i['banking']\\n position=i['position']\\n latitude=position['lat']\\n longitude=position['lng']\\n bike_stands=i['bike_stands']\\n status=i['status']\\n insert_data(name, number, address, banking, latitude, longitude, bike_stands, status)\",\n \"def get_data(self):\\n data = {}\\n _priv = self.get('_private', [])\\n\\n def check_data(v):\\n if isinstance(v, Yaco):\\n v = v.get_data()\\n elif isinstance(v, list):\\n v = [check_data(x) for x in v]\\n return v\\n\\n for k in list(self.keys()):\\n if k in _priv:\\n continue\\n if isinstance(k, (str)) and k and k[0] == '_':\\n continue\\n # print self.keys()\\n # print k, 'x' * 30\\n data[k] = check_data(self[k])\\n return data\",\n \"def get_json(self):\\r\\n self.get_recordrange()\\r\\n [\\r\\n self.json_data.setter(n, self.get_data(\\\"json\\\", x))\\r\\n for n, x in enumerate(tqdm(self.iterlist))\\r\\n ]\",\n \"def dict(self):\\n return {\\\"data\\\": self.data.dict(), \\\"inventory\\\": self.inventory.dict()}\",\n \"def generate_data_list(self, data_list):\\n sol = []\\n for i in data_list:\\n sol.append({\\n \\\"data_sig\\\": i[0],\\n \\\"data\\\": pickle.loads(i[1]),\\n \\\"attachment\\\": i[2],\\n \\\"likes\\\":pickle.loads(i[3]),\\n \\\"dislikes\\\":pickle.loads(i[4]),\\n \\\"owner\\\": i[5]\\n })\\n return sol\",\n \"def get_keys_from_list():\\n json_data = request.get_json()\\n\\n d = dict()\\n d['elements'] = list()\\n settings.setOptionsFile(get_info('uid'))\\n fn = settings.getHistoROOTFileName()\\n rfn = settings.getReferenceROOTFileName()\\n# open root file stored in the root database\\n f = ROOT.TFile(fn)\\n# open reference root file stored in the root database\\n rf = ROOT.TFile(rfn)\\n\\n for values in json_data.itervalues():\\n for k in values:\\n subd = dict()\\n subd[\\\"index\\\"] = k[\\\"index\\\"]\\n if fn != k[\\\"file\\\"]: \\n fn = k[\\\"file\\\"]\\n settings.setHistoROOTFileName(fn)\\n f = ROOT.TFile(fn)\\n print \\\"histogram :>>>>>: \\\",k[\\\"histogram\\\"]\\n subd[\\\"data\\\"] = eval(cppyy.gbl.getDictionary(f,k[\\\"histogram\\\"]))\\n if rfn != k[\\\"referenceFile\\\"]: \\n rfn = k[\\\"referenceFile\\\"]\\n settings.setReferenceROOTFileName(rfn)\\n rf = ROOT.TFile(rfn)\\n subd[\\\"refdata\\\"] = eval(cppyy.gbl.getDictionary(rf,k[\\\"reference\\\"]))\\n d['elements'].append(subd)\\n\\n f.Close()\\n rf.Close()\\n\\n return jsonify(d)\",\n \"def getDictWells(self):\\n #Method begins here\\n #nx=self.__grid['nx'] #From the geometry in grid\\n ny=self.__grid['ny']\\n nz=self.__grid['nz']\\n minx=self.__grid['ox']\\n miny=self.__grid['oy']\\n minz=self.__grid['oz']\\n rx=self.__grid['dx']\\n ry=self.__grid['dy']\\n rz=self.__grid['dz']\\n \\n # well package\\n # Remember to use zero-based layer, row, column indices!\\n lcoordw=np.zeros((self.__nwells,3),dtype=np.int32)\\n for i in range (self.__nwells):\\n lcoordw[i,0]=floor((self.__dflst.iloc[i,3]-minx)/rx)\\n #In MODFLOW y ans z coordinates are inverted\\n lcoordw[i,1]=floor((miny+ry*ny-self.__dflst.iloc[i,4])/ry)\\n lcoordw[i,2]=floor((minz+rz*nz-self.__dflst.iloc[i,5])/rz)\\n \\n nper=self.__df.getForcPer()\\n wel_sp = {} \\n for i in range(nper):\\n lst=[]\\n for j in range(self.__nwells):\\n pumping_rate=self.__dfwells.iloc[i+1,j+1]\\n lst.append( [lcoordw[j,2], lcoordw[j,1], lcoordw[j,0], pumping_rate] )\\n wel_sp[i]=lst\\n print(wel_sp)\\n \\n print('*--- Succesfull reading of wells ---*')\\n \\n return wel_sp\",\n \"def get_all_data():\\n return jsonify(service.get_all_data())\",\n \"def to_dict(self, complete=False):\\n if complete:\\n data = self.complete_data.to_dict('list')\\n else:\\n data = self.data.to_dict('list')\\n # Get metadata.\\n data.update(wildtype=self.wildtype, mutations=self.mutations)\\n return data\",\n \"def metadata(self):\\n self.data_as_dict = {}\\n for ele in self.data:\\n self.data_as_dict[ele.name] = ele.value\\n return self.data_as_dict\",\n \"def json(self) -> List[Dict[str, Union[str, List[str]]]]:\\n model_json: List[Dict[str, Union[str, List[str]]]] = []\\n for phrase in self.phrase_index:\\n entry = {'phrase': phrase}\\n if phrase in self.has_variants:\\n entry['variants'] = list(self.has_variants[phrase])\\n if phrase in self.has_labels:\\n entry['label'] = list(self.has_labels[phrase])\\n if phrase in self.custom:\\n entry['custom'] = self.custom[phrase]\\n model_json += [entry]\\n return model_json\",\n \"def data_list(self):\\n # return [data_object, data_object, ...]\\n return self._data_list\",\n \"def get_all(self):\\n # read log\\n d = {}\\n log = self.get_logfile()\\n if not os.path.isfile(log):\\n return d\\n f = open(log, \\\"r\\\")\\n if f.mode == 'r':\\n lines = f.readlines()\\n for line in lines:\\n dline = json.loads(line)\\n d.update(dline)\\n f.close()\\n return d\",\n \"def _data_list(json: 'a json'):\\n data = json['Time Series (Daily)']\\n return list(data.items())\",\n \"def get_data(data_type):\\n json_list = list()\\n for source in data_type.value:\\n json_list += open_json_file(source)\\n return json_list\",\n \"def get_data(self):\\n if not hasattr(self, 'json_data'):\\n self.json_data = collections.OrderedDict()\\n return self.json_data\",\n \"def data_json(request):\\n json_data = []\\n for resource in Resource.objects.all():\\n record = {} \\n record['title'] = resource.name\\n record['description'] = resource.description\\n record['keyword'] = resource.csw_keywords.split(',')\\n record['modified'] = resource.last_updated\\n record['publisher'] = resource.organization\\n record['contactPoint'] = resource.metadata_contact\\n record['mbox'] = resource.contact_email\\n record['identifier'] = resource.csw_identifier\\n if resource.is_published:\\n record['accessLevel'] = 'public'\\n else:\\n record['accessLevel'] = 'non-public'\\n\\n json_data.append(record)\\n\\n return HttpResponse(json.dumps(json_data), 'application/json')\",\n \"def get_json(self):\\n return {\\n \\\"power\\\": self.get_power(), \\n \\\"timestamp\\\": self.get_timestamp(), \\n \\\"shortage\\\": self.get_shortage()\\n }\",\n \"def data():\\n return volumes_fetchers.get_json_data()\",\n \"def json(self):\\n json_data = {}\\n\\n for item in self.iteritems():\\n try:\\n value = item.get_value(json_serializable=True)\\n except TypeError:\\n value = \\\"NOT_SERIALIZABLE\\\"\\n item_data = {\\n \\\"tags\\\": list(item.tags),\\n \\\"value\\\": value,\\n }\\n if item.name in json_data:\\n json_data[item.name].append(item_data)\\n else:\\n json_data[item.name] = [item_data]\\n\\n return json_data\",\n \"def raw_data() -> Dict:\\n return {\\\"neighbourhood\\\":\\\"Buttes-Montmartre\\\",\\\"room_type\\\":\\\"Entire home/apt\\\",\\\"minimum_nights\\\":1.555,\\\"mois\\\":2,\\\"voyageurs\\\":2.5,\\\"chambres\\\":1,\\\"lits\\\":1,\\\"salle_de_bains\\\":1}\",\n \"def get_json(ticker_symbol):\\n s_data = Stock.objects.filter(ticker=ticker_symbol).order_by('-date')\\n json_file = []\\n for data in s_data:\\n json_obj = StockSerializer(data).data\\n json_file.append(json_obj)\\n return json_file\",\n \"def to_dict(self):\\n# \\\"\\\"\\\" The JSON model used is like:\\n# <code>\\n#{\\n# \\\"duration\\\": 15,\\n# \\\"url\\\": \\\"url1\\\",\\n# \\\"selections\\\": [{\\n# \\\"annotations\\\": [{\\n# \\\"author\\\": \\\"\\\",\\n# \\\"description\\\": \\\"speaker\\\",\\n# \\\"keyword\\\": \\\"john\\\",\\n# \\\"lang\\\": \\\"EN\\\"\\n# },\\n# {\\n# \\\"author\\\": \\\"\\\",\\n# \\\"description\\\": \\\"speakerLabel\\\",\\n# \\\"keyword\\\": \\\"S0\\\",\\n# \\\"lang\\\": \\\"EN\\\"\\n# }\\n# , {\\n# \\\"author\\\": \\\"\\\",\\n# \\\"description\\\": \\\"gender\\\",\\n# \\\"keyword\\\": \\\"F\\\",\\n# \\\"lang\\\": \\\"EN\\\" \\n# }],\\n# \\\"resolution\\\": \\\"0x0\\\",\\n# \\\"selW\\\": 20,\\n# \\\"selH\\\": 15,\\n# \\\"selY\\\": 10,\\n# \\\"selX\\\": 10,\\n# \\\"startTime\\\" : 0,\\n# \\\"endTime\\\" : 10\\n# \\n# }]\\n#}\\n# </code>\\n# \\n# \\\"\\\"\\\"\\n\\n dic = {\\\"duration\\\": self.get_duration(),\\n \\\"url\\\": self._filename,\\n \\\"db\\\":self.get_db().get_path(),\\n \\\"selections\\\": [] }\\n for seg in self.get_time_slices():\\n dic['selections'].append({\\n \\\"startTime\\\": float(seg[0]) / 100.0,\\n \\\"endTime\\\": float(seg[1]) / 100.0,\\n 'speaker': seg[-2],\\n 'speakerLabel': seg[-1],\\n 'gender': seg[2],\\n 'speakers': seg[3]\\n })\\n return dic\",\n \"def get_structure(self):\\n main = {}\\n for line in self.load():\\n match = re.match('^\\\\s*([A-Za-z0-9_]+)(\\\\((\\\\d+)\\\\))?=(.*)$', line)\\n if match:\\n key = match.group(1)\\n index = match.group(3)\\n value = match.group(4)\\n if index is None:\\n main[key] = self.parse_data_value(value)\\n else:\\n if key not in main:\\n main[key] = []\\n main[key].append(self.parse_data_value(value))\\n #else:\\n # print(line)\\n return main\",\n \"def data():\\n print (\\\"&\\\")\\n res = {}\\n\\t\\n # Load Data\\n with open(DATA_PATH_TRAIN, 'rb') as f:\\n data = pickle.load(f)\\n\\t\\t\\n for d in data:\\n for j in range(len(d)):\\n if not d[j][\\\"addinfo\\\"][\\\"path\\\"] in res:\\n res[d[j][\\\"addinfo\\\"][\\\"path\\\"]] = {}\\n d[j][\\\"environment\\\"][\\\"text\\\"] = d[j][\\\"addinfo\\\"][\\\"text\\\"]\\n res[d[j][\\\"addinfo\\\"][\\\"path\\\"]][d[j][\\\"addinfo\\\"][\\\"line\\\"]] = d[j][\\\"environment\\\"]\\n\\t \\t\\n with open(DATA_PATH_TEST, 'rb') as f:\\n data = pickle.load(f)\\n\\t\\t\\n for d in data:\\n for j in range(len(d)):\\n if not d[j][\\\"addinfo\\\"][\\\"path\\\"] in res:\\n res[d[j][\\\"addinfo\\\"][\\\"path\\\"]] = {}\\n d[j][\\\"environment\\\"][\\\"text\\\"] = d[j][\\\"addinfo\\\"][\\\"text\\\"]\\n res[d[j][\\\"addinfo\\\"][\\\"path\\\"]][d[j][\\\"addinfo\\\"][\\\"line\\\"]] = d[j][\\\"environment\\\"]\\n\\t\\t\\t\\n with open('tasks/env/data/data.json', 'w') as outfile:\\n json.dump(res, outfile)\",\n \"def getAllData(self):\\r\\n return self.data\",\n \"def set_data(self):\\n data = [\\n {\\\"ASN1P-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"ASN1P\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n {\\\"asn1rt-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"asn1rt\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n {\\\"cadessignature-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"cadessignature\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n { \\\"JCP-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"JCP\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n { \\\"JCPRequest-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"JCPRequest\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }}\\n ]\\n return data\",\n \"def list(self):\\n return {\\n k: json.loads(v)\\n for k, v in iteritems(self._db.hgetall(self.index))\\n }\",\n \"def jsons(self):\\n if self._jsons is None:\\n self.make_jsons()\\n return self._jsons\",\n \"def json(self):\\n return {\\\"name\\\": self.name,\\n \\\"items\\\": [item.json() for item in self.item]} # self.name==storeObj.name and self.item==storeObj.item\",\n \"def _get_data(self):\\n data = {}\\n\\n for name, field in self._get_fields().items():\\n if isinstance(field, fields.Factory):\\n # skip for factories for now\\n continue\\n value = getattr(self, name)\\n raw_value = field.to_raw(value)\\n if isinstance(field, fields.Secret):\\n data[f\\\"__{name}\\\"] = raw_value\\n else:\\n data[name] = raw_value\\n\\n return data\",\n \"def wem_market_value_all():\\n __query = \\\"\\\"\\\"\\n select\\n date_trunc('month', wfs.trading_interval) AS trading_day,\\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\\n wf.fueltech_id\\n from wem_facility_scada wfs\\n left join wem_facility wf on wfs.facility_id = wf.code\\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\\n where\\n wf.fueltech_id is not null\\n group by 1, wf.fueltech_id\\n order by 1 desc, 2 asc\\n \\\"\\\"\\\"\\n\\n query = __query.format()\\n\\n json_envelope = {}\\n\\n with engine.connect() as c:\\n rows = c.execute(query)\\n\\n current_tech = None\\n\\n for row in rows:\\n\\n current_tech = row[2]\\n\\n if current_tech not in json_envelope.keys():\\n json_envelope[current_tech] = {\\n \\\"id\\\": f\\\"wem.fuel_tech.{current_tech}.market_value\\\",\\n \\\"fuel_tech\\\": current_tech,\\n \\\"region\\\": \\\"wa\\\",\\n \\\"type\\\": \\\"market_value\\\",\\n \\\"units\\\": \\\"AUD\\\",\\n \\\"history\\\": {\\n \\\"interval\\\": \\\"1M\\\",\\n \\\"start\\\": None,\\n \\\"last\\\": None,\\n \\\"data\\\": [],\\n },\\n }\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] == None\\n or row[0] < json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] = row[0]\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] == None\\n or row[0] > json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] = row[0]\\n\\n json_envelope[current_tech][\\\"history\\\"][\\\"data\\\"].append(row[1])\\n\\n return [json_envelope[i] for i in json_envelope.keys()]\",\n \"def get_info(self) -> List[Dict]:\\n return [{} for _ in range(self.num)]\",\n \"def parse_source_json(data):\\n data_dict=[]\\n for item in data:\\n tmp_dict ={}\\n tmp_dict[\\\"_id\\\"]=item[\\\"POIID\\\"]\\n tmp_dict[\\\"POIName\\\"]=item[\\\"POIName\\\"]\\n tmp_dict[\\\"Address\\\"]=item[\\\"Address\\\"]\\n try:\\n if item[\\\"isDining\\\"]=='Y':\\n tmp_dict[\\\"isDining\\\"] = True\\n else:\\n tmp_dict[\\\"isDining\\\"] = False\\n except:\\n tmp_dict[\\\"isDining\\\"] = None\\n tmp_dict[\\\"location\\\"] = {\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [item[\\\"X\\\"],item[\\\"Y\\\"]]}\\n \\n data_dict.append(tmp_dict)\\n return data_dict\",\n \"def get_json_ldata( location_list ):\\n\\tdata = []\\n\\tfor idx,location in enumerate( location_list ):\\n\\t\\ttemp = {\\n\\t\\t\\t'name' \\t\\t\\t: location.get_name(),\\n\\t\\t\\t'url' \\t\\t\\t: location.get_url(),\\n\\t\\t\\t'population' \\t: location.get_population(),\\n\\t\\t\\t'type'\\t\\t\\t: location.get_location_type(),\\n\\t\\t\\t'tweet_count'\\t: location.tweet_count\\n\\t\\t}\\n\\t\\t# data[ str( idx ) ] = temp\\n\\t\\tdata.append(temp)\\n\\treturn data\",\n \"def export_data(self):\\n\\n # We need to remove spaces for TW-1504, use custom separators\\n data_tuples = ((key, self._serialize(key, value))\\n for key, value in six.iteritems(self._data))\\n\\n # Empty string denotes empty serialized value, we do not want\\n # to pass that to TaskWarrior.\\n data_tuples = filter(lambda t: t[1] is not '', data_tuples)\\n data = dict(data_tuples)\\n return json.dumps(data, separators=(',',':'))\",\n \"def dict(self):\\n return self.__data_dict\",\n \"def get_all(self):\\n self.data = list(self.data)\\n return self.data\",\n \"def _collect_data(self):\\n data = {\\n \\\"K\\\": self.K,\\n \\\"root\\\": self.root\\n }\\n return data\",\n \"def get_json(self):\\n data = {}\\n data['ip'] = self.ip\\n\\n try:\\n data['country'] = self.processedvtdata[\\\"country\\\"]\\n except KeyError:\\n data['country'] = 'None'\\n try:\\n data['as'] = self.processedvtdata[\\\"as_owner\\\"]\\n except KeyError:\\n data['as'] = 'None'\\n try:\\n data['rdns'] = self.processedvtdata[\\\"self.reversedns\\\"]\\n except KeyError:\\n data['rdns'] = 'None'\\n try:\\n data['label'] = self.expertlabel\\n except AttributeError:\\n data['label'] = ''\\n\\n # geodata\\n #{\\\"status\\\":\\\"success\\\",\\\"country\\\":\\\"Yemen\\\",\\\"countryCode\\\":\\\"YE\\\",\\\"region\\\":\\\"SA\\\",\\\"regionName\\\":\\\"Amanat Alasimah\\\",\\\"city\\\":\\\"Sanaa\\\",\\\"zip\\\":\\\"\\\",\\\"lat\\\":15.3522,\\\"lon\\\":44.2095,\\\"timezone\\\":\\\"Asia/Aden\\\",\\\"isp\\\":\\\"Public Telecommunication Corporation\\\",\\\"org\\\":\\\"YemenNet\\\",\\\"as\\\":\\\"AS30873 Public Telecommunication Corporation\\\",\\\"query\\\":\\\"134.35.218.63\\\"}\\n if self.geodata:\\n data['geodata'] = self.geodata\\n \\n # vt resolutions. Is a list\\n data['vt'] = {}\\n try:\\n if self.processedvtdata['resolutions'] != 'None':\\n data['vt']['resolutions'] = []\\n for count, resolution_tuple in enumerate(self.processedvtdata['resolutions']):\\n if count >= self.amount_to_print:\\n break\\n temp = {}\\n temp['date'] = resolution_tuple[0]\\n temp['domain'] = resolution_tuple[1]\\n data['vt']['resolutions'].append(temp)\\n except KeyError:\\n pass\\n\\n # vt urls. Is a list\\n try:\\n if self.processedvtdata['detected_urls'] != 'None':\\n data['vt']['detected_urls'] = []\\n for count, url_tuple in enumerate(self.processedvtdata['detected_urls']):\\n if count >= self.amount_to_print:\\n break\\n temp = {}\\n temp['date'] = url_tuple[0]\\n temp['url'] = url_tuple[1][0]\\n temp['detections'] = str(url_tuple[1][1]) + '/' + str(url_tuple[1][2])\\n data['vt']['detected_urls'].append(temp)\\n except KeyError:\\n pass\\n\\n\\n # vt detected communicating samples. Is a list\\n try:\\n if self.processedvtdata['detected_communicating_samples'] != 'None':\\n data['vt']['detected_communicating_samples'] = []\\n for count, communcating_tuple in enumerate(self.processedvtdata['detected_communicating_samples']):\\n if count >= self.amount_to_print:\\n break\\n temp = {}\\n temp['date'] = communcating_tuple[0]\\n temp['detections'] = str(communcating_tuple[1][0]) + '/' + str(communcating_tuple[1][1])\\n temp['sha256'] = communcating_tuple[1][2]\\n data['vt']['detected_communicating_samples'].append(temp)\\n except AttributeError:\\n pass\\n\\n # vt detected downloaded samples. Is a list\\n try:\\n if self.processedvtdata['detected_downloaded_samples'] != 'None':\\n data['vt']['detected_downloaded_samples'] = []\\n for count, detected_tuple in enumerate(self.processedvtdata['detected_downloaded_samples']):\\n if count >= self.amount_to_print:\\n break\\n temp = {}\\n temp['date'] = detected_tuple[0]\\n temp['detections'] = str(detected_tuple[1][0]) + '/' + str(detected_tuple[1][1])\\n temp['sha256'] = detected_tuple[1][2]\\n data['vt']['detected_downloaded_samples'].append(temp)\\n except AttributeError:\\n pass\\n\\n # vt referrer downloaded samples. Is a list\\n try:\\n if self.processedvtdata['detected_referrer_samples'] != 'None':\\n data['vt']['detected_referrer_samples'] = []\\n for count, referrer_tuple in enumerate(self.processedvtdata['detected_referrer_samples']):\\n if count >= self.amount_to_print:\\n break\\n temp = {}\\n temp['sha256'] = referrer_tuple[0]\\n temp['detections'] = str(referrer_tuple[1][0]) + '/' + str(referrer_tuple[1][1])\\n data['vt']['detected_referrer_samples'].append(temp)\\n except AttributeError:\\n pass\\n\\n # pt data\\n data['pt'] = {}\\n if self.processedptdata:\\n count = 0\\n data['pt']['passive_dns'] = []\\n for result in self.processedptdata_results:\\n if count >= self.amount_to_print:\\n break\\n temp = {}\\n temp['lastseen'] = result[0]\\n temp['firstseen'] = result[1][0]\\n temp['hostname'] = result[1][1]\\n data['pt']['passive_dns'].append(temp)\\n count += 1\\n\\n # shodan data\\n try:\\n if self.shodandata:\\n data['shodan'] = self.shodandata\\n except AttributeError:\\n pass\\n\\n data = json.dumps(data)\\n return data\",\n \"def get_all(self):\\n return {\\\"parcels\\\": self.db}, 200\",\n \"def json(self):\\n return [t.json for t in self.values()]\",\n \"def to_list_of_dicts(self) -> List[Dict[str, Any]]:\\n return [this.to_dict() for this in self._elements]\",\n \"def logs(self):\\n dataTuples = list()\\n try:\\n with sqlite3.connect(self.db_file) as conn:\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM curves\\\")\\n dataTuples = cur.fetchall()\\n return [{\\\"id\\\": a[0], \\\"name\\\": a[1], \\\"units\\\": a[2],\\n \\\"descr\\\": a[3]} for a in dataTuples]\\n except:\\n return dataTuples\",\n \"def to_dict(self):\\n print(\\\"\\\\n\\\\nSTARTING...\\\")\\n ea = db.session.query(entity_assets).filter(entity_assets.c.entity_id == self.id).all()\\n print(\\\"\\\\n\\\\nmade it\\\", ea)\\n em = db.session.query(entity_meters).filter(entity_meters.c.entity_id == self.id).all()\\n est = db.session.query(entity_statuses).filter(entity_statuses.c.entity_id == self.id).all()\\n \\n return {\\n \\\"id\\\": self.id,\\n \\\"user_id\\\": self.user_id,\\n \\\"creator\\\": self.user.username,\\n \\\"type\\\": self.type,\\n \\\"category\\\": self.category,\\n \\\"title\\\": self.title,\\n \\\"description\\\": self.description,\\n \\\"color\\\": self.color,\\n \\\"icon\\\": self.icon,\\n \\\"image\\\": self.image,\\n \\\"created_at\\\": self.created_at,\\n\\n \\\"location_id\\\": self.location_id,\\n \\\"generated_id\\\": self.generated_id,\\n \\n \\\"assets\\\": [(a.asset_id, a.quantity) for a in ea],\\n \\\"statuses\\\": [(s.status_id, s.expiry) for s in est],\\n \\\"meters\\\": [(m.meter_id, m.points) for m in em],\\n \\\"slots\\\": [(slot.slot_id, slot.filler_id) for slot in self.entity_slots],\\n }\",\n \"def dicts(self):\\n if self._dicts is None:\\n self.make_dicts()\\n return self._dicts\",\n \"def data_dict0():\\n\\n # 0- Sample from detectron2 -> 5 different sections.\\n info_val0 = [{\\\"date_created\\\": \\\"2020-03-15 04:59:45.442988\\\",\\n \\\"description\\\": \\\"Automatically generated COCO json file for Detectron2.\\\"}]\\n images0 = [{\\\"id\\\": \\\"image\\\", \\\"width\\\": 100,\\n \\\"height\\\": 100, \\\"file_name\\\": \\\"image.png\\\"}]\\n annotations0 = [{\\\"id\\\": 1, \\\"image_id\\\": \\\"image\\\", \\\"bbox\\\": [70.0, 30.0, 30.0, 40.0],\\n \\\"area\\\": 1200.0, \\\"iscrowd\\\": 0, \\\"category_id\\\": 0}]\\n categories0 = [{\\\"id\\\": 0, \\\"name\\\": \\\"first\\\"}]\\n licence0 = 'null'\\n\\n return [{\\\"info\\\": info_val0,\\n \\\"images\\\": images0,\\n \\\"annotations\\\": annotations0,\\n \\\"categories\\\": categories0,\\n \\\"licenses\\\": licence0}]\",\n \"def get_json(self):\\n json_item = {\\\"id: \\\": self.id,\\n \\\"question: \\\": self.question,\\n \\\"documents: \\\": self.documents,\\n \\\"document_ids: \\\": self.document_ids,\\n \\\"gold answers: \\\": self.gold}\\n return json_item\",\n \"def get_dict(self):\\n return self._data\",\n \"def get_list():\\n hash_map_list = model.hash_table.find()\\n data = dict(success=True, hash_map_list=hash_map_list)\\n return data\",\n \"def get_full_data(self):\\n to_return = {}\\n keywords = Keywords()\\n for word in self.keywords:\\n to_return[word] = keywords[word].get_info()\\n return to_return\",\n \"def get_all_items():\\n try:\\n return get_json(get_data())\\n except ValueError as e:\\n print(e)\\n except TypeError as e:\\n print(e)\",\n \"def Mydata():\\n\\n stmt = db.session.query(Appsdata).statement\\n df = pd.read_sql_query(stmt, db.session.bind)\\n \\n return jsonify(df.to_dict())\",\n \"def bridge_all_json():\\n return {\\n \\\"base_stations\\\": [\\n {\\n \\\"id\\\": 12345,\\n \\\"name\\\": None,\\n \\\"mode\\\": \\\"home\\\",\\n \\\"hardware_id\\\": \\\"0x1234567890abcdef\\\",\\n \\\"hardware_revision\\\": 4,\\n \\\"firmware_version\\\": {\\n \\\"wifi\\\": \\\"0.121.0\\\",\\n \\\"wifi_app\\\": \\\"3.3.0\\\",\\n \\\"silabs\\\": \\\"1.0.1\\\",\\n },\\n \\\"missing_at\\\": None,\\n \\\"created_at\\\": \\\"2019-04-30T01:43:50.497Z\\\",\\n \\\"updated_at\\\": \\\"2019-04-30T01:44:43.749Z\\\",\\n \\\"system_id\\\": 12345,\\n \\\"firmware\\\": {\\\"wifi\\\": \\\"0.121.0\\\", \\\"wifi_app\\\": \\\"3.3.0\\\", \\\"silabs\\\": \\\"1.0.1\\\"},\\n \\\"links\\\": {\\\"system\\\": 12345},\\n }\\n ]\\n }\",\n \"def to_dict(self) -> List[dict]:\\n return asyncio.run(self.parse(key=\\\"series\\\"))\",\n \"def toJSON(self):\\r\\n\\r\\n jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n if len(self.slctData) > 100:\\r\\n self.getSimMatSummary(100)\\r\\n jsonToRet.append(self.summaryOrdering)\\r\\n for i in range(0,len(self.simMatSmm)):\\r\\n for n in self.simMatSmm[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n\\r\\n jsonToRet.append(self.patchOrdering)\\r\\n # jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n for i in range(0,len(self.simMat)):\\r\\n for n in self.simMat[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n return jsonToRet\",\n \"def _get_data_as_json(self, data):\\n data = self._get_data_as_df(data)\\n data = data.to_json(orient=\\\"records\\\")\\n char_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)\\n return list(map(''.join, zip(*[iter(data)] * char_per_line)))\",\n \"def GetDict(self):\\n # CL actions are be stored in self._cl_action_list instead of\\n # in self._metadata_dict['cl_actions'], because _cl_action_list\\n # is potentially a multiprocess.lis. So, _cl_action_list needs to\\n # be copied into a normal list.\\n temp = self._metadata_dict.copy()\\n temp['cl_actions'] = list(self._cl_action_list)\\n\\n # Similarly, the per-board dicts are stored in a flattened form in\\n # _per_board_dict. Un-flatten into nested dict.\\n per_board_dict = {}\\n for k, v in self._per_board_dict.items():\\n board, key = k.split(':')\\n board_dict = per_board_dict.setdefault(board, {})\\n if key:\\n board_dict[key] = v\\n\\n temp['board-metadata'] = per_board_dict\\n return temp\",\n \"def to_listing_dict(self) -> dict:\\n data = super().to_listing_dict()\\n return data\",\n \"def get_dict(self):\\n return\",\n \"def list_items(self) -> List[Dict[str, Any]]:\\n return [c.to_dict() for c in self._objects.values()]\",\n \"def listings_data():\\n\\n stmt = db.session.query(nyc).statement\\n df = pd.read_sql_query(stmt, db.session.bind)\\n df[\\\"latitude\\\"] = pd.to_numeric(df[\\\"latitude\\\"])\\n df[\\\"longitude\\\"] = pd.to_numeric(df[\\\"longitude\\\"])\\n df[\\\"accommodates\\\"] = pd.to_numeric(df[\\\"accommodates\\\"])\\n\\n data = df.to_dict(orient='index')\\n # Create a dictionary entry for each row of metadata information\\n # data = {}\\n # for result in results:\\n #\\n # data[\\\"ID\\\"] = result[0]\\n # data[\\\"LISTING_URL\\\"] = result[1]\\n # data[\\\"NAME\\\"] = result[2]\\n # data[\\\"HOST_ID\\\"] = result[3]\\n # data[\\\"NEIGHBORHOOD\\\"] = result[4]\\n # data[\\\"NEIGHBORHOOD_GROUP\\\"] = result[5]\\n # data[\\\"CITY\\\"] = result[6]\\n # data[\\\"ZIPCODE\\\"] = result[7]\\n # data[\\\"LAT\\\"] = float(result[8])\\n # data[\\\"LON\\\"] = float(result[9])\\n #\\n # print(data)\\n\\n return jsonify(data)\",\n \"def to_dict(self) -> List[Dict[str, Any]]:\\n return [x.to_dict() for x in self.params]\",\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 book_list_manipulation():\\n global book_list\\n book_json = {}\\n for book in book_list:\\n book_container = {'title': book.title,\\n 'author': book.author,\\n 'read': str(book.read),\\n 'rating': book.rating,\\n 'review': book.review,\\n 'date read': book.date_read,\\n 'id': str(book.id)}\\n book_json[str(book.id)] = book_container\\n\\n return book_json\",\n \"def get_json():\\n data_list = []\\n\\n for page in range(1,13):\\n url = BASE_URL + STYLE_URL + \\\"&\\\" + PAGINATION_URL + str(page)\\n print(page, \\\"pages processed\\\")\\n try:\\n response = requests.get(url, timeout=METADATA_REQUEST_TIMEOUT)\\n data = response.json()['Paintings']\\n parse_data(data_list, data)\\n except requests.exceptions.RequestException as e:\\n print(e)\\n sys.exit(1)\\n\\n return data_list\",\n \"def upload_initial_data():\\n data = {\\\"updated\\\":1512866292573,\\\"data\\\":[[[\\\"DAY %\\\",\\\"PPS (CAD)\\\",\\\"TKR\\\",\\\"NAME\\\",\\\"SECTOR\\\",\\\"EX.\\\",\\\"ALT. TKR\\\",\\\"VOL\\\",\\\"VOL @ CRNT $\\\",\\\"MC ($MM)\\\"],[\\\"5.84%\\\",\\\"1.45\\\",\\\"ABCN\\\",\\\"ABcann Medicinals\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"ABCCF\\\",\\\"901,940\\\",\\\"1,307,813\\\",\\\"78.49\\\"],[\\\"6.14%\\\",\\\"7.09\\\",\\\"ACB\\\",\\\"Aurora Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"ACBFF\\\",\\\"13,927,205\\\",\\\"98,743,883.45\\\",\\\"2,861.53\\\"],[\\\"9.09%\\\",\\\"0.24\\\",\\\"ACG\\\",\\\"Alliance Growers \\\",\\\"AgTech\\\",\\\"CNSX\\\",\\\"--\\\",\\\"1,189,385\\\",\\\"285,452.4\\\",\\\"\\\"],[\\\"0.00%\\\",\\\"0.10\\\",\\\"AFI\\\",\\\"Affinor Growers\\\",\\\"AgTech\\\",\\\"CNSX\\\",\\\"RSSFF\\\",\\\"210,310\\\",\\\"21,031\\\",\\\"\\\"],[\\\"3.22%\\\",\\\"13.47\\\",\\\"APH\\\",\\\"Aphria\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"APHQF\\\",\\\"2,663,133\\\",\\\"35,872,401.51\\\",\\\"2,042.08\\\"],[\\\"13.95%\\\",\\\"0.25\\\",\\\"ATT\\\",\\\"Abattis Bio\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"ATTBF\\\",\\\"2,706,357\\\",\\\"676,589.25\\\",\\\"39.86\\\"],[\\\"0.00%\\\",\\\"2.03\\\",\\\"BE\\\",\\\"Beleave\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"BLEVF\\\",\\\"597,909\\\",\\\"1,213,755.27\\\",\\\"\\\"],[\\\"1.28%\\\",\\\"1.58\\\",\\\"BLO\\\",\\\"Cannabix Tech\\\",\\\"LawTech\\\",\\\"CNSX\\\",\\\"BLOZF\\\",\\\"465,869\\\",\\\"736,073.02\\\",\\\"136.61\\\"],[\\\"-2.20%\\\",\\\"0.89\\\",\\\"CBW\\\",\\\"Cannabis Wheaton \\\",\\\"Investing & Finance\\\",\\\"CVE\\\",\\\"KWFLF\\\",\\\"815,477\\\",\\\"725,774.53\\\",\\\"234.57\\\"],[\\\"-0.65%\\\",\\\"19.93\\\",\\\"CMED\\\",\\\"Cannimed\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"CMMDF\\\",\\\"130,722\\\",\\\"2,605,289.46\\\",\\\"457.69\\\"],[\\\"12.73%\\\",\\\"0.62\\\",\\\"CMM\\\",\\\"Canabo Medical\\\",\\\"MedCare\\\",\\\"CVE\\\",\\\"CAMDF\\\",\\\"330,404\\\",\\\"204,850.48\\\",\\\"23.54\\\"],[\\\"-2.87%\\\",\\\"2.71\\\",\\\"CRZ\\\",\\\"Cannaroyalty\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"CNNRF\\\",\\\"961,449\\\",\\\"2,605,526.79\\\",\\\"115.09\\\"],[\\\"-6.67%\\\",\\\"0.28\\\",\\\"CYX\\\",\\\"Calyx Bio\\\",\\\"AgTech\\\",\\\"CVE\\\",\\\"CLYXF\\\",\\\"2,120,562\\\",\\\"593,757.36\\\",\\\"24.23\\\"],[\\\"0.00%\\\",\\\"1.23\\\",\\\"DOJA\\\",\\\"DOJA Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"DJACF\\\",\\\"206,635\\\",\\\"254,161.05\\\",\\\"72.27\\\"],[\\\"-4.40%\\\",\\\"0.44\\\",\\\"DVA\\\",\\\"Delivra\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"--\\\",\\\"89,485\\\",\\\"39,373.4\\\",\\\"19.55\\\"],[\\\"6.52%\\\",\\\"0.25\\\",\\\"EAT\\\",\\\"Nutritional High\\\",\\\"Marijuana Edibles & Extracts\\\",\\\"CNSX\\\",\\\"SPLIF\\\",\\\"3,067,636\\\",\\\"766,909\\\",\\\"61.54\\\"],[\\\"-1.20%\\\",\\\"1.64\\\",\\\"EMC\\\",\\\"Emblem\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"EMMBF\\\",\\\"411,764\\\",\\\"675,292.96\\\",\\\"130.60\\\"],[\\\"2.05%\\\",\\\"3.98\\\",\\\"EMH\\\",\\\"Emerald\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"TBQBF\\\",\\\"1,430,067\\\",\\\"5,691,666.66\\\",\\\"374.34\\\"],[\\\"-5.88%\\\",\\\"0.48\\\",\\\"FFT\\\",\\\"Future Farm Tech\\\",\\\"AgTech\\\",\\\"CNSX\\\",\\\"AGSTF\\\",\\\"1,291,240\\\",\\\"619,795.2\\\",\\\"0.61\\\"],[\\\"1.06%\\\",\\\"1.90\\\",\\\"FIRE\\\",\\\"Supreme Pharma\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"SPRWF\\\",\\\"1,275,906\\\",\\\"2,424,221.4\\\",\\\"391.96\\\"],[\\\"5.26%\\\",\\\"0.10\\\",\\\"GHG\\\",\\\"Global Hemp\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"GBHPF\\\",\\\"764,350\\\",\\\"76,435\\\",\\\"\\\"],[\\\"3.28%\\\",\\\"0.31\\\",\\\"GLH\\\",\\\"Golden Leaf\\\",\\\"Marijuana Products\\\",\\\"CNSX\\\",\\\"GLDFF\\\",\\\"4,298,567\\\",\\\"1,332,555.77\\\",\\\"116.96\\\"],[\\\"-1.96%\\\",\\\"0.50\\\",\\\"HC\\\",\\\"High Hampton Holdings\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"--\\\",\\\"727,116\\\",\\\"363,558\\\",\\\"\\\"],[\\\"1.89%\\\",\\\"0.54\\\",\\\"HIP\\\",\\\"Newstirke Resources \\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"NWKRF\\\",\\\"431,875\\\",\\\"233,212.5\\\",\\\"210.35\\\"],[\\\"8.91%\\\",\\\"1.10\\\",\\\"HVST\\\",\\\"Harvest One Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"HRVOF\\\",\\\"2,192,877\\\",\\\"2,412,164.7\\\",\\\"98.10\\\"],[\\\"8.89%\\\",\\\"0.98\\\",\\\"ICC\\\",\\\"International Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"ICCLF\\\",\\\"123,538\\\",\\\"121,067.24\\\",\\\"110.84\\\"],[\\\"0.00%\\\",\\\"1.62\\\",\\\"IMH\\\",\\\"Invictus MD\\\",\\\"Investing & Finance\\\",\\\"CVE\\\",\\\"IVITF\\\",\\\"781,924\\\",\\\"1,266,716.88\\\",\\\"129.87\\\"],[\\\"12.50%\\\",\\\"0.90\\\",\\\"IN\\\",\\\"Inmed Pharma\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"IMLFF\\\",\\\"3,846,586\\\",\\\"3,461,927.4\\\",\\\"\\\"],[\\\"2.27%\\\",\\\"1.80\\\",\\\"ISOL\\\",\\\"Isodiol International \\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"LAGBF\\\",\\\"8,514,952\\\",\\\"15,326,913.6\\\",\\\"\\\"],[\\\"7.84%\\\",\\\"0.28\\\",\\\"KALY\\\",\\\"Kalytera Therapeutics\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"QUEZD\\\",\\\"5,634,186\\\",\\\"1,577,572.08\\\",\\\"34.74\\\"],[\\\"-1.72%\\\",\\\"0.57\\\",\\\"LDS\\\",\\\"Lifestyle Delivery Systems\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"LDSYF\\\",\\\"685,628\\\",\\\"390,807.96\\\",\\\"51.44\\\"],[\\\"0.19%\\\",\\\"15.50\\\",\\\"LEAF\\\",\\\"MedReleaf Corp\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"MEDFF\\\",\\\"229,190\\\",\\\"3,552,445\\\",\\\"1,459.18\\\"],[\\\"2.33%\\\",\\\"0.44\\\",\\\"LIB\\\",\\\"Liberty Leaf Holdings\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"LIBFF\\\",\\\"4,555,082\\\",\\\"2,004,236.08\\\",\\\"\\\"],[\\\"10.42%\\\",\\\"1.59\\\",\\\"LXX\\\",\\\"Lexaria Bio\\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"LXRP\\\",\\\"1,523,338\\\",\\\"2,422,107.42\\\",\\\"\\\"],[\\\"-1.38%\\\",\\\"2.14\\\",\\\"MARI\\\",\\\"Maricann Group\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"MRRCF\\\",\\\"678,106\\\",\\\"1,451,146.84\\\",\\\"157.10\\\"],[\\\"3.26%\\\",\\\"0.95\\\",\\\"MDM\\\",\\\"Marapharm\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"MRPHF\\\",\\\"209,019\\\",\\\"198,568.05\\\",\\\"\\\"],[\\\"0.00%\\\",\\\"0.57\\\",\\\"MGW\\\",\\\"Maple Leaf Green World\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"MGWFF\\\",\\\"367,479\\\",\\\"209,463.03\\\",\\\"83.83\\\"],[\\\"7.37%\\\",\\\"1.02\\\",\\\"MJ\\\",\\\"True Leaf\\\",\\\"Hemp Pet Chews\\\",\\\"CNSX\\\",\\\"TLFMF\\\",\\\"164,101\\\",\\\"167,383.02\\\",\\\"\\\"],[\\\"2.27%\\\",\\\"4.50\\\",\\\"MJN\\\",\\\"Pharmacan /Cronos\\\",\\\"Investing & Finance\\\",\\\"CVE\\\",\\\"PRMCF\\\",\\\"419,922\\\",\\\"1,889,649\\\",\\\"675.43\\\"],[\\\"4.23%\\\",\\\"2.71\\\",\\\"MYM\\\",\\\"My Marijuana\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"--\\\",\\\"1,066,122\\\",\\\"2,889,190.62\\\",\\\"\\\"],[\\\"4.40%\\\",\\\"0.95\\\",\\\"N\\\",\\\"Namaste Tech\\\",\\\"Consumption Devices\\\",\\\"CNSX\\\",\\\"NXTTF\\\",\\\"5,714,764\\\",\\\"5,429,025.8\\\",\\\"192.50\\\"],[\\\"0.00%\\\",\\\"0.10\\\",\\\"NF\\\",\\\"New Age Farm\\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"NWGFF\\\",\\\"3,938,476\\\",\\\"393,847.6\\\",\\\"\\\"],[\\\"-7.27%\\\",\\\"0.25\\\",\\\"NSP\\\",\\\"Naturally Splendid\\\",\\\"Hemp Products\\\",\\\"CVE\\\",\\\"NSPDF\\\",\\\"484,812\\\",\\\"121,203\\\",\\\"24.42\\\"],[\\\"4.99%\\\",\\\"3.79\\\",\\\"OGI\\\",\\\"Organigram\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"OGRMF\\\",\\\"3,654,843\\\",\\\"13,851,854.97\\\",\\\"375.89\\\"],[\\\"1.15%\\\",\\\"0.88\\\",\\\"PUF\\\",\\\"PUF Ventures\\\",\\\"Consumption Devices\\\",\\\"CNSX\\\",\\\"PUFXF\\\",\\\"719,534\\\",\\\"633,189.92\\\",\\\"45.85\\\"],[\\\"10.68%\\\",\\\"1.14\\\",\\\"RHT\\\",\\\"Reliq Health Tech\\\",\\\"Mobile Software\\\",\\\"CVE\\\",\\\"RQHTF\\\",\\\"1,564,567\\\",\\\"1,783,606.38\\\",\\\"98.74\\\"],[\\\"4.05%\\\",\\\"1.80\\\",\\\"RTI\\\",\\\"Radient Technologies\\\",\\\"Extraction\\\",\\\"CVE\\\",\\\"RDDTF\\\",\\\"2,181,473\\\",\\\"3,926,651.4\\\",\\\"345.53\\\"],[\\\"3.64%\\\",\\\"0.28\\\",\\\"RVV\\\",\\\"Revive Therapeutics\\\",\\\"Medication\\\",\\\"CVE\\\",\\\"RVVTF\\\",\\\"399,705\\\",\\\"111,917.4\\\",\\\"15.50\\\"],[\\\"-2.90%\\\",\\\"0.67\\\",\\\"SUN\\\",\\\"Wildflower\\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"WLDFF\\\",\\\"87,197\\\",\\\"58,421.99\\\",\\\"29.48\\\"],[\\\"-0.67%\\\",\\\"4.45\\\",\\\"SXP\\\",\\\"Supremex\\\",\\\"Packaging\\\",\\\"TSE\\\",\\\"SUMXF\\\",\\\"27,015\\\",\\\"120,216.75\\\",\\\"126.40\\\"],[\\\"0.00%\\\",\\\"0.76\\\",\\\"TBP\\\",\\\"Tetra Bio-Pharma\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"GRPOF\\\",\\\"497,745\\\",\\\"378,286.2\\\",\\\"88.67\\\"],[\\\"2.44%\\\",\\\"2.10\\\",\\\"TER\\\",\\\"TerrAscend Corp\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"--\\\",\\\"270,176\\\",\\\"567,369.6\\\",\\\"\\\"],[\\\"4.29%\\\",\\\"0.73\\\",\\\"THC\\\",\\\"THC Biomed\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"THCBF\\\",\\\"818,162\\\",\\\"597,258.26\\\",\\\"81.29\\\"],[\\\"3.55%\\\",\\\"3.21\\\",\\\"THCX\\\",\\\"Hydropothecary Corp\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"HYYDF\\\",\\\"1,581,640\\\",\\\"5,077,064.4\\\",\\\"282.37\\\"],[\\\"8.22%\\\",\\\"0.79\\\",\\\"TNY\\\",\\\"Tinley Beverage Co\\\",\\\"Beverage\\\",\\\"CNSX\\\",\\\"QRSRF\\\",\\\"945,154\\\",\\\"746,671.66\\\",\\\"57.81\\\"],[\\\"3.49%\\\",\\\"7.70\\\",\\\"TRST\\\",\\\"CannTrust\\\",\\\"Cultivation & Biotech\\\",\\\"CNSX\\\",\\\"CNTTF\\\",\\\"368,892\\\",\\\"2,840,468.4\\\",\\\"699.98\\\"],[\\\"-8.04%\\\",\\\"1.03\\\",\\\"VGW\\\",\\\"Valens Groworks\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"MYMSF\\\",\\\"23,285\\\",\\\"23,983.55\\\",\\\"62.77\\\"],[\\\"0.00%\\\",\\\"0.52\\\",\\\"VIN\\\",\\\"Vinergy Resources\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"VNNYF\\\",\\\"0\\\",\\\"\\\",\\\"\\\"],[\\\"-2.50%\\\",\\\"0.39\\\",\\\"VP\\\",\\\"Vodis Pharma\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"VDQSF\\\",\\\"52,661\\\",\\\"20,537.79\\\",\\\"\\\"],[\\\"6.67%\\\",\\\"0.80\\\",\\\"VRT\\\",\\\"Veritas Pharma\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"VRTHF\\\",\\\"377,901\\\",\\\"302,320.8\\\",\\\"\\\"],[\\\"6.41%\\\",\\\"19.42\\\",\\\"WEED\\\",\\\"Canopy Growth\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"TWMJF\\\",\\\"4,940,034\\\",\\\"95,935,460.28\\\",\\\"3,706.63\\\"],[\\\"6.25%\\\",\\\"2.38\\\",\\\"WMD\\\",\\\"WeedMD\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"WDDMF\\\",\\\"1,174,148\\\",\\\"2,794,472.24\\\",\\\"124.71\\\"],[\\\"3.36%\\\",\\\"14.75\\\",\\\"HMMJ\\\",\\\"Horizons Marijuana Life Sciences\\\",\\\"Canadian Marijuana ETF\\\",\\\"TSE\\\",\\\"HMLSF\\\",\\\"336,579\\\",\\\"4,964,540.25\\\",\\\"197.64\\\"]]],\\\"sheetnames\\\":[\\\"ALLSHOW\\\"]}\\n \\n exchange_suffixes = {'TSE': 'TO', 'CVE': 'V'}\\n\\n # create sector\\n sector, _ = Sector.objects.get_or_create(name=\\\"Cannabis\\\", slug='cannabis')\\n\\n # create currency\\n currency, _ = Currency.objects.get_or_create(symbol='CAD', defaults={'character':'$', 'name':'Canadian Dollar'})\\n us_currency, _ = Currency.objects.get_or_create(symbol='USD', defaults={'character':'$', 'name':'US Dollar'})\\n\\n # OTC exchange\\n otc, _ = Exchange.objects.get_or_create(symbol='OTC', defaults={'name':'OTC', 'currency': us_currency})\\n\\n # iterate over each item in our table, make the items\\n for row in data[\\\"data\\\"][0][1:]:\\n # percent = float(row[0].replace(\\\"%\\\",\\\"\\\"))\\n suffix = exchange_suffixes[row[5]] if row[5] in exchange_suffixes else ''\\n exchange, _ = Exchange.objects.get_or_create(symbol=row[5], defaults={'name':row[5], 'currency':currency, 'ticker_suffix': suffix})\\n company, _ = Company.objects.get_or_create(name=row[3], defaults={'sector':sector})\\n stock, _ = Stock.objects.get_or_create(ticker=row[2], defaults={\\n 'company': company,\\n 'exchange': exchange,\\n 'market_cap': float(row[9].replace(\\\",\\\",\\\"\\\")) * 1000000 if row[9] else 0.0,\\n # 'previous_close': float(row[1]) - float(row[1]) * percent / 100,\\n # 'open': float(row[1]),\\n # 'current': float(row[1]),\\n # 'volume': float(row[8].replace(\\\",\\\",\\\"\\\")) if row[8] else 0.0,\\n })\\n stock.save()\\n\\n if row[4]:\\n tag, _ = Tag.objects.get_or_create(name=row[4])\\n company.tags.add(tag)\\n\\n if row[6] and not row[6] == \\\"--\\\":\\n stock, _ = Stock.objects.get_or_create(ticker=row[6], defaults={'company':company, 'exchange':otc})\\n\\n\\n print data\",\n \"def get_from_file() -> List[Dict]:\\n with open(get_file_path('all'), 'r', encoding='utf-8') as file:\\n return json.load(file)\",\n \"def record_data(self) -> dict:\\n data = {}\\n for provider in self.providers.values():\\n data.update(provider.record_data())\\n\\n for obs in self.observations.values():\\n data.update(obs.record_data())\\n\\n return data\",\n \"def serialise(self):\\n return {\\n 'id': self.id,\\n 'name': self.name,\\n 'items': [i.serialise for i in self.items]\\n }\",\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_json_adata( activity_list ):\\n\\tdata = []\\n\\tfor idx,activity in enumerate( activity_list ):\\n\\t\\ttemp = {\\n\\t\\t\\t'name' \\t\\t\\t: activity.get_name(),\\n\\t\\t\\t'url' \\t\\t\\t: activity.get_url(),\\n\\t\\t\\t'type'\\t\\t\\t: activity.get_activity_type(),\\n\\t\\t\\t'hour'\\t\\t\\t: activity.get_hours(),\\n\\t\\t\\t'locations'\\t\\t: activity.get_location_list(),\\n\\t\\t\\t'tweet_count'\\t: activity.tweet_count\\n\\t\\t}\\n\\t\\t# data[ str( idx ) ] = temp\\n\\t\\tdata.append(temp)\\n\\treturn data\",\n \"def fullLoad(f_list):\\n\\n t_1 = datetime.now()\\n\\n id_list = []\\n data_dict = {}\\n\\n for i, f in enumerate(f_list):\\n \\n i_t_1 = datetime.now()\\n\\n r_f = open(f, \\\"rb\\\")\\n r_data = r_f.read().split(\\\"\\\\n\\\")\\n\\n ln_r_data = len(r_data)\\n\\n for j, r in enumerate(r_data[:-1]):\\n j_r = json.loads(r)\\n if j_r[\\\"_id\\\"] not in id_list:\\n id_list.append(j_r[\\\"_id\\\"])\\n date = datetime.strptime(j_r[\\\"pub_date\\\"][0:10], \\\"%Y-%m-%d\\\")\\n if date not in data_dict:\\n data_dict[date] = \\\"\\\"\\n data_dict[date] += \\\" %s\\\" % j_r[\\\"lead_paragraph\\\"]\\n print (j * 100.) / ln_r_data, datetime.now() - t_1, datetime.now() - i_t_1, i \\n return data_dict\",\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]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7234665","0.714661","0.68842065","0.68358326","0.66206723","0.6602693","0.6447181","0.64074695","0.64045924","0.63781536","0.6338094","0.6336256","0.63291097","0.6313858","0.62933797","0.62647957","0.6225904","0.62003684","0.6192421","0.61876845","0.6187121","0.61675364","0.6164842","0.6159393","0.6154688","0.6150421","0.61491746","0.6145599","0.6139965","0.61397445","0.61237985","0.61087906","0.6101439","0.6097859","0.6094148","0.6073623","0.6068147","0.6066518","0.6064681","0.60627025","0.60562277","0.6049435","0.60471106","0.6026064","0.60235226","0.6013818","0.60044783","0.5998908","0.59908545","0.5985339","0.5981154","0.59804857","0.59767747","0.5975106","0.59575635","0.5926365","0.5919761","0.591821","0.59053594","0.59039074","0.59032965","0.5903095","0.5901011","0.5892747","0.5884549","0.5881839","0.5869349","0.58667594","0.5862533","0.58619416","0.5861265","0.58534145","0.58504134","0.5827115","0.5826798","0.58220404","0.5816065","0.5814621","0.58140683","0.5811726","0.5797186","0.57893926","0.578717","0.5785832","0.5783649","0.57826","0.5781446","0.57785827","0.5775638","0.57740915","0.5772444","0.5767285","0.5749404","0.5741356","0.5738794","0.57313347","0.5726386","0.5725167","0.5723953","0.57227385"],"string":"[\n \"0.7234665\",\n \"0.714661\",\n \"0.68842065\",\n \"0.68358326\",\n \"0.66206723\",\n \"0.6602693\",\n \"0.6447181\",\n \"0.64074695\",\n \"0.64045924\",\n \"0.63781536\",\n \"0.6338094\",\n \"0.6336256\",\n \"0.63291097\",\n \"0.6313858\",\n \"0.62933797\",\n \"0.62647957\",\n \"0.6225904\",\n \"0.62003684\",\n \"0.6192421\",\n \"0.61876845\",\n \"0.6187121\",\n \"0.61675364\",\n \"0.6164842\",\n \"0.6159393\",\n \"0.6154688\",\n \"0.6150421\",\n \"0.61491746\",\n \"0.6145599\",\n \"0.6139965\",\n \"0.61397445\",\n \"0.61237985\",\n \"0.61087906\",\n \"0.6101439\",\n \"0.6097859\",\n \"0.6094148\",\n \"0.6073623\",\n \"0.6068147\",\n \"0.6066518\",\n \"0.6064681\",\n \"0.60627025\",\n \"0.60562277\",\n \"0.6049435\",\n \"0.60471106\",\n \"0.6026064\",\n \"0.60235226\",\n \"0.6013818\",\n \"0.60044783\",\n \"0.5998908\",\n \"0.59908545\",\n \"0.5985339\",\n \"0.5981154\",\n \"0.59804857\",\n \"0.59767747\",\n \"0.5975106\",\n \"0.59575635\",\n \"0.5926365\",\n \"0.5919761\",\n \"0.591821\",\n \"0.59053594\",\n \"0.59039074\",\n \"0.59032965\",\n \"0.5903095\",\n \"0.5901011\",\n \"0.5892747\",\n \"0.5884549\",\n \"0.5881839\",\n \"0.5869349\",\n \"0.58667594\",\n \"0.5862533\",\n \"0.58619416\",\n \"0.5861265\",\n \"0.58534145\",\n \"0.58504134\",\n \"0.5827115\",\n \"0.5826798\",\n \"0.58220404\",\n \"0.5816065\",\n \"0.5814621\",\n \"0.58140683\",\n \"0.5811726\",\n \"0.5797186\",\n \"0.57893926\",\n \"0.578717\",\n \"0.5785832\",\n \"0.5783649\",\n \"0.57826\",\n \"0.5781446\",\n \"0.57785827\",\n \"0.5775638\",\n \"0.57740915\",\n \"0.5772444\",\n \"0.5767285\",\n \"0.5749404\",\n \"0.5741356\",\n \"0.5738794\",\n \"0.57313347\",\n \"0.5726386\",\n \"0.5725167\",\n \"0.5723953\",\n \"0.57227385\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5839698"},"document_rank":{"kind":"string","value":"73"}}},{"rowIdx":75794,"cells":{"query":{"kind":"string","value":"Returns a dictionary with drawdown data and well parameters needed for analysis models in chen"},"document":{"kind":"string","value":"def to_model(self):\n out_dict = _deepcopy(self.parameters)\n out_dict[\"x\"] = self.drawdown.x.copy()\n out_dict[\"y\"] = self.drawdown.y.copy()\n if self._type == 2:\n out_dict[\"wtype\"] = 2\n elif self._type == 3:\n out_dict[\"wtype\"] = 3\n return(out_dict)"},"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 asDict(self) -> dict:\n return {\n \"predominant_occlusion\": self.predominantOcclusion.value,\n \"estimations\": {\n \"chin\": self.chin,\n \"mouth\": self.mouth,\n \"clear\": self.clear,\n \"correct\": self.correct,\n \"partially\": self.partially,\n \"full\": self.full,\n },\n }","def describe(self) -> Dict:\n\n desc = {\n \"Win\": {\n \"max\": np.max(self.Win),\n \"min\": np.min(self.Win),\n \"mean\": np.mean(self.Win),\n \"median\": np.median(self.Win),\n \"std\": np.std(self.Win)\n },\n \"W\": {\n \"max\": np.max(self.W),\n \"min\": np.min(self.W),\n \"mean\": np.mean(self.W),\n \"median\": np.median(self.W),\n \"std\": np.std(self.W),\n \"sr\": max(abs(linalg.eig(self.W)[0]))\n }\n }\n if self.Wfb is not None:\n desc[\"Wfb\"] = {\n \"max\": np.max(self.Wfb),\n \"min\": np.min(self.Wfb),\n \"mean\": np.mean(self.Wfb),\n \"median\": np.median(self.Wfb),\n \"std\": np.std(self.Wfb)\n }\n if self.Wout is not None:\n desc[\"Wout\"] = {\n \"max\": np.max(self.Wout),\n \"min\": np.min(self.Wout),\n \"mean\": np.mean(self.Wout),\n \"median\": np.median(self.Wout),\n \"std\": np.std(self.Wout)\n }\n return desc","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def getDictWells(self):\n #Method begins here\n #nx=self.__grid['nx'] #From the geometry in grid\n ny=self.__grid['ny']\n nz=self.__grid['nz']\n minx=self.__grid['ox']\n miny=self.__grid['oy']\n minz=self.__grid['oz']\n rx=self.__grid['dx']\n ry=self.__grid['dy']\n rz=self.__grid['dz']\n \n # well package\n # Remember to use zero-based layer, row, column indices!\n lcoordw=np.zeros((self.__nwells,3),dtype=np.int32)\n for i in range (self.__nwells):\n lcoordw[i,0]=floor((self.__dflst.iloc[i,3]-minx)/rx)\n #In MODFLOW y ans z coordinates are inverted\n lcoordw[i,1]=floor((miny+ry*ny-self.__dflst.iloc[i,4])/ry)\n lcoordw[i,2]=floor((minz+rz*nz-self.__dflst.iloc[i,5])/rz)\n \n nper=self.__df.getForcPer()\n wel_sp = {} \n for i in range(nper):\n lst=[]\n for j in range(self.__nwells):\n pumping_rate=self.__dfwells.iloc[i+1,j+1]\n lst.append( [lcoordw[j,2], lcoordw[j,1], lcoordw[j,0], pumping_rate] )\n wel_sp[i]=lst\n print(wel_sp)\n \n print('*--- Succesfull reading of wells ---*')\n \n return wel_sp","def buildParamsDict(self):\n self.params_dict = {\n \"img_dir\": self.savePathJoin(\"Images\"),\n \"depth_dir\": self.savePathJoin(\"Depth\"),\n \"back_of_dir\": self.savePathJoin(\"Back_Of\"),\n \"of_dir\": self.savePathJoin(\"Of\"),\n \"save_dir\": self.user[\"Save\"],\n \"high\": self.high,\n \"low\": self.low,\n \"run_dict\": self.run_dict,\n \"of_model\": self.app.get_resource(\n os.path.join(\"of_models\", \"network-default.pytorch\")\n ),\n \"depth_model\": self.app.get_resource(\n os.path.join(\"depth_models\", \"model_city2kitti.meta\")\n ),\n \"yolo_weights\": self.app.get_resource(\n os.path.join(\"yolo\", \"yolov3.weights\")\n ),\n \"yolo_v\": self.app.get_resource(os.path.join(\"yolo\", \"yolov3.cfg\")),\n \"coco_names\": self.app.get_resource(os.path.join(\"yolo\", \"coco.names\")),\n \"object_detection_dir\": self.savePathJoin(\"ObjectDetection\"),\n \"plot_speed_dir\": PLOT_SPEED_DIR,\n \"plot_crash_dir\": PLOT_CRASH_DIR,\n \"numbers_dir\": NP_DIR,\n \"plot_error_dir\": PLOT_ERROR_DIR,\n \"speed_gt\": self.user[\"GT\"],\n \"vid_path\": self.user[\"Video\"],\n \"super_pixel_method\": self.super_pixel_method,\n \"super_pixel_dir\": SUPER_PIXEL_DIR,\n \"send_video_frame\": False,\n \"create_csv\": self.ui.c_csv.isChecked(),\n \"create_draw\": self.ui.c_draw.isChecked(),\n \"create_velocity\": self.ui.c_velocity.isChecked(),\n \"create_video_fps\": int(self.ui.t_fps.text()),\n \"optimize_params\": self.ui.c_optimize.isChecked(),\n \"super_pixel_label_dir\": os.path.join(\n self.savePathJoin(\"Super_Pixel\"), self.super_pixel_method\n ),\n }","def test_to_rich_dict(self):\n F81().to_rich_dict()\n HKY85().to_rich_dict()\n GN().to_rich_dict()\n # TODO need to assess ability to reconstruct from this","def data(self):\n return {\"estimate\":self.get_estimate(),\n \"samplers\":self.samplers}","def parameters(self):\n res = dict()\n res[\"population_size\"] = self.population_size\n res[\"mutation_prob\"] = self.mutation_prob\n res[\"crossover\"] = self.crossover\n res[\"selection\"] = self.selection\n res[\"sigma\"] = self.sigma\n res[\"crossover_method\"] = self.crossover_method\n res[\"selection_method\"] = self.selection_method\n res[\"best_rate\"] = self.best_rate\n res[\"n_parents\"] = self.n_parents\n res[\"model_parameters\"] = self.model.total_parameters()\n res[\"IDCT_from\"] = self.IDCT_from\n res[\"elitism\"] = self.elitism\n return res","def getRigBuildData(self):\n\n data = super(SimpleControlComponentGuide, self).getRigBuildData()\n\n data[\"ctrlSize\"] = self.ctrlSizeInputAttr.getValue()\n data[\"ctrlXfo\"] = self.mainCtrl.xfo\n\n return data","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rbk[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd_emu[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rbk,Rdy,Rqcd_emu\")","def _get_fitted_params(self):\n return {}","def load_demo():\n\tprint(\"\"\"\n\tBreast Cancer Wisconsin dataset. It contains a total of 569 samples of tumor and malignant cells. \n\tData labeled 1 corresponds to malignant cells, while data labeled 0 corresponds to benign cells. \n\tThe 30 characteristics contain real values obtained from images of cell nuclei. For more information:\n\n\t\t\thttp://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)\n\n\n\tThe returned value is a dictionary where 'x_data' are the predictor variables, 'y_data' the class \n\tlabels and 'features' the name of the characteristics.\n\t\"\"\")\n\tpath = '/'.join(os.path.abspath(pywinEA.__file__).split('/')[:-1])\n\t\n\tdata = pd.read_csv(path+'/dataset/data/BreastCancerWisconsin.csv', index_col=0)\n\tx_data = data.iloc[:, 1:].values\n\ty_data = data.iloc[:, 0].values\n\tfeatures = data.columns[1:].values\n\n\t# Transform labels\n\ty_data[np.where(y_data == 'M')] = 1\n\ty_data[np.where(y_data == 'B')] = 0\n\ty_data = y_data.astype(int)\n\n\treturn {'x_data': x_data, 'y_data': y_data, 'features': features}","def qasmCircuitResults(self):\n returnedDictionary={}\n self.circutDrawing = self.draw()\n self.blochSpheres=self.separatedBlochSpheres()\n returnedDictionary[\"wires\"]=self.num_qubits\n returnedDictionary[\"probabilities\"] = self.separatedProbabilities()\n #returnedDictionary[\"blochSpheres\"] = self.separatedBlochSpheres()\n returnedDictionary[\"diracNotation\"] = self.diracNotation()\n returnedDictionary['chart'] = self.graph()\n returnedDictionary[\"link\"] = \"\"\n #returnedDictionary[\"qasmRows\"] = np.transpose(cols).tolist()\n \n if self.API_TOKEN != \"\":\n returnedDictionary[\"link\"] = self.runOnIBMQ()\n \n return returnedDictionary","def dic_info(self):\n\n info = {}\n try:\n info[\"DIC\"] = self.mc.DIC\n info[\"deviance\"] = np.mean(self.mc.db.trace(\"deviance\")(), axis=0)\n info[\"pD\"] = info[\"DIC\"] - info[\"deviance\"]\n except pm.ZeroProbability:\n info[\"DIC\"] = np.nan\n info[\"deviance\"] = np.nan\n info[\"pD\"] = np.nan\n\n return info","def get_params(self, deep=False):\n return {\"alpha\": self.alpha, \"beta\": self.beta, \"gamma\": self.gamma, \"W\": self.W, \"bias\": self.bias, \"add_bias\": self.add_bias, \"opts\": self.opts}","def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }","def paraChck(**kwargs):\n import sys\n\n \n def_val = {\n 'x_train':None,\n 'y_train':None,\n 'x_test':None,\n 'y_test':None,\n 'channel':1,\n 'input_img_cols':72,\n 'input_img_rows':72,\n 'nb_classes':13,\n 'nb_epoch': 5,\n 'batch_size' : 16,\n 'dict_label' : None} # default parameteters value\n\n diff = set(kwargs.keys()) - set(def_val.keys())\n if diff:\n print(\"Invalid args:\",tuple(diff),file=sys.stderr)\n return\n\n def_val.update(kwargs)\n return def_val","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)')","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(\"dAfb[0.,-0.75,0.75]\");\n self.modelBuilder.doVar(\"dA0[0.0, -1.0, 1.0]\");\n #self.modelBuilder.doSet(\"POI\",\"dAfb,dA0\")\n self.modelBuilder.doSet(\"POI\",\"dAfb\")\n self.modelBuilder.factory_('expr::mAfb(\"@0+@1\",eAfb,dAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0+@1)\",eA0,dA0)')\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)')","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(\"mAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"mA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"eAfb,mAfb\")\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)')","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"kappa_W[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_Z[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_tau[1,0.0,3.0]\")\n self.modelBuilder.doVar(\"kappa_mu[1,0.0,5.0]\") \n self.modelBuilder.factory_(\"expr::kappa_mu_expr(\\\"@0*@1+(1-@0)*@2\\\", CMS_use_kmu[0], kappa_mu, kappa_tau)\")\n self.modelBuilder.doVar(\"kappa_t[1,0.0,4.0]\")\n # additional kappa for the anomalous coupling\n self.modelBuilder.doVar(\"kappa_tilde_t[0.0,0.0,4.0]\")\n self.modelBuilder.doVar(\"kappa_b[1,0.0,3.0]\")\n if not self.resolved:\n self.modelBuilder.doVar(\"kappa_g[1,0.0,2.0]\")\n self.modelBuilder.doVar(\"kappa_gam[1,0.0,2.5]\")\n\tself.modelBuilder.doVar(\"BRinv[0,0,1]\")\n self.modelBuilder.out.var(\"BRinv\").setConstant(True)\n # adding additional kappa to list of parameters of interest\n pois = 'kappa_W,kappa_Z,kappa_tau,kappa_t,kappa_tilde_t,kappa_b'\n if not self.resolved:\n pois += ',kappa_g,kappa_gam'\n self.doMH()\n self.modelBuilder.doSet(\"POI\",pois)\n # use modified Higgs Builder\n self.SMH = AnomalousTopHiggsBuilder(self.modelBuilder)\n self.setup()","def parameters(self):\n return {\n 'base':self.base.parameters(),\n 'material':[m.parameters() for m in self.material],\n 'fraction':self.fraction,\n }","def game_info(tytuł, gatunek, **cechy):\r\n\tgra = {}\r\n\tgra['Tytuł'] = tytuł\r\n\tgra['Gatunek'] = gatunek\r\n\t\r\n\tfor k, v in cechy.items():\r\n\t\tgra[k] = v\r\n\t\r\n\treturn gra","def getParameters(self):\n return {'channels':self._channels, 'means':self._means, 'stds':self._stds}","def read_parameters():\n hdulist1 = pf.open(source+'/kids_data/KiDS_DR3.1_G9_ugri_shear.fits')\n param1 = hdulist1[1].data['e1'][:sample]\n param2 = hdulist1[1].data['e2'][:sample]\n weights = hdulist1[1].data['weight'][:sample]\n return param1, param2, weights","def chanDefaults(self) -> Dict[str, Any]:\n chanH = {}\n chanH[\"gain_stage1\"] = 1\n chanH[\"gain_stage2\"] = 1\n chanH[\"hchopper\"] = 0 # this depends on sample frequency\n chanH[\"echopper\"] = 0\n # channel output information (sensor_type, channel_type, ts_lsb, pos_x1, pos_x2, pos_y1, pos_y2, pos_z1, pos_z2, sensor_sernum)\n chanH[\"ats_data_file\"] = \"\"\n chanH[\"num_samples\"] = 0\n chanH[\"sensor_type\"] = \"\"\n chanH[\"channel_type\"] = \"\"\n chanH[\"ts_lsb\"] = 1\n # the lsb/scaling is not applied. data is raw voltage which needs to be scaled\n # an lsb is constructed from the scaling in the XTR/XTRX file to take the data to mV\n chanH[\"scaling_applied\"] = False # check this\n chanH[\"pos_x1\"] = 0\n chanH[\"pos_x2\"] = 0\n chanH[\"pos_y1\"] = 0\n chanH[\"pos_y2\"] = 0\n chanH[\"pos_z1\"] = 0\n chanH[\"pos_z2\"] = 0\n chanH[\"sensor_sernum\"] = 0\n return chanH","def get_params(self) -> Dict:\n params: Dict = {}\n params['g_leak'] = self.leak.get_g()\n params['g_kvhh'] = self.kvhh.get_g()\n params['g_cav'] = self.cav.get_g()\n params['g_kca'] = self.kca.get_g()\n params['g_nap'] = self.nap.get_g()\n params['t_ca'] = self.tau_ca\n return params","def gather_experiment_parameters(self):\n consts = win32com.client.constants.__dicts__[0]\n exp_params = [r for r in consts.keys() if len(r.split(\"EXP_\")) > 1]\n dm_params = [r for r in consts.keys() if len(r.split(\"DM_\")) > 1]\n self.app_param = {} \n self.appdoc_param = {} \n for p in exp_params:\n self.app_param.update({p:self.app.GetParam(consts[p])})\n\n for p in dm_params:\n #self.appdoc_param.update({p:self.app.GetParam(consts[p])}) bug? call appdoc? CP\n\n self.appdoc_param.update({p:self.app.GetParam(consts[p])})","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 getMyQuadInfo(self):\n d = self.getMyInfoAsDict()\n d['components'] = self.getMyDictInfo('components')\n d['weapons'] = self.getMyDictInfo('weapons')\n return d","def getRigBuildData(self):\n\n # Values\n mouthPosition = self.jawCtrl.xfo.tr\n jawEndPosition = self.jawEndCtrl.xfo.tr\n mouthLen = mouthPosition.subtract(jawEndPosition).length()\n\n # Calculate Mouth Xfo\n\n # atVector\n # mouthUpV = Vec3(0.0, 1.0, 0.0)\n\n # rootToEnd = jawEndPosition.subtract(mouthPosition).unit()\n # rootToUpV = mouthUpV.subtract(mouthPosition).unit()\n # bone1ZAxis = rootToUpV.cross(rootToEnd).unit()\n # bone1Normal = bone1ZAxis.cross(rootToEnd).unit()\n\n jawXfo = self.jawEndCtrl.xfo\n # jawXfo.setFromVectors(rootToEnd, bone1Normal, bone1ZAxis, mouthPosition)\n\n\n\n data = super(OSSMouthGuide, self).getRigBuildData()\n\n # should include getCurveData\n data = self.saveAllObjectData(data, \"Control\")\n data = self.saveAllObjectData(data, \"Transform\")\n data['jawXfo'] = self.jawCtrl.xfo\n data['mouthLen'] = mouthLen\n return data","def build(self):\n return self.hyperparams.items()","def getDictCWells(self,itype):\n #Method begins here\n #nx=self.__grid['nx'] #From the geometry in grid\n ny=self.__grid['ny']\n nz=self.__grid['nz']\n minx=self.__grid['ox']\n miny=self.__grid['oy']\n minz=self.__grid['oz']\n rx=self.__grid['dx']\n ry=self.__grid['dy']\n rz=self.__grid['dz']\n \n # well package\n # Remember to use zero-based layer, row, column indices!\n lcoordw=np.zeros((self.__ncwells,3),dtype=np.int32)\n for i in range (self.__ncwells):\n lcoordw[i,0]=floor((self.__dfclst.iloc[i,3]-minx)/rx)\n #In MODFLOW y ans z coordinates are inverted\n lcoordw[i,1]=floor((miny+ry*ny-self.__dfclst.iloc[i,4])/ry)\n lcoordw[i,2]=floor((minz+rz*nz-self.__dfclst.iloc[i,5])/rz)\n \n nper=self.__df.getForcPer()\n ssm_data = {}\n print('Number of conc periods='+str(nper)) \n for i in range(nper):\n lst=[]\n for j in range(self.__ncwells):\n conc_rate=self.__dfcwells.iloc[i+1,j+1]\n lst.append( [ lcoordw[j,2], lcoordw[j,1], lcoordw[j,0], conc_rate, itype['WEL'] ] )\n ssm_data[i]=lst\n print(ssm_data)\n \n print('*--- Succesfull reading of concentration wells ---*')\n \n return ssm_data","def get_params(self):\n return {\"d\": \"155\"}","def get_wells_info(self):\n prod_info = self.read_wells_include_file(\n rel_path='INCLUDE/Produtores.inc')\n prod_info['well_type'] = 'prod'\n inj_info = self.read_wells_include_file(\n rel_path='INCLUDE/Injetores.inc')\n inj_info['well_type'] = 'inj'\n return pd.concat([prod_info, inj_info], ignore_index=True)","def draggableCircuitResults(self):\n returnedDictionary={}\n self.blochSpheres=self.separatedBlochSpheres()\n returnedDictionary[\"probabilities\"] = self.separatedProbabilities()\n #returnedDictionary[\"blochSpheres\"] = self.separatedBlochSpheres()\n returnedDictionary[\"diracNotation\"] = self.diracNotation()\n returnedDictionary[\"link\"] = \"\"\n returnedDictionary['chart'] = self.graph()\n try:\n returnedDictionary[\"qasm\"] = self.circuit.qasm()\n except Exception:\n #str(Exception)\n returnedDictionary[\"qasm\"] = \"//You are using custom gate\\n//with size more than 2 qubits\\n//sorry, this version doesn't support that\\n//qiskit version 0.19.1\"\n \n if self.API_TOKEN != \"\":\n returnedDictionary[\"link\"] = self.runOnIBMQ()\n \n return returnedDictionary","def get_model_descs():\n model_descs = {}\n for (dirpath, dirnames, filenames) in walk('.'):\n for filename in filenames:\n if 'model_desc' in filename:\n numwavelets = None\n wavelet_name = filename.split('.')[1]\n if '-' in wavelet_name:\n wavelet_name = wavelet_name.split('-')[0]\n dirs = str(dirpath).split('/')\n for direc in dirs:\n if 'w' in direc:\n numwavelets = int(direc.split('w')[1].split('.')[0])\n break\n if numwavelets is None:\n print(join(dirpath, filename) + \" is skipped\")\n else:\n if numwavelets not in model_descs.keys():\n model_descs[numwavelets] = {}\n if wavelet_name not in model_descs[numwavelets].keys():\n model_descs[numwavelets][wavelet_name] = {}\n model_descs[numwavelets][wavelet_name][join(dirpath,\\\n filename)] = str(dirpath) + \"/\"\n return model_descs","def get_hyperparams(self):","def get_obj_desc():\n\n attributes = {'results' : ['aperiodic_params_', 'peak_params_',\n 'r_squared_', 'error_',\n '_gaussian_params'],\n 'settings' : ['peak_width_limits', 'max_n_peaks',\n 'min_peak_height', 'peak_threshold',\n 'aperiodic_mode'],\n 'data' : ['power_spectrum', 'freq_range', 'freq_res'],\n 'data_info' : ['freq_range', 'freq_res'],\n 'arrays' : ['freqs', 'power_spectrum', 'aperiodic_params_',\n 'peak_params_', '_gaussian_params'],\n 'model_components' : ['_spectrum_flat', '_spectrum_peak_rm',\n '_ap_fit', '_peak_fit']}\n\n return attributes","def dump(self):\n cfg = {\n \"Detector\" : \n { \n \"8680\": \n {\n \"CONVENTIONAL\": \n {\n \"1.0\": \n {\n \"1x\": (3.87, 8.51),\n \"2.4x\": (1.6, 6.74),\n \"4.9x\": (0.72, 6.23)\n },\n \n \"3.0\": \n {\n \"1x\": (10.12, 14.07),\n \"2.4x\": (4.2, 11.17),\n \"4.9x\": (1.89, 10)\n }\n \n },\n \n \n \"EMGAIN\": \n {\n \"1.0\" : \n {\n \"1x\": (18.97, 32.25),\n \"2.4x\": (7.61, 19.41),\n \"4.9x\": (3.47, 16.31)\n },\n \n \"3.0\" : \n {\n \"1x\": (46.56, 54.01),\n \"2.4x\": (19.82, 33.3),\n \"4.9x\": (8.84, 26.25)\n },\n \n \"5.0\" : \n {\n \"1x\": (46.49, 70.66),\n \"2.4x\": (19.53, 45.11),\n \"4.9x\": (8.9, 35.87)\n },\n \n \"10.0\" : \n {\n \"2.4x\": (22.45, 52.98),\n \"4.9x\": (10.43, 45.37),\n } \n } \n }, \n \n \"8325\": \n {\n \"CONVENTIONAL\": \n {\n \"1.0\": \n {\n \"1x\": (3.98, 8.64),\n \"2.5x\": (1.6, 6.75),\n \"5.1x\": (0.72, 6.23)\n },\n \n \"3.0\": \n {\n \"1x\": (10.45, 14.42),\n \"2.5x\": (4.14, 10.97),\n \"5.1x\": (1.89, 10.24)\n }\n },\n \n \"EMGAIN\": \n {\n \"1.0\" : \n {\n \"1\": (19.73, 34.13),\n \"2.5x\": (7.88, 20.49),\n \"5.1x\": (3.54, 16.99)\n }, \n \n \"3.0\" : \n {\n \"1\": (48.23, 54.5),\n \"2.5x\": (19.77, 33.41),\n \"5.1x\": (9.04, 27.84)\n },\n \n \"5.0\" : \n {\n \"1\": (50.66, 77.0),\n \"2.5x\": (20.46, 48.08),\n \"5.1x\": (8.7, 35.5)\n },\n \n \"10.0\" : \n {\n \"2.5x\": (22.44, 53.63),\n \"5.1x\": (11.3, 52.55),\n } \n }\n },\n \n \"10522\": \n {\n \"CONVENTIONAL\": \n {\n \"0.1\": \n {\n \"Gain 1\": (3.37, 8.54),\n \"Gain 2\": (0.8, 3.15),\n },\n \n \"1.0\": \n {\n \"Gain 1\": (3.36, 6.51),\n \"Gain 2\": (0.79, 4.59),\n }\n },\n \n \"EMGAIN\": \n {\n \"1.0\" : \n {\n \"Gain 1\": (16.4, 25.1),\n \"Gain 2\": (3.96, 12.3),\n }, \n \n \"10.0\" : \n {\n \"Gain 1\": (16.5, 76.0),\n \"Gain 2\": (4.05, 45.1),\n },\n \n \"20.0\" : \n {\n \"Gain 1\": (17.2, 193.0),\n \"Gain 2\": (4.64, 76.6),\n },\n \n \"30.0\" : \n {\n \"Gain 1\": (18.2, 272.0),\n \"Gain 2\": (5.46, 145.0),\n } \n }\n },\n \n \"10570\": \n {\n \"CONVENTIONAL\": \n {\n \"0.1\": \n {\n \"Gain 1\": (3.31, 8.82),\n \"Gain 2\": (0.8, 3.39),\n },\n \n \"1.0\": \n {\n \"Gain 1\": (3.30, 6.52),\n \"Gain 2\": (0.79, 4.83),\n }\n },\n \n \"EMGAIN\": \n {\n \"1.0\" : \n {\n \"Gain 1\": (16.4, 25.1),\n \"Gain 2\": (4.01, 12.6),\n }, \n \n \"10.0\" : \n {\n \"Gain 1\": (16.5, 85.6),\n \"Gain 2\": (4.06, 42.7),\n },\n \n \"20.0\" : \n {\n \"Gain 1\": (17.5, 142.0),\n \"Gain 2\": (4.81, 76.0),\n },\n \n \"30.0\" : \n {\n \"Gain 1\": (19.3, 256.0),\n \"Gain 2\": (5.88, 166.0),\n } \n }\n }\n },\n \n \"Phot\" :\n {\n \"fwhmpsf\": 6.0,\n \"sigma\": 10.0,\n \"exposure\": \"EXPTIME\",\n \"calgorithm\": \"centroid\",\n \"cbox\" : 8,\n \"maxshift\": 5, \n \"salgorithm\": \"median\",\n \"annulus\": 14,\n \"dannulus\": 16,\n \"apertures\": 12,\n \"zmag\": 27.11\n }\n }\n \n \n # Dump the configuration to json output file\n with open(self.conf_fname, \"w\") as fd:\n json.dump(cfg, fd) \n \n return","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.7,0.7]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n # ss templates\n self.modelBuilder.doVar(\"R_ee_os_fakes[0.6,0.0,1.0]\");\n self.modelBuilder.doVar(\"ee16_fakes_norm[1.0, 0.01, 10.]\");\n self.modelBuilder.doVar(\"ee17_fakes_norm[1.0, 0.01, 10.]\");\n self.modelBuilder.doVar(\"ee18_fakes_norm[1.0, 0.01, 10.]\");\n #Remember, cant use spaces in these formulas!\n #self.modelBuilder.options.verbose = 10\n self.modelBuilder.factory_('expr::R_ee16_qcd_os(\"@0*@1\",ee16_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee17_qcd_os(\"@0*@1\",ee17_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee18_qcd_os(\"@0*@1\",ee18_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee16_qcd_ss(\"@0*(1.0-@1)\",ee16_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee17_qcd_ss(\"@0*(1.0-@1)\",ee17_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee18_qcd_ss(\"@0*(1.0-@1)\",ee18_fakes_norm,R_ee_os_fakes)')\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def get_dic(self):\n dic = {\n 'size': self.size,\n 'bounds': self.bounds,\n 'visible': self.visible,\n 'is_static': self.is_static,\n 'options': self.options,\n 'primitive_type': self.primitive_type,\n 'constrain_ratio': self.constrain_ratio,\n 'constrain_navigation': self.constrain_navigation,\n 'framebuffer': self.framebuffer,\n # 'beforeclear': self.beforeclear,\n 'variables': self.get_variables_list(),\n 'vertex_shader': self.vertex_shader,\n 'fragment_shader': self.fragment_shader,\n }\n return dic","def get_params(self):\n return {'classifier': self.classifier,\n 'grid_param': self.grid_param,\n 'n_param_comb': self.n_param_comb,\n 'top_bagging': self.bagging,\n 'bagging_param': self.bagging_param,\n 'comb_seed': self.comb_seed}","def child_analyse(self, name, child):\n try:\n module = child.Conv2d\n analyse_dict = {\"in_channels\": module.in_channels,\n \"out_channels\": module.out_channels,\n \"kernel_size\": module.kernel_size[0],\n \"stride\": module.stride[0],\n \"padding\": (module.kernel_size[0] - 1) // 2,\n \"bias\": False if module.bias is None else True,\n \"shortcut\": False}\n except AttributeError:\n try:\n analyse_dict = {\"shortcut\": True,\n \"from\": self.model.module_defs[int(name)]['from']}\n except KeyError:\n analyse_dict = None\n\n return analyse_dict","def dictOfDraws(self):\n return dict()","def training_info(self):\n pass","def raw_data() -> Dict:\n return {\"neighbourhood\":\"Buttes-Montmartre\",\"room_type\":\"Entire home/apt\",\"minimum_nights\":1.555,\"mois\":2,\"voyageurs\":2.5,\"chambres\":1,\"lits\":1,\"salle_de_bains\":1}","def nn_data(self, chrome):\n decoded_chrome = self.decode(chrome)\n data = dict()\n data[\"num_input_nodes\"] = 9\n data[\"num_hidden_nodes\"] = 5\n data[\"num_output_nodes\"] = 20\n data[\"inputs\"] = data_helpers.get_data(self.current_board)\n data[\"first_weights\"] = decoded_chrome[0]\n data[\"second_weights\"] = decoded_chrome[1]\n data[\"third_weights\"] = decoded_chrome[2]\n data[\"first_thresholds\"] = decoded_chrome[3]\n data[\"second_thresholds\"] = decoded_chrome[4]\n data[\"third_thresholds\"] = decoded_chrome[5]\n return data","def get_params(self):\n return {'threshold': self.threshold,\n 'subsample': self.subsample,\n 'estimator': self.estimator,\n 'n_folds': self.n_folds,\n 'stratify': self.stratify,\n 'random_state': self.random_state,\n 'n_jobs': self.n_jobs}","def get_params_info(cls):\n return dict(\n config='laygo configuration dictionary.',\n threshold='transistor threshold flavor.',\n draw_boundaries='True to draw boundaries.',\n num_blk='number of driver segments.',\n show_pins='True to draw pin geometries.',\n )","def data_dict(self) -> dict:\n return self.design.renderers.gds.options","def build_data(self):\n from desiutil.io import combine_dicts\n # Loop on exposures\n odict = {}\n for qanight in self.qa_nights:\n for qaexp in qanight.qa_exps:\n # Get the exposure dict\n idict = write_qa_exposure('foo', qaexp, ret_dict=True)\n odict = combine_dicts(odict, idict)\n # Finish\n self.data = odict","def parameters(self):\n return {\"W\": self.W,\n \"T\": self.T,\n \"P\": self.P}","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rdy,Rqcd\")","def getDefaultParameterValues(self):\r\n dct = {}\r\n self.initializeRoadRunnerModel()\r\n self.roadrunnerModel.reset()\r\n for parameterName in self.parametersToFit:\r\n dct[parameterName] = self.roadrunnerModel.model[parameterName]\r\n return dct","def get_resulting_hypo_params(self, injkey):\n h0_params = self.fid_values[injkey][\n 'h0_fit_to_%s'%(self.labels.dict['data'])]['params']\n h1_params = self.fid_values[injkey][\n 'h1_fit_to_%s'%(self.labels.dict['data'])]['params']\n return h0_params, h1_params","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doSet(\"POI\",\"Afb\")\n\n # ss templates\n self.modelBuilder.doVar(\"Dilu_ratio[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_mumu_ss[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_ee_ss[1.0,0.0,10.0]\");\n \n self.modelBuilder.factory_('expr::Rpl(\"0.5*(1.+@0*@1)\",Afb, Dilu_ratio)')\n self.modelBuilder.factory_('expr::Rmn(\"0.5*(1.-@0*@1)\",Afb, Dilu_ratio)')","def doParametersOfInterest(self):\r\n if self.fg4fixed:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0]\")\r\n self.modelBuilder.doVar(\"r[1,0,4]\")\r\n print \"Fixing CMS_zz4l_fg4\"\r\n poi = \"r\"\r\n else:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg4\"):\r\n print \"have fg4 inside\"\r\n else:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0.,-1,1]\")\r\n poi = \"CMS_zz4l_fg4\"\r\n if self.cPOI:\r\n if self.modelBuilder.out.var(\"cww_zz\"):\r\n print \"have czz_ww inside\"\r\n else:\r\n self.modelBuilder.doVar(\"cww_zz[0.5,-10,10]\")\r\n poi += \",cww_zz\"\r\n\r\n if self.fg2POI:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg2\"):\r\n print \"have fg2 inside\"\r\n else:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg2[0.,0,1]\")\r\n poi += \",CMS_zz4l_fg2\"\r\n if self.muFloating:\r\n self.modelBuilder.doVar(\"r[1,0,2000]\")\r\n if self.muAsPOI:\r\n print \"Treating r as a POI\"\r\n poi += \",r\"\r\n else:\r\n self.modelBuilder.out.var(\"r\").setAttribute(\"flatParam\")\r\n if self.phiFloating:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg4phi\"):\r\n print \"have fg4phi inside\"\r\n else: \r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4phi[0.,-3.1415926,3.1415926]\")\r\n if self.phiPOI:\r\n poi += \",CMS_zz4l_fg4phi\"\r\n else:\r\n self.modelBuilder.out.var(\"CMS_zz4l_fg4phi\").setAttribute(\"flatParam\")\r\n if self.phi2Floating:\r\n #self.modelBuilder.doVar(\"CMS_zz4l_fg4phi[0.,-math.pi,math.pi]\")\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg2phi\"):\r\n print \"have fg2phi inside\"\r\n else: \r\n self.modelBuilder.doVar(\"CMS_zz4l_fg2phi[0.,-3.1415926,3.1415926]\")\r\n self.modelBuilder.out.var(\"CMS_zz4l_fg2phi\").setAttribute(\"flatParam\")\r\n \r\n self.modelBuilder.doSet(\"POI\",poi)","def wypisz_info(self):\n print(f\"Samochód: {self.producent} {self.model}\")","def get_prob_params():\n prob = Namespace()\n prob.study_name = STUDY_NAME\n if IS_DEBUG:\n prob.num_trials = 3\n prob.max_capital = 10\n else:\n prob.num_trials = NUM_TRIALS\n prob.max_capital = MAX_CAPITAL\n # Common\n prob.time_distro = TIME_DISTRO\n prob.num_workers = NUM_WORKERS\n _study_params = {\n 'branin': ('synthetic/branin/config_mf.json',\n branin_mf, cost_branin_mf, 0.1, 0, 1),\n 'hartmann3_2': ('synthetic/hartmann3_2/config_mf.json',\n hartmann3_2_mf, cost_hartmann3_2_mf, 0.1, 0, 1),\n 'hartmann6_4': ('synthetic/hartmann6_4/config_mf.json',\n hartmann6_4_mf, cost_hartmann6_4_mf, 0.1, 0, 1),\n 'borehole_6': ('synthetic/borehole_6/config_mf.json',\n borehole_6_mf, cost_borehole_6_mf, 1, 0, 1),\n 'park2_4': ('synthetic/park2_4/config_mf.json',\n park2_4_mf, cost_park2_4_mf, 0.3, 0, 1),\n 'park2_3': ('synthetic/park2_3/config_mf.json',\n park2_3_mf, cost_park2_3_mf, 0.1, 0, 1),\n 'park1_3': ('synthetic/park1_3/config_mf.json',\n park1_3_mf, cost_park1_3_mf, 0.5, 0, 1),\n }\n (domain_config_file_suffix, raw_func, raw_fidel_cost_func, _fc_noise_scale,\n _initial_pool_size, _) = _study_params[prob.study_name]\n domain_config_file = os.path.join(DRAGONFLY_EXPERIMENTS_DIR, domain_config_file_suffix)\n # noisy\n prob.noisy_evals = NOISY_EVALS\n if NOISY_EVALS:\n noise_type = 'gauss'\n noise_scale = _fc_noise_scale\n else:\n noise_type = 'no_noise'\n noise_scale = None\n # Create domain, function_caller and worker_manager\n config = load_config_file(domain_config_file)\n func_caller = get_multifunction_caller_from_config(raw_func, config,\n raw_fidel_cost_func=raw_fidel_cost_func, noise_type=noise_type,\n noise_scale=noise_scale)\n # Set max_capital\n if hasattr(func_caller, 'fidel_cost_func'):\n prob.max_capital = prob.max_capital * \\\n func_caller.fidel_cost_func(func_caller.fidel_to_opt)\n else:\n prob.max_capital = prob.max_capital\n # Store everything in prob\n prob.func_caller = func_caller\n prob.worker_manager = SyntheticWorkerManager(prob.num_workers,\n time_distro='caller_eval_cost')\n prob.save_file_prefix = prob.study_name + ('-debug' if IS_DEBUG else '')\n prob.methods = METHODS\n prob.save_results_dir = SAVE_RESULTS_DIR\n prob.reporter = get_reporter('default')\n # evaluation options\n prob.evaluation_options = Namespace(prev_eval_points='none',\n initial_pool_size=_initial_pool_size)\n return prob","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 default_params():\n params = {}\n params['dataset'] = 'adult'\n params['engines'] = ['MD','RDA']\n params['iters'] = 10000\n params['epsilon'] = 1.0\n params['delta'] = 0.0\n params['bounded'] = True\n params['frequency'] = 1\n params['seed'] = 0\n params['save'] = None\n params['load'] = None\n params['plot'] = None\n\n return params","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def getOutputParams(self):\n out = {}\n out[\"g_C\"] = self.ureg.Quantity(self.g_C, self.gBase).to(self.gOutput)\n out[\"gamma_C\"] = self.ureg.Quantity(self.gamma_C,\n self.gammaBase).to(self.gammaOutput)\n out[\"m\"] = self.ureg.Quantity(self.m, self.mBase).to(self.mOutput)\n out[\"charL\"] = self.ureg.Quantity(self.charL,\n self.charLBase).to(self.charLOutput)\n out[\"charT\"] = self.ureg.Quantity(self.charT,\n self.charTBase).to(self.charTOutput)\n out[\"g_R\"] = self.ureg.Quantity(self.g_R, self.gBase).to(self.gOutput)\n out[\"R\"] = self.ureg.Quantity(self.R, self.rBase).to(self.rOutput)\n out[\"gamma_R\"] = self.ureg.Quantity(self.gamma_R,\n self.gammaBase).to(self.gammaOutput)\n if self.singleComp:\n out[\"gamma_nl\"] = self.ureg.Quantity(self.gamma_nl,\n self.gammaNlBase).\\\n to(self.gammaNlOutput)\n return out","def get_model_and_assets():\n\n return common.read_model('cloth_corner.xml'),common.ASSETS","def load_demographic_data(self):\n\n # load household size distribution and age distribution\n self.hh_comp = load_probs(os.path.join(self.params['resource_prefix'],\n self.params['hh_composition']), False)\n self.params['age_cutoffs'] = [int(x) for x in self.hh_comp[0][1:][0]] # yuk!\n self.params['adult_age'] = int(float(self.params['adult_age']))\n self.age_dist = load_probs(os.path.join(self.params['resource_prefix'],\n self.params['age_distribution']))\n\n annual_factor = self.params['t_dur']/365.0\n\n # load and scale MORTALITY rates\n self.death_rates = {}\n self.death_rates[0] = self.parse_age_rates(os.path.join(\n self.params['resource_prefix'],\n self.params['death_rates_m']), annual_factor, True)\n self.death_rates[1] = self.parse_age_rates(os.path.join(\n self.params['resource_prefix'],\n self.params['death_rates_f']), annual_factor, True)\n\n ### load FERTILITY age probs (don't require scaling) for closed pops\n self.fertility_age_probs = load_prob_tables(os.path.join(\n self.params['resource_prefix'],\n self.params['fertility_age_probs']))\n self.fertility_parity_probs = None\n #load_probs_new(os.path.join(\n # self.params['resource_prefix'],\n # self.params['fertility_parity_probs']))\n self.fertility_age_rates = self.parse_age_rates(os.path.join(\n self.params['resource_prefix'],\n self.params['fertility_age_rates']), annual_factor, False)\n self.new_marriage_years = float(self.params['new_marriage_years'])\n self.new_marriage_fertility = float(self.params['marriage_based_fertility'])\n self.single_mother_fertility = float(self.params['single_mother_fertility'])\n self.params['partner_age_diff'] = float(self.params['partner_age_diff'])\n self.params['partner_age_sd'] = float(self.params['partner_age_sd'])\n\n ### load and scale leave/couple/divorce and growth rates\n if self.params['dyn_rates']:\n # rates will be a list of annual values\n self.params['leaving_probs'] = load_prob_list(os.path.join(\n self.params['resource_prefix'], self.params['leaving_prob_file']))\n self.params['couple_probs'] = load_prob_list(os.path.join(\n self.params['resource_prefix'], self.params['couple_prob_file']))\n self.params['divorce_probs'] = load_prob_list(os.path.join(\n self.params['resource_prefix'], self.params['divorce_prob_file']))\n self.params['growth_rates'] = load_prob_list(os.path.join(\n self.params['resource_prefix'], self.params['growth_rate_file']))\n self.params['imm_rates'] = load_prob_list(os.path.join(\n self.params['resource_prefix'], self.params['imm_rate_file']))\n\n self.params_adj['leaving_probs'] = [adjust_prob(x, self.params['t_dur'])\n for x in self.params['leaving_probs']]\n self.params_adj['couple_probs'] = [adjust_prob(x, self.params['t_dur'])\n for x in self.params['couple_probs']]\n self.params_adj['divorce_probs'] = [adjust_prob(x, self.params['t_dur'])\n for x in self.params['divorce_probs']]\n self.params_adj['growth_rates'] = [adjust_prob(x, self.params['t_dur'])\n for x in self.params['growth_rates']]\n self.params_adj['imm_rates'] = [adjust_prob(x, self.params['t_dur'])\n for x in self.params['imm_rates']]\n\n self.dyn_years = min(len(self.death_rates[0][0])-1, len(self.fertility_age_probs)-1,\n len(self.params_adj['leaving_probs'])-1, len(self.params_adj['couple_probs'])-1,\n len(self.params_adj['divorce_probs'])-1, len(self.params_adj['growth_rates'])-1)\n\n else:\n # adjust demographic event probabilities according to time step\n self.params_adj['couple_probs'] = [adjust_prob(\n self.params['couple_prob'], self.params['t_dur'])]\n self.params_adj['leaving_probs'] = [adjust_prob(\n self.params['leaving_prob'], self.params['t_dur'])]\n self.params_adj['divorce_probs'] = [adjust_prob(\n self.params['divorce_prob'], self.params['t_dur'])]\n self.params_adj['growth_rates'] = [adjust_prob(\n self.params['growth_rate'], self.params['t_dur'])]\n self.params_adj['imm_rates'] = [adjust_prob(\n self.params['imm_rate'], self.params['t_dur'])]","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_guides(data):\n return data.groups[\"Frames\"].objects","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def whatareks():\n\n print(\"loading data\")\n dataset = datagen.DataSet(datapath)\n dataset.load()\n\n for k, ens in enumerate(dataset.ensembles):\n\n print(\n f\"k: {k} --> b={ens.b}, N={ens.grid_shape[0]}, iternum={ens.iternum}\")","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 1e-7\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def _get_gedi1b_main_data_dict(self) -> dict:\n data = {\n # General identifiable data\n \"granule_name\": [self.parent_granule.filename] * self.n_shots,\n \"shot_number\": self[\"shot_number\"][:],\n \"beam_type\": [self.beam_type] * self.n_shots,\n \"beam_name\": [self.name] * self.n_shots,\n # Temporal data\n \"delta_time\": self[\"delta_time\"][:],\n # Quality data\n \"degrade\": self[\"geolocation/degrade\"][:],\n \"stale_return_flag\": self[\"stale_return_flag\"][:],\n \"solar_elevation\": self[\"geolocation/solar_elevation\"][:],\n \"solar_azimuth\": self[\"geolocation/solar_elevation\"][:],\n \"rx_energy\": self[\"rx_energy\"][:],\n # DEM\n \"dem_tandemx\": self[\"geolocation/digital_elevation_model\"][:],\n \"dem_srtm\": self[\"geolocation/digital_elevation_model_srtm\"][:],\n # geolocation bin0\n \"latitude_bin0\": self[\"geolocation/latitude_bin0\"][:],\n \"latitude_bin0_error\": self[\"geolocation/latitude_bin0_error\"][:],\n \"longitude_bin0\": self[\"geolocation/longitude_bin0\"][:],\n \"longitude_bin0_error\": self[\"geolocation/longitude_bin0_error\"][:],\n \"elevation_bin0\": self[\"geolocation/elevation_bin0\"][:],\n \"elevation_bin0_error\": self[\"geolocation/elevation_bin0_error\"][:],\n # geolocation lastbin\n \"latitude_lastbin\": self[\"geolocation/latitude_lastbin\"][:],\n \"latitude_lastbin_error\": self[\"geolocation/latitude_lastbin_error\"][:],\n \"longitude_lastbin\": self[\"geolocation/longitude_lastbin\"][:],\n \"longitude_lastbin_error\": self[\"geolocation/longitude_lastbin_error\"][:],\n \"elevation_lastbin\": self[\"geolocation/elevation_lastbin\"][:],\n \"elevation_lastbin_error\": self[\"geolocation/elevation_lastbin_error\"][:],\n # relative waveform position info in beam and ssub-granule\n \"waveform_start\": self[\"rx_sample_start_index\"][:] - 1,\n \"waveform_count\": self[\"rx_sample_count\"][:],\n }\n return data","def get_configspace(self):\n cd = self.cd\n sp_dict = {}\n sp_dict['epochs'] = int(cd['epochs'])\n sp_dict['gamma'] = self._get_range_uniform('gamma', cd)\n sp_dict['multilabel'] = self._get_atomic('multilabel', cd)\n sp_dict['lr'] = self._get_range_uniform('lr', cd)\n sp_dict['optimizer'] = self._get_categorical('optimizer', cd)\n sp_dict['n_latent'] = self._get_range_integer('n_latent',cd)\n sp_dict['enc_hidden_dim'] = self._get_range_integer('enc_hidden_dim', cd)\n sp_dict['batch_size'] = self._get_range_integer('batch_size', cd)\n sp_dict['coherence_loss_wt'] = self._get_range_uniform('coherence_loss_wt', cd) or 0.0\n sp_dict['redundancy_loss_wt'] = self._get_range_uniform('redundancy_loss_wt', cd) or 0.0\n sp_dict['num_enc_layers'] = self._get_range_integer('num_enc_layers', cd) or 1\n sp_dict['enc_dr'] = self._get_range_uniform('enc_dr', cd) or 0.0\n sp_dict['covar_net_layers'] = self._get_range_integer('covar_net_layers', cd) or 1\n sp_dict['classifier_dropout'] = self._get_range_uniform('classifier_dropout', cd) or 0.1\n\n embedding_types = cd['embedding']\n embedding_space = [] \n for et in embedding_types:\n if et['source'] == 'random':\n embedding_space.append(ag.space.Dict(**{'source': 'random', 'size': self._get_range_integer('size', et)}))\n else:\n fixed_assigned = et.get('fixed')\n if fixed_assigned is None:\n embedding_space.append(ag.space.Dict(**{'source': et['source'], 'fixed': ag.space.Bool()}))\n else:\n embedding_space.append(ag.space.Dict(**{'source': et['source'], 'fixed': fixed_assigned.lower()}))\n sp_dict['embedding'] = ag.space.Categorical(*embedding_space)\n\n latent_types = cd['latent_distribution']\n latent_space = []\n for lt in latent_types:\n dist_type = lt['dist_type']\n if dist_type == 'vmf':\n latent_space.append(ag.space.Dict(**{'dist_type': 'vmf', 'kappa': self._get_range_uniform('kappa', lt)}))\n elif dist_type == 'logistic_gaussian':\n latent_space.append(ag.space.Dict(**{'dist_type': 'logistic_gaussian', 'alpha': self._get_range_uniform('alpha', lt)}))\n else:\n latent_space.append(ag.space.Dict(**{'dist_type': 'gaussian'}))\n sp_dict['latent_distribution'] = ag.space.Categorical(*latent_space)\n return sp_dict","def _describe(self) -> Dict[str, Any]:","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 0.3\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_params(self):\n outputs = ['sample',\n 'ratio_params',\n 'despike_params',\n 'autorange_params',\n 'bkgcorrect_params']\n\n out = {}\n for o in outputs:\n out[o] = getattr(self, o)\n\n out['filter_params'] = self.filt.params\n out['filter_sequence'] = self.filt.sequence\n out['filter_used'] = self.filt.make_keydict()\n\n return out","def to_dict(self):\n result_dict = {}\n result_dict['evaluations'] = self.evaluations\n result_dict['dof'] = self.dof\n result_dict['variances'] = self.variances\n result_dict['noise_ceiling'] = self.noise_ceiling\n result_dict['method'] = self.method\n result_dict['cv_method'] = self.cv_method\n result_dict['models'] = {}\n for i_model in range(len(self.models)):\n key = 'model_%d' % i_model\n result_dict['models'][key] = self.models[i_model].to_dict()\n return result_dict","def df_2_dict(df,band_list,lens_model_list,source_model_list,lens_light_model_list):\n \n import re\n from lenstronomy.Util.param_util import ellipticity2phi_q\n from lenstronomy.Util.param_util import shear_cartesian2polar\n \n import pandas as pd\n \n\n model_kwarg_names = get_kwarg_names(lens_model_list,source_model_list,\n lens_light_model_list,None)\n \n IDs = df.loc[:,'ID']\n chi_sq = df.loc[:,'reduced chi^2']\n \n lens_dict = {}\n \n for i,prof in enumerate(lens_model_list):\n lens_dict[prof] = {}\n for param in model_kwarg_names['kwargs_lens'][i]:\n col = df.loc[:,'{}_lens.{}'.format(prof,param)]\n col_array = col.values\n lens_dict[prof][param] = col_array\n \n if 'e1' in model_kwarg_names['kwargs_lens'][i]:\n lens_dict[prof]['q'] = np.array([])\n lens_dict[prof]['phi'] = np.array([]) \n for j in range(len(lens_dict[prof]['e1'])):\n phi,q = ellipticity2phi_q(lens_dict[prof]['e1'][j],lens_dict[prof]['e2'][j])\n lens_dict[prof]['q'] = np.append(lens_dict[prof]['q'],q)\n lens_dict[prof]['phi'] = np.append(lens_dict[prof]['phi'],phi)\n elif 'gamma1' in model_kwarg_names['kwargs_lens'][i]:\n lens_dict[prof]['gamma'] = np.array([])\n lens_dict[prof]['theta'] = np.array([])\n for j in range(len(lens_dict[prof]['gamma1'])):\n theta,gamma = shear_cartesian2polar(lens_dict[prof]['gamma1'][j],lens_dict[prof]['gamma2'][j])\n lens_dict[prof]['gamma'] = np.append(lens_dict[prof]['gamma'],gamma)\n lens_dict[prof]['theta'] = np.append(lens_dict[prof]['theta'],theta)\n \n \n source_dict = {}\n lens_light_dict = {}\n \n for i,band in enumerate(band_list):\n for j,prof in enumerate(source_model_list):\n key = '{} Band: {}'.format(band,prof)\n source_dict[key] = {}\n for param in model_kwarg_names['kwargs_source'][j]:\n col = df.loc[:,'{} Band: {}_source.{}'.format(band,prof,param)]\n col_array = col.values\n source_dict[key][param] = col_array\n \n if 'e1' in model_kwarg_names['kwargs_source'][j]:\n source_dict[key]['q'] = np.array([])\n source_dict[key]['phi'] = np.array([]) \n for k in range(len(source_dict[key]['e1'])):\n phi,q = ellipticity2phi_q(source_dict[key]['e1'][k],source_dict[key]['e2'][k])\n source_dict[key]['q'] = np.append(source_dict[key]['q'],q)\n source_dict[key]['phi'] = np.append(source_dict[key]['phi'],phi)\n \n for j,prof in enumerate(lens_light_model_list):\n key = '{} Band: {}'.format(band,prof)\n lens_light_dict[key] = {}\n for param in model_kwarg_names['kwargs_lens_light'][j]:\n col = df.loc[:,'{} Band: {}_lens_light.{}'.format(band,prof,param)]\n col_array = col.values\n lens_light_dict[key][param] = col_array\n\n if 'e1' in model_kwarg_names['kwargs_lens_light'][j]:\n lens_light_dict[key]['q'] = np.array([])\n lens_light_dict[key]['phi'] = np.array([]) \n for k in range(len(lens_light_dict[key]['e1'])):\n phi,q = ellipticity2phi_q(lens_light_dict[key]['e1'][k],lens_light_dict[key]['e2'][k])\n lens_light_dict[key]['q'] = np.append(lens_light_dict[key]['q'],q)\n lens_light_dict[key]['phi'] = np.append(lens_light_dict[key]['phi'],phi)\n \n params_dict = {'Object IDs': IDs.values,'Reduced Chi^2': chi_sq.values,\n 'lens': lens_dict, 'source': source_dict, 'lens_light': lens_light_dict}\n \n return params_dict","def setup(self):\n header_print(self.data['intro'])\n header_print(self.data['help'])\n random.shuffle(self.data['draw'])\n random.shuffle(self.data['locations'])\n random.shuffle(self.data['events'])\n random.shuffle(self.data['aces'])\n random.shuffle(self.data['personalities'])\n self.stats = {\n 'round': 0,\n 'powers': {\n 'MOONS': 6,\n 'SUNS': 6,\n 'WAVES': 6,\n 'LEAVES': 6,\n 'WYRMS': 6,\n 'KNOTS': 6,\n },\n 'hand': self.data['draw'][:],\n 'discard': [],\n 'active': [],\n 'opponent': {},\n }","def build_parameters(pobj):\n ViscosityWilke.build_parameters(pobj)","def info_view(self):\n view_dict = dict(\n env_class=self.env_class,\n model_structure=None,\n model_kwargs={key: value for key, value in self.model_kwargs.items() \n if isinstance(value,(str,int,float,tuple,list,dict)) \n and len(str(value))<100},\n model_class=self.model_class,\n hyperparams=self.hyperparams)\n return view_dict","def create_data_dict(model, chardict):\n \n # Less than 25 isn't part of original paper, but word2vec has some outrageous entries\n def include_word(word, chardict):\n \"\"\"\n Function to determine if word can be included and perform any parsing\n \"\"\"\n if (all(char in chardict.keys() for char in word)) & (len(word)<=25):\n # Some word2vec entries are all capitals and generally are acronyms.\n # This is unlikely to be learnable\n if not word.isupper():\n return True\n\n return False\n\n\n # Create list of words which will be used in training/testing our model\n all_words = dict({})\n\n # For every word in word2vec model establish if it is \"allowed\"; if it is\n # add the word to our all_words dict, with the embedding as the value\n for idx, key in enumerate(model.wv.vocab):\n if include_word(key, chardict):\n all_words[key] = model.wv[key]\n else:\n pass\n \n return all_words","def _get_model(self):\n\n parameters = {keys._topology:self.topology,\n keys._size:self.size,\n keys._name:self.name,\n #keys._output_activation:self._outActiv_fun_key,\n #keys._hidden_activation:self._hiddenActiv_fun_key,\n keys._learning_rate:self.learningRate,\n keys._momentum:self.momentum}\n\n return parameters","def get_params(self):\n return {\n \"nspecies\": self.nspecies,\n \"lmax\": self.lmax,\n \"nmax\": self.nmax,\n \"rcut\": self.rcut,\n \"sigma\": self.sigma,\n \"trans_width\": self.trans_width\n }","def get_model_config(model_name, args):\n if model_name == 'WaveGlow':\n model_config = dict(\n n_mel_channels=args.n_mel_channels,\n n_flows=args.flows,\n n_group=args.groups,\n n_early_every=args.early_every,\n n_early_size=args.early_size,\n WN_config=dict(\n n_layers=args.wn_layers,\n kernel_size=args.wn_kernel_size,\n n_channels=args.wn_channels\n )\n )\n return model_config\n elif model_name == 'FastPitch':\n model_config = dict(\n # io\n n_mel_channels=args.n_mel_channels,\n # symbols\n n_symbols=len(get_symbols(args.symbol_set)),\n padding_idx=get_pad_idx(args.symbol_set),\n symbols_embedding_dim=args.symbols_embedding_dim,\n # input FFT\n in_fft_n_layers=args.in_fft_n_layers,\n in_fft_n_heads=args.in_fft_n_heads,\n in_fft_d_head=args.in_fft_d_head,\n in_fft_conv1d_kernel_size=args.in_fft_conv1d_kernel_size,\n in_fft_conv1d_filter_size=args.in_fft_conv1d_filter_size,\n in_fft_output_size=args.in_fft_output_size,\n p_in_fft_dropout=args.p_in_fft_dropout,\n p_in_fft_dropatt=args.p_in_fft_dropatt,\n p_in_fft_dropemb=args.p_in_fft_dropemb,\n # output FFT\n out_fft_n_layers=args.out_fft_n_layers,\n out_fft_n_heads=args.out_fft_n_heads,\n out_fft_d_head=args.out_fft_d_head,\n out_fft_conv1d_kernel_size=args.out_fft_conv1d_kernel_size,\n out_fft_conv1d_filter_size=args.out_fft_conv1d_filter_size,\n out_fft_output_size=args.out_fft_output_size,\n p_out_fft_dropout=args.p_out_fft_dropout,\n p_out_fft_dropatt=args.p_out_fft_dropatt,\n p_out_fft_dropemb=args.p_out_fft_dropemb,\n # duration predictor\n dur_predictor_kernel_size=args.dur_predictor_kernel_size,\n dur_predictor_filter_size=args.dur_predictor_filter_size,\n p_dur_predictor_dropout=args.p_dur_predictor_dropout,\n dur_predictor_n_layers=args.dur_predictor_n_layers,\n # pitch predictor\n pitch_predictor_kernel_size=args.pitch_predictor_kernel_size,\n pitch_predictor_filter_size=args.pitch_predictor_filter_size,\n p_pitch_predictor_dropout=args.p_pitch_predictor_dropout,\n pitch_predictor_n_layers=args.pitch_predictor_n_layers,\n # pitch conditioning\n pitch_embedding_kernel_size=args.pitch_embedding_kernel_size,\n # speakers parameters\n n_speakers=args.n_speakers,\n speaker_emb_weight=args.speaker_emb_weight,\n # energy predictor\n energy_predictor_kernel_size=args.energy_predictor_kernel_size,\n energy_predictor_filter_size=args.energy_predictor_filter_size,\n p_energy_predictor_dropout=args.p_energy_predictor_dropout,\n energy_predictor_n_layers=args.energy_predictor_n_layers,\n # energy conditioning\n energy_conditioning=args.energy_conditioning,\n energy_embedding_kernel_size=args.energy_embedding_kernel_size,\n )\n return model_config\n\n else:\n raise NotImplementedError(model_name)","def getActualConfig(self) -> object:\n if not self.debug:\n ntraces = int(self.getNumberOfTraces())\n traces = []\n for i in range(1,ntraces+1):\n self.selectTrace(i)\n data = self.getData()\n if i == 1:\n title = \"S11\"\n elif i == 2:\n title = \"S21\"\n elif i == 3:\n title = \"S12\"\n else:\n title = \"S22\"\n trace={\n 'number': i,\n 'xMin': self.getStartFrequency(),\n 'xMax': self.getStopFrequency(),\n 'yMin': self.getmindbm(i),#min([x['y'] for x in data]), #getmindbm(),\n 'yMax': self.getmaxdbm(i),#max([x['y'] for x in data]), #getmaxdbm(),\n 'xScale': \"linear\",#self.getxscale()\n 'yScale': \"linear\",#self.getyscale(),\n 'type': \"bode\",#self.getTypeFormat(),\n 'title': title,#self.getTraceTitle(i),\n 'xLabel': \"Freq [Hz]\",#getxLabel(),\n 'yLabel': \"dBm\", #getyLabel()\n 'data': data,\n 'yPDiv': self.getYPDiv(i)\n }\n traces.append(trace) \n ret = {\n 'traces': traces, \n 'sweepResolution': self.getSweepResolution(),\n 'IFBW': self.getIFBW() \n }\n else:\n trace1 = {\n 'number': 1,\n 'xMin': 100,\n 'xMax': 1000,\n 'yMin': 100,\n 'yMax': 1000,\n 'xScale': 'logarithmic',\n 'yScale': 'logarithmic',\n 'type': 'bode',\n 'title': 'S11',\n 'xLabel': 'Freq',\n 'yLabel': 'dBm',\n 'yPDiv': 10,\n 'data': [\n {'x': 100,'y': 100},\n {'x': 200,'y': 150},\n {'x': 500,'y': 300},\n {'x': 1000,'y': 800}\n ]\n }\n trace2 = {\n 'number': 2,\n 'xMin': 1,\n 'xMax': 100,\n 'yMin': 1,\n 'yMax': 1000,\n 'xScale': 'linear',\n 'yScale': 'linear',\n 'type': 'bode',\n 'title': 'S21',\n 'xLabel': 'Freq',\n 'yLabel': 'dBm',\n 'yPDiv': 10,\n 'data': [\n {'x': 1,'y': 100},\n {'x': 20,'y': 250},\n {'x': 50,'y': 200},\n {'x': 100,'y': 600}\n ]\n }\n trace3 = {\n 'number': 3,\n 'xMin': 500,\n 'xMax': 10000,\n 'yMin': 100,\n 'yMax': 10000,\n 'xScale': 'linear',\n 'yScale': 'logarithmic',\n 'type': 'bode',\n 'title': 'S12',\n 'xLabel': 'Freq',\n 'yLabel': 'dBm',\n 'yPDiv': 10,\n 'data': [\n {'x': 500,'y': 100},\n {'x': 2000,'y': 1000},\n {'x': 5000,'y': 3000},\n {'x': 10000,'y': 8000}\n ]\n }\n trace4 = {\n 'number': 4,\n 'xMin': 100,\n 'xMax': 10000,\n 'yMin': 500,\n 'yMax': 10000,\n 'xScale': 'logarithmic',\n 'yScale': 'linear',\n 'type': 'bode',\n 'title': 'S22',\n 'xLabel': 'Freq',\n 'yLabel': 'dBm',\n 'yPDiv': 10,\n 'data': [\n {'x': 100,'y': 500},\n {'x': 2000,'y': 5000},\n {'x': 5000,'y': 2000},\n {'x': 10000,'y': 4000}\n ]\n }\n ret = {\n 'traces': [ trace1, trace2, trace3, trace4 ], \n 'sweepResolution': 401,\n 'IFBW': 10000 \n }\n return ret","def get_variables():\n policer_data = {\n \"policer_data\": {\n \"name\": \"policy1\",\n \"cir\": 450,\n \"cb\": 50000,\n \"rate-type\": \"kbps\",\n \"round-type\": \"closest\",\n \"type\": \"1r2c\",\n \"conform-action\": {\n \"meter-action-type\": \"meter-action-transmit\"\n },\n \"exceed-action\": {\n \"meter-action-type\": \"meter-action-drop\"\n }\n },\n \"policer_data_oper\": {\n \"name\": \"policy1\",\n \"cir\": 450,\n \"cb\": 50000,\n \"rate-type\": \"kbps\",\n \"round-type\": \"closest\",\n \"type\": \"1r2c\",\n \"conform-action\": {\n \"meter-action-type\": \"policer:meter-action-transmit\"\n },\n \"exceed-action\": {\n \"meter-action-type\": \"policer:meter-action-drop\"\n }\n },\n \"policer_data_2\": {\n \"name\": \"policy1\",\n \"cir\": 900,\n \"cb\": 50000,\n \"rate-type\": \"kbps\",\n \"round-type\": \"closest\",\n \"type\": \"1r2c\",\n \"conform-action\": {\n \"meter-action-type\": \"meter-action-transmit\"\n },\n \"exceed-action\": {\n \"meter-action-type\": \"meter-action-drop\"\n }\n },\n \"policer_data_oper_2\": {\n \"name\": \"policy1\",\n \"cir\": 900,\n \"cb\": 50000,\n \"rate-type\": \"kbps\",\n \"round-type\": \"closest\",\n \"type\": \"1r2c\",\n \"conform-action\": {\n \"meter-action-type\": \"policer:meter-action-transmit\"\n },\n \"exceed-action\": {\n \"meter-action-type\": \"policer:meter-action-drop\"\n }\n },\n \"policer_data_3\": {\n \"name\": \"policy1\",\n \"cir\": 100,\n \"eir\": 150,\n \"cb\": 200,\n \"eb\": 300,\n \"rate-type\": \"pps\",\n \"round-type\": \"closest\",\n \"type\": \"2r3c-2698\",\n \"conform-action\": {\n \"meter-action-type\": \"meter-action-transmit\"\n },\n \"exceed-action\": {\n \"meter-action-type\": \"meter-action-mark-dscp\",\n \"dscp\": \"AF22\"\n },\n \"violate-action\": {\n \"meter-action-type\": \"meter-action-drop\"\n },\n \"color-aware\": True\n },\n \"policer_data_oper_3\": {\n \"name\": \"policy1\",\n \"cir\": 100,\n \"eir\": 150,\n \"cb\": 200,\n \"eb\": 300,\n \"rate-type\": \"pps\",\n \"round-type\": \"closest\",\n \"type\": \"2r3c-2698\",\n \"conform-action\": {\n \"meter-action-type\": \"policer:meter-action-transmit\"\n },\n \"exceed-action\": {\n \"meter-action-type\": \"policer:meter-action-mark-dscp\",\n },\n \"violate-action\": {\n \"meter-action-type\": \"policer:meter-action-drop\"\n },\n \"color-aware\": True\n },\n\n \"acl_tables\": {\n # settings for policer tables\n \"hc_acl_table\": {\n \"name\": \"table0\",\n \"nbuckets\": 2,\n \"memory_size\": 1048576,\n \"skip_n_vectors\": 12,\n \"miss_next\": \"permit\",\n \"mask\": \"00:00:00:00:00:00:00:00:00:00:00:00:ff:ff:ff:ff\"\n },\n # setting for acl sessions\n \"hc_acl_session\": {\n \"match\": \"00:00:00:00:00:00:00:00:00:00:00:00:C0:A8:7A:01\",\n \"policer_hit_next\": \"policy1\",\n \"color_classfier\": \"exceed-color\",\n },\n \"hc_acl_session2\": {\n \"match\": \"00:00:00:00:00:00:00:00:00:00:00:00:C0:A8:7A:02\",\n \"policer_hit_next\": \"policy1\",\n \"color_classfier\": \"exceed-color\",\n },\n },\n }\n return policer_data","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['low' ] = self.low\n paramDict['high' ] = self.high\n paramDict['alpha'] = self.alpha\n paramDict['beta' ] = self.beta\n return paramDict","def get_design(model):\n parameters = ['v', 'gamma', 's', 'tau', 't0']\n\n subject_idx = model.data['subject']\n subjects = subject_idx.unique()\n\n design = dict()\n design['factors'] = list({value for key, value in model.depends_on.items()\n if value is not None})\n design['factor_conditions'] = {factor: model.data[factor].unique()\n for factor in design['factors']}\n\n for parameter in parameters:\n design[parameter] = dict()\n # adding an index defining which entry in data belongs to which subject\n design[parameter]['subject_index'] = subject_idx[:].values.astype(\n np.int)\n\n dependence = model.depends_on.get(parameter)\n design[parameter]['dependence'] = dependence\n\n if dependence is not None:\n # extract condition levels\n conditions = model.data[dependence].unique()\n design[parameter]['conditions'] = conditions\n\n # Initialize empty design matrix D for this parameter\n D = np.zeros((subjects.size, conditions.size), dtype=np.int)\n # create parameter mapping containing condition names and indices (i.e., columns of design matrix)\n design[parameter]['condition_mapping'] = {condition: c\n for c, condition in enumerate(conditions)}\n # create an array to index which condition each trial-entry in the data belongs to\n design[parameter]['condition_index'] = np.zeros_like(\n subject_idx, dtype=np.int)\n\n # For each condition level\n for c, condition in enumerate(conditions):\n design[parameter][condition] = dict()\n\n # mark which trial-entries belong to this condition\n design[parameter]['condition_index'][model.data[dependence]\n == condition] = c\n\n # Subset data to condition-specific data\n data_subset = model.data[model.data[dependence]\n == condition].copy()\n\n # find all subject_IDs in this condition\n subject_subset = data_subset['subject'].unique()\n\n # within this level, generate mapping between subject_ids and indices\n # e.g., gamma_high[5] corresponds to subject_id 10\n design[parameter][condition]['subject_mapping'] = {int(subject): s\n for s, subject in enumerate(subject_subset)}\n # attach a list of all subjects in this condition\n design[parameter][condition]['subjects'] = subject_subset\n\n # Set cells with subject in this condition to 1\n for s, subject in enumerate(subject_subset):\n D[np.int(subject), c] = np.int(s+1)\n else:\n D = (np.arange(subjects.size)[:, None] + 1).astype(np.int)\n design[parameter]['conditions'] = None\n design[parameter]['condition_index'] = np.zeros_like(\n subject_idx, dtype=np.int)\n\n # Save design matrix D\n design[parameter]['D'] = D\n\n # Detect within / between subject design\n if D.shape[1] == 1:\n design_type = 'fixed'\n elif np.all((D != 0).sum(axis=1) > 1):\n design_type = 'within'\n elif np.all((D != 0).sum(axis=1) == 1):\n design_type = 'between'\n else:\n design_type = 'mixed'\n design[parameter]['type'] = design_type\n\n return design","def load_mat_dict():\n param_df = pd.DataFrame(loadmat(\n 'X:/YuxiangWang/DataAnalysis/skinMechanicsAll/analysis/' +\n 'ViscoAnalysis052013/strain_level_2py.mat')['qlv2tFixPara'])\n param_df.columns = ['tau1', 'tau2', 'g1', 'g2', 'ginf', 'mu', 'alpha',\n 'r2', 'stretch', 'thickness', 'skin_id', 'ramp_time']\n param_df['skin_id'] = param_df['skin_id'].astype('i')\n param_df['thickness'] *= 1e3 # Change unit to microns\n return param_df","def get_params(self):\n return {'k': self.k, 'q': self.q, 'sigma_s': self.sigma_s, 'm': self.m}","def main(self):\n\n assault_mech_df = self.get_mech_df(url=self.assault_url)\n heavy_mech_df = self.get_mech_df(url=self.heavy_url)\n medium_mech_df = self.get_mech_df(url=self.medium_url)\n light_mech_df = self.get_mech_df(url=self.light_url)\n all_weights_df = pd.concat([assault_mech_df, heavy_mech_df, medium_mech_df, \n light_mech_df])\n\n self.save_data(assault_mech_df, \"assault\")\n self.save_data(heavy_mech_df, \"heavy\")\n self.save_data(medium_mech_df, \"medium\")\n self.save_data(light_mech_df, \"light\")\n self.save_data(all_weights_df, \"all_weights\")\n #get maximum new columns needed for splitting variants\n max_cols = all_weights_df.variants.apply(lambda x: len(x)).max()\n melt_cols = []\n\n for i in range(max_cols):\n all_weights_df[\"var_\"+str(i)] = \"\"\n melt_cols.append(\"var_\"+str(i))\n\n variant_weights_df = pd.DataFrame()\n for index, row in all_weights_df.iterrows():\n for i in range(len(row[\"variants\"])):\n #add each variant to variant weights as a row with mech, tonnage, variant\n new_row_dict = {\n \"mech_name\":row[\"mechs\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"variants\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\n\n for i in range(len(row[\"hero_chassis\"])):\n new_row_dict = {\n \"mech_name\":row[\"hero_names\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"hero_chassis\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\n\n\n for i in range(len(row[\"special_variants\"])):\n new_row_dict = {\n \"mech_name\":row[\"mechs\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"special_variants\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df]) \n\n #add champion variants by matching on \n for i in range(len(row[\"champion_variants\"])):\n new_row_dict = {\n \"mech_name\":row[\"mechs\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"champion_variants\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\n #remove duplicate rows \n variant_weights_df = variant_weights_df[variant_weights_df.duplicated(keep=\"first\")==False]\n self.save_data(variant_weights_df, \"variant_weights\")","def smp_dict():\n out = base_dict()\n out['mro']['current'] = ['Sample']\n out['name']['current'] = 'Sample'\n ao(out, 'idx', 'Integer', attr=['Hidden'])\n ao(out, 'ii', 'Integer', attr=['Hidden'])\n ao(out, 'initialDimension', 'Float', 0., name='Initial Dimension')\n return out","def default_pypeit_par(cls):\n par = super().default_pypeit_par()\n\n par['rdx']['detnum'] = [(1,2,3)]\n\n # Adjustments to parameters for Keck HIRES\n turn_on = dict(use_biasimage=False, use_overscan=True, overscan_method='median',\n use_darkimage=False, use_illumflat=False, use_pixelflat=False,\n use_specillum=False)\n par.reset_all_processimages_par(**turn_on)\n par['calibrations']['traceframe']['process']['overscan_method'] = 'median'\n\n # Right now we are using the overscan and not biases becuase the\n # standards are read with a different read mode and we don't yet have\n # the option to use different sets of biases for different standards,\n # or use the overscan for standards but not for science frames\n # TODO testing\n par['scienceframe']['process']['use_biasimage'] = False\n par['scienceframe']['process']['use_illumflat'] = False\n par['scienceframe']['process']['use_pixelflat'] = False\n par['calibrations']['standardframe']['process']['use_illumflat'] = False\n par['calibrations']['standardframe']['process']['use_pixelflat'] = False\n # par['scienceframe']['useframe'] ='overscan'\n\n par['calibrations']['slitedges']['edge_thresh'] = 8.0\n par['calibrations']['slitedges']['fit_order'] = 8\n par['calibrations']['slitedges']['max_shift_adj'] = 0.5\n par['calibrations']['slitedges']['trace_thresh'] = 10.\n par['calibrations']['slitedges']['left_right_pca'] = True\n par['calibrations']['slitedges']['length_range'] = 0.3\n par['calibrations']['slitedges']['max_nudge'] = 0.\n par['calibrations']['slitedges']['overlap'] = True\n par['calibrations']['slitedges']['dlength_range'] = 0.25\n\n # These are the defaults\n par['calibrations']['tilts']['tracethresh'] = 15\n par['calibrations']['tilts']['spat_order'] = 3\n par['calibrations']['tilts']['spec_order'] = 5 # [5, 5, 5] + 12*[7] # + [5]\n\n # 1D wavelength solution\n par['calibrations']['wavelengths']['lamps'] = ['ThAr']\n # This is for 1x1 binning. TODO GET BINNING SORTED OUT!!\n par['calibrations']['wavelengths']['rms_threshold'] = 0.50\n par['calibrations']['wavelengths']['sigdetect'] = 5.0\n par['calibrations']['wavelengths']['n_final'] = 4 #[3] + 13 * [4] + [3]\n # This is for 1x1 binning. Needs to be divided by binning for binned data!!\n par['calibrations']['wavelengths']['fwhm'] = 8.0\n # Reidentification parameters\n par['calibrations']['wavelengths']['method'] = 'echelle'\n # TODO: the arxived solution is for 1x1 binning. It needs to be\n # generalized for different binning!\n #par['calibrations']['wavelengths']['reid_arxiv'] = 'vlt_xshooter_vis1x1.fits'\n par['calibrations']['wavelengths']['cc_thresh'] = 0.50\n par['calibrations']['wavelengths']['cc_local_thresh'] = 0.50\n# par['calibrations']['wavelengths']['ech_fix_format'] = True\n # Echelle parameters\n par['calibrations']['wavelengths']['echelle'] = True\n par['calibrations']['wavelengths']['ech_nspec_coeff'] = 4\n par['calibrations']['wavelengths']['ech_norder_coeff'] = 4\n par['calibrations']['wavelengths']['ech_sigrej'] = 3.0\n par['calibrations']['wavelengths']['ech_separate_2d'] = True\n\n # Flats\n par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.90\n par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.10\n\n # Extraction\n par['reduce']['skysub']['bspline_spacing'] = 0.6\n par['reduce']['skysub']['global_sky_std'] = False\n # local sky subtraction operates on entire slit\n par['reduce']['extraction']['model_full_slit'] = True\n # Mask 3 edges pixels since the slit is short, insted of default (5,5)\n par['reduce']['findobj']['find_trim_edge'] = [3, 3]\n # Continnum order for determining thresholds\n\n # Sensitivity function parameters\n par['sensfunc']['algorithm'] = 'IR'\n par['sensfunc']['polyorder'] = 11 #[9, 11, 11, 9, 9, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7]\n par['sensfunc']['IR']['telgridfile'] = 'TelFit_MaunaKea_3100_26100_R20000.fits'\n return par","def get_params(self) -> Dict:\n params: Dict = {}\n channel: str\n for channel in list(self.channel.keys()):\n if self.channel_bool[channel]:\n params[channel] = self.channel[channel].get_g()\n if self.tau_ca != float('inf'):\n params['t_ca'] = self.tau_ca\n return params","def init_params():\n p = {}\n \n # p['rootFolder'] = 'C:/Users/Umberto Gostoli/SPHSU/Social Care Model II'\n # p['rootFolder'] = 'N:/Social Care Model Paper III'\n \n p['noPolicySim'] = False\n p['multiprocessing'] = True\n p['numberProcessors'] = 9\n p['numRepeats'] = 3\n \n p['startYear'] = 1860\n p['endYear'] = 2040\n p['thePresent'] = 2012\n p['statsCollectFrom'] = 1990\n p['regressionCollectFrom'] = 1960 \n p['implementPoliciesFromYear'] = 2020\n p['yearOutcome'] = 2015\n \n p['favouriteSeed'] = 123\n p['loadFromFile'] = False\n p['verboseDebugging'] = False\n p['singleRunGraphs'] = False\n p['saveChecks'] = True\n p['getCheckVariablesAtYear'] = 2015\n # To change through command-line arguments\n\n p['numberPolicyParameters'] = 2\n p['valuesPerParam'] = 1\n p['numberScenarios'] = 3\n \n ############ Policy Parameters #######################\n p['incomeCareParam'] = 0.0005 #[0.00025 - 0.001]\n p['taxBreakRate'] = 0.0\n p['ageOfRetirement'] = 65\n p['socialSupportLevel'] = 5\n # p['educationCosts']\n #############################################################\n p['socialCareCreditShare'] = 0.0\n p['maxWtWChildAge'] = 5\n # The basics: starting population and year, etc.\n \n p['discountingFactor'] = 0.03\n \n \n p['initialPop'] = 600 \n \n p['minStartAge'] = 24\n p['maxStartAge'] = 45\n p['numberClasses'] = 5\n p['socialClasses'] = ['unskilled', 'skilled', 'lower', 'middle', 'upper']\n p['initialClassShares'] = [0.2, 0.25, 0.3, 0.2, 0.05]\n p['initialUnemployment'] = [0.25, 0.2, 0.15, 0.1, 0.1]\n p['unemploymentAgeBandParam'] = 0.3\n \n # doDeath function parameters\n p['mortalityBias'] = 0.85 # After 1950\n p['careNeedBias'] = 0.9\n p['unmetCareNeedBias'] = 0.5\n p['baseDieProb'] = 0.0001\n p['babyDieProb'] = 0.005\n p['maleAgeScaling'] = 14.0\n p['maleAgeDieProb'] = 0.00021\n p['femaleAgeScaling'] = 15.5\n p['femaleAgeDieProb'] = 0.00019\n \n p['orphansRelocationParam'] = 0.5\n \n # doBirths function parameters\n p['minPregnancyAge'] = 17\n p['maxPregnancyAge'] = 42\n p['growingPopBirthProb'] = 0.215\n p['fertilityCorrector'] = 1.0\n p['fertilityBias'] = 0.9\n \n # careTransitions function parameters\n p['zeroYearCare'] = 80.0\n p['childcareDecreaseRate'] = 0.25\n p['personCareProb'] = 0.0008\n p['maleAgeCareScaling'] = 18.0 # p['maleAgeCareProb'] = 0.0008\n p['femaleAgeCareScaling'] = 19.0 # p['femaleAgeCareProb'] = 0.0008\n p['baseCareProb'] = 0.0002\n p['careBias'] = 0.9\n p['careTransitionRate'] = 0.7\n\n p['unmetNeedExponent'] = 1.0 # 0.005 #[0.005 - 0.02]\n \n p['numCareLevels'] = 5\n p['careLevelNames'] = ['none','low','moderate','substantial','critical']\n p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 32.0, 80.0 ]\n p['quantumCare'] = 4.0\n \n # careSupplies getCare and probSuppliers function parameters\n \n ######## Key parameter 1 ##############\n \n \n p['weeklyHours'] = 40.0\n \n \n p['priceChildCare'] = 0.76 # 6 \n p['schoolAge'] = 5\n p['maxFormalChildcareHours'] = 48\n p['schoolHours'] = 30\n p['freeChildcareHours'] = 15\n p['workingParentsFreeChildcareHours'] = 30\n p['minAgeStartChildCareSupport'] = 3\n p['minAgeStartChildCareSupportByIncome'] = 2\n p['maxHouseholdIncomeChildCareSupport'] = 40 # 320\n \n ######## Key parameter 2 ##############\n # 5: No public supply \n \n p['retiredHours'] = [48.0, 36.0, 20.0, 10.0] # 60.0\n p['studentHours'] = [24.0, 16.0, 8.0, 4.0]\n p['teenAgersHours'] = [16.0, 0.0, 0.0, 0.0]\n p['unemployedHours'] = [32.0, 24.0, 16.0, 8.0]\n p['employedHours'] = [28.0, 20.0, 12.0, 8.0]\n p['formalCareDiscountFactor'] = 0.5\n \n p['socialNetworkDistances'] = [0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0]\n p['networkDistanceParam'] = 2.0\n p['socialCareWeightBias'] = 1.0\n p['unmetCareNeedDiscountParam'] = 0.5\n p['shareUnmetNeedDiscountParam'] = 0.5\n # p['pastShareUnmetNeedWeight'] = 0.5\n \n \n \n p['networkSizeParam'] = 10.0 # 1.0\n \n p['careSupplyBias'] = 0.5\n p['careIncomeParam'] = 0.001\n \n # Hospitalization Costs\n p['qalyBeta'] = 0.18\n p['qalyAlpha'] = 1.5\n p['qalyDiscountRate'] = 0.035\n p['qalyIndexes'] = [1.0, 0.8, 0.6, 0.4, 0.2]\n p['unmetCareHealthParam'] = 0.1\n p['hospitalizationParam'] = 0.5\n p['needLevelParam'] = 2.0\n p['unmetSocialCareParam'] = 2.0\n p['costHospitalizationPerDay'] = 400\n \n # ageTransitions, enterWorkForce and marketWage functions parameters\n p['ageTeenagers'] = 12\n p['minWorkingAge'] = 16\n \n ######## Key parameter 3 ##############\n \n p['careBankingSchemeOn'] = False\n p['socialCareBankingAge'] = 65\n \n p['absoluteCreditQuantity'] = False\n p['quantityYearlyIncrease'] = 0.0\n p['socialCareCreditQuantity'] = 0\n p['kinshipNetworkCarePropension'] = 0.5\n p['volunteersCarePropensionCoefficient'] = 0.01\n p['pensionContributionRate'] = 0.05\n \n p['hillHealthLevelThreshold'] = 3\n p['seriouslyHillSupportRate'] = 0.5\n \n ### Prices ####\n p['pricePublicSocialCare'] = 20.0 # [2.55] # 20\n p['priceSocialCare'] = 17.0 # [2.29] # 18\n p['taxBrackets'] = [663, 228, 0] # [28.16, 110.23] # [221, 865]\n p['taxBandsNumber'] = 3\n p['bandsTaxationRates'] = [0.4, 0.2, 0.0] # [0.0, 0.2, 0.4]\n # Tax Break Policy\n\n \n p['pensionWage'] = [5.0, 7.0, 10.0, 13.0, 18.0] # [0.64, 0.89, 1.27, 1.66, 2.29] # \n p['incomeInitialLevels'] = [5.0, 7.0, 9.0, 11.0, 14.0] #[0.64, 0.89, 1.15, 1.40, 1.78] # \n p['incomeFinalLevels'] = [10.0, 15.0, 22.0, 33.0, 50.0] #[1.27, 1.91, 2.80, 4.21, 6.37] # \n p['educationCosts'] = [0.0, 100.0, 150.0, 200.0] #[0.0, 12.74, 19.12, 25.49] # \n \n # Priced growth #####\n p['wageGrowthRate'] = 1.0 # 1.01338 # \n\n p['incomeGrowthRate'] = [0.4, 0.35, 0.35, 0.3, 0.25]\n \n # SES inter-generational mobility parameters\n p['leaveHomeStudentsProb'] = 0.5\n \n p['eduWageSensitivity'] = 0.2 # 0.5\n p['eduRankSensitivity'] = 3.0 # 5.0\n p['costantIncomeParam'] = 80.0 # 20.0\n p['costantEduParam'] = 10.0 # 10.0\n p['careEducationParam'] = 0.005 # 0.04\n \n \n \n # p['incEduExp'] = 0.25\n p['educationLevels'] = ['GCSE', 'A-Level', 'HND', 'Degree', 'Higher Degree']\n p['workingAge'] = [16, 18, 20, 22, 24]\n \n # doDivorce function parameters\n p['basicDivorceRate'] = 0.06\n p['variableDivorce'] = 0.06\n p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n p['divorceBias'] = 1.0\n \n # doMarriages function parameters\n p['deltageProb'] = [0.0, 0.1, 0.25, 0.4, 0.2, 0.05]\n p['incomeMarriageParam'] = 0.025\n p['studentFactorParam'] = 0.5\n ######## Key parameter 4 ##############\n p['betaGeoExp'] = 2.0 #[1.0 - 4.0]\n \n p['betaSocExp'] = 2.0\n p['rankGenderBias'] = 0.5\n p['basicMaleMarriageProb'] = 0.9\n p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0, 0.0 ]\n \n # jobMarket, updateWork and unemploymentRate functions parameters\n p['unemploymentClassBias'] = 0.75\n p['unemploymentAgeBias'] = [1.0, 0.55, 0.35, 0.25, 0.2, 0.2]\n p['numberAgeBands'] = 6\n p['jobMobilitySlope'] = 0.004\n p['jobMobilityIntercept'] = 0.05\n p['ageBiasParam'] = [7.0, 3.0, 1.0, 0.5, 0.35, 0.15]\n p['deltaIncomeExp'] = 0.05\n p['unemployedCareBurdernParam'] = 0.025\n # Potential key parameter\n p['relocationCareLossExp'] = 1.0 # 40.0 # \n p['incomeSocialCostRelativeWeight'] = 0.5\n \n p['firingParam'] = 0.2\n p['wageVar'] = 0.06\n p['workDiscountingTime'] = 0.75 # 0.8\n p['sizeWeightParam'] = 0.7\n p['minClassWeightParam'] = 1.0\n p['incomeDiscountingExponent'] = 4.0\n p['discountingMultiplier'] = 2.0\n #p['incomeDiscountingParam'] = 2.0\n \n # relocationPensioners function parameters\n p['agingParentsMoveInWithKids'] = 0.1\n p['variableMoveBack'] = 0.1\n p['retiredRelocationParam'] = 0.001 # 0.005\n \n # houseMap function parameters\n p['geoDistanceSensitivityParam'] = 2.0\n p['socDistanceSensitivityParam'] = 2.0\n p['classAffinityWeight'] = 4.0\n p['distanceSensitivityParam'] = 0.5\n \n # relocationProb function parameters\n p['baseRelocatingProb'] = 0.05\n p['relocationParameter'] = 1.0 \n p['apprenticesRelocationProb'] = 0.5\n #p['expReloc'] = 1.0\n \n # computeRelocationCost and relocation Propensity functions parameters\n p['yearsInTownSensitivityParam'] = 0.5\n \n ######## Key parameter 5 ##############\n p['relocationCostParam'] = 0.5 # 1.0 \n \n ######## Key parameter 6 ##############\n p['propensityRelocationParam'] = 2.0 # 2.0 \n p['denRelocationWeight'] = 0.5\n \n \n ## Description of the map, towns, and houses\n p['mapGridXDimension'] = 8\n p['mapGridYDimension'] = 12 \n p['townGridDimension'] = 70\n p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]\n p['ukMap'] = [[ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],\n [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],\n [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],\n [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],\n [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],\n [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ]]\n p['ukClassBias'] = [[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\n [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],\n [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],\n [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]\n p['mapDensityModifier'] = 0.6\n # p['numHouseClasses'] = 3\n # p['houseClasses'] = ['small','medium','large']\n \n ## Graphical interface details\n p['interactiveGraphics'] = False #True\n p['delayTime'] = 0.0\n p['screenWidth'] = 1300\n p['screenHeight'] = 700\n p['bgColour'] = 'black'\n p['mainFont'] = 'Helvetica 18'\n p['fontColour'] = 'white'\n p['dateX'] = 70\n p['dateY'] = 20\n p['popX'] = 70\n p['popY'] = 50\n p['pixelsInPopPyramid'] = 2000\n p['num5YearAgeClasses'] = 28\n p['careLevelColour'] = ['blue','green','yellow','orange','red']\n p['houseSizeColour'] = ['brown','purple','yellow']\n p['pixelsPerTown'] = 56\n p['maxTextUpdateList'] = 22\n \n # p['eduEduSensitivity'] = 0.5\n # p['mortalityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\n # p['fertilityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\n # p['divorceBias'] = [2.0, 1.5, 1.0, 0.75, 0.5]\n\n ## Transitions to care statistics\n \n ## Availability of care statistics\n \n #p['childHours'] = 5.0\n # p['employedHours'] = 12.0\n #p['homeAdultHours'] = 30.0\n #p['workingAdultHours'] = 25.0\n #p['maxEmployedHours'] = 60.0\n \n #p['lowCareHandicap'] = 0.5\n #p['hourlyCostOfCare'] = 20.0\n \n ## Fertility statistics\n \n # p['steadyPopBirthProb'] = 0.13\n # p['transitionYear'] = 1965\n \n ## Class and employment statistics\n # p['numClasses'] = 5\n # p['occupationClasses'] = ['lower','intermediate','higher']\n # p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]\n\n ## Age transition statistics\n # p['ageOfAdulthood'] = 17\n \n ## Marriage function parameters\n \n # p['basicFemaleMarriageProb'] = 0.25\n # p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['femaleMarriageProb'] = [0.01, 0.15, 0.3, 0.2, 0.1, 0.1, 0.06, 0.05, 0.02, 0.01, 0.01, 0.005]\n # p['maleMarriageProb'] = [0.005, 0.08, 0.25, 0.25, 0.15, 0.1, 0.07, 0.05, 0.03, 0.02, 0.01, 0.005]\n \n ## Leaving home and moving around statistics\n # p['probApartWillMoveTogether'] = 0.3\n # p['coupleMovesToExistingHousehold'] = 0.3\n # p['basicProbAdultMoveOut'] = 0.22\n # p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['basicProbSingleMove'] = 0.05\n # p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['basicProbFamilyMove'] = 0.03\n # p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]\n\n \n return p","def parameters(self):\n return {}","def createDict( self ):\n d = {}\n devTup = ( 'endcap', 'comp', 'shutter','397intensity' )\n for dev in devTup:\n d[dev] = {'devChannels':{}}\n endcap = ( ( 1, 1 ), ( 2, 0 ) )\n comp = ( ( 1, 4 ), ( 2, 2 ), ( 'common', 3 ) )\n shutter = ( ( 1, 5 ), ( 2, 6 ), ( 3, 7 ) )\n intensity397 = (('397intensity',8),)\n chanTup = ( endcap, comp, shutter ,intensity397 )\n for dev, value in zip( devTup, chanTup ):\n for chanPair in value:\n d[dev]['devChannels'][chanPair[0]] = {'value':None, 'channel':chanPair[1]}\n ecRange = ( 0.0, 40.0 )\n compRange = ( -40.0, 40.0 )\n shutterRange = ( 0.0, 5.0 )\n intensity397Range = (0.0,2500.0)\n rangeTup = ( ecRange, compRange, shutterRange, intensity397Range )\n for dev, value in zip( devTup, rangeTup ): d[dev]['range'] = value\n self.dcDict = d"],"string":"[\n \"def asDict(self) -> dict:\\n return {\\n \\\"predominant_occlusion\\\": self.predominantOcclusion.value,\\n \\\"estimations\\\": {\\n \\\"chin\\\": self.chin,\\n \\\"mouth\\\": self.mouth,\\n \\\"clear\\\": self.clear,\\n \\\"correct\\\": self.correct,\\n \\\"partially\\\": self.partially,\\n \\\"full\\\": self.full,\\n },\\n }\",\n \"def describe(self) -> Dict:\\n\\n desc = {\\n \\\"Win\\\": {\\n \\\"max\\\": np.max(self.Win),\\n \\\"min\\\": np.min(self.Win),\\n \\\"mean\\\": np.mean(self.Win),\\n \\\"median\\\": np.median(self.Win),\\n \\\"std\\\": np.std(self.Win)\\n },\\n \\\"W\\\": {\\n \\\"max\\\": np.max(self.W),\\n \\\"min\\\": np.min(self.W),\\n \\\"mean\\\": np.mean(self.W),\\n \\\"median\\\": np.median(self.W),\\n \\\"std\\\": np.std(self.W),\\n \\\"sr\\\": max(abs(linalg.eig(self.W)[0]))\\n }\\n }\\n if self.Wfb is not None:\\n desc[\\\"Wfb\\\"] = {\\n \\\"max\\\": np.max(self.Wfb),\\n \\\"min\\\": np.min(self.Wfb),\\n \\\"mean\\\": np.mean(self.Wfb),\\n \\\"median\\\": np.median(self.Wfb),\\n \\\"std\\\": np.std(self.Wfb)\\n }\\n if self.Wout is not None:\\n desc[\\\"Wout\\\"] = {\\n \\\"max\\\": np.max(self.Wout),\\n \\\"min\\\": np.min(self.Wout),\\n \\\"mean\\\": np.mean(self.Wout),\\n \\\"median\\\": np.median(self.Wout),\\n \\\"std\\\": np.std(self.Wout)\\n }\\n return desc\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def getDictWells(self):\\n #Method begins here\\n #nx=self.__grid['nx'] #From the geometry in grid\\n ny=self.__grid['ny']\\n nz=self.__grid['nz']\\n minx=self.__grid['ox']\\n miny=self.__grid['oy']\\n minz=self.__grid['oz']\\n rx=self.__grid['dx']\\n ry=self.__grid['dy']\\n rz=self.__grid['dz']\\n \\n # well package\\n # Remember to use zero-based layer, row, column indices!\\n lcoordw=np.zeros((self.__nwells,3),dtype=np.int32)\\n for i in range (self.__nwells):\\n lcoordw[i,0]=floor((self.__dflst.iloc[i,3]-minx)/rx)\\n #In MODFLOW y ans z coordinates are inverted\\n lcoordw[i,1]=floor((miny+ry*ny-self.__dflst.iloc[i,4])/ry)\\n lcoordw[i,2]=floor((minz+rz*nz-self.__dflst.iloc[i,5])/rz)\\n \\n nper=self.__df.getForcPer()\\n wel_sp = {} \\n for i in range(nper):\\n lst=[]\\n for j in range(self.__nwells):\\n pumping_rate=self.__dfwells.iloc[i+1,j+1]\\n lst.append( [lcoordw[j,2], lcoordw[j,1], lcoordw[j,0], pumping_rate] )\\n wel_sp[i]=lst\\n print(wel_sp)\\n \\n print('*--- Succesfull reading of wells ---*')\\n \\n return wel_sp\",\n \"def buildParamsDict(self):\\n self.params_dict = {\\n \\\"img_dir\\\": self.savePathJoin(\\\"Images\\\"),\\n \\\"depth_dir\\\": self.savePathJoin(\\\"Depth\\\"),\\n \\\"back_of_dir\\\": self.savePathJoin(\\\"Back_Of\\\"),\\n \\\"of_dir\\\": self.savePathJoin(\\\"Of\\\"),\\n \\\"save_dir\\\": self.user[\\\"Save\\\"],\\n \\\"high\\\": self.high,\\n \\\"low\\\": self.low,\\n \\\"run_dict\\\": self.run_dict,\\n \\\"of_model\\\": self.app.get_resource(\\n os.path.join(\\\"of_models\\\", \\\"network-default.pytorch\\\")\\n ),\\n \\\"depth_model\\\": self.app.get_resource(\\n os.path.join(\\\"depth_models\\\", \\\"model_city2kitti.meta\\\")\\n ),\\n \\\"yolo_weights\\\": self.app.get_resource(\\n os.path.join(\\\"yolo\\\", \\\"yolov3.weights\\\")\\n ),\\n \\\"yolo_v\\\": self.app.get_resource(os.path.join(\\\"yolo\\\", \\\"yolov3.cfg\\\")),\\n \\\"coco_names\\\": self.app.get_resource(os.path.join(\\\"yolo\\\", \\\"coco.names\\\")),\\n \\\"object_detection_dir\\\": self.savePathJoin(\\\"ObjectDetection\\\"),\\n \\\"plot_speed_dir\\\": PLOT_SPEED_DIR,\\n \\\"plot_crash_dir\\\": PLOT_CRASH_DIR,\\n \\\"numbers_dir\\\": NP_DIR,\\n \\\"plot_error_dir\\\": PLOT_ERROR_DIR,\\n \\\"speed_gt\\\": self.user[\\\"GT\\\"],\\n \\\"vid_path\\\": self.user[\\\"Video\\\"],\\n \\\"super_pixel_method\\\": self.super_pixel_method,\\n \\\"super_pixel_dir\\\": SUPER_PIXEL_DIR,\\n \\\"send_video_frame\\\": False,\\n \\\"create_csv\\\": self.ui.c_csv.isChecked(),\\n \\\"create_draw\\\": self.ui.c_draw.isChecked(),\\n \\\"create_velocity\\\": self.ui.c_velocity.isChecked(),\\n \\\"create_video_fps\\\": int(self.ui.t_fps.text()),\\n \\\"optimize_params\\\": self.ui.c_optimize.isChecked(),\\n \\\"super_pixel_label_dir\\\": os.path.join(\\n self.savePathJoin(\\\"Super_Pixel\\\"), self.super_pixel_method\\n ),\\n }\",\n \"def test_to_rich_dict(self):\\n F81().to_rich_dict()\\n HKY85().to_rich_dict()\\n GN().to_rich_dict()\\n # TODO need to assess ability to reconstruct from this\",\n \"def data(self):\\n return {\\\"estimate\\\":self.get_estimate(),\\n \\\"samplers\\\":self.samplers}\",\n \"def parameters(self):\\n res = dict()\\n res[\\\"population_size\\\"] = self.population_size\\n res[\\\"mutation_prob\\\"] = self.mutation_prob\\n res[\\\"crossover\\\"] = self.crossover\\n res[\\\"selection\\\"] = self.selection\\n res[\\\"sigma\\\"] = self.sigma\\n res[\\\"crossover_method\\\"] = self.crossover_method\\n res[\\\"selection_method\\\"] = self.selection_method\\n res[\\\"best_rate\\\"] = self.best_rate\\n res[\\\"n_parents\\\"] = self.n_parents\\n res[\\\"model_parameters\\\"] = self.model.total_parameters()\\n res[\\\"IDCT_from\\\"] = self.IDCT_from\\n res[\\\"elitism\\\"] = self.elitism\\n return res\",\n \"def getRigBuildData(self):\\n\\n data = super(SimpleControlComponentGuide, self).getRigBuildData()\\n\\n data[\\\"ctrlSize\\\"] = self.ctrlSizeInputAttr.getValue()\\n data[\\\"ctrlXfo\\\"] = self.mainCtrl.xfo\\n\\n return data\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rbk[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd_emu[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rbk,Rdy,Rqcd_emu\\\")\",\n \"def _get_fitted_params(self):\\n return {}\",\n \"def load_demo():\\n\\tprint(\\\"\\\"\\\"\\n\\tBreast Cancer Wisconsin dataset. It contains a total of 569 samples of tumor and malignant cells. \\n\\tData labeled 1 corresponds to malignant cells, while data labeled 0 corresponds to benign cells. \\n\\tThe 30 characteristics contain real values obtained from images of cell nuclei. For more information:\\n\\n\\t\\t\\thttp://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)\\n\\n\\n\\tThe returned value is a dictionary where 'x_data' are the predictor variables, 'y_data' the class \\n\\tlabels and 'features' the name of the characteristics.\\n\\t\\\"\\\"\\\")\\n\\tpath = '/'.join(os.path.abspath(pywinEA.__file__).split('/')[:-1])\\n\\t\\n\\tdata = pd.read_csv(path+'/dataset/data/BreastCancerWisconsin.csv', index_col=0)\\n\\tx_data = data.iloc[:, 1:].values\\n\\ty_data = data.iloc[:, 0].values\\n\\tfeatures = data.columns[1:].values\\n\\n\\t# Transform labels\\n\\ty_data[np.where(y_data == 'M')] = 1\\n\\ty_data[np.where(y_data == 'B')] = 0\\n\\ty_data = y_data.astype(int)\\n\\n\\treturn {'x_data': x_data, 'y_data': y_data, 'features': features}\",\n \"def qasmCircuitResults(self):\\n returnedDictionary={}\\n self.circutDrawing = self.draw()\\n self.blochSpheres=self.separatedBlochSpheres()\\n returnedDictionary[\\\"wires\\\"]=self.num_qubits\\n returnedDictionary[\\\"probabilities\\\"] = self.separatedProbabilities()\\n #returnedDictionary[\\\"blochSpheres\\\"] = self.separatedBlochSpheres()\\n returnedDictionary[\\\"diracNotation\\\"] = self.diracNotation()\\n returnedDictionary['chart'] = self.graph()\\n returnedDictionary[\\\"link\\\"] = \\\"\\\"\\n #returnedDictionary[\\\"qasmRows\\\"] = np.transpose(cols).tolist()\\n \\n if self.API_TOKEN != \\\"\\\":\\n returnedDictionary[\\\"link\\\"] = self.runOnIBMQ()\\n \\n return returnedDictionary\",\n \"def dic_info(self):\\n\\n info = {}\\n try:\\n info[\\\"DIC\\\"] = self.mc.DIC\\n info[\\\"deviance\\\"] = np.mean(self.mc.db.trace(\\\"deviance\\\")(), axis=0)\\n info[\\\"pD\\\"] = info[\\\"DIC\\\"] - info[\\\"deviance\\\"]\\n except pm.ZeroProbability:\\n info[\\\"DIC\\\"] = np.nan\\n info[\\\"deviance\\\"] = np.nan\\n info[\\\"pD\\\"] = np.nan\\n\\n return info\",\n \"def get_params(self, deep=False):\\n return {\\\"alpha\\\": self.alpha, \\\"beta\\\": self.beta, \\\"gamma\\\": self.gamma, \\\"W\\\": self.W, \\\"bias\\\": self.bias, \\\"add_bias\\\": self.add_bias, \\\"opts\\\": self.opts}\",\n \"def paramDetails(cls):\\n return {\\n 'dim': (10, 20, 2, 20),\\n 'nIter': (1, 10, 2, 5),\\n 'lamb': (.1, 1., .1, .05),\\n 'alph': (30, 50, 5, 40)\\n }\",\n \"def paraChck(**kwargs):\\n import sys\\n\\n \\n def_val = {\\n 'x_train':None,\\n 'y_train':None,\\n 'x_test':None,\\n 'y_test':None,\\n 'channel':1,\\n 'input_img_cols':72,\\n 'input_img_rows':72,\\n 'nb_classes':13,\\n 'nb_epoch': 5,\\n 'batch_size' : 16,\\n 'dict_label' : None} # default parameteters value\\n\\n diff = set(kwargs.keys()) - set(def_val.keys())\\n if diff:\\n print(\\\"Invalid args:\\\",tuple(diff),file=sys.stderr)\\n return\\n\\n def_val.update(kwargs)\\n return def_val\",\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 \"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(\\\"dAfb[0.,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"dA0[0.0, -1.0, 1.0]\\\");\\n #self.modelBuilder.doSet(\\\"POI\\\",\\\"dAfb,dA0\\\")\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"dAfb\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0+@1\\\",eAfb,dAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0+@1)\\\",eA0,dA0)')\\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 \"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(\\\"mAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"mA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"eAfb,mAfb\\\")\\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 \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"kappa_W[1,0.0,2.0]\\\") \\n self.modelBuilder.doVar(\\\"kappa_Z[1,0.0,2.0]\\\") \\n self.modelBuilder.doVar(\\\"kappa_tau[1,0.0,3.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_mu[1,0.0,5.0]\\\") \\n self.modelBuilder.factory_(\\\"expr::kappa_mu_expr(\\\\\\\"@0*@1+(1-@0)*@2\\\\\\\", CMS_use_kmu[0], kappa_mu, kappa_tau)\\\")\\n self.modelBuilder.doVar(\\\"kappa_t[1,0.0,4.0]\\\")\\n # additional kappa for the anomalous coupling\\n self.modelBuilder.doVar(\\\"kappa_tilde_t[0.0,0.0,4.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_b[1,0.0,3.0]\\\")\\n if not self.resolved:\\n self.modelBuilder.doVar(\\\"kappa_g[1,0.0,2.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_gam[1,0.0,2.5]\\\")\\n\\tself.modelBuilder.doVar(\\\"BRinv[0,0,1]\\\")\\n self.modelBuilder.out.var(\\\"BRinv\\\").setConstant(True)\\n # adding additional kappa to list of parameters of interest\\n pois = 'kappa_W,kappa_Z,kappa_tau,kappa_t,kappa_tilde_t,kappa_b'\\n if not self.resolved:\\n pois += ',kappa_g,kappa_gam'\\n self.doMH()\\n self.modelBuilder.doSet(\\\"POI\\\",pois)\\n # use modified Higgs Builder\\n self.SMH = AnomalousTopHiggsBuilder(self.modelBuilder)\\n self.setup()\",\n \"def parameters(self):\\n return {\\n 'base':self.base.parameters(),\\n 'material':[m.parameters() for m in self.material],\\n 'fraction':self.fraction,\\n }\",\n \"def game_info(tytuł, gatunek, **cechy):\\r\\n\\tgra = {}\\r\\n\\tgra['Tytuł'] = tytuł\\r\\n\\tgra['Gatunek'] = gatunek\\r\\n\\t\\r\\n\\tfor k, v in cechy.items():\\r\\n\\t\\tgra[k] = v\\r\\n\\t\\r\\n\\treturn gra\",\n \"def getParameters(self):\\n return {'channels':self._channels, 'means':self._means, 'stds':self._stds}\",\n \"def read_parameters():\\n hdulist1 = pf.open(source+'/kids_data/KiDS_DR3.1_G9_ugri_shear.fits')\\n param1 = hdulist1[1].data['e1'][:sample]\\n param2 = hdulist1[1].data['e2'][:sample]\\n weights = hdulist1[1].data['weight'][:sample]\\n return param1, param2, weights\",\n \"def chanDefaults(self) -> Dict[str, Any]:\\n chanH = {}\\n chanH[\\\"gain_stage1\\\"] = 1\\n chanH[\\\"gain_stage2\\\"] = 1\\n chanH[\\\"hchopper\\\"] = 0 # this depends on sample frequency\\n chanH[\\\"echopper\\\"] = 0\\n # channel output information (sensor_type, channel_type, ts_lsb, pos_x1, pos_x2, pos_y1, pos_y2, pos_z1, pos_z2, sensor_sernum)\\n chanH[\\\"ats_data_file\\\"] = \\\"\\\"\\n chanH[\\\"num_samples\\\"] = 0\\n chanH[\\\"sensor_type\\\"] = \\\"\\\"\\n chanH[\\\"channel_type\\\"] = \\\"\\\"\\n chanH[\\\"ts_lsb\\\"] = 1\\n # the lsb/scaling is not applied. data is raw voltage which needs to be scaled\\n # an lsb is constructed from the scaling in the XTR/XTRX file to take the data to mV\\n chanH[\\\"scaling_applied\\\"] = False # check this\\n chanH[\\\"pos_x1\\\"] = 0\\n chanH[\\\"pos_x2\\\"] = 0\\n chanH[\\\"pos_y1\\\"] = 0\\n chanH[\\\"pos_y2\\\"] = 0\\n chanH[\\\"pos_z1\\\"] = 0\\n chanH[\\\"pos_z2\\\"] = 0\\n chanH[\\\"sensor_sernum\\\"] = 0\\n return chanH\",\n \"def get_params(self) -> Dict:\\n params: Dict = {}\\n params['g_leak'] = self.leak.get_g()\\n params['g_kvhh'] = self.kvhh.get_g()\\n params['g_cav'] = self.cav.get_g()\\n params['g_kca'] = self.kca.get_g()\\n params['g_nap'] = self.nap.get_g()\\n params['t_ca'] = self.tau_ca\\n return params\",\n \"def gather_experiment_parameters(self):\\n consts = win32com.client.constants.__dicts__[0]\\n exp_params = [r for r in consts.keys() if len(r.split(\\\"EXP_\\\")) > 1]\\n dm_params = [r for r in consts.keys() if len(r.split(\\\"DM_\\\")) > 1]\\n self.app_param = {} \\n self.appdoc_param = {} \\n for p in exp_params:\\n self.app_param.update({p:self.app.GetParam(consts[p])})\\n\\n for p in dm_params:\\n #self.appdoc_param.update({p:self.app.GetParam(consts[p])}) bug? call appdoc? CP\\n\\n self.appdoc_param.update({p:self.app.GetParam(consts[p])})\",\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 getMyQuadInfo(self):\\n d = self.getMyInfoAsDict()\\n d['components'] = self.getMyDictInfo('components')\\n d['weapons'] = self.getMyDictInfo('weapons')\\n return d\",\n \"def getRigBuildData(self):\\n\\n # Values\\n mouthPosition = self.jawCtrl.xfo.tr\\n jawEndPosition = self.jawEndCtrl.xfo.tr\\n mouthLen = mouthPosition.subtract(jawEndPosition).length()\\n\\n # Calculate Mouth Xfo\\n\\n # atVector\\n # mouthUpV = Vec3(0.0, 1.0, 0.0)\\n\\n # rootToEnd = jawEndPosition.subtract(mouthPosition).unit()\\n # rootToUpV = mouthUpV.subtract(mouthPosition).unit()\\n # bone1ZAxis = rootToUpV.cross(rootToEnd).unit()\\n # bone1Normal = bone1ZAxis.cross(rootToEnd).unit()\\n\\n jawXfo = self.jawEndCtrl.xfo\\n # jawXfo.setFromVectors(rootToEnd, bone1Normal, bone1ZAxis, mouthPosition)\\n\\n\\n\\n data = super(OSSMouthGuide, self).getRigBuildData()\\n\\n # should include getCurveData\\n data = self.saveAllObjectData(data, \\\"Control\\\")\\n data = self.saveAllObjectData(data, \\\"Transform\\\")\\n data['jawXfo'] = self.jawCtrl.xfo\\n data['mouthLen'] = mouthLen\\n return data\",\n \"def build(self):\\n return self.hyperparams.items()\",\n \"def getDictCWells(self,itype):\\n #Method begins here\\n #nx=self.__grid['nx'] #From the geometry in grid\\n ny=self.__grid['ny']\\n nz=self.__grid['nz']\\n minx=self.__grid['ox']\\n miny=self.__grid['oy']\\n minz=self.__grid['oz']\\n rx=self.__grid['dx']\\n ry=self.__grid['dy']\\n rz=self.__grid['dz']\\n \\n # well package\\n # Remember to use zero-based layer, row, column indices!\\n lcoordw=np.zeros((self.__ncwells,3),dtype=np.int32)\\n for i in range (self.__ncwells):\\n lcoordw[i,0]=floor((self.__dfclst.iloc[i,3]-minx)/rx)\\n #In MODFLOW y ans z coordinates are inverted\\n lcoordw[i,1]=floor((miny+ry*ny-self.__dfclst.iloc[i,4])/ry)\\n lcoordw[i,2]=floor((minz+rz*nz-self.__dfclst.iloc[i,5])/rz)\\n \\n nper=self.__df.getForcPer()\\n ssm_data = {}\\n print('Number of conc periods='+str(nper)) \\n for i in range(nper):\\n lst=[]\\n for j in range(self.__ncwells):\\n conc_rate=self.__dfcwells.iloc[i+1,j+1]\\n lst.append( [ lcoordw[j,2], lcoordw[j,1], lcoordw[j,0], conc_rate, itype['WEL'] ] )\\n ssm_data[i]=lst\\n print(ssm_data)\\n \\n print('*--- Succesfull reading of concentration wells ---*')\\n \\n return ssm_data\",\n \"def get_params(self):\\n return {\\\"d\\\": \\\"155\\\"}\",\n \"def get_wells_info(self):\\n prod_info = self.read_wells_include_file(\\n rel_path='INCLUDE/Produtores.inc')\\n prod_info['well_type'] = 'prod'\\n inj_info = self.read_wells_include_file(\\n rel_path='INCLUDE/Injetores.inc')\\n inj_info['well_type'] = 'inj'\\n return pd.concat([prod_info, inj_info], ignore_index=True)\",\n \"def draggableCircuitResults(self):\\n returnedDictionary={}\\n self.blochSpheres=self.separatedBlochSpheres()\\n returnedDictionary[\\\"probabilities\\\"] = self.separatedProbabilities()\\n #returnedDictionary[\\\"blochSpheres\\\"] = self.separatedBlochSpheres()\\n returnedDictionary[\\\"diracNotation\\\"] = self.diracNotation()\\n returnedDictionary[\\\"link\\\"] = \\\"\\\"\\n returnedDictionary['chart'] = self.graph()\\n try:\\n returnedDictionary[\\\"qasm\\\"] = self.circuit.qasm()\\n except Exception:\\n #str(Exception)\\n returnedDictionary[\\\"qasm\\\"] = \\\"//You are using custom gate\\\\n//with size more than 2 qubits\\\\n//sorry, this version doesn't support that\\\\n//qiskit version 0.19.1\\\"\\n \\n if self.API_TOKEN != \\\"\\\":\\n returnedDictionary[\\\"link\\\"] = self.runOnIBMQ()\\n \\n return returnedDictionary\",\n \"def get_model_descs():\\n model_descs = {}\\n for (dirpath, dirnames, filenames) in walk('.'):\\n for filename in filenames:\\n if 'model_desc' in filename:\\n numwavelets = None\\n wavelet_name = filename.split('.')[1]\\n if '-' in wavelet_name:\\n wavelet_name = wavelet_name.split('-')[0]\\n dirs = str(dirpath).split('/')\\n for direc in dirs:\\n if 'w' in direc:\\n numwavelets = int(direc.split('w')[1].split('.')[0])\\n break\\n if numwavelets is None:\\n print(join(dirpath, filename) + \\\" is skipped\\\")\\n else:\\n if numwavelets not in model_descs.keys():\\n model_descs[numwavelets] = {}\\n if wavelet_name not in model_descs[numwavelets].keys():\\n model_descs[numwavelets][wavelet_name] = {}\\n model_descs[numwavelets][wavelet_name][join(dirpath,\\\\\\n filename)] = str(dirpath) + \\\"/\\\"\\n return model_descs\",\n \"def get_hyperparams(self):\",\n \"def get_obj_desc():\\n\\n attributes = {'results' : ['aperiodic_params_', 'peak_params_',\\n 'r_squared_', 'error_',\\n '_gaussian_params'],\\n 'settings' : ['peak_width_limits', 'max_n_peaks',\\n 'min_peak_height', 'peak_threshold',\\n 'aperiodic_mode'],\\n 'data' : ['power_spectrum', 'freq_range', 'freq_res'],\\n 'data_info' : ['freq_range', 'freq_res'],\\n 'arrays' : ['freqs', 'power_spectrum', 'aperiodic_params_',\\n 'peak_params_', '_gaussian_params'],\\n 'model_components' : ['_spectrum_flat', '_spectrum_peak_rm',\\n '_ap_fit', '_peak_fit']}\\n\\n return attributes\",\n \"def dump(self):\\n cfg = {\\n \\\"Detector\\\" : \\n { \\n \\\"8680\\\": \\n {\\n \\\"CONVENTIONAL\\\": \\n {\\n \\\"1.0\\\": \\n {\\n \\\"1x\\\": (3.87, 8.51),\\n \\\"2.4x\\\": (1.6, 6.74),\\n \\\"4.9x\\\": (0.72, 6.23)\\n },\\n \\n \\\"3.0\\\": \\n {\\n \\\"1x\\\": (10.12, 14.07),\\n \\\"2.4x\\\": (4.2, 11.17),\\n \\\"4.9x\\\": (1.89, 10)\\n }\\n \\n },\\n \\n \\n \\\"EMGAIN\\\": \\n {\\n \\\"1.0\\\" : \\n {\\n \\\"1x\\\": (18.97, 32.25),\\n \\\"2.4x\\\": (7.61, 19.41),\\n \\\"4.9x\\\": (3.47, 16.31)\\n },\\n \\n \\\"3.0\\\" : \\n {\\n \\\"1x\\\": (46.56, 54.01),\\n \\\"2.4x\\\": (19.82, 33.3),\\n \\\"4.9x\\\": (8.84, 26.25)\\n },\\n \\n \\\"5.0\\\" : \\n {\\n \\\"1x\\\": (46.49, 70.66),\\n \\\"2.4x\\\": (19.53, 45.11),\\n \\\"4.9x\\\": (8.9, 35.87)\\n },\\n \\n \\\"10.0\\\" : \\n {\\n \\\"2.4x\\\": (22.45, 52.98),\\n \\\"4.9x\\\": (10.43, 45.37),\\n } \\n } \\n }, \\n \\n \\\"8325\\\": \\n {\\n \\\"CONVENTIONAL\\\": \\n {\\n \\\"1.0\\\": \\n {\\n \\\"1x\\\": (3.98, 8.64),\\n \\\"2.5x\\\": (1.6, 6.75),\\n \\\"5.1x\\\": (0.72, 6.23)\\n },\\n \\n \\\"3.0\\\": \\n {\\n \\\"1x\\\": (10.45, 14.42),\\n \\\"2.5x\\\": (4.14, 10.97),\\n \\\"5.1x\\\": (1.89, 10.24)\\n }\\n },\\n \\n \\\"EMGAIN\\\": \\n {\\n \\\"1.0\\\" : \\n {\\n \\\"1\\\": (19.73, 34.13),\\n \\\"2.5x\\\": (7.88, 20.49),\\n \\\"5.1x\\\": (3.54, 16.99)\\n }, \\n \\n \\\"3.0\\\" : \\n {\\n \\\"1\\\": (48.23, 54.5),\\n \\\"2.5x\\\": (19.77, 33.41),\\n \\\"5.1x\\\": (9.04, 27.84)\\n },\\n \\n \\\"5.0\\\" : \\n {\\n \\\"1\\\": (50.66, 77.0),\\n \\\"2.5x\\\": (20.46, 48.08),\\n \\\"5.1x\\\": (8.7, 35.5)\\n },\\n \\n \\\"10.0\\\" : \\n {\\n \\\"2.5x\\\": (22.44, 53.63),\\n \\\"5.1x\\\": (11.3, 52.55),\\n } \\n }\\n },\\n \\n \\\"10522\\\": \\n {\\n \\\"CONVENTIONAL\\\": \\n {\\n \\\"0.1\\\": \\n {\\n \\\"Gain 1\\\": (3.37, 8.54),\\n \\\"Gain 2\\\": (0.8, 3.15),\\n },\\n \\n \\\"1.0\\\": \\n {\\n \\\"Gain 1\\\": (3.36, 6.51),\\n \\\"Gain 2\\\": (0.79, 4.59),\\n }\\n },\\n \\n \\\"EMGAIN\\\": \\n {\\n \\\"1.0\\\" : \\n {\\n \\\"Gain 1\\\": (16.4, 25.1),\\n \\\"Gain 2\\\": (3.96, 12.3),\\n }, \\n \\n \\\"10.0\\\" : \\n {\\n \\\"Gain 1\\\": (16.5, 76.0),\\n \\\"Gain 2\\\": (4.05, 45.1),\\n },\\n \\n \\\"20.0\\\" : \\n {\\n \\\"Gain 1\\\": (17.2, 193.0),\\n \\\"Gain 2\\\": (4.64, 76.6),\\n },\\n \\n \\\"30.0\\\" : \\n {\\n \\\"Gain 1\\\": (18.2, 272.0),\\n \\\"Gain 2\\\": (5.46, 145.0),\\n } \\n }\\n },\\n \\n \\\"10570\\\": \\n {\\n \\\"CONVENTIONAL\\\": \\n {\\n \\\"0.1\\\": \\n {\\n \\\"Gain 1\\\": (3.31, 8.82),\\n \\\"Gain 2\\\": (0.8, 3.39),\\n },\\n \\n \\\"1.0\\\": \\n {\\n \\\"Gain 1\\\": (3.30, 6.52),\\n \\\"Gain 2\\\": (0.79, 4.83),\\n }\\n },\\n \\n \\\"EMGAIN\\\": \\n {\\n \\\"1.0\\\" : \\n {\\n \\\"Gain 1\\\": (16.4, 25.1),\\n \\\"Gain 2\\\": (4.01, 12.6),\\n }, \\n \\n \\\"10.0\\\" : \\n {\\n \\\"Gain 1\\\": (16.5, 85.6),\\n \\\"Gain 2\\\": (4.06, 42.7),\\n },\\n \\n \\\"20.0\\\" : \\n {\\n \\\"Gain 1\\\": (17.5, 142.0),\\n \\\"Gain 2\\\": (4.81, 76.0),\\n },\\n \\n \\\"30.0\\\" : \\n {\\n \\\"Gain 1\\\": (19.3, 256.0),\\n \\\"Gain 2\\\": (5.88, 166.0),\\n } \\n }\\n }\\n },\\n \\n \\\"Phot\\\" :\\n {\\n \\\"fwhmpsf\\\": 6.0,\\n \\\"sigma\\\": 10.0,\\n \\\"exposure\\\": \\\"EXPTIME\\\",\\n \\\"calgorithm\\\": \\\"centroid\\\",\\n \\\"cbox\\\" : 8,\\n \\\"maxshift\\\": 5, \\n \\\"salgorithm\\\": \\\"median\\\",\\n \\\"annulus\\\": 14,\\n \\\"dannulus\\\": 16,\\n \\\"apertures\\\": 12,\\n \\\"zmag\\\": 27.11\\n }\\n }\\n \\n \\n # Dump the configuration to json output file\\n with open(self.conf_fname, \\\"w\\\") as fd:\\n json.dump(cfg, fd) \\n \\n return\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.7,0.7]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"R_ee_os_fakes[0.6,0.0,1.0]\\\");\\n self.modelBuilder.doVar(\\\"ee16_fakes_norm[1.0, 0.01, 10.]\\\");\\n self.modelBuilder.doVar(\\\"ee17_fakes_norm[1.0, 0.01, 10.]\\\");\\n self.modelBuilder.doVar(\\\"ee18_fakes_norm[1.0, 0.01, 10.]\\\");\\n #Remember, cant use spaces in these formulas!\\n #self.modelBuilder.options.verbose = 10\\n self.modelBuilder.factory_('expr::R_ee16_qcd_os(\\\"@0*@1\\\",ee16_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee17_qcd_os(\\\"@0*@1\\\",ee17_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee18_qcd_os(\\\"@0*@1\\\",ee18_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee16_qcd_ss(\\\"@0*(1.0-@1)\\\",ee16_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee17_qcd_ss(\\\"@0*(1.0-@1)\\\",ee17_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee18_qcd_ss(\\\"@0*(1.0-@1)\\\",ee18_fakes_norm,R_ee_os_fakes)')\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def get_dic(self):\\n dic = {\\n 'size': self.size,\\n 'bounds': self.bounds,\\n 'visible': self.visible,\\n 'is_static': self.is_static,\\n 'options': self.options,\\n 'primitive_type': self.primitive_type,\\n 'constrain_ratio': self.constrain_ratio,\\n 'constrain_navigation': self.constrain_navigation,\\n 'framebuffer': self.framebuffer,\\n # 'beforeclear': self.beforeclear,\\n 'variables': self.get_variables_list(),\\n 'vertex_shader': self.vertex_shader,\\n 'fragment_shader': self.fragment_shader,\\n }\\n return dic\",\n \"def get_params(self):\\n return {'classifier': self.classifier,\\n 'grid_param': self.grid_param,\\n 'n_param_comb': self.n_param_comb,\\n 'top_bagging': self.bagging,\\n 'bagging_param': self.bagging_param,\\n 'comb_seed': self.comb_seed}\",\n \"def child_analyse(self, name, child):\\n try:\\n module = child.Conv2d\\n analyse_dict = {\\\"in_channels\\\": module.in_channels,\\n \\\"out_channels\\\": module.out_channels,\\n \\\"kernel_size\\\": module.kernel_size[0],\\n \\\"stride\\\": module.stride[0],\\n \\\"padding\\\": (module.kernel_size[0] - 1) // 2,\\n \\\"bias\\\": False if module.bias is None else True,\\n \\\"shortcut\\\": False}\\n except AttributeError:\\n try:\\n analyse_dict = {\\\"shortcut\\\": True,\\n \\\"from\\\": self.model.module_defs[int(name)]['from']}\\n except KeyError:\\n analyse_dict = None\\n\\n return analyse_dict\",\n \"def dictOfDraws(self):\\n return dict()\",\n \"def training_info(self):\\n pass\",\n \"def raw_data() -> Dict:\\n return {\\\"neighbourhood\\\":\\\"Buttes-Montmartre\\\",\\\"room_type\\\":\\\"Entire home/apt\\\",\\\"minimum_nights\\\":1.555,\\\"mois\\\":2,\\\"voyageurs\\\":2.5,\\\"chambres\\\":1,\\\"lits\\\":1,\\\"salle_de_bains\\\":1}\",\n \"def nn_data(self, chrome):\\n decoded_chrome = self.decode(chrome)\\n data = dict()\\n data[\\\"num_input_nodes\\\"] = 9\\n data[\\\"num_hidden_nodes\\\"] = 5\\n data[\\\"num_output_nodes\\\"] = 20\\n data[\\\"inputs\\\"] = data_helpers.get_data(self.current_board)\\n data[\\\"first_weights\\\"] = decoded_chrome[0]\\n data[\\\"second_weights\\\"] = decoded_chrome[1]\\n data[\\\"third_weights\\\"] = decoded_chrome[2]\\n data[\\\"first_thresholds\\\"] = decoded_chrome[3]\\n data[\\\"second_thresholds\\\"] = decoded_chrome[4]\\n data[\\\"third_thresholds\\\"] = decoded_chrome[5]\\n return data\",\n \"def get_params(self):\\n return {'threshold': self.threshold,\\n 'subsample': self.subsample,\\n 'estimator': self.estimator,\\n 'n_folds': self.n_folds,\\n 'stratify': self.stratify,\\n 'random_state': self.random_state,\\n 'n_jobs': self.n_jobs}\",\n \"def get_params_info(cls):\\n return dict(\\n config='laygo configuration dictionary.',\\n threshold='transistor threshold flavor.',\\n draw_boundaries='True to draw boundaries.',\\n num_blk='number of driver segments.',\\n show_pins='True to draw pin geometries.',\\n )\",\n \"def data_dict(self) -> dict:\\n return self.design.renderers.gds.options\",\n \"def build_data(self):\\n from desiutil.io import combine_dicts\\n # Loop on exposures\\n odict = {}\\n for qanight in self.qa_nights:\\n for qaexp in qanight.qa_exps:\\n # Get the exposure dict\\n idict = write_qa_exposure('foo', qaexp, ret_dict=True)\\n odict = combine_dicts(odict, idict)\\n # Finish\\n self.data = odict\",\n \"def parameters(self):\\n return {\\\"W\\\": self.W,\\n \\\"T\\\": self.T,\\n \\\"P\\\": self.P}\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rdy,Rqcd\\\")\",\n \"def getDefaultParameterValues(self):\\r\\n dct = {}\\r\\n self.initializeRoadRunnerModel()\\r\\n self.roadrunnerModel.reset()\\r\\n for parameterName in self.parametersToFit:\\r\\n dct[parameterName] = self.roadrunnerModel.model[parameterName]\\r\\n return dct\",\n \"def get_resulting_hypo_params(self, injkey):\\n h0_params = self.fid_values[injkey][\\n 'h0_fit_to_%s'%(self.labels.dict['data'])]['params']\\n h1_params = self.fid_values[injkey][\\n 'h1_fit_to_%s'%(self.labels.dict['data'])]['params']\\n return h0_params, h1_params\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"Dilu_ratio[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_mumu_ss[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_ee_ss[1.0,0.0,10.0]\\\");\\n \\n self.modelBuilder.factory_('expr::Rpl(\\\"0.5*(1.+@0*@1)\\\",Afb, Dilu_ratio)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"0.5*(1.-@0*@1)\\\",Afb, Dilu_ratio)')\",\n \"def doParametersOfInterest(self):\\r\\n if self.fg4fixed:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4[0]\\\")\\r\\n self.modelBuilder.doVar(\\\"r[1,0,4]\\\")\\r\\n print \\\"Fixing CMS_zz4l_fg4\\\"\\r\\n poi = \\\"r\\\"\\r\\n else:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg4\\\"):\\r\\n print \\\"have fg4 inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4[0.,-1,1]\\\")\\r\\n poi = \\\"CMS_zz4l_fg4\\\"\\r\\n if self.cPOI:\\r\\n if self.modelBuilder.out.var(\\\"cww_zz\\\"):\\r\\n print \\\"have czz_ww inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"cww_zz[0.5,-10,10]\\\")\\r\\n poi += \\\",cww_zz\\\"\\r\\n\\r\\n if self.fg2POI:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg2\\\"):\\r\\n print \\\"have fg2 inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg2[0.,0,1]\\\")\\r\\n poi += \\\",CMS_zz4l_fg2\\\"\\r\\n if self.muFloating:\\r\\n self.modelBuilder.doVar(\\\"r[1,0,2000]\\\")\\r\\n if self.muAsPOI:\\r\\n print \\\"Treating r as a POI\\\"\\r\\n poi += \\\",r\\\"\\r\\n else:\\r\\n self.modelBuilder.out.var(\\\"r\\\").setAttribute(\\\"flatParam\\\")\\r\\n if self.phiFloating:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg4phi\\\"):\\r\\n print \\\"have fg4phi inside\\\"\\r\\n else: \\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4phi[0.,-3.1415926,3.1415926]\\\")\\r\\n if self.phiPOI:\\r\\n poi += \\\",CMS_zz4l_fg4phi\\\"\\r\\n else:\\r\\n self.modelBuilder.out.var(\\\"CMS_zz4l_fg4phi\\\").setAttribute(\\\"flatParam\\\")\\r\\n if self.phi2Floating:\\r\\n #self.modelBuilder.doVar(\\\"CMS_zz4l_fg4phi[0.,-math.pi,math.pi]\\\")\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg2phi\\\"):\\r\\n print \\\"have fg2phi inside\\\"\\r\\n else: \\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg2phi[0.,-3.1415926,3.1415926]\\\")\\r\\n self.modelBuilder.out.var(\\\"CMS_zz4l_fg2phi\\\").setAttribute(\\\"flatParam\\\")\\r\\n \\r\\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def wypisz_info(self):\\n print(f\\\"Samochód: {self.producent} {self.model}\\\")\",\n \"def get_prob_params():\\n prob = Namespace()\\n prob.study_name = STUDY_NAME\\n if IS_DEBUG:\\n prob.num_trials = 3\\n prob.max_capital = 10\\n else:\\n prob.num_trials = NUM_TRIALS\\n prob.max_capital = MAX_CAPITAL\\n # Common\\n prob.time_distro = TIME_DISTRO\\n prob.num_workers = NUM_WORKERS\\n _study_params = {\\n 'branin': ('synthetic/branin/config_mf.json',\\n branin_mf, cost_branin_mf, 0.1, 0, 1),\\n 'hartmann3_2': ('synthetic/hartmann3_2/config_mf.json',\\n hartmann3_2_mf, cost_hartmann3_2_mf, 0.1, 0, 1),\\n 'hartmann6_4': ('synthetic/hartmann6_4/config_mf.json',\\n hartmann6_4_mf, cost_hartmann6_4_mf, 0.1, 0, 1),\\n 'borehole_6': ('synthetic/borehole_6/config_mf.json',\\n borehole_6_mf, cost_borehole_6_mf, 1, 0, 1),\\n 'park2_4': ('synthetic/park2_4/config_mf.json',\\n park2_4_mf, cost_park2_4_mf, 0.3, 0, 1),\\n 'park2_3': ('synthetic/park2_3/config_mf.json',\\n park2_3_mf, cost_park2_3_mf, 0.1, 0, 1),\\n 'park1_3': ('synthetic/park1_3/config_mf.json',\\n park1_3_mf, cost_park1_3_mf, 0.5, 0, 1),\\n }\\n (domain_config_file_suffix, raw_func, raw_fidel_cost_func, _fc_noise_scale,\\n _initial_pool_size, _) = _study_params[prob.study_name]\\n domain_config_file = os.path.join(DRAGONFLY_EXPERIMENTS_DIR, domain_config_file_suffix)\\n # noisy\\n prob.noisy_evals = NOISY_EVALS\\n if NOISY_EVALS:\\n noise_type = 'gauss'\\n noise_scale = _fc_noise_scale\\n else:\\n noise_type = 'no_noise'\\n noise_scale = None\\n # Create domain, function_caller and worker_manager\\n config = load_config_file(domain_config_file)\\n func_caller = get_multifunction_caller_from_config(raw_func, config,\\n raw_fidel_cost_func=raw_fidel_cost_func, noise_type=noise_type,\\n noise_scale=noise_scale)\\n # Set max_capital\\n if hasattr(func_caller, 'fidel_cost_func'):\\n prob.max_capital = prob.max_capital * \\\\\\n func_caller.fidel_cost_func(func_caller.fidel_to_opt)\\n else:\\n prob.max_capital = prob.max_capital\\n # Store everything in prob\\n prob.func_caller = func_caller\\n prob.worker_manager = SyntheticWorkerManager(prob.num_workers,\\n time_distro='caller_eval_cost')\\n prob.save_file_prefix = prob.study_name + ('-debug' if IS_DEBUG else '')\\n prob.methods = METHODS\\n prob.save_results_dir = SAVE_RESULTS_DIR\\n prob.reporter = get_reporter('default')\\n # evaluation options\\n prob.evaluation_options = Namespace(prev_eval_points='none',\\n initial_pool_size=_initial_pool_size)\\n return prob\",\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 default_params():\\n params = {}\\n params['dataset'] = 'adult'\\n params['engines'] = ['MD','RDA']\\n params['iters'] = 10000\\n params['epsilon'] = 1.0\\n params['delta'] = 0.0\\n params['bounded'] = True\\n params['frequency'] = 1\\n params['seed'] = 0\\n params['save'] = None\\n params['load'] = None\\n params['plot'] = None\\n\\n return params\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def getOutputParams(self):\\n out = {}\\n out[\\\"g_C\\\"] = self.ureg.Quantity(self.g_C, self.gBase).to(self.gOutput)\\n out[\\\"gamma_C\\\"] = self.ureg.Quantity(self.gamma_C,\\n self.gammaBase).to(self.gammaOutput)\\n out[\\\"m\\\"] = self.ureg.Quantity(self.m, self.mBase).to(self.mOutput)\\n out[\\\"charL\\\"] = self.ureg.Quantity(self.charL,\\n self.charLBase).to(self.charLOutput)\\n out[\\\"charT\\\"] = self.ureg.Quantity(self.charT,\\n self.charTBase).to(self.charTOutput)\\n out[\\\"g_R\\\"] = self.ureg.Quantity(self.g_R, self.gBase).to(self.gOutput)\\n out[\\\"R\\\"] = self.ureg.Quantity(self.R, self.rBase).to(self.rOutput)\\n out[\\\"gamma_R\\\"] = self.ureg.Quantity(self.gamma_R,\\n self.gammaBase).to(self.gammaOutput)\\n if self.singleComp:\\n out[\\\"gamma_nl\\\"] = self.ureg.Quantity(self.gamma_nl,\\n self.gammaNlBase).\\\\\\n to(self.gammaNlOutput)\\n return out\",\n \"def get_model_and_assets():\\n\\n return common.read_model('cloth_corner.xml'),common.ASSETS\",\n \"def load_demographic_data(self):\\n\\n # load household size distribution and age distribution\\n self.hh_comp = load_probs(os.path.join(self.params['resource_prefix'],\\n self.params['hh_composition']), False)\\n self.params['age_cutoffs'] = [int(x) for x in self.hh_comp[0][1:][0]] # yuk!\\n self.params['adult_age'] = int(float(self.params['adult_age']))\\n self.age_dist = load_probs(os.path.join(self.params['resource_prefix'],\\n self.params['age_distribution']))\\n\\n annual_factor = self.params['t_dur']/365.0\\n\\n # load and scale MORTALITY rates\\n self.death_rates = {}\\n self.death_rates[0] = self.parse_age_rates(os.path.join(\\n self.params['resource_prefix'],\\n self.params['death_rates_m']), annual_factor, True)\\n self.death_rates[1] = self.parse_age_rates(os.path.join(\\n self.params['resource_prefix'],\\n self.params['death_rates_f']), annual_factor, True)\\n\\n ### load FERTILITY age probs (don't require scaling) for closed pops\\n self.fertility_age_probs = load_prob_tables(os.path.join(\\n self.params['resource_prefix'],\\n self.params['fertility_age_probs']))\\n self.fertility_parity_probs = None\\n #load_probs_new(os.path.join(\\n # self.params['resource_prefix'],\\n # self.params['fertility_parity_probs']))\\n self.fertility_age_rates = self.parse_age_rates(os.path.join(\\n self.params['resource_prefix'],\\n self.params['fertility_age_rates']), annual_factor, False)\\n self.new_marriage_years = float(self.params['new_marriage_years'])\\n self.new_marriage_fertility = float(self.params['marriage_based_fertility'])\\n self.single_mother_fertility = float(self.params['single_mother_fertility'])\\n self.params['partner_age_diff'] = float(self.params['partner_age_diff'])\\n self.params['partner_age_sd'] = float(self.params['partner_age_sd'])\\n\\n ### load and scale leave/couple/divorce and growth rates\\n if self.params['dyn_rates']:\\n # rates will be a list of annual values\\n self.params['leaving_probs'] = load_prob_list(os.path.join(\\n self.params['resource_prefix'], self.params['leaving_prob_file']))\\n self.params['couple_probs'] = load_prob_list(os.path.join(\\n self.params['resource_prefix'], self.params['couple_prob_file']))\\n self.params['divorce_probs'] = load_prob_list(os.path.join(\\n self.params['resource_prefix'], self.params['divorce_prob_file']))\\n self.params['growth_rates'] = load_prob_list(os.path.join(\\n self.params['resource_prefix'], self.params['growth_rate_file']))\\n self.params['imm_rates'] = load_prob_list(os.path.join(\\n self.params['resource_prefix'], self.params['imm_rate_file']))\\n\\n self.params_adj['leaving_probs'] = [adjust_prob(x, self.params['t_dur'])\\n for x in self.params['leaving_probs']]\\n self.params_adj['couple_probs'] = [adjust_prob(x, self.params['t_dur'])\\n for x in self.params['couple_probs']]\\n self.params_adj['divorce_probs'] = [adjust_prob(x, self.params['t_dur'])\\n for x in self.params['divorce_probs']]\\n self.params_adj['growth_rates'] = [adjust_prob(x, self.params['t_dur'])\\n for x in self.params['growth_rates']]\\n self.params_adj['imm_rates'] = [adjust_prob(x, self.params['t_dur'])\\n for x in self.params['imm_rates']]\\n\\n self.dyn_years = min(len(self.death_rates[0][0])-1, len(self.fertility_age_probs)-1,\\n len(self.params_adj['leaving_probs'])-1, len(self.params_adj['couple_probs'])-1,\\n len(self.params_adj['divorce_probs'])-1, len(self.params_adj['growth_rates'])-1)\\n\\n else:\\n # adjust demographic event probabilities according to time step\\n self.params_adj['couple_probs'] = [adjust_prob(\\n self.params['couple_prob'], self.params['t_dur'])]\\n self.params_adj['leaving_probs'] = [adjust_prob(\\n self.params['leaving_prob'], self.params['t_dur'])]\\n self.params_adj['divorce_probs'] = [adjust_prob(\\n self.params['divorce_prob'], self.params['t_dur'])]\\n self.params_adj['growth_rates'] = [adjust_prob(\\n self.params['growth_rate'], self.params['t_dur'])]\\n self.params_adj['imm_rates'] = [adjust_prob(\\n self.params['imm_rate'], self.params['t_dur'])]\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_guides(data):\\n return data.groups[\\\"Frames\\\"].objects\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def whatareks():\\n\\n print(\\\"loading data\\\")\\n dataset = datagen.DataSet(datapath)\\n dataset.load()\\n\\n for k, ens in enumerate(dataset.ensembles):\\n\\n print(\\n f\\\"k: {k} --> b={ens.b}, N={ens.grid_shape[0]}, iternum={ens.iternum}\\\")\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 1e-7\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def _get_gedi1b_main_data_dict(self) -> dict:\\n data = {\\n # General identifiable data\\n \\\"granule_name\\\": [self.parent_granule.filename] * self.n_shots,\\n \\\"shot_number\\\": self[\\\"shot_number\\\"][:],\\n \\\"beam_type\\\": [self.beam_type] * self.n_shots,\\n \\\"beam_name\\\": [self.name] * self.n_shots,\\n # Temporal data\\n \\\"delta_time\\\": self[\\\"delta_time\\\"][:],\\n # Quality data\\n \\\"degrade\\\": self[\\\"geolocation/degrade\\\"][:],\\n \\\"stale_return_flag\\\": self[\\\"stale_return_flag\\\"][:],\\n \\\"solar_elevation\\\": self[\\\"geolocation/solar_elevation\\\"][:],\\n \\\"solar_azimuth\\\": self[\\\"geolocation/solar_elevation\\\"][:],\\n \\\"rx_energy\\\": self[\\\"rx_energy\\\"][:],\\n # DEM\\n \\\"dem_tandemx\\\": self[\\\"geolocation/digital_elevation_model\\\"][:],\\n \\\"dem_srtm\\\": self[\\\"geolocation/digital_elevation_model_srtm\\\"][:],\\n # geolocation bin0\\n \\\"latitude_bin0\\\": self[\\\"geolocation/latitude_bin0\\\"][:],\\n \\\"latitude_bin0_error\\\": self[\\\"geolocation/latitude_bin0_error\\\"][:],\\n \\\"longitude_bin0\\\": self[\\\"geolocation/longitude_bin0\\\"][:],\\n \\\"longitude_bin0_error\\\": self[\\\"geolocation/longitude_bin0_error\\\"][:],\\n \\\"elevation_bin0\\\": self[\\\"geolocation/elevation_bin0\\\"][:],\\n \\\"elevation_bin0_error\\\": self[\\\"geolocation/elevation_bin0_error\\\"][:],\\n # geolocation lastbin\\n \\\"latitude_lastbin\\\": self[\\\"geolocation/latitude_lastbin\\\"][:],\\n \\\"latitude_lastbin_error\\\": self[\\\"geolocation/latitude_lastbin_error\\\"][:],\\n \\\"longitude_lastbin\\\": self[\\\"geolocation/longitude_lastbin\\\"][:],\\n \\\"longitude_lastbin_error\\\": self[\\\"geolocation/longitude_lastbin_error\\\"][:],\\n \\\"elevation_lastbin\\\": self[\\\"geolocation/elevation_lastbin\\\"][:],\\n \\\"elevation_lastbin_error\\\": self[\\\"geolocation/elevation_lastbin_error\\\"][:],\\n # relative waveform position info in beam and ssub-granule\\n \\\"waveform_start\\\": self[\\\"rx_sample_start_index\\\"][:] - 1,\\n \\\"waveform_count\\\": self[\\\"rx_sample_count\\\"][:],\\n }\\n return data\",\n \"def get_configspace(self):\\n cd = self.cd\\n sp_dict = {}\\n sp_dict['epochs'] = int(cd['epochs'])\\n sp_dict['gamma'] = self._get_range_uniform('gamma', cd)\\n sp_dict['multilabel'] = self._get_atomic('multilabel', cd)\\n sp_dict['lr'] = self._get_range_uniform('lr', cd)\\n sp_dict['optimizer'] = self._get_categorical('optimizer', cd)\\n sp_dict['n_latent'] = self._get_range_integer('n_latent',cd)\\n sp_dict['enc_hidden_dim'] = self._get_range_integer('enc_hidden_dim', cd)\\n sp_dict['batch_size'] = self._get_range_integer('batch_size', cd)\\n sp_dict['coherence_loss_wt'] = self._get_range_uniform('coherence_loss_wt', cd) or 0.0\\n sp_dict['redundancy_loss_wt'] = self._get_range_uniform('redundancy_loss_wt', cd) or 0.0\\n sp_dict['num_enc_layers'] = self._get_range_integer('num_enc_layers', cd) or 1\\n sp_dict['enc_dr'] = self._get_range_uniform('enc_dr', cd) or 0.0\\n sp_dict['covar_net_layers'] = self._get_range_integer('covar_net_layers', cd) or 1\\n sp_dict['classifier_dropout'] = self._get_range_uniform('classifier_dropout', cd) or 0.1\\n\\n embedding_types = cd['embedding']\\n embedding_space = [] \\n for et in embedding_types:\\n if et['source'] == 'random':\\n embedding_space.append(ag.space.Dict(**{'source': 'random', 'size': self._get_range_integer('size', et)}))\\n else:\\n fixed_assigned = et.get('fixed')\\n if fixed_assigned is None:\\n embedding_space.append(ag.space.Dict(**{'source': et['source'], 'fixed': ag.space.Bool()}))\\n else:\\n embedding_space.append(ag.space.Dict(**{'source': et['source'], 'fixed': fixed_assigned.lower()}))\\n sp_dict['embedding'] = ag.space.Categorical(*embedding_space)\\n\\n latent_types = cd['latent_distribution']\\n latent_space = []\\n for lt in latent_types:\\n dist_type = lt['dist_type']\\n if dist_type == 'vmf':\\n latent_space.append(ag.space.Dict(**{'dist_type': 'vmf', 'kappa': self._get_range_uniform('kappa', lt)}))\\n elif dist_type == 'logistic_gaussian':\\n latent_space.append(ag.space.Dict(**{'dist_type': 'logistic_gaussian', 'alpha': self._get_range_uniform('alpha', lt)}))\\n else:\\n latent_space.append(ag.space.Dict(**{'dist_type': 'gaussian'}))\\n sp_dict['latent_distribution'] = ag.space.Categorical(*latent_space)\\n return sp_dict\",\n \"def _describe(self) -> Dict[str, Any]:\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 0.3\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_params(self):\\n outputs = ['sample',\\n 'ratio_params',\\n 'despike_params',\\n 'autorange_params',\\n 'bkgcorrect_params']\\n\\n out = {}\\n for o in outputs:\\n out[o] = getattr(self, o)\\n\\n out['filter_params'] = self.filt.params\\n out['filter_sequence'] = self.filt.sequence\\n out['filter_used'] = self.filt.make_keydict()\\n\\n return out\",\n \"def to_dict(self):\\n result_dict = {}\\n result_dict['evaluations'] = self.evaluations\\n result_dict['dof'] = self.dof\\n result_dict['variances'] = self.variances\\n result_dict['noise_ceiling'] = self.noise_ceiling\\n result_dict['method'] = self.method\\n result_dict['cv_method'] = self.cv_method\\n result_dict['models'] = {}\\n for i_model in range(len(self.models)):\\n key = 'model_%d' % i_model\\n result_dict['models'][key] = self.models[i_model].to_dict()\\n return result_dict\",\n \"def df_2_dict(df,band_list,lens_model_list,source_model_list,lens_light_model_list):\\n \\n import re\\n from lenstronomy.Util.param_util import ellipticity2phi_q\\n from lenstronomy.Util.param_util import shear_cartesian2polar\\n \\n import pandas as pd\\n \\n\\n model_kwarg_names = get_kwarg_names(lens_model_list,source_model_list,\\n lens_light_model_list,None)\\n \\n IDs = df.loc[:,'ID']\\n chi_sq = df.loc[:,'reduced chi^2']\\n \\n lens_dict = {}\\n \\n for i,prof in enumerate(lens_model_list):\\n lens_dict[prof] = {}\\n for param in model_kwarg_names['kwargs_lens'][i]:\\n col = df.loc[:,'{}_lens.{}'.format(prof,param)]\\n col_array = col.values\\n lens_dict[prof][param] = col_array\\n \\n if 'e1' in model_kwarg_names['kwargs_lens'][i]:\\n lens_dict[prof]['q'] = np.array([])\\n lens_dict[prof]['phi'] = np.array([]) \\n for j in range(len(lens_dict[prof]['e1'])):\\n phi,q = ellipticity2phi_q(lens_dict[prof]['e1'][j],lens_dict[prof]['e2'][j])\\n lens_dict[prof]['q'] = np.append(lens_dict[prof]['q'],q)\\n lens_dict[prof]['phi'] = np.append(lens_dict[prof]['phi'],phi)\\n elif 'gamma1' in model_kwarg_names['kwargs_lens'][i]:\\n lens_dict[prof]['gamma'] = np.array([])\\n lens_dict[prof]['theta'] = np.array([])\\n for j in range(len(lens_dict[prof]['gamma1'])):\\n theta,gamma = shear_cartesian2polar(lens_dict[prof]['gamma1'][j],lens_dict[prof]['gamma2'][j])\\n lens_dict[prof]['gamma'] = np.append(lens_dict[prof]['gamma'],gamma)\\n lens_dict[prof]['theta'] = np.append(lens_dict[prof]['theta'],theta)\\n \\n \\n source_dict = {}\\n lens_light_dict = {}\\n \\n for i,band in enumerate(band_list):\\n for j,prof in enumerate(source_model_list):\\n key = '{} Band: {}'.format(band,prof)\\n source_dict[key] = {}\\n for param in model_kwarg_names['kwargs_source'][j]:\\n col = df.loc[:,'{} Band: {}_source.{}'.format(band,prof,param)]\\n col_array = col.values\\n source_dict[key][param] = col_array\\n \\n if 'e1' in model_kwarg_names['kwargs_source'][j]:\\n source_dict[key]['q'] = np.array([])\\n source_dict[key]['phi'] = np.array([]) \\n for k in range(len(source_dict[key]['e1'])):\\n phi,q = ellipticity2phi_q(source_dict[key]['e1'][k],source_dict[key]['e2'][k])\\n source_dict[key]['q'] = np.append(source_dict[key]['q'],q)\\n source_dict[key]['phi'] = np.append(source_dict[key]['phi'],phi)\\n \\n for j,prof in enumerate(lens_light_model_list):\\n key = '{} Band: {}'.format(band,prof)\\n lens_light_dict[key] = {}\\n for param in model_kwarg_names['kwargs_lens_light'][j]:\\n col = df.loc[:,'{} Band: {}_lens_light.{}'.format(band,prof,param)]\\n col_array = col.values\\n lens_light_dict[key][param] = col_array\\n\\n if 'e1' in model_kwarg_names['kwargs_lens_light'][j]:\\n lens_light_dict[key]['q'] = np.array([])\\n lens_light_dict[key]['phi'] = np.array([]) \\n for k in range(len(lens_light_dict[key]['e1'])):\\n phi,q = ellipticity2phi_q(lens_light_dict[key]['e1'][k],lens_light_dict[key]['e2'][k])\\n lens_light_dict[key]['q'] = np.append(lens_light_dict[key]['q'],q)\\n lens_light_dict[key]['phi'] = np.append(lens_light_dict[key]['phi'],phi)\\n \\n params_dict = {'Object IDs': IDs.values,'Reduced Chi^2': chi_sq.values,\\n 'lens': lens_dict, 'source': source_dict, 'lens_light': lens_light_dict}\\n \\n return params_dict\",\n \"def setup(self):\\n header_print(self.data['intro'])\\n header_print(self.data['help'])\\n random.shuffle(self.data['draw'])\\n random.shuffle(self.data['locations'])\\n random.shuffle(self.data['events'])\\n random.shuffle(self.data['aces'])\\n random.shuffle(self.data['personalities'])\\n self.stats = {\\n 'round': 0,\\n 'powers': {\\n 'MOONS': 6,\\n 'SUNS': 6,\\n 'WAVES': 6,\\n 'LEAVES': 6,\\n 'WYRMS': 6,\\n 'KNOTS': 6,\\n },\\n 'hand': self.data['draw'][:],\\n 'discard': [],\\n 'active': [],\\n 'opponent': {},\\n }\",\n \"def build_parameters(pobj):\\n ViscosityWilke.build_parameters(pobj)\",\n \"def info_view(self):\\n view_dict = dict(\\n env_class=self.env_class,\\n model_structure=None,\\n model_kwargs={key: value for key, value in self.model_kwargs.items() \\n if isinstance(value,(str,int,float,tuple,list,dict)) \\n and len(str(value))<100},\\n model_class=self.model_class,\\n hyperparams=self.hyperparams)\\n return view_dict\",\n \"def create_data_dict(model, chardict):\\n \\n # Less than 25 isn't part of original paper, but word2vec has some outrageous entries\\n def include_word(word, chardict):\\n \\\"\\\"\\\"\\n Function to determine if word can be included and perform any parsing\\n \\\"\\\"\\\"\\n if (all(char in chardict.keys() for char in word)) & (len(word)<=25):\\n # Some word2vec entries are all capitals and generally are acronyms.\\n # This is unlikely to be learnable\\n if not word.isupper():\\n return True\\n\\n return False\\n\\n\\n # Create list of words which will be used in training/testing our model\\n all_words = dict({})\\n\\n # For every word in word2vec model establish if it is \\\"allowed\\\"; if it is\\n # add the word to our all_words dict, with the embedding as the value\\n for idx, key in enumerate(model.wv.vocab):\\n if include_word(key, chardict):\\n all_words[key] = model.wv[key]\\n else:\\n pass\\n \\n return all_words\",\n \"def _get_model(self):\\n\\n parameters = {keys._topology:self.topology,\\n keys._size:self.size,\\n keys._name:self.name,\\n #keys._output_activation:self._outActiv_fun_key,\\n #keys._hidden_activation:self._hiddenActiv_fun_key,\\n keys._learning_rate:self.learningRate,\\n keys._momentum:self.momentum}\\n\\n return parameters\",\n \"def get_params(self):\\n return {\\n \\\"nspecies\\\": self.nspecies,\\n \\\"lmax\\\": self.lmax,\\n \\\"nmax\\\": self.nmax,\\n \\\"rcut\\\": self.rcut,\\n \\\"sigma\\\": self.sigma,\\n \\\"trans_width\\\": self.trans_width\\n }\",\n \"def get_model_config(model_name, args):\\n if model_name == 'WaveGlow':\\n model_config = dict(\\n n_mel_channels=args.n_mel_channels,\\n n_flows=args.flows,\\n n_group=args.groups,\\n n_early_every=args.early_every,\\n n_early_size=args.early_size,\\n WN_config=dict(\\n n_layers=args.wn_layers,\\n kernel_size=args.wn_kernel_size,\\n n_channels=args.wn_channels\\n )\\n )\\n return model_config\\n elif model_name == 'FastPitch':\\n model_config = dict(\\n # io\\n n_mel_channels=args.n_mel_channels,\\n # symbols\\n n_symbols=len(get_symbols(args.symbol_set)),\\n padding_idx=get_pad_idx(args.symbol_set),\\n symbols_embedding_dim=args.symbols_embedding_dim,\\n # input FFT\\n in_fft_n_layers=args.in_fft_n_layers,\\n in_fft_n_heads=args.in_fft_n_heads,\\n in_fft_d_head=args.in_fft_d_head,\\n in_fft_conv1d_kernel_size=args.in_fft_conv1d_kernel_size,\\n in_fft_conv1d_filter_size=args.in_fft_conv1d_filter_size,\\n in_fft_output_size=args.in_fft_output_size,\\n p_in_fft_dropout=args.p_in_fft_dropout,\\n p_in_fft_dropatt=args.p_in_fft_dropatt,\\n p_in_fft_dropemb=args.p_in_fft_dropemb,\\n # output FFT\\n out_fft_n_layers=args.out_fft_n_layers,\\n out_fft_n_heads=args.out_fft_n_heads,\\n out_fft_d_head=args.out_fft_d_head,\\n out_fft_conv1d_kernel_size=args.out_fft_conv1d_kernel_size,\\n out_fft_conv1d_filter_size=args.out_fft_conv1d_filter_size,\\n out_fft_output_size=args.out_fft_output_size,\\n p_out_fft_dropout=args.p_out_fft_dropout,\\n p_out_fft_dropatt=args.p_out_fft_dropatt,\\n p_out_fft_dropemb=args.p_out_fft_dropemb,\\n # duration predictor\\n dur_predictor_kernel_size=args.dur_predictor_kernel_size,\\n dur_predictor_filter_size=args.dur_predictor_filter_size,\\n p_dur_predictor_dropout=args.p_dur_predictor_dropout,\\n dur_predictor_n_layers=args.dur_predictor_n_layers,\\n # pitch predictor\\n pitch_predictor_kernel_size=args.pitch_predictor_kernel_size,\\n pitch_predictor_filter_size=args.pitch_predictor_filter_size,\\n p_pitch_predictor_dropout=args.p_pitch_predictor_dropout,\\n pitch_predictor_n_layers=args.pitch_predictor_n_layers,\\n # pitch conditioning\\n pitch_embedding_kernel_size=args.pitch_embedding_kernel_size,\\n # speakers parameters\\n n_speakers=args.n_speakers,\\n speaker_emb_weight=args.speaker_emb_weight,\\n # energy predictor\\n energy_predictor_kernel_size=args.energy_predictor_kernel_size,\\n energy_predictor_filter_size=args.energy_predictor_filter_size,\\n p_energy_predictor_dropout=args.p_energy_predictor_dropout,\\n energy_predictor_n_layers=args.energy_predictor_n_layers,\\n # energy conditioning\\n energy_conditioning=args.energy_conditioning,\\n energy_embedding_kernel_size=args.energy_embedding_kernel_size,\\n )\\n return model_config\\n\\n else:\\n raise NotImplementedError(model_name)\",\n \"def getActualConfig(self) -> object:\\n if not self.debug:\\n ntraces = int(self.getNumberOfTraces())\\n traces = []\\n for i in range(1,ntraces+1):\\n self.selectTrace(i)\\n data = self.getData()\\n if i == 1:\\n title = \\\"S11\\\"\\n elif i == 2:\\n title = \\\"S21\\\"\\n elif i == 3:\\n title = \\\"S12\\\"\\n else:\\n title = \\\"S22\\\"\\n trace={\\n 'number': i,\\n 'xMin': self.getStartFrequency(),\\n 'xMax': self.getStopFrequency(),\\n 'yMin': self.getmindbm(i),#min([x['y'] for x in data]), #getmindbm(),\\n 'yMax': self.getmaxdbm(i),#max([x['y'] for x in data]), #getmaxdbm(),\\n 'xScale': \\\"linear\\\",#self.getxscale()\\n 'yScale': \\\"linear\\\",#self.getyscale(),\\n 'type': \\\"bode\\\",#self.getTypeFormat(),\\n 'title': title,#self.getTraceTitle(i),\\n 'xLabel': \\\"Freq [Hz]\\\",#getxLabel(),\\n 'yLabel': \\\"dBm\\\", #getyLabel()\\n 'data': data,\\n 'yPDiv': self.getYPDiv(i)\\n }\\n traces.append(trace) \\n ret = {\\n 'traces': traces, \\n 'sweepResolution': self.getSweepResolution(),\\n 'IFBW': self.getIFBW() \\n }\\n else:\\n trace1 = {\\n 'number': 1,\\n 'xMin': 100,\\n 'xMax': 1000,\\n 'yMin': 100,\\n 'yMax': 1000,\\n 'xScale': 'logarithmic',\\n 'yScale': 'logarithmic',\\n 'type': 'bode',\\n 'title': 'S11',\\n 'xLabel': 'Freq',\\n 'yLabel': 'dBm',\\n 'yPDiv': 10,\\n 'data': [\\n {'x': 100,'y': 100},\\n {'x': 200,'y': 150},\\n {'x': 500,'y': 300},\\n {'x': 1000,'y': 800}\\n ]\\n }\\n trace2 = {\\n 'number': 2,\\n 'xMin': 1,\\n 'xMax': 100,\\n 'yMin': 1,\\n 'yMax': 1000,\\n 'xScale': 'linear',\\n 'yScale': 'linear',\\n 'type': 'bode',\\n 'title': 'S21',\\n 'xLabel': 'Freq',\\n 'yLabel': 'dBm',\\n 'yPDiv': 10,\\n 'data': [\\n {'x': 1,'y': 100},\\n {'x': 20,'y': 250},\\n {'x': 50,'y': 200},\\n {'x': 100,'y': 600}\\n ]\\n }\\n trace3 = {\\n 'number': 3,\\n 'xMin': 500,\\n 'xMax': 10000,\\n 'yMin': 100,\\n 'yMax': 10000,\\n 'xScale': 'linear',\\n 'yScale': 'logarithmic',\\n 'type': 'bode',\\n 'title': 'S12',\\n 'xLabel': 'Freq',\\n 'yLabel': 'dBm',\\n 'yPDiv': 10,\\n 'data': [\\n {'x': 500,'y': 100},\\n {'x': 2000,'y': 1000},\\n {'x': 5000,'y': 3000},\\n {'x': 10000,'y': 8000}\\n ]\\n }\\n trace4 = {\\n 'number': 4,\\n 'xMin': 100,\\n 'xMax': 10000,\\n 'yMin': 500,\\n 'yMax': 10000,\\n 'xScale': 'logarithmic',\\n 'yScale': 'linear',\\n 'type': 'bode',\\n 'title': 'S22',\\n 'xLabel': 'Freq',\\n 'yLabel': 'dBm',\\n 'yPDiv': 10,\\n 'data': [\\n {'x': 100,'y': 500},\\n {'x': 2000,'y': 5000},\\n {'x': 5000,'y': 2000},\\n {'x': 10000,'y': 4000}\\n ]\\n }\\n ret = {\\n 'traces': [ trace1, trace2, trace3, trace4 ], \\n 'sweepResolution': 401,\\n 'IFBW': 10000 \\n }\\n return ret\",\n \"def get_variables():\\n policer_data = {\\n \\\"policer_data\\\": {\\n \\\"name\\\": \\\"policy1\\\",\\n \\\"cir\\\": 450,\\n \\\"cb\\\": 50000,\\n \\\"rate-type\\\": \\\"kbps\\\",\\n \\\"round-type\\\": \\\"closest\\\",\\n \\\"type\\\": \\\"1r2c\\\",\\n \\\"conform-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-transmit\\\"\\n },\\n \\\"exceed-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-drop\\\"\\n }\\n },\\n \\\"policer_data_oper\\\": {\\n \\\"name\\\": \\\"policy1\\\",\\n \\\"cir\\\": 450,\\n \\\"cb\\\": 50000,\\n \\\"rate-type\\\": \\\"kbps\\\",\\n \\\"round-type\\\": \\\"closest\\\",\\n \\\"type\\\": \\\"1r2c\\\",\\n \\\"conform-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-transmit\\\"\\n },\\n \\\"exceed-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-drop\\\"\\n }\\n },\\n \\\"policer_data_2\\\": {\\n \\\"name\\\": \\\"policy1\\\",\\n \\\"cir\\\": 900,\\n \\\"cb\\\": 50000,\\n \\\"rate-type\\\": \\\"kbps\\\",\\n \\\"round-type\\\": \\\"closest\\\",\\n \\\"type\\\": \\\"1r2c\\\",\\n \\\"conform-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-transmit\\\"\\n },\\n \\\"exceed-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-drop\\\"\\n }\\n },\\n \\\"policer_data_oper_2\\\": {\\n \\\"name\\\": \\\"policy1\\\",\\n \\\"cir\\\": 900,\\n \\\"cb\\\": 50000,\\n \\\"rate-type\\\": \\\"kbps\\\",\\n \\\"round-type\\\": \\\"closest\\\",\\n \\\"type\\\": \\\"1r2c\\\",\\n \\\"conform-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-transmit\\\"\\n },\\n \\\"exceed-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-drop\\\"\\n }\\n },\\n \\\"policer_data_3\\\": {\\n \\\"name\\\": \\\"policy1\\\",\\n \\\"cir\\\": 100,\\n \\\"eir\\\": 150,\\n \\\"cb\\\": 200,\\n \\\"eb\\\": 300,\\n \\\"rate-type\\\": \\\"pps\\\",\\n \\\"round-type\\\": \\\"closest\\\",\\n \\\"type\\\": \\\"2r3c-2698\\\",\\n \\\"conform-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-transmit\\\"\\n },\\n \\\"exceed-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-mark-dscp\\\",\\n \\\"dscp\\\": \\\"AF22\\\"\\n },\\n \\\"violate-action\\\": {\\n \\\"meter-action-type\\\": \\\"meter-action-drop\\\"\\n },\\n \\\"color-aware\\\": True\\n },\\n \\\"policer_data_oper_3\\\": {\\n \\\"name\\\": \\\"policy1\\\",\\n \\\"cir\\\": 100,\\n \\\"eir\\\": 150,\\n \\\"cb\\\": 200,\\n \\\"eb\\\": 300,\\n \\\"rate-type\\\": \\\"pps\\\",\\n \\\"round-type\\\": \\\"closest\\\",\\n \\\"type\\\": \\\"2r3c-2698\\\",\\n \\\"conform-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-transmit\\\"\\n },\\n \\\"exceed-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-mark-dscp\\\",\\n },\\n \\\"violate-action\\\": {\\n \\\"meter-action-type\\\": \\\"policer:meter-action-drop\\\"\\n },\\n \\\"color-aware\\\": True\\n },\\n\\n \\\"acl_tables\\\": {\\n # settings for policer tables\\n \\\"hc_acl_table\\\": {\\n \\\"name\\\": \\\"table0\\\",\\n \\\"nbuckets\\\": 2,\\n \\\"memory_size\\\": 1048576,\\n \\\"skip_n_vectors\\\": 12,\\n \\\"miss_next\\\": \\\"permit\\\",\\n \\\"mask\\\": \\\"00:00:00:00:00:00:00:00:00:00:00:00:ff:ff:ff:ff\\\"\\n },\\n # setting for acl sessions\\n \\\"hc_acl_session\\\": {\\n \\\"match\\\": \\\"00:00:00:00:00:00:00:00:00:00:00:00:C0:A8:7A:01\\\",\\n \\\"policer_hit_next\\\": \\\"policy1\\\",\\n \\\"color_classfier\\\": \\\"exceed-color\\\",\\n },\\n \\\"hc_acl_session2\\\": {\\n \\\"match\\\": \\\"00:00:00:00:00:00:00:00:00:00:00:00:C0:A8:7A:02\\\",\\n \\\"policer_hit_next\\\": \\\"policy1\\\",\\n \\\"color_classfier\\\": \\\"exceed-color\\\",\\n },\\n },\\n }\\n return policer_data\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['low' ] = self.low\\n paramDict['high' ] = self.high\\n paramDict['alpha'] = self.alpha\\n paramDict['beta' ] = self.beta\\n return paramDict\",\n \"def get_design(model):\\n parameters = ['v', 'gamma', 's', 'tau', 't0']\\n\\n subject_idx = model.data['subject']\\n subjects = subject_idx.unique()\\n\\n design = dict()\\n design['factors'] = list({value for key, value in model.depends_on.items()\\n if value is not None})\\n design['factor_conditions'] = {factor: model.data[factor].unique()\\n for factor in design['factors']}\\n\\n for parameter in parameters:\\n design[parameter] = dict()\\n # adding an index defining which entry in data belongs to which subject\\n design[parameter]['subject_index'] = subject_idx[:].values.astype(\\n np.int)\\n\\n dependence = model.depends_on.get(parameter)\\n design[parameter]['dependence'] = dependence\\n\\n if dependence is not None:\\n # extract condition levels\\n conditions = model.data[dependence].unique()\\n design[parameter]['conditions'] = conditions\\n\\n # Initialize empty design matrix D for this parameter\\n D = np.zeros((subjects.size, conditions.size), dtype=np.int)\\n # create parameter mapping containing condition names and indices (i.e., columns of design matrix)\\n design[parameter]['condition_mapping'] = {condition: c\\n for c, condition in enumerate(conditions)}\\n # create an array to index which condition each trial-entry in the data belongs to\\n design[parameter]['condition_index'] = np.zeros_like(\\n subject_idx, dtype=np.int)\\n\\n # For each condition level\\n for c, condition in enumerate(conditions):\\n design[parameter][condition] = dict()\\n\\n # mark which trial-entries belong to this condition\\n design[parameter]['condition_index'][model.data[dependence]\\n == condition] = c\\n\\n # Subset data to condition-specific data\\n data_subset = model.data[model.data[dependence]\\n == condition].copy()\\n\\n # find all subject_IDs in this condition\\n subject_subset = data_subset['subject'].unique()\\n\\n # within this level, generate mapping between subject_ids and indices\\n # e.g., gamma_high[5] corresponds to subject_id 10\\n design[parameter][condition]['subject_mapping'] = {int(subject): s\\n for s, subject in enumerate(subject_subset)}\\n # attach a list of all subjects in this condition\\n design[parameter][condition]['subjects'] = subject_subset\\n\\n # Set cells with subject in this condition to 1\\n for s, subject in enumerate(subject_subset):\\n D[np.int(subject), c] = np.int(s+1)\\n else:\\n D = (np.arange(subjects.size)[:, None] + 1).astype(np.int)\\n design[parameter]['conditions'] = None\\n design[parameter]['condition_index'] = np.zeros_like(\\n subject_idx, dtype=np.int)\\n\\n # Save design matrix D\\n design[parameter]['D'] = D\\n\\n # Detect within / between subject design\\n if D.shape[1] == 1:\\n design_type = 'fixed'\\n elif np.all((D != 0).sum(axis=1) > 1):\\n design_type = 'within'\\n elif np.all((D != 0).sum(axis=1) == 1):\\n design_type = 'between'\\n else:\\n design_type = 'mixed'\\n design[parameter]['type'] = design_type\\n\\n return design\",\n \"def load_mat_dict():\\n param_df = pd.DataFrame(loadmat(\\n 'X:/YuxiangWang/DataAnalysis/skinMechanicsAll/analysis/' +\\n 'ViscoAnalysis052013/strain_level_2py.mat')['qlv2tFixPara'])\\n param_df.columns = ['tau1', 'tau2', 'g1', 'g2', 'ginf', 'mu', 'alpha',\\n 'r2', 'stretch', 'thickness', 'skin_id', 'ramp_time']\\n param_df['skin_id'] = param_df['skin_id'].astype('i')\\n param_df['thickness'] *= 1e3 # Change unit to microns\\n return param_df\",\n \"def get_params(self):\\n return {'k': self.k, 'q': self.q, 'sigma_s': self.sigma_s, 'm': self.m}\",\n \"def main(self):\\n\\n assault_mech_df = self.get_mech_df(url=self.assault_url)\\n heavy_mech_df = self.get_mech_df(url=self.heavy_url)\\n medium_mech_df = self.get_mech_df(url=self.medium_url)\\n light_mech_df = self.get_mech_df(url=self.light_url)\\n all_weights_df = pd.concat([assault_mech_df, heavy_mech_df, medium_mech_df, \\n light_mech_df])\\n\\n self.save_data(assault_mech_df, \\\"assault\\\")\\n self.save_data(heavy_mech_df, \\\"heavy\\\")\\n self.save_data(medium_mech_df, \\\"medium\\\")\\n self.save_data(light_mech_df, \\\"light\\\")\\n self.save_data(all_weights_df, \\\"all_weights\\\")\\n #get maximum new columns needed for splitting variants\\n max_cols = all_weights_df.variants.apply(lambda x: len(x)).max()\\n melt_cols = []\\n\\n for i in range(max_cols):\\n all_weights_df[\\\"var_\\\"+str(i)] = \\\"\\\"\\n melt_cols.append(\\\"var_\\\"+str(i))\\n\\n variant_weights_df = pd.DataFrame()\\n for index, row in all_weights_df.iterrows():\\n for i in range(len(row[\\\"variants\\\"])):\\n #add each variant to variant weights as a row with mech, tonnage, variant\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"mechs\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"variants\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\\n\\n for i in range(len(row[\\\"hero_chassis\\\"])):\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"hero_names\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"hero_chassis\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\\n\\n\\n for i in range(len(row[\\\"special_variants\\\"])):\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"mechs\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"special_variants\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df]) \\n\\n #add champion variants by matching on \\n for i in range(len(row[\\\"champion_variants\\\"])):\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"mechs\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"champion_variants\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\\n #remove duplicate rows \\n variant_weights_df = variant_weights_df[variant_weights_df.duplicated(keep=\\\"first\\\")==False]\\n self.save_data(variant_weights_df, \\\"variant_weights\\\")\",\n \"def smp_dict():\\n out = base_dict()\\n out['mro']['current'] = ['Sample']\\n out['name']['current'] = 'Sample'\\n ao(out, 'idx', 'Integer', attr=['Hidden'])\\n ao(out, 'ii', 'Integer', attr=['Hidden'])\\n ao(out, 'initialDimension', 'Float', 0., name='Initial Dimension')\\n return out\",\n \"def default_pypeit_par(cls):\\n par = super().default_pypeit_par()\\n\\n par['rdx']['detnum'] = [(1,2,3)]\\n\\n # Adjustments to parameters for Keck HIRES\\n turn_on = dict(use_biasimage=False, use_overscan=True, overscan_method='median',\\n use_darkimage=False, use_illumflat=False, use_pixelflat=False,\\n use_specillum=False)\\n par.reset_all_processimages_par(**turn_on)\\n par['calibrations']['traceframe']['process']['overscan_method'] = 'median'\\n\\n # Right now we are using the overscan and not biases becuase the\\n # standards are read with a different read mode and we don't yet have\\n # the option to use different sets of biases for different standards,\\n # or use the overscan for standards but not for science frames\\n # TODO testing\\n par['scienceframe']['process']['use_biasimage'] = False\\n par['scienceframe']['process']['use_illumflat'] = False\\n par['scienceframe']['process']['use_pixelflat'] = False\\n par['calibrations']['standardframe']['process']['use_illumflat'] = False\\n par['calibrations']['standardframe']['process']['use_pixelflat'] = False\\n # par['scienceframe']['useframe'] ='overscan'\\n\\n par['calibrations']['slitedges']['edge_thresh'] = 8.0\\n par['calibrations']['slitedges']['fit_order'] = 8\\n par['calibrations']['slitedges']['max_shift_adj'] = 0.5\\n par['calibrations']['slitedges']['trace_thresh'] = 10.\\n par['calibrations']['slitedges']['left_right_pca'] = True\\n par['calibrations']['slitedges']['length_range'] = 0.3\\n par['calibrations']['slitedges']['max_nudge'] = 0.\\n par['calibrations']['slitedges']['overlap'] = True\\n par['calibrations']['slitedges']['dlength_range'] = 0.25\\n\\n # These are the defaults\\n par['calibrations']['tilts']['tracethresh'] = 15\\n par['calibrations']['tilts']['spat_order'] = 3\\n par['calibrations']['tilts']['spec_order'] = 5 # [5, 5, 5] + 12*[7] # + [5]\\n\\n # 1D wavelength solution\\n par['calibrations']['wavelengths']['lamps'] = ['ThAr']\\n # This is for 1x1 binning. TODO GET BINNING SORTED OUT!!\\n par['calibrations']['wavelengths']['rms_threshold'] = 0.50\\n par['calibrations']['wavelengths']['sigdetect'] = 5.0\\n par['calibrations']['wavelengths']['n_final'] = 4 #[3] + 13 * [4] + [3]\\n # This is for 1x1 binning. Needs to be divided by binning for binned data!!\\n par['calibrations']['wavelengths']['fwhm'] = 8.0\\n # Reidentification parameters\\n par['calibrations']['wavelengths']['method'] = 'echelle'\\n # TODO: the arxived solution is for 1x1 binning. It needs to be\\n # generalized for different binning!\\n #par['calibrations']['wavelengths']['reid_arxiv'] = 'vlt_xshooter_vis1x1.fits'\\n par['calibrations']['wavelengths']['cc_thresh'] = 0.50\\n par['calibrations']['wavelengths']['cc_local_thresh'] = 0.50\\n# par['calibrations']['wavelengths']['ech_fix_format'] = True\\n # Echelle parameters\\n par['calibrations']['wavelengths']['echelle'] = True\\n par['calibrations']['wavelengths']['ech_nspec_coeff'] = 4\\n par['calibrations']['wavelengths']['ech_norder_coeff'] = 4\\n par['calibrations']['wavelengths']['ech_sigrej'] = 3.0\\n par['calibrations']['wavelengths']['ech_separate_2d'] = True\\n\\n # Flats\\n par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.90\\n par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.10\\n\\n # Extraction\\n par['reduce']['skysub']['bspline_spacing'] = 0.6\\n par['reduce']['skysub']['global_sky_std'] = False\\n # local sky subtraction operates on entire slit\\n par['reduce']['extraction']['model_full_slit'] = True\\n # Mask 3 edges pixels since the slit is short, insted of default (5,5)\\n par['reduce']['findobj']['find_trim_edge'] = [3, 3]\\n # Continnum order for determining thresholds\\n\\n # Sensitivity function parameters\\n par['sensfunc']['algorithm'] = 'IR'\\n par['sensfunc']['polyorder'] = 11 #[9, 11, 11, 9, 9, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7]\\n par['sensfunc']['IR']['telgridfile'] = 'TelFit_MaunaKea_3100_26100_R20000.fits'\\n return par\",\n \"def get_params(self) -> Dict:\\n params: Dict = {}\\n channel: str\\n for channel in list(self.channel.keys()):\\n if self.channel_bool[channel]:\\n params[channel] = self.channel[channel].get_g()\\n if self.tau_ca != float('inf'):\\n params['t_ca'] = self.tau_ca\\n return params\",\n \"def init_params():\\n p = {}\\n \\n # p['rootFolder'] = 'C:/Users/Umberto Gostoli/SPHSU/Social Care Model II'\\n # p['rootFolder'] = 'N:/Social Care Model Paper III'\\n \\n p['noPolicySim'] = False\\n p['multiprocessing'] = True\\n p['numberProcessors'] = 9\\n p['numRepeats'] = 3\\n \\n p['startYear'] = 1860\\n p['endYear'] = 2040\\n p['thePresent'] = 2012\\n p['statsCollectFrom'] = 1990\\n p['regressionCollectFrom'] = 1960 \\n p['implementPoliciesFromYear'] = 2020\\n p['yearOutcome'] = 2015\\n \\n p['favouriteSeed'] = 123\\n p['loadFromFile'] = False\\n p['verboseDebugging'] = False\\n p['singleRunGraphs'] = False\\n p['saveChecks'] = True\\n p['getCheckVariablesAtYear'] = 2015\\n # To change through command-line arguments\\n\\n p['numberPolicyParameters'] = 2\\n p['valuesPerParam'] = 1\\n p['numberScenarios'] = 3\\n \\n ############ Policy Parameters #######################\\n p['incomeCareParam'] = 0.0005 #[0.00025 - 0.001]\\n p['taxBreakRate'] = 0.0\\n p['ageOfRetirement'] = 65\\n p['socialSupportLevel'] = 5\\n # p['educationCosts']\\n #############################################################\\n p['socialCareCreditShare'] = 0.0\\n p['maxWtWChildAge'] = 5\\n # The basics: starting population and year, etc.\\n \\n p['discountingFactor'] = 0.03\\n \\n \\n p['initialPop'] = 600 \\n \\n p['minStartAge'] = 24\\n p['maxStartAge'] = 45\\n p['numberClasses'] = 5\\n p['socialClasses'] = ['unskilled', 'skilled', 'lower', 'middle', 'upper']\\n p['initialClassShares'] = [0.2, 0.25, 0.3, 0.2, 0.05]\\n p['initialUnemployment'] = [0.25, 0.2, 0.15, 0.1, 0.1]\\n p['unemploymentAgeBandParam'] = 0.3\\n \\n # doDeath function parameters\\n p['mortalityBias'] = 0.85 # After 1950\\n p['careNeedBias'] = 0.9\\n p['unmetCareNeedBias'] = 0.5\\n p['baseDieProb'] = 0.0001\\n p['babyDieProb'] = 0.005\\n p['maleAgeScaling'] = 14.0\\n p['maleAgeDieProb'] = 0.00021\\n p['femaleAgeScaling'] = 15.5\\n p['femaleAgeDieProb'] = 0.00019\\n \\n p['orphansRelocationParam'] = 0.5\\n \\n # doBirths function parameters\\n p['minPregnancyAge'] = 17\\n p['maxPregnancyAge'] = 42\\n p['growingPopBirthProb'] = 0.215\\n p['fertilityCorrector'] = 1.0\\n p['fertilityBias'] = 0.9\\n \\n # careTransitions function parameters\\n p['zeroYearCare'] = 80.0\\n p['childcareDecreaseRate'] = 0.25\\n p['personCareProb'] = 0.0008\\n p['maleAgeCareScaling'] = 18.0 # p['maleAgeCareProb'] = 0.0008\\n p['femaleAgeCareScaling'] = 19.0 # p['femaleAgeCareProb'] = 0.0008\\n p['baseCareProb'] = 0.0002\\n p['careBias'] = 0.9\\n p['careTransitionRate'] = 0.7\\n\\n p['unmetNeedExponent'] = 1.0 # 0.005 #[0.005 - 0.02]\\n \\n p['numCareLevels'] = 5\\n p['careLevelNames'] = ['none','low','moderate','substantial','critical']\\n p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 32.0, 80.0 ]\\n p['quantumCare'] = 4.0\\n \\n # careSupplies getCare and probSuppliers function parameters\\n \\n ######## Key parameter 1 ##############\\n \\n \\n p['weeklyHours'] = 40.0\\n \\n \\n p['priceChildCare'] = 0.76 # 6 \\n p['schoolAge'] = 5\\n p['maxFormalChildcareHours'] = 48\\n p['schoolHours'] = 30\\n p['freeChildcareHours'] = 15\\n p['workingParentsFreeChildcareHours'] = 30\\n p['minAgeStartChildCareSupport'] = 3\\n p['minAgeStartChildCareSupportByIncome'] = 2\\n p['maxHouseholdIncomeChildCareSupport'] = 40 # 320\\n \\n ######## Key parameter 2 ##############\\n # 5: No public supply \\n \\n p['retiredHours'] = [48.0, 36.0, 20.0, 10.0] # 60.0\\n p['studentHours'] = [24.0, 16.0, 8.0, 4.0]\\n p['teenAgersHours'] = [16.0, 0.0, 0.0, 0.0]\\n p['unemployedHours'] = [32.0, 24.0, 16.0, 8.0]\\n p['employedHours'] = [28.0, 20.0, 12.0, 8.0]\\n p['formalCareDiscountFactor'] = 0.5\\n \\n p['socialNetworkDistances'] = [0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0]\\n p['networkDistanceParam'] = 2.0\\n p['socialCareWeightBias'] = 1.0\\n p['unmetCareNeedDiscountParam'] = 0.5\\n p['shareUnmetNeedDiscountParam'] = 0.5\\n # p['pastShareUnmetNeedWeight'] = 0.5\\n \\n \\n \\n p['networkSizeParam'] = 10.0 # 1.0\\n \\n p['careSupplyBias'] = 0.5\\n p['careIncomeParam'] = 0.001\\n \\n # Hospitalization Costs\\n p['qalyBeta'] = 0.18\\n p['qalyAlpha'] = 1.5\\n p['qalyDiscountRate'] = 0.035\\n p['qalyIndexes'] = [1.0, 0.8, 0.6, 0.4, 0.2]\\n p['unmetCareHealthParam'] = 0.1\\n p['hospitalizationParam'] = 0.5\\n p['needLevelParam'] = 2.0\\n p['unmetSocialCareParam'] = 2.0\\n p['costHospitalizationPerDay'] = 400\\n \\n # ageTransitions, enterWorkForce and marketWage functions parameters\\n p['ageTeenagers'] = 12\\n p['minWorkingAge'] = 16\\n \\n ######## Key parameter 3 ##############\\n \\n p['careBankingSchemeOn'] = False\\n p['socialCareBankingAge'] = 65\\n \\n p['absoluteCreditQuantity'] = False\\n p['quantityYearlyIncrease'] = 0.0\\n p['socialCareCreditQuantity'] = 0\\n p['kinshipNetworkCarePropension'] = 0.5\\n p['volunteersCarePropensionCoefficient'] = 0.01\\n p['pensionContributionRate'] = 0.05\\n \\n p['hillHealthLevelThreshold'] = 3\\n p['seriouslyHillSupportRate'] = 0.5\\n \\n ### Prices ####\\n p['pricePublicSocialCare'] = 20.0 # [2.55] # 20\\n p['priceSocialCare'] = 17.0 # [2.29] # 18\\n p['taxBrackets'] = [663, 228, 0] # [28.16, 110.23] # [221, 865]\\n p['taxBandsNumber'] = 3\\n p['bandsTaxationRates'] = [0.4, 0.2, 0.0] # [0.0, 0.2, 0.4]\\n # Tax Break Policy\\n\\n \\n p['pensionWage'] = [5.0, 7.0, 10.0, 13.0, 18.0] # [0.64, 0.89, 1.27, 1.66, 2.29] # \\n p['incomeInitialLevels'] = [5.0, 7.0, 9.0, 11.0, 14.0] #[0.64, 0.89, 1.15, 1.40, 1.78] # \\n p['incomeFinalLevels'] = [10.0, 15.0, 22.0, 33.0, 50.0] #[1.27, 1.91, 2.80, 4.21, 6.37] # \\n p['educationCosts'] = [0.0, 100.0, 150.0, 200.0] #[0.0, 12.74, 19.12, 25.49] # \\n \\n # Priced growth #####\\n p['wageGrowthRate'] = 1.0 # 1.01338 # \\n\\n p['incomeGrowthRate'] = [0.4, 0.35, 0.35, 0.3, 0.25]\\n \\n # SES inter-generational mobility parameters\\n p['leaveHomeStudentsProb'] = 0.5\\n \\n p['eduWageSensitivity'] = 0.2 # 0.5\\n p['eduRankSensitivity'] = 3.0 # 5.0\\n p['costantIncomeParam'] = 80.0 # 20.0\\n p['costantEduParam'] = 10.0 # 10.0\\n p['careEducationParam'] = 0.005 # 0.04\\n \\n \\n \\n # p['incEduExp'] = 0.25\\n p['educationLevels'] = ['GCSE', 'A-Level', 'HND', 'Degree', 'Higher Degree']\\n p['workingAge'] = [16, 18, 20, 22, 24]\\n \\n # doDivorce function parameters\\n p['basicDivorceRate'] = 0.06\\n p['variableDivorce'] = 0.06\\n p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n p['divorceBias'] = 1.0\\n \\n # doMarriages function parameters\\n p['deltageProb'] = [0.0, 0.1, 0.25, 0.4, 0.2, 0.05]\\n p['incomeMarriageParam'] = 0.025\\n p['studentFactorParam'] = 0.5\\n ######## Key parameter 4 ##############\\n p['betaGeoExp'] = 2.0 #[1.0 - 4.0]\\n \\n p['betaSocExp'] = 2.0\\n p['rankGenderBias'] = 0.5\\n p['basicMaleMarriageProb'] = 0.9\\n p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0, 0.0 ]\\n \\n # jobMarket, updateWork and unemploymentRate functions parameters\\n p['unemploymentClassBias'] = 0.75\\n p['unemploymentAgeBias'] = [1.0, 0.55, 0.35, 0.25, 0.2, 0.2]\\n p['numberAgeBands'] = 6\\n p['jobMobilitySlope'] = 0.004\\n p['jobMobilityIntercept'] = 0.05\\n p['ageBiasParam'] = [7.0, 3.0, 1.0, 0.5, 0.35, 0.15]\\n p['deltaIncomeExp'] = 0.05\\n p['unemployedCareBurdernParam'] = 0.025\\n # Potential key parameter\\n p['relocationCareLossExp'] = 1.0 # 40.0 # \\n p['incomeSocialCostRelativeWeight'] = 0.5\\n \\n p['firingParam'] = 0.2\\n p['wageVar'] = 0.06\\n p['workDiscountingTime'] = 0.75 # 0.8\\n p['sizeWeightParam'] = 0.7\\n p['minClassWeightParam'] = 1.0\\n p['incomeDiscountingExponent'] = 4.0\\n p['discountingMultiplier'] = 2.0\\n #p['incomeDiscountingParam'] = 2.0\\n \\n # relocationPensioners function parameters\\n p['agingParentsMoveInWithKids'] = 0.1\\n p['variableMoveBack'] = 0.1\\n p['retiredRelocationParam'] = 0.001 # 0.005\\n \\n # houseMap function parameters\\n p['geoDistanceSensitivityParam'] = 2.0\\n p['socDistanceSensitivityParam'] = 2.0\\n p['classAffinityWeight'] = 4.0\\n p['distanceSensitivityParam'] = 0.5\\n \\n # relocationProb function parameters\\n p['baseRelocatingProb'] = 0.05\\n p['relocationParameter'] = 1.0 \\n p['apprenticesRelocationProb'] = 0.5\\n #p['expReloc'] = 1.0\\n \\n # computeRelocationCost and relocation Propensity functions parameters\\n p['yearsInTownSensitivityParam'] = 0.5\\n \\n ######## Key parameter 5 ##############\\n p['relocationCostParam'] = 0.5 # 1.0 \\n \\n ######## Key parameter 6 ##############\\n p['propensityRelocationParam'] = 2.0 # 2.0 \\n p['denRelocationWeight'] = 0.5\\n \\n \\n ## Description of the map, towns, and houses\\n p['mapGridXDimension'] = 8\\n p['mapGridYDimension'] = 12 \\n p['townGridDimension'] = 70\\n p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]\\n p['ukMap'] = [[ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],\\n [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],\\n [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],\\n [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],\\n [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],\\n [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ]]\\n p['ukClassBias'] = [[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\\n [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],\\n [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],\\n [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]\\n p['mapDensityModifier'] = 0.6\\n # p['numHouseClasses'] = 3\\n # p['houseClasses'] = ['small','medium','large']\\n \\n ## Graphical interface details\\n p['interactiveGraphics'] = False #True\\n p['delayTime'] = 0.0\\n p['screenWidth'] = 1300\\n p['screenHeight'] = 700\\n p['bgColour'] = 'black'\\n p['mainFont'] = 'Helvetica 18'\\n p['fontColour'] = 'white'\\n p['dateX'] = 70\\n p['dateY'] = 20\\n p['popX'] = 70\\n p['popY'] = 50\\n p['pixelsInPopPyramid'] = 2000\\n p['num5YearAgeClasses'] = 28\\n p['careLevelColour'] = ['blue','green','yellow','orange','red']\\n p['houseSizeColour'] = ['brown','purple','yellow']\\n p['pixelsPerTown'] = 56\\n p['maxTextUpdateList'] = 22\\n \\n # p['eduEduSensitivity'] = 0.5\\n # p['mortalityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\\n # p['fertilityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\\n # p['divorceBias'] = [2.0, 1.5, 1.0, 0.75, 0.5]\\n\\n ## Transitions to care statistics\\n \\n ## Availability of care statistics\\n \\n #p['childHours'] = 5.0\\n # p['employedHours'] = 12.0\\n #p['homeAdultHours'] = 30.0\\n #p['workingAdultHours'] = 25.0\\n #p['maxEmployedHours'] = 60.0\\n \\n #p['lowCareHandicap'] = 0.5\\n #p['hourlyCostOfCare'] = 20.0\\n \\n ## Fertility statistics\\n \\n # p['steadyPopBirthProb'] = 0.13\\n # p['transitionYear'] = 1965\\n \\n ## Class and employment statistics\\n # p['numClasses'] = 5\\n # p['occupationClasses'] = ['lower','intermediate','higher']\\n # p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]\\n\\n ## Age transition statistics\\n # p['ageOfAdulthood'] = 17\\n \\n ## Marriage function parameters\\n \\n # p['basicFemaleMarriageProb'] = 0.25\\n # p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['femaleMarriageProb'] = [0.01, 0.15, 0.3, 0.2, 0.1, 0.1, 0.06, 0.05, 0.02, 0.01, 0.01, 0.005]\\n # p['maleMarriageProb'] = [0.005, 0.08, 0.25, 0.25, 0.15, 0.1, 0.07, 0.05, 0.03, 0.02, 0.01, 0.005]\\n \\n ## Leaving home and moving around statistics\\n # p['probApartWillMoveTogether'] = 0.3\\n # p['coupleMovesToExistingHousehold'] = 0.3\\n # p['basicProbAdultMoveOut'] = 0.22\\n # p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['basicProbSingleMove'] = 0.05\\n # p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['basicProbFamilyMove'] = 0.03\\n # p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]\\n\\n \\n return p\",\n \"def parameters(self):\\n return {}\",\n \"def createDict( self ):\\n d = {}\\n devTup = ( 'endcap', 'comp', 'shutter','397intensity' )\\n for dev in devTup:\\n d[dev] = {'devChannels':{}}\\n endcap = ( ( 1, 1 ), ( 2, 0 ) )\\n comp = ( ( 1, 4 ), ( 2, 2 ), ( 'common', 3 ) )\\n shutter = ( ( 1, 5 ), ( 2, 6 ), ( 3, 7 ) )\\n intensity397 = (('397intensity',8),)\\n chanTup = ( endcap, comp, shutter ,intensity397 )\\n for dev, value in zip( devTup, chanTup ):\\n for chanPair in value:\\n d[dev]['devChannels'][chanPair[0]] = {'value':None, 'channel':chanPair[1]}\\n ecRange = ( 0.0, 40.0 )\\n compRange = ( -40.0, 40.0 )\\n shutterRange = ( 0.0, 5.0 )\\n intensity397Range = (0.0,2500.0)\\n rangeTup = ( ecRange, compRange, shutterRange, intensity397Range )\\n for dev, value in zip( devTup, rangeTup ): d[dev]['range'] = value\\n self.dcDict = d\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58797055","0.58303946","0.5813848","0.5742892","0.57119375","0.5697148","0.5641197","0.5615532","0.5615115","0.561501","0.55502295","0.5531489","0.55249536","0.5515042","0.5483852","0.54467213","0.54290205","0.54283315","0.54066515","0.5398945","0.53961486","0.5392474","0.53664625","0.53561056","0.5354998","0.53481483","0.5341616","0.533744","0.5332413","0.531877","0.531043","0.5301442","0.5296798","0.5285573","0.52721596","0.5269258","0.5267342","0.5262863","0.52571136","0.52538645","0.5244538","0.52421355","0.52368486","0.5213594","0.52135235","0.52112204","0.52109855","0.52103245","0.5207281","0.52038324","0.5200885","0.51990837","0.51864207","0.51708674","0.51691693","0.51657534","0.5159795","0.51597387","0.5150617","0.51458204","0.5142881","0.5140502","0.51399076","0.5139542","0.5139061","0.51361114","0.51316446","0.51313","0.51237106","0.51186055","0.51186055","0.51172596","0.5115031","0.5112914","0.5110353","0.5107561","0.51065075","0.5102917","0.51023114","0.50903475","0.5082556","0.5077262","0.5067388","0.50659823","0.5065646","0.50500697","0.5049312","0.5045133","0.5042386","0.5037356","0.5034197","0.5025006","0.5018809","0.5017807","0.50151896","0.50086397","0.5000955","0.5000767","0.500075","0.4998039"],"string":"[\n \"0.58797055\",\n \"0.58303946\",\n \"0.5813848\",\n \"0.5742892\",\n \"0.57119375\",\n \"0.5697148\",\n \"0.5641197\",\n \"0.5615532\",\n \"0.5615115\",\n \"0.561501\",\n \"0.55502295\",\n \"0.5531489\",\n \"0.55249536\",\n \"0.5515042\",\n \"0.5483852\",\n \"0.54467213\",\n \"0.54290205\",\n \"0.54283315\",\n \"0.54066515\",\n \"0.5398945\",\n \"0.53961486\",\n \"0.5392474\",\n \"0.53664625\",\n \"0.53561056\",\n \"0.5354998\",\n \"0.53481483\",\n \"0.5341616\",\n \"0.533744\",\n \"0.5332413\",\n \"0.531877\",\n \"0.531043\",\n \"0.5301442\",\n \"0.5296798\",\n \"0.5285573\",\n \"0.52721596\",\n \"0.5269258\",\n \"0.5267342\",\n \"0.5262863\",\n \"0.52571136\",\n \"0.52538645\",\n \"0.5244538\",\n \"0.52421355\",\n \"0.52368486\",\n \"0.5213594\",\n \"0.52135235\",\n \"0.52112204\",\n \"0.52109855\",\n \"0.52103245\",\n \"0.5207281\",\n \"0.52038324\",\n \"0.5200885\",\n \"0.51990837\",\n \"0.51864207\",\n \"0.51708674\",\n \"0.51691693\",\n \"0.51657534\",\n \"0.5159795\",\n \"0.51597387\",\n \"0.5150617\",\n \"0.51458204\",\n \"0.5142881\",\n \"0.5140502\",\n \"0.51399076\",\n \"0.5139542\",\n \"0.5139061\",\n \"0.51361114\",\n \"0.51316446\",\n \"0.51313\",\n \"0.51237106\",\n \"0.51186055\",\n \"0.51186055\",\n \"0.51172596\",\n \"0.5115031\",\n \"0.5112914\",\n \"0.5110353\",\n \"0.5107561\",\n \"0.51065075\",\n \"0.5102917\",\n \"0.51023114\",\n \"0.50903475\",\n \"0.5082556\",\n \"0.5077262\",\n \"0.5067388\",\n \"0.50659823\",\n \"0.5065646\",\n \"0.50500697\",\n \"0.5049312\",\n \"0.5045133\",\n \"0.5042386\",\n \"0.5037356\",\n \"0.5034197\",\n \"0.5025006\",\n \"0.5018809\",\n \"0.5017807\",\n \"0.50151896\",\n \"0.50086397\",\n \"0.5000955\",\n \"0.5000767\",\n \"0.500075\",\n \"0.4998039\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56081396"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":75795,"cells":{"query":{"kind":"string","value":"Updates the well or piezometer object using an input dictionary"},"document":{"kind":"string","value":"def update(self, new_data):\n if type(new_data) is not dict:\n raise TypeError(\"Input parameter must be a dict\")\n # Update parameters\n self._type = new_data.get(\"_type\", self._type)\n self.time_units = new_data.get(\"time_units\", self.time_units)\n self.len_units = new_data.get(\"len_units\", self.len_units)\n self.parameters = new_data.get(\"parameters\", self.parameters)\n # Update drawdown\n self.drawdown.update(new_data.get(\"drawdown\", self.drawdown.to_dict()))\n # Update data\n if \"data\" in new_data:\n n = len(new_data[\"data\"])\n if n > 0:\n self.reset_data()\n for i in range(n):\n self.add_data(0, 0)\n self.data[i].update(new_data[\"data\"][i])\n # End Function"},"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 update(self,d:dict):\n for name,(value,n) in d.items():\n if n==0:\n continue\n self.meters[name].update(value,n)","def update(self,dict):\n for key in list(dict.keys()):\n print(\"Hey, I'm updating something\")\n self.d[key]=dict[key]","def update_dict(new,old):","def update(self, new_data):\n if type(new_data) is not dict:\n raise TypeError(\"Input parameter must be a dict\")\n # Update parameters\n self._type = new_data.get(\"_type\", self._type)\n self.time_units = new_data.get(\"time_units\", self.time_units)\n self.len_units = new_data.get(\"len_units\", self.len_units)\n self.pump_units = new_data.get(\"pump_units\", self.pump_units)\n self.parameters = new_data.get(\"parameters\", self.parameters)\n # Update pumping rate\n self.pumprate.update(new_data.get(\"pumprate\", self.pumprate.to_dict()))\n # Update data\n if \"wells\" in new_data:\n n = len(new_data[\"wells\"])\n if n > 1:\n self.delete_all_wells()\n for i in range(n):\n self.add_well(0, 0, new_data[\"wells\"][i][\"_type\"] - 2)\n self.wells[i].update(new_data[\"wells\"][i])\n # End Function","def _update_object(self, data_dict):\r\n pass","def update(self):\n changes = {}\n for coord in INDICES: # the need for two for loops is necessary\n if self.chart[coord] == ALIVE and (\n self.number_of_neighbors(coord) < 2 or self.number_of_neighbors(coord) > 3):\n changes[coord] = KILL\n elif self.number_of_neighbors(coord) == 3:\n changes[coord] = REVIVE\n for coord in changes.keys(): # because the evolution is discrete\n if changes[coord] == KILL:\n self.kill(coord)\n elif changes[coord] == REVIVE:\n self.givebirth(coord)","def test_updatewithdict(self):\n s1 = Square(10, 2, 1)\n s1_dictionary = s1.to_dictionary()\n s2 = Square(1, 1)\n s2.update(**s1_dictionary)\n self.assertEqual(s2.size, 10)\n self.assertEqual(s2.x, 2)\n self.assertEqual(s2.y, 1)","def update(self, d):\n for k in d:\n self[k] = d[k]","def update(self, d):\n for key, value in d.items():\n self.__setitem__(key, value)","def update(self, d):\n for key, value in d.items():\n self.__setitem__(key, value)","def updateFromDict(self, data):\n for key, value in data.items():\n setattr(self, key, value)","def update(self):\n self.data.update()\n\n sensor_type = self.entity_description.key\n if sensor_type == \"light\":\n self._attr_native_value = self.data.light\n elif sensor_type == \"light_red\":\n self._attr_native_value = self.data.light_red\n elif sensor_type == \"light_green\":\n self._attr_native_value = self.data.light_green\n elif sensor_type == \"light_blue\":\n self._attr_native_value = self.data.light_blue\n elif sensor_type == \"accelerometer_x\":\n self._attr_native_value = self.data.accelerometer_x\n elif sensor_type == \"accelerometer_y\":\n self._attr_native_value = self.data.accelerometer_y\n elif sensor_type == \"accelerometer_z\":\n self._attr_native_value = self.data.accelerometer_z\n elif sensor_type == \"magnetometer_x\":\n self._attr_native_value = self.data.magnetometer_x\n elif sensor_type == \"magnetometer_y\":\n self._attr_native_value = self.data.magnetometer_y\n elif sensor_type == \"magnetometer_z\":\n self._attr_native_value = self.data.magnetometer_z\n elif sensor_type == \"temperature\":\n self._attr_native_value = self.data.temperature\n elif sensor_type == \"pressure\":\n self._attr_native_value = self.data.pressure\n elif sensor_type == \"voltage_0\":\n self._attr_native_value = self.data.voltage_0\n elif sensor_type == \"voltage_1\":\n self._attr_native_value = self.data.voltage_1\n elif sensor_type == \"voltage_2\":\n self._attr_native_value = self.data.voltage_2\n elif sensor_type == \"voltage_3\":\n self._attr_native_value = self.data.voltage_3","def updateInstrumentParameterValue(tablews, paramdict):\n paramnames = paramdict.keys()\n for parname in paramnames: \n parvalue = paramdict[parname]\n\t# print \"%s = %f\" % (parname, parvalue)\n\tif parname.count(\"Chi2\") == 0:\n\t # Only update parameters nothing to do with chi2\n UpdatePeakParameterTableValue(InputWorkspace=tablews,\n\t\tColumn='Value',\n ParameterNames=[parname],\n\t\tNewFloatValue=parvalue)\n\n return","def update_knobs(self):\n self.previous_knobs = self.current_knobs\n self.current_knobs = {'Modulation' : 'fsk',\n 'Rs' : 0,\n 'EIRP' : 0,\n 'Speed' : 0}","def update(self, params):\n for gauge in self.gauges:\n self.safexec(gauge.update, params)","def _update(self, datapoints):\r\n if len(datapoints) == 1:\r\n timestamp, value = datapoints[0]\r\n whisper.update(self.path, value, timestamp)\r\n else:\r\n whisper.update_many(self.path, datapoints)","def add_to_dict(param_dict):\n ### Sample - Int\n sample_s = param_dict['ml_args'].sample_s\n ### Sample - Mr\n sample_Mr = param_dict['ml_args'].sample_Mr\n ## Sample volume\n # Units (Mpc/h)**3\n volume_sample = { '18': 37820 / 0.01396,\n '19': 6046016.60311 ,\n '20': 2.40481e7 ,\n '21': 8.79151e7 }\n vol_mr = volume_sample[sample_s]\n ##\n ## Choice of Centrals and Satellites\n cens = int(1)\n sats = int(0)\n ## Other constants\n # Speed of light - In km/s\n speed_c = ac.c.to(u.km/u.s).value\n ## Number of CPU's to use\n cpu_number = int(cpu_count() * param_dict['cpu_frac'])\n ##\n ## Plotting constants\n plot_dict = { 'size_label':23,\n 'size_title':25,\n 'color_ham' :'red',\n 'color_dyn' :'blue'}\n ##\n ## Catalogue Prefix string\n catl_str_fig = param_dict['ml_args'].catl_alg_comp_fig_str()\n ##\n ## Saving to `param_dict`\n param_dict['sample_s' ] = sample_s\n param_dict['sample_Mr' ] = sample_Mr\n param_dict['vol_mr' ] = vol_mr\n param_dict['cens' ] = cens\n param_dict['sats' ] = sats\n param_dict['speed_c' ] = speed_c\n param_dict['cpu_number' ] = cpu_number\n param_dict['plot_dict' ] = plot_dict\n param_dict['catl_str_fig'] = catl_str_fig\n\n return param_dict","def update(self):\n self.data_service.update()\n self._state = self.data_service.data.get(self._json_key)\n self._attributes = self.data_service.attributes.get(self._json_key)\n self._unit_of_measurement = self.data_service.unit","def update(self):\n self.data_service.update()\n self._state = self.data_service.data.get(self._json_key)\n self._attributes = self.data_service.attributes.get(self._json_key)\n self._unit_of_measurement = self.data_service.unit","def _update_dicts(game, synergy, counter):\n radiant_win, radiant_heroes, dire_heroes = game[1], game[2], game[3]\n\n radiant_heroes = map(int, radiant_heroes.split(','))\n dire_heroes = map(int, dire_heroes.split(','))\n\n for i in range(5):\n for j in range(5):\n if i != j:\n synergy['games'][radiant_heroes[i] - 1, radiant_heroes[j] - 1] += 1\n synergy['games'][dire_heroes[i] - 1, dire_heroes[j] - 1] += 1\n\n if radiant_win:\n synergy['wins'][radiant_heroes[i] - 1, radiant_heroes[j] - 1] += 1\n else:\n synergy['wins'][dire_heroes[i] - 1, dire_heroes[j] - 1] += 1\n\n counter['games'][radiant_heroes[i] - 1, dire_heroes[j] - 1] += 1\n counter['games'][dire_heroes[i] - 1, radiant_heroes[j] - 1] += 1\n\n if radiant_win:\n counter['wins'][radiant_heroes[i] - 1, dire_heroes[j] - 1] += 1\n else:\n counter['wins'][dire_heroes[i] - 1, radiant_heroes[j] - 1] += 1","def update(self,**kwargs):\n self._update_from_kwargs(**kwargs)\n #--- calc estimated chops from chop length\n self._calc_estimated_chops_from_timepoints()\n #--- adjust chops for stimulusresponse\n self._adjust_chops_for_stimulus_response()\n #--- update annotations\n self._update_annotations()\n #--- get info\n if self.verbose:\n self.GetInfo()\n #--- show plot\n if self.show:\n self.show_chops()","def update(self):\r\n self._data.update()\r\n if self._data == None:\r\n _Log.error('No data!')\r\n\r\n\r\n expedite = self._data.data['trafficinfo']['evaluation']['expedite']\r\n congested = self._data.data['trafficinfo']['evaluation']['congested']\r\n blocked = self._data.data['trafficinfo']['evaluation']['blocked']\r\n unknown = self._data.data['trafficinfo']['evaluation']['unknown']\r\n description = self._data.data['trafficinfo']['evaluation']['description']\r\n status = self._data.data['trafficinfo']['evaluation']['status']\r\n if status == '0':\r\n traffic_status = '未知'\r\n elif status == '1':\r\n traffic_status = '畅通'\r\n elif status == '2':\r\n traffic_status = '缓行'\r\n elif status == '3':\r\n traffic_status = '拥堵'\r\n elif status == '4':\r\n traffic_status = '超堵'\r\n else:\r\n traffic_status = '无数据'\r\n self._state = traffic_status\r\n\r\n adddict = {\r\n '畅通所占百分比':expedite,\r\n '缓行所占百分比':congested,\r\n '拥堵所占百分比':blocked,\r\n '未知路段所占百分比':unknown,\r\n '道路描述':description,\r\n '路况': self._data.data['trafficinfo']['description'],\r\n '更新时间':datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\r\n self.attributes=adddict","def update(self, update, **kwargs):\n try:\n self.saved = False\n function_to_call = self.update_dic[update]\n function_to_call(**kwargs)\n except KeyError as e:\n pass","def update(self,ztate):\n \n if not ztate: return\n assert isinstance(ztate,State) , 'must update with another State-type'\n for key in self.keys():\n if isinstance(ztate[key],dict):\n self[key].update( ztate[key] )\n elif ztate[key]:\n self[key] = ztate[key]\n \n self.set_timestamp()","def update(self, upddict):\n\t\tfor (key, value) in upddict.iteritems():\n\t\t\tsetattr(self, key, value)","def update(self, *args, **kwargs):\n other = dict(*args, **kwargs)\n for key, value in other.items():\n if not isinstance(value, Detector):\n raise TypeError(\n \"Values must be {}, not {} for key {}\".format(\n Detector, type(value), key\n )\n )\n super().update(other)","def update(self, **kwargs):\n for name, item in itertools.chain(\n self._cal_objs.items(),\n self._noise_objs.items()):\n logger.debug(\"update {}\".format(item))\n item.update(**kwargs)","def update_metric_dict_overall(self, metric_dict, update_dict, phase):\n for key in update_dict.keys():\n metric_dict[phase][key].append(update_dict[key])\n return metric_dict","def _update_(self, data):\n self.heroes = dict((hero, Hero(hero, data[hero])) for hero in data)","def normal_update(self, game, elfDict):\n self.game = game # update game\n self.my_elves = [elf for elf in elfDict.values() if not elf.elf.already_acted] # update self.my_elves\n self.game = game # update self.game\n self.portals.portals_update(game) # update portals (the object)\n self.my_castle = game.get_my_castle()","def update(self):\n self.data.update()\n stats = self.data.stats\n ticker = self.data.ticker\n\n if self.type == \"exchangerate\":\n self._attr_state = ticker[self._currency].p15min\n self._attr_unit_of_measurement = self._currency\n elif self.type == \"trade_volume_btc\":\n self._attr_state = f\"{stats.trade_volume_btc:.1f}\"\n elif self.type == \"miners_revenue_usd\":\n self._attr_state = f\"{stats.miners_revenue_usd:.0f}\"\n elif self.type == \"btc_mined\":\n self._attr_state = str(stats.btc_mined * 0.00000001)\n elif self.type == \"trade_volume_usd\":\n self._attr_state = f\"{stats.trade_volume_usd:.1f}\"\n elif self.type == \"difficulty\":\n self._attr_state = f\"{stats.difficulty:.0f}\"\n elif self.type == \"minutes_between_blocks\":\n self._attr_state = f\"{stats.minutes_between_blocks:.2f}\"\n elif self.type == \"number_of_transactions\":\n self._attr_state = str(stats.number_of_transactions)\n elif self.type == \"hash_rate\":\n self._attr_state = f\"{stats.hash_rate * 0.000001:.1f}\"\n elif self.type == \"timestamp\":\n self._attr_state = stats.timestamp\n elif self.type == \"mined_blocks\":\n self._attr_state = str(stats.mined_blocks)\n elif self.type == \"blocks_size\":\n self._attr_state = f\"{stats.blocks_size:.1f}\"\n elif self.type == \"total_fees_btc\":\n self._attr_state = f\"{stats.total_fees_btc * 0.00000001:.2f}\"\n elif self.type == \"total_btc_sent\":\n self._attr_state = f\"{stats.total_btc_sent * 0.00000001:.2f}\"\n elif self.type == \"estimated_btc_sent\":\n self._attr_state = f\"{stats.estimated_btc_sent * 0.00000001:.2f}\"\n elif self.type == \"total_btc\":\n self._attr_state = f\"{stats.total_btc * 0.00000001:.2f}\"\n elif self.type == \"total_blocks\":\n self._attr_state = f\"{stats.total_blocks:.0f}\"\n elif self.type == \"next_retarget\":\n self._attr_state = f\"{stats.next_retarget:.2f}\"\n elif self.type == \"estimated_transaction_volume_usd\":\n self._attr_state = f\"{stats.estimated_transaction_volume_usd:.2f}\"\n elif self.type == \"miners_revenue_btc\":\n self._attr_state = f\"{stats.miners_revenue_btc * 0.00000001:.1f}\"\n elif self.type == \"market_price_usd\":\n self._attr_state = f\"{stats.market_price_usd:.2f}\"","def _update_(self, data):\n # imports\n from intervals import Interval, LinkedIntervalSet\n \n # create a LinkedIntervalSet to store the time-based states for this\n # hero\n states = [Interval(dict((HER_KWD_MAP.get(k, k), d[1][k]) for k in d[1]), d[0]) for d in data]\n self.states = LinkedIntervalSet.from_starts(states)","def update(self, dayHour, comicId):\n #TODO test\n self.loadComic(comicId)\n\n #Sprint \"Predictor.update(\", comicId, \")\"\n\n\n for i in range(1):\n # Stop weeding?\n stopWeeding = False\n\n if self.__predictorData == None:\n print \"Line 252 (NONE)\"\n\n if (self.__predictorData.isWeeding()):\n tgm = time.gmtime()\n sec = time.mktime(time.strptime(str(tgm.tm_year) +' '+ str(tgm.tm_mon) +' '+ str(tgm.tm_mday) +' '+ str(tgm.tm_hour) +' '+ str(tgm.tm_min) +' '+ str(tgm.tm_sec), '%Y %m %d %H %M %S'))\n # print \"weeding times:\", self.__predictorData.getWeedingStart(), sec\n if (sec >= self.__predictorData.getWeedingStart() + 7*24*self.hourScale):\n self.stopWeeding(dayHour)\n stopWeeding = True\n\n if (self.__predictorData.isWeeding() or stopWeeding):\n # Weeding\n uRanges = self.__predictorData.getUpdateRanges()\n if (len(uRanges) > 0):\n if (self.__predictorData.addDayHour(dayHour)):\n break\n # if (self.inURange(self.incDayHour(dayHour, -self.rangeWidth), uRanges[-1])):\n # uRangeLast = self.incDayHour(uRanges[-1]['position'], uRanges[-1]['width'])\n # #self.__predictorData.addUpdateRange(self.addUpdateRange(incDayHour(uRangeLast, Predictor.rangeWidth + 1)))\n # self.__predictorData.addUpdateRange(self.incDayHour(uRangeLast, Predictor.rangeWidth + 1))\n # break \n self.__predictorData.addUpdateRange(self.blankUpdateRange(dayHour))\n else:\n # Regular update\n if (not self.__predictorData.addDayHour(dayHour)):\n # Add dayHour to nearest range\n updateRanges = self.__predictorData.getUpdateRanges()\n dist = {}\n dh = self.dayHourToHours(dayHour)\n for i, uRange in enumerate(updateRanges):\n ur_pos_hours = self.dayHourToHours(uRange['position'])\n ur_width = uRange['width']\n hours = self.dayHourToHours(dayHour)\n dist[min(abs(ur_pos_hours - ur_width - hours), abs(ur_pos_hours + ur_width - hours))] = i\n self.__predictorData.addDayHourToURange(dayHour, dist[min(dist.keys())])\n\n \n self.__predictorData.setUpdateRanges(self.calculateURPositions(self.__predictorData.getUpdateRanges()))\n self.__predictorData.setSchedule(self.calculateScheduleUR(self.__predictorData.getUpdateRanges()))\n \n self.updatePredictorList(self.__predictorData.getSchedule(), comicId)\n self.saveComic(comicId)","def update(self, dt):\n self.level.update(self.keys, dt)","def update(self, arg=None, **kwargs):\n if arg:\n if hasattr(arg, 'keys'):\n for k in arg: self[k] = arg[k]\n else:\n for k, v in arg: self[k] = v\n\n if kwargs:\n for k in kwargs: self[k] = kwargs[k]","def update_properties(self, update_dict: Dict[str, Any]):\n self.event_properties.update(update_dict)","def update_compartments(self, food_glucose):\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())","def update(self, other_dict=None, **f):\n changed = False\n if other_dict:\n for (key, value) in other_dict.items() if hasattr(other_dict, 'items') else other_dict:\n self.__setitem__(key, value, bulk_change=True)\n changed = True\n if f:\n for (key, value) in f.items():\n self.__setitem__(key, value, bulk_change=True)\n changed = True\n changed and self._changed()","def update():","def update():","def update_from_dict(self, data: dict):\n self.algo = data[\"algo\"]\n self.version = data[\"sbx\"]\n self.next_session = data[\"next\"]\n self.last_session = data[\"last\"]\n self.past_quality = unpack_int_list(data[\"pastq\"])\n\n # Revert to length of past_quality if reps are not set\n possible_rep = len(self.past_quality)\n self.actual_repetitions = data.get(\"reps\", possible_rep)\n\n # Other keys are used by algorithm\n self.algo_state = data.copy()\n for required_key in REQUIRED_FIELDS:\n del self.algo_state[required_key]","def update(self, *args, **kwargs):\n super(ReadOnlyDict, self).update(*args, **kwargs) # pragma: no cover","def update_asop_dict(asop_dict,region,coords,color,all_settings):\n # Set unique color\n asop_dict['color'] = color\n\n # Apply any general user settings\n asop_dict['grid_desc'] = all_settings.get('grid','native')\n asop_dict['grid_type'] = all_settings.get('grid','native')\n asop_dict['region_name'] = region\n asop_dict['region_desc'] = region.replace('_',' ')\n asop_dict['region'] = coords\n\n # Edit dx for region\n mean_lat = np.mean(coords[0:2])\n asop_dict['dx'] = asop_dict['dx'] * np.cos(np.radians(mean_lat))\n all_settings.pop('infile','') # key not allowed\n for key in asop_dict:\n if key in all_settings:\n asop_dict[key] = all_settings[key]\n\n # Apply any specific file settings\n infile = os.path.basename(asop_dict['infile'])\n file_settings = settings.get(infile,{})\n file_settings.pop('infile','') # key not allowed\n file_settings.pop('region','')\n if file_settings:\n for key in file_settings:\n asop_dict[key] = file_settings[key]\n if 'legend_name' not in file_settings:\n asop_dict['legend_name'] = asop_dict['name'].replace('_',' ')\n\n print('---> Final data dictionary:')\n print(json.dumps(asop_dict, sort_keys=True, indent=2))\n\n return asop_dict","def _update_loose (self, dict):\n self.__dict__.update(dict)","def update( d, **kw):\n d.update( d, **kw )\n return d","def update(self, *args, **kw):\n pass","def _measurement_update(self):\n pass","def update(self):\n self.data.update()\n for sensor in self.data.daikinskyport.get_sensors(self._index):\n if sensor[\"type\"] == self._type and self._sensor_name == sensor[\"name\"]:\n self._state = sensor[\"value\"]","def update(self, temperature, humidity, co2, voc, voltage,\n baseline=False, fullupdate=False):\n self._add_borders()\n self._add_temperature(temperature)\n self._add_humidity(humidity)\n self._add_co2(co2)\n self._add_voc(voc)\n self._add_voltage(voltage)\n self._add_baseline_indicator(baseline)\n self._update_screen(fullupdate)","def update(self, params):","def soft_update(self, data):\n if not data:\n return\n\n for key, value in list(data.items()):\n\n old_value = super(Yaco, self).get(key, None)\n\n if isinstance(value, dict):\n if old_value and isinstance(old_value, Yaco):\n old_value.soft_update(value)\n if old_value:\n # there is an older value - not a dict - cannot overwrite\n continue\n else:\n # no old value - overwrite all you like\n super(Yaco, self).__setitem__(key, Yaco(value))\n elif isinstance(value, list):\n # parse the list to see if there are dicts - which\n # need to be translated to Yaco objects\n if not old_value:\n new_value = self._list_parser(value)\n super(Yaco, self).__setitem__(key, new_value)\n else:\n if not old_value:\n super(Yaco, self).__setitem__(key, value)","def update_data():\n pass","def set_params(self, dic):\n if dic is not None:\n for key, val in zip(dic.keys(), dic.values()):\n if key in self.__dict__.keys():\n self.__dict__[key] = val\n\n if 'scale_params' in self.__dict__.keys():\n self.scale_params.set_params(dic)\n if 'atmospheric_params' in self.__dict__.keys():\n if self.atmospheric_params is not None:\n self.atmospheric_params.set_params(dic)\n\n if 'atemperature_params' in self.__dict__.keys():\n if self.atemperature_params is not None:\n self.atemperature_params.set_params(dic)\n\n if 'oceanic_params' in self.__dict__.keys():\n if self.oceanic_params is not None:\n self.oceanic_params.set_params(dic)\n\n if 'ground_params' in self.__dict__.keys():\n if self.ground_params is not None:\n self.ground_params.set_params(dic)\n\n if 'otemperature_params' in self.__dict__.keys():\n if self.gotemperature_params is not None:\n self.gotemperature_params.set_params(dic)\n\n if 'gtemperature_params' in self.__dict__.keys():\n if self.gotemperature_params is not None:\n self.gotemperature_params.set_params(dic)","def update(self,z_t):\n # YOUR CODE HERE\n pass","def update(world):\r\n infect = infection(world['SIR'], infection_rate, incubation_rate)\r\n disperse = dispersion(world['SIR'], dispersion_kernel, dispersion_rates)\r\n world['SIR'] += dt*( infect + disperse)\r\n world['t'] += dt","def update_from_dict(self, data: dict) -> \"Device\":\n if \"info\" in data and data[\"info\"]:\n self.info = Info.from_dict(data[\"info\"])\n\n if \"locations\" in data and data[\"locations\"]:\n locations = [Location.from_dict(location) for location in data[\"locations\"]]\n self.locations = locations\n\n return self","def update(self, *args, **kwargs):\n sqrlist = [\"id\", \"size\", \"x\", \"y\"]\n if args and len(args) != 0:\n for i in range(len(sqrlist)):\n if i < len(args):\n # call to setter method\n setattr(self, sqrlist[i], args[i])\n else:\n if kwargs and len(kwargs) != 0:\n for k in sqrlist:\n for key, value in kwargs.items():\n if k == key:\n setattr(self, key, value)","def setFromDict(self, designObject, myShipDict):\n self.myDesign = designObject\n self.designID = designObject.id\n self.componentdata = designObject.componentdata\n self.weapondata = designObject.weapondata\n self.myShipHull = designObject.myShipHull\n designAttrDict = designObject.getMyInfoAsDict()\n self.currentISP = myShipDict['currentISP']\n\n for position, dQuad in myShipDict['quads'].iteritems():\n newQuad = quad.Quad(dQuad)\n newQuad.setMyParent(self)\n # weapons have to be created first\n for id, dWeap in dQuad['weapons'].iteritems():\n newWeap = weapon.Weapon(dWeap)\n newWeap.setMyQuad(newQuad)\n for id, dComp in dQuad['components'].iteritems():\n newComp = component.Component(dComp)\n newComp.setMyQuad(newQuad)\n newQuad.setMyStatus()\n self.quads[newQuad.position] = newQuad\n newQuad.resetDefences()\n newQuad.reloadAmmo()\n\n self.setMyStatus()\n self.name = designObject.name + '-' + self.id","def edit_pressure(dictionary, domian_json, pressure=0):\n\n\ttry:\n\t\ttqdm.write(\"{}: Editing pressure file: {}\".format(datetime.datetime.now(),dictionary))\n\t\tpressureDict = ParsedParameterFile(dictionary)\n\n\t\twith open(domian_json, 'r') as f:\n\t\t\tdomain_dict = json.load(f)\n\n\t\tboundaryField = {\"vessel\": {\"type\": \"zeroGradient\"}, \"walls\":{\"type\":\"empty\"}}\t\n\t\tfor key, value in domain_dict.items():\n\t\t\ttry:\t\t\t\n\t\t\t\tif value[\"type\"] == \"inlet\":\n\t\t\t\t\tboundaryField.update({key: { \n\t\t\t\t\t\t\"type\": \"zeroGradient\"}})\n\t\t\t\telif value[\"type\"] == \"outlet\":\n\t\t\t\t\tboundaryField.update({key: { \n\t\t\t\t\t\t\"type\": \"fixedValue\",\n\t\t\t\t\t\t\"value\":\"uniform 0 \"}}\n\t\t\t\t\t\t)\n\t\t\t\telse:\n\t\t\t\t\tcontinue\n\t\t\texcept:\n\t\t\t\tcontinue\n\n\t\tpressureDict[\"boundaryField\"] = boundaryField\n\n\t\ttry:\n\t\t\tpressureDict.writeFile()\n\t\texcept IOError:\n\t\t\ttqdm.write(\"Can't write file. Content would have been:\")\n\t\t\ttqdm.write(pressureDict)\n\n\texcept IOError:\n\t\ttqdm.write(dictionary, \" does not exist\")\n\t\treturn 1","def update(self, profile: Dict[datetime.time, float]) -> None:\n\n if self._profile is None:\n self._profile = profile\n else:\n self._profile.update(profile)","def update_from_dict(self, dictionary):\n for key in dictionary:\n setattr(self, key, dictionary[key])\n return self.to_dict()","def _update(self, var, channels, value):\n\n if type(channels) is int:\n channels = [channels]\n\n if var =='frequency':\n for channel in channels:\n self.frequencies[channel] = value\n elif var =='phase':\n for channel in channels:\n self.phases[channel] = value\n elif var == 'amplitude':\n for channel in channels:\n self.amplitudes[channel] = value","def update( ):\r\n pass","def update_record(self, d: dict) -> None:\n super().update_record(d)\n d.update(\n dataset_doses=str_list(self.doses),\n dataset_ns=str_list(self.ns),\n dataset_stdevs=str_list(self.stdevs),\n dataset_means=str_list(self.means),\n )","def update_weather(self, weather: Weather) -> None:\n new_weather = carla.WeatherParameters()\n if weather == Weather.SUNSHINE:\n new_weather = carla.WeatherParameters.ClearNoon\n elif weather == Weather.RAIN:\n new_weather = carla.WeatherParameters(precipitation=100.0, wetness=100.0)\n elif weather == Weather.FOG:\n new_weather = carla.WeatherParameters(fog_density=100.0, fog_distance=0.0,fog_falloff=0.5)\n self.carla_world.set_weather(new_weather)","def update(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)","def update(self, *args, **kwargs):\n if kwargs is not None:\n for key, value in kwargs.items():\n setattr(self, key, value)","def update_params(coeffs_dict, params):\n for key in coeffs_dict.keys():\n try:\n # Batman\n params.__dict__[key] = coeffs_dict[key]\n except:\n # Normal dictionary\n params[key] = coeffs_dict[key]\n return params","def test_updatewithdictionarybycomparingdictionaries(self):\n s1 = Square(10, 2, 1, 9)\n s1_dictionary = s1.to_dictionary()\n s2 = Square(1, 1)\n s2.update(**s1_dictionary)\n self.assertEqual(s1.__dict__, s2.__dict__)","def Increase_Dictionary(dictio, data, index):\n for line in data:\n split = line.split()\n name = split[0] + \" \" + split[1]\n fmi = float(split[2])\n dictio.setdefault(name, [np.Inf, np.Inf, np.Inf])[index] = fmi\n return dictio","def update_tool(self, tool_number, **kwargs):\r\n if kwargs.get('feed_rate') is not None:\r\n self.tools[tool_number].feed_rate = kwargs.get('feed_rate')\r\n if kwargs.get('retract_rate') is not None:\r\n self.tools[tool_number].retract_rate = kwargs.get('retract_rate')\r\n if kwargs.get('rpm') is not None:\r\n self.tools[tool_number].rpm = kwargs.get('rpm')\r\n if kwargs.get('diameter') is not None:\r\n self.tools[tool_number].diameter = kwargs.get('diameter')\r\n if kwargs.get('max_hit_count') is not None:\r\n self.tools[tool_number].max_hit_count = kwargs.get('max_hit_count')\r\n if kwargs.get('depth_offset') is not None:\r\n self.tools[tool_number].depth_offset = kwargs.get('depth_offset')\r\n # Update drill hits\r\n newtool = self.tools[tool_number]\r\n for hit in self.hits:\r\n if hit.tool.number == newtool.number:\r\n hit.tool = newtool","def _update(self, handle, value):\n _LOGGER.debug(\n \"%s: %15s temperature = %-2d.%-2d, humidity = %3d\",\n handle,\n self.name,\n value[0],\n value[2],\n value[1],\n )\n self.data[\"temp\"] = float(\"%d.%d\" % (value[0], value[2]))\n self.data[\"humid\"] = value[1]","def report_update():\r\n resources[\"water\"] = resources[\"water\"] - MENU[order][\"ingredients\"][\"water\"]\r\n resources[\"milk\"] = resources[\"milk\"] - MENU[order][\"ingredients\"][\"milk\"]\r\n resources[\"coffee\"] = resources[\"coffee\"] - MENU[order][\"ingredients\"][\"coffee\"]\r\n resources[\"money\"] = resources[\"money\"] + total","def update_info(job_path,\n vals,\n param_names,\n prop_name):\n\n with open(job_path, \"r\") as f:\n info = json.load(f)\n\n real_names = []\n real_vals = []\n\n for param_name, val in zip(param_names, vals):\n if param_name.startswith(\"log_\"):\n # if anything starts with \"log_\" (e.g. \"log_schnet_dropout\"),\n # exponentiate its value to get the actual number\n real_names.append(param_name.replace(\"log_\", \"\"))\n real_vals.append(np.exp(val))\n else:\n real_names.append(param_name)\n real_vals.append(val)\n\n # update values\n for param_type, val in zip(real_names, real_vals):\n if 'dropout' in param_type:\n update_dropout(info=info,\n dropout=val,\n dropout_type=param_type,\n prop_name=prop_name)\n\n elif param_type == \"num_heads\":\n update_heads(info=info,\n heads=val)\n\n elif param_type == \"attention_type\":\n info[\"model_params\"][\"boltzmann_dict\"][\"type\"] = val\n\n else:\n if param_type not in info[\"model_params\"]:\n msg = (f\"Warning: assuming that {param_type} \"\n \"is just a key in `model_params`, but \"\n \"it is not currently in `model_params` in \"\n \"the config file. If it should be in a \"\n \"different location then you will need \"\n \"to write a custom function for updating \"\n \"it.\")\n\n fprint(msg)\n\n update_general(info, key=param_type, val=val)\n\n # save\n with open(job_path, \"w\") as f:\n json.dump(info, f, indent=4, sort_keys=True)","def data_dict_update(self, change):\n self.data_dict = change['value']","def update_device(self, dev_dict):\n # Note(jprabh1x): added bus,slot,function into fields dict as \n # seperate fields.\n no_changes = ('status', 'instance_uuid', 'id', 'extra_info', 'workload')\n map(lambda x: dev_dict.pop(x, None),\n [key for key in no_changes])\n\n # Note(jprabh1x): populating values for bus,slot,function from address in dev_dict.\n if dev_dict.has_key(\"address\"):\n \t\taddress = pci_utils.parse_address(dev_dict[\"address\"])\n \t\tdev_dict.update({'bus':str(address[1]), 'slot':str(address[2]), 'function':str(address[3])})\n for k, v in dev_dict.items():\n if k in self.fields.keys():\n self[k] = v\n else:\n extra_info = self.extra_info\n extra_info.update({k: str(v)})\n self.extra_info = extra_info","def update(self, values):\n for k, v in values.items():\n setattr(self, k, v)","def update(*args):","def updateParameters(self, paramDict):\n\n params = ['taux', 'mu', 'G', 'alpha_0', 'delta', 'p', 'I0', 'kparam']\n\n # Now set the parameters\n for k in paramDict.keys():\n mycode = 'self.' + k + \"=paramDict[\\'\" + k + \"\\']\"\n exec(mycode)","def update_parameters(updates):\r\n for (key, val) in updates.items():\r\n par[key] = val\r\n print('Updating:', key, '-->', val)\r\n update_dependencies()","def update_general(info, key, val):\n\n info[\"model_params\"][key] = val","def update(self, data: Mapping[str, np.ndarray]) -> Self:\n\n raise NotImplementedError","def __init__(self, sensor):\n self.sensor = sensor\n self.temperature = None\n self.humitidy = None\n self.update()","def update(self, *args, **kwargs):\n if args:\n if args is not None:\n lista = [\"id\", \"size\", \"x\", \"y\"]\n for i, j in zip(args, lista):\n setattr(self, j, i)\n else:\n for key, value in kwargs.items():\n setattr(self, key, value)","def update(self, **vars):\n for name in vars:\n # Use __setitem__ for all effects\n self[name] = vars[name]","def update(self, **kwargs):\n for k, v in kwargs.iteritems():\n if hasattr(self, k):\n setattr(self, k, v)","def update(self, *args, **kwargs):\n if len(args):\n for i, val in enumerate(args):\n if i == 0:\n self.id = val\n elif i == 1:\n self.size = val\n elif i == 2:\n self.x = val\n elif i == 3:\n self.y = val\n else:\n for key, val in kwargs.items():\n if key == \"id\":\n self.id = val\n if key == \"size\":\n self.size = val\n if key == \"x\":\n self.x = val\n if key == \"y\":\n self.y = val","def update(self):\n dht = self.sensor(self.pin)\n try:\n temperature = dht.temperature\n humidity = dht.humidity\n except RuntimeError:\n _LOGGER.debug(\"Unexpected value from DHT sensor: %s\", self.name)\n except Exception: # pylint: disable=broad-except\n _LOGGER.exception(\"Error updating DHT sensor: %s\", self.name)\n else:\n if temperature:\n self.data[SENSOR_TEMPERATURE] = temperature\n if humidity:\n self.data[SENSOR_HUMIDITY] = humidity\n finally:\n dht.exit()","def update(self):\n if self._api is not None:\n \"\"\"Fetch the latest data\"\"\"\n self._api.get()\n \n \"\"\"set our sensor values\"\"\"\n self._current_temperature = self._api._temperature\n self._current_humidity = self._api._humidity\n self._target_temperature_high = self._api._coolto\n self._target_temperature_low = self._api._heatto\n self._away = self._api._awaymode\n \n \"\"\"Create a textual representations\"\"\"\n self._current_operation_mode = \"Unknown\"\n if self._api._opmode == 0:\n self._current_operation_mode = 'Off';\n elif self._api._opmode == 1:\n self._current_operation_mode = 'Heat only';\n elif self._api._opmode == 2:\n self._current_operation_mode = 'Cool only';\n elif self._api._opmode == 3:\n self._current_operation_mode = 'Heat & Cool'; \n self._current_fan_mode = \"Unknown\"\n \"\"\"Create a textual representations\"\"\"\n if self._api._fanmode == 0:\n self._current_fan_mode = 'Auto';\n elif self._api._fanmode == 1:\n self._current_fan_mode = 'On';\n elif self._api._fanmode == 2:\n self._current_fan_mode = 'Circulate';\n \"\"\"Create a textual representation\"\"\"\n if self._api._state == 0:\n self._current_state = 'Idle';\n elif self._api._state == 1:\n self._current_state = 'Heating';\n elif self._api._state == 2:\n self._current_state = 'Cooling';","def update_all_dict_bywhereclause(cls, updatedict, whereclause):\n return cls.dbm().modelclass_update_all_dict_bywhereclause(cls, updatedict, whereclause)","def gen_update(params, data):\n result = {}\n for key, value in data.iteritems():\n if key in params:\n result[key] = value\n return result","def visit_dict(self, sydict):\n self.current.update(sydict)","def apply_updates(\n self, unused_unit, unused_updated_unit_dict, unused_errors):\n raise Exception('Not implemented')","def update(self, *args, **kwargs):\n if args:\n if len(args) != 1:\n raise TypeError(f\"update expected at most 1 argument, got {len(args)}\")\n arg = args[0]\n if hasattr(arg, \"keys\"):\n super().update(normalize(arg, cls=self.__class__))\n else:\n try:\n for k, v in arg:\n super().update(normalize({k: v}, cls=self.__class__))\n except Exception:\n raise ValueError(\n \"dictionary update sequence element #0 has length \"\n f\"{ len(arg[0]) }; 2 is required\"\n )\n for k in kwargs:\n super().update(normalize({k: kwargs[k]}, cls=self.__class__))","def __init__(self):\n self.sensor_value = dict()","def update(self, config_dict):\r\n self._update(config_dict, allow_new_keys=True)","def update_meters(self):\n self.previous_meters = self.current_meters\n self.current_meters = {'X' : 0,\n 'Y' : 0,\n 'RSSI' : 0}","def updateMoney(self, moneydict):\n for currency_serial, amounts in moneydict.iteritems():\n self.money[currency_serial] = amounts[0]","def update_state(self, **kwargs):\n\n for name in self.metrics:\n\n metric = self.metrics[name]\n\n argspec = inspect.getfullargspec(metric.update_state)\n\n kwargs_to_pass = {k: kwargs[k] for k in kwargs if k in argspec.args}\n\n metric.update_state(**kwargs_to_pass)","def update_freqdict(seq, freqdict):\n return"],"string":"[\n \"def update(self,d:dict):\\n for name,(value,n) in d.items():\\n if n==0:\\n continue\\n self.meters[name].update(value,n)\",\n \"def update(self,dict):\\n for key in list(dict.keys()):\\n print(\\\"Hey, I'm updating something\\\")\\n self.d[key]=dict[key]\",\n \"def update_dict(new,old):\",\n \"def update(self, new_data):\\n if type(new_data) is not dict:\\n raise TypeError(\\\"Input parameter must be a dict\\\")\\n # Update parameters\\n self._type = new_data.get(\\\"_type\\\", self._type)\\n self.time_units = new_data.get(\\\"time_units\\\", self.time_units)\\n self.len_units = new_data.get(\\\"len_units\\\", self.len_units)\\n self.pump_units = new_data.get(\\\"pump_units\\\", self.pump_units)\\n self.parameters = new_data.get(\\\"parameters\\\", self.parameters)\\n # Update pumping rate\\n self.pumprate.update(new_data.get(\\\"pumprate\\\", self.pumprate.to_dict()))\\n # Update data\\n if \\\"wells\\\" in new_data:\\n n = len(new_data[\\\"wells\\\"])\\n if n > 1:\\n self.delete_all_wells()\\n for i in range(n):\\n self.add_well(0, 0, new_data[\\\"wells\\\"][i][\\\"_type\\\"] - 2)\\n self.wells[i].update(new_data[\\\"wells\\\"][i])\\n # End Function\",\n \"def _update_object(self, data_dict):\\r\\n pass\",\n \"def update(self):\\n changes = {}\\n for coord in INDICES: # the need for two for loops is necessary\\n if self.chart[coord] == ALIVE and (\\n self.number_of_neighbors(coord) < 2 or self.number_of_neighbors(coord) > 3):\\n changes[coord] = KILL\\n elif self.number_of_neighbors(coord) == 3:\\n changes[coord] = REVIVE\\n for coord in changes.keys(): # because the evolution is discrete\\n if changes[coord] == KILL:\\n self.kill(coord)\\n elif changes[coord] == REVIVE:\\n self.givebirth(coord)\",\n \"def test_updatewithdict(self):\\n s1 = Square(10, 2, 1)\\n s1_dictionary = s1.to_dictionary()\\n s2 = Square(1, 1)\\n s2.update(**s1_dictionary)\\n self.assertEqual(s2.size, 10)\\n self.assertEqual(s2.x, 2)\\n self.assertEqual(s2.y, 1)\",\n \"def update(self, d):\\n for k in d:\\n self[k] = d[k]\",\n \"def update(self, d):\\n for key, value in d.items():\\n self.__setitem__(key, value)\",\n \"def update(self, d):\\n for key, value in d.items():\\n self.__setitem__(key, value)\",\n \"def updateFromDict(self, data):\\n for key, value in data.items():\\n setattr(self, key, value)\",\n \"def update(self):\\n self.data.update()\\n\\n sensor_type = self.entity_description.key\\n if sensor_type == \\\"light\\\":\\n self._attr_native_value = self.data.light\\n elif sensor_type == \\\"light_red\\\":\\n self._attr_native_value = self.data.light_red\\n elif sensor_type == \\\"light_green\\\":\\n self._attr_native_value = self.data.light_green\\n elif sensor_type == \\\"light_blue\\\":\\n self._attr_native_value = self.data.light_blue\\n elif sensor_type == \\\"accelerometer_x\\\":\\n self._attr_native_value = self.data.accelerometer_x\\n elif sensor_type == \\\"accelerometer_y\\\":\\n self._attr_native_value = self.data.accelerometer_y\\n elif sensor_type == \\\"accelerometer_z\\\":\\n self._attr_native_value = self.data.accelerometer_z\\n elif sensor_type == \\\"magnetometer_x\\\":\\n self._attr_native_value = self.data.magnetometer_x\\n elif sensor_type == \\\"magnetometer_y\\\":\\n self._attr_native_value = self.data.magnetometer_y\\n elif sensor_type == \\\"magnetometer_z\\\":\\n self._attr_native_value = self.data.magnetometer_z\\n elif sensor_type == \\\"temperature\\\":\\n self._attr_native_value = self.data.temperature\\n elif sensor_type == \\\"pressure\\\":\\n self._attr_native_value = self.data.pressure\\n elif sensor_type == \\\"voltage_0\\\":\\n self._attr_native_value = self.data.voltage_0\\n elif sensor_type == \\\"voltage_1\\\":\\n self._attr_native_value = self.data.voltage_1\\n elif sensor_type == \\\"voltage_2\\\":\\n self._attr_native_value = self.data.voltage_2\\n elif sensor_type == \\\"voltage_3\\\":\\n self._attr_native_value = self.data.voltage_3\",\n \"def updateInstrumentParameterValue(tablews, paramdict):\\n paramnames = paramdict.keys()\\n for parname in paramnames: \\n parvalue = paramdict[parname]\\n\\t# print \\\"%s = %f\\\" % (parname, parvalue)\\n\\tif parname.count(\\\"Chi2\\\") == 0:\\n\\t # Only update parameters nothing to do with chi2\\n UpdatePeakParameterTableValue(InputWorkspace=tablews,\\n\\t\\tColumn='Value',\\n ParameterNames=[parname],\\n\\t\\tNewFloatValue=parvalue)\\n\\n return\",\n \"def update_knobs(self):\\n self.previous_knobs = self.current_knobs\\n self.current_knobs = {'Modulation' : 'fsk',\\n 'Rs' : 0,\\n 'EIRP' : 0,\\n 'Speed' : 0}\",\n \"def update(self, params):\\n for gauge in self.gauges:\\n self.safexec(gauge.update, params)\",\n \"def _update(self, datapoints):\\r\\n if len(datapoints) == 1:\\r\\n timestamp, value = datapoints[0]\\r\\n whisper.update(self.path, value, timestamp)\\r\\n else:\\r\\n whisper.update_many(self.path, datapoints)\",\n \"def add_to_dict(param_dict):\\n ### Sample - Int\\n sample_s = param_dict['ml_args'].sample_s\\n ### Sample - Mr\\n sample_Mr = param_dict['ml_args'].sample_Mr\\n ## Sample volume\\n # Units (Mpc/h)**3\\n volume_sample = { '18': 37820 / 0.01396,\\n '19': 6046016.60311 ,\\n '20': 2.40481e7 ,\\n '21': 8.79151e7 }\\n vol_mr = volume_sample[sample_s]\\n ##\\n ## Choice of Centrals and Satellites\\n cens = int(1)\\n sats = int(0)\\n ## Other constants\\n # Speed of light - In km/s\\n speed_c = ac.c.to(u.km/u.s).value\\n ## Number of CPU's to use\\n cpu_number = int(cpu_count() * param_dict['cpu_frac'])\\n ##\\n ## Plotting constants\\n plot_dict = { 'size_label':23,\\n 'size_title':25,\\n 'color_ham' :'red',\\n 'color_dyn' :'blue'}\\n ##\\n ## Catalogue Prefix string\\n catl_str_fig = param_dict['ml_args'].catl_alg_comp_fig_str()\\n ##\\n ## Saving to `param_dict`\\n param_dict['sample_s' ] = sample_s\\n param_dict['sample_Mr' ] = sample_Mr\\n param_dict['vol_mr' ] = vol_mr\\n param_dict['cens' ] = cens\\n param_dict['sats' ] = sats\\n param_dict['speed_c' ] = speed_c\\n param_dict['cpu_number' ] = cpu_number\\n param_dict['plot_dict' ] = plot_dict\\n param_dict['catl_str_fig'] = catl_str_fig\\n\\n return param_dict\",\n \"def update(self):\\n self.data_service.update()\\n self._state = self.data_service.data.get(self._json_key)\\n self._attributes = self.data_service.attributes.get(self._json_key)\\n self._unit_of_measurement = self.data_service.unit\",\n \"def update(self):\\n self.data_service.update()\\n self._state = self.data_service.data.get(self._json_key)\\n self._attributes = self.data_service.attributes.get(self._json_key)\\n self._unit_of_measurement = self.data_service.unit\",\n \"def _update_dicts(game, synergy, counter):\\n radiant_win, radiant_heroes, dire_heroes = game[1], game[2], game[3]\\n\\n radiant_heroes = map(int, radiant_heroes.split(','))\\n dire_heroes = map(int, dire_heroes.split(','))\\n\\n for i in range(5):\\n for j in range(5):\\n if i != j:\\n synergy['games'][radiant_heroes[i] - 1, radiant_heroes[j] - 1] += 1\\n synergy['games'][dire_heroes[i] - 1, dire_heroes[j] - 1] += 1\\n\\n if radiant_win:\\n synergy['wins'][radiant_heroes[i] - 1, radiant_heroes[j] - 1] += 1\\n else:\\n synergy['wins'][dire_heroes[i] - 1, dire_heroes[j] - 1] += 1\\n\\n counter['games'][radiant_heroes[i] - 1, dire_heroes[j] - 1] += 1\\n counter['games'][dire_heroes[i] - 1, radiant_heroes[j] - 1] += 1\\n\\n if radiant_win:\\n counter['wins'][radiant_heroes[i] - 1, dire_heroes[j] - 1] += 1\\n else:\\n counter['wins'][dire_heroes[i] - 1, radiant_heroes[j] - 1] += 1\",\n \"def update(self,**kwargs):\\n self._update_from_kwargs(**kwargs)\\n #--- calc estimated chops from chop length\\n self._calc_estimated_chops_from_timepoints()\\n #--- adjust chops for stimulusresponse\\n self._adjust_chops_for_stimulus_response()\\n #--- update annotations\\n self._update_annotations()\\n #--- get info\\n if self.verbose:\\n self.GetInfo()\\n #--- show plot\\n if self.show:\\n self.show_chops()\",\n \"def update(self):\\r\\n self._data.update()\\r\\n if self._data == None:\\r\\n _Log.error('No data!')\\r\\n\\r\\n\\r\\n expedite = self._data.data['trafficinfo']['evaluation']['expedite']\\r\\n congested = self._data.data['trafficinfo']['evaluation']['congested']\\r\\n blocked = self._data.data['trafficinfo']['evaluation']['blocked']\\r\\n unknown = self._data.data['trafficinfo']['evaluation']['unknown']\\r\\n description = self._data.data['trafficinfo']['evaluation']['description']\\r\\n status = self._data.data['trafficinfo']['evaluation']['status']\\r\\n if status == '0':\\r\\n traffic_status = '未知'\\r\\n elif status == '1':\\r\\n traffic_status = '畅通'\\r\\n elif status == '2':\\r\\n traffic_status = '缓行'\\r\\n elif status == '3':\\r\\n traffic_status = '拥堵'\\r\\n elif status == '4':\\r\\n traffic_status = '超堵'\\r\\n else:\\r\\n traffic_status = '无数据'\\r\\n self._state = traffic_status\\r\\n\\r\\n adddict = {\\r\\n '畅通所占百分比':expedite,\\r\\n '缓行所占百分比':congested,\\r\\n '拥堵所占百分比':blocked,\\r\\n '未知路段所占百分比':unknown,\\r\\n '道路描述':description,\\r\\n '路况': self._data.data['trafficinfo']['description'],\\r\\n '更新时间':datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\\r\\n self.attributes=adddict\",\n \"def update(self, update, **kwargs):\\n try:\\n self.saved = False\\n function_to_call = self.update_dic[update]\\n function_to_call(**kwargs)\\n except KeyError as e:\\n pass\",\n \"def update(self,ztate):\\n \\n if not ztate: return\\n assert isinstance(ztate,State) , 'must update with another State-type'\\n for key in self.keys():\\n if isinstance(ztate[key],dict):\\n self[key].update( ztate[key] )\\n elif ztate[key]:\\n self[key] = ztate[key]\\n \\n self.set_timestamp()\",\n \"def update(self, upddict):\\n\\t\\tfor (key, value) in upddict.iteritems():\\n\\t\\t\\tsetattr(self, key, value)\",\n \"def update(self, *args, **kwargs):\\n other = dict(*args, **kwargs)\\n for key, value in other.items():\\n if not isinstance(value, Detector):\\n raise TypeError(\\n \\\"Values must be {}, not {} for key {}\\\".format(\\n Detector, type(value), key\\n )\\n )\\n super().update(other)\",\n \"def update(self, **kwargs):\\n for name, item in itertools.chain(\\n self._cal_objs.items(),\\n self._noise_objs.items()):\\n logger.debug(\\\"update {}\\\".format(item))\\n item.update(**kwargs)\",\n \"def update_metric_dict_overall(self, metric_dict, update_dict, phase):\\n for key in update_dict.keys():\\n metric_dict[phase][key].append(update_dict[key])\\n return metric_dict\",\n \"def _update_(self, data):\\n self.heroes = dict((hero, Hero(hero, data[hero])) for hero in data)\",\n \"def normal_update(self, game, elfDict):\\n self.game = game # update game\\n self.my_elves = [elf for elf in elfDict.values() if not elf.elf.already_acted] # update self.my_elves\\n self.game = game # update self.game\\n self.portals.portals_update(game) # update portals (the object)\\n self.my_castle = game.get_my_castle()\",\n \"def update(self):\\n self.data.update()\\n stats = self.data.stats\\n ticker = self.data.ticker\\n\\n if self.type == \\\"exchangerate\\\":\\n self._attr_state = ticker[self._currency].p15min\\n self._attr_unit_of_measurement = self._currency\\n elif self.type == \\\"trade_volume_btc\\\":\\n self._attr_state = f\\\"{stats.trade_volume_btc:.1f}\\\"\\n elif self.type == \\\"miners_revenue_usd\\\":\\n self._attr_state = f\\\"{stats.miners_revenue_usd:.0f}\\\"\\n elif self.type == \\\"btc_mined\\\":\\n self._attr_state = str(stats.btc_mined * 0.00000001)\\n elif self.type == \\\"trade_volume_usd\\\":\\n self._attr_state = f\\\"{stats.trade_volume_usd:.1f}\\\"\\n elif self.type == \\\"difficulty\\\":\\n self._attr_state = f\\\"{stats.difficulty:.0f}\\\"\\n elif self.type == \\\"minutes_between_blocks\\\":\\n self._attr_state = f\\\"{stats.minutes_between_blocks:.2f}\\\"\\n elif self.type == \\\"number_of_transactions\\\":\\n self._attr_state = str(stats.number_of_transactions)\\n elif self.type == \\\"hash_rate\\\":\\n self._attr_state = f\\\"{stats.hash_rate * 0.000001:.1f}\\\"\\n elif self.type == \\\"timestamp\\\":\\n self._attr_state = stats.timestamp\\n elif self.type == \\\"mined_blocks\\\":\\n self._attr_state = str(stats.mined_blocks)\\n elif self.type == \\\"blocks_size\\\":\\n self._attr_state = f\\\"{stats.blocks_size:.1f}\\\"\\n elif self.type == \\\"total_fees_btc\\\":\\n self._attr_state = f\\\"{stats.total_fees_btc * 0.00000001:.2f}\\\"\\n elif self.type == \\\"total_btc_sent\\\":\\n self._attr_state = f\\\"{stats.total_btc_sent * 0.00000001:.2f}\\\"\\n elif self.type == \\\"estimated_btc_sent\\\":\\n self._attr_state = f\\\"{stats.estimated_btc_sent * 0.00000001:.2f}\\\"\\n elif self.type == \\\"total_btc\\\":\\n self._attr_state = f\\\"{stats.total_btc * 0.00000001:.2f}\\\"\\n elif self.type == \\\"total_blocks\\\":\\n self._attr_state = f\\\"{stats.total_blocks:.0f}\\\"\\n elif self.type == \\\"next_retarget\\\":\\n self._attr_state = f\\\"{stats.next_retarget:.2f}\\\"\\n elif self.type == \\\"estimated_transaction_volume_usd\\\":\\n self._attr_state = f\\\"{stats.estimated_transaction_volume_usd:.2f}\\\"\\n elif self.type == \\\"miners_revenue_btc\\\":\\n self._attr_state = f\\\"{stats.miners_revenue_btc * 0.00000001:.1f}\\\"\\n elif self.type == \\\"market_price_usd\\\":\\n self._attr_state = f\\\"{stats.market_price_usd:.2f}\\\"\",\n \"def _update_(self, data):\\n # imports\\n from intervals import Interval, LinkedIntervalSet\\n \\n # create a LinkedIntervalSet to store the time-based states for this\\n # hero\\n states = [Interval(dict((HER_KWD_MAP.get(k, k), d[1][k]) for k in d[1]), d[0]) for d in data]\\n self.states = LinkedIntervalSet.from_starts(states)\",\n \"def update(self, dayHour, comicId):\\n #TODO test\\n self.loadComic(comicId)\\n\\n #Sprint \\\"Predictor.update(\\\", comicId, \\\")\\\"\\n\\n\\n for i in range(1):\\n # Stop weeding?\\n stopWeeding = False\\n\\n if self.__predictorData == None:\\n print \\\"Line 252 (NONE)\\\"\\n\\n if (self.__predictorData.isWeeding()):\\n tgm = time.gmtime()\\n sec = time.mktime(time.strptime(str(tgm.tm_year) +' '+ str(tgm.tm_mon) +' '+ str(tgm.tm_mday) +' '+ str(tgm.tm_hour) +' '+ str(tgm.tm_min) +' '+ str(tgm.tm_sec), '%Y %m %d %H %M %S'))\\n # print \\\"weeding times:\\\", self.__predictorData.getWeedingStart(), sec\\n if (sec >= self.__predictorData.getWeedingStart() + 7*24*self.hourScale):\\n self.stopWeeding(dayHour)\\n stopWeeding = True\\n\\n if (self.__predictorData.isWeeding() or stopWeeding):\\n # Weeding\\n uRanges = self.__predictorData.getUpdateRanges()\\n if (len(uRanges) > 0):\\n if (self.__predictorData.addDayHour(dayHour)):\\n break\\n # if (self.inURange(self.incDayHour(dayHour, -self.rangeWidth), uRanges[-1])):\\n # uRangeLast = self.incDayHour(uRanges[-1]['position'], uRanges[-1]['width'])\\n # #self.__predictorData.addUpdateRange(self.addUpdateRange(incDayHour(uRangeLast, Predictor.rangeWidth + 1)))\\n # self.__predictorData.addUpdateRange(self.incDayHour(uRangeLast, Predictor.rangeWidth + 1))\\n # break \\n self.__predictorData.addUpdateRange(self.blankUpdateRange(dayHour))\\n else:\\n # Regular update\\n if (not self.__predictorData.addDayHour(dayHour)):\\n # Add dayHour to nearest range\\n updateRanges = self.__predictorData.getUpdateRanges()\\n dist = {}\\n dh = self.dayHourToHours(dayHour)\\n for i, uRange in enumerate(updateRanges):\\n ur_pos_hours = self.dayHourToHours(uRange['position'])\\n ur_width = uRange['width']\\n hours = self.dayHourToHours(dayHour)\\n dist[min(abs(ur_pos_hours - ur_width - hours), abs(ur_pos_hours + ur_width - hours))] = i\\n self.__predictorData.addDayHourToURange(dayHour, dist[min(dist.keys())])\\n\\n \\n self.__predictorData.setUpdateRanges(self.calculateURPositions(self.__predictorData.getUpdateRanges()))\\n self.__predictorData.setSchedule(self.calculateScheduleUR(self.__predictorData.getUpdateRanges()))\\n \\n self.updatePredictorList(self.__predictorData.getSchedule(), comicId)\\n self.saveComic(comicId)\",\n \"def update(self, dt):\\n self.level.update(self.keys, dt)\",\n \"def update(self, arg=None, **kwargs):\\n if arg:\\n if hasattr(arg, 'keys'):\\n for k in arg: self[k] = arg[k]\\n else:\\n for k, v in arg: self[k] = v\\n\\n if kwargs:\\n for k in kwargs: self[k] = kwargs[k]\",\n \"def update_properties(self, update_dict: Dict[str, Any]):\\n self.event_properties.update(update_dict)\",\n \"def update_compartments(self, food_glucose):\\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())\",\n \"def update(self, other_dict=None, **f):\\n changed = False\\n if other_dict:\\n for (key, value) in other_dict.items() if hasattr(other_dict, 'items') else other_dict:\\n self.__setitem__(key, value, bulk_change=True)\\n changed = True\\n if f:\\n for (key, value) in f.items():\\n self.__setitem__(key, value, bulk_change=True)\\n changed = True\\n changed and self._changed()\",\n \"def update():\",\n \"def update():\",\n \"def update_from_dict(self, data: dict):\\n self.algo = data[\\\"algo\\\"]\\n self.version = data[\\\"sbx\\\"]\\n self.next_session = data[\\\"next\\\"]\\n self.last_session = data[\\\"last\\\"]\\n self.past_quality = unpack_int_list(data[\\\"pastq\\\"])\\n\\n # Revert to length of past_quality if reps are not set\\n possible_rep = len(self.past_quality)\\n self.actual_repetitions = data.get(\\\"reps\\\", possible_rep)\\n\\n # Other keys are used by algorithm\\n self.algo_state = data.copy()\\n for required_key in REQUIRED_FIELDS:\\n del self.algo_state[required_key]\",\n \"def update(self, *args, **kwargs):\\n super(ReadOnlyDict, self).update(*args, **kwargs) # pragma: no cover\",\n \"def update_asop_dict(asop_dict,region,coords,color,all_settings):\\n # Set unique color\\n asop_dict['color'] = color\\n\\n # Apply any general user settings\\n asop_dict['grid_desc'] = all_settings.get('grid','native')\\n asop_dict['grid_type'] = all_settings.get('grid','native')\\n asop_dict['region_name'] = region\\n asop_dict['region_desc'] = region.replace('_',' ')\\n asop_dict['region'] = coords\\n\\n # Edit dx for region\\n mean_lat = np.mean(coords[0:2])\\n asop_dict['dx'] = asop_dict['dx'] * np.cos(np.radians(mean_lat))\\n all_settings.pop('infile','') # key not allowed\\n for key in asop_dict:\\n if key in all_settings:\\n asop_dict[key] = all_settings[key]\\n\\n # Apply any specific file settings\\n infile = os.path.basename(asop_dict['infile'])\\n file_settings = settings.get(infile,{})\\n file_settings.pop('infile','') # key not allowed\\n file_settings.pop('region','')\\n if file_settings:\\n for key in file_settings:\\n asop_dict[key] = file_settings[key]\\n if 'legend_name' not in file_settings:\\n asop_dict['legend_name'] = asop_dict['name'].replace('_',' ')\\n\\n print('---> Final data dictionary:')\\n print(json.dumps(asop_dict, sort_keys=True, indent=2))\\n\\n return asop_dict\",\n \"def _update_loose (self, dict):\\n self.__dict__.update(dict)\",\n \"def update( d, **kw):\\n d.update( d, **kw )\\n return d\",\n \"def update(self, *args, **kw):\\n pass\",\n \"def _measurement_update(self):\\n pass\",\n \"def update(self):\\n self.data.update()\\n for sensor in self.data.daikinskyport.get_sensors(self._index):\\n if sensor[\\\"type\\\"] == self._type and self._sensor_name == sensor[\\\"name\\\"]:\\n self._state = sensor[\\\"value\\\"]\",\n \"def update(self, temperature, humidity, co2, voc, voltage,\\n baseline=False, fullupdate=False):\\n self._add_borders()\\n self._add_temperature(temperature)\\n self._add_humidity(humidity)\\n self._add_co2(co2)\\n self._add_voc(voc)\\n self._add_voltage(voltage)\\n self._add_baseline_indicator(baseline)\\n self._update_screen(fullupdate)\",\n \"def update(self, params):\",\n \"def soft_update(self, data):\\n if not data:\\n return\\n\\n for key, value in list(data.items()):\\n\\n old_value = super(Yaco, self).get(key, None)\\n\\n if isinstance(value, dict):\\n if old_value and isinstance(old_value, Yaco):\\n old_value.soft_update(value)\\n if old_value:\\n # there is an older value - not a dict - cannot overwrite\\n continue\\n else:\\n # no old value - overwrite all you like\\n super(Yaco, self).__setitem__(key, Yaco(value))\\n elif isinstance(value, list):\\n # parse the list to see if there are dicts - which\\n # need to be translated to Yaco objects\\n if not old_value:\\n new_value = self._list_parser(value)\\n super(Yaco, self).__setitem__(key, new_value)\\n else:\\n if not old_value:\\n super(Yaco, self).__setitem__(key, value)\",\n \"def update_data():\\n pass\",\n \"def set_params(self, dic):\\n if dic is not None:\\n for key, val in zip(dic.keys(), dic.values()):\\n if key in self.__dict__.keys():\\n self.__dict__[key] = val\\n\\n if 'scale_params' in self.__dict__.keys():\\n self.scale_params.set_params(dic)\\n if 'atmospheric_params' in self.__dict__.keys():\\n if self.atmospheric_params is not None:\\n self.atmospheric_params.set_params(dic)\\n\\n if 'atemperature_params' in self.__dict__.keys():\\n if self.atemperature_params is not None:\\n self.atemperature_params.set_params(dic)\\n\\n if 'oceanic_params' in self.__dict__.keys():\\n if self.oceanic_params is not None:\\n self.oceanic_params.set_params(dic)\\n\\n if 'ground_params' in self.__dict__.keys():\\n if self.ground_params is not None:\\n self.ground_params.set_params(dic)\\n\\n if 'otemperature_params' in self.__dict__.keys():\\n if self.gotemperature_params is not None:\\n self.gotemperature_params.set_params(dic)\\n\\n if 'gtemperature_params' in self.__dict__.keys():\\n if self.gotemperature_params is not None:\\n self.gotemperature_params.set_params(dic)\",\n \"def update(self,z_t):\\n # YOUR CODE HERE\\n pass\",\n \"def update(world):\\r\\n infect = infection(world['SIR'], infection_rate, incubation_rate)\\r\\n disperse = dispersion(world['SIR'], dispersion_kernel, dispersion_rates)\\r\\n world['SIR'] += dt*( infect + disperse)\\r\\n world['t'] += dt\",\n \"def update_from_dict(self, data: dict) -> \\\"Device\\\":\\n if \\\"info\\\" in data and data[\\\"info\\\"]:\\n self.info = Info.from_dict(data[\\\"info\\\"])\\n\\n if \\\"locations\\\" in data and data[\\\"locations\\\"]:\\n locations = [Location.from_dict(location) for location in data[\\\"locations\\\"]]\\n self.locations = locations\\n\\n return self\",\n \"def update(self, *args, **kwargs):\\n sqrlist = [\\\"id\\\", \\\"size\\\", \\\"x\\\", \\\"y\\\"]\\n if args and len(args) != 0:\\n for i in range(len(sqrlist)):\\n if i < len(args):\\n # call to setter method\\n setattr(self, sqrlist[i], args[i])\\n else:\\n if kwargs and len(kwargs) != 0:\\n for k in sqrlist:\\n for key, value in kwargs.items():\\n if k == key:\\n setattr(self, key, value)\",\n \"def setFromDict(self, designObject, myShipDict):\\n self.myDesign = designObject\\n self.designID = designObject.id\\n self.componentdata = designObject.componentdata\\n self.weapondata = designObject.weapondata\\n self.myShipHull = designObject.myShipHull\\n designAttrDict = designObject.getMyInfoAsDict()\\n self.currentISP = myShipDict['currentISP']\\n\\n for position, dQuad in myShipDict['quads'].iteritems():\\n newQuad = quad.Quad(dQuad)\\n newQuad.setMyParent(self)\\n # weapons have to be created first\\n for id, dWeap in dQuad['weapons'].iteritems():\\n newWeap = weapon.Weapon(dWeap)\\n newWeap.setMyQuad(newQuad)\\n for id, dComp in dQuad['components'].iteritems():\\n newComp = component.Component(dComp)\\n newComp.setMyQuad(newQuad)\\n newQuad.setMyStatus()\\n self.quads[newQuad.position] = newQuad\\n newQuad.resetDefences()\\n newQuad.reloadAmmo()\\n\\n self.setMyStatus()\\n self.name = designObject.name + '-' + self.id\",\n \"def edit_pressure(dictionary, domian_json, pressure=0):\\n\\n\\ttry:\\n\\t\\ttqdm.write(\\\"{}: Editing pressure file: {}\\\".format(datetime.datetime.now(),dictionary))\\n\\t\\tpressureDict = ParsedParameterFile(dictionary)\\n\\n\\t\\twith open(domian_json, 'r') as f:\\n\\t\\t\\tdomain_dict = json.load(f)\\n\\n\\t\\tboundaryField = {\\\"vessel\\\": {\\\"type\\\": \\\"zeroGradient\\\"}, \\\"walls\\\":{\\\"type\\\":\\\"empty\\\"}}\\t\\n\\t\\tfor key, value in domain_dict.items():\\n\\t\\t\\ttry:\\t\\t\\t\\n\\t\\t\\t\\tif value[\\\"type\\\"] == \\\"inlet\\\":\\n\\t\\t\\t\\t\\tboundaryField.update({key: { \\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"zeroGradient\\\"}})\\n\\t\\t\\t\\telif value[\\\"type\\\"] == \\\"outlet\\\":\\n\\t\\t\\t\\t\\tboundaryField.update({key: { \\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"fixedValue\\\",\\n\\t\\t\\t\\t\\t\\t\\\"value\\\":\\\"uniform 0 \\\"}}\\n\\t\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\tpressureDict[\\\"boundaryField\\\"] = boundaryField\\n\\n\\t\\ttry:\\n\\t\\t\\tpressureDict.writeFile()\\n\\t\\texcept IOError:\\n\\t\\t\\ttqdm.write(\\\"Can't write file. Content would have been:\\\")\\n\\t\\t\\ttqdm.write(pressureDict)\\n\\n\\texcept IOError:\\n\\t\\ttqdm.write(dictionary, \\\" does not exist\\\")\\n\\t\\treturn 1\",\n \"def update(self, profile: Dict[datetime.time, float]) -> None:\\n\\n if self._profile is None:\\n self._profile = profile\\n else:\\n self._profile.update(profile)\",\n \"def update_from_dict(self, dictionary):\\n for key in dictionary:\\n setattr(self, key, dictionary[key])\\n return self.to_dict()\",\n \"def _update(self, var, channels, value):\\n\\n if type(channels) is int:\\n channels = [channels]\\n\\n if var =='frequency':\\n for channel in channels:\\n self.frequencies[channel] = value\\n elif var =='phase':\\n for channel in channels:\\n self.phases[channel] = value\\n elif var == 'amplitude':\\n for channel in channels:\\n self.amplitudes[channel] = value\",\n \"def update( ):\\r\\n pass\",\n \"def update_record(self, d: dict) -> None:\\n super().update_record(d)\\n d.update(\\n dataset_doses=str_list(self.doses),\\n dataset_ns=str_list(self.ns),\\n dataset_stdevs=str_list(self.stdevs),\\n dataset_means=str_list(self.means),\\n )\",\n \"def update_weather(self, weather: Weather) -> None:\\n new_weather = carla.WeatherParameters()\\n if weather == Weather.SUNSHINE:\\n new_weather = carla.WeatherParameters.ClearNoon\\n elif weather == Weather.RAIN:\\n new_weather = carla.WeatherParameters(precipitation=100.0, wetness=100.0)\\n elif weather == Weather.FOG:\\n new_weather = carla.WeatherParameters(fog_density=100.0, fog_distance=0.0,fog_falloff=0.5)\\n self.carla_world.set_weather(new_weather)\",\n \"def update(self, **kwargs):\\n for key, val in kwargs.items():\\n setattr(self, key, val)\",\n \"def update(self, *args, **kwargs):\\n if kwargs is not None:\\n for key, value in kwargs.items():\\n setattr(self, key, value)\",\n \"def update_params(coeffs_dict, params):\\n for key in coeffs_dict.keys():\\n try:\\n # Batman\\n params.__dict__[key] = coeffs_dict[key]\\n except:\\n # Normal dictionary\\n params[key] = coeffs_dict[key]\\n return params\",\n \"def test_updatewithdictionarybycomparingdictionaries(self):\\n s1 = Square(10, 2, 1, 9)\\n s1_dictionary = s1.to_dictionary()\\n s2 = Square(1, 1)\\n s2.update(**s1_dictionary)\\n self.assertEqual(s1.__dict__, s2.__dict__)\",\n \"def Increase_Dictionary(dictio, data, index):\\n for line in data:\\n split = line.split()\\n name = split[0] + \\\" \\\" + split[1]\\n fmi = float(split[2])\\n dictio.setdefault(name, [np.Inf, np.Inf, np.Inf])[index] = fmi\\n return dictio\",\n \"def update_tool(self, tool_number, **kwargs):\\r\\n if kwargs.get('feed_rate') is not None:\\r\\n self.tools[tool_number].feed_rate = kwargs.get('feed_rate')\\r\\n if kwargs.get('retract_rate') is not None:\\r\\n self.tools[tool_number].retract_rate = kwargs.get('retract_rate')\\r\\n if kwargs.get('rpm') is not None:\\r\\n self.tools[tool_number].rpm = kwargs.get('rpm')\\r\\n if kwargs.get('diameter') is not None:\\r\\n self.tools[tool_number].diameter = kwargs.get('diameter')\\r\\n if kwargs.get('max_hit_count') is not None:\\r\\n self.tools[tool_number].max_hit_count = kwargs.get('max_hit_count')\\r\\n if kwargs.get('depth_offset') is not None:\\r\\n self.tools[tool_number].depth_offset = kwargs.get('depth_offset')\\r\\n # Update drill hits\\r\\n newtool = self.tools[tool_number]\\r\\n for hit in self.hits:\\r\\n if hit.tool.number == newtool.number:\\r\\n hit.tool = newtool\",\n \"def _update(self, handle, value):\\n _LOGGER.debug(\\n \\\"%s: %15s temperature = %-2d.%-2d, humidity = %3d\\\",\\n handle,\\n self.name,\\n value[0],\\n value[2],\\n value[1],\\n )\\n self.data[\\\"temp\\\"] = float(\\\"%d.%d\\\" % (value[0], value[2]))\\n self.data[\\\"humid\\\"] = value[1]\",\n \"def report_update():\\r\\n resources[\\\"water\\\"] = resources[\\\"water\\\"] - MENU[order][\\\"ingredients\\\"][\\\"water\\\"]\\r\\n resources[\\\"milk\\\"] = resources[\\\"milk\\\"] - MENU[order][\\\"ingredients\\\"][\\\"milk\\\"]\\r\\n resources[\\\"coffee\\\"] = resources[\\\"coffee\\\"] - MENU[order][\\\"ingredients\\\"][\\\"coffee\\\"]\\r\\n resources[\\\"money\\\"] = resources[\\\"money\\\"] + total\",\n \"def update_info(job_path,\\n vals,\\n param_names,\\n prop_name):\\n\\n with open(job_path, \\\"r\\\") as f:\\n info = json.load(f)\\n\\n real_names = []\\n real_vals = []\\n\\n for param_name, val in zip(param_names, vals):\\n if param_name.startswith(\\\"log_\\\"):\\n # if anything starts with \\\"log_\\\" (e.g. \\\"log_schnet_dropout\\\"),\\n # exponentiate its value to get the actual number\\n real_names.append(param_name.replace(\\\"log_\\\", \\\"\\\"))\\n real_vals.append(np.exp(val))\\n else:\\n real_names.append(param_name)\\n real_vals.append(val)\\n\\n # update values\\n for param_type, val in zip(real_names, real_vals):\\n if 'dropout' in param_type:\\n update_dropout(info=info,\\n dropout=val,\\n dropout_type=param_type,\\n prop_name=prop_name)\\n\\n elif param_type == \\\"num_heads\\\":\\n update_heads(info=info,\\n heads=val)\\n\\n elif param_type == \\\"attention_type\\\":\\n info[\\\"model_params\\\"][\\\"boltzmann_dict\\\"][\\\"type\\\"] = val\\n\\n else:\\n if param_type not in info[\\\"model_params\\\"]:\\n msg = (f\\\"Warning: assuming that {param_type} \\\"\\n \\\"is just a key in `model_params`, but \\\"\\n \\\"it is not currently in `model_params` in \\\"\\n \\\"the config file. If it should be in a \\\"\\n \\\"different location then you will need \\\"\\n \\\"to write a custom function for updating \\\"\\n \\\"it.\\\")\\n\\n fprint(msg)\\n\\n update_general(info, key=param_type, val=val)\\n\\n # save\\n with open(job_path, \\\"w\\\") as f:\\n json.dump(info, f, indent=4, sort_keys=True)\",\n \"def data_dict_update(self, change):\\n self.data_dict = change['value']\",\n \"def update_device(self, dev_dict):\\n # Note(jprabh1x): added bus,slot,function into fields dict as \\n # seperate fields.\\n no_changes = ('status', 'instance_uuid', 'id', 'extra_info', 'workload')\\n map(lambda x: dev_dict.pop(x, None),\\n [key for key in no_changes])\\n\\n # Note(jprabh1x): populating values for bus,slot,function from address in dev_dict.\\n if dev_dict.has_key(\\\"address\\\"):\\n \\t\\taddress = pci_utils.parse_address(dev_dict[\\\"address\\\"])\\n \\t\\tdev_dict.update({'bus':str(address[1]), 'slot':str(address[2]), 'function':str(address[3])})\\n for k, v in dev_dict.items():\\n if k in self.fields.keys():\\n self[k] = v\\n else:\\n extra_info = self.extra_info\\n extra_info.update({k: str(v)})\\n self.extra_info = extra_info\",\n \"def update(self, values):\\n for k, v in values.items():\\n setattr(self, k, v)\",\n \"def update(*args):\",\n \"def updateParameters(self, paramDict):\\n\\n params = ['taux', 'mu', 'G', 'alpha_0', 'delta', 'p', 'I0', 'kparam']\\n\\n # Now set the parameters\\n for k in paramDict.keys():\\n mycode = 'self.' + k + \\\"=paramDict[\\\\'\\\" + k + \\\"\\\\']\\\"\\n exec(mycode)\",\n \"def update_parameters(updates):\\r\\n for (key, val) in updates.items():\\r\\n par[key] = val\\r\\n print('Updating:', key, '-->', val)\\r\\n update_dependencies()\",\n \"def update_general(info, key, val):\\n\\n info[\\\"model_params\\\"][key] = val\",\n \"def update(self, data: Mapping[str, np.ndarray]) -> Self:\\n\\n raise NotImplementedError\",\n \"def __init__(self, sensor):\\n self.sensor = sensor\\n self.temperature = None\\n self.humitidy = None\\n self.update()\",\n \"def update(self, *args, **kwargs):\\n if args:\\n if args is not None:\\n lista = [\\\"id\\\", \\\"size\\\", \\\"x\\\", \\\"y\\\"]\\n for i, j in zip(args, lista):\\n setattr(self, j, i)\\n else:\\n for key, value in kwargs.items():\\n setattr(self, key, value)\",\n \"def update(self, **vars):\\n for name in vars:\\n # Use __setitem__ for all effects\\n self[name] = vars[name]\",\n \"def update(self, **kwargs):\\n for k, v in kwargs.iteritems():\\n if hasattr(self, k):\\n setattr(self, k, v)\",\n \"def update(self, *args, **kwargs):\\n if len(args):\\n for i, val in enumerate(args):\\n if i == 0:\\n self.id = val\\n elif i == 1:\\n self.size = val\\n elif i == 2:\\n self.x = val\\n elif i == 3:\\n self.y = val\\n else:\\n for key, val in kwargs.items():\\n if key == \\\"id\\\":\\n self.id = val\\n if key == \\\"size\\\":\\n self.size = val\\n if key == \\\"x\\\":\\n self.x = val\\n if key == \\\"y\\\":\\n self.y = val\",\n \"def update(self):\\n dht = self.sensor(self.pin)\\n try:\\n temperature = dht.temperature\\n humidity = dht.humidity\\n except RuntimeError:\\n _LOGGER.debug(\\\"Unexpected value from DHT sensor: %s\\\", self.name)\\n except Exception: # pylint: disable=broad-except\\n _LOGGER.exception(\\\"Error updating DHT sensor: %s\\\", self.name)\\n else:\\n if temperature:\\n self.data[SENSOR_TEMPERATURE] = temperature\\n if humidity:\\n self.data[SENSOR_HUMIDITY] = humidity\\n finally:\\n dht.exit()\",\n \"def update(self):\\n if self._api is not None:\\n \\\"\\\"\\\"Fetch the latest data\\\"\\\"\\\"\\n self._api.get()\\n \\n \\\"\\\"\\\"set our sensor values\\\"\\\"\\\"\\n self._current_temperature = self._api._temperature\\n self._current_humidity = self._api._humidity\\n self._target_temperature_high = self._api._coolto\\n self._target_temperature_low = self._api._heatto\\n self._away = self._api._awaymode\\n \\n \\\"\\\"\\\"Create a textual representations\\\"\\\"\\\"\\n self._current_operation_mode = \\\"Unknown\\\"\\n if self._api._opmode == 0:\\n self._current_operation_mode = 'Off';\\n elif self._api._opmode == 1:\\n self._current_operation_mode = 'Heat only';\\n elif self._api._opmode == 2:\\n self._current_operation_mode = 'Cool only';\\n elif self._api._opmode == 3:\\n self._current_operation_mode = 'Heat & Cool'; \\n self._current_fan_mode = \\\"Unknown\\\"\\n \\\"\\\"\\\"Create a textual representations\\\"\\\"\\\"\\n if self._api._fanmode == 0:\\n self._current_fan_mode = 'Auto';\\n elif self._api._fanmode == 1:\\n self._current_fan_mode = 'On';\\n elif self._api._fanmode == 2:\\n self._current_fan_mode = 'Circulate';\\n \\\"\\\"\\\"Create a textual representation\\\"\\\"\\\"\\n if self._api._state == 0:\\n self._current_state = 'Idle';\\n elif self._api._state == 1:\\n self._current_state = 'Heating';\\n elif self._api._state == 2:\\n self._current_state = 'Cooling';\",\n \"def update_all_dict_bywhereclause(cls, updatedict, whereclause):\\n return cls.dbm().modelclass_update_all_dict_bywhereclause(cls, updatedict, whereclause)\",\n \"def gen_update(params, data):\\n result = {}\\n for key, value in data.iteritems():\\n if key in params:\\n result[key] = value\\n return result\",\n \"def visit_dict(self, sydict):\\n self.current.update(sydict)\",\n \"def apply_updates(\\n self, unused_unit, unused_updated_unit_dict, unused_errors):\\n raise Exception('Not implemented')\",\n \"def update(self, *args, **kwargs):\\n if args:\\n if len(args) != 1:\\n raise TypeError(f\\\"update expected at most 1 argument, got {len(args)}\\\")\\n arg = args[0]\\n if hasattr(arg, \\\"keys\\\"):\\n super().update(normalize(arg, cls=self.__class__))\\n else:\\n try:\\n for k, v in arg:\\n super().update(normalize({k: v}, cls=self.__class__))\\n except Exception:\\n raise ValueError(\\n \\\"dictionary update sequence element #0 has length \\\"\\n f\\\"{ len(arg[0]) }; 2 is required\\\"\\n )\\n for k in kwargs:\\n super().update(normalize({k: kwargs[k]}, cls=self.__class__))\",\n \"def __init__(self):\\n self.sensor_value = dict()\",\n \"def update(self, config_dict):\\r\\n self._update(config_dict, allow_new_keys=True)\",\n \"def update_meters(self):\\n self.previous_meters = self.current_meters\\n self.current_meters = {'X' : 0,\\n 'Y' : 0,\\n 'RSSI' : 0}\",\n \"def updateMoney(self, moneydict):\\n for currency_serial, amounts in moneydict.iteritems():\\n self.money[currency_serial] = amounts[0]\",\n \"def update_state(self, **kwargs):\\n\\n for name in self.metrics:\\n\\n metric = self.metrics[name]\\n\\n argspec = inspect.getfullargspec(metric.update_state)\\n\\n kwargs_to_pass = {k: kwargs[k] for k in kwargs if k in argspec.args}\\n\\n metric.update_state(**kwargs_to_pass)\",\n \"def update_freqdict(seq, freqdict):\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6955226","0.63827276","0.63701075","0.62050605","0.60651165","0.601024","0.5964989","0.5824813","0.5740105","0.5740105","0.571362","0.57038397","0.5703277","0.5690079","0.568872","0.5649773","0.5644877","0.5637672","0.5637672","0.5636201","0.56254166","0.5616359","0.56047213","0.56040215","0.5593615","0.5592204","0.557957","0.5573038","0.5566676","0.5559075","0.5556366","0.55531365","0.5552392","0.5551197","0.5545829","0.5542443","0.55336773","0.5526177","0.5522443","0.5522443","0.5518496","0.55156857","0.5513465","0.5512714","0.54895693","0.54875994","0.548313","0.54816103","0.54774845","0.5466441","0.5465696","0.54590297","0.5454321","0.54511034","0.5431339","0.54298496","0.54040366","0.5403866","0.5400378","0.5389972","0.53790027","0.5370858","0.53678846","0.53643906","0.5363816","0.5350061","0.5347113","0.5339905","0.5338653","0.53351194","0.53347605","0.5334636","0.5331131","0.53253317","0.5318522","0.5317002","0.53143024","0.5301473","0.5299183","0.5293653","0.5288518","0.5286624","0.5284256","0.52823806","0.5273784","0.52734375","0.5271361","0.5270544","0.52670187","0.5266873","0.5261936","0.52555275","0.524767","0.52472264","0.5246398","0.5245401","0.52383167","0.52381265","0.5236189","0.5232331"],"string":"[\n \"0.6955226\",\n \"0.63827276\",\n \"0.63701075\",\n \"0.62050605\",\n \"0.60651165\",\n \"0.601024\",\n \"0.5964989\",\n \"0.5824813\",\n \"0.5740105\",\n \"0.5740105\",\n \"0.571362\",\n \"0.57038397\",\n \"0.5703277\",\n \"0.5690079\",\n \"0.568872\",\n \"0.5649773\",\n \"0.5644877\",\n \"0.5637672\",\n \"0.5637672\",\n \"0.5636201\",\n \"0.56254166\",\n \"0.5616359\",\n \"0.56047213\",\n \"0.56040215\",\n \"0.5593615\",\n \"0.5592204\",\n \"0.557957\",\n \"0.5573038\",\n \"0.5566676\",\n \"0.5559075\",\n \"0.5556366\",\n \"0.55531365\",\n \"0.5552392\",\n \"0.5551197\",\n \"0.5545829\",\n \"0.5542443\",\n \"0.55336773\",\n \"0.5526177\",\n \"0.5522443\",\n \"0.5522443\",\n \"0.5518496\",\n \"0.55156857\",\n \"0.5513465\",\n \"0.5512714\",\n \"0.54895693\",\n \"0.54875994\",\n \"0.548313\",\n \"0.54816103\",\n \"0.54774845\",\n \"0.5466441\",\n \"0.5465696\",\n \"0.54590297\",\n \"0.5454321\",\n \"0.54511034\",\n \"0.5431339\",\n \"0.54298496\",\n \"0.54040366\",\n \"0.5403866\",\n \"0.5400378\",\n \"0.5389972\",\n \"0.53790027\",\n \"0.5370858\",\n \"0.53678846\",\n \"0.53643906\",\n \"0.5363816\",\n \"0.5350061\",\n \"0.5347113\",\n \"0.5339905\",\n \"0.5338653\",\n \"0.53351194\",\n \"0.53347605\",\n \"0.5334636\",\n \"0.5331131\",\n \"0.53253317\",\n \"0.5318522\",\n \"0.5317002\",\n \"0.53143024\",\n \"0.5301473\",\n \"0.5299183\",\n \"0.5293653\",\n \"0.5288518\",\n \"0.5286624\",\n \"0.5284256\",\n \"0.52823806\",\n \"0.5273784\",\n \"0.52734375\",\n \"0.5271361\",\n \"0.5270544\",\n \"0.52670187\",\n \"0.5266873\",\n \"0.5261936\",\n \"0.52555275\",\n \"0.524767\",\n \"0.52472264\",\n \"0.5246398\",\n \"0.5245401\",\n \"0.52383167\",\n \"0.52381265\",\n \"0.5236189\",\n \"0.5232331\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61816335"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":75796,"cells":{"query":{"kind":"string","value":"Verify well parameters and returns warnings according to possible errors"},"document":{"kind":"string","value":"def validate_parameters(self):\n\n flag = True\n warnings = \"\"\n # Check radius\n r = self.parameters.get('r', 0)\n if type(r) not in [int, float]:\n flag = False\n warnings += \"Radius r must be a float value\\n\"\n else:\n if r <= 0:\n flag = False\n warnings += \"Radius r must be higher than 0\\n\"\n # Check if is full penetrating\n op = self.parameters.get('full', False)\n\n if not op:\n # Check observation well length\n if 'd' in self.parameters and 'l' in self.parameters:\n d = self.parameters.get('d', -1)\n l = self.parameters.get('l', -1)\n if type(l) not in [int, float]:\n flag = False\n warnings += \"Depth of well bottom must be a float value\\n\"\n else:\n if l < 0:\n flag = False\n warnings += \"Depth l must be higher than 0\\n\"\n if type(d) not in [int, float]:\n flag = False\n warnings += \"Depth of well screen must be a float value\\n\"\n else:\n if d < 0 or d > l:\n flag = False\n warnings += \"Depth d must be in range 0 <= d <= l\\n\"\n # Check piezometer depth\n elif 'z' in self.parameters:\n z = self.parameters.get('z', -1)\n if type(z) not in [int, float]:\n flag = False\n warnings += \"Depth of piezometer must be a float value\\n\"\n else:\n if z < 0:\n flag = False\n warnings += \"Depth z must be higher than 0\\n\"\n else:\n flag = False\n warnings += \"Well don't contain well depth attributes\\n\"\n return(flag, warnings) # End Function"},"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 validate_parameters(self):\n\n flag = True\n warnings = \"\"\n # Check radius\n r = self.parameters.get('rw', 0)\n if type(r) not in [int, float]:\n flag = False\n warnings += \"Well radius rw must be a float value\\n\"\n else:\n if r <= 0:\n flag = False\n warnings += \"Well radius rw must be higher than 0\\n\"\n # Check if is full penetrating\n op = self.parameters.get('full', False)\n\n if not op:\n # Check observation well length\n if 'd' in self.parameters and 'l' in self.parameters:\n d = self.parameters.get('d', -1)\n l = self.parameters.get('l', -1)\n if type(l) not in [int, float]:\n flag = False\n warnings += \"Depth of well bottom must be a float value\\n\"\n else:\n if l < 0:\n flag = False\n warnings += \"Depth l must be higher than 0\\n\"\n if type(d) not in [int, float]:\n flag = False\n warnings += \"Depth of well screen must be a float value\\n\"\n else:\n if d < 0 or d > l:\n flag = False\n warnings += \"Depth d must be in range 0 <= d <= l\\n\"\n return(flag, warnings) # End Function","def Check(self, parameters):","def check_errors(self) -> None:","def _check_params(self):\n\t\tstrange_param_helper = False\n\t\tfor param in self.params:\n\t\t\n\t\t\t# It could be that the param encapsulates several values (e.g., \"FLUX_RADIUS(10)\")\n\t\t\t# So we have to dissect this\n\t\t\tmatch = re.compile(\"(\\w*)\\(\\d*\\)\").match(param)\n\t\t\tif match:\n\t\t\t\tcleanparam = match.group(1)\n\t\t\telse:\n\t\t\t\tcleanparam = param\n\t\t\t\t\n\t\t\tif cleanparam not in self.fullparamlist:\n\t\t\t\tlogger.warning(\"Parameter '%s' seems strange and might be unknown to SExtractor\" \\\n % (param))\n\t\t\t\tstrange_param_helper = True\n\t\t\t\t\n\t\tif strange_param_helper:\n\t\t\tlogger.warning(\"Known parameters are: %s\" % (self.fullparamtxt))","def _check_params(self):\n pass","def check_params(self):\n raise NotImplementedError","def check_params(self):\r\n \r\n # TODO: More cases?\r\n\r\n if self.N <= 0:\r\n print('Bad Parameter: N')\r\n \r\n if self.Ha_tally <= 0 or self.Ha_tally > self.N:\r\n print('Bad Parameter: Reported winner tally')\r\n \r\n if len(self.round_sched) < 1 or not self.check_inc_sched(self.round_sched):\r\n print('Bad Parameter: Round Schedule')\r\n\r\n if self.alpha <= 0 or self.alpha >= .5:\r\n print('Bad Parameter: Alpha')","def test_checkParameters(self):\n self.failUnlessEqual(self.nice.opts['long'], \"Alpha\")\n self.failUnlessEqual(self.nice.opts['another'], \"Beta\")\n self.failUnlessEqual(self.nice.opts['longonly'], \"noshort\")\n self.failUnlessEqual(self.nice.opts['shortless'], \"Gamma\")","def test_wrong_parameters(self):\n with self.assertWarns(RuntimeWarning):\n Parameters(1, mu=3, lambda_=2)","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def check_validity(self):","def validate():","def test_wrong_input():\n dwd = DwdWeatherWarningsAPI(None)\n assert not dwd.data_valid\n assert dwd.warncell_id is None\n assert dwd.warncell_name is None\n assert dwd.last_update is None\n assert dwd.current_warning_level is None\n assert dwd.expected_warning_level is None\n assert dwd.current_warnings is None\n assert dwd.expected_warnings is None","def check():","def test_kyc_get_validation_legal(self):\n pass","def has_warnings(self) -> bool:","def check_invalid_args_general(config):\n # Not mathematically correct, but might be required if prior is not\n # appropriate.\n if hasattr(config, 'kl_scale') and config.kl_scale != 1.0:\n warnings.warn('Prior matching term will be scaled by %f.'\n % config.kl_scale)\n\n if hasattr(config, 'store_final_model') and \\\n hasattr(config, 'train_from_scratch') and \\\n config.store_final_model and config.train_from_scratch:\n warnings.warn('Note, when training from scratch, the final model is ' +\n 'only trained on the last task!')","def warnings(self) -> List[Error]:","def check_all_parameters(self):\n global dtParameterDesc\n self.message = ''\n ok = True\n for par in self.parameters:\n if par in dtParameterDesc:\n pardata = dtParameterDesc[par]\n check = self.check_parameter(par, self.parameters[par])\n ok = ok and check\n if not check:\n self.message += ('\\n' if self.message != '' else '') + pardata[dtg.LANG] +\\\n (' вне диапазона' if dtg.LANG == 'ru' else ' out of range')\n\n return ok","def _getIsValidParameters(self):\n return True, ''","def validate(self):\n\n\tmissing = []\n\tbadcheck = []\n\tfor name, checkfunc, params in self._required:\n\t try:\n\t\targ = self.make_required(name)\n\t\tif checkfunc is not None:\n\t\t if params is not None:\n\t\t\tparams = (self.param_map[name], arg) + params\n\t\t else:\n\t\t\tparams = (self.param_map[name], arg)\n\t\t try:\n\t\t\tapply(checkfunc, params)\n\t\t except ValidationError, msg:\n\t\t\tbadcheck.append(msg)\n\t except ValidationError, args:\n\t\tmissing.append(args)\n\n\tfor (name, checkfunc, params) in self._optional:\n\t tup = self.make_optional(name)\n\t if tup and checkfunc is not None:\n\t\tif params is not None:\n\t\t params = (self.param_map[name], tup) + params\n\t\telse:\n\t\t params = (self.param_map[name], tup)\n\t\ttry:\n\t\t apply(checkfunc, params)\n\t\texcept ValidationError, msg:\n\t\t badcheck.append(msg)\n\n\tif (missing or badcheck) and self.log_errors:\n\t self.log_error(missing, badcheck)\n\n\tif (missing or badcheck) and self.generate_error_page:\n\t self.generate_HTML(missing, badcheck)\n\n\tself.missing = missing\n\tself.badcheck = badcheck\n\n\treturn not (missing or badcheck)","def checkParameters(self):\n EDVerbose.DEBUG(\"EDPluginExecGnomv0_1.checkParameters\")\n self.checkMandatoryParameters(self.dataInput, \"Data Input is None\")\n self.checkMandatoryParameters(self.dataInput.experimentalDataQ, \"Scattering vector values are missing\")\n self.checkMandatoryParameters(self.dataInput.experimentalDataValues, \"Experimental intensity values are missing\")","def verify():","def check(self):\n\n Rbo = self.get_Rbo()\n alpha = self.comp_alpha()\n\n if self.W0 < (self.W2 + self.W3):\n raise S51_WCheckError(\"You must have W2+W3 < W0\")\n\n if Rbo < self.H0 + self.H2:\n raise S51_RHCheckError(\"You must have H0+H2 < Rbo\")\n\n if alpha > pi / 2:\n raise S51_AlphaCheckError(\"You must have alpha < pi/2\")","def ok(*args):","def _check_input_data(self):\n\n n0, n1, corr, pval = np.nan, np.nan, np.nan, np.nan\n\n error_code_test = 0\n error_text_test = 'No error occurred'\n try:\n error_code_test, error_msg = self._check_notnull()\n if error_code_test == 0:\n error_code_test, error_msg, n0, n1 = self._check_group_obs(self.test_min_data)\n if error_code_test == 0:\n error_code_test, error_msg, corr, pval = \\\n self._check_spearman_corr(self.min_corr, self.max_p)\n if error_code_test != 0:\n error_text_test = str(error_msg)\n except:\n error_code_test = 9\n error_text_test = 'Unknown Error'\n\n self.checkstats = {'n0': n0, 'n1': n1, 'frame_spearmanR': corr, 'frame_corrPval': pval}\n\n self.error_code_test = error_code_test\n self.error_text_test = error_text_test\n\n return self.error_code_test, self.error_text_test","def sanity_check(self):\n pass","def test_check_data_fields_errors_warnings(self):\r\n\r\n header =\\\r\n ['SampleID',\r\n 'BarcodeSequence',\r\n 'LinkerPrimerSequence',\r\n 'Description']\r\n mapping_data = [['s1', 'ACGT', 'AAAxA', 's1_data'],\r\n ['s_2', 'CGTA', 'AAAA', 's2_data']]\r\n errors = []\r\n warnings = []\r\n\r\n errors, warnings = check_data_fields(header,\r\n mapping_data, errors, warnings)\r\n\r\n expected_errors = ['Invalid DNA sequence detected: AAAxA\\t1,2']\r\n expected_warnings = ['Invalid characters found in s_2\\t2,0']\r\n\r\n self.assertEqual(errors, expected_errors)\r\n self.assertEqual(warnings, expected_warnings)","def checkParameters(self):\n self.DEBUG(\"EDPluginWaitMultiFile.checkParameters\")\n self.checkMandatoryParameters(self.dataInput, \"Data Input is None\")\n self.checkMandatoryParameters(self.dataInput.expectedFile, \"Data Input is None\")\n self.checkMandatoryParameters(self.dataInput.expectedSize, \"Data Input is None\")","def NoMore():\n\n if assem.MoreParameters():\n errors.DoWarning('extrign', False)","def NoMore():\n\n if assem.MoreParameters():\n errors.DoWarning('extrign', False)","def warning(self, *args, **kwargs):","def test_bad_values(self):\n self.assertOK([60])\n self.assertRaisesInternalError([59.9])\n self.assertRaisesInternalError([''])\n self.assertRaisesInternalError([';'])\n self.assertRaisesInternalError(['('])\n self.assertRaisesInternalError([None])","def _check_validity(self):\n pass","def verifications(sv):\r\n verif_no_special(sv) # check no special character remains (fatal error) \r\n verif_functions(sv) # check if all functions are defined (fatal error)\r\n verif_similar_names(sv) # check similar names for typing errors (warning)\r\n verif_stochastic(sv) # make sure stochastic objects are not used in expressions\r\n verif_unused(sv) # check physical and logical configurations match \r","def __validate():\n # TODO: implement","def checkParameters(self):\n self.DEBUG(\"EDPluginExecDatGnomv1_0.checkParameters\")\n self.checkMandatoryParameters(self.dataInput, \"Data Input is None\")\n self.checkMandatoryParameters(self.dataInput.inputCurve, \"No input Curve file provided\")","def __check(self):\n if self.probid is None:\n raise ProblemConfigError('problem %s has no probid' % self.problem_name)\n if self.color is None:\n raise ProblemConfigError('problem %s has no color' % self.problem_name)\n if self.samples is None:\n raise ProblemConfigError('problem %s has no sample' % self.problem_name)","def checkParamsError(self):\n # check if parameter combinations match with the simulation filename.\n for i, f in enumerate(self.yadeDataFiles):\n # get the file name fore the suffix\n f = f.split('.' + f.split('.')[-1])[0]\n # get parameters from the remaining string\n paramsString = f.split('_')[-self.numParams:]\n # element wise comparison of the parameter vector\n if not (np.abs((np.float64(paramsString) - self.getSmcSamples()[-1][i])\n / self.getSmcSamples()[-1][i] < 1e-10).all()):\n raise RuntimeError(\n \"Parameters \" + \", \".join(\n [\"%s\" % v for v in self.getSmcSamples()[-1][i]]) + \" do not match with the data file name \" + f)","def check_hparams(self, hparams):\n error_messages = []\n\n # Check global params.\n feature_names = hparams.get_feature_names()\n global_values, per_feature_values = hparams.get_global_and_feature_params(\n ['num_keypoints', 'missing_input_value',\n 'missing_output_value'], feature_names)\n global_param_error_messages = self._check_param_configuration(\n *global_values)\n if global_param_error_messages:\n error_messages.append('Error message for global param:')\n error_messages += global_param_error_messages\n\n # Check per feature params. hparams.get_feature_names() will only return\n # feature names that sets per feature parameters.\n for feature_idx in range(len(per_feature_values)):\n per_feature_param_error_messages = self._check_param_configuration(\n *per_feature_values[feature_idx])\n if per_feature_param_error_messages:\n error_messages.append(\n 'Error message for %s feature param:' % feature_names[feature_idx])\n error_messages += per_feature_param_error_messages\n\n if error_messages:\n raise ValueError(\n 'Hyperparameter configuration cannot be used in the calibrated '\n 'lattice estimator. Error messages report the issue per feature, but'\n ' the parameter may be inherited from global parameter.\\nDetailed '\n 'error messsages\\n%s' % '\\n'.join(error_messages))","def _check_input(self, func, args, kwargs):\n fullargspec = inspect.getfullargspec(func)\n return_msg = ''\n if fullargspec.varkw is None:\n for key in kwargs:\n if not key in fullargspec.kwonlyargs:\n return_msg += f'[Error]: not support param `{key}`. \\n'\n if fullargspec.varargs is None:\n if len(fullargspec.args) == 0:\n max_args_len = 0\n else:\n max_args_len = len(fullargspec.args)-1 if fullargspec.args[0] == 'self' else len(fullargspec.args)\n defaults_nums = 0 if fullargspec.defaults is None else len(fullargspec.defaults)\n min_args_len = max_args_len - defaults_nums\n if len(args) < min_args_len:\n return_msg += f'[Error]: have min {min_args_len} input, but you input {len(args)} args. \\n'\n if max_args_len < len(args):\n return_msg += f'[Error]: have max {max_args_len} input, but you input {len(args)} args. \\n'\n return return_msg","def check(self):\n if 'MISFIT' not in PAR:\n setattr(PAR, 'MISFIT', 'Waveform')\n\n if 'CHANNELS' not in PAR:\n raise ParameterError(PAR, 'CHANNELS')\n\n if 'READER' not in PAR:\n raise ParameterError(PAR, 'READER')\n\n if 'WRITER' not in PAR:\n setattr(PAR, 'WRITER', PAR.READER)\n\n if 'NORMALIZE' not in PAR:\n setattr(PAR, 'NORMALIZE', True)\n\n # mute settings\n if 'MUTE' not in PAR:\n setattr(PAR, 'MUTE', False)\n\n if 'MUTESLOPE' not in PAR:\n setattr(PAR, 'MUTESLOPE', 0.)\n\n if 'MUTECONST' not in PAR:\n setattr(PAR, 'MUTECONST', 0.)\n\n # filter settings\n if 'BANDPASS' not in PAR:\n setattr(PAR, 'BANDPASS', False)\n\n if 'FREQLO' not in PAR:\n setattr(PAR, 'FREQLO', 0.)\n\n if 'FREQHI' not in PAR:\n setattr(PAR, 'FREQHI', 0.)\n\n # assertions\n if PAR.READER not in dir(readers):\n print msg.ReaderError\n raise ParameterError()\n\n if PAR.WRITER not in dir(writers):\n print msg.WriterError\n raise ParameterError()","def check(self) -> None:","def parameter_check(coordinates, \n neighbors, \n bandwidth, \n convergence, \n percentage):\n # Create a boolean vector to keep track of incorrect inputs\n incorrect_inputs = np.zeros(5, dtype = bool)\n # Check whether two-dimensional coordinates are provided\n if not type(coordinates) == np.ndarray:\n incorrect_inputs[0] = True\n elif not coordinates.shape[1] == 2:\n incorrect_inputs[0] = True\n # Check whether neighbors is a positive integer or float\n if not ((type(neighbors) == int and neighbors > 0)\n and not ((type(neighbors) == float) \n and (neighbors > 0)\n and (neighbors.is_integer() == True))):\n incorrect_inputs[1] = True\n # Check whether bandwidth is a positive integer or float\n if not bandwidth == None:\n if not ((type(bandwidth) == int and bandwidth > 0)\n or (type(bandwidth) == float) and bandwidth > 0):\n incorrect_inputs[2] = True\n # Check whether convergence is a positive integer or float\n if not convergence == None:\n if not ((type(convergence) == int and convergence > 0)\n or (type(convergence) == float) and convergence > 0):\n incorrect_inputs[3] = True\n # Check whether percentage is a valid percentage value\n if not percentage == None:\n if not ((type(percentage) == int and percentage >= 0 \n and percentage <= 100)\n or ((type(percentage) == float) and percentage >= 0 \n and percentage <= 100)):\n incorrect_inputs[4] = True\n # Define error messages for each parameter failing the tests\n errors = ['ERROR: coordinates: Must be a 2-column numpy.ndarray',\n 'ERROR: neighbors: Must be a whole-number int or float > 0',\n 'ERROR: bandwidth: Must be an int or float > 0, or None',\n 'ERROR: convergence: Must be an int or float > 0, or None',\n 'ERROR: percentage: Must be an int or float in [0, 100], or None']\n # Print eventual error messages and terminate the code\n if any(value == True for value in incorrect_inputs):\n for i in range(0, len(errors)):\n if incorrect_inputs[i] == True:\n print(errors[i])\n sys.exit()","def test_basic1(self):\r\n self.validate((2, 2, 3, 3), (2, 2, 2, 2), 'valid', verify_grad=False)","def checkSuccess():\n try:\n relin = np.genfromtxt(\"{}reliability.in\".format(basedir), dtype=str)\n # Accurate argument order\n args = [str(\"\\'\"+probpath+\"\\'\"), str(\"\\'\"+obpath+\"\\'\"),\n str(\"\\'\"+outfile+\"\\'\"),\n fcsthr, str(\"\\'\"+variable+\"\\'\"),\n rthresh, sixhour, nbrhd, rbox_bounds[0],\n rbox_bounds[1], rbox_bounds[2], rbox_bounds[3]]\n success = True # Assume success initially\n # Ensure that each argument was placed into the proper line of the\n # reliability input file\n for ind, line in enumerate(relin):\n # If an argument doesn't line up with the rel in arg, set False\n print(str(args[ind]).replace('\\\\', ''), line)\n if (str(args[ind]).replace('\\\\', '') != line):\n success = False\n print(success)\n except:\n success = False\n return success","def checkSuccess():\n try:\n relin = np.genfromtxt(\"{}reliability.in\".format(basedir), dtype=str)\n # Accurate argument order\n args = [str(\"\\'\"+probpath+\"\\'\"), str(\"\\'\"+obpath+\"\\'\"),\n str(\"\\'\"+outfile+\"\\'\"),\n fcsthr, str(\"\\'\"+variable+\"\\'\"),\n rthresh, sixhour, nbrhd, rbox_bounds[0],\n rbox_bounds[1], rbox_bounds[2], rbox_bounds[3]]\n success = True # Assume success initially\n # Ensure that each argument was placed into the proper line of the\n # reliability input file\n for ind, line in enumerate(relin):\n # If an argument doesn't line up with the rel in arg, set False\n print(str(args[ind]).replace('\\\\', ''), line)\n if (str(args[ind]).replace('\\\\', '') != line):\n success = False\n print(success)\n except:\n success = False\n return success","def checkParameters(self):\n EDVerbose.DEBUG(\"EDPluginExecVideov10.checkParameters\")\n self.checkMandatoryParameters(self.getDataInput(), \"Data Input is None\")\n self.checkMandatoryParameters(self.getDataInput().getInputImagePath(), \"inputImage list is None\")\n for oneXSDataFile in self.getDataInput().getInputImagePath():\n self.checkMandatoryParameters(oneXSDataFile.getPath().getValue(), \"input Image does not exist\" + oneXSDataFile.marshal())","def test_non_integral_validation(self):","def test_non_integral_validation(self):","def checkParameters(self):\n self.DEBUG(\"EDPluginControlStitchImagev1_0.checkParameters\")\n self.checkMandatoryParameters(self.getDataInput(), \"Data Input is None\")\n self.checkMandatoryParameters(self.getDataInput().getInputImages(), \"No input Images\")","def _check_param(in_params, req_param, opt_param=list()):\n for param in req_param:\n if param not in in_params:\n raise ValueError('{} parameter is required'.format(param))\n defined_param = set(req_param + opt_param)\n for param in in_params:\n if param not in defined_param:\n print(\n \"WARNING: received unexpected parameter {}\".format(param))","def verify(self):\n \n # Check non-optional args\n pList = ['basePath', 'dateStart', 'dateStop','sensor']\n for p in pList:\n if not hasattr(self, p):\n self.showUsage()\n raise '\\nMissing keyword arg: %s.\\n' % p\n \n if not self.isValidBasePath():\n raise '\\nbasePath (%s) does not exist.\\n' % self.basePath\n\n if not isValidPimsDateString(self.dateStart):\n raise '\\ndateStart (%s) is not valid\\n' % self.dateStart\n else:\n self.uStart = stringTimeToUnix(self.dateStart)\n\n if not isValidPimsDateString(self.dateStop):\n raise '\\ndateStop (%s) is not valid\\n' % self.dateStop\n else:\n self.uStop = stringTimeToUnix(self.dateStop)","def __check_args(self):\n self.__check_args_type()\n self.__check_args_val()","def check(self):\n invalid = []\n\n if not self.route:\n invalid.append(('route', 'missing'))\n elif not self.route[1] in ['GET', 'POST', 'PUT']:\n invalid.append(('route', 'invalid method: %s' % self.route[1]))\n\n has_2xx = False\n for rcode in self.return_codes:\n code = rcode[0]\n if code >= 200 and code < 300:\n has_2xx = True\n break\n if not has_2xx:\n invalid.append(('return_codes', 'Missing succes return code doc'))\n\n if self.client_auth is None:\n invalid.append(\n ('client_auth', 'Please provide client auth requirement'))\n\n if self.user_auth is None:\n invalid.append(\n ('user_auth', 'Please provide user auth requirement'))\n\n if invalid:\n msgs = []\n for error in invalid:\n msgs.append(\"%s: %s\" % error)\n raise ValueError(\n \"APIFunc for %s is invalid: %s\"\n % (self.viewfunc.__name__,\n ', '.join(msgs)))","def __verify_arguments(self):\n if len(self.__pointer_data) == 0:\n raise ValueError(\n \"Input data is empty (size: '%d').\" % len(self.__pointer_data)\n )\n\n if self.__number_clusters <= 0:\n raise ValueError(\n \"Amount of cluster (current value: '%d') for allocation should be greater than 0.\"\n % self.__number_clusters\n )\n\n if self.__numlocal < 0:\n raise ValueError(\n \"Local minima (current value: '%d') should be greater or equal to 0.\"\n % self.__numlocal\n )\n\n if self.__maxneighbor < 0:\n raise ValueError(\n \"Maximum number of neighbors (current value: '%d') should be greater or \"\n \"equal to 0.\" % self.__maxneighbor\n )","def test_runtime_warnings(self):\n\t\tprint(\"test runtime warning: sqrt(-1)=\", np.sqrt(-1.0))\n\t\tassert(True)","def testErrorChecking(self):\n discardLs = [17,18,19,20]\n table = {'row1':[1,2,3,4,5],'row2':[6,7,'7s',9,10],'row3':[11,12,13],'row4':[14,15,16]}\n blackJackObj = BlackJack()\n ## Overload Constructor\n blackJackObj.overloadConstructor(table, discardLs)\n self.assertEquals(True,blackJackObj.errorChecking('10')) # empty pos\n self.assertEquals(False,blackJackObj.errorChecking('8')) # taken pos\n self.assertEquals(False,blackJackObj.errorChecking('A')) # invalid input\n self.assertEquals(False,blackJackObj.errorChecking('21')) # out of range","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def verify_pln(self):\n\n warnings = []\n\n self._populate_uncertainties()\n\n # The transitref and transiturl actually end up stored in the 'transit'\n # ExoParam due to the ref and url splits. Pull these out and set the\n # transit entries to the proper pointers.\n if self.transit.value == 1:\n if is_empty(self.transit.reference):\n self.transit.reference = \"__TRANSITREF\"\n if is_empty(self.transit.url):\n self.transit.url = \"__TRANSITURL\"\n\n # If the transit depth is not provided, but an Rp/R* ratio is,\n # calculate the depth value.\n if is_empty(self.depth.value) and is_valid(self.rr.value):\n self.depth.value = self.rr.value ** 2\n if isinstance(self.rr.uncertainty, Decimal):\n self.depth.uncertainty = self.rr.uncertainty * 2\n if isinstance(self.rr.uncertainty_upper, Decimal):\n self.depth.uncertainty_upper = self.rr.uncertainty_upper * 2\n self.depth.reference = \"Calculated from Rp/R*\"\n self.depth.url = self.rr.reference\n\n # If the orbital eccentricity value is 0 and a TT value is provided,\n # use the same values for T0 as well.\n if self.ecc.value == Decimal(0) and is_empty(self.om.value):\n self.om.value = Decimal(90)\n self.om.reference = \"Set to 90 deg with ecc~0\"\n print(\"set omega to 90\")\n if is_valid(self.tt.value):\n print(\"copying TT to T0\")\n self.t0.copy_values(self.tt)\n # OM may already be set to 90.\n elif self.ecc.value == 0 and self.om.value == 90:\n if str(self.tt.value) != \"NaN\":\n print(\"copying TT to T0\")\n self.t0.copy_values(self.tt)\n\n # Set the FREEZE_ECC flag if ECC=0 and no uncertainty is provided.\n if self.ecc.value == 0 and is_empty(self.ecc.uncertainty):\n self.freeze_ecc.value = 1\n\n # Set the MULT flag if NCOMP is more than 1 planet.\n if self.ncomp.value > 1:\n self.mult.value = 1\n\n # Set the TREND flag if a DVDT value is provided.\n if not is_empty(self.dvdt.value):\n self.trend.value = 1\n\n # Exclude planets with period uncertainty >10%.\n self.per.check_constrained(0.1)\n if not self.per.well_constrained:\n self.exclude()\n warnings.append(\"<uncertain PER>\")\n\n # Warn of planets with K speeds <2 m/s.\n if is_valid(self.k.value):\n if self.k.value < 2:\n # self.exclude()\n warnings.append(\"<low K value>\")\n\n # Make sure RA string uses spaces.\n if not is_empty(self.ra_string.value):\n if \"h\" in self.ra_string.value:\n new_value = self.ra_string.value.replace(\"h\", \" \")\n new_value = new_value.replace(\"m\", \" \")\n new_value = new_value.replace(\"s\", \"\")\n self.ra_string.value = new_value\n\n # Make sure DEC string uses spaces.\n if not is_empty(self.dec_string.value):\n if \"d\" in self.dec_string.value:\n new_value = self.dec_string.value.replace(\"d\", \" \")\n new_value = new_value.replace(\"m\", \" \")\n new_value = new_value.replace(\"s\", \"\")\n self.dec_string.value = new_value\n\n # Display warnings generated by final adjustments.\n if len(warnings) > 0:\n print(\"<<<{0} GOT {1} WARNING(S)>>>\".format(self.name.value,\n len(warnings)\n )\n )\n [print(x) for x in warnings]","def checkParameters(self):\n self.DEBUG(\"EDPluginExecDatcmpv2_0.checkParameters\")\n self.checkMandatoryParameters(self.getDataInput(), \"Data Input is None\")\n self.checkMandatoryParameters(self.getDataInput().inputCurve, \"No input 1D curves file provided\")","def check_params (self, doclet):\n\n if not doclet.undocumented:\n try:\n param_names = { p.name for p in doclet.params }\n meta_param_names = set (doclet.meta.code.paramnames)\n\n for name in param_names - meta_param_names:\n doclet.warn (\"Documented parameter %s not found on signature\" % name)\n\n for name in meta_param_names - param_names:\n doclet.warn (\"Undocumented parameter %s\" % name)\n\n except AttributeError:\n pass\n\n return doclet","def input_check(self):\n\n if self.species == 'He': assert self.line_model == 'voigt'\n n_upper_range, e_dens_range, temp_range, b_field_range = get_param_ranges(self.line_model)\n\n if np.isnan(n_upper_range).sum() <= 1:\n assert (self.n_upper in range(n_upper_range[0], n_upper_range[1]))\n if np.isnan(e_dens_range).sum() <= 1:\n assert (e_dens_range[0] <= self.e_dens <= e_dens_range[1])\n if np.isnan(temp_range).sum() <= 1:\n assert (temp_range[0] <= self.temp <= temp_range[1])\n if np.isnan(b_field_range).sum() <= 1:\n assert (b_field_range[0] <= self.b_field <= b_field_range[1])","def sanityCheck(parameters):\n if not parameters: printUsage(); sys.exit()\n\n # these may differ depending on type of processing to do\n padPath = parameters['padPath']\n dateStart = parameters['dateStart']\n dateStop = parameters['dateStop']\n sensor = parameters['sensor']\n abbr = parameters['abbr']\n whichAx = parameters['whichAx']\n pm = parameters['pm']\n tag = parameters['tag']\n Nfft = parameters['Nfft']\n No = parameters['No']\n \n if not os.path.isdir(padPath): print '%s does not exist' % padPath; sys.exit()\n if not(pm in ['+','-']): print 'bad pm flag (%s): it should be either (+) for demean OR (-) for keep mean' % pm; sys.exit()\n\n return padPath,dateStart,dateStop,sensor,abbr,whichAx,pm,tag,Nfft,No","def test_Validator_is_valid_two_arguments(self):\n\n validator = validators.Draft7Validator({})\n with self.assertWarns(DeprecationWarning) as w:\n result = validator.is_valid(\"foo\", {\"type\": \"number\"})\n\n self.assertFalse(result)\n self.assertEqual(w.filename, __file__)\n self.assertTrue(\n str(w.warning).startswith(\n \"Passing a schema to Validator.is_valid is deprecated \",\n ),\n )","def test_buoy_format2():\n with pytest.raises(AssertionError) as err_info:\n Check_BuoyDC.check_buoy_format(buoy_format_fail_2)\n assert str(err_info.value) == 'Input length incorrect, see instructions'","def checkNeededParams(self):\n for clp,value in self.neededParamsNames.items():\n if value[0] not in self.neededParams:\n print >> sys.stderr, clp+\" is a mandatory parameter \"\n self.printUsage()\n sys.exit(1)","def test_buoy_format1():\n with pytest.raises(AssertionError) as err_info:\n Check_BuoyDC.check_buoy_format(buoy_format_fail_1)\n assert str(err_info.value) == 'Input formatted incorrectly, see instructions'","def check(self):\r\n self.check_probabilities()\r\n self.check_sum()","def test__validate_topic__2():\n for input_value in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_topic(input_value)","def check_additional_input():\r\n\r\n # Check if the cluster center input is correct\r\n RM.check_if_matrix(clust_cent, 'The cluster centers')\r\n RM.warn_if_bigger(clust_cent.shape[1], meta_model.get_in_par_means().shape[1],\r\n 'The number of input parameters in the cluster centers',\r\n 'the number of input parameters - 1')\r\n RM.check_if_bigger(clust_cent.shape[1], meta_model.get_in_par_means().shape[1] - 1,\r\n 'The number of input parameters',\r\n 'the number of input parameters in the cluster centers')\r\n\r\n bounds = meta_model.get_in_par_intervals()\r\n\r\n for j in range(clust_cent.shape[0]):\r\n for i in range(bounds.shape[0]):\r\n RM.check_if_in_interval(bounds[i], clust_cent[j, i], i, ' In cluster center %x, the value')\r\n\r\n def check_PLSR_input():\r\n \"\"\" Checks model data of PLSR\r\n\r\n :return: Checks model data of PLSR\r\n \"\"\"\r\n\r\n RM.check_if_ndim_array(model_data, 3, 'Model data')\r\n RM.check_if_bigger(model_data.shape[1], meta_model.get_in_par_means().shape[1],\r\n 'The number of input parameters in the solution matrix',\r\n 'the number of input parameters')\r\n RM.warn_if_bigger(model_data.shape[1], meta_model.get_in_par_means().shape[1] + 1,\r\n 'The number of input parameters',\r\n 'the number of input parameters in the solution matrix')\r\n RM.check_if_bigger(model_data.shape[2], meta_model.get_out_par_means().shape[1] - 1,\r\n 'The number of output parameters in the solution matrix',\r\n 'the number of output parameters')\r\n RM.warn_if_bigger(model_data.shape[2], meta_model.get_out_par_means().shape[1],\r\n 'The number of output parameters',\r\n 'the number of output parameters in the solution matrix')\r\n\r\n # Check if the additional data is correct\r\n\r\n if meta_model.get_type() == 'PLSR': # Additional check-up for PLSR\r\n check_PLSR_input()\r\n\r\n elif meta_model.get_type() == 'DLU': # Additional check-up for DLU\r\n raise TypeError('This part is not implemented yet')\r\n\r\n # if not isinstance(model_data, np.ndarray):\r\n # raise TypeError('The cluster input and output data is not stored in a multidimensional array')\r\n #\r\n # for clust_data in model_data:\r\n #\r\n # if not isinstance(clust_data[0], np.matrix) or not isinstance(clust_data[1], np.matrix):\r\n # raise TypeError('One of the input or output databases is not a matrix')\r\n #\r\n # if clust_data[0].shape[1] > meta_model.get_in_par_means().shape[1]:\r\n # warnings.warn('The number of input parameters for the input database of the clusters is bigger '\r\n # 'than the actual number of input parameters')\r\n #\r\n # elif clust_data[0].shape[1] < meta_model.get_in_par_means().shape[1]:\r\n # raise TypeError('The number of input parameters for the input database of the clusters is '\r\n # 'smaller than the actual numbers of input parameters')\r\n #\r\n # if clust_data[1].shape[1] > meta_model.get_out_par_means().shape[1]:\r\n # raise TypeError('The number of output parameters for the output database of the clusters is '\r\n # 'bigger than the actual number of output parameters')\r\n #\r\n # elif clust_data[1].shape[1] < meta_model.get_out_par_means().shape[1]:\r\n # raise TypeError('The number of output parameters for the output database of the clusters is '\r\n # 'smaller than the actual numbers of output parameters')\r\n #\r\n # if clust_data[0].shape[0] != clust_data[1].shape[0]:\r\n # raise TypeError('The number rows in the input and output database differ from each other')\r\n\r\n else: # No check-up is done when the meta-model is an unknown version\r\n warnings.warn('The additional cluster data can not be checked, for this kind of meta-model')\r\n\r\n RM.check_if_same_size(clust_cent.shape[0], model_data.shape[0],\r\n 'The number of clusters according to the cluster centers',\r\n 'The number of clusters according to the model_data')","def validate(self):\n errors = []\n app = errors.append\n\n if not self.hint_cores >= self.mpi_procs * self.omp_threads >= self.min_cores:\n app(\"self.hint_cores >= mpi_procs * omp_threads >= self.min_cores not satisfied\")\n\n if self.omp_threads > self.hw.cores_per_node:\n app(\"omp_threads > hw.cores_per_node\")\n\n if self.mem_per_proc > self.hw.mem_per_node:\n app(\"mem_mb >= self.hw.mem_per_node\")\n\n if not self.max_mem_per_proc >= self.mem_per_proc >= self.min_mem_per_proc:\n app(\"self.max_mem_per_proc >= mem_mb >= self.min_mem_per_proc not satisfied\")\n\n if self.priority <= 0:\n app(\"priority must be > 0\")\n\n if not (1 <= self.min_cores <= self.hw.num_cores >= self.hint_cores):\n app(\"1 <= min_cores <= hardware num_cores >= hint_cores not satisfied\")\n\n if errors:\n raise self.Error(str(self) + \"\\n\".join(errors))","def __verify_arguments(self):\r\n if len(self.__pointer_data) == 0:\r\n raise ValueError(\"Input data is empty (size: '%d').\" % len(self.__pointer_data))\r\n\r\n if self.__number_clusters <= 0:\r\n raise ValueError(\"Amount of cluster (current value: '%d') for allocation should be greater than 0.\" %\r\n self.__number_clusters)\r\n\r\n if self.__numlocal < 0:\r\n raise ValueError(\"Local minima (current value: '%d') should be greater or equal to 0.\" % self.__numlocal)\r\n\r\n if self.__maxneighbor < 0:\r\n raise ValueError(\"Maximum number of neighbors (current value: '%d') should be greater or \"\r\n \"equal to 0.\" % self.__maxneighbor)","def check_parameters(args):\n inference_algorithm = args[\"inference_algorithm\"]\n combination_algorithm = args[\"combination_algorithm\"]\n measures = args[\"measures\"]\n prior = args[\"prior\"]\n inverse_dynamics_model_checkpoint = args[\"inverse_dynamics_model_checkpoint\"]\n\n check_in(\n \"inference_algorithm\",\n inference_algorithm,\n [\n \"rlsp\",\n \"latent_rlsp\",\n \"latent_rlsp_ablation\",\n \"sampling\",\n \"deviation\",\n \"reachability\",\n \"spec\",\n ],\n )\n check_in(\n \"combination_algorithm\",\n combination_algorithm,\n (\"additive\", \"bayesian\", \"latent_vi\", \"latent_ppo\"),\n )\n check_in(\"prior\", prior, [\"gaussian\", \"laplace\", \"uniform\"])\n\n for i, measure in enumerate(measures):\n check_in(\n \"measure {}\".format(i),\n measure,\n [\"inferred_reward\", \"true_reward\", \"final_reward\", \"model_training_error\"],\n )\n\n if combination_algorithm == \"bayesian\":\n check_in(\"inference_algorithm\", inference_algorithm, [\"rlsp\", \"sampling\"])\n\n if inference_algorithm == \"latent_rlsp\":\n check_not_none(\n \"inverse_dynamics_model_checkpoint\", inverse_dynamics_model_checkpoint\n )\n\n if (\n combination_algorithm.startswith(\"latent\")\n and inference_algorithm != \"latent_rlsp\"\n ):\n raise ValueError(\n \"combination_algorithm 'latent' should only be used with 'latent_rlsp'\"\n )","def check_user_parameters(user_information):\n\n email = user_information.get('email', '')\n sub_end_date = user_information.get('subenddate', '')\n vineyards = user_information.get('vineyards', '')\n current_date = datetime.date.today().strftime('%Y-%m-%d')\n new_user_id = user_information.get('userid', '')\n is_admin = user_information.get('admin', '')\n is_enable = user_information.get('enable', '')\n\n try:\n message = (\n 'Validating submitted user parameters.'\n )\n logger.info(message)\n check_user_id(new_user_id)\n if email != '':\n if re.match(r\"[^@]+@[^@]+\\.[^@]+\", email) is None:\n raise PlantalyticsDataException(EMAIL_INVALID)\n check_subscription_end_date(sub_end_date, current_date)\n if is_admin != '':\n if not isinstance(is_admin, bool):\n raise PlantalyticsDataException(DATA_INVALID)\n if is_enable != '':\n if not isinstance(is_enable, bool):\n raise PlantalyticsDataException(DATA_INVALID)\n if vineyards != '':\n for vineyard_id in vineyards:\n if int(vineyard_id) < 0:\n raise PlantalyticsDataException(VINEYARD_BAD_ID)\n message = (\n 'Submitted user parameters successfully validated.'\n )\n logger.info(message)\n except PlantalyticsException as e:\n raise e\n except ValueError as e:\n raise e\n except Exception as e:\n raise e","def f_check_adr_parameters_correctness(dict):\n\n if int(dict[\"operation_mode_num\"]) not in (0, 1, 2, 3, 4, 5, 6):\n print('\\n Error!!! Operation mode is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"FFT_size_samples\"]) not in (2048, 4096, 8192, 16384, 32768):\n print('\\n Error!!! FFT size is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"spectra_averaging\"]) < 16 or int(dict[\"spectra_averaging\"]) > 32768:\n print('\\n Error!!! Spectra averaging number is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"start_line_freq\"]) not in (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16): # 0 … (SFFT-1024)/1024\n print('\\n Error!!! Start frequency line is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"width_line_freq\"]) not in (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16):\n print('\\n Error!!! Frequency width line is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"width_line_freq\"]) > ((int(dict[\"FFT_size_samples\"]) - int(dict[\"start_line_freq\"]) * 1024) / 1024): # 1 … (SFFT-SLINE*1024)/1024\n print('\\n Error!!! Frequency width is bigger than FFT size allows!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"clock_source\"]) not in (0, 1):\n print('\\n Error!!! Clock source is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"sum_diff_mode_num\"]) not in (0, 1):\n print('\\n Error!!! Sum-diff mode is wrong!\\n')\n sys.exit(' Program stopped!')\n\n if int(dict[\"data_file_size\"]) < -1 or int(dict[\"data_file_size\"]) > 4096:\n print('\\n Error!!! File size value is wrong!\\n')\n sys.exit(' Program stopped!')\n\n '''\n if (int(dict[\"chan_diff_delay\"]) < 0 or int(parameters_dict[\"chan_diff_dalay\"]) > 1024):\n print('\\n Error!!! Channel difference delay is wrong!\\n')\n sys.exit(' Program stopped!')\n '''\n\n # print('\\n ADR parameters from file are correct!\\n')\n\n return dict","def test_check_inputs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n with warnings.catch_warnings(record=True):\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.students == game._all_students\n assert game.projects == game._all_projects\n assert game.supervisors == game._all_supervisors","def check_errors(self):\n raise NotImplementedError(\"Implement it in a subclass.\")","def test_validate_and_logger(self):\n data = V20CredProblemReport(\n description={\n \"en\": \"Insufficient credit\",\n \"code\": \"invalid_code\",\n },\n ).serialize()\n self._caplog.set_level(logging.WARNING)\n V20CredProblemReportSchema().validate_fields(data)\n assert \"Unexpected error code received\" in self._caplog.text","def validate_args(args):\n\n if args.batch_size % args.batch_splits != 0:\n raise ValueError(BATCH_SIZE_SPLIT_ERR.format(args.batch_size, args.batch_splits))\n\n if args.data_parallel and args.model_parallel:\n raise ValueError(DATA_AND_MODEL_PARALLEL_ERR)\n\n if args.class_bal and args.year_weighted_class_bal:\n raise ValueError(CONFLICTING_WEIGHTED_SAMPLING_ERR)\n\n assert args.ten_fold_test_index in range(-1, 10)","def test__validate_status__1():\n for input_value in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_status(input_value)","def checkUserInput(self, stguess):\n lines = stguess.splitlines()\n if len(lines) == 0:\n return False\n err = \"\"\n for l, line in enumerate(lines):\n items = line.split(',')\n np = len(items)\n if np != 4 and np != 6:\n err = \"Wrong number of parameters\"\n else:\n for p in range(0, np):\n val = items[p].strip()\n if p == 0:\n if not val in ['G', 'L', 'P', 'AG', 'AL', 'AP']:\n err = \"Unknown peak type\"\n else:\n if not isNumber(val):\n err = \"Parameter {0} in not numeric\".format(p+1)\n break\n v = float(val)\n if p == 1: # xm\n if v < self.data[0].min() or v > self.data[0].max():\n err = \"Parameter {0} out of range\".format(p+1)\n break\n if p == 2: # amp\n if v < self.data[1].min() * 1.1 \\\n or v > self.data[1].max() * 1.1:\n err = \"Parameter {0} out of range\".format(p+1)\n break\n if p == 3: # w\n xspan = getSpan(self.data[0])\n if v < xspan / self.npt or v > (xspan / 2):\n err = \"Parameter {0} out of range\".format(p+1)\n break\n if p == 4: # asym\n maxasym = 1000 / self.data[0].max()\n if v < -maxasym or v > maxasym:\n err = \"Parameter {0} out of range\".format(p+1)\n break\n if p == 5: # Lfrac\n if v < 0.0 or v > 1.0:\n err = \"Parameter {0} out of range\".format(p+1)\n break\n if err:\n break\n if err:\n errmsg = \"Error in peak {0}:\\n{1}\".format(l+1, err)\n QtWidgets.QMessageBox.warning(self.parent, self.title, errmsg)\n return False\n return True","def check_all_user_inputs_valid(self):\n self.check_RNN_layers_valid()\n self.check_activations_valid()\n self.check_embedding_dimensions_valid()\n self.check_initialiser_valid()\n self.check_y_range_values_valid()\n self.check_return_final_seq_only_valid()","def warning_check(self, rule_to_check, valid):\r\n for warning in self.warning_functions:\r\n warning(rule_to_check, valid)","def __check(self):\n assert self.name is not None, \"Empty name!\"\n assert self.in_spc, \"Empty in_spc!\"\n assert self.out_spc, \"Empty out_spc!\"\n assert self.num_clss > 0, \"Invalid number of output classes!\"\n if not isinstance(self, SvmSklearnWrapper):\n assert self.los_fnc is not None, \"No loss function!\"\n assert self.opt is not None, \"No optimizer!\"","def run_parameters_validations(self):\n if self.risk_rule:\n if 'connectApi' not in self.services:\n return_error(\"You entered a risk rule but the 'connectApi' service is not chosen. \"\n \"Add the 'connectApi' service to the list or remove the risk rule.\")\n else:\n for risk_rule in self.risk_rule:\n if not is_valid_risk_rule(self, risk_rule):\n return_error(f\"The given risk rule: {risk_rule} does not exist,\"\n f\"please make sure you entered it correctly. \\n\"\n f\"To see all available risk rules run the '!rf-get-risk-rules' command.\")\n\n if self.fusion_file_path is not None:\n if 'fusion' not in self.services:\n return_error(\"You entered a fusion file path but the 'fusion' service is not chosen. \"\n \"Add the 'fusion' service to the list or remove the fusion file path.\")","def check_parameters_valid(self) :\n for check_parameter in self.parameters :\n if (not self.parameters[check_parameter]['set']) :\n error_message = \"Missing key -> '\" + check_parameter + \"'\"\n if (Config.logger) :\n dtpo_log('fatal', error_message)\n raise ParseError(error_message)\n\n if self.parameters[check_parameter]['type'] == 'dir' :\n value = self.parameters[check_parameter]['value']\n return_string = check_directory_permissions(value)\n if return_string :\n error_message = \"{0} not accessible \" \\\n \"-> {1}\".format(\n check_parameter,\n return_string)\n raise ParseError(error_message)\n elif self.parameters[check_parameter]['type'] == 'file' :\n value = self.parameters[check_parameter]['value']\n try :\n file_pointer = open(value)\n file_pointer.close()\n except IOError as io_error :\n error_message = \"File {0} not accessible -> {2}\" \\\n .format(\n check_parameter,\n self.parameters[check_parameter]['value'],\n str(io_error))\n raise ParseError(error_message)","def checkParameters(self):\n EDVerbose.DEBUG(\"EDPluginControlAbsorptionv0_1.checkParameters\")\n self.checkMandatoryParameters(self.getDataInput(), \"Data Input is None\")","def test_failed_parameter_verification() -> None:\n name = create_random_alphanumeric(10)\n # The lower bound must be smaller than the upper bound!\n parameters = [5, 1, 3]\n\n with pytest.raises(ValueError):\n UnivDist(\n name=name, distribution=DISTRIBUTION_NAME, parameters=parameters\n )\n\n # The mid-point value must be between lower and upper bounds!\n parameters = [3, 4, 10]\n\n with pytest.raises(ValueError):\n UnivDist(\n name=name, distribution=DISTRIBUTION_NAME, parameters=parameters\n )","def sanity_check(self):\n return True","def check_args():\n schema = Schema({\n 'FOLDREC': Use(open, error='FOLDREC file should be readable'),\n 'CLUSTAL': Use(open, error='CLUSTAL file should be readable'),\n 'CCMPRED': Use(open, error='CCMPRED file should be readable'),\n '--metafold': Use(open, error='METAFOLD_FILE should be readable'),\n '--nb_pdb': And(Use(int), lambda n: 1 <= n <= 405,\n error='--nb_pdb=NUM should be integer 1 <= N <= 405'),\n '--dssp': Use(open, error='dssp/mkdssp should be readable'),\n '--dope': Use(open, error='dope file should be readable'),\n '--benchmark': Use(open, error='BENCHMARK_FILE should be readable'),\n '--cpu': And(Use(int), lambda n: 0 <= n <= cpu_count(),\n error='--cpus=NUM should be integer 1 <= N <= ' + str(cpu_count())),\n # The output PATH is created (if not exists) at the end of the program\n # so we skip the check.\n object: object})\n try:\n schema.validate(ARGUMENTS)\n except SchemaError as err:\n exit(err)","def validate(self):","def validate(self):"],"string":"[\n \"def validate_parameters(self):\\n\\n flag = True\\n warnings = \\\"\\\"\\n # Check radius\\n r = self.parameters.get('rw', 0)\\n if type(r) not in [int, float]:\\n flag = False\\n warnings += \\\"Well radius rw must be a float value\\\\n\\\"\\n else:\\n if r <= 0:\\n flag = False\\n warnings += \\\"Well radius rw must be higher than 0\\\\n\\\"\\n # Check if is full penetrating\\n op = self.parameters.get('full', False)\\n\\n if not op:\\n # Check observation well length\\n if 'd' in self.parameters and 'l' in self.parameters:\\n d = self.parameters.get('d', -1)\\n l = self.parameters.get('l', -1)\\n if type(l) not in [int, float]:\\n flag = False\\n warnings += \\\"Depth of well bottom must be a float value\\\\n\\\"\\n else:\\n if l < 0:\\n flag = False\\n warnings += \\\"Depth l must be higher than 0\\\\n\\\"\\n if type(d) not in [int, float]:\\n flag = False\\n warnings += \\\"Depth of well screen must be a float value\\\\n\\\"\\n else:\\n if d < 0 or d > l:\\n flag = False\\n warnings += \\\"Depth d must be in range 0 <= d <= l\\\\n\\\"\\n return(flag, warnings) # End Function\",\n \"def Check(self, parameters):\",\n \"def check_errors(self) -> None:\",\n \"def _check_params(self):\\n\\t\\tstrange_param_helper = False\\n\\t\\tfor param in self.params:\\n\\t\\t\\n\\t\\t\\t# It could be that the param encapsulates several values (e.g., \\\"FLUX_RADIUS(10)\\\")\\n\\t\\t\\t# So we have to dissect this\\n\\t\\t\\tmatch = re.compile(\\\"(\\\\w*)\\\\(\\\\d*\\\\)\\\").match(param)\\n\\t\\t\\tif match:\\n\\t\\t\\t\\tcleanparam = match.group(1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcleanparam = param\\n\\t\\t\\t\\t\\n\\t\\t\\tif cleanparam not in self.fullparamlist:\\n\\t\\t\\t\\tlogger.warning(\\\"Parameter '%s' seems strange and might be unknown to SExtractor\\\" \\\\\\n % (param))\\n\\t\\t\\t\\tstrange_param_helper = True\\n\\t\\t\\t\\t\\n\\t\\tif strange_param_helper:\\n\\t\\t\\tlogger.warning(\\\"Known parameters are: %s\\\" % (self.fullparamtxt))\",\n \"def _check_params(self):\\n pass\",\n \"def check_params(self):\\n raise NotImplementedError\",\n \"def check_params(self):\\r\\n \\r\\n # TODO: More cases?\\r\\n\\r\\n if self.N <= 0:\\r\\n print('Bad Parameter: N')\\r\\n \\r\\n if self.Ha_tally <= 0 or self.Ha_tally > self.N:\\r\\n print('Bad Parameter: Reported winner tally')\\r\\n \\r\\n if len(self.round_sched) < 1 or not self.check_inc_sched(self.round_sched):\\r\\n print('Bad Parameter: Round Schedule')\\r\\n\\r\\n if self.alpha <= 0 or self.alpha >= .5:\\r\\n print('Bad Parameter: Alpha')\",\n \"def test_checkParameters(self):\\n self.failUnlessEqual(self.nice.opts['long'], \\\"Alpha\\\")\\n self.failUnlessEqual(self.nice.opts['another'], \\\"Beta\\\")\\n self.failUnlessEqual(self.nice.opts['longonly'], \\\"noshort\\\")\\n self.failUnlessEqual(self.nice.opts['shortless'], \\\"Gamma\\\")\",\n \"def test_wrong_parameters(self):\\n with self.assertWarns(RuntimeWarning):\\n Parameters(1, mu=3, lambda_=2)\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def check_validity(self):\",\n \"def validate():\",\n \"def test_wrong_input():\\n dwd = DwdWeatherWarningsAPI(None)\\n assert not dwd.data_valid\\n assert dwd.warncell_id is None\\n assert dwd.warncell_name is None\\n assert dwd.last_update is None\\n assert dwd.current_warning_level is None\\n assert dwd.expected_warning_level is None\\n assert dwd.current_warnings is None\\n assert dwd.expected_warnings is None\",\n \"def check():\",\n \"def test_kyc_get_validation_legal(self):\\n pass\",\n \"def has_warnings(self) -> bool:\",\n \"def check_invalid_args_general(config):\\n # Not mathematically correct, but might be required if prior is not\\n # appropriate.\\n if hasattr(config, 'kl_scale') and config.kl_scale != 1.0:\\n warnings.warn('Prior matching term will be scaled by %f.'\\n % config.kl_scale)\\n\\n if hasattr(config, 'store_final_model') and \\\\\\n hasattr(config, 'train_from_scratch') and \\\\\\n config.store_final_model and config.train_from_scratch:\\n warnings.warn('Note, when training from scratch, the final model is ' +\\n 'only trained on the last task!')\",\n \"def warnings(self) -> List[Error]:\",\n \"def check_all_parameters(self):\\n global dtParameterDesc\\n self.message = ''\\n ok = True\\n for par in self.parameters:\\n if par in dtParameterDesc:\\n pardata = dtParameterDesc[par]\\n check = self.check_parameter(par, self.parameters[par])\\n ok = ok and check\\n if not check:\\n self.message += ('\\\\n' if self.message != '' else '') + pardata[dtg.LANG] +\\\\\\n (' вне диапазона' if dtg.LANG == 'ru' else ' out of range')\\n\\n return ok\",\n \"def _getIsValidParameters(self):\\n return True, ''\",\n \"def validate(self):\\n\\n\\tmissing = []\\n\\tbadcheck = []\\n\\tfor name, checkfunc, params in self._required:\\n\\t try:\\n\\t\\targ = self.make_required(name)\\n\\t\\tif checkfunc is not None:\\n\\t\\t if params is not None:\\n\\t\\t\\tparams = (self.param_map[name], arg) + params\\n\\t\\t else:\\n\\t\\t\\tparams = (self.param_map[name], arg)\\n\\t\\t try:\\n\\t\\t\\tapply(checkfunc, params)\\n\\t\\t except ValidationError, msg:\\n\\t\\t\\tbadcheck.append(msg)\\n\\t except ValidationError, args:\\n\\t\\tmissing.append(args)\\n\\n\\tfor (name, checkfunc, params) in self._optional:\\n\\t tup = self.make_optional(name)\\n\\t if tup and checkfunc is not None:\\n\\t\\tif params is not None:\\n\\t\\t params = (self.param_map[name], tup) + params\\n\\t\\telse:\\n\\t\\t params = (self.param_map[name], tup)\\n\\t\\ttry:\\n\\t\\t apply(checkfunc, params)\\n\\t\\texcept ValidationError, msg:\\n\\t\\t badcheck.append(msg)\\n\\n\\tif (missing or badcheck) and self.log_errors:\\n\\t self.log_error(missing, badcheck)\\n\\n\\tif (missing or badcheck) and self.generate_error_page:\\n\\t self.generate_HTML(missing, badcheck)\\n\\n\\tself.missing = missing\\n\\tself.badcheck = badcheck\\n\\n\\treturn not (missing or badcheck)\",\n \"def checkParameters(self):\\n EDVerbose.DEBUG(\\\"EDPluginExecGnomv0_1.checkParameters\\\")\\n self.checkMandatoryParameters(self.dataInput, \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.dataInput.experimentalDataQ, \\\"Scattering vector values are missing\\\")\\n self.checkMandatoryParameters(self.dataInput.experimentalDataValues, \\\"Experimental intensity values are missing\\\")\",\n \"def verify():\",\n \"def check(self):\\n\\n Rbo = self.get_Rbo()\\n alpha = self.comp_alpha()\\n\\n if self.W0 < (self.W2 + self.W3):\\n raise S51_WCheckError(\\\"You must have W2+W3 < W0\\\")\\n\\n if Rbo < self.H0 + self.H2:\\n raise S51_RHCheckError(\\\"You must have H0+H2 < Rbo\\\")\\n\\n if alpha > pi / 2:\\n raise S51_AlphaCheckError(\\\"You must have alpha < pi/2\\\")\",\n \"def ok(*args):\",\n \"def _check_input_data(self):\\n\\n n0, n1, corr, pval = np.nan, np.nan, np.nan, np.nan\\n\\n error_code_test = 0\\n error_text_test = 'No error occurred'\\n try:\\n error_code_test, error_msg = self._check_notnull()\\n if error_code_test == 0:\\n error_code_test, error_msg, n0, n1 = self._check_group_obs(self.test_min_data)\\n if error_code_test == 0:\\n error_code_test, error_msg, corr, pval = \\\\\\n self._check_spearman_corr(self.min_corr, self.max_p)\\n if error_code_test != 0:\\n error_text_test = str(error_msg)\\n except:\\n error_code_test = 9\\n error_text_test = 'Unknown Error'\\n\\n self.checkstats = {'n0': n0, 'n1': n1, 'frame_spearmanR': corr, 'frame_corrPval': pval}\\n\\n self.error_code_test = error_code_test\\n self.error_text_test = error_text_test\\n\\n return self.error_code_test, self.error_text_test\",\n \"def sanity_check(self):\\n pass\",\n \"def test_check_data_fields_errors_warnings(self):\\r\\n\\r\\n header =\\\\\\r\\n ['SampleID',\\r\\n 'BarcodeSequence',\\r\\n 'LinkerPrimerSequence',\\r\\n 'Description']\\r\\n mapping_data = [['s1', 'ACGT', 'AAAxA', 's1_data'],\\r\\n ['s_2', 'CGTA', 'AAAA', 's2_data']]\\r\\n errors = []\\r\\n warnings = []\\r\\n\\r\\n errors, warnings = check_data_fields(header,\\r\\n mapping_data, errors, warnings)\\r\\n\\r\\n expected_errors = ['Invalid DNA sequence detected: AAAxA\\\\t1,2']\\r\\n expected_warnings = ['Invalid characters found in s_2\\\\t2,0']\\r\\n\\r\\n self.assertEqual(errors, expected_errors)\\r\\n self.assertEqual(warnings, expected_warnings)\",\n \"def checkParameters(self):\\n self.DEBUG(\\\"EDPluginWaitMultiFile.checkParameters\\\")\\n self.checkMandatoryParameters(self.dataInput, \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.dataInput.expectedFile, \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.dataInput.expectedSize, \\\"Data Input is None\\\")\",\n \"def NoMore():\\n\\n if assem.MoreParameters():\\n errors.DoWarning('extrign', False)\",\n \"def NoMore():\\n\\n if assem.MoreParameters():\\n errors.DoWarning('extrign', False)\",\n \"def warning(self, *args, **kwargs):\",\n \"def test_bad_values(self):\\n self.assertOK([60])\\n self.assertRaisesInternalError([59.9])\\n self.assertRaisesInternalError([''])\\n self.assertRaisesInternalError([';'])\\n self.assertRaisesInternalError(['('])\\n self.assertRaisesInternalError([None])\",\n \"def _check_validity(self):\\n pass\",\n \"def verifications(sv):\\r\\n verif_no_special(sv) # check no special character remains (fatal error) \\r\\n verif_functions(sv) # check if all functions are defined (fatal error)\\r\\n verif_similar_names(sv) # check similar names for typing errors (warning)\\r\\n verif_stochastic(sv) # make sure stochastic objects are not used in expressions\\r\\n verif_unused(sv) # check physical and logical configurations match \\r\",\n \"def __validate():\\n # TODO: implement\",\n \"def checkParameters(self):\\n self.DEBUG(\\\"EDPluginExecDatGnomv1_0.checkParameters\\\")\\n self.checkMandatoryParameters(self.dataInput, \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.dataInput.inputCurve, \\\"No input Curve file provided\\\")\",\n \"def __check(self):\\n if self.probid is None:\\n raise ProblemConfigError('problem %s has no probid' % self.problem_name)\\n if self.color is None:\\n raise ProblemConfigError('problem %s has no color' % self.problem_name)\\n if self.samples is None:\\n raise ProblemConfigError('problem %s has no sample' % self.problem_name)\",\n \"def checkParamsError(self):\\n # check if parameter combinations match with the simulation filename.\\n for i, f in enumerate(self.yadeDataFiles):\\n # get the file name fore the suffix\\n f = f.split('.' + f.split('.')[-1])[0]\\n # get parameters from the remaining string\\n paramsString = f.split('_')[-self.numParams:]\\n # element wise comparison of the parameter vector\\n if not (np.abs((np.float64(paramsString) - self.getSmcSamples()[-1][i])\\n / self.getSmcSamples()[-1][i] < 1e-10).all()):\\n raise RuntimeError(\\n \\\"Parameters \\\" + \\\", \\\".join(\\n [\\\"%s\\\" % v for v in self.getSmcSamples()[-1][i]]) + \\\" do not match with the data file name \\\" + f)\",\n \"def check_hparams(self, hparams):\\n error_messages = []\\n\\n # Check global params.\\n feature_names = hparams.get_feature_names()\\n global_values, per_feature_values = hparams.get_global_and_feature_params(\\n ['num_keypoints', 'missing_input_value',\\n 'missing_output_value'], feature_names)\\n global_param_error_messages = self._check_param_configuration(\\n *global_values)\\n if global_param_error_messages:\\n error_messages.append('Error message for global param:')\\n error_messages += global_param_error_messages\\n\\n # Check per feature params. hparams.get_feature_names() will only return\\n # feature names that sets per feature parameters.\\n for feature_idx in range(len(per_feature_values)):\\n per_feature_param_error_messages = self._check_param_configuration(\\n *per_feature_values[feature_idx])\\n if per_feature_param_error_messages:\\n error_messages.append(\\n 'Error message for %s feature param:' % feature_names[feature_idx])\\n error_messages += per_feature_param_error_messages\\n\\n if error_messages:\\n raise ValueError(\\n 'Hyperparameter configuration cannot be used in the calibrated '\\n 'lattice estimator. Error messages report the issue per feature, but'\\n ' the parameter may be inherited from global parameter.\\\\nDetailed '\\n 'error messsages\\\\n%s' % '\\\\n'.join(error_messages))\",\n \"def _check_input(self, func, args, kwargs):\\n fullargspec = inspect.getfullargspec(func)\\n return_msg = ''\\n if fullargspec.varkw is None:\\n for key in kwargs:\\n if not key in fullargspec.kwonlyargs:\\n return_msg += f'[Error]: not support param `{key}`. \\\\n'\\n if fullargspec.varargs is None:\\n if len(fullargspec.args) == 0:\\n max_args_len = 0\\n else:\\n max_args_len = len(fullargspec.args)-1 if fullargspec.args[0] == 'self' else len(fullargspec.args)\\n defaults_nums = 0 if fullargspec.defaults is None else len(fullargspec.defaults)\\n min_args_len = max_args_len - defaults_nums\\n if len(args) < min_args_len:\\n return_msg += f'[Error]: have min {min_args_len} input, but you input {len(args)} args. \\\\n'\\n if max_args_len < len(args):\\n return_msg += f'[Error]: have max {max_args_len} input, but you input {len(args)} args. \\\\n'\\n return return_msg\",\n \"def check(self):\\n if 'MISFIT' not in PAR:\\n setattr(PAR, 'MISFIT', 'Waveform')\\n\\n if 'CHANNELS' not in PAR:\\n raise ParameterError(PAR, 'CHANNELS')\\n\\n if 'READER' not in PAR:\\n raise ParameterError(PAR, 'READER')\\n\\n if 'WRITER' not in PAR:\\n setattr(PAR, 'WRITER', PAR.READER)\\n\\n if 'NORMALIZE' not in PAR:\\n setattr(PAR, 'NORMALIZE', True)\\n\\n # mute settings\\n if 'MUTE' not in PAR:\\n setattr(PAR, 'MUTE', False)\\n\\n if 'MUTESLOPE' not in PAR:\\n setattr(PAR, 'MUTESLOPE', 0.)\\n\\n if 'MUTECONST' not in PAR:\\n setattr(PAR, 'MUTECONST', 0.)\\n\\n # filter settings\\n if 'BANDPASS' not in PAR:\\n setattr(PAR, 'BANDPASS', False)\\n\\n if 'FREQLO' not in PAR:\\n setattr(PAR, 'FREQLO', 0.)\\n\\n if 'FREQHI' not in PAR:\\n setattr(PAR, 'FREQHI', 0.)\\n\\n # assertions\\n if PAR.READER not in dir(readers):\\n print msg.ReaderError\\n raise ParameterError()\\n\\n if PAR.WRITER not in dir(writers):\\n print msg.WriterError\\n raise ParameterError()\",\n \"def check(self) -> None:\",\n \"def parameter_check(coordinates, \\n neighbors, \\n bandwidth, \\n convergence, \\n percentage):\\n # Create a boolean vector to keep track of incorrect inputs\\n incorrect_inputs = np.zeros(5, dtype = bool)\\n # Check whether two-dimensional coordinates are provided\\n if not type(coordinates) == np.ndarray:\\n incorrect_inputs[0] = True\\n elif not coordinates.shape[1] == 2:\\n incorrect_inputs[0] = True\\n # Check whether neighbors is a positive integer or float\\n if not ((type(neighbors) == int and neighbors > 0)\\n and not ((type(neighbors) == float) \\n and (neighbors > 0)\\n and (neighbors.is_integer() == True))):\\n incorrect_inputs[1] = True\\n # Check whether bandwidth is a positive integer or float\\n if not bandwidth == None:\\n if not ((type(bandwidth) == int and bandwidth > 0)\\n or (type(bandwidth) == float) and bandwidth > 0):\\n incorrect_inputs[2] = True\\n # Check whether convergence is a positive integer or float\\n if not convergence == None:\\n if not ((type(convergence) == int and convergence > 0)\\n or (type(convergence) == float) and convergence > 0):\\n incorrect_inputs[3] = True\\n # Check whether percentage is a valid percentage value\\n if not percentage == None:\\n if not ((type(percentage) == int and percentage >= 0 \\n and percentage <= 100)\\n or ((type(percentage) == float) and percentage >= 0 \\n and percentage <= 100)):\\n incorrect_inputs[4] = True\\n # Define error messages for each parameter failing the tests\\n errors = ['ERROR: coordinates: Must be a 2-column numpy.ndarray',\\n 'ERROR: neighbors: Must be a whole-number int or float > 0',\\n 'ERROR: bandwidth: Must be an int or float > 0, or None',\\n 'ERROR: convergence: Must be an int or float > 0, or None',\\n 'ERROR: percentage: Must be an int or float in [0, 100], or None']\\n # Print eventual error messages and terminate the code\\n if any(value == True for value in incorrect_inputs):\\n for i in range(0, len(errors)):\\n if incorrect_inputs[i] == True:\\n print(errors[i])\\n sys.exit()\",\n \"def test_basic1(self):\\r\\n self.validate((2, 2, 3, 3), (2, 2, 2, 2), 'valid', verify_grad=False)\",\n \"def checkSuccess():\\n try:\\n relin = np.genfromtxt(\\\"{}reliability.in\\\".format(basedir), dtype=str)\\n # Accurate argument order\\n args = [str(\\\"\\\\'\\\"+probpath+\\\"\\\\'\\\"), str(\\\"\\\\'\\\"+obpath+\\\"\\\\'\\\"),\\n str(\\\"\\\\'\\\"+outfile+\\\"\\\\'\\\"),\\n fcsthr, str(\\\"\\\\'\\\"+variable+\\\"\\\\'\\\"),\\n rthresh, sixhour, nbrhd, rbox_bounds[0],\\n rbox_bounds[1], rbox_bounds[2], rbox_bounds[3]]\\n success = True # Assume success initially\\n # Ensure that each argument was placed into the proper line of the\\n # reliability input file\\n for ind, line in enumerate(relin):\\n # If an argument doesn't line up with the rel in arg, set False\\n print(str(args[ind]).replace('\\\\\\\\', ''), line)\\n if (str(args[ind]).replace('\\\\\\\\', '') != line):\\n success = False\\n print(success)\\n except:\\n success = False\\n return success\",\n \"def checkSuccess():\\n try:\\n relin = np.genfromtxt(\\\"{}reliability.in\\\".format(basedir), dtype=str)\\n # Accurate argument order\\n args = [str(\\\"\\\\'\\\"+probpath+\\\"\\\\'\\\"), str(\\\"\\\\'\\\"+obpath+\\\"\\\\'\\\"),\\n str(\\\"\\\\'\\\"+outfile+\\\"\\\\'\\\"),\\n fcsthr, str(\\\"\\\\'\\\"+variable+\\\"\\\\'\\\"),\\n rthresh, sixhour, nbrhd, rbox_bounds[0],\\n rbox_bounds[1], rbox_bounds[2], rbox_bounds[3]]\\n success = True # Assume success initially\\n # Ensure that each argument was placed into the proper line of the\\n # reliability input file\\n for ind, line in enumerate(relin):\\n # If an argument doesn't line up with the rel in arg, set False\\n print(str(args[ind]).replace('\\\\\\\\', ''), line)\\n if (str(args[ind]).replace('\\\\\\\\', '') != line):\\n success = False\\n print(success)\\n except:\\n success = False\\n return success\",\n \"def checkParameters(self):\\n EDVerbose.DEBUG(\\\"EDPluginExecVideov10.checkParameters\\\")\\n self.checkMandatoryParameters(self.getDataInput(), \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.getDataInput().getInputImagePath(), \\\"inputImage list is None\\\")\\n for oneXSDataFile in self.getDataInput().getInputImagePath():\\n self.checkMandatoryParameters(oneXSDataFile.getPath().getValue(), \\\"input Image does not exist\\\" + oneXSDataFile.marshal())\",\n \"def test_non_integral_validation(self):\",\n \"def test_non_integral_validation(self):\",\n \"def checkParameters(self):\\n self.DEBUG(\\\"EDPluginControlStitchImagev1_0.checkParameters\\\")\\n self.checkMandatoryParameters(self.getDataInput(), \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.getDataInput().getInputImages(), \\\"No input Images\\\")\",\n \"def _check_param(in_params, req_param, opt_param=list()):\\n for param in req_param:\\n if param not in in_params:\\n raise ValueError('{} parameter is required'.format(param))\\n defined_param = set(req_param + opt_param)\\n for param in in_params:\\n if param not in defined_param:\\n print(\\n \\\"WARNING: received unexpected parameter {}\\\".format(param))\",\n \"def verify(self):\\n \\n # Check non-optional args\\n pList = ['basePath', 'dateStart', 'dateStop','sensor']\\n for p in pList:\\n if not hasattr(self, p):\\n self.showUsage()\\n raise '\\\\nMissing keyword arg: %s.\\\\n' % p\\n \\n if not self.isValidBasePath():\\n raise '\\\\nbasePath (%s) does not exist.\\\\n' % self.basePath\\n\\n if not isValidPimsDateString(self.dateStart):\\n raise '\\\\ndateStart (%s) is not valid\\\\n' % self.dateStart\\n else:\\n self.uStart = stringTimeToUnix(self.dateStart)\\n\\n if not isValidPimsDateString(self.dateStop):\\n raise '\\\\ndateStop (%s) is not valid\\\\n' % self.dateStop\\n else:\\n self.uStop = stringTimeToUnix(self.dateStop)\",\n \"def __check_args(self):\\n self.__check_args_type()\\n self.__check_args_val()\",\n \"def check(self):\\n invalid = []\\n\\n if not self.route:\\n invalid.append(('route', 'missing'))\\n elif not self.route[1] in ['GET', 'POST', 'PUT']:\\n invalid.append(('route', 'invalid method: %s' % self.route[1]))\\n\\n has_2xx = False\\n for rcode in self.return_codes:\\n code = rcode[0]\\n if code >= 200 and code < 300:\\n has_2xx = True\\n break\\n if not has_2xx:\\n invalid.append(('return_codes', 'Missing succes return code doc'))\\n\\n if self.client_auth is None:\\n invalid.append(\\n ('client_auth', 'Please provide client auth requirement'))\\n\\n if self.user_auth is None:\\n invalid.append(\\n ('user_auth', 'Please provide user auth requirement'))\\n\\n if invalid:\\n msgs = []\\n for error in invalid:\\n msgs.append(\\\"%s: %s\\\" % error)\\n raise ValueError(\\n \\\"APIFunc for %s is invalid: %s\\\"\\n % (self.viewfunc.__name__,\\n ', '.join(msgs)))\",\n \"def __verify_arguments(self):\\n if len(self.__pointer_data) == 0:\\n raise ValueError(\\n \\\"Input data is empty (size: '%d').\\\" % len(self.__pointer_data)\\n )\\n\\n if self.__number_clusters <= 0:\\n raise ValueError(\\n \\\"Amount of cluster (current value: '%d') for allocation should be greater than 0.\\\"\\n % self.__number_clusters\\n )\\n\\n if self.__numlocal < 0:\\n raise ValueError(\\n \\\"Local minima (current value: '%d') should be greater or equal to 0.\\\"\\n % self.__numlocal\\n )\\n\\n if self.__maxneighbor < 0:\\n raise ValueError(\\n \\\"Maximum number of neighbors (current value: '%d') should be greater or \\\"\\n \\\"equal to 0.\\\" % self.__maxneighbor\\n )\",\n \"def test_runtime_warnings(self):\\n\\t\\tprint(\\\"test runtime warning: sqrt(-1)=\\\", np.sqrt(-1.0))\\n\\t\\tassert(True)\",\n \"def testErrorChecking(self):\\n discardLs = [17,18,19,20]\\n table = {'row1':[1,2,3,4,5],'row2':[6,7,'7s',9,10],'row3':[11,12,13],'row4':[14,15,16]}\\n blackJackObj = BlackJack()\\n ## Overload Constructor\\n blackJackObj.overloadConstructor(table, discardLs)\\n self.assertEquals(True,blackJackObj.errorChecking('10')) # empty pos\\n self.assertEquals(False,blackJackObj.errorChecking('8')) # taken pos\\n self.assertEquals(False,blackJackObj.errorChecking('A')) # invalid input\\n self.assertEquals(False,blackJackObj.errorChecking('21')) # out of range\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def verify_pln(self):\\n\\n warnings = []\\n\\n self._populate_uncertainties()\\n\\n # The transitref and transiturl actually end up stored in the 'transit'\\n # ExoParam due to the ref and url splits. Pull these out and set the\\n # transit entries to the proper pointers.\\n if self.transit.value == 1:\\n if is_empty(self.transit.reference):\\n self.transit.reference = \\\"__TRANSITREF\\\"\\n if is_empty(self.transit.url):\\n self.transit.url = \\\"__TRANSITURL\\\"\\n\\n # If the transit depth is not provided, but an Rp/R* ratio is,\\n # calculate the depth value.\\n if is_empty(self.depth.value) and is_valid(self.rr.value):\\n self.depth.value = self.rr.value ** 2\\n if isinstance(self.rr.uncertainty, Decimal):\\n self.depth.uncertainty = self.rr.uncertainty * 2\\n if isinstance(self.rr.uncertainty_upper, Decimal):\\n self.depth.uncertainty_upper = self.rr.uncertainty_upper * 2\\n self.depth.reference = \\\"Calculated from Rp/R*\\\"\\n self.depth.url = self.rr.reference\\n\\n # If the orbital eccentricity value is 0 and a TT value is provided,\\n # use the same values for T0 as well.\\n if self.ecc.value == Decimal(0) and is_empty(self.om.value):\\n self.om.value = Decimal(90)\\n self.om.reference = \\\"Set to 90 deg with ecc~0\\\"\\n print(\\\"set omega to 90\\\")\\n if is_valid(self.tt.value):\\n print(\\\"copying TT to T0\\\")\\n self.t0.copy_values(self.tt)\\n # OM may already be set to 90.\\n elif self.ecc.value == 0 and self.om.value == 90:\\n if str(self.tt.value) != \\\"NaN\\\":\\n print(\\\"copying TT to T0\\\")\\n self.t0.copy_values(self.tt)\\n\\n # Set the FREEZE_ECC flag if ECC=0 and no uncertainty is provided.\\n if self.ecc.value == 0 and is_empty(self.ecc.uncertainty):\\n self.freeze_ecc.value = 1\\n\\n # Set the MULT flag if NCOMP is more than 1 planet.\\n if self.ncomp.value > 1:\\n self.mult.value = 1\\n\\n # Set the TREND flag if a DVDT value is provided.\\n if not is_empty(self.dvdt.value):\\n self.trend.value = 1\\n\\n # Exclude planets with period uncertainty >10%.\\n self.per.check_constrained(0.1)\\n if not self.per.well_constrained:\\n self.exclude()\\n warnings.append(\\\"<uncertain PER>\\\")\\n\\n # Warn of planets with K speeds <2 m/s.\\n if is_valid(self.k.value):\\n if self.k.value < 2:\\n # self.exclude()\\n warnings.append(\\\"<low K value>\\\")\\n\\n # Make sure RA string uses spaces.\\n if not is_empty(self.ra_string.value):\\n if \\\"h\\\" in self.ra_string.value:\\n new_value = self.ra_string.value.replace(\\\"h\\\", \\\" \\\")\\n new_value = new_value.replace(\\\"m\\\", \\\" \\\")\\n new_value = new_value.replace(\\\"s\\\", \\\"\\\")\\n self.ra_string.value = new_value\\n\\n # Make sure DEC string uses spaces.\\n if not is_empty(self.dec_string.value):\\n if \\\"d\\\" in self.dec_string.value:\\n new_value = self.dec_string.value.replace(\\\"d\\\", \\\" \\\")\\n new_value = new_value.replace(\\\"m\\\", \\\" \\\")\\n new_value = new_value.replace(\\\"s\\\", \\\"\\\")\\n self.dec_string.value = new_value\\n\\n # Display warnings generated by final adjustments.\\n if len(warnings) > 0:\\n print(\\\"<<<{0} GOT {1} WARNING(S)>>>\\\".format(self.name.value,\\n len(warnings)\\n )\\n )\\n [print(x) for x in warnings]\",\n \"def checkParameters(self):\\n self.DEBUG(\\\"EDPluginExecDatcmpv2_0.checkParameters\\\")\\n self.checkMandatoryParameters(self.getDataInput(), \\\"Data Input is None\\\")\\n self.checkMandatoryParameters(self.getDataInput().inputCurve, \\\"No input 1D curves file provided\\\")\",\n \"def check_params (self, doclet):\\n\\n if not doclet.undocumented:\\n try:\\n param_names = { p.name for p in doclet.params }\\n meta_param_names = set (doclet.meta.code.paramnames)\\n\\n for name in param_names - meta_param_names:\\n doclet.warn (\\\"Documented parameter %s not found on signature\\\" % name)\\n\\n for name in meta_param_names - param_names:\\n doclet.warn (\\\"Undocumented parameter %s\\\" % name)\\n\\n except AttributeError:\\n pass\\n\\n return doclet\",\n \"def input_check(self):\\n\\n if self.species == 'He': assert self.line_model == 'voigt'\\n n_upper_range, e_dens_range, temp_range, b_field_range = get_param_ranges(self.line_model)\\n\\n if np.isnan(n_upper_range).sum() <= 1:\\n assert (self.n_upper in range(n_upper_range[0], n_upper_range[1]))\\n if np.isnan(e_dens_range).sum() <= 1:\\n assert (e_dens_range[0] <= self.e_dens <= e_dens_range[1])\\n if np.isnan(temp_range).sum() <= 1:\\n assert (temp_range[0] <= self.temp <= temp_range[1])\\n if np.isnan(b_field_range).sum() <= 1:\\n assert (b_field_range[0] <= self.b_field <= b_field_range[1])\",\n \"def sanityCheck(parameters):\\n if not parameters: printUsage(); sys.exit()\\n\\n # these may differ depending on type of processing to do\\n padPath = parameters['padPath']\\n dateStart = parameters['dateStart']\\n dateStop = parameters['dateStop']\\n sensor = parameters['sensor']\\n abbr = parameters['abbr']\\n whichAx = parameters['whichAx']\\n pm = parameters['pm']\\n tag = parameters['tag']\\n Nfft = parameters['Nfft']\\n No = parameters['No']\\n \\n if not os.path.isdir(padPath): print '%s does not exist' % padPath; sys.exit()\\n if not(pm in ['+','-']): print 'bad pm flag (%s): it should be either (+) for demean OR (-) for keep mean' % pm; sys.exit()\\n\\n return padPath,dateStart,dateStop,sensor,abbr,whichAx,pm,tag,Nfft,No\",\n \"def test_Validator_is_valid_two_arguments(self):\\n\\n validator = validators.Draft7Validator({})\\n with self.assertWarns(DeprecationWarning) as w:\\n result = validator.is_valid(\\\"foo\\\", {\\\"type\\\": \\\"number\\\"})\\n\\n self.assertFalse(result)\\n self.assertEqual(w.filename, __file__)\\n self.assertTrue(\\n str(w.warning).startswith(\\n \\\"Passing a schema to Validator.is_valid is deprecated \\\",\\n ),\\n )\",\n \"def test_buoy_format2():\\n with pytest.raises(AssertionError) as err_info:\\n Check_BuoyDC.check_buoy_format(buoy_format_fail_2)\\n assert str(err_info.value) == 'Input length incorrect, see instructions'\",\n \"def checkNeededParams(self):\\n for clp,value in self.neededParamsNames.items():\\n if value[0] not in self.neededParams:\\n print >> sys.stderr, clp+\\\" is a mandatory parameter \\\"\\n self.printUsage()\\n sys.exit(1)\",\n \"def test_buoy_format1():\\n with pytest.raises(AssertionError) as err_info:\\n Check_BuoyDC.check_buoy_format(buoy_format_fail_1)\\n assert str(err_info.value) == 'Input formatted incorrectly, see instructions'\",\n \"def check(self):\\r\\n self.check_probabilities()\\r\\n self.check_sum()\",\n \"def test__validate_topic__2():\\n for input_value in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_topic(input_value)\",\n \"def check_additional_input():\\r\\n\\r\\n # Check if the cluster center input is correct\\r\\n RM.check_if_matrix(clust_cent, 'The cluster centers')\\r\\n RM.warn_if_bigger(clust_cent.shape[1], meta_model.get_in_par_means().shape[1],\\r\\n 'The number of input parameters in the cluster centers',\\r\\n 'the number of input parameters - 1')\\r\\n RM.check_if_bigger(clust_cent.shape[1], meta_model.get_in_par_means().shape[1] - 1,\\r\\n 'The number of input parameters',\\r\\n 'the number of input parameters in the cluster centers')\\r\\n\\r\\n bounds = meta_model.get_in_par_intervals()\\r\\n\\r\\n for j in range(clust_cent.shape[0]):\\r\\n for i in range(bounds.shape[0]):\\r\\n RM.check_if_in_interval(bounds[i], clust_cent[j, i], i, ' In cluster center %x, the value')\\r\\n\\r\\n def check_PLSR_input():\\r\\n \\\"\\\"\\\" Checks model data of PLSR\\r\\n\\r\\n :return: Checks model data of PLSR\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n RM.check_if_ndim_array(model_data, 3, 'Model data')\\r\\n RM.check_if_bigger(model_data.shape[1], meta_model.get_in_par_means().shape[1],\\r\\n 'The number of input parameters in the solution matrix',\\r\\n 'the number of input parameters')\\r\\n RM.warn_if_bigger(model_data.shape[1], meta_model.get_in_par_means().shape[1] + 1,\\r\\n 'The number of input parameters',\\r\\n 'the number of input parameters in the solution matrix')\\r\\n RM.check_if_bigger(model_data.shape[2], meta_model.get_out_par_means().shape[1] - 1,\\r\\n 'The number of output parameters in the solution matrix',\\r\\n 'the number of output parameters')\\r\\n RM.warn_if_bigger(model_data.shape[2], meta_model.get_out_par_means().shape[1],\\r\\n 'The number of output parameters',\\r\\n 'the number of output parameters in the solution matrix')\\r\\n\\r\\n # Check if the additional data is correct\\r\\n\\r\\n if meta_model.get_type() == 'PLSR': # Additional check-up for PLSR\\r\\n check_PLSR_input()\\r\\n\\r\\n elif meta_model.get_type() == 'DLU': # Additional check-up for DLU\\r\\n raise TypeError('This part is not implemented yet')\\r\\n\\r\\n # if not isinstance(model_data, np.ndarray):\\r\\n # raise TypeError('The cluster input and output data is not stored in a multidimensional array')\\r\\n #\\r\\n # for clust_data in model_data:\\r\\n #\\r\\n # if not isinstance(clust_data[0], np.matrix) or not isinstance(clust_data[1], np.matrix):\\r\\n # raise TypeError('One of the input or output databases is not a matrix')\\r\\n #\\r\\n # if clust_data[0].shape[1] > meta_model.get_in_par_means().shape[1]:\\r\\n # warnings.warn('The number of input parameters for the input database of the clusters is bigger '\\r\\n # 'than the actual number of input parameters')\\r\\n #\\r\\n # elif clust_data[0].shape[1] < meta_model.get_in_par_means().shape[1]:\\r\\n # raise TypeError('The number of input parameters for the input database of the clusters is '\\r\\n # 'smaller than the actual numbers of input parameters')\\r\\n #\\r\\n # if clust_data[1].shape[1] > meta_model.get_out_par_means().shape[1]:\\r\\n # raise TypeError('The number of output parameters for the output database of the clusters is '\\r\\n # 'bigger than the actual number of output parameters')\\r\\n #\\r\\n # elif clust_data[1].shape[1] < meta_model.get_out_par_means().shape[1]:\\r\\n # raise TypeError('The number of output parameters for the output database of the clusters is '\\r\\n # 'smaller than the actual numbers of output parameters')\\r\\n #\\r\\n # if clust_data[0].shape[0] != clust_data[1].shape[0]:\\r\\n # raise TypeError('The number rows in the input and output database differ from each other')\\r\\n\\r\\n else: # No check-up is done when the meta-model is an unknown version\\r\\n warnings.warn('The additional cluster data can not be checked, for this kind of meta-model')\\r\\n\\r\\n RM.check_if_same_size(clust_cent.shape[0], model_data.shape[0],\\r\\n 'The number of clusters according to the cluster centers',\\r\\n 'The number of clusters according to the model_data')\",\n \"def validate(self):\\n errors = []\\n app = errors.append\\n\\n if not self.hint_cores >= self.mpi_procs * self.omp_threads >= self.min_cores:\\n app(\\\"self.hint_cores >= mpi_procs * omp_threads >= self.min_cores not satisfied\\\")\\n\\n if self.omp_threads > self.hw.cores_per_node:\\n app(\\\"omp_threads > hw.cores_per_node\\\")\\n\\n if self.mem_per_proc > self.hw.mem_per_node:\\n app(\\\"mem_mb >= self.hw.mem_per_node\\\")\\n\\n if not self.max_mem_per_proc >= self.mem_per_proc >= self.min_mem_per_proc:\\n app(\\\"self.max_mem_per_proc >= mem_mb >= self.min_mem_per_proc not satisfied\\\")\\n\\n if self.priority <= 0:\\n app(\\\"priority must be > 0\\\")\\n\\n if not (1 <= self.min_cores <= self.hw.num_cores >= self.hint_cores):\\n app(\\\"1 <= min_cores <= hardware num_cores >= hint_cores not satisfied\\\")\\n\\n if errors:\\n raise self.Error(str(self) + \\\"\\\\n\\\".join(errors))\",\n \"def __verify_arguments(self):\\r\\n if len(self.__pointer_data) == 0:\\r\\n raise ValueError(\\\"Input data is empty (size: '%d').\\\" % len(self.__pointer_data))\\r\\n\\r\\n if self.__number_clusters <= 0:\\r\\n raise ValueError(\\\"Amount of cluster (current value: '%d') for allocation should be greater than 0.\\\" %\\r\\n self.__number_clusters)\\r\\n\\r\\n if self.__numlocal < 0:\\r\\n raise ValueError(\\\"Local minima (current value: '%d') should be greater or equal to 0.\\\" % self.__numlocal)\\r\\n\\r\\n if self.__maxneighbor < 0:\\r\\n raise ValueError(\\\"Maximum number of neighbors (current value: '%d') should be greater or \\\"\\r\\n \\\"equal to 0.\\\" % self.__maxneighbor)\",\n \"def check_parameters(args):\\n inference_algorithm = args[\\\"inference_algorithm\\\"]\\n combination_algorithm = args[\\\"combination_algorithm\\\"]\\n measures = args[\\\"measures\\\"]\\n prior = args[\\\"prior\\\"]\\n inverse_dynamics_model_checkpoint = args[\\\"inverse_dynamics_model_checkpoint\\\"]\\n\\n check_in(\\n \\\"inference_algorithm\\\",\\n inference_algorithm,\\n [\\n \\\"rlsp\\\",\\n \\\"latent_rlsp\\\",\\n \\\"latent_rlsp_ablation\\\",\\n \\\"sampling\\\",\\n \\\"deviation\\\",\\n \\\"reachability\\\",\\n \\\"spec\\\",\\n ],\\n )\\n check_in(\\n \\\"combination_algorithm\\\",\\n combination_algorithm,\\n (\\\"additive\\\", \\\"bayesian\\\", \\\"latent_vi\\\", \\\"latent_ppo\\\"),\\n )\\n check_in(\\\"prior\\\", prior, [\\\"gaussian\\\", \\\"laplace\\\", \\\"uniform\\\"])\\n\\n for i, measure in enumerate(measures):\\n check_in(\\n \\\"measure {}\\\".format(i),\\n measure,\\n [\\\"inferred_reward\\\", \\\"true_reward\\\", \\\"final_reward\\\", \\\"model_training_error\\\"],\\n )\\n\\n if combination_algorithm == \\\"bayesian\\\":\\n check_in(\\\"inference_algorithm\\\", inference_algorithm, [\\\"rlsp\\\", \\\"sampling\\\"])\\n\\n if inference_algorithm == \\\"latent_rlsp\\\":\\n check_not_none(\\n \\\"inverse_dynamics_model_checkpoint\\\", inverse_dynamics_model_checkpoint\\n )\\n\\n if (\\n combination_algorithm.startswith(\\\"latent\\\")\\n and inference_algorithm != \\\"latent_rlsp\\\"\\n ):\\n raise ValueError(\\n \\\"combination_algorithm 'latent' should only be used with 'latent_rlsp'\\\"\\n )\",\n \"def check_user_parameters(user_information):\\n\\n email = user_information.get('email', '')\\n sub_end_date = user_information.get('subenddate', '')\\n vineyards = user_information.get('vineyards', '')\\n current_date = datetime.date.today().strftime('%Y-%m-%d')\\n new_user_id = user_information.get('userid', '')\\n is_admin = user_information.get('admin', '')\\n is_enable = user_information.get('enable', '')\\n\\n try:\\n message = (\\n 'Validating submitted user parameters.'\\n )\\n logger.info(message)\\n check_user_id(new_user_id)\\n if email != '':\\n if re.match(r\\\"[^@]+@[^@]+\\\\.[^@]+\\\", email) is None:\\n raise PlantalyticsDataException(EMAIL_INVALID)\\n check_subscription_end_date(sub_end_date, current_date)\\n if is_admin != '':\\n if not isinstance(is_admin, bool):\\n raise PlantalyticsDataException(DATA_INVALID)\\n if is_enable != '':\\n if not isinstance(is_enable, bool):\\n raise PlantalyticsDataException(DATA_INVALID)\\n if vineyards != '':\\n for vineyard_id in vineyards:\\n if int(vineyard_id) < 0:\\n raise PlantalyticsDataException(VINEYARD_BAD_ID)\\n message = (\\n 'Submitted user parameters successfully validated.'\\n )\\n logger.info(message)\\n except PlantalyticsException as e:\\n raise e\\n except ValueError as e:\\n raise e\\n except Exception as e:\\n raise e\",\n \"def f_check_adr_parameters_correctness(dict):\\n\\n if int(dict[\\\"operation_mode_num\\\"]) not in (0, 1, 2, 3, 4, 5, 6):\\n print('\\\\n Error!!! Operation mode is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"FFT_size_samples\\\"]) not in (2048, 4096, 8192, 16384, 32768):\\n print('\\\\n Error!!! FFT size is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"spectra_averaging\\\"]) < 16 or int(dict[\\\"spectra_averaging\\\"]) > 32768:\\n print('\\\\n Error!!! Spectra averaging number is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"start_line_freq\\\"]) not in (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16): # 0 … (SFFT-1024)/1024\\n print('\\\\n Error!!! Start frequency line is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"width_line_freq\\\"]) not in (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16):\\n print('\\\\n Error!!! Frequency width line is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"width_line_freq\\\"]) > ((int(dict[\\\"FFT_size_samples\\\"]) - int(dict[\\\"start_line_freq\\\"]) * 1024) / 1024): # 1 … (SFFT-SLINE*1024)/1024\\n print('\\\\n Error!!! Frequency width is bigger than FFT size allows!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"clock_source\\\"]) not in (0, 1):\\n print('\\\\n Error!!! Clock source is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"sum_diff_mode_num\\\"]) not in (0, 1):\\n print('\\\\n Error!!! Sum-diff mode is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n if int(dict[\\\"data_file_size\\\"]) < -1 or int(dict[\\\"data_file_size\\\"]) > 4096:\\n print('\\\\n Error!!! File size value is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n\\n '''\\n if (int(dict[\\\"chan_diff_delay\\\"]) < 0 or int(parameters_dict[\\\"chan_diff_dalay\\\"]) > 1024):\\n print('\\\\n Error!!! Channel difference delay is wrong!\\\\n')\\n sys.exit(' Program stopped!')\\n '''\\n\\n # print('\\\\n ADR parameters from file are correct!\\\\n')\\n\\n return dict\",\n \"def test_check_inputs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n with warnings.catch_warnings(record=True):\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.students == game._all_students\\n assert game.projects == game._all_projects\\n assert game.supervisors == game._all_supervisors\",\n \"def check_errors(self):\\n raise NotImplementedError(\\\"Implement it in a subclass.\\\")\",\n \"def test_validate_and_logger(self):\\n data = V20CredProblemReport(\\n description={\\n \\\"en\\\": \\\"Insufficient credit\\\",\\n \\\"code\\\": \\\"invalid_code\\\",\\n },\\n ).serialize()\\n self._caplog.set_level(logging.WARNING)\\n V20CredProblemReportSchema().validate_fields(data)\\n assert \\\"Unexpected error code received\\\" in self._caplog.text\",\n \"def validate_args(args):\\n\\n if args.batch_size % args.batch_splits != 0:\\n raise ValueError(BATCH_SIZE_SPLIT_ERR.format(args.batch_size, args.batch_splits))\\n\\n if args.data_parallel and args.model_parallel:\\n raise ValueError(DATA_AND_MODEL_PARALLEL_ERR)\\n\\n if args.class_bal and args.year_weighted_class_bal:\\n raise ValueError(CONFLICTING_WEIGHTED_SAMPLING_ERR)\\n\\n assert args.ten_fold_test_index in range(-1, 10)\",\n \"def test__validate_status__1():\\n for input_value in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_status(input_value)\",\n \"def checkUserInput(self, stguess):\\n lines = stguess.splitlines()\\n if len(lines) == 0:\\n return False\\n err = \\\"\\\"\\n for l, line in enumerate(lines):\\n items = line.split(',')\\n np = len(items)\\n if np != 4 and np != 6:\\n err = \\\"Wrong number of parameters\\\"\\n else:\\n for p in range(0, np):\\n val = items[p].strip()\\n if p == 0:\\n if not val in ['G', 'L', 'P', 'AG', 'AL', 'AP']:\\n err = \\\"Unknown peak type\\\"\\n else:\\n if not isNumber(val):\\n err = \\\"Parameter {0} in not numeric\\\".format(p+1)\\n break\\n v = float(val)\\n if p == 1: # xm\\n if v < self.data[0].min() or v > self.data[0].max():\\n err = \\\"Parameter {0} out of range\\\".format(p+1)\\n break\\n if p == 2: # amp\\n if v < self.data[1].min() * 1.1 \\\\\\n or v > self.data[1].max() * 1.1:\\n err = \\\"Parameter {0} out of range\\\".format(p+1)\\n break\\n if p == 3: # w\\n xspan = getSpan(self.data[0])\\n if v < xspan / self.npt or v > (xspan / 2):\\n err = \\\"Parameter {0} out of range\\\".format(p+1)\\n break\\n if p == 4: # asym\\n maxasym = 1000 / self.data[0].max()\\n if v < -maxasym or v > maxasym:\\n err = \\\"Parameter {0} out of range\\\".format(p+1)\\n break\\n if p == 5: # Lfrac\\n if v < 0.0 or v > 1.0:\\n err = \\\"Parameter {0} out of range\\\".format(p+1)\\n break\\n if err:\\n break\\n if err:\\n errmsg = \\\"Error in peak {0}:\\\\n{1}\\\".format(l+1, err)\\n QtWidgets.QMessageBox.warning(self.parent, self.title, errmsg)\\n return False\\n return True\",\n \"def check_all_user_inputs_valid(self):\\n self.check_RNN_layers_valid()\\n self.check_activations_valid()\\n self.check_embedding_dimensions_valid()\\n self.check_initialiser_valid()\\n self.check_y_range_values_valid()\\n self.check_return_final_seq_only_valid()\",\n \"def warning_check(self, rule_to_check, valid):\\r\\n for warning in self.warning_functions:\\r\\n warning(rule_to_check, valid)\",\n \"def __check(self):\\n assert self.name is not None, \\\"Empty name!\\\"\\n assert self.in_spc, \\\"Empty in_spc!\\\"\\n assert self.out_spc, \\\"Empty out_spc!\\\"\\n assert self.num_clss > 0, \\\"Invalid number of output classes!\\\"\\n if not isinstance(self, SvmSklearnWrapper):\\n assert self.los_fnc is not None, \\\"No loss function!\\\"\\n assert self.opt is not None, \\\"No optimizer!\\\"\",\n \"def run_parameters_validations(self):\\n if self.risk_rule:\\n if 'connectApi' not in self.services:\\n return_error(\\\"You entered a risk rule but the 'connectApi' service is not chosen. \\\"\\n \\\"Add the 'connectApi' service to the list or remove the risk rule.\\\")\\n else:\\n for risk_rule in self.risk_rule:\\n if not is_valid_risk_rule(self, risk_rule):\\n return_error(f\\\"The given risk rule: {risk_rule} does not exist,\\\"\\n f\\\"please make sure you entered it correctly. \\\\n\\\"\\n f\\\"To see all available risk rules run the '!rf-get-risk-rules' command.\\\")\\n\\n if self.fusion_file_path is not None:\\n if 'fusion' not in self.services:\\n return_error(\\\"You entered a fusion file path but the 'fusion' service is not chosen. \\\"\\n \\\"Add the 'fusion' service to the list or remove the fusion file path.\\\")\",\n \"def check_parameters_valid(self) :\\n for check_parameter in self.parameters :\\n if (not self.parameters[check_parameter]['set']) :\\n error_message = \\\"Missing key -> '\\\" + check_parameter + \\\"'\\\"\\n if (Config.logger) :\\n dtpo_log('fatal', error_message)\\n raise ParseError(error_message)\\n\\n if self.parameters[check_parameter]['type'] == 'dir' :\\n value = self.parameters[check_parameter]['value']\\n return_string = check_directory_permissions(value)\\n if return_string :\\n error_message = \\\"{0} not accessible \\\" \\\\\\n \\\"-> {1}\\\".format(\\n check_parameter,\\n return_string)\\n raise ParseError(error_message)\\n elif self.parameters[check_parameter]['type'] == 'file' :\\n value = self.parameters[check_parameter]['value']\\n try :\\n file_pointer = open(value)\\n file_pointer.close()\\n except IOError as io_error :\\n error_message = \\\"File {0} not accessible -> {2}\\\" \\\\\\n .format(\\n check_parameter,\\n self.parameters[check_parameter]['value'],\\n str(io_error))\\n raise ParseError(error_message)\",\n \"def checkParameters(self):\\n EDVerbose.DEBUG(\\\"EDPluginControlAbsorptionv0_1.checkParameters\\\")\\n self.checkMandatoryParameters(self.getDataInput(), \\\"Data Input is None\\\")\",\n \"def test_failed_parameter_verification() -> None:\\n name = create_random_alphanumeric(10)\\n # The lower bound must be smaller than the upper bound!\\n parameters = [5, 1, 3]\\n\\n with pytest.raises(ValueError):\\n UnivDist(\\n name=name, distribution=DISTRIBUTION_NAME, parameters=parameters\\n )\\n\\n # The mid-point value must be between lower and upper bounds!\\n parameters = [3, 4, 10]\\n\\n with pytest.raises(ValueError):\\n UnivDist(\\n name=name, distribution=DISTRIBUTION_NAME, parameters=parameters\\n )\",\n \"def sanity_check(self):\\n return True\",\n \"def check_args():\\n schema = Schema({\\n 'FOLDREC': Use(open, error='FOLDREC file should be readable'),\\n 'CLUSTAL': Use(open, error='CLUSTAL file should be readable'),\\n 'CCMPRED': Use(open, error='CCMPRED file should be readable'),\\n '--metafold': Use(open, error='METAFOLD_FILE should be readable'),\\n '--nb_pdb': And(Use(int), lambda n: 1 <= n <= 405,\\n error='--nb_pdb=NUM should be integer 1 <= N <= 405'),\\n '--dssp': Use(open, error='dssp/mkdssp should be readable'),\\n '--dope': Use(open, error='dope file should be readable'),\\n '--benchmark': Use(open, error='BENCHMARK_FILE should be readable'),\\n '--cpu': And(Use(int), lambda n: 0 <= n <= cpu_count(),\\n error='--cpus=NUM should be integer 1 <= N <= ' + str(cpu_count())),\\n # The output PATH is created (if not exists) at the end of the program\\n # so we skip the check.\\n object: object})\\n try:\\n schema.validate(ARGUMENTS)\\n except SchemaError as err:\\n exit(err)\",\n \"def validate(self):\",\n \"def validate(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7049662","0.7046296","0.6972314","0.69019","0.67996854","0.6766278","0.67596585","0.6748443","0.6729965","0.6717157","0.66976315","0.66686934","0.6619799","0.65809375","0.65628576","0.64479285","0.6446445","0.64453197","0.642674","0.6337774","0.6332849","0.63279086","0.63171345","0.631054","0.6309994","0.6307619","0.6292396","0.62842166","0.6272857","0.625615","0.625615","0.62417203","0.6234877","0.6232026","0.62318623","0.6231148","0.622687","0.6226052","0.6222267","0.6216256","0.6212527","0.621139","0.6202895","0.61993265","0.6188981","0.6184103","0.6184103","0.6178157","0.61609477","0.61609477","0.615019","0.6119985","0.6110561","0.6100453","0.6082646","0.6074613","0.6073154","0.606408","0.6063797","0.6063797","0.6063797","0.6063797","0.6063797","0.6063797","0.6063797","0.6063797","0.6062133","0.60555136","0.60502046","0.6046029","0.6038767","0.6031627","0.6025555","0.6025347","0.6007366","0.6004877","0.6002821","0.5995878","0.59879875","0.5980181","0.5972823","0.5965321","0.5962036","0.59616166","0.5954179","0.5952208","0.59519166","0.5948973","0.5947721","0.5946392","0.5939844","0.5939221","0.59372866","0.5925443","0.5917923","0.5908478","0.59016067","0.5898066","0.5895947","0.5895947"],"string":"[\n \"0.7049662\",\n \"0.7046296\",\n \"0.6972314\",\n \"0.69019\",\n \"0.67996854\",\n \"0.6766278\",\n \"0.67596585\",\n \"0.6748443\",\n \"0.6729965\",\n \"0.6717157\",\n \"0.66976315\",\n \"0.66686934\",\n \"0.6619799\",\n \"0.65809375\",\n \"0.65628576\",\n \"0.64479285\",\n \"0.6446445\",\n \"0.64453197\",\n \"0.642674\",\n \"0.6337774\",\n \"0.6332849\",\n \"0.63279086\",\n \"0.63171345\",\n \"0.631054\",\n \"0.6309994\",\n \"0.6307619\",\n \"0.6292396\",\n \"0.62842166\",\n \"0.6272857\",\n \"0.625615\",\n \"0.625615\",\n \"0.62417203\",\n \"0.6234877\",\n \"0.6232026\",\n \"0.62318623\",\n \"0.6231148\",\n \"0.622687\",\n \"0.6226052\",\n \"0.6222267\",\n \"0.6216256\",\n \"0.6212527\",\n \"0.621139\",\n \"0.6202895\",\n \"0.61993265\",\n \"0.6188981\",\n \"0.6184103\",\n \"0.6184103\",\n \"0.6178157\",\n \"0.61609477\",\n \"0.61609477\",\n \"0.615019\",\n \"0.6119985\",\n \"0.6110561\",\n \"0.6100453\",\n \"0.6082646\",\n \"0.6074613\",\n \"0.6073154\",\n \"0.606408\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6063797\",\n \"0.6062133\",\n \"0.60555136\",\n \"0.60502046\",\n \"0.6046029\",\n \"0.6038767\",\n \"0.6031627\",\n \"0.6025555\",\n \"0.6025347\",\n \"0.6007366\",\n \"0.6004877\",\n \"0.6002821\",\n \"0.5995878\",\n \"0.59879875\",\n \"0.5980181\",\n \"0.5972823\",\n \"0.5965321\",\n \"0.5962036\",\n \"0.59616166\",\n \"0.5954179\",\n \"0.5952208\",\n \"0.59519166\",\n \"0.5948973\",\n \"0.5947721\",\n \"0.5946392\",\n \"0.5939844\",\n \"0.5939221\",\n \"0.59372866\",\n \"0.5925443\",\n \"0.5917923\",\n \"0.5908478\",\n \"0.59016067\",\n \"0.5898066\",\n \"0.5895947\",\n \"0.5895947\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6827856"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":75797,"cells":{"query":{"kind":"string","value":"Create a piezometer object (works similar to Observation well)"},"document":{"kind":"string","value":"def __init__(self, name=\"\", description=\"\", time_units=\"s\", len_units=\"m\"):\n super(Piezometer, self).__init__()\n\n # Set general info\n self._type = 3 # piezometer id\n self.parameters = {'full': True, # is full penetrating?\n 'r': 1., # distance until pumping well in length units\n 'z': 1.} # piezometer depth in length units\n\n # Set data\n self.name = name\n self.description = description\n\n self.time_units = time_units\n self.len_units = len_units\n\n # Create drawdown data\n self.drawdown = _Data(dtype=1, name=name, description=description)\n self.drawdown.set_units(self.time_units, self.len_units)"},"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 lick_piezo():\n lp_raw = read_npy_file('lickPiezo.raw.npy')\n lp_timestamps = read_npy_file('lickPiezo.timestamps.npy')\n lp_rate = get_rate(lp_timestamps)\n lick_piezo_ts = TimeSeries(\n name='lickPiezo',\n starting_time=lp_timestamps[0, 1],\n rate=lp_rate,\n data=np.ravel(lp_raw),\n unit='V',\n description='Voltage values from a thin-film piezo connected to the '\n 'lick spout, so that values are proportional to deflection '\n 'of the spout and licks can be detected as peaks of the signal.'\n )\n nwb_file.add_acquisition(lick_piezo_ts)","def __init__(self,make,model,year):\r\n self.make=make\r\n self.model=model\r\n self.year=year\r\n self.odometer_reading=0","def __init__(self, make, model, year):\r\n self.make = make\r\n self.model = model\r\n self.year = year\r\n self.odometer_reading = 0","def __init__(self, make, model, year):\r\n self.make = make\r\n self.model = model\r\n self.year = year\r\n self.odometer_reading = 0","def __init__(self, make, model, year):\r\n self.make = make\r\n self.model = model\r\n self.year = year\r\n self.odometer_reading = 0","def __init__(self, make, model, year):\r\n self.make = make\r\n self.model = model\r\n self.year = year\r\n self.odometer_reading = 0","def __init__(self, make, model, year):\r\n self.make = make\r\n self.model = model\r\n self.year = year\r\n self.odometer_reading = 0","def __init__(self, manufacturer, model, year):\n self.manufacturer = manufacturer\n self.model = model\n self.year = year\n self.odometer = 0","def __init__(self):\n raise NotImplementedError('cannot create independent arc')","def prada(self):\n scale_factor = 1.0 / (1.0 + self.snapshot.header.redshift)\n r200c_physical = self.r200c * scale_factor / 1000.0 # units Mpc\n\n v200 = (\n (self.snapshot.const.G * self.m200c)\n / r200c_physical\n * self.snapshot.const.Mpc ** 2\n / 1000.0 ** 2\n ) ** 0.5 # units km/s\n\n def y(x, vmax, v200):\n func = np.log(1 + x) - (x / (1 + x))\n return ((0.216 * x) / func) ** 0.5 - (vmax / v200)\n\n concentration = np.zeros((len(self.vmax)))\n for halo in range(self.N_halos):\n if v200[halo] > self.vmax[halo]:\n concentration[halo] = -9999.0\n else:\n try:\n concentration[halo] = newton(\n y, x0=5.0, args=(self.vmax[halo], v200[halo])\n )\n except:\n concentration[halo] = -9999.0\n\n return concentration","def __init__(self, make, model, year=2009):\r\n self.make = make\r\n self.model = model\r\n self.year = year\r\n self.odometer = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def func_piezo(self,piezo):\n self.write('SOURce:VOLTage:PIEZo '+str(piezo))\n self.read()","def __init__(self,naca_name,N,plt=True):\n # Name of the airfoil\n self.name = naca_name\n # Number of points\n self.N = N/2\n # [m] Points of the airfoil (_u: upper, _l: lower)\n self.xu = np.linspace(0,1,N)\n self.xl = np.linspace(0,1,N)\n self.x = np.concatenate((self.xu, np.flip(self.xl)))\n self.yu = np.empty((N))\n self.yl = np.empty((N))\n self.ytu = np.empty((N))\n self.ytl = np.empty((N))\n self.z = np.concatenate((self.yu, np.flip(self.yl)))\n self.y = np.zeros((2*N))\n self.dyc = np.zeros((N))\n # Camber line y position (_u: upper, _l: lower)\n self.yc = np.zeros((N))\n # [m] Chord length\n self.c = 1\n # Macximum camber in chord length\n self.m = 0.01*int(naca_name[-4:-3])\n # Distance of the maximum camber from the leading edge\n self.p = 0.1*int(naca_name[-3:-2])\n # Thickness of the airfoil\n self.t = 0.01*int(naca_name[-2:])\n # Leading edge radius\n self.r = 1.019*self.t**2 / self.c\n # Plotting variable\n self.plot = plt","def __init__(self, p) -> None:\n self._p = p\n self._delegate = TwoQubitAsymmetricDepolarizingChannel(p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15)","def __init__(self, make, model, year):\r\n self.make = make # Este MAKE depois do = é o mesmo dos Parametros a cima.\r\n self.model = model\r\n self.year = year\r\n self.odometer_reading = 0","def __init__(self, make, model, year):\n\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0","def _createObj(self) -> None:\n phase_img = skimage.img_as_float(skimage.data.camera())[::-1, ::-1]\n mod_img = skimage.img_as_float(skimage.data.immunohistochemistry()[:, :, 0])[::-1, ::-1]\n mod = skimage.transform.resize(mod_img, self.shape,\n mode='wrap', preserve_range=True)\n phase = skimage.transform.resize(phase_img, self.shape,\n mode='wrap', preserve_range=True)\n\n # Setting the ranges\n phase = (phase - np.min(phase)) / (np.max(phase) - np.min(phase)) * self.phase_range\n mod = (mod - np.min(mod)) / (np.max(mod) - np.min(mod)) * self.mod_range\n\n # Centering the phase at 0.\n phase = np.angle(np.exp(1j * (phase - scipy.stats.circmean(phase))))\n obj = (mod * np.exp(1j * phase)).astype('complex64')\n self._setObjArrayValues(obj)","def __init__(self, a, p=1):\n self.a = a # strength\n self.p = p # period\n self._construct_doffsets()\n self._compute_soffsets()","def __init__(self,mass,time,**kwargs):\n self.setTime(time)\n ParticlePhaseCoordinates.__init__(self,mass,**kwargs)","def _construct_pie(self):\n\n # count labels and instances\n label_count = count(self.labels)\n label_instances = [instance for instance in label_count.values()]\n colours = [\n rgb2hex(\n self.clusters[label]/255) for label in label_count.keys()]\n self.pie = plot.figure()\n plot.pie(\n x=label_instances, labels=label_count.keys(\n ), colors=colours, autopct=\"%.2f%%\")\n plot.tight_layout()\n plot.axis('equal')","def __init__(self, name, gauge, window = Amount(1, Time.SECONDS), clock = time):\r\n self._clock = clock\r\n self._gauge = gauge\r\n self._samples = []\r\n self._window = window\r\n NamedGauge.__init__(self, '%s_per_%s%s' % (name, window.amount(), window.unit()))","def __init__(self):\n super().__init__()\n self.mu = 0.0\n self.type = 'Poisson'\n self.hasInfiniteBound = True\n self.distType = 'Discrete'\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self):\n super().__init__()\n self.dynamic = True # from base class, indicates time-dependence is handled internally\n self.numBins = None # integer number of bins to use in creating the duration curve. TODO default?\n self.targets = None # list of strings, variables to apply postprocessor to","def __init__(\n self,\n oxygen: MeasurementStatistics,\n pulse: MeasurementStatistics,\n blood_pressure_systolic: MeasurementStatistics,\n blood_pressure_diastolic: MeasurementStatistics,\n ):\n self.oxygen = oxygen\n self.pulse = pulse\n self.blood_pressure_systolic = blood_pressure_systolic\n self.blood_pressure_diastolic = blood_pressure_diastolic","def __init__(self,E,px,py,pz):\n Particle.__init__(self)\n self.E=float(E)\n self.px=float(px)\n self.py=float(py)\n self.pz=float(pz)\n self.cal_pt()\n self.cal_phi()\n self.cal_eta()\n #self.cal_mass()\n #print self.E,self.px,self.py,self.pz\n #print self.pt,self.phi,self.eta","def __init__(self, data, times): #want to prevent infinite results with a circle class\n self.data = data\n self.times = times","def __init__(self,):\r\n self.g = 9.81\r\n self.l = 0.5\r\n self.m1 = 1.0\r\n self.m2 = 1.0\r\n self.m3 = 1.0\r\n self.r1 = 1.0\r\n self.r2 = 1.0\r\n self.tau = 0.001\r\n self.theta1 = 1.0\r\n self.theta2 = 1.0\r\n self.theta3 = 1.0","def __init__(self,dos):\n self.e = self.double(dos.e,-1.0)\n self.g = self.double(dos.g)\n self.gz = self.double(dos.gz)\n self.cutoffInd = dos.cutoffInd\n self.cutoff = dos.cutoff\n self.de = dos.de","def __init__(self, x, y , planet):\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.planet = planet\n\t\tself.battery = 100","def __init__(self):\n self.x = 0.0\n self.y = 0.0\n self.theta = 0.0\n self.total_distance_covered = 0.0","def __init__(self, servo_gpio, pi=None, pulse_left_ns=2500, pulse_right_ns=1000, pulse_centre_ns=None):\n\n self.gpio = servo_gpio\n\n if pi is None:\n self.pi = pi = pigpio.pi()\n else:\n self.pi = pi\n\n self.pulse_left_ns = pulse_left_ns\n self.pulse_right_ns = pulse_right_ns\n\n if pulse_centre_ns is None:\n self.pulse_centre_ns = ((pulse_left_ns - pulse_right_ns) // 2) + pulse_right_ns","def __init__(self,min_instances=30, drift_level=3.0):\n\n from math import sqrt\n self.min_instances = min_instances\n self.drift_level = float(drift_level)\n self.i = None\n self.pi = None\n self.si = None\n self.pi_min = None\n self.si_min = None\n self.sqrt=sqrt\n self.reset()","def __init__(self, pitch=30, pitch_type='duo', Z=4, Alt = 100):\n \n self.pitch_type = pitch_type\n self.pitch = pitch\n self.Z = Z\n self.Alt = Alt\n \n \n # set the Ce value (exposure coeff NA 2.16)\n self.Ce = 1\n \n # set the Ct value (thermal coeff NA 2.17)\n self.Ct = 1\n \n # snow load shjape coefficients\n if self.pitch_type == 'mono':\n if self.pitch <= 30:\n self.mu = 0.80\n elif 30 < self.pitch <= 60:\n self.mu = 0.80 * (60 - self.pitch) / 30\n else:\n self.mu = 0.0\n elif self.pitch_type == 'duo':\n if self.pitch <= 15:\n self.mu = 0.80\n elif 15 < self.pitch <= 30:\n self.mu = 0.80 + 0.40*(self.pitch - 15) / 15\n elif 30 < self.pitch <= 60:\n self.mu = 1.2*(60 - self.pitch) / 30\n else:\n self.mu = 0.0\n else:\n self.mu = 0.80 # end conservative number\n \n # calculate the value of the snow load on the ground \n self.sk = (0.15 + (0.1 * self.Z + 0.05) + ((self.Alt - 100) / 525))\n \n # calculate the roof snow load\n self.s = self.mu * self.Ce * self.Ct * self.sk","def __init__(self, radius=0.5, extra={}):\n self.radius = radius\n self.sensors = [] # array of Attached\n self.id_sensors = None\n self.id_dynamics = None # XXX\n self.dynamics = None\n self.extra = extra\n\n self.primitives = set()\n\n # Needs to be initialized before calling certain functions\n self._state = None","def __init__(self):\n # Set constants\n self.fromHztoeV = 6.58e-16\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\n self.mtoev = 1/(1.97 * 1e-7) \n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\n self.Om0 = cosmo.Om0 #total matter \n self.OLambda0 = cosmo.Ode0 # cosmological constant\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\n PSgal1h = np.loadtxt(\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\")\n PSgal2h = np.loadtxt(\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\")\n self.Mpc = 1e3 * const.kpc.value\n self.zmin = 0.001\n self.zmax = 30.001\n self.zbins = 301\n self.h = cosmo.h\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\n self.k_vect = PSgal1h[:,0]* self.h\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\n self.Power = self.Power1h + self.Power2h\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))","def _goniometer(self):\n\n return self._goniometer_factory.single_axis_reverse()","def create_observation(self):","def create_observation(self):","def __init__(self, value):\n if isinstance(value, Pressure):\n value = Pascal(value.base_value).bar.value\n self.base_value = value * 100000.0\n self.value = float(value)","def __init__(self, obj, parent=None):\n logger.info('%s initialization' % obj.name)\n # Call the constructor of the parent class\n super(self.__class__,self).__init__(obj, parent)\n\n self.local_data['x'] = 0.0\n self.local_data['y'] = 0.0\n self.local_data['z'] = 0.0\n self.local_data['yaw'] = 0.0\n self.local_data['pitch'] = 0.0\n self.local_data['roll'] = 0.0\n self.local_data['seg0'] = 0.0\n self.local_data['seg1'] = 0.0\n self.local_data['seg2'] = 0.0\n self.local_data['seg3'] = 0.0\n self.local_data['seg4'] = 0.0\n self.local_data['seg5'] = 0.0\n self.local_data['seg6'] = 0.0\n logger.info('Component initialized')\n\n # The axis along which the different segments rotate\n # Considering the rotation of the arm as installed in Jido\n self._dofs = ['z', '-y', 'z', 'y', 'z', '-y', 'z']","def __init__(self, date_time, diastolic):\n Encounter.__init__(self, date_time)\n self.__diastolic = diastolic","def __init__(self, coordinates, emissivity = 0.4): \n\t\tsuper().__init__(coordinates)\n\t\tself.emissivity = emissivity\n\t\tself.type = 'thermometer'","def pontos(self):\n \n self.sc = 1. \n self.x = self.sc*np.array([-155., -139.4, -124., -108.5, -93., -77.5, -62., -46.5, -31., -15.5, 0, 15.5, 31., 46.5, 62., 77.5, 93., 108.5, 124., 139.5, 155.])\n self.y = self.sc*np.array([ 9.23, 14.37, 18.98, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 21.55, 14.37, 3.59])\n self.px_index = len(self.x)\n #self.py_index = len(self.x)/2\n\n self.coord = np.array([self.x,self.y,np.full(len(self.x),self.z)])\n \n self.x = self.x[::-1]\n self.y = -self.y[::-1] \n self.new = np.array([self.x,self.y,np.full(len(self.x),self.z)])\n self.coord = np.array([np.append(self.coord[0],self.new[0]),np.append(self.coord[1],self.new[1]),np.append(self.coord[2],self.new[2])])\n self.coord = np.array([np.append(self.coord[0],self.coord[0,0]),np.append(self.coord[1],self.coord[1,0]),np.append(self.coord[2],self.coord[2,0])])\n\n self.coord[0] = self.coord[0] - (np.amax(self.coord[0])+np.amin(self.coord[0]))/2\n self.coord[1] = self.coord[1] + (np.amax(self.coord[1])-np.amin(self.coord[1]))/2 \n \n self.coordi = np.array(self.coord)\n \n self.cg = np.array([0 + self.dx, self.H/2 + self.dy, self.z]) \n self.cgi = np.array(self.cg)\n \n self.thi = 0. + self.dth \n self.th = float(self.thi) \n \n self.coordnav(self.dx,self.dy,self.dth)","def __init__(self, n_pe, laser=None, bubble_radius=None, propagation_distance=None):\n self.npe = n_pe.to(\"1/cm**3\")\n self.λp = np.sqrt(np.pi / (r_e * self.npe)).to(\"micrometer\")\n self.kp = (2 * np.pi / self.λp).to(\"1/micrometer\")\n self.ωp = (u.clight * self.kp).to(\"1/femtosecond\")\n\n self.Ewb = (u.me * u.clight * self.ωp / np.abs(u.qe)).to(\"megavolt/mm\")\n\n self.lp = (u.clight / self.ωp).to(\"micrometer\")\n self.tp = (1 / self.ωp).to(\"femtosecond\")\n\n if laser:\n self.laser = laser\n\n self.γp = (self.laser.ωL / self.ωp).to(\"dimensionless\")\n self.Pc = (17 * self.γp ** 2 * u.gigawatt).to(\"terawatt\")\n\n self.dephasing = (\n 4 / 3 * self.γp ** 2 * np.sqrt(self.laser.a0) / self.kp\n ).to(\"mm\")\n self.depletion = (self.γp ** 2 * u.clight * self.laser.τL).to(\"mm\")\n\n self.Ez_avg = (self.Ewb * np.sqrt(self.laser.a0) / 2).to(\"megavolt/mm\")\n\n if propagation_distance:\n self.Lacc = propagation_distance.to(\"mm\")\n else:\n self.Lacc = self.dephasing\n\n self.ΔE = (np.abs(u.qe) * self.Ez_avg * self.Lacc).to(\"megaelectronvolt\")\n\n if bubble_radius:\n self.R = bubble_radius.to(\"micrometer\")\n\n self.N = (1 / 30 * (self.kp * self.R) ** 3 / (self.kp * r_e)).to(\n \"dimensionless\"\n )\n self.Q = (self.N * np.abs(u.qe)).to(\"picocoulomb\")\n\n self.η = (self.N * self.ΔE / self.laser.ɛL).to(\"dimensionless\")\n else:\n self.R = None\n else:\n self.laser = None\n self.R = None","def __init__(self, win, angle, velocity, height): \r\n\r\n self.proj = Projectile(angle, velocity, height) \r\n self.marker = Circle(Point(0, height), 3) \r\n self.marker.setFill(\"red\")\r\n self.marker.setOutline(\"red\")\r\n self.marker.draw(win)","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n self.odometer_reading = 0 #Setting a default value for an Attribute","def __init__(self, name, freq, waypoint_specified, waypoint_bc):\n self.dt = 1.0/freq\n self.uav = name\n self.wp_specfied = waypoint_specified\n self.wp_bc = waypoint_bc\n self.start_time = time.time()\n self.average_speed = 3.0\n self.reduced_speed = 0.5\n # specify start/intermediate/end points and its deviratives \n self.no_of_segments = 7\n self.wp_callback_counter = 0\n self.trajectory_constructed = False\n\n self.r = 4 # corresponding to snap which is 4th derivative\n self.N = 7# degree of polynomial \n \n self.pub = rospy.Publisher('/'+self.uav+'/PolynomialTrajectory', PolynomialTrajectory, queue_size = 1, tcp_nodelay = True)\n \n rospy.Subscriber('/'+self.uav + '/odometry_sensor1/odometry', Odometry, self.currentstate_callback, queue_size = 1, tcp_nodelay = True) \n rospy.Subscriber('/'+self.uav+'/waypoint_publisher', Pose, self.waypoint_callback, queue_size = 1, tcp_nodelay=True)\n\n #try: \n # rospy.Subscriber('/'+self.uav+'/waypoint_publisher', Pose, self.waypoint_callback, queue_size = 1, tcp_nodelay=True)\n # rospy.Subscriber('/'+self.uav + '/odometry_sensor/odometry', Odometry, self.currentstate_callback, queue_size = 1, tcp_nodelay = True)\n #except: \n # print 'Either waypoints or odometry is not available.'","def pie(t,length,n):\n\tangle=360/n\n\tlength2=math.sqrt((length**2)*2-(2*length*length)*math.cos(math.radians(angle)))\n\tangle2=(180-angle)/2\n\n\tfor i in range(n):\n\t\tt.fd(length)\n\t\tt.lt(180-angle2)\t\n\t\tt.fd(length2)\n\t\tt.lt(180-angle2)\n\t\tt.fd(length)\n\t\tt.rt(180)","def __init__(self, t=0.01):\r\n\r\n\t\tself.t = float(t)\r\n\t\t\r\n\t\t#Control constants\r\n\t\t#Altitude\r\n\t\t#P\r\n\t\tkp1=3\r\n\t\t#PID\r\n\t\tkp2=6\r\n\t\tki2=0\r\n\t\tkd2=2.5\r\n\t\t#PID\r\n\t\tkp3=0.75\r\n\t\tki3=1.5\r\n\t\tkd3=0\r\n\t\t\r\n\t\t#Pitch\r\n\t\t#P\r\n\t\tkp1_theta=4.5\r\n\t\t#PID\r\n\t\tkp2_theta=0.15\r\n\t\tki2_theta=0.1\r\n\t\tkd2_theta=0.004\r\n\t\t\r\n\t\t#Roll\r\n\t\t#P\r\n\t\tkp1_phi=4.5\r\n\t\t#PID\r\n\t\tkp2_phi=0.15\r\n\t\tki2_phi=0.1\r\n\t\tkd2_phi=0.004\r\n\t\t\r\n\t\t#Yaw\r\n\t\t#P\r\n\t\tkp1_psi=10\r\n\t\t#PID\r\n\t\tkp2_psi=0.2\r\n\t\tki2_psi=0.02\r\n\t\tkd2_psi=0\r\n\t\t\r\n\t\t#Control\r\n\t\timax1=10\r\n\t\tt_max1=200\r\n\t\timax2=10\r\n\t\tt_max2=120\r\n\t\t\r\n\t\t#Constrains\r\n\t\trate_imax = 50\r\n\t\tac_imax = 40\r\n\t\tt_min = 800\r\n\t\tt_max = 1500\r\n\t\t\r\n\t\t#Goals\r\n\t\tself.target_z=3\r\n\t\tself.target_theta=0\r\n\t\tself.target_phi=0\r\n\t\tself.target_psi=0\r\n\r\n\t\t#Initial Conditions\r\n\t\tself.w=[1000, 1000, 1000, 1000]\r\n\t\t#self.ev = [0, 0, 1, 0, 0, 0, 0, 0, -9.81, 0.4, -0.2, 1, 0.1, -0.1, 0.2, 0.01, -0.01, 0.02]\r\n\t\tself.ev = [0, 0, 0, 0, 0, 0, 0, 0, -9.81, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\n\t\t#Create controllers\r\n\t\tself.altitudeController = control.controlAlt (self.t, kp1, kp2, ki2, kd2, kp3, ki3, kd3, rate_imax, ac_imax,t_min,t_max)\r\n\t\tself.thetaController = control.controlAngle (self.t, kp1_theta, kp2_theta, ki2_theta, kd2_theta, imax1, t_max1)\r\n\t\tself.phiController = control.controlAngle (self.t, kp1_phi, kp2_phi, ki2_phi, kd2_phi, imax1, t_max1)\r\n\t\tself.psiController = control.controlAngle (self.t, kp1_psi, kp2_psi, ki2_psi, kd2_psi, imax2, t_max2)","def __init__(self):\n super().__init__()\n self.p = 0.0\n self.type = 'Geometric'\n self.distType = 'Discrete'\n self.lowerBound = 0.0\n self.upperBound = 1.0\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, plant):\n\n self.plant = plant\n\n self.x = np.zeros(2*self.plant.dof) # position, velocity\n self.t = 0.0 # time\n\n self.reset_data_recorder()","def __init__(self, r, r_2, pie_d, pie_d_2, lmb_d, lmb_d_2, pie_s, pie_s_2, b_s, b_s_2):\n lma_d=(pie_d-(1+r))\n lma_d_2=(pie_d_2-(1+r_2))\n a_s=((1+r)-pie_s)\n a_s_2=((1+r_2)-pie_s_2)\n \n self.lma_d, self.lma_d_2, self.lmb_d, self.lmb_d_2, self.a_s, self.a_s_2, self.b_s, self.b_s_2 = lma_d , lma_d_2 , lmb_d , lmb_d_2, a_s, a_s_2 , b_s, b_s_2\n if lma_d < a_s :\n raise ValueError('Insufficient demand.')\n elif lma_d_2 < a_s_2 :\n raise ValueError('Insufficient demand on 2.')","def _create_example_door():\n return Door({\"warning\": False, \"closed\": True, \"locked\": False})","def create_base(inst):\r\n\r\n strophe = []\r\n chorus = []\r\n chord = C_SCALE[0]\r\n \r\n for i in range(4):\r\n strophe.append(chord) \r\n chord = get_next_chord(chord)\r\n \r\n for i in range(4):\r\n chorus.append(chord) \r\n chord = get_next_chord(chord)\r\n \r\n chord_progression = strophe + strophe + chorus + strophe\r\n \r\n for c in chord_progression:\r\n meas = inst.NewMeasure()\r\n add_chord(meas, c)\r\n\r\n return chord_progression","def __init__(self, time, rate, type='sine'):\n self.t_v = np.zeros(time*rate)\n self.heading = np.zeros(self.t_v.shape)\n self.commands = np.zeros(self.t_v.shape)\n self.derivative = np.zeros(self.t_v.shape)","def __init__(self):\n\n\n self.dtype = np.dtype([\n ('fault_flags', np.uint32),\n ('raw_x', np.int16),\n ('raw_y', np.int16),\n ('raw_z', np.int16),\n ('accel_x', np.float32),\n ('accel_y', np.float32),\n ('accel_z', np.float32), \n ('pitch', np.float32),\n ('roll', np.float32), \n ])\n \n self._accel_indices = [0, 4, 5, 6]\n \n self.data = np.array([(0, 0.1, 12, 1234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234)], dtype=self.dtype)\n self.data['fault_flags'] = 0\n self.data['raw_x'] = 0.1\n self.data['raw_y'] = 12\n self.data['raw_z'] = 31929\n self.data['accel_x'] = 0.12345678901234\n self.data['accel_y'] = 0.23456789012345\n self.data['accel_z'] = 0.34567890123456\n self.data['pitch'] = 0.1000\n self.data['roll'] = 0.2000\n\n\n #print len(self.data.tostring(order=\"C\"))","def __init__(\n self,\n oxygen: int = None,\n pulse: int = None,\n blood_pressure_systolic: int = None,\n blood_pressure_diastolic: int = None,\n ):\n self.oxygen = oxygen\n self.pulse = pulse\n self.blood_pressure_systolic = blood_pressure_systolic\n self.blood_pressure_diastolic = blood_pressure_diastolic","def __init__(self, value):\n if isinstance(value, Pressure):\n value = Pascal(value.base_value).atmosphere.value\n self.base_value = value * 101325.0\n self.value = float(value)","def __init__(self, mass, x, y,px=0.0,py=0.0):\n self.mass = mass\n self.position = np.array([x,y])\n self.momentum = np.array([px,py])","def __init__(self, parent):\n super(Demo5, self).__init__(parent)\n self.angle = 0.0\n self.replication = 1.0\n self.offset = 0.0\n self.deltaRep = 1\n self.revolution = 0\n self.stepsPer90 = 180\n self.stepsLeft = self.stepsPer90\n self.deltaAng = 90.0\n self.deltaOff = 0.15\n self.spin = True\n self.x2yAspect = 1.0\n self.texture = None","def __init__(self):\n self.Robot = Robot()\n self.Omega = matrix()\n # self.Omega.value[0][0] = 1.0\n # self.Omega.value[1][1] = 1.0\n self.Xi = matrix()\n # Xi.value[0][0] = 0.0\n # Xi.value[1][0] = 0.0\n self.measure = {}\n self.landMarkCount = 0\n self.init = False\n self.bearing = 0\n self.x = 0\n self.y = 0\n \n # TODO","def __init__(self, n_points, kind='cubic'):\n self.n_points = n_points\n self.kind = kind","def __init__(self, markers):\n self.markers = markers\n self.last_time = None # Used to keep track of time between measurements \n self.Q_t = np.eye(2)\n self.R_t = np.eye(3)\n # YOUR CODE HERE","def __init__(self):\n super().__init__()\n self.n = 0.0\n self.p = 0.0\n self.type = 'Binomial'\n self.hasInfiniteBound = True\n self.distType = 'Discrete'\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, win, angle, velocity, height):\n \n self.proj = Projectile(angle, velocity, height)\n self.marker = Circle(Point(0,height), 3)\n self.marker.setFill(\"red\")\n self.marker.setOutline(\"red\")\n self.marker.draw(win)","def __init__(self, name=None, dss=28, date=None, project='SolarPatrol'):\n self.logger = logging.getLogger(logger.name+\".Observation\")\n DR.Observation.__init__(self, name=date, date=date, dss=dss, \n project=project)\n self.extended_init()\n \n #self.obs =Astronomy.Ephem.DSS(dss)\n #y,d = date.split('/')\n #self.year = int(y); self.DOY = int(d)\n #projdatapath, self.sessionpath, rawdatapath = \\\n # DR.get_obs_dirs(project, dss, self.year, self.DOY,\n # datafmt=None)","def __init__(self, timer=120, rate=1, percent=0):\n self.timer = timer\n self.rate = rate\n self.percent = percent","def __init__(self):\n self.temperature = 70.0\n self.status = \"sunny\"","def __init__(self):\n self.temperature = 70.0\n self.status = \"sunny\"","def __init__(self):\n self.temperature = 70.0\n self.status = \"sunny\"","def __init__(self):\n self.center = Point()\n self.velocity = Velocity()","def __init__(self, data=(1.0, numpy.array((0., 0., 0.), 'f'))):\n if len(data) == 2:\n self.real = data[0]\n try:\n theta = numpy.arccos(self.real)\n self.pure = numpy.array((data[1][0], data[1][1], data[1][2]), 'f')\n except:\n raise ValueError(\"The real part must be between -1.0 and 1.0\")\n elif len(data) == 4:\n theta = numpy.pi * data[3] / 360.\n self.real = numpy.cos(theta)\n self.pure = numpy.sin(theta) * numpy.array((data[0], data[1],\n data[2]), 'f')\n else:\n raise ValueError(\"Args must be (x,y,z,theta) or (real,pure)\")\n self.normal()","def __init__(self):\n self.ascii_filename = None\n\n # name and calc_id\n self.name = None\n self.calc_id = None\n self.i_ts = None\n self.time = None\n # data members\n self.x = None\n self.p = None\n # particle number density normalized to nGJ: n/n_{GJ} \n self.fmci_XP = None\n # PSR parameters\n self.PSR_P = None\n self.PSR_B12 = None\n self.PSR_Lcm = None\n self.PSR_Theta = None\n self.PSR_Chi = None","def __init__(\n self,\n pin,\n min_pulse_width=500,\n max_pulse_width=2500,\n min_full_sweep_time=0.5,\n rotation_update_freq=25,\n ):\n assert (\n 500 <= min_pulse_width <= 2500\n ), \"min_pulse_width should be between 500 and 2500\"\n assert (\n 500 <= max_pulse_width <= 2500\n ), \"max_pulse_width should be between 500 and 2500\"\n assert (\n min_pulse_width < max_pulse_width\n ), \"min_pulse_width should be less than max_pulse_width\"\n assert (\n min_full_sweep_time > 0\n ), \"min_full_sweep_time should be positive\"\n assert (\n rotation_update_freq > 0\n ), \"rotation_update_freq should be positive\"\n\n self._pin = pin\n self._min_pulse_width = min_pulse_width\n self._max_pulse_width = max_pulse_width\n self._mid_pulse_width = (max_pulse_width + min_pulse_width) / 2\n self._max_pos_change_per_update = 2 / (\n rotation_update_freq * min_full_sweep_time\n )\n self._rotation_update_freq = rotation_update_freq\n self._latest_rotation_start_time = None\n self._position = None\n self._rotation_speed = 0\n self._stopped = False\n\n self._pi = pigpio.pi()\n if not self._pi.connected:\n raise RuntimeError(\"Could not connect to pigpio daemon\")\n self._pi.set_mode(self._pin, pigpio.OUTPUT)\n self._pi.set_servo_pulsewidth(self._pin, 0)","def new_gas_particle():\n function = LegacyFunctionSpecification()\n function.must_handle_array = True\n function.addParameter('index_of_the_particle', dtype='int32', direction=function.OUT, description =\n \"\"\"\n An index assigned to the newly created particle.\n This index is supposed to be a local index for the code\n (and not valid in other instances of the code or in other codes)\n \"\"\"\n )\n for par in [\"x\", \"y\", \"z\"]:\n function.addParameter(par, dtype='float64', unit=generic_unit_system.length, direction=function.IN, \n description = \"The initial position vector of the particle\")\n function.addParameter('radius', dtype='float64', unit=generic_unit_system.length, direction=function.IN, description = \"The radius of the particle\")\n for par in [\"red\", \"green\", \"blue\"]:\n function.addParameter(par, dtype='float64', direction=function.IN, \n description = \"The RGB color of the particle\")\n function.addParameter(\"alpha\", dtype='float64', direction=function.IN, description = \"The opacity of the particle\", default = 1.0)\n function.addParameter('npoints', dtype='int32', direction=function.LENGTH)\n function.result_type = 'int32'\n return function","def __init__(self, num_points=5000):\n self.num_points = num_points\n\n # All motion starts at (0,0).\n self.x_values = [0]\n self.y_values = [0]","def __init__(self, make, model, year):\n self.make = make\n self.model = model\n self.year = year\n # default value for a data attribute not matched by a parameter\n self.odometer_reading = 0","def __init__(self):\n self.risepower = 4.\n self.min_event_amplitude = 5.0e-12 # pA default\n self.template = None\n pass","def __init__(self, sensor):\n self.sensor = sensor\n self.temperature = None\n self.humitidy = None\n self.update()","def __init__(self):\n self.set_recharge(random.randint(1000, 2000) / 10000)\n operator_count = random.randint(1, 3)\n self.operators = [Solder() for _ in range(0, operator_count)]\n list_operators = [i.get_health for i in self.operators]\n self.set_health(sum(list_operators) / len(list_operators))","def __init__(self):\n super().__init__()\n self.apex = 0.0 # peak location\n self.min = None # domain lower boundary\n self.max = None # domain upper boundary\n self.type = 'Triangular'\n self.distType = 'Continuous'\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def __init__(self):\n self.wnd = WindSensor()","def photometric_calibration():\n pass","def __init__(self, nav_data, div=5, dx=0.0, dy=1.6757135024103853, dth=0.0, pc1=1/3, pc2=9/24, ph=0.2): \n self.div = div\n self.t = np.arange(self.div)/self.div\n self.L = nav_data.loc['Loa']\n self.H = nav_data.loc['B']\n self.T = nav_data.loc['T']\n self.dx = dx \n self.dy = dy\n self.dth = dth\n self.c1 = self.L*pc1\n self.c2 = self.L*pc2\n self.h2 = self.H*ph\n self.Rz = rotation.matrix([0,0,1],dth) \n\n self.LWT = 0.50*nav_data.loc['Delta_m'] \n self.Md = nav_data.loc['Delta_m'] #self.LWT + self.DWT\n self.D = nav_data.loc['De']\n self.G = self.T - self.D \n self.z = nav_data.loc['KG'] - self.D","def __init__(self, position=np.array([0,0,0], dtype=float),\n velocity=np.array([0,0,0], dtype=float), acceleration=np.array([0,0,0],\n dtype=float), name='A Particle', restMass=1.0, charge=const.elementary_charge):\n self.name = name\n self.position = np.array(position, dtype=float)\n self.velocity = np.array(velocity, dtype=float)\n self.acceleration = np.array(acceleration, dtype=float)\n self.restMass = restMass\n self.charge = charge\n self.electricField = PointElectricFieldClass(sourceParticle=self\n , name='Field from %s'%(self.name))\n self.magneticField = PointMagneticFieldClass(sourceParticle=self\n , name='Field from %s'%(self.name))","def __init__(self, x_coor, x_speed, y_coor, y_speed, direction):\n self.__x_coor = x_coor\n self.__x_speed = x_speed\n self.__y_coor = y_coor\n self.__y_speed = y_speed\n self.__direction = direction\n self.__radius = self.TORPEDO_RADIUS","def __init__ (self,\n elevation = 45.0,\n start_angle = 0.0,\n latitude = 78.0,\n longitude = 0.0,\n rot_per_min = 1,\n rev_days = 15):\n\n self.elevation = np.radians (elevation) # Units: [rad]\n self.start_angle = np.radians (start_angle) # Units: [rad]\n\n self.base_lat = np.radians (latitude) # Units: [rad]\n self.base_long = np.radians (longitude) # Units: [rad]\n\n self.omega_rot = 2 * np.pi * rot_per_min / 60 # Units: [rad/s]\n self.rev_days = rev_days # Units: [day]","def __init__(self, name=''):\n self.domain = (0, np.pi)\n self._evaluator_overrides = None\n self._name = name\n self._user_data = dict()\n self.__loaded_from = None","def create_door(tick, x, y, z):\n door = nbt.TAG_Compound()\n door['TS'] = nbt.TAG_Int(tick)\n door['X'] = nbt.TAG_Int(x)\n door['Y'] = nbt.TAG_Int(y)\n door['Z'] = nbt.TAG_Int(z)\n return door","def get_pzo(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.PZO(data)\n if result is None:\n raise IndicatorException\n return result","def particle(self) -> Particle:\n return Particle()","def particle(self) -> Particle:\n return Particle()"],"string":"[\n \"def lick_piezo():\\n lp_raw = read_npy_file('lickPiezo.raw.npy')\\n lp_timestamps = read_npy_file('lickPiezo.timestamps.npy')\\n lp_rate = get_rate(lp_timestamps)\\n lick_piezo_ts = TimeSeries(\\n name='lickPiezo',\\n starting_time=lp_timestamps[0, 1],\\n rate=lp_rate,\\n data=np.ravel(lp_raw),\\n unit='V',\\n description='Voltage values from a thin-film piezo connected to the '\\n 'lick spout, so that values are proportional to deflection '\\n 'of the spout and licks can be detected as peaks of the signal.'\\n )\\n nwb_file.add_acquisition(lick_piezo_ts)\",\n \"def __init__(self,make,model,year):\\r\\n self.make=make\\r\\n self.model=model\\r\\n self.year=year\\r\\n self.odometer_reading=0\",\n \"def __init__(self, make, model, year):\\r\\n self.make = make\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\r\\n self.make = make\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\r\\n self.make = make\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\r\\n self.make = make\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\r\\n self.make = make\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer_reading = 0\",\n \"def __init__(self, manufacturer, model, year):\\n self.manufacturer = manufacturer\\n self.model = model\\n self.year = year\\n self.odometer = 0\",\n \"def __init__(self):\\n raise NotImplementedError('cannot create independent arc')\",\n \"def prada(self):\\n scale_factor = 1.0 / (1.0 + self.snapshot.header.redshift)\\n r200c_physical = self.r200c * scale_factor / 1000.0 # units Mpc\\n\\n v200 = (\\n (self.snapshot.const.G * self.m200c)\\n / r200c_physical\\n * self.snapshot.const.Mpc ** 2\\n / 1000.0 ** 2\\n ) ** 0.5 # units km/s\\n\\n def y(x, vmax, v200):\\n func = np.log(1 + x) - (x / (1 + x))\\n return ((0.216 * x) / func) ** 0.5 - (vmax / v200)\\n\\n concentration = np.zeros((len(self.vmax)))\\n for halo in range(self.N_halos):\\n if v200[halo] > self.vmax[halo]:\\n concentration[halo] = -9999.0\\n else:\\n try:\\n concentration[halo] = newton(\\n y, x0=5.0, args=(self.vmax[halo], v200[halo])\\n )\\n except:\\n concentration[halo] = -9999.0\\n\\n return concentration\",\n \"def __init__(self, make, model, year=2009):\\r\\n self.make = make\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def func_piezo(self,piezo):\\n self.write('SOURce:VOLTage:PIEZo '+str(piezo))\\n self.read()\",\n \"def __init__(self,naca_name,N,plt=True):\\n # Name of the airfoil\\n self.name = naca_name\\n # Number of points\\n self.N = N/2\\n # [m] Points of the airfoil (_u: upper, _l: lower)\\n self.xu = np.linspace(0,1,N)\\n self.xl = np.linspace(0,1,N)\\n self.x = np.concatenate((self.xu, np.flip(self.xl)))\\n self.yu = np.empty((N))\\n self.yl = np.empty((N))\\n self.ytu = np.empty((N))\\n self.ytl = np.empty((N))\\n self.z = np.concatenate((self.yu, np.flip(self.yl)))\\n self.y = np.zeros((2*N))\\n self.dyc = np.zeros((N))\\n # Camber line y position (_u: upper, _l: lower)\\n self.yc = np.zeros((N))\\n # [m] Chord length\\n self.c = 1\\n # Macximum camber in chord length\\n self.m = 0.01*int(naca_name[-4:-3])\\n # Distance of the maximum camber from the leading edge\\n self.p = 0.1*int(naca_name[-3:-2])\\n # Thickness of the airfoil\\n self.t = 0.01*int(naca_name[-2:])\\n # Leading edge radius\\n self.r = 1.019*self.t**2 / self.c\\n # Plotting variable\\n self.plot = plt\",\n \"def __init__(self, p) -> None:\\n self._p = p\\n self._delegate = TwoQubitAsymmetricDepolarizingChannel(p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15)\",\n \"def __init__(self, make, model, year):\\r\\n self.make = make # Este MAKE depois do = é o mesmo dos Parametros a cima.\\r\\n self.model = model\\r\\n self.year = year\\r\\n self.odometer_reading = 0\",\n \"def __init__(self, make, model, year):\\n\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0\",\n \"def _createObj(self) -> None:\\n phase_img = skimage.img_as_float(skimage.data.camera())[::-1, ::-1]\\n mod_img = skimage.img_as_float(skimage.data.immunohistochemistry()[:, :, 0])[::-1, ::-1]\\n mod = skimage.transform.resize(mod_img, self.shape,\\n mode='wrap', preserve_range=True)\\n phase = skimage.transform.resize(phase_img, self.shape,\\n mode='wrap', preserve_range=True)\\n\\n # Setting the ranges\\n phase = (phase - np.min(phase)) / (np.max(phase) - np.min(phase)) * self.phase_range\\n mod = (mod - np.min(mod)) / (np.max(mod) - np.min(mod)) * self.mod_range\\n\\n # Centering the phase at 0.\\n phase = np.angle(np.exp(1j * (phase - scipy.stats.circmean(phase))))\\n obj = (mod * np.exp(1j * phase)).astype('complex64')\\n self._setObjArrayValues(obj)\",\n \"def __init__(self, a, p=1):\\n self.a = a # strength\\n self.p = p # period\\n self._construct_doffsets()\\n self._compute_soffsets()\",\n \"def __init__(self,mass,time,**kwargs):\\n self.setTime(time)\\n ParticlePhaseCoordinates.__init__(self,mass,**kwargs)\",\n \"def _construct_pie(self):\\n\\n # count labels and instances\\n label_count = count(self.labels)\\n label_instances = [instance for instance in label_count.values()]\\n colours = [\\n rgb2hex(\\n self.clusters[label]/255) for label in label_count.keys()]\\n self.pie = plot.figure()\\n plot.pie(\\n x=label_instances, labels=label_count.keys(\\n ), colors=colours, autopct=\\\"%.2f%%\\\")\\n plot.tight_layout()\\n plot.axis('equal')\",\n \"def __init__(self, name, gauge, window = Amount(1, Time.SECONDS), clock = time):\\r\\n self._clock = clock\\r\\n self._gauge = gauge\\r\\n self._samples = []\\r\\n self._window = window\\r\\n NamedGauge.__init__(self, '%s_per_%s%s' % (name, window.amount(), window.unit()))\",\n \"def __init__(self):\\n super().__init__()\\n self.mu = 0.0\\n self.type = 'Poisson'\\n self.hasInfiniteBound = True\\n self.distType = 'Discrete'\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self):\\n super().__init__()\\n self.dynamic = True # from base class, indicates time-dependence is handled internally\\n self.numBins = None # integer number of bins to use in creating the duration curve. TODO default?\\n self.targets = None # list of strings, variables to apply postprocessor to\",\n \"def __init__(\\n self,\\n oxygen: MeasurementStatistics,\\n pulse: MeasurementStatistics,\\n blood_pressure_systolic: MeasurementStatistics,\\n blood_pressure_diastolic: MeasurementStatistics,\\n ):\\n self.oxygen = oxygen\\n self.pulse = pulse\\n self.blood_pressure_systolic = blood_pressure_systolic\\n self.blood_pressure_diastolic = blood_pressure_diastolic\",\n \"def __init__(self,E,px,py,pz):\\n Particle.__init__(self)\\n self.E=float(E)\\n self.px=float(px)\\n self.py=float(py)\\n self.pz=float(pz)\\n self.cal_pt()\\n self.cal_phi()\\n self.cal_eta()\\n #self.cal_mass()\\n #print self.E,self.px,self.py,self.pz\\n #print self.pt,self.phi,self.eta\",\n \"def __init__(self, data, times): #want to prevent infinite results with a circle class\\n self.data = data\\n self.times = times\",\n \"def __init__(self,):\\r\\n self.g = 9.81\\r\\n self.l = 0.5\\r\\n self.m1 = 1.0\\r\\n self.m2 = 1.0\\r\\n self.m3 = 1.0\\r\\n self.r1 = 1.0\\r\\n self.r2 = 1.0\\r\\n self.tau = 0.001\\r\\n self.theta1 = 1.0\\r\\n self.theta2 = 1.0\\r\\n self.theta3 = 1.0\",\n \"def __init__(self,dos):\\n self.e = self.double(dos.e,-1.0)\\n self.g = self.double(dos.g)\\n self.gz = self.double(dos.gz)\\n self.cutoffInd = dos.cutoffInd\\n self.cutoff = dos.cutoff\\n self.de = dos.de\",\n \"def __init__(self, x, y , planet):\\n\\t\\tself.x = x\\n\\t\\tself.y = y\\n\\t\\tself.planet = planet\\n\\t\\tself.battery = 100\",\n \"def __init__(self):\\n self.x = 0.0\\n self.y = 0.0\\n self.theta = 0.0\\n self.total_distance_covered = 0.0\",\n \"def __init__(self, servo_gpio, pi=None, pulse_left_ns=2500, pulse_right_ns=1000, pulse_centre_ns=None):\\n\\n self.gpio = servo_gpio\\n\\n if pi is None:\\n self.pi = pi = pigpio.pi()\\n else:\\n self.pi = pi\\n\\n self.pulse_left_ns = pulse_left_ns\\n self.pulse_right_ns = pulse_right_ns\\n\\n if pulse_centre_ns is None:\\n self.pulse_centre_ns = ((pulse_left_ns - pulse_right_ns) // 2) + pulse_right_ns\",\n \"def __init__(self,min_instances=30, drift_level=3.0):\\n\\n from math import sqrt\\n self.min_instances = min_instances\\n self.drift_level = float(drift_level)\\n self.i = None\\n self.pi = None\\n self.si = None\\n self.pi_min = None\\n self.si_min = None\\n self.sqrt=sqrt\\n self.reset()\",\n \"def __init__(self, pitch=30, pitch_type='duo', Z=4, Alt = 100):\\n \\n self.pitch_type = pitch_type\\n self.pitch = pitch\\n self.Z = Z\\n self.Alt = Alt\\n \\n \\n # set the Ce value (exposure coeff NA 2.16)\\n self.Ce = 1\\n \\n # set the Ct value (thermal coeff NA 2.17)\\n self.Ct = 1\\n \\n # snow load shjape coefficients\\n if self.pitch_type == 'mono':\\n if self.pitch <= 30:\\n self.mu = 0.80\\n elif 30 < self.pitch <= 60:\\n self.mu = 0.80 * (60 - self.pitch) / 30\\n else:\\n self.mu = 0.0\\n elif self.pitch_type == 'duo':\\n if self.pitch <= 15:\\n self.mu = 0.80\\n elif 15 < self.pitch <= 30:\\n self.mu = 0.80 + 0.40*(self.pitch - 15) / 15\\n elif 30 < self.pitch <= 60:\\n self.mu = 1.2*(60 - self.pitch) / 30\\n else:\\n self.mu = 0.0\\n else:\\n self.mu = 0.80 # end conservative number\\n \\n # calculate the value of the snow load on the ground \\n self.sk = (0.15 + (0.1 * self.Z + 0.05) + ((self.Alt - 100) / 525))\\n \\n # calculate the roof snow load\\n self.s = self.mu * self.Ce * self.Ct * self.sk\",\n \"def __init__(self, radius=0.5, extra={}):\\n self.radius = radius\\n self.sensors = [] # array of Attached\\n self.id_sensors = None\\n self.id_dynamics = None # XXX\\n self.dynamics = None\\n self.extra = extra\\n\\n self.primitives = set()\\n\\n # Needs to be initialized before calling certain functions\\n self._state = None\",\n \"def __init__(self):\\n # Set constants\\n self.fromHztoeV = 6.58e-16\\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\\n self.mtoev = 1/(1.97 * 1e-7) \\n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\\n self.Om0 = cosmo.Om0 #total matter \\n self.OLambda0 = cosmo.Ode0 # cosmological constant\\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\\n PSgal1h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\\\")\\n PSgal2h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\\\")\\n self.Mpc = 1e3 * const.kpc.value\\n self.zmin = 0.001\\n self.zmax = 30.001\\n self.zbins = 301\\n self.h = cosmo.h\\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\\n self.k_vect = PSgal1h[:,0]* self.h\\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\\n self.Power = self.Power1h + self.Power2h\\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))\",\n \"def _goniometer(self):\\n\\n return self._goniometer_factory.single_axis_reverse()\",\n \"def create_observation(self):\",\n \"def create_observation(self):\",\n \"def __init__(self, value):\\n if isinstance(value, Pressure):\\n value = Pascal(value.base_value).bar.value\\n self.base_value = value * 100000.0\\n self.value = float(value)\",\n \"def __init__(self, obj, parent=None):\\n logger.info('%s initialization' % obj.name)\\n # Call the constructor of the parent class\\n super(self.__class__,self).__init__(obj, parent)\\n\\n self.local_data['x'] = 0.0\\n self.local_data['y'] = 0.0\\n self.local_data['z'] = 0.0\\n self.local_data['yaw'] = 0.0\\n self.local_data['pitch'] = 0.0\\n self.local_data['roll'] = 0.0\\n self.local_data['seg0'] = 0.0\\n self.local_data['seg1'] = 0.0\\n self.local_data['seg2'] = 0.0\\n self.local_data['seg3'] = 0.0\\n self.local_data['seg4'] = 0.0\\n self.local_data['seg5'] = 0.0\\n self.local_data['seg6'] = 0.0\\n logger.info('Component initialized')\\n\\n # The axis along which the different segments rotate\\n # Considering the rotation of the arm as installed in Jido\\n self._dofs = ['z', '-y', 'z', 'y', 'z', '-y', 'z']\",\n \"def __init__(self, date_time, diastolic):\\n Encounter.__init__(self, date_time)\\n self.__diastolic = diastolic\",\n \"def __init__(self, coordinates, emissivity = 0.4): \\n\\t\\tsuper().__init__(coordinates)\\n\\t\\tself.emissivity = emissivity\\n\\t\\tself.type = 'thermometer'\",\n \"def pontos(self):\\n \\n self.sc = 1. \\n self.x = self.sc*np.array([-155., -139.4, -124., -108.5, -93., -77.5, -62., -46.5, -31., -15.5, 0, 15.5, 31., 46.5, 62., 77.5, 93., 108.5, 124., 139.5, 155.])\\n self.y = self.sc*np.array([ 9.23, 14.37, 18.98, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 23.6, 21.55, 14.37, 3.59])\\n self.px_index = len(self.x)\\n #self.py_index = len(self.x)/2\\n\\n self.coord = np.array([self.x,self.y,np.full(len(self.x),self.z)])\\n \\n self.x = self.x[::-1]\\n self.y = -self.y[::-1] \\n self.new = np.array([self.x,self.y,np.full(len(self.x),self.z)])\\n self.coord = np.array([np.append(self.coord[0],self.new[0]),np.append(self.coord[1],self.new[1]),np.append(self.coord[2],self.new[2])])\\n self.coord = np.array([np.append(self.coord[0],self.coord[0,0]),np.append(self.coord[1],self.coord[1,0]),np.append(self.coord[2],self.coord[2,0])])\\n\\n self.coord[0] = self.coord[0] - (np.amax(self.coord[0])+np.amin(self.coord[0]))/2\\n self.coord[1] = self.coord[1] + (np.amax(self.coord[1])-np.amin(self.coord[1]))/2 \\n \\n self.coordi = np.array(self.coord)\\n \\n self.cg = np.array([0 + self.dx, self.H/2 + self.dy, self.z]) \\n self.cgi = np.array(self.cg)\\n \\n self.thi = 0. + self.dth \\n self.th = float(self.thi) \\n \\n self.coordnav(self.dx,self.dy,self.dth)\",\n \"def __init__(self, n_pe, laser=None, bubble_radius=None, propagation_distance=None):\\n self.npe = n_pe.to(\\\"1/cm**3\\\")\\n self.λp = np.sqrt(np.pi / (r_e * self.npe)).to(\\\"micrometer\\\")\\n self.kp = (2 * np.pi / self.λp).to(\\\"1/micrometer\\\")\\n self.ωp = (u.clight * self.kp).to(\\\"1/femtosecond\\\")\\n\\n self.Ewb = (u.me * u.clight * self.ωp / np.abs(u.qe)).to(\\\"megavolt/mm\\\")\\n\\n self.lp = (u.clight / self.ωp).to(\\\"micrometer\\\")\\n self.tp = (1 / self.ωp).to(\\\"femtosecond\\\")\\n\\n if laser:\\n self.laser = laser\\n\\n self.γp = (self.laser.ωL / self.ωp).to(\\\"dimensionless\\\")\\n self.Pc = (17 * self.γp ** 2 * u.gigawatt).to(\\\"terawatt\\\")\\n\\n self.dephasing = (\\n 4 / 3 * self.γp ** 2 * np.sqrt(self.laser.a0) / self.kp\\n ).to(\\\"mm\\\")\\n self.depletion = (self.γp ** 2 * u.clight * self.laser.τL).to(\\\"mm\\\")\\n\\n self.Ez_avg = (self.Ewb * np.sqrt(self.laser.a0) / 2).to(\\\"megavolt/mm\\\")\\n\\n if propagation_distance:\\n self.Lacc = propagation_distance.to(\\\"mm\\\")\\n else:\\n self.Lacc = self.dephasing\\n\\n self.ΔE = (np.abs(u.qe) * self.Ez_avg * self.Lacc).to(\\\"megaelectronvolt\\\")\\n\\n if bubble_radius:\\n self.R = bubble_radius.to(\\\"micrometer\\\")\\n\\n self.N = (1 / 30 * (self.kp * self.R) ** 3 / (self.kp * r_e)).to(\\n \\\"dimensionless\\\"\\n )\\n self.Q = (self.N * np.abs(u.qe)).to(\\\"picocoulomb\\\")\\n\\n self.η = (self.N * self.ΔE / self.laser.ɛL).to(\\\"dimensionless\\\")\\n else:\\n self.R = None\\n else:\\n self.laser = None\\n self.R = None\",\n \"def __init__(self, win, angle, velocity, height): \\r\\n\\r\\n self.proj = Projectile(angle, velocity, height) \\r\\n self.marker = Circle(Point(0, height), 3) \\r\\n self.marker.setFill(\\\"red\\\")\\r\\n self.marker.setOutline(\\\"red\\\")\\r\\n self.marker.draw(win)\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n self.odometer_reading = 0 #Setting a default value for an Attribute\",\n \"def __init__(self, name, freq, waypoint_specified, waypoint_bc):\\n self.dt = 1.0/freq\\n self.uav = name\\n self.wp_specfied = waypoint_specified\\n self.wp_bc = waypoint_bc\\n self.start_time = time.time()\\n self.average_speed = 3.0\\n self.reduced_speed = 0.5\\n # specify start/intermediate/end points and its deviratives \\n self.no_of_segments = 7\\n self.wp_callback_counter = 0\\n self.trajectory_constructed = False\\n\\n self.r = 4 # corresponding to snap which is 4th derivative\\n self.N = 7# degree of polynomial \\n \\n self.pub = rospy.Publisher('/'+self.uav+'/PolynomialTrajectory', PolynomialTrajectory, queue_size = 1, tcp_nodelay = True)\\n \\n rospy.Subscriber('/'+self.uav + '/odometry_sensor1/odometry', Odometry, self.currentstate_callback, queue_size = 1, tcp_nodelay = True) \\n rospy.Subscriber('/'+self.uav+'/waypoint_publisher', Pose, self.waypoint_callback, queue_size = 1, tcp_nodelay=True)\\n\\n #try: \\n # rospy.Subscriber('/'+self.uav+'/waypoint_publisher', Pose, self.waypoint_callback, queue_size = 1, tcp_nodelay=True)\\n # rospy.Subscriber('/'+self.uav + '/odometry_sensor/odometry', Odometry, self.currentstate_callback, queue_size = 1, tcp_nodelay = True)\\n #except: \\n # print 'Either waypoints or odometry is not available.'\",\n \"def pie(t,length,n):\\n\\tangle=360/n\\n\\tlength2=math.sqrt((length**2)*2-(2*length*length)*math.cos(math.radians(angle)))\\n\\tangle2=(180-angle)/2\\n\\n\\tfor i in range(n):\\n\\t\\tt.fd(length)\\n\\t\\tt.lt(180-angle2)\\t\\n\\t\\tt.fd(length2)\\n\\t\\tt.lt(180-angle2)\\n\\t\\tt.fd(length)\\n\\t\\tt.rt(180)\",\n \"def __init__(self, t=0.01):\\r\\n\\r\\n\\t\\tself.t = float(t)\\r\\n\\t\\t\\r\\n\\t\\t#Control constants\\r\\n\\t\\t#Altitude\\r\\n\\t\\t#P\\r\\n\\t\\tkp1=3\\r\\n\\t\\t#PID\\r\\n\\t\\tkp2=6\\r\\n\\t\\tki2=0\\r\\n\\t\\tkd2=2.5\\r\\n\\t\\t#PID\\r\\n\\t\\tkp3=0.75\\r\\n\\t\\tki3=1.5\\r\\n\\t\\tkd3=0\\r\\n\\t\\t\\r\\n\\t\\t#Pitch\\r\\n\\t\\t#P\\r\\n\\t\\tkp1_theta=4.5\\r\\n\\t\\t#PID\\r\\n\\t\\tkp2_theta=0.15\\r\\n\\t\\tki2_theta=0.1\\r\\n\\t\\tkd2_theta=0.004\\r\\n\\t\\t\\r\\n\\t\\t#Roll\\r\\n\\t\\t#P\\r\\n\\t\\tkp1_phi=4.5\\r\\n\\t\\t#PID\\r\\n\\t\\tkp2_phi=0.15\\r\\n\\t\\tki2_phi=0.1\\r\\n\\t\\tkd2_phi=0.004\\r\\n\\t\\t\\r\\n\\t\\t#Yaw\\r\\n\\t\\t#P\\r\\n\\t\\tkp1_psi=10\\r\\n\\t\\t#PID\\r\\n\\t\\tkp2_psi=0.2\\r\\n\\t\\tki2_psi=0.02\\r\\n\\t\\tkd2_psi=0\\r\\n\\t\\t\\r\\n\\t\\t#Control\\r\\n\\t\\timax1=10\\r\\n\\t\\tt_max1=200\\r\\n\\t\\timax2=10\\r\\n\\t\\tt_max2=120\\r\\n\\t\\t\\r\\n\\t\\t#Constrains\\r\\n\\t\\trate_imax = 50\\r\\n\\t\\tac_imax = 40\\r\\n\\t\\tt_min = 800\\r\\n\\t\\tt_max = 1500\\r\\n\\t\\t\\r\\n\\t\\t#Goals\\r\\n\\t\\tself.target_z=3\\r\\n\\t\\tself.target_theta=0\\r\\n\\t\\tself.target_phi=0\\r\\n\\t\\tself.target_psi=0\\r\\n\\r\\n\\t\\t#Initial Conditions\\r\\n\\t\\tself.w=[1000, 1000, 1000, 1000]\\r\\n\\t\\t#self.ev = [0, 0, 1, 0, 0, 0, 0, 0, -9.81, 0.4, -0.2, 1, 0.1, -0.1, 0.2, 0.01, -0.01, 0.02]\\r\\n\\t\\tself.ev = [0, 0, 0, 0, 0, 0, 0, 0, -9.81, 0, 0, 0, 0, 0, 0, 0, 0, 0]\\r\\n\\r\\n\\t\\t#Create controllers\\r\\n\\t\\tself.altitudeController = control.controlAlt (self.t, kp1, kp2, ki2, kd2, kp3, ki3, kd3, rate_imax, ac_imax,t_min,t_max)\\r\\n\\t\\tself.thetaController = control.controlAngle (self.t, kp1_theta, kp2_theta, ki2_theta, kd2_theta, imax1, t_max1)\\r\\n\\t\\tself.phiController = control.controlAngle (self.t, kp1_phi, kp2_phi, ki2_phi, kd2_phi, imax1, t_max1)\\r\\n\\t\\tself.psiController = control.controlAngle (self.t, kp1_psi, kp2_psi, ki2_psi, kd2_psi, imax2, t_max2)\",\n \"def __init__(self):\\n super().__init__()\\n self.p = 0.0\\n self.type = 'Geometric'\\n self.distType = 'Discrete'\\n self.lowerBound = 0.0\\n self.upperBound = 1.0\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, plant):\\n\\n self.plant = plant\\n\\n self.x = np.zeros(2*self.plant.dof) # position, velocity\\n self.t = 0.0 # time\\n\\n self.reset_data_recorder()\",\n \"def __init__(self, r, r_2, pie_d, pie_d_2, lmb_d, lmb_d_2, pie_s, pie_s_2, b_s, b_s_2):\\n lma_d=(pie_d-(1+r))\\n lma_d_2=(pie_d_2-(1+r_2))\\n a_s=((1+r)-pie_s)\\n a_s_2=((1+r_2)-pie_s_2)\\n \\n self.lma_d, self.lma_d_2, self.lmb_d, self.lmb_d_2, self.a_s, self.a_s_2, self.b_s, self.b_s_2 = lma_d , lma_d_2 , lmb_d , lmb_d_2, a_s, a_s_2 , b_s, b_s_2\\n if lma_d < a_s :\\n raise ValueError('Insufficient demand.')\\n elif lma_d_2 < a_s_2 :\\n raise ValueError('Insufficient demand on 2.')\",\n \"def _create_example_door():\\n return Door({\\\"warning\\\": False, \\\"closed\\\": True, \\\"locked\\\": False})\",\n \"def create_base(inst):\\r\\n\\r\\n strophe = []\\r\\n chorus = []\\r\\n chord = C_SCALE[0]\\r\\n \\r\\n for i in range(4):\\r\\n strophe.append(chord) \\r\\n chord = get_next_chord(chord)\\r\\n \\r\\n for i in range(4):\\r\\n chorus.append(chord) \\r\\n chord = get_next_chord(chord)\\r\\n \\r\\n chord_progression = strophe + strophe + chorus + strophe\\r\\n \\r\\n for c in chord_progression:\\r\\n meas = inst.NewMeasure()\\r\\n add_chord(meas, c)\\r\\n\\r\\n return chord_progression\",\n \"def __init__(self, time, rate, type='sine'):\\n self.t_v = np.zeros(time*rate)\\n self.heading = np.zeros(self.t_v.shape)\\n self.commands = np.zeros(self.t_v.shape)\\n self.derivative = np.zeros(self.t_v.shape)\",\n \"def __init__(self):\\n\\n\\n self.dtype = np.dtype([\\n ('fault_flags', np.uint32),\\n ('raw_x', np.int16),\\n ('raw_y', np.int16),\\n ('raw_z', np.int16),\\n ('accel_x', np.float32),\\n ('accel_y', np.float32),\\n ('accel_z', np.float32), \\n ('pitch', np.float32),\\n ('roll', np.float32), \\n ])\\n \\n self._accel_indices = [0, 4, 5, 6]\\n \\n self.data = np.array([(0, 0.1, 12, 1234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234)], dtype=self.dtype)\\n self.data['fault_flags'] = 0\\n self.data['raw_x'] = 0.1\\n self.data['raw_y'] = 12\\n self.data['raw_z'] = 31929\\n self.data['accel_x'] = 0.12345678901234\\n self.data['accel_y'] = 0.23456789012345\\n self.data['accel_z'] = 0.34567890123456\\n self.data['pitch'] = 0.1000\\n self.data['roll'] = 0.2000\\n\\n\\n #print len(self.data.tostring(order=\\\"C\\\"))\",\n \"def __init__(\\n self,\\n oxygen: int = None,\\n pulse: int = None,\\n blood_pressure_systolic: int = None,\\n blood_pressure_diastolic: int = None,\\n ):\\n self.oxygen = oxygen\\n self.pulse = pulse\\n self.blood_pressure_systolic = blood_pressure_systolic\\n self.blood_pressure_diastolic = blood_pressure_diastolic\",\n \"def __init__(self, value):\\n if isinstance(value, Pressure):\\n value = Pascal(value.base_value).atmosphere.value\\n self.base_value = value * 101325.0\\n self.value = float(value)\",\n \"def __init__(self, mass, x, y,px=0.0,py=0.0):\\n self.mass = mass\\n self.position = np.array([x,y])\\n self.momentum = np.array([px,py])\",\n \"def __init__(self, parent):\\n super(Demo5, self).__init__(parent)\\n self.angle = 0.0\\n self.replication = 1.0\\n self.offset = 0.0\\n self.deltaRep = 1\\n self.revolution = 0\\n self.stepsPer90 = 180\\n self.stepsLeft = self.stepsPer90\\n self.deltaAng = 90.0\\n self.deltaOff = 0.15\\n self.spin = True\\n self.x2yAspect = 1.0\\n self.texture = None\",\n \"def __init__(self):\\n self.Robot = Robot()\\n self.Omega = matrix()\\n # self.Omega.value[0][0] = 1.0\\n # self.Omega.value[1][1] = 1.0\\n self.Xi = matrix()\\n # Xi.value[0][0] = 0.0\\n # Xi.value[1][0] = 0.0\\n self.measure = {}\\n self.landMarkCount = 0\\n self.init = False\\n self.bearing = 0\\n self.x = 0\\n self.y = 0\\n \\n # TODO\",\n \"def __init__(self, n_points, kind='cubic'):\\n self.n_points = n_points\\n self.kind = kind\",\n \"def __init__(self, markers):\\n self.markers = markers\\n self.last_time = None # Used to keep track of time between measurements \\n self.Q_t = np.eye(2)\\n self.R_t = np.eye(3)\\n # YOUR CODE HERE\",\n \"def __init__(self):\\n super().__init__()\\n self.n = 0.0\\n self.p = 0.0\\n self.type = 'Binomial'\\n self.hasInfiniteBound = True\\n self.distType = 'Discrete'\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, win, angle, velocity, height):\\n \\n self.proj = Projectile(angle, velocity, height)\\n self.marker = Circle(Point(0,height), 3)\\n self.marker.setFill(\\\"red\\\")\\n self.marker.setOutline(\\\"red\\\")\\n self.marker.draw(win)\",\n \"def __init__(self, name=None, dss=28, date=None, project='SolarPatrol'):\\n self.logger = logging.getLogger(logger.name+\\\".Observation\\\")\\n DR.Observation.__init__(self, name=date, date=date, dss=dss, \\n project=project)\\n self.extended_init()\\n \\n #self.obs =Astronomy.Ephem.DSS(dss)\\n #y,d = date.split('/')\\n #self.year = int(y); self.DOY = int(d)\\n #projdatapath, self.sessionpath, rawdatapath = \\\\\\n # DR.get_obs_dirs(project, dss, self.year, self.DOY,\\n # datafmt=None)\",\n \"def __init__(self, timer=120, rate=1, percent=0):\\n self.timer = timer\\n self.rate = rate\\n self.percent = percent\",\n \"def __init__(self):\\n self.temperature = 70.0\\n self.status = \\\"sunny\\\"\",\n \"def __init__(self):\\n self.temperature = 70.0\\n self.status = \\\"sunny\\\"\",\n \"def __init__(self):\\n self.temperature = 70.0\\n self.status = \\\"sunny\\\"\",\n \"def __init__(self):\\n self.center = Point()\\n self.velocity = Velocity()\",\n \"def __init__(self, data=(1.0, numpy.array((0., 0., 0.), 'f'))):\\n if len(data) == 2:\\n self.real = data[0]\\n try:\\n theta = numpy.arccos(self.real)\\n self.pure = numpy.array((data[1][0], data[1][1], data[1][2]), 'f')\\n except:\\n raise ValueError(\\\"The real part must be between -1.0 and 1.0\\\")\\n elif len(data) == 4:\\n theta = numpy.pi * data[3] / 360.\\n self.real = numpy.cos(theta)\\n self.pure = numpy.sin(theta) * numpy.array((data[0], data[1],\\n data[2]), 'f')\\n else:\\n raise ValueError(\\\"Args must be (x,y,z,theta) or (real,pure)\\\")\\n self.normal()\",\n \"def __init__(self):\\n self.ascii_filename = None\\n\\n # name and calc_id\\n self.name = None\\n self.calc_id = None\\n self.i_ts = None\\n self.time = None\\n # data members\\n self.x = None\\n self.p = None\\n # particle number density normalized to nGJ: n/n_{GJ} \\n self.fmci_XP = None\\n # PSR parameters\\n self.PSR_P = None\\n self.PSR_B12 = None\\n self.PSR_Lcm = None\\n self.PSR_Theta = None\\n self.PSR_Chi = None\",\n \"def __init__(\\n self,\\n pin,\\n min_pulse_width=500,\\n max_pulse_width=2500,\\n min_full_sweep_time=0.5,\\n rotation_update_freq=25,\\n ):\\n assert (\\n 500 <= min_pulse_width <= 2500\\n ), \\\"min_pulse_width should be between 500 and 2500\\\"\\n assert (\\n 500 <= max_pulse_width <= 2500\\n ), \\\"max_pulse_width should be between 500 and 2500\\\"\\n assert (\\n min_pulse_width < max_pulse_width\\n ), \\\"min_pulse_width should be less than max_pulse_width\\\"\\n assert (\\n min_full_sweep_time > 0\\n ), \\\"min_full_sweep_time should be positive\\\"\\n assert (\\n rotation_update_freq > 0\\n ), \\\"rotation_update_freq should be positive\\\"\\n\\n self._pin = pin\\n self._min_pulse_width = min_pulse_width\\n self._max_pulse_width = max_pulse_width\\n self._mid_pulse_width = (max_pulse_width + min_pulse_width) / 2\\n self._max_pos_change_per_update = 2 / (\\n rotation_update_freq * min_full_sweep_time\\n )\\n self._rotation_update_freq = rotation_update_freq\\n self._latest_rotation_start_time = None\\n self._position = None\\n self._rotation_speed = 0\\n self._stopped = False\\n\\n self._pi = pigpio.pi()\\n if not self._pi.connected:\\n raise RuntimeError(\\\"Could not connect to pigpio daemon\\\")\\n self._pi.set_mode(self._pin, pigpio.OUTPUT)\\n self._pi.set_servo_pulsewidth(self._pin, 0)\",\n \"def new_gas_particle():\\n function = LegacyFunctionSpecification()\\n function.must_handle_array = True\\n function.addParameter('index_of_the_particle', dtype='int32', direction=function.OUT, description =\\n \\\"\\\"\\\"\\n An index assigned to the newly created particle.\\n This index is supposed to be a local index for the code\\n (and not valid in other instances of the code or in other codes)\\n \\\"\\\"\\\"\\n )\\n for par in [\\\"x\\\", \\\"y\\\", \\\"z\\\"]:\\n function.addParameter(par, dtype='float64', unit=generic_unit_system.length, direction=function.IN, \\n description = \\\"The initial position vector of the particle\\\")\\n function.addParameter('radius', dtype='float64', unit=generic_unit_system.length, direction=function.IN, description = \\\"The radius of the particle\\\")\\n for par in [\\\"red\\\", \\\"green\\\", \\\"blue\\\"]:\\n function.addParameter(par, dtype='float64', direction=function.IN, \\n description = \\\"The RGB color of the particle\\\")\\n function.addParameter(\\\"alpha\\\", dtype='float64', direction=function.IN, description = \\\"The opacity of the particle\\\", default = 1.0)\\n function.addParameter('npoints', dtype='int32', direction=function.LENGTH)\\n function.result_type = 'int32'\\n return function\",\n \"def __init__(self, num_points=5000):\\n self.num_points = num_points\\n\\n # All motion starts at (0,0).\\n self.x_values = [0]\\n self.y_values = [0]\",\n \"def __init__(self, make, model, year):\\n self.make = make\\n self.model = model\\n self.year = year\\n # default value for a data attribute not matched by a parameter\\n self.odometer_reading = 0\",\n \"def __init__(self):\\n self.risepower = 4.\\n self.min_event_amplitude = 5.0e-12 # pA default\\n self.template = None\\n pass\",\n \"def __init__(self, sensor):\\n self.sensor = sensor\\n self.temperature = None\\n self.humitidy = None\\n self.update()\",\n \"def __init__(self):\\n self.set_recharge(random.randint(1000, 2000) / 10000)\\n operator_count = random.randint(1, 3)\\n self.operators = [Solder() for _ in range(0, operator_count)]\\n list_operators = [i.get_health for i in self.operators]\\n self.set_health(sum(list_operators) / len(list_operators))\",\n \"def __init__(self):\\n super().__init__()\\n self.apex = 0.0 # peak location\\n self.min = None # domain lower boundary\\n self.max = None # domain upper boundary\\n self.type = 'Triangular'\\n self.distType = 'Continuous'\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def __init__(self):\\n self.wnd = WindSensor()\",\n \"def photometric_calibration():\\n pass\",\n \"def __init__(self, nav_data, div=5, dx=0.0, dy=1.6757135024103853, dth=0.0, pc1=1/3, pc2=9/24, ph=0.2): \\n self.div = div\\n self.t = np.arange(self.div)/self.div\\n self.L = nav_data.loc['Loa']\\n self.H = nav_data.loc['B']\\n self.T = nav_data.loc['T']\\n self.dx = dx \\n self.dy = dy\\n self.dth = dth\\n self.c1 = self.L*pc1\\n self.c2 = self.L*pc2\\n self.h2 = self.H*ph\\n self.Rz = rotation.matrix([0,0,1],dth) \\n\\n self.LWT = 0.50*nav_data.loc['Delta_m'] \\n self.Md = nav_data.loc['Delta_m'] #self.LWT + self.DWT\\n self.D = nav_data.loc['De']\\n self.G = self.T - self.D \\n self.z = nav_data.loc['KG'] - self.D\",\n \"def __init__(self, position=np.array([0,0,0], dtype=float),\\n velocity=np.array([0,0,0], dtype=float), acceleration=np.array([0,0,0],\\n dtype=float), name='A Particle', restMass=1.0, charge=const.elementary_charge):\\n self.name = name\\n self.position = np.array(position, dtype=float)\\n self.velocity = np.array(velocity, dtype=float)\\n self.acceleration = np.array(acceleration, dtype=float)\\n self.restMass = restMass\\n self.charge = charge\\n self.electricField = PointElectricFieldClass(sourceParticle=self\\n , name='Field from %s'%(self.name))\\n self.magneticField = PointMagneticFieldClass(sourceParticle=self\\n , name='Field from %s'%(self.name))\",\n \"def __init__(self, x_coor, x_speed, y_coor, y_speed, direction):\\n self.__x_coor = x_coor\\n self.__x_speed = x_speed\\n self.__y_coor = y_coor\\n self.__y_speed = y_speed\\n self.__direction = direction\\n self.__radius = self.TORPEDO_RADIUS\",\n \"def __init__ (self,\\n elevation = 45.0,\\n start_angle = 0.0,\\n latitude = 78.0,\\n longitude = 0.0,\\n rot_per_min = 1,\\n rev_days = 15):\\n\\n self.elevation = np.radians (elevation) # Units: [rad]\\n self.start_angle = np.radians (start_angle) # Units: [rad]\\n\\n self.base_lat = np.radians (latitude) # Units: [rad]\\n self.base_long = np.radians (longitude) # Units: [rad]\\n\\n self.omega_rot = 2 * np.pi * rot_per_min / 60 # Units: [rad/s]\\n self.rev_days = rev_days # Units: [day]\",\n \"def __init__(self, name=''):\\n self.domain = (0, np.pi)\\n self._evaluator_overrides = None\\n self._name = name\\n self._user_data = dict()\\n self.__loaded_from = None\",\n \"def create_door(tick, x, y, z):\\n door = nbt.TAG_Compound()\\n door['TS'] = nbt.TAG_Int(tick)\\n door['X'] = nbt.TAG_Int(x)\\n door['Y'] = nbt.TAG_Int(y)\\n door['Z'] = nbt.TAG_Int(z)\\n return door\",\n \"def get_pzo(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.PZO(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def particle(self) -> Particle:\\n return Particle()\",\n \"def particle(self) -> Particle:\\n return Particle()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5920001","0.57205135","0.5660947","0.5660947","0.5660947","0.5660947","0.5660947","0.56580734","0.5646192","0.5608879","0.56043893","0.55835843","0.55835843","0.55835843","0.55835843","0.55835843","0.55835843","0.55835843","0.55545354","0.5550657","0.5526975","0.54765284","0.546936","0.5465043","0.5462492","0.54616565","0.5459737","0.542245","0.54208535","0.54175454","0.5412927","0.537839","0.53739315","0.5357907","0.53483164","0.5333282","0.5331511","0.5325196","0.5323875","0.5321311","0.52957785","0.52940184","0.5291564","0.5281499","0.5281499","0.5270803","0.5261867","0.5259023","0.52510506","0.5250755","0.52455944","0.5212697","0.5208612","0.5198209","0.51923865","0.5176874","0.51742524","0.51717997","0.5164674","0.51617837","0.51612735","0.51608986","0.5154742","0.51510364","0.51401097","0.5136272","0.51321816","0.5129064","0.51189876","0.5112993","0.5110088","0.5103251","0.50997716","0.5098745","0.50953215","0.50953215","0.50953215","0.5088809","0.508489","0.5084715","0.5082583","0.5080073","0.50788474","0.5076427","0.5063198","0.5061906","0.505981","0.50596815","0.50557935","0.5055531","0.5051125","0.50461155","0.504018","0.5038156","0.50336355","0.5029146","0.5019985","0.50155556","0.5013907","0.5013907"],"string":"[\n \"0.5920001\",\n \"0.57205135\",\n \"0.5660947\",\n \"0.5660947\",\n \"0.5660947\",\n \"0.5660947\",\n \"0.5660947\",\n \"0.56580734\",\n \"0.5646192\",\n \"0.5608879\",\n \"0.56043893\",\n \"0.55835843\",\n \"0.55835843\",\n \"0.55835843\",\n \"0.55835843\",\n \"0.55835843\",\n \"0.55835843\",\n \"0.55835843\",\n \"0.55545354\",\n \"0.5550657\",\n \"0.5526975\",\n \"0.54765284\",\n \"0.546936\",\n \"0.5465043\",\n \"0.5462492\",\n \"0.54616565\",\n \"0.5459737\",\n \"0.542245\",\n \"0.54208535\",\n \"0.54175454\",\n \"0.5412927\",\n \"0.537839\",\n \"0.53739315\",\n \"0.5357907\",\n \"0.53483164\",\n \"0.5333282\",\n \"0.5331511\",\n \"0.5325196\",\n \"0.5323875\",\n \"0.5321311\",\n \"0.52957785\",\n \"0.52940184\",\n \"0.5291564\",\n \"0.5281499\",\n \"0.5281499\",\n \"0.5270803\",\n \"0.5261867\",\n \"0.5259023\",\n \"0.52510506\",\n \"0.5250755\",\n \"0.52455944\",\n \"0.5212697\",\n \"0.5208612\",\n \"0.5198209\",\n \"0.51923865\",\n \"0.5176874\",\n \"0.51742524\",\n \"0.51717997\",\n \"0.5164674\",\n \"0.51617837\",\n \"0.51612735\",\n \"0.51608986\",\n \"0.5154742\",\n \"0.51510364\",\n \"0.51401097\",\n \"0.5136272\",\n \"0.51321816\",\n \"0.5129064\",\n \"0.51189876\",\n \"0.5112993\",\n \"0.5110088\",\n \"0.5103251\",\n \"0.50997716\",\n \"0.5098745\",\n \"0.50953215\",\n \"0.50953215\",\n \"0.50953215\",\n \"0.5088809\",\n \"0.508489\",\n \"0.5084715\",\n \"0.5082583\",\n \"0.5080073\",\n \"0.50788474\",\n \"0.5076427\",\n \"0.5063198\",\n \"0.5061906\",\n \"0.505981\",\n \"0.50596815\",\n \"0.50557935\",\n \"0.5055531\",\n \"0.5051125\",\n \"0.50461155\",\n \"0.504018\",\n \"0.5038156\",\n \"0.50336355\",\n \"0.5029146\",\n \"0.5019985\",\n \"0.50155556\",\n \"0.5013907\",\n \"0.5013907\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6613623"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75798,"cells":{"query":{"kind":"string","value":"method to validate my input"},"document":{"kind":"string","value":"def validate_empty(self):\n result = \"\"\n if(not re.search(\"[a-zA-Z0-9]\", self.title) or not\n re.search(\"^(\\s|\\S)*(\\S)+(\\s|\\S)*$\", self.body)):\n result = \"INCORRECT INPUT, YOU CAN'T SUBMIT EMPTY FIELD OR FIRST CHARACTER SHOULD BE ALPHA NUMERIC\"\n else:\n result = True\n return result"},"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 validate():","def validate_input(self, *args):\n return","def isInputValid(self, input):\r\n pass","def check_validity(self):","def __validate():\n # TODO: implement","def validate(self):","def validate(self):","def _validate_input(self):\n self.data.validate()\n self.meta_hybridizer.validate_input()","def validate(ob):","def validate_input():\n pass_length = None\n account_name = None\n method = None\n in_value = None\n parser = ArgumentParser()\n parser.add_argument(\"-l\", \"--length\",\n type=int,\n help=\"length of password\")\n\n parser.add_argument(\"-a\", \"--account\",\n help=\"account or site name\")\n\n parser.add_argument(\"-m\", \"--method\",\n help=\"method to take [S|C|R|U|D]\")\n\n parser.add_argument(\"-v\", \"--value\",\n help=\"Do not generate, store this value\")\n\n args = parser.parse_args()\n if args.length:\n pass_length = int(args.length)\n\n if args.account:\n account_name = str(args.account)\n\n if args.method:\n method = str.upper(args.method)\n\n if args.value:\n in_value = str(args.value)\n\n return (pass_length, account_name, method, in_value)","def _validate(self):\n pass","def validate(cls, data, errors):","def __check_validation(input_string):\n if not input_string:\n raise NullInputException(\"Input string should be not empty\")\n if type(input_string) != str:\n raise NonStringInputException(\"Input value should be a string\")\n if len(input_string) >= 200:\n raise TooLongInputException(\"Input string should be less than 200 characters\")\n for i in input_string:\n if not i.isalpha():\n raise NonStringInputException(\"All input value characters should be an alpha\")","def question_new_validate():","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self, text):\n raise NotImplementedError()","def validate_input(self):\n self._validate_limits_cols_prefixed()\n self._validate_fillna_cols_prefixed()\n self._validate_ratio_input()","def validate_inputs(self, extra_inputs=[]):\n pass","def _check_validity(self):\n pass","def validate(self):\n raise NotImplementedError","def validate(self):\n raise NotImplementedError","def validate(self,data):\r\n for person in data:\r\n #check the format is a letter and 3 digit e.g A002 or a002\r\n \r\n if (re.match(r'[A-Z][0-9]{3}', (person[0]).lower())):\r\n print (person[0])\r\n else:\r\n print(person[0] + \" \" + 'is incorrect ID, '\r\n ' must contains a letter and 3 digits e.g a002')\r\n \r\n #check the format is either M/F/Male/Female\r\n \r\n if (person[1] == \"M\" or (person[1]).upper() == \"F\" or\r\n person[1] == \"Male\" or person[1] == \"Female\"):\r\n print (person[1])\r\n else:\r\n print(person[1] + \" \" + 'is incorrect Gender, '\r\n ' must either be M and Male or F and Female')\r\n \r\n #check age is valid entry and match with date\r\n \r\n if (re.match(r'[0-9]{2}', person[2]) and person[2] == self.valid_age(person[6])):\r\n print (person[2])\r\n elif (person[2] != self.valid_age(person[2])):\r\n print(\"Does not match with your birthday, invalid age\")\r\n else:\r\n print(person[2] + \" \" + 'age must be an integer')\r\n \r\n #check sales is 3 interger value\r\n if (re.match(r'[0-9]{3}', person[3])):\r\n return (person[3])\r\n else:\r\n print(person[3] + \" \" + 'is incorrect sales number, '\r\n 'must be a 2 interger number')\r\n \r\n #check BMI is either Normal / Overweight / Obesity or Underweight\r\n if (re.match(r'\\b(NORMAL|OVERWEIGHT|OBESITY|UNDERWEIGHT)\\b',(person[4]).upper())):\r\n print (person[4])\r\n else:\r\n print(person[4] + \" \" ' is incorrect BMI value, '\r\n 'must select from Normal, Overweight, Obesity or Underweight')\r\n \r\n #check Income is float\r\n\r\n if (re.match(r'[0-9]{2,3}', person[5])):\r\n print (person[5])\r\n else:\r\n print(person[5] + \" \" + 'is incorrect income, '\r\n 'must be a interger number') \r\n \r\n #check birthday\r\n \r\n if (self.valid_date(person[6]) and person[2] == self.valid_age(person[6]) ):\r\n print (person[6])\r\n else:\r\n print(person[2] + \" \" + 'is incorrect date format, '\r\n 'must contain DD-MM-YYYY or DD-MM-YY and seperated by -')\r\n \r\n return readFile","def _validate(self, *_):\n provider = self.provider_entry.get_text()\n username = self.account_name_entry.get_text()\n token = \"\".join(self.token_entry.get_text().split())\n\n if not username:\n self.account_name_entry.get_style_context().add_class(\"error\")\n valid_name = False\n else:\n self.account_name_entry.get_style_context().remove_class(\"error\")\n valid_name = True\n\n if not provider:\n self.provider_combobox.get_style_context().add_class(\"error\")\n valid_provider = False\n else:\n self.provider_combobox.get_style_context().remove_class(\"error\")\n valid_provider = True\n\n if (not token or not OTP.is_valid(token)) and not self.props.is_edit:\n self.token_entry.get_style_context().add_class(\"error\")\n valid_token = False\n else:\n self.token_entry.get_style_context().remove_class(\"error\")\n valid_token = True\n\n self.emit(\"changed\", all([valid_name, valid_provider, valid_token]))","def validate(self, data):\n raise NotImplementedError(\"Inherit this class and override this method.\")","def validate(info):\n\n\tif info == \"\": \n\t\treturn False\n\telse:\n\t\tif len(info) < 5 or len(info) > 32:\n\t\t\treturn False\n\t\telse:\n\t\t\treturn True","def validate(self, name, values):\r\n \r\n pass","def _is_valid_input(self, parameter_name):\n raise NotImplementedError()","def validate(self):\n return (self.check_input_digits_count()\n and self.check_if_input_is_int()\n and self.check_if_input_digits_are_unique())","def validate(self, document) -> None:\n\n # document.text will have value in two cases, after we pressed enter in the prompt or when navigating down\n # the autocomplete commands list. In the second case there is no need to press enter to trigger this method,\n # but in those cases self.validation_type == ''\n typed = document.text\n\n if typed:\n if self.validation_type == \"number\":\n regex = r\"^-?\\d+$\"\n\n if not re.search(regex, typed):\n\n raise ValidationError(\n message=\"Please input a positive or negative number.\"\n )\n elif self.validation_type == \"yes_no\":\n regex = r\"^[yYnN]$\"\n\n if not re.search(regex, typed):\n raise ValidationError(message=\"Please type y, n, Y or N.\")\n elif self.validation_type == \"text_max_len\":\n if len(typed) > 100:\n raise ValidationError(message=\"La oración debe tener menos de 100 caracteres.\")\n else:\n raise ValidationError(message=\"Internal Error: Wrong validation type\")","def validate(self):\n ...","def validate(self, value, clean=True):\n pass","def validate(self, value, clean=True):\n pass","def test_validate_input_good_input(self):\r\n _validate_input(self.dist_matrix_header, self.dist_matrix,\r\n self.mapping_header, self.mapping, \"Treatment\")","def validate(self, *args, **kwargs):\n raise NotImplementedError('Validate not implemented')","def _validate_input(self):\n\n if is_empty(self.message) == True:\n raise ValidationException(\"Message cannont be empty.\")","def validate(self):\n try:\n self.values.clear()\n self.values.append(int(self.e1.get()))\n except ValueError:\n messagebox.showwarning(\n \"Bad input\",\n \"Illegal values, please try again.\")\n return False\n\n return True","def validate(self):\n raise NotImplementedError(\"validate function needs to be implemented for validators\")","def __validate(self):\n pass","def __validateInput(self, fx: str, ux:str, lx:str) -> bool:\r\n # validate the input fields\r\n if fx == \"\" or ux == \"\" or lx == \"\":\r\n self.errorMessage = self.errorMessageMissingFields\r\n self.__showErrorMessage()\r\n return False\r\n\r\n # validate the limits\r\n self.lowerX = lx\r\n self.upperX = ux\r\n # check if numeric\r\n try:\r\n self.upperX = float(self.upperX)\r\n self.lowerX = float(self.lowerX)\r\n except:\r\n self.errorMessage = self.errorMessageLimitsNotNumeric\r\n self.__showErrorMessage()\r\n return False\r\n \r\n # check for inquality\r\n if self.lowerX > self.upperX:\r\n self.errorMessage = self.errorMessageLimitsNotOrdered\r\n self.upperXField.setText(str(self.lowerX))\r\n self.lowerXField.setText(str(self.upperX))\r\n self.lowerX, self.upperX = self.upperX, self.lowerX\r\n ##################################\r\n # validate and process the input function\r\n self.inputFunction = fx\r\n try:\r\n self.inputFunction = self.inputFunction.replace(\" \", \"\").replace(\"^\", \"**\").replace(\"sqrt\", \"np.sqrt\")\r\n self.inputFunction = self.inputFunction.replace(\"e**\", \"np.exp\").replace(\"log\", \"np.log\") \r\n self.inputFunction = self.inputFunction.replace(\"sin\", \"np.sin\").replace(\"cos\", \"np.cos\").replace(\"tan\", \"np.tan\")\r\n\r\n except:\r\n self.errorMessage = self.errorMessageNonValidFunction\r\n self.__showErrorMessage()\r\n return True","def validate(self):\n raise NotImplementedError()","def getValidation(myInput):\r\n if myInput == \"\":\r\n print('You did not enter the number of bugs collected.')\r\n return -1\r\n elif myInput.isnumeric() == False:\r\n print('You entered a negative or a text value, please enter numerical digits only.')\r\n return -1\r\n elif myInput.isnumeric() == True:\r\n return int(myInput)\r\n else:\r\n print('There has been a read error, please reenter your number')\r\n return -1","def validate(self):\n raise NotImplementedError('validate method not implemented.')","def input_validation(input_: str) -> bool:\n return fullmatch('[1-9]', input_) is not None","def valid(self, *args, **kwargs) -> Any:\n pass","def _validate_input(self):\n\n try:\n expected_type(str, self.venue_id, \"venue_id\")\n expected_type(datetime.datetime, self.timestamp_utc, \"timestamp_utc\")\n\n expected_type(VenueStreamType, self.measurement_type, \"measurement_type\")\n\n expected_type(int, self.number_of_people, \"number_of_people\")\n\n if self.measurement_type is VenueStreamType.ABSOLUTE:\n if self.operator:\n raise ValueError(\"The stream type for the venue doesn't allow passing an Event operator\")\n\n elif self.measurement_type is VenueStreamType.EVENT:\n expected_type(EventStreamOperator, self.operator, \"operator\")\n else:\n raise ValueError(\"Unsupported member of the VenueStreamType enum\")\n\n if self.metadata:\n expected_type(dict, self.metadata, \"metadata\")\n\n except Exception as ex:\n raise ValueError(\"Validation of input failed. Reason: %s\" % str(ex))","def is_valid(self, value):\r\n pass","def test_validate_input_valid(self):\n final_config = self.dtm1.validate_input('00001111')\n nose.assert_equal(final_config[0], 'q4')\n nose.assert_equal(str(final_config[1]), 'TMTape(\\'xxxxyyyy.\\')')","def validate_user_input(user_input):\n responses = ['t', 'r', 'q']\n return user_input in responses","def validate(self):\n self._check_type()","def valid(self):\n pass","def validateInputType(self, inputType):\n raise NotImplementedError()","def validate(self):\r\n return 1","def validate(self):\r\n return 1","def validate(self):\r\n return 1","def validate(self):\r\n return 1","def validate(self):\r\n return 1","def _validate(self, value):\n return True","def __validate__(self):","def checkUserInput(self):\n prm = []\n err = \"\"\n guess = self.text.text()\n items = str(guess).split(',')\n if len(items) != 2:\n err = \"Two parameters must be given\"\n else:\n for i in range(0, len(items)):\n val = items[i].strip()\n if not isNumber(val):\n err = \"Parameter {0} is not numeric\".format(i + 1)\n break\n if float(val) < 0.0:\n err = \"Parameter {0} is negative\".format(i + 1)\n break\n val = float(val)\n if i == 0 and val > self.yspan:\n err = \"minHeight is too large\"\n break\n if i == 1:\n if val < self.xspan/self.npt or val > self.xspan/2:\n err = \"minWidth is too large\"\n break\n prm.append(val)\n if err:\n errmsg = \"Incorrect input:\\n{0}\".format(err)\n QtWidgets.QMessageBox.warning(self, self.title, errmsg)\n return False\n\n # Store parameters values in global variables for the next call\n global lastfilename, lastmph, lastmpw\n lastfilename = self.pltw.filename\n self.mph = lastmph = prm[0]\n self.mpw = lastmpw = prm[1]\n return True","def validate(self):\n return 1","def is_valid(self):\r\n raise NotImplementedError","def validate_input(input_name:str, input_value:any) -> str:\n input_validate = ''\n MONTHS = MONTHS_LIST + ['All']\n DAYS = DAYS_LIST + ['All']\n if not isinstance(input_value, str) or not input_value.strip() or input_value.isdigit():\n input_validate = f'Warrning --> Please enter the {input_name} as it mentioned in the Question.\\n'\n \n elif input_name == 'city' and input_value.title().strip() not in ['Chicago', 'New York City', 'Washington']:\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\n'\n \n elif input_name == 'filters' and input_value.lower().strip() not in ['both', 'day', 'month', 'none']:\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\n'\n \n elif input_name == 'month' and input_value.title().strip() not in MONTHS:\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\n'\n \n elif input_name == 'day' and input_value.title().strip() not in DAYS:\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\n'\n return input_validate","def test_validate_input_rejection(self):\n with nose.assert_raises(exceptions.RejectionError):\n self.dtm1.validate_input('000011')","def _validate(self, instance, value):","def validate_input(**kwargs):\n for name, value in kwargs.items():\n if name == \"x\" or name == \"y\":\n if type(value) != int:\n raise TypeError(\"{} must be an integer\".format(name))\n elif value < 0:\n raise ValueError(\"{} must be >= 0\".format(name))\n else:\n if type(value) != int:\n raise TypeError(\"{} must be an integer\".format(name))\n elif value <= 0:\n raise ValueError(\"{} must be > 0\".format(name))","def validate(cls, **kwargs: Any) -> None: # pragma no cover","def validate(entry: _LexiconEntry) -> None:\n _entry_has_required_fields(entry)\n _entry_field_values_are_not_empty(entry)\n _entry_field_values_does_not_contain_infix_whitespace(entry)\n _entry_tag_is_valid(entry)\n _entry_compound_annotation_is_valid(entry)\n _entry_morphophonemics_annotation_is_valid(entry)\n _entry_features_annotation_is_valid(entry)\n _entry_has_required_features(entry)\n _entry_required_features_are_valid(entry)\n _entry_optional_features_are_valid(entry)\n _entry_features_are_not_redundant(entry)","def _check_for_incomplete_input(self):\n pass","def Validate(self):\n raise NotImplementedError","def validate(self, instance, value):","def validate(self, instance, value):","def validate(prop, string, node, match, entry_start, entry_end):\n return True","def __init__(self):\n # assert(self.validate('61882613000194'))\n # assert(self.validate('061882613000190'))\n # assert(not self.validate('61882613000195'))\n pass","def is_valid(self): # -> bool:\n ...","def is_valid(self):\n raise NotImplementedError","def test_validate_input_rejection_invalid_symbol(self):\n with nose.assert_raises(exceptions.RejectionError):\n self.dtm1.validate_input('02')","def validate_format(self):\n raise NotImplementedError()","def test_validate(self):\n pass","def test_non_required_validation(self):\r\n Text().validate('')\r\n Text().validate(None)","def validate(self, value):\n return True","def __input_validator(msg):\n\n\t\tstatus = msg[\"status\"]\n\n\t\tif status == 1:\n\t\t\treturn status\n\t\telif status == 0:\n\t\t\tprint(msg[\"body\"])\n\t\telif status == -1:\n\t\t\tprint(\"Please enter something!\")\n\t\telif status == -2:\n\t\t\tprint(\"Your command {} is invalid\".format(msg[\"verb\"]))\n\t\telif status == -3:\n\t\t\tprint(\"No argument given after {}\".format(msg[\"verb\"]))","def _check_value(self):\n value = str(self._value_field.toPlainText())\n if value=='': return True\n ACCEPTABLES_CHARS = ('1', '2', '3', '4', '5', '6', '7', '8', '9', '0',\n '.', ',', ';', ' ', '\\n', '-')\n\n for char in value:\n if not char in ACCEPTABLES_CHARS:\n return False\n if Variable.is_acceptable_arg(value):\n rows, columns = np.matrix(value).shape\n return 1 <= rows <= 4 and 1 <= columns <= 4\n else:\n return False","def __validate_input(self, request_data):\n call_id = request_data.get(strings.CALL_ID_KEY)\n request_timestamp = request_data.get(strings.TIMESTAMP_KEY)\n request_start = request_data.get(strings.START_KEY)\n validation = None\n if call_id and request_timestamp and request_start is not None:\n call_detail_query = CallDetail.objects.filter(call_id=call_id)\n if call_detail_query:\n if len(call_detail_query) < CALL_DETAILS_LIMIT:\n stored_call_detail = call_detail_query[0]\n if isinstance(request_start, str):\n if request_start in strings.TRUE_VALUES:\n request_start = True\n else:\n request_start = False\n if stored_call_detail.start == request_start:\n validation = {strings.INPUT_ERROR_KEY:\n strings.START_END_ERROR}\n stored_timestamp = standardize_date(\n stored_call_detail.timestamp,\n strings.COMPLETE_DATE_PATTERN)\n request_timestamp = standardize_date(request_timestamp,\n strings.\n COMPLETE_DATE_PATTERN)\n if stored_timestamp == request_timestamp:\n validation = {strings.INPUT_ERROR_KEY:\n strings.EQUAL_TIMESTAMPS_ERROR}\n if stored_call_detail.start and not request_start:\n if stored_timestamp > request_timestamp:\n validation = {strings.INPUT_ERROR_KEY:\n strings.SOONER_END_ERROR}\n elif not stored_call_detail.start and request_start:\n if stored_timestamp < request_timestamp:\n validation = {strings.INPUT_ERROR_KEY:\n strings.SOONER_END_ERROR}\n else:\n validation = {strings.INPUT_ERROR_KEY:\n strings.CALL_LIMIT_ERROR}\n\n return validation","def input_check(self):\n\n if self.species == 'He': assert self.line_model == 'voigt'\n n_upper_range, e_dens_range, temp_range, b_field_range = get_param_ranges(self.line_model)\n\n if np.isnan(n_upper_range).sum() <= 1:\n assert (self.n_upper in range(n_upper_range[0], n_upper_range[1]))\n if np.isnan(e_dens_range).sum() <= 1:\n assert (e_dens_range[0] <= self.e_dens <= e_dens_range[1])\n if np.isnan(temp_range).sum() <= 1:\n assert (temp_range[0] <= self.temp <= temp_range[1])\n if np.isnan(b_field_range).sum() <= 1:\n assert (b_field_range[0] <= self.b_field <= b_field_range[1])","def check_all_user_inputs_valid(self):\n self.check_RNN_layers_valid()\n self.check_activations_valid()\n self.check_embedding_dimensions_valid()\n self.check_initialiser_valid()\n self.check_y_range_values_valid()\n self.check_return_final_seq_only_valid()","def validate(self):\r\n # Check KeyError\r\n try:\r\n self.fields[\"product_name_fr\"]\r\n self.fields[\"generic_name\"]\r\n self.fields[\"url\"]\r\n self.fields[\"nutrition_grade_fr\"]\r\n self.fields[\"categories\"]\r\n self.fields[\"stores\"]\r\n self.fields[\"brands\"]\r\n except KeyError:\r\n return False\r\n\r\n # Check empty field and lenght of generic_name\r\n for key, value in self.fields.items():\r\n if value == '':\r\n return False\r\n break\r\n if key == \"generic_name\":\r\n if len(value) > 255:\r\n return False\r\n\r\n try:\r\n self.categories = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"categories\"], 100)\r\n self.stores = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"stores\"], 45)\r\n self.brands = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"brands\"], 45)\r\n self.category_index = self.categories.index(self.category)\r\n except KeyError:\r\n return False\r\n except ValueError:\r\n return False\r\n except AttributeError:\r\n self.errors += 1\r\n print(self.errors)\r\n return False","def validate(self):\n stguess = self.text.toPlainText()\n if not self.pkfitdlg.checkUserInput(stguess):\n return\n self.stguess = stguess\n self.accept()","def validate(self):\n\n def checknum(val):\n \"\"\"\n checks if the string entered is a nonnegative number\n\n Args:\n\n val: the string to check if is a number\n\n Returns:\n\n True if the string is a nonnegative number\n\n \"\"\"\n\n if len(val) == 0:\n return False\n\n for i in range(len(val)):\n if not val[i].isdigit():\n return False\n\n return True\n\n # get user entry from inputs\n width = self.width_input.get()\n height = self.height_input.get()\n mines = self.mines_input.get()\n\n # check that all entries are numbers\n if not checknum(width):\n messagebox.showerror(\"Entry Error\", \"Board width must be a number!\")\n elif not checknum(height):\n messagebox.showerror(\"Entry Error\", \"Board height must be a number!\")\n elif not checknum(mines):\n messagebox.showerror(\"Entry Error\", \"Mine count must be a number!\")\n else:\n\n width = int(width)\n height = int(height)\n mines = int(mines)\n\n # if they are all numbers, check that they are in bounds\n if width < 2:\n messagebox.showerror(\"Entry Error\", \"Too small of a width\")\n elif width > 20:\n messagebox.showerror(\"Entry Error\", \"Too large of a width\")\n elif height < 2:\n messagebox.showerror(\"Entry Error\", \"Too small of a height\")\n elif height > 20:\n messagebox.showerror(\"Entry Error\", \"Too large of a height\")\n elif mines >= width * height:\n messagebox.showerror(\"Entry Error\", \"Too many mines!\")\n elif mines < 1:\n messagebox.showerror(\"Entry Error\", \"Too few mines!\")\n else:\n # hide setup window and tell executive instance to start game\n self.setup_window.withdraw()\n self.exec.setup(width, height, mines)","def validate(self):\n # rVal = False\n # return rVal\n return True","def __check_inputs__(self):\n # | - __check_inputs__\n # #####################################################################\n stop_mode = self.stop_mode\n stop_num_generations = self.stop_num_generations\n # #####################################################################\n\n if stop_mode == \"num_generations\":\n mess_i = \"stop_mode='num_generations', \\\n Must pass int to 'stop_num_generations'\"\n assert type(stop_num_generations) == type(1), mess_i\n #__|","def test_is_valid():\n # Expected input.\n assert make_european.is_valid('12/31/2015:john.a.graham@gmail.com')\n # Check 'at' typo.\n assert not make_european.is_valid('12/31/2015:john.a.grahamgmail.com')\n # Check colon typo.\n assert not make_european.is_valid('12/31/2015::john.a.graham@gmail.com')\n # Check forward slash replacement.\n assert not make_european.is_valid('12.31.2015:john.a.graham@gmail.com')\n # Check order.\n assert not make_european.is_valid('john.a.graham@gmail.com:12/31/2015')","def _is_valid(self):\n self._is_allows_valid()\n self._is_denies_valid()","def validate_result(self):\n raise NotImplementedError","def _validate_params(self):\n raise NotImplementedError('Must be implemented in subclasses.')"],"string":"[\n \"def validate():\",\n \"def validate_input(self, *args):\\n return\",\n \"def isInputValid(self, input):\\r\\n pass\",\n \"def check_validity(self):\",\n \"def __validate():\\n # TODO: implement\",\n \"def validate(self):\",\n \"def validate(self):\",\n \"def _validate_input(self):\\n self.data.validate()\\n self.meta_hybridizer.validate_input()\",\n \"def validate(ob):\",\n \"def validate_input():\\n pass_length = None\\n account_name = None\\n method = None\\n in_value = None\\n parser = ArgumentParser()\\n parser.add_argument(\\\"-l\\\", \\\"--length\\\",\\n type=int,\\n help=\\\"length of password\\\")\\n\\n parser.add_argument(\\\"-a\\\", \\\"--account\\\",\\n help=\\\"account or site name\\\")\\n\\n parser.add_argument(\\\"-m\\\", \\\"--method\\\",\\n help=\\\"method to take [S|C|R|U|D]\\\")\\n\\n parser.add_argument(\\\"-v\\\", \\\"--value\\\",\\n help=\\\"Do not generate, store this value\\\")\\n\\n args = parser.parse_args()\\n if args.length:\\n pass_length = int(args.length)\\n\\n if args.account:\\n account_name = str(args.account)\\n\\n if args.method:\\n method = str.upper(args.method)\\n\\n if args.value:\\n in_value = str(args.value)\\n\\n return (pass_length, account_name, method, in_value)\",\n \"def _validate(self):\\n pass\",\n \"def validate(cls, data, errors):\",\n \"def __check_validation(input_string):\\n if not input_string:\\n raise NullInputException(\\\"Input string should be not empty\\\")\\n if type(input_string) != str:\\n raise NonStringInputException(\\\"Input value should be a string\\\")\\n if len(input_string) >= 200:\\n raise TooLongInputException(\\\"Input string should be less than 200 characters\\\")\\n for i in input_string:\\n if not i.isalpha():\\n raise NonStringInputException(\\\"All input value characters should be an alpha\\\")\",\n \"def question_new_validate():\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self, text):\\n raise NotImplementedError()\",\n \"def validate_input(self):\\n self._validate_limits_cols_prefixed()\\n self._validate_fillna_cols_prefixed()\\n self._validate_ratio_input()\",\n \"def validate_inputs(self, extra_inputs=[]):\\n pass\",\n \"def _check_validity(self):\\n pass\",\n \"def validate(self):\\n raise NotImplementedError\",\n \"def validate(self):\\n raise NotImplementedError\",\n \"def validate(self,data):\\r\\n for person in data:\\r\\n #check the format is a letter and 3 digit e.g A002 or a002\\r\\n \\r\\n if (re.match(r'[A-Z][0-9]{3}', (person[0]).lower())):\\r\\n print (person[0])\\r\\n else:\\r\\n print(person[0] + \\\" \\\" + 'is incorrect ID, '\\r\\n ' must contains a letter and 3 digits e.g a002')\\r\\n \\r\\n #check the format is either M/F/Male/Female\\r\\n \\r\\n if (person[1] == \\\"M\\\" or (person[1]).upper() == \\\"F\\\" or\\r\\n person[1] == \\\"Male\\\" or person[1] == \\\"Female\\\"):\\r\\n print (person[1])\\r\\n else:\\r\\n print(person[1] + \\\" \\\" + 'is incorrect Gender, '\\r\\n ' must either be M and Male or F and Female')\\r\\n \\r\\n #check age is valid entry and match with date\\r\\n \\r\\n if (re.match(r'[0-9]{2}', person[2]) and person[2] == self.valid_age(person[6])):\\r\\n print (person[2])\\r\\n elif (person[2] != self.valid_age(person[2])):\\r\\n print(\\\"Does not match with your birthday, invalid age\\\")\\r\\n else:\\r\\n print(person[2] + \\\" \\\" + 'age must be an integer')\\r\\n \\r\\n #check sales is 3 interger value\\r\\n if (re.match(r'[0-9]{3}', person[3])):\\r\\n return (person[3])\\r\\n else:\\r\\n print(person[3] + \\\" \\\" + 'is incorrect sales number, '\\r\\n 'must be a 2 interger number')\\r\\n \\r\\n #check BMI is either Normal / Overweight / Obesity or Underweight\\r\\n if (re.match(r'\\\\b(NORMAL|OVERWEIGHT|OBESITY|UNDERWEIGHT)\\\\b',(person[4]).upper())):\\r\\n print (person[4])\\r\\n else:\\r\\n print(person[4] + \\\" \\\" ' is incorrect BMI value, '\\r\\n 'must select from Normal, Overweight, Obesity or Underweight')\\r\\n \\r\\n #check Income is float\\r\\n\\r\\n if (re.match(r'[0-9]{2,3}', person[5])):\\r\\n print (person[5])\\r\\n else:\\r\\n print(person[5] + \\\" \\\" + 'is incorrect income, '\\r\\n 'must be a interger number') \\r\\n \\r\\n #check birthday\\r\\n \\r\\n if (self.valid_date(person[6]) and person[2] == self.valid_age(person[6]) ):\\r\\n print (person[6])\\r\\n else:\\r\\n print(person[2] + \\\" \\\" + 'is incorrect date format, '\\r\\n 'must contain DD-MM-YYYY or DD-MM-YY and seperated by -')\\r\\n \\r\\n return readFile\",\n \"def _validate(self, *_):\\n provider = self.provider_entry.get_text()\\n username = self.account_name_entry.get_text()\\n token = \\\"\\\".join(self.token_entry.get_text().split())\\n\\n if not username:\\n self.account_name_entry.get_style_context().add_class(\\\"error\\\")\\n valid_name = False\\n else:\\n self.account_name_entry.get_style_context().remove_class(\\\"error\\\")\\n valid_name = True\\n\\n if not provider:\\n self.provider_combobox.get_style_context().add_class(\\\"error\\\")\\n valid_provider = False\\n else:\\n self.provider_combobox.get_style_context().remove_class(\\\"error\\\")\\n valid_provider = True\\n\\n if (not token or not OTP.is_valid(token)) and not self.props.is_edit:\\n self.token_entry.get_style_context().add_class(\\\"error\\\")\\n valid_token = False\\n else:\\n self.token_entry.get_style_context().remove_class(\\\"error\\\")\\n valid_token = True\\n\\n self.emit(\\\"changed\\\", all([valid_name, valid_provider, valid_token]))\",\n \"def validate(self, data):\\n raise NotImplementedError(\\\"Inherit this class and override this method.\\\")\",\n \"def validate(info):\\n\\n\\tif info == \\\"\\\": \\n\\t\\treturn False\\n\\telse:\\n\\t\\tif len(info) < 5 or len(info) > 32:\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\treturn True\",\n \"def validate(self, name, values):\\r\\n \\r\\n pass\",\n \"def _is_valid_input(self, parameter_name):\\n raise NotImplementedError()\",\n \"def validate(self):\\n return (self.check_input_digits_count()\\n and self.check_if_input_is_int()\\n and self.check_if_input_digits_are_unique())\",\n \"def validate(self, document) -> None:\\n\\n # document.text will have value in two cases, after we pressed enter in the prompt or when navigating down\\n # the autocomplete commands list. In the second case there is no need to press enter to trigger this method,\\n # but in those cases self.validation_type == ''\\n typed = document.text\\n\\n if typed:\\n if self.validation_type == \\\"number\\\":\\n regex = r\\\"^-?\\\\d+$\\\"\\n\\n if not re.search(regex, typed):\\n\\n raise ValidationError(\\n message=\\\"Please input a positive or negative number.\\\"\\n )\\n elif self.validation_type == \\\"yes_no\\\":\\n regex = r\\\"^[yYnN]$\\\"\\n\\n if not re.search(regex, typed):\\n raise ValidationError(message=\\\"Please type y, n, Y or N.\\\")\\n elif self.validation_type == \\\"text_max_len\\\":\\n if len(typed) > 100:\\n raise ValidationError(message=\\\"La oración debe tener menos de 100 caracteres.\\\")\\n else:\\n raise ValidationError(message=\\\"Internal Error: Wrong validation type\\\")\",\n \"def validate(self):\\n ...\",\n \"def validate(self, value, clean=True):\\n pass\",\n \"def validate(self, value, clean=True):\\n pass\",\n \"def test_validate_input_good_input(self):\\r\\n _validate_input(self.dist_matrix_header, self.dist_matrix,\\r\\n self.mapping_header, self.mapping, \\\"Treatment\\\")\",\n \"def validate(self, *args, **kwargs):\\n raise NotImplementedError('Validate not implemented')\",\n \"def _validate_input(self):\\n\\n if is_empty(self.message) == True:\\n raise ValidationException(\\\"Message cannont be empty.\\\")\",\n \"def validate(self):\\n try:\\n self.values.clear()\\n self.values.append(int(self.e1.get()))\\n except ValueError:\\n messagebox.showwarning(\\n \\\"Bad input\\\",\\n \\\"Illegal values, please try again.\\\")\\n return False\\n\\n return True\",\n \"def validate(self):\\n raise NotImplementedError(\\\"validate function needs to be implemented for validators\\\")\",\n \"def __validate(self):\\n pass\",\n \"def __validateInput(self, fx: str, ux:str, lx:str) -> bool:\\r\\n # validate the input fields\\r\\n if fx == \\\"\\\" or ux == \\\"\\\" or lx == \\\"\\\":\\r\\n self.errorMessage = self.errorMessageMissingFields\\r\\n self.__showErrorMessage()\\r\\n return False\\r\\n\\r\\n # validate the limits\\r\\n self.lowerX = lx\\r\\n self.upperX = ux\\r\\n # check if numeric\\r\\n try:\\r\\n self.upperX = float(self.upperX)\\r\\n self.lowerX = float(self.lowerX)\\r\\n except:\\r\\n self.errorMessage = self.errorMessageLimitsNotNumeric\\r\\n self.__showErrorMessage()\\r\\n return False\\r\\n \\r\\n # check for inquality\\r\\n if self.lowerX > self.upperX:\\r\\n self.errorMessage = self.errorMessageLimitsNotOrdered\\r\\n self.upperXField.setText(str(self.lowerX))\\r\\n self.lowerXField.setText(str(self.upperX))\\r\\n self.lowerX, self.upperX = self.upperX, self.lowerX\\r\\n ##################################\\r\\n # validate and process the input function\\r\\n self.inputFunction = fx\\r\\n try:\\r\\n self.inputFunction = self.inputFunction.replace(\\\" \\\", \\\"\\\").replace(\\\"^\\\", \\\"**\\\").replace(\\\"sqrt\\\", \\\"np.sqrt\\\")\\r\\n self.inputFunction = self.inputFunction.replace(\\\"e**\\\", \\\"np.exp\\\").replace(\\\"log\\\", \\\"np.log\\\") \\r\\n self.inputFunction = self.inputFunction.replace(\\\"sin\\\", \\\"np.sin\\\").replace(\\\"cos\\\", \\\"np.cos\\\").replace(\\\"tan\\\", \\\"np.tan\\\")\\r\\n\\r\\n except:\\r\\n self.errorMessage = self.errorMessageNonValidFunction\\r\\n self.__showErrorMessage()\\r\\n return True\",\n \"def validate(self):\\n raise NotImplementedError()\",\n \"def getValidation(myInput):\\r\\n if myInput == \\\"\\\":\\r\\n print('You did not enter the number of bugs collected.')\\r\\n return -1\\r\\n elif myInput.isnumeric() == False:\\r\\n print('You entered a negative or a text value, please enter numerical digits only.')\\r\\n return -1\\r\\n elif myInput.isnumeric() == True:\\r\\n return int(myInput)\\r\\n else:\\r\\n print('There has been a read error, please reenter your number')\\r\\n return -1\",\n \"def validate(self):\\n raise NotImplementedError('validate method not implemented.')\",\n \"def input_validation(input_: str) -> bool:\\n return fullmatch('[1-9]', input_) is not None\",\n \"def valid(self, *args, **kwargs) -> Any:\\n pass\",\n \"def _validate_input(self):\\n\\n try:\\n expected_type(str, self.venue_id, \\\"venue_id\\\")\\n expected_type(datetime.datetime, self.timestamp_utc, \\\"timestamp_utc\\\")\\n\\n expected_type(VenueStreamType, self.measurement_type, \\\"measurement_type\\\")\\n\\n expected_type(int, self.number_of_people, \\\"number_of_people\\\")\\n\\n if self.measurement_type is VenueStreamType.ABSOLUTE:\\n if self.operator:\\n raise ValueError(\\\"The stream type for the venue doesn't allow passing an Event operator\\\")\\n\\n elif self.measurement_type is VenueStreamType.EVENT:\\n expected_type(EventStreamOperator, self.operator, \\\"operator\\\")\\n else:\\n raise ValueError(\\\"Unsupported member of the VenueStreamType enum\\\")\\n\\n if self.metadata:\\n expected_type(dict, self.metadata, \\\"metadata\\\")\\n\\n except Exception as ex:\\n raise ValueError(\\\"Validation of input failed. Reason: %s\\\" % str(ex))\",\n \"def is_valid(self, value):\\r\\n pass\",\n \"def test_validate_input_valid(self):\\n final_config = self.dtm1.validate_input('00001111')\\n nose.assert_equal(final_config[0], 'q4')\\n nose.assert_equal(str(final_config[1]), 'TMTape(\\\\'xxxxyyyy.\\\\')')\",\n \"def validate_user_input(user_input):\\n responses = ['t', 'r', 'q']\\n return user_input in responses\",\n \"def validate(self):\\n self._check_type()\",\n \"def valid(self):\\n pass\",\n \"def validateInputType(self, inputType):\\n raise NotImplementedError()\",\n \"def validate(self):\\r\\n return 1\",\n \"def validate(self):\\r\\n return 1\",\n \"def validate(self):\\r\\n return 1\",\n \"def validate(self):\\r\\n return 1\",\n \"def validate(self):\\r\\n return 1\",\n \"def _validate(self, value):\\n return True\",\n \"def __validate__(self):\",\n \"def checkUserInput(self):\\n prm = []\\n err = \\\"\\\"\\n guess = self.text.text()\\n items = str(guess).split(',')\\n if len(items) != 2:\\n err = \\\"Two parameters must be given\\\"\\n else:\\n for i in range(0, len(items)):\\n val = items[i].strip()\\n if not isNumber(val):\\n err = \\\"Parameter {0} is not numeric\\\".format(i + 1)\\n break\\n if float(val) < 0.0:\\n err = \\\"Parameter {0} is negative\\\".format(i + 1)\\n break\\n val = float(val)\\n if i == 0 and val > self.yspan:\\n err = \\\"minHeight is too large\\\"\\n break\\n if i == 1:\\n if val < self.xspan/self.npt or val > self.xspan/2:\\n err = \\\"minWidth is too large\\\"\\n break\\n prm.append(val)\\n if err:\\n errmsg = \\\"Incorrect input:\\\\n{0}\\\".format(err)\\n QtWidgets.QMessageBox.warning(self, self.title, errmsg)\\n return False\\n\\n # Store parameters values in global variables for the next call\\n global lastfilename, lastmph, lastmpw\\n lastfilename = self.pltw.filename\\n self.mph = lastmph = prm[0]\\n self.mpw = lastmpw = prm[1]\\n return True\",\n \"def validate(self):\\n return 1\",\n \"def is_valid(self):\\r\\n raise NotImplementedError\",\n \"def validate_input(input_name:str, input_value:any) -> str:\\n input_validate = ''\\n MONTHS = MONTHS_LIST + ['All']\\n DAYS = DAYS_LIST + ['All']\\n if not isinstance(input_value, str) or not input_value.strip() or input_value.isdigit():\\n input_validate = f'Warrning --> Please enter the {input_name} as it mentioned in the Question.\\\\n'\\n \\n elif input_name == 'city' and input_value.title().strip() not in ['Chicago', 'New York City', 'Washington']:\\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\\\n'\\n \\n elif input_name == 'filters' and input_value.lower().strip() not in ['both', 'day', 'month', 'none']:\\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\\\n'\\n \\n elif input_name == 'month' and input_value.title().strip() not in MONTHS:\\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\\\n'\\n \\n elif input_name == 'day' and input_value.title().strip() not in DAYS:\\n input_validate = f'Please enter the {input_name} as it mentioned in the Question.\\\\n'\\n return input_validate\",\n \"def test_validate_input_rejection(self):\\n with nose.assert_raises(exceptions.RejectionError):\\n self.dtm1.validate_input('000011')\",\n \"def _validate(self, instance, value):\",\n \"def validate_input(**kwargs):\\n for name, value in kwargs.items():\\n if name == \\\"x\\\" or name == \\\"y\\\":\\n if type(value) != int:\\n raise TypeError(\\\"{} must be an integer\\\".format(name))\\n elif value < 0:\\n raise ValueError(\\\"{} must be >= 0\\\".format(name))\\n else:\\n if type(value) != int:\\n raise TypeError(\\\"{} must be an integer\\\".format(name))\\n elif value <= 0:\\n raise ValueError(\\\"{} must be > 0\\\".format(name))\",\n \"def validate(cls, **kwargs: Any) -> None: # pragma no cover\",\n \"def validate(entry: _LexiconEntry) -> None:\\n _entry_has_required_fields(entry)\\n _entry_field_values_are_not_empty(entry)\\n _entry_field_values_does_not_contain_infix_whitespace(entry)\\n _entry_tag_is_valid(entry)\\n _entry_compound_annotation_is_valid(entry)\\n _entry_morphophonemics_annotation_is_valid(entry)\\n _entry_features_annotation_is_valid(entry)\\n _entry_has_required_features(entry)\\n _entry_required_features_are_valid(entry)\\n _entry_optional_features_are_valid(entry)\\n _entry_features_are_not_redundant(entry)\",\n \"def _check_for_incomplete_input(self):\\n pass\",\n \"def Validate(self):\\n raise NotImplementedError\",\n \"def validate(self, instance, value):\",\n \"def validate(self, instance, value):\",\n \"def validate(prop, string, node, match, entry_start, entry_end):\\n return True\",\n \"def __init__(self):\\n # assert(self.validate('61882613000194'))\\n # assert(self.validate('061882613000190'))\\n # assert(not self.validate('61882613000195'))\\n pass\",\n \"def is_valid(self): # -> bool:\\n ...\",\n \"def is_valid(self):\\n raise NotImplementedError\",\n \"def test_validate_input_rejection_invalid_symbol(self):\\n with nose.assert_raises(exceptions.RejectionError):\\n self.dtm1.validate_input('02')\",\n \"def validate_format(self):\\n raise NotImplementedError()\",\n \"def test_validate(self):\\n pass\",\n \"def test_non_required_validation(self):\\r\\n Text().validate('')\\r\\n Text().validate(None)\",\n \"def validate(self, value):\\n return True\",\n \"def __input_validator(msg):\\n\\n\\t\\tstatus = msg[\\\"status\\\"]\\n\\n\\t\\tif status == 1:\\n\\t\\t\\treturn status\\n\\t\\telif status == 0:\\n\\t\\t\\tprint(msg[\\\"body\\\"])\\n\\t\\telif status == -1:\\n\\t\\t\\tprint(\\\"Please enter something!\\\")\\n\\t\\telif status == -2:\\n\\t\\t\\tprint(\\\"Your command {} is invalid\\\".format(msg[\\\"verb\\\"]))\\n\\t\\telif status == -3:\\n\\t\\t\\tprint(\\\"No argument given after {}\\\".format(msg[\\\"verb\\\"]))\",\n \"def _check_value(self):\\n value = str(self._value_field.toPlainText())\\n if value=='': return True\\n ACCEPTABLES_CHARS = ('1', '2', '3', '4', '5', '6', '7', '8', '9', '0',\\n '.', ',', ';', ' ', '\\\\n', '-')\\n\\n for char in value:\\n if not char in ACCEPTABLES_CHARS:\\n return False\\n if Variable.is_acceptable_arg(value):\\n rows, columns = np.matrix(value).shape\\n return 1 <= rows <= 4 and 1 <= columns <= 4\\n else:\\n return False\",\n \"def __validate_input(self, request_data):\\n call_id = request_data.get(strings.CALL_ID_KEY)\\n request_timestamp = request_data.get(strings.TIMESTAMP_KEY)\\n request_start = request_data.get(strings.START_KEY)\\n validation = None\\n if call_id and request_timestamp and request_start is not None:\\n call_detail_query = CallDetail.objects.filter(call_id=call_id)\\n if call_detail_query:\\n if len(call_detail_query) < CALL_DETAILS_LIMIT:\\n stored_call_detail = call_detail_query[0]\\n if isinstance(request_start, str):\\n if request_start in strings.TRUE_VALUES:\\n request_start = True\\n else:\\n request_start = False\\n if stored_call_detail.start == request_start:\\n validation = {strings.INPUT_ERROR_KEY:\\n strings.START_END_ERROR}\\n stored_timestamp = standardize_date(\\n stored_call_detail.timestamp,\\n strings.COMPLETE_DATE_PATTERN)\\n request_timestamp = standardize_date(request_timestamp,\\n strings.\\n COMPLETE_DATE_PATTERN)\\n if stored_timestamp == request_timestamp:\\n validation = {strings.INPUT_ERROR_KEY:\\n strings.EQUAL_TIMESTAMPS_ERROR}\\n if stored_call_detail.start and not request_start:\\n if stored_timestamp > request_timestamp:\\n validation = {strings.INPUT_ERROR_KEY:\\n strings.SOONER_END_ERROR}\\n elif not stored_call_detail.start and request_start:\\n if stored_timestamp < request_timestamp:\\n validation = {strings.INPUT_ERROR_KEY:\\n strings.SOONER_END_ERROR}\\n else:\\n validation = {strings.INPUT_ERROR_KEY:\\n strings.CALL_LIMIT_ERROR}\\n\\n return validation\",\n \"def input_check(self):\\n\\n if self.species == 'He': assert self.line_model == 'voigt'\\n n_upper_range, e_dens_range, temp_range, b_field_range = get_param_ranges(self.line_model)\\n\\n if np.isnan(n_upper_range).sum() <= 1:\\n assert (self.n_upper in range(n_upper_range[0], n_upper_range[1]))\\n if np.isnan(e_dens_range).sum() <= 1:\\n assert (e_dens_range[0] <= self.e_dens <= e_dens_range[1])\\n if np.isnan(temp_range).sum() <= 1:\\n assert (temp_range[0] <= self.temp <= temp_range[1])\\n if np.isnan(b_field_range).sum() <= 1:\\n assert (b_field_range[0] <= self.b_field <= b_field_range[1])\",\n \"def check_all_user_inputs_valid(self):\\n self.check_RNN_layers_valid()\\n self.check_activations_valid()\\n self.check_embedding_dimensions_valid()\\n self.check_initialiser_valid()\\n self.check_y_range_values_valid()\\n self.check_return_final_seq_only_valid()\",\n \"def validate(self):\\r\\n # Check KeyError\\r\\n try:\\r\\n self.fields[\\\"product_name_fr\\\"]\\r\\n self.fields[\\\"generic_name\\\"]\\r\\n self.fields[\\\"url\\\"]\\r\\n self.fields[\\\"nutrition_grade_fr\\\"]\\r\\n self.fields[\\\"categories\\\"]\\r\\n self.fields[\\\"stores\\\"]\\r\\n self.fields[\\\"brands\\\"]\\r\\n except KeyError:\\r\\n return False\\r\\n\\r\\n # Check empty field and lenght of generic_name\\r\\n for key, value in self.fields.items():\\r\\n if value == '':\\r\\n return False\\r\\n break\\r\\n if key == \\\"generic_name\\\":\\r\\n if len(value) > 255:\\r\\n return False\\r\\n\\r\\n try:\\r\\n self.categories = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"categories\\\"], 100)\\r\\n self.stores = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"stores\\\"], 45)\\r\\n self.brands = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"brands\\\"], 45)\\r\\n self.category_index = self.categories.index(self.category)\\r\\n except KeyError:\\r\\n return False\\r\\n except ValueError:\\r\\n return False\\r\\n except AttributeError:\\r\\n self.errors += 1\\r\\n print(self.errors)\\r\\n return False\",\n \"def validate(self):\\n stguess = self.text.toPlainText()\\n if not self.pkfitdlg.checkUserInput(stguess):\\n return\\n self.stguess = stguess\\n self.accept()\",\n \"def validate(self):\\n\\n def checknum(val):\\n \\\"\\\"\\\"\\n checks if the string entered is a nonnegative number\\n\\n Args:\\n\\n val: the string to check if is a number\\n\\n Returns:\\n\\n True if the string is a nonnegative number\\n\\n \\\"\\\"\\\"\\n\\n if len(val) == 0:\\n return False\\n\\n for i in range(len(val)):\\n if not val[i].isdigit():\\n return False\\n\\n return True\\n\\n # get user entry from inputs\\n width = self.width_input.get()\\n height = self.height_input.get()\\n mines = self.mines_input.get()\\n\\n # check that all entries are numbers\\n if not checknum(width):\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Board width must be a number!\\\")\\n elif not checknum(height):\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Board height must be a number!\\\")\\n elif not checknum(mines):\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Mine count must be a number!\\\")\\n else:\\n\\n width = int(width)\\n height = int(height)\\n mines = int(mines)\\n\\n # if they are all numbers, check that they are in bounds\\n if width < 2:\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Too small of a width\\\")\\n elif width > 20:\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Too large of a width\\\")\\n elif height < 2:\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Too small of a height\\\")\\n elif height > 20:\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Too large of a height\\\")\\n elif mines >= width * height:\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Too many mines!\\\")\\n elif mines < 1:\\n messagebox.showerror(\\\"Entry Error\\\", \\\"Too few mines!\\\")\\n else:\\n # hide setup window and tell executive instance to start game\\n self.setup_window.withdraw()\\n self.exec.setup(width, height, mines)\",\n \"def validate(self):\\n # rVal = False\\n # return rVal\\n return True\",\n \"def __check_inputs__(self):\\n # | - __check_inputs__\\n # #####################################################################\\n stop_mode = self.stop_mode\\n stop_num_generations = self.stop_num_generations\\n # #####################################################################\\n\\n if stop_mode == \\\"num_generations\\\":\\n mess_i = \\\"stop_mode='num_generations', \\\\\\n Must pass int to 'stop_num_generations'\\\"\\n assert type(stop_num_generations) == type(1), mess_i\\n #__|\",\n \"def test_is_valid():\\n # Expected input.\\n assert make_european.is_valid('12/31/2015:john.a.graham@gmail.com')\\n # Check 'at' typo.\\n assert not make_european.is_valid('12/31/2015:john.a.grahamgmail.com')\\n # Check colon typo.\\n assert not make_european.is_valid('12/31/2015::john.a.graham@gmail.com')\\n # Check forward slash replacement.\\n assert not make_european.is_valid('12.31.2015:john.a.graham@gmail.com')\\n # Check order.\\n assert not make_european.is_valid('john.a.graham@gmail.com:12/31/2015')\",\n \"def _is_valid(self):\\n self._is_allows_valid()\\n self._is_denies_valid()\",\n \"def validate_result(self):\\n raise NotImplementedError\",\n \"def _validate_params(self):\\n raise NotImplementedError('Must be implemented in subclasses.')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8256197","0.8026426","0.7914555","0.7372203","0.7364157","0.73189014","0.73189014","0.71831155","0.71817976","0.7078171","0.7075113","0.7054536","0.69421256","0.69395375","0.69082284","0.69082284","0.69082284","0.69082284","0.69082284","0.69082284","0.69082284","0.69082284","0.6903115","0.68981475","0.68829185","0.68712074","0.68664205","0.68664205","0.6832605","0.6826368","0.68239254","0.6818242","0.67918557","0.67759156","0.6773342","0.6769238","0.6769137","0.6735501","0.6735501","0.6730767","0.6730262","0.6714094","0.6709741","0.6690288","0.66653","0.6658711","0.6624492","0.6613381","0.6606166","0.65905076","0.6587446","0.6584235","0.6574786","0.65712976","0.6562435","0.6558467","0.65433985","0.6540048","0.65332925","0.65332925","0.65332925","0.65332925","0.65332925","0.65217805","0.6514309","0.6498754","0.64973897","0.64970016","0.6493305","0.6487793","0.64753854","0.6466516","0.6445149","0.6442289","0.64379865","0.6418079","0.64102197","0.64102197","0.63884866","0.63850397","0.63840604","0.6376482","0.63742167","0.6371945","0.63695353","0.6361775","0.6348556","0.63394856","0.63378876","0.63373464","0.6320875","0.63045233","0.629116","0.6289452","0.6283581","0.62824804","0.62813956","0.6274982","0.62596303","0.6256742","0.6248285"],"string":"[\n \"0.8256197\",\n \"0.8026426\",\n \"0.7914555\",\n \"0.7372203\",\n \"0.7364157\",\n \"0.73189014\",\n \"0.73189014\",\n \"0.71831155\",\n \"0.71817976\",\n \"0.7078171\",\n \"0.7075113\",\n \"0.7054536\",\n \"0.69421256\",\n \"0.69395375\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.69082284\",\n \"0.6903115\",\n \"0.68981475\",\n \"0.68829185\",\n \"0.68712074\",\n \"0.68664205\",\n \"0.68664205\",\n \"0.6832605\",\n \"0.6826368\",\n \"0.68239254\",\n \"0.6818242\",\n \"0.67918557\",\n \"0.67759156\",\n \"0.6773342\",\n \"0.6769238\",\n \"0.6769137\",\n \"0.6735501\",\n \"0.6735501\",\n \"0.6730767\",\n \"0.6730262\",\n \"0.6714094\",\n \"0.6709741\",\n \"0.6690288\",\n \"0.66653\",\n \"0.6658711\",\n \"0.6624492\",\n \"0.6613381\",\n \"0.6606166\",\n \"0.65905076\",\n \"0.6587446\",\n \"0.6584235\",\n \"0.6574786\",\n \"0.65712976\",\n \"0.6562435\",\n \"0.6558467\",\n \"0.65433985\",\n \"0.6540048\",\n \"0.65332925\",\n \"0.65332925\",\n \"0.65332925\",\n \"0.65332925\",\n \"0.65332925\",\n \"0.65217805\",\n \"0.6514309\",\n \"0.6498754\",\n \"0.64973897\",\n \"0.64970016\",\n \"0.6493305\",\n \"0.6487793\",\n \"0.64753854\",\n \"0.6466516\",\n \"0.6445149\",\n \"0.6442289\",\n \"0.64379865\",\n \"0.6418079\",\n \"0.64102197\",\n \"0.64102197\",\n \"0.63884866\",\n \"0.63850397\",\n \"0.63840604\",\n \"0.6376482\",\n \"0.63742167\",\n \"0.6371945\",\n \"0.63695353\",\n \"0.6361775\",\n \"0.6348556\",\n \"0.63394856\",\n \"0.63378876\",\n \"0.63373464\",\n \"0.6320875\",\n \"0.63045233\",\n \"0.629116\",\n \"0.6289452\",\n \"0.6283581\",\n \"0.62824804\",\n \"0.62813956\",\n \"0.6274982\",\n \"0.62596303\",\n \"0.6256742\",\n \"0.6248285\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75799,"cells":{"query":{"kind":"string","value":"Connects to postgres db"},"document":{"kind":"string","value":"def connect_to_postgres():\n try:\n conn = psycopg2.connect(database=os.environ['POSTGRES_DATABASE'], user=os.environ['POSTGRES_USER'],\n password=os.environ['POSTGRES_PASSWORD'], host=os.environ['POSTGRES_HOST'])\n return conn\n except psycopg2.Error as err:\n logging.exception('Unable to connect to postgres db %s', err)\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 connect_to_postgres():\n postgres_creds = os.environ.get(\"POSTGRES_CREDS\")\n if not postgres_creds:\n postgres_creds = get_secret(\"POSTGRES_CREDS\")\n print(\"Using {} Postgres database credentials\".format(postgres_creds))\n postgres_url = os.environ.get(\"POSTGRES\")\n if not postgres_url:\n postgres_url = get_secret(\"POSTGRES\")\n return connect(postgres_url)","def connect_to_db():\n return pg.connect(DB_CONN_STRING)","def connect_postgress():\n conn = psycopg2.connect(\n host=\"postgres-data\",\n port=5432,\n database=\"sc\",\n user=\"postgres\",\n password=\"postgres\")\n return conn","def connect():\n connection = psycopg2.connect(host='localhost', database='huwebshop', user='postgres', password='Xplod_555')\n return connection","def connect():\r\n params = config()\r\n print('Connecting to the PostgreSQL database...')\r\n global conn\r\n conn = psycopg2.connect(**params)","def connect(self):\n self.conn = psycopg2.connect(database=self.config['dbname'], user=self.config['dbuser'], password=self.config['dbpassword'], host=self.config['dbhost'])","def connect_to_postgres():\n con = pg2.connect(host='this_postgres', user='postgres', database='postgres',password='example')\n return con, con.cursor()","def connect_db():\n try:\n conn = psycopg2.connect(\"dbname='gis' user='postgres' host='localhost' password='postgres'\")\n except:\n print(\"I am unable to connect to the database\")\n print(\"Connencted\")\n return conn","def connect_db(self):\n # connect to database\n connection = psycopg2.connect(\n database = \"postgres\",\n user = \"postgres\",\n password = os.getenv('DB_PASSWORD'),\n host = \"groalives.cvslmiksgnix.us-east-1.rds.amazonaws.com\",\n port = '5432'\n )\n # create cursor that is used throughout\n\n try:\n self.cursor_dog = connection.cursor()\n print(\"Connected!\")\n except:\n print(\"Connection problem chief!\")","def connect_to_db():\n try:\n conn = psycopg2.connect(database='postgres',\n user='postgres',\n password='docker',\n host='localhost',\n port='5432')\n print('Opened database successfully')\n return conn\n except Exception as e:\n print(str(e))\n raise e","def connect():\n return psycopg2.connect(\"dbname=forum\")","def connect():\n\t\n\tglobal _conn\n\turlparse.uses_netloc.append('postgres')\n\turl = urlparse.urlparse(config.get_database())\n\ttry:\n\t\t_conn = psycopg2.connect(\n\t\tdatabase = url.path[1:],\n\t\tuser = url.username,\n\t\tpassword = url.password,\n\t\thost = url.hostname,\n\t\tport = url.port)\n\n\t\tprint('Connected to database!')\n\t\treturn True\n\texcept:\n\t\tprint('Connection to database failed.')\n\t\texit()","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n return psycopg2.connect(\"dbname=tournament\")","def __connect(host):\n global pg_pool\n pg_config['host'] = host\n pg_pool = SimpleConnectionPool(1, 1, **pg_config)","def connect_db():\n debug(\"Connecting to DB.\")\n conn = psycopg2.connect(host=IP_ADDR, user=\"postgres\", password=\"rhodes\"\n , dbname=\"exchangedb\", cursor_factory=psycopg2.extras.DictCursor)\n return conn","def _connect_to_db(self) -> None:\n self._connection = psycopg2.connect(database=self.dbname,\n user=self.dbuser, password=self.dbpassword,\n host=self.dbhost, port=str(self.dbport))","def connect_db():\n conn = None\n try:\n # read connection parameters\n params = config()\n\n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n\t\t\n # create a cursor\n cur = conn.cursor()\n \n\t# execute a statement\n print('PostgreSQL database version:')\n cur.execute('SELECT version()')\n\n # display the PostgreSQL database server version\n db_version = cur.fetchone()\n print(db_version)\n \n\t# close the communication with the PostgreSQL\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()\n print('Database connection closed.')","def connect():\n\n try:\n return psycopg2.connect(\n dbname = os.environ['DATABASE_NAME'],\n user = os.environ['DATABASE_USER'],\n password = os.environ['DATABASE_PASSWORD'],\n host = ['DATABASE_HOST'],\n port = ['DATABASE_PORT']\n )\n\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)","def connect_to_db(self):\n\t\t# connection = psycopg2.connect(database=config.database, user=config.user,password = config.password)\n\t\tconnection = psycopg2.connect(database=config.database, user=config.user)\n\t\treturn connection","def psycopg2_connect(tunnel):\n\n # Create a database connection\n connection = psycopg2.connect(\n database=config.dbreddit['dbname'],\n user=config.dbreddit['user'],\n password=config.dbreddit['password'],\n host=tunnel.local_bind_host,\n port=tunnel.local_bind_port,\n )\n connection.set_session(autocommit=True)\n print('Database connection successful')\n return connection","def connect():\n try:\n return psycopg2.connect(\"dbname=tournament\")\n except psycopg2.Error:\n print \"Cannot connect to the database\"","def _dbconnect(self):\n user = mm_cfg.STORM_MEMBER_DB_USER\n password = mm_cfg.STORM_MEMBER_DB_PASS\n host = mm_cfg.STORM_MEMBER_DB_HOST\n dbname = mm_cfg.STORM_MEMBER_DB_NAME\n\n\n db = 'postgres://'+user+':'+password+'@'+host+'/'+dbname\n return create_database(db)","def connect_db():\n conn = psycopg2.connect(\n database = app.config['DATABASE'],\n host = app.config['HOST'],\n port = app.config['PORT'])\n conn.autocommit = True\n return conn","def connect(db: str=None) -> Connection:\n return pgsql.connect(\"postgres.internal\", db or \"sysadmins\")","def connect():\n try:\n return psycopg2.connect(\"dbname=tournament\")\n except:\n print(\"Connection failed\")","def connect():\n # returns the connection object to tournament\n # database from PostgreSQL\n return psycopg2.connect(\"dbname=tournament\")","def connect():\n conn = None\n try:\n # read connection parameters\n params = config()\n\n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n\n # create a cursor\n cur = conn.cursor()\n\n # execute a statement\n print('PostgreSQL database version:')\n cur.execute('SELECT version()')\n\n # display the PostgreSQL database server version\n db_version = cur.fetchone()\n print(db_version)\n\n # close the communication with the PostgreSQL\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()\n print('Database connection closed.')","def connect(self):\n\t\t# PostgreSQL PyPgSQL\n\t#\tcp = adbapi.ConnectionPool(\"pyPgSQL.PgSQL\", database=\"test\")\n\t\t# MySQL\n\t\tself.dbpool = adbapi.ConnectionPool('MySQLdb',\n\t\t\thost = self.settings.get('hostname', 'localhost'),\n\t\t\tport = self.settings.get('port', 3306),\n\t\t\tdb = self.settings.get('database'),\n\t\t\tuser = self.settings.get('username'),\n\t\t\tpasswd = self.settings.get('password'),\n\t\t\tcursorclass = MySQLdb.cursors.DictCursor,\n\t\t\tcharset = 'utf8',\n\t\t\tuse_unicode = True,\n\t\t)","def pq_connect(self):\n\t\ttry:\n\t\t\tself.connection = psycopg2.connect(**self.pq_params)\n\t\texcept psycopg2.DatabaseError as error:\n\t\t\tprint(error)","def connect_db():\n db = psycopg2.connect(\n dbname=app.config['DBNAME'],\n user=app.config['DBUSER'],\n password=app.config['DBPASSWORD'],\n host=app.config['DBHOST'],\n port=app.config['DBPORT']\n )\n return db","def connect():\n try:\n conn = psycopg2.connect(\"dbname=tournament\")\n return conn\n except:\n print \" I am unable to connect to the database\"","def connect(params_dic):\n conn = None\n try:\n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params_dic)\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n sys.exit(1) \n return conn","def create_connection(self):\n try:\n self.conn = psycopg2.connect(host=self.host, port=self.port, database=self.database, user=self.user, password=self.password)\n\n except:\n print(\"Unable to connect to the database. Please check your options and try again.\")\n exit()","def connectDB(self): \n #connect to the database\n try:\n print(self.pg_dbname)\n self.conn = psycopg2.connect(\"dbname=%s user=%s password=%s host=%s port=%s\" % (self.pg_dbname, self.pg_username, self.pg_password, self.pg_host, self.pg_port))\n print(\"connected!\")\n except psycopg2.Error as e:\n print(\"I am unable to connect to the database\")\n print(e)\n\n #define cursor\n self.cur = self.conn.cursor()","def psg_connect(self, conf=conf):\r\n try:\r\n self.connection = psycopg2.connect(user=self.conf[\"test_env\"][\"user\"],\r\n password=self.conf[\"test_env\"][\"user\"],\r\n host=self.conf[\"test_env\"][\"host\"],\r\n port=self.conf[\"test_env\"][\"port\"],\r\n database=self.conf[\"test_env\"][\"defaultDB\"])\r\n self.cursor = self.connection.cursor()\r\n print(\"PostgreSQL server information: \",self.connection.get_dsn_parameters(), \"\\n\")\r\n self.cursor.execute(\"SELECT version();\")\r\n record = self.cursor.fetchone()\r\n print(\"You are connected to - \", record, \"\\n\")\r\n\r\n except (Exception, Error) as error:\r\n print(\"Error while connecting to PostgreSQL\", error)\r\n finally:\r\n return self.cursor","def connect_to_db():\n\n host = 'tweetsdb.cqfliozhpzqt.us-west-2.rds.amazonaws.com:5432'\n dbname = 'tweetsdb'\n\n user = ''\n pswd = ''\n\n with open('db_credentials', 'r') as f:\n credentials = f.readlines()\n f.close()\n \n user = credentials[0].rstrip()\n pswd = credentials[1].rstrip()\n\n connection = psycopg2.connect(\n database=dbname,\n user=user,\n password=pswd,\n host=host.split(':')[0],\n port=5432)\n\n return connection","def connect(db_ini=None):\n conn = None\n curr = None\n try:\n # read connection parameters\n params = PostgresStuff.load_config(db_ini)\n print (params)\n\n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n\n # create a cursor\n cur = conn.cursor()\n\n # execute a statement\n print('PostgreSQL database version:')\n cur.execute('SELECT version()')\n\n # display the PostgreSQL database server version\n db_version = cur.fetchone()\n print(db_version)\n\n # close the communication with the PostgreSQL\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n return conn","def connect():\n try:\n return psycopg2.connect(\"dbname=tournament\")\n\n except:\n print('An error occured connecting to the database \"tournament\".')\n sys.exit()","def connect_db(autocommit = True):\n uses_netloc.append('postgres')\n url = urlparse(DATABASE_URL)\n\n try:\n conn = connect(\n database = url.path[1:],\n user = url.username,\n password = url.password,\n host = url.hostname,\n port = url.port\n )\n conn.autocommit = autocommit\n return conn\n\n except Error as e:\n print(e)","def connect():\n cf = configparser.ConfigParser()\n cf.read(\"config.ini\")\n try:\n connection = psycopg2.connect(user=cf.get('postgres', 'user'),\n password=cf.get('postgres', 'password'),\n host=cf.get('postgres', 'host'),\n port=cf.get('postgres', 'port'),\n database=cf.get('postgres', 'database')\n )\n return connection\n except (Exception, psycopg2.Error) as error:\n print(\"Error while connecting to PostgresSQL\", error)","def pg_get_conn(database=\"fakenews\", user=\"fakenews\", password=\"fnd\"):\n try:\n conn = psycopg2.connect(database=\"fakenews\",\n user=\"fakenews\", password=\"fnd\", host='localhost', port='5432')\n return conn\n except Exception as e:\n print(str(e))","def connect(logger):\n\n global _conn\n urllib.parse.uses_netloc.append(\"postgres\")\n url = urllib.parse.urlparse(DATABASE_URL)\n try:\n _conn = psycopg2.connect(\n database = url.path[1:],\n user = url.username,\n password = url.password,\n host = url.hostname,\n port = url.port)\n return True\n except:\n logger.critical(\"Connection to database failed. Exiting...\")\n exit(1)","def cli(ctx, db, user, password, verbose):\n click.echo('Connecting %s@%s' % (user, db))\n logging.basicConfig(level=logging.DEBUG if verbose else logging.INFO, format='%(levelname)s: %(message)s')\n ctx.obj = psycopg2.connect(database=db, user=user, password=password)","def connect():\n conn = None\n try:\n # read connection parameters\n params = config(filename='./configs/database.ini',\n section='postgresql')\n\n # connect to the PostgreSQL server\n logger.info('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n\n # create a cursor\n cur = conn.cursor()\n\n # execute a statement\n logger.debug('PostgreSQL database version:')\n cur.execute('SELECT version()')\n\n # display the PostgreSQL database server version\n db_version = cur.fetchone()\n logger.debug(db_version)\n\n # close the communication with the PostgreSQL\n cur.close()\n return conn\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n return None","def connect(host, database, user, password, port):\n conn = None\n try:\n # read connection parameters\n params = dict()\n params['host'] = host\n params['database'] = database\n params['user'] = user\n params['password'] = password\n params['port'] = port\n\n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psy.connect(**params)\n conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)\n\n\n except (Exception, psy.DatabaseError) as error:\n print(error)\n print(\"Erreur\")\n sys.exit()\n\n return conn","def connect(self):\n\n self._dbcon = psycopg2.connect(\n database=self._dbname, host=self._host, port=self._port, user=self._username, password=self._password)\n self.result_as_dict(self._result_as_dict)","def connect_database(dbname, user, pwd, host_ip=\"127.0.0.1\", port=\"5432\"):\n \n # connect to database\n conn = psycopg2.connect(f\"host={host_ip} port={port} dbname={dbname} user={user} password={pwd}\")\n cur = conn.cursor()\n \n return cur, conn","def _connect(self):\n try:\n self.conn = psycopg2.connect(\n host=self.host,\n user=self.username,\n password=self.password,\n port=self.port,\n dbname=self.dbname\n )\n except psycopg2.DatabaseError as e:\n logger.error(e)\n raise e\n logger.info('Connection opened successfully.')","def connect_to_db(config: Config):\n logger.info(f'resolving db host from {config.db_host} DNS: {config.flyio_dns}')\n db_host_ip = resolve_db_host(config)\n logger.info(f'resolved db host ip: {db_host_ip}')\n\n return psycopg2.connect(\n # NOTE: for demo, we use database template1\n (f\"dbname='template1' \"\n f\"user='{config.db_user}' \"\n f\"host='{db_host_ip}' \"\n f\"password='{config.db_password}'\")\n )","def initialize_db(password):\n print('Starting...')\n global conn\n\n try:\n conn = psycopg2.connect(database=\"postgres\",#default databse in postgreSQL\n user=\"postgres\",#username of postgreSQL\n password=password,#password of that user in postgreSQL\n host=\"127.0.0.1\",\n port=\"5432\")\n except psycopg2.OperationalError as exception:\n messagebox.showerror(\"password\",\"PASSWORD IS INCORRECT. TRY AGAIN...\")\n sys.exit()","def connect_psycopg2():\n server = yaml.load(open('config.yaml', 'r'))\n\n server = server['servers'][229]\n\n host = server['host']\n database = server['database']\n user = server['user']\n password = server['password']\n\n conn = psycopg2.connect(\n host=host, port=5432, database=database,\n user=user, password=password)\n return conn","def connect(self):\n # use our connection values to establish a connection\n self.conn = psycopg2.connect(self.connectString)\n # create a psycopg2 cursor that can execute queries\n self.cursor = self.conn.cursor()\n connectionStringEnding = self.databaseName + ' at ' + self.databaseHost\n \"\"\"\n if (self.cursor):\n print('connection made to ' + connectionStringEnding)\n else:\n print('connection problem with ' + connectionStringEnding)\"\"\"","def postgresql():\n icanhaz.postgres.server()\n\n icanhaz.postgres.user('pguser', 'foo')\n assert fabtools.postgres.user_exists('pguser')\n\n icanhaz.postgres.database('pgdb', 'pguser')\n assert fabtools.postgres.database_exists('pgdb')","def connect_db():\n\n # use nullpool because pooling breaks unit tests and we don't need the performance\n return sqlalchemy.create_engine(\n 'postgresql://' +\n app.config[ 'DATABASE_USER' ] + ':' +\n app.config[ 'DATABASE_PASSWORD' ] + '@' +\n app.config[ 'DATABASE_HOST' ] + '/' +\n app.config[ 'DATABASE' ],\n poolclass = sqlalchemy.pool.NullPool\n )","def make_postgres(host, port, database, user, password):\n if host is None:\n return NoDb()\n return psycopg2.connect(host=host, port=port, database=database, user=user, password=password)","def connect(path=''):\n conn = None\n try:\n # read connection parameters\n params = utils.config(path)\n\n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n\n # create a cursor\n cur = conn.cursor()\n\n # execute a statement\n print('PostgreSQL database version:')\n cur.execute('SELECT version()')\n\n # display the PostgreSQL database server version\n db_version = cur.fetchone()\n print(db_version)\n\n # close the communication with the PostgreSQL\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()\n print('Database connection closed.')","def sqlconnect(host, database, user, password):\r\n # Assert valid connection\r\n conn = pg.connect(host = host, database=database, user = user, password = password)\r\n cur = conn.cursor()\r\n return cur","def startConnection(self):\n try:\n self.conn = psycopg2.connect(\"dbname='library' user='postgres' host='localhost' password='Codechef'\")\n # self.conn = psycopg2.connect(\"dbname='library' user='postgres' host='localhost' password='Codechef'\")\n # self.conn = psycopg2.connect(\"dbname='db_b130974cs' user='postgres' host='localhost' password='Codechef'\")\n except:\n print \"I am unable to connect to the database\"\n print \"connected to database...\"\n self.schema = SchemaGraph(self.conn)","def connect_db():\n \n con = None\n try:\n \n # connect to the PostgreSQL server\n \n con = psycopg2.connect(host=config_esql.server,\n database=config_esql.database,\n user=config_esql.database,\n password=config_esql.password)\t\n # create a cursor\n return (con)\n \n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n print ('Could not connect to DB. Exiting..')\n sys.exit(2)","def create_connection(self):\r\n dbname = \"dbname = \"+\"'\"+self.dbname+\"' \"\r\n user = \"user = \"+\"'\"+self.user+\"' \"\r\n host = \"host = \"+\"'\"+self.host+\"' \"\r\n password = \"password = \"+\"'\"+self.password+\"' \"\r\n connection_info = dbname + user + host + password\r\n try:\r\n conn = psycopg2.connect(connection_info)\r\n except psycopg2.Error:\r\n print(\"Unable to connect to DB\")\r\n return conn","def connect(params_dic):\n conn = None\n try:\n # connect to the PostgreSQL server\n logger.info('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params_dic)\n except (Exception, psycopg2.DatabaseError) as error:\n logger.error(error)\n return None\n # sys.exit(1)\n logger.info(\"Connection successful\")\n return conn","def create_connection():\n config = ConfigParser.ConfigParser()\n config.read(\"config.ini\")\n localhost = config.get('postgresql','localhost')\n database = config.get('postgresql','database')\n username = config.get('postgresql','username')\n password = config.get('postgresql','password')\n port = config.get('postgresql', 'port')\n conn_string = \"host='\" + localhost + \"' port='\" + port + \"' dbname='\" + database + \"' user='\" + username + \"' password='\" + password + \"'\"\n print(\"Connecting to database...\")\n conn = psycopg2.connect(conn_string)\n print(\"Connection established!\")\n return(conn)","def connect(self):\n try:\n connect = psycopg2.connect(\"\"\"\n dbname=ovdata_db \n user=larsstegman \n host=localhost \n password=password\n \"\"\")\n return connect\n except Exception as e:\n return ConnectionError()","def create_connection():\r\n\r\n conn = None\r\n try: \r\n # Connect to database\r\n conn = psycopg2.connect(user=data[\"user\"],\r\n password=data[\"pass\"],\r\n host=data[\"host\"],\r\n port=data[\"port\"],\r\n database=data[\"db\"]) \r\n return conn\r\n except (Exception, Error) as error:\r\n print(\"Error while connecting to PostgreSQL: \", error)\r\n return conn","def createConnection(self):\r\n conn_string = \"host='{}' dbname='{}' user='{}' password='{}' port={}\".format(\r\n self.host, self.database, self.user, self.password, self.port)\r\n return psycopg2.connect(conn_string)","def connect():\n\n conn = None\n try:\n # read connection parameters\n params = config()\n\n # connect to the PostgreSQL server\n print('Connection to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n\n # create a cursor\n cur = conn.cursor()\n\n print('PostgreSQL database version:')\n cur.execute('SELECT version()')\n\n db_version = cur.fetchone()\n print(db_version)\n\n # Close the connection\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print('Unable to connect!\\n{0}').format(error)\n sys.exit(1)\n \n return conn","def conn(self):\n self.cnx = psycopg2.connect(**self.dbConfig) \n self.cur = self.cnx.cursor()","def connect():\n conn = None\n try:\n print \"Connecting to ssm database...\"\n conn = psycopg2.connect(host=\"\",\n database=\"\",\n user=\"\",\n port=\"\")\n except (Exception, psycopg2.DatabaseError) as error:\n print error\n return conn","def connect():\n try:\n if not hasattr(connect, \"conn\"):\n connect.conn = pg.connect(\n user=DB_USER,\n password=DB_PASSWORD,\n host=DB_HOST,\n port=DB_PORT,\n database=DB_NAME)\n connect.conn.set_client_encoding('UTF8')\n elif connect.conn is None or connect.conn.closed != 0:\n connect.conn = pg.connect(\n user=DB_USER,\n password=DB_PASSWORD,\n host=DB_HOST,\n port=DB_PORT,\n database=DB_NAME)\n connect.conn.set_client_encoding('UTF8')\n return connect.conn\n except pg.OperationalError as pgoe:\n logging.error(pgoe.message)\n raise\n except Exception:\n logging.error(\"Failed to connect to database.\")\n raise","def _postgresql(self):\n logging.debug(\"Configured to use Postgresql for a database\")\n try:\n import pypostgresql\n except ImportError:\n print(WARN + \"You must install 'pypostgresql'\")\n os._exit(1)\n db_host, db_name, db_user, db_password = self._db_credentials()\n postgres = \"postgresql+pypostgresql://%s:%s@%s/%s\" % (\n db_user,\n db_password,\n db_host,\n db_name,\n )\n if self._test_connection(postgres):\n return postgres\n else:\n logging.fatal(\"Cannot connect to database with any available driver\")\n os._exit(1)","def createConnection(user, password, server, port, db):\n Database.globalUser = user\n try:\n Database.connection = psycopg2.connect(user=user,\n password=password,\n host=server,\n port=port,\n database=db)\n return Database.connection\n except (Exception, psycopg2.Error) as error:\n print(\"Error conectando a PostgreSQL\", error)\n return None","def get_postgredb_connection(properties):\n try:\n return postgreconnect(host=properties[0],\n database=properties[1],\n user=properties[2],\n password=properties[3])\n except postgreError as error:\n print(\"Error while connecting to PostgreSQL Database\", error)","def doconn(desc):\n debug(\"doconn({},{},{})\".format(desc['host'], desc['user'], desc['database']))\n # debug(\"doconn({},{},{},{})\".format(desc['host'], desc['user'], desc['database'], desc['password']))\n ret = psycopg2.connect(**desc)\n ret.autocommit = True\n return ret","def connect_trades_db():\n\n conn = psycopg2.connect(os.environ.get('TRADES_DATABASE_URL'))\n\n return conn","def dbConnect(database, user, password, host, autocommit = True):\n trace(\"connecting to database %s as %s on host %s\" % (database, user, host))\n con =psycopg2.connect(database = database, user = user, password = password, host = host)\n con.autocommit = autocommit\n return con","def databaseConnect():\n connection = None\n try:\n params = config()\n connection = psycopg2.connect(**params)\n cursor = connection.cursor()\n\n \"\"\"for query in createTables():\n cursor.execute(query)\"\"\"\n [cursor.execute(query) for query in createTables()]\n\n cursor.close()\n connection.commit()\n\n except (Exception, psycopg2.DatabaseError) as errorDescription:\n print('Error description:\\n', errorDescription)\n finally:\n if connection is not None:\n connection.close()","def database_connect():\n # Read the config file\n config = configparser.ConfigParser()\n config.read('config.ini')\n if 'database' not in config['DATABASE']:\n config['DATABASE']['database'] = config['DATABASE']['user']\n\n # Create a connection to the database\n connection = None\n try:\n # Parses the config file and connects using the connect string\n connection = pg8000.connect(database=config['DATABASE']['database'],\n user=config['DATABASE']['user'],\n password=config['DATABASE']['password'],\n host=config['DATABASE']['host'])\n except pg8000.OperationalError as operation_error:\n print(\"\"\"Error, you haven't updated your config.ini or you have a bad\n connection, please try again. (Update your files first, then check\n internet connection)\n \"\"\")\n print(operation_error)\n return None\n\n # return the connection to use\n return connection","def connect_to_db(app):\n\n # Configure to use my PstgreSQL database\n # ***come back to this to make sure db name is correct ***\n app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql:///legislature'\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n db.app = app\n db.init_app(app)","def connect(self):\n if self.connection is not None:\n logger.info(\" connection: %s \" % (self.connection is not None))\n return self.connection\n try:\n self.connection = DataPostgres.connect(**self.options)\n except Exception as e:\n logger.critical(\"Unable to connect to DB: {0}\".format(e.message))\n raise\n\n return self.connection","def connect_db(user, password, db, host='localhost', port=5432):\n url = 'postgresql://{}:{}@{}:{}/{}'\n url = url.format(user, password, host, port, db)\n con = sqlalchemy.create_engine(url, client_encoding='utf8')\n meta = sqlalchemy.MetaData(bind=con, reflect=True)\n return con, meta","def connect_to_database():\n engine = create_engine('postgresql://catalog:password@localhost/catalog')\n Base.metadata.bind = engine\n db_session = sessionmaker(bind=engine)\n session = db_session()\n return session","def connect_transactions_db():\n\n conn = psycopg2.connect(os.environ.get('TRANSACTIONS_DATABASE_URL'))\n\n return conn","def connect_to_db(app):\n\n\t#Configure to use our Postgres databse\n\tapp.config['SQLALCHEMY_DATABASE_URI'] ='postgresql:///results' #double check this \n\tapp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\tdb.app = app\n\tdb.init_app(app)","def make_db_conn():\n\n global DB_CONN\n DB_CONN = psycopg2.connect(\"dbname=tornadowatch user=postgres host=localhost port=6432\")\n DB_CONN.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)","def connect(db_config):\n conn = None\n try:\n print('Connecting to db...')\n url = \"dbname={} user={} password={} host={} port={}\".\\\n format(db_config['dbname'], db_config['user'],\n db_config['password'], db_config['host'],\n db_config['port'])\n conn = psycopg2.connect(url)\n except(Exception, psycopg2.DatabaseError) as error:\n print(\"Warning: Connection Error\", error)\n finally:\n if conn is not None:\n print(\"Connected to db\")\n db_connection['connection'] = conn\n return conn","def connect(db_name):\n try:\n db = psycopg2.connect(\"dbname={}\".format('news'))\n c = db.cursor()\n return db, c\n except psycopg2.Error as e:\n print(\"Unable to connect to database\")","def connect(db_name='news'):\n try:\n db = psycopg2.connect(dbname=\"{}\".format(db_name))\n cursor = db.cursor()\n return db, cursor\n except psycopg2.DatabaseError as e:\n print('Error connecting to the database: {}'.format(e.pgerror))","def connect():\n conn = None\n try:\n # read connection parameters\n params = config()\n \n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n \n # create a cursor\n cur = conn.cursor()\n \n # execute a statement\n print('PostgreSQL database version:')\n cur.execute('SELECT version()')\n \n # display the PostgreSQL database server version\n db_version = cur.fetchone()\n print(db_version)\n \n return conn, cur\n # close the communication with the PostgreSQL\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()\n print('Database connection closed.')","def connect_db(self):\n try:\n self.connection = self.engine.connect()\n except Exception:\n self.print_std_error()","def connect_sqlalchemy():\n \n path = os.path.dirname(os.path.abspath(__file__))\n server = yaml.load(open(path + '/config.yaml', 'r'))\n \n server = server['servers'][229]\n \n host = server['host']\n database = server['database']\n user = server['user']\n password = server['password']\n\n from sqlalchemy import create_engine\n url = 'postgresql://{}:{}@{}/{}'\n url = url.format(user, password, host, database)\n return create_engine(url)"],"string":"[\n \"def connect_to_postgres():\\n postgres_creds = os.environ.get(\\\"POSTGRES_CREDS\\\")\\n if not postgres_creds:\\n postgres_creds = get_secret(\\\"POSTGRES_CREDS\\\")\\n print(\\\"Using {} Postgres database credentials\\\".format(postgres_creds))\\n postgres_url = os.environ.get(\\\"POSTGRES\\\")\\n if not postgres_url:\\n postgres_url = get_secret(\\\"POSTGRES\\\")\\n return connect(postgres_url)\",\n \"def connect_to_db():\\n return pg.connect(DB_CONN_STRING)\",\n \"def connect_postgress():\\n conn = psycopg2.connect(\\n host=\\\"postgres-data\\\",\\n port=5432,\\n database=\\\"sc\\\",\\n user=\\\"postgres\\\",\\n password=\\\"postgres\\\")\\n return conn\",\n \"def connect():\\n connection = psycopg2.connect(host='localhost', database='huwebshop', user='postgres', password='Xplod_555')\\n return connection\",\n \"def connect():\\r\\n params = config()\\r\\n print('Connecting to the PostgreSQL database...')\\r\\n global conn\\r\\n conn = psycopg2.connect(**params)\",\n \"def connect(self):\\n self.conn = psycopg2.connect(database=self.config['dbname'], user=self.config['dbuser'], password=self.config['dbpassword'], host=self.config['dbhost'])\",\n \"def connect_to_postgres():\\n con = pg2.connect(host='this_postgres', user='postgres', database='postgres',password='example')\\n return con, con.cursor()\",\n \"def connect_db():\\n try:\\n conn = psycopg2.connect(\\\"dbname='gis' user='postgres' host='localhost' password='postgres'\\\")\\n except:\\n print(\\\"I am unable to connect to the database\\\")\\n print(\\\"Connencted\\\")\\n return conn\",\n \"def connect_db(self):\\n # connect to database\\n connection = psycopg2.connect(\\n database = \\\"postgres\\\",\\n user = \\\"postgres\\\",\\n password = os.getenv('DB_PASSWORD'),\\n host = \\\"groalives.cvslmiksgnix.us-east-1.rds.amazonaws.com\\\",\\n port = '5432'\\n )\\n # create cursor that is used throughout\\n\\n try:\\n self.cursor_dog = connection.cursor()\\n print(\\\"Connected!\\\")\\n except:\\n print(\\\"Connection problem chief!\\\")\",\n \"def connect_to_db():\\n try:\\n conn = psycopg2.connect(database='postgres',\\n user='postgres',\\n password='docker',\\n host='localhost',\\n port='5432')\\n print('Opened database successfully')\\n return conn\\n except Exception as e:\\n print(str(e))\\n raise e\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=forum\\\")\",\n \"def connect():\\n\\t\\n\\tglobal _conn\\n\\turlparse.uses_netloc.append('postgres')\\n\\turl = urlparse.urlparse(config.get_database())\\n\\ttry:\\n\\t\\t_conn = psycopg2.connect(\\n\\t\\tdatabase = url.path[1:],\\n\\t\\tuser = url.username,\\n\\t\\tpassword = url.password,\\n\\t\\thost = url.hostname,\\n\\t\\tport = url.port)\\n\\n\\t\\tprint('Connected to database!')\\n\\t\\treturn True\\n\\texcept:\\n\\t\\tprint('Connection to database failed.')\\n\\t\\texit()\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def __connect(host):\\n global pg_pool\\n pg_config['host'] = host\\n pg_pool = SimpleConnectionPool(1, 1, **pg_config)\",\n \"def connect_db():\\n debug(\\\"Connecting to DB.\\\")\\n conn = psycopg2.connect(host=IP_ADDR, user=\\\"postgres\\\", password=\\\"rhodes\\\"\\n , dbname=\\\"exchangedb\\\", cursor_factory=psycopg2.extras.DictCursor)\\n return conn\",\n \"def _connect_to_db(self) -> None:\\n self._connection = psycopg2.connect(database=self.dbname,\\n user=self.dbuser, password=self.dbpassword,\\n host=self.dbhost, port=str(self.dbport))\",\n \"def connect_db():\\n conn = None\\n try:\\n # read connection parameters\\n params = config()\\n\\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n\\t\\t\\n # create a cursor\\n cur = conn.cursor()\\n \\n\\t# execute a statement\\n print('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n\\n # display the PostgreSQL database server version\\n db_version = cur.fetchone()\\n print(db_version)\\n \\n\\t# close the communication with the PostgreSQL\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\\n print('Database connection closed.')\",\n \"def connect():\\n\\n try:\\n return psycopg2.connect(\\n dbname = os.environ['DATABASE_NAME'],\\n user = os.environ['DATABASE_USER'],\\n password = os.environ['DATABASE_PASSWORD'],\\n host = ['DATABASE_HOST'],\\n port = ['DATABASE_PORT']\\n )\\n\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\",\n \"def connect_to_db(self):\\n\\t\\t# connection = psycopg2.connect(database=config.database, user=config.user,password = config.password)\\n\\t\\tconnection = psycopg2.connect(database=config.database, user=config.user)\\n\\t\\treturn connection\",\n \"def psycopg2_connect(tunnel):\\n\\n # Create a database connection\\n connection = psycopg2.connect(\\n database=config.dbreddit['dbname'],\\n user=config.dbreddit['user'],\\n password=config.dbreddit['password'],\\n host=tunnel.local_bind_host,\\n port=tunnel.local_bind_port,\\n )\\n connection.set_session(autocommit=True)\\n print('Database connection successful')\\n return connection\",\n \"def connect():\\n try:\\n return psycopg2.connect(\\\"dbname=tournament\\\")\\n except psycopg2.Error:\\n print \\\"Cannot connect to the database\\\"\",\n \"def _dbconnect(self):\\n user = mm_cfg.STORM_MEMBER_DB_USER\\n password = mm_cfg.STORM_MEMBER_DB_PASS\\n host = mm_cfg.STORM_MEMBER_DB_HOST\\n dbname = mm_cfg.STORM_MEMBER_DB_NAME\\n\\n\\n db = 'postgres://'+user+':'+password+'@'+host+'/'+dbname\\n return create_database(db)\",\n \"def connect_db():\\n conn = psycopg2.connect(\\n database = app.config['DATABASE'],\\n host = app.config['HOST'],\\n port = app.config['PORT'])\\n conn.autocommit = True\\n return conn\",\n \"def connect(db: str=None) -> Connection:\\n return pgsql.connect(\\\"postgres.internal\\\", db or \\\"sysadmins\\\")\",\n \"def connect():\\n try:\\n return psycopg2.connect(\\\"dbname=tournament\\\")\\n except:\\n print(\\\"Connection failed\\\")\",\n \"def connect():\\n # returns the connection object to tournament\\n # database from PostgreSQL\\n return psycopg2.connect(\\\"dbname=tournament\\\")\",\n \"def connect():\\n conn = None\\n try:\\n # read connection parameters\\n params = config()\\n\\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n\\n # create a cursor\\n cur = conn.cursor()\\n\\n # execute a statement\\n print('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n\\n # display the PostgreSQL database server version\\n db_version = cur.fetchone()\\n print(db_version)\\n\\n # close the communication with the PostgreSQL\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\\n print('Database connection closed.')\",\n \"def connect(self):\\n\\t\\t# PostgreSQL PyPgSQL\\n\\t#\\tcp = adbapi.ConnectionPool(\\\"pyPgSQL.PgSQL\\\", database=\\\"test\\\")\\n\\t\\t# MySQL\\n\\t\\tself.dbpool = adbapi.ConnectionPool('MySQLdb',\\n\\t\\t\\thost = self.settings.get('hostname', 'localhost'),\\n\\t\\t\\tport = self.settings.get('port', 3306),\\n\\t\\t\\tdb = self.settings.get('database'),\\n\\t\\t\\tuser = self.settings.get('username'),\\n\\t\\t\\tpasswd = self.settings.get('password'),\\n\\t\\t\\tcursorclass = MySQLdb.cursors.DictCursor,\\n\\t\\t\\tcharset = 'utf8',\\n\\t\\t\\tuse_unicode = True,\\n\\t\\t)\",\n \"def pq_connect(self):\\n\\t\\ttry:\\n\\t\\t\\tself.connection = psycopg2.connect(**self.pq_params)\\n\\t\\texcept psycopg2.DatabaseError as error:\\n\\t\\t\\tprint(error)\",\n \"def connect_db():\\n db = psycopg2.connect(\\n dbname=app.config['DBNAME'],\\n user=app.config['DBUSER'],\\n password=app.config['DBPASSWORD'],\\n host=app.config['DBHOST'],\\n port=app.config['DBPORT']\\n )\\n return db\",\n \"def connect():\\n try:\\n conn = psycopg2.connect(\\\"dbname=tournament\\\")\\n return conn\\n except:\\n print \\\" I am unable to connect to the database\\\"\",\n \"def connect(params_dic):\\n conn = None\\n try:\\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params_dic)\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n sys.exit(1) \\n return conn\",\n \"def create_connection(self):\\n try:\\n self.conn = psycopg2.connect(host=self.host, port=self.port, database=self.database, user=self.user, password=self.password)\\n\\n except:\\n print(\\\"Unable to connect to the database. Please check your options and try again.\\\")\\n exit()\",\n \"def connectDB(self): \\n #connect to the database\\n try:\\n print(self.pg_dbname)\\n self.conn = psycopg2.connect(\\\"dbname=%s user=%s password=%s host=%s port=%s\\\" % (self.pg_dbname, self.pg_username, self.pg_password, self.pg_host, self.pg_port))\\n print(\\\"connected!\\\")\\n except psycopg2.Error as e:\\n print(\\\"I am unable to connect to the database\\\")\\n print(e)\\n\\n #define cursor\\n self.cur = self.conn.cursor()\",\n \"def psg_connect(self, conf=conf):\\r\\n try:\\r\\n self.connection = psycopg2.connect(user=self.conf[\\\"test_env\\\"][\\\"user\\\"],\\r\\n password=self.conf[\\\"test_env\\\"][\\\"user\\\"],\\r\\n host=self.conf[\\\"test_env\\\"][\\\"host\\\"],\\r\\n port=self.conf[\\\"test_env\\\"][\\\"port\\\"],\\r\\n database=self.conf[\\\"test_env\\\"][\\\"defaultDB\\\"])\\r\\n self.cursor = self.connection.cursor()\\r\\n print(\\\"PostgreSQL server information: \\\",self.connection.get_dsn_parameters(), \\\"\\\\n\\\")\\r\\n self.cursor.execute(\\\"SELECT version();\\\")\\r\\n record = self.cursor.fetchone()\\r\\n print(\\\"You are connected to - \\\", record, \\\"\\\\n\\\")\\r\\n\\r\\n except (Exception, Error) as error:\\r\\n print(\\\"Error while connecting to PostgreSQL\\\", error)\\r\\n finally:\\r\\n return self.cursor\",\n \"def connect_to_db():\\n\\n host = 'tweetsdb.cqfliozhpzqt.us-west-2.rds.amazonaws.com:5432'\\n dbname = 'tweetsdb'\\n\\n user = ''\\n pswd = ''\\n\\n with open('db_credentials', 'r') as f:\\n credentials = f.readlines()\\n f.close()\\n \\n user = credentials[0].rstrip()\\n pswd = credentials[1].rstrip()\\n\\n connection = psycopg2.connect(\\n database=dbname,\\n user=user,\\n password=pswd,\\n host=host.split(':')[0],\\n port=5432)\\n\\n return connection\",\n \"def connect(db_ini=None):\\n conn = None\\n curr = None\\n try:\\n # read connection parameters\\n params = PostgresStuff.load_config(db_ini)\\n print (params)\\n\\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n\\n # create a cursor\\n cur = conn.cursor()\\n\\n # execute a statement\\n print('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n\\n # display the PostgreSQL database server version\\n db_version = cur.fetchone()\\n print(db_version)\\n\\n # close the communication with the PostgreSQL\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n return conn\",\n \"def connect():\\n try:\\n return psycopg2.connect(\\\"dbname=tournament\\\")\\n\\n except:\\n print('An error occured connecting to the database \\\"tournament\\\".')\\n sys.exit()\",\n \"def connect_db(autocommit = True):\\n uses_netloc.append('postgres')\\n url = urlparse(DATABASE_URL)\\n\\n try:\\n conn = connect(\\n database = url.path[1:],\\n user = url.username,\\n password = url.password,\\n host = url.hostname,\\n port = url.port\\n )\\n conn.autocommit = autocommit\\n return conn\\n\\n except Error as e:\\n print(e)\",\n \"def connect():\\n cf = configparser.ConfigParser()\\n cf.read(\\\"config.ini\\\")\\n try:\\n connection = psycopg2.connect(user=cf.get('postgres', 'user'),\\n password=cf.get('postgres', 'password'),\\n host=cf.get('postgres', 'host'),\\n port=cf.get('postgres', 'port'),\\n database=cf.get('postgres', 'database')\\n )\\n return connection\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error while connecting to PostgresSQL\\\", error)\",\n \"def pg_get_conn(database=\\\"fakenews\\\", user=\\\"fakenews\\\", password=\\\"fnd\\\"):\\n try:\\n conn = psycopg2.connect(database=\\\"fakenews\\\",\\n user=\\\"fakenews\\\", password=\\\"fnd\\\", host='localhost', port='5432')\\n return conn\\n except Exception as e:\\n print(str(e))\",\n \"def connect(logger):\\n\\n global _conn\\n urllib.parse.uses_netloc.append(\\\"postgres\\\")\\n url = urllib.parse.urlparse(DATABASE_URL)\\n try:\\n _conn = psycopg2.connect(\\n database = url.path[1:],\\n user = url.username,\\n password = url.password,\\n host = url.hostname,\\n port = url.port)\\n return True\\n except:\\n logger.critical(\\\"Connection to database failed. Exiting...\\\")\\n exit(1)\",\n \"def cli(ctx, db, user, password, verbose):\\n click.echo('Connecting %s@%s' % (user, db))\\n logging.basicConfig(level=logging.DEBUG if verbose else logging.INFO, format='%(levelname)s: %(message)s')\\n ctx.obj = psycopg2.connect(database=db, user=user, password=password)\",\n \"def connect():\\n conn = None\\n try:\\n # read connection parameters\\n params = config(filename='./configs/database.ini',\\n section='postgresql')\\n\\n # connect to the PostgreSQL server\\n logger.info('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n\\n # create a cursor\\n cur = conn.cursor()\\n\\n # execute a statement\\n logger.debug('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n\\n # display the PostgreSQL database server version\\n db_version = cur.fetchone()\\n logger.debug(db_version)\\n\\n # close the communication with the PostgreSQL\\n cur.close()\\n return conn\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n return None\",\n \"def connect(host, database, user, password, port):\\n conn = None\\n try:\\n # read connection parameters\\n params = dict()\\n params['host'] = host\\n params['database'] = database\\n params['user'] = user\\n params['password'] = password\\n params['port'] = port\\n\\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psy.connect(**params)\\n conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)\\n\\n\\n except (Exception, psy.DatabaseError) as error:\\n print(error)\\n print(\\\"Erreur\\\")\\n sys.exit()\\n\\n return conn\",\n \"def connect(self):\\n\\n self._dbcon = psycopg2.connect(\\n database=self._dbname, host=self._host, port=self._port, user=self._username, password=self._password)\\n self.result_as_dict(self._result_as_dict)\",\n \"def connect_database(dbname, user, pwd, host_ip=\\\"127.0.0.1\\\", port=\\\"5432\\\"):\\n \\n # connect to database\\n conn = psycopg2.connect(f\\\"host={host_ip} port={port} dbname={dbname} user={user} password={pwd}\\\")\\n cur = conn.cursor()\\n \\n return cur, conn\",\n \"def _connect(self):\\n try:\\n self.conn = psycopg2.connect(\\n host=self.host,\\n user=self.username,\\n password=self.password,\\n port=self.port,\\n dbname=self.dbname\\n )\\n except psycopg2.DatabaseError as e:\\n logger.error(e)\\n raise e\\n logger.info('Connection opened successfully.')\",\n \"def connect_to_db(config: Config):\\n logger.info(f'resolving db host from {config.db_host} DNS: {config.flyio_dns}')\\n db_host_ip = resolve_db_host(config)\\n logger.info(f'resolved db host ip: {db_host_ip}')\\n\\n return psycopg2.connect(\\n # NOTE: for demo, we use database template1\\n (f\\\"dbname='template1' \\\"\\n f\\\"user='{config.db_user}' \\\"\\n f\\\"host='{db_host_ip}' \\\"\\n f\\\"password='{config.db_password}'\\\")\\n )\",\n \"def initialize_db(password):\\n print('Starting...')\\n global conn\\n\\n try:\\n conn = psycopg2.connect(database=\\\"postgres\\\",#default databse in postgreSQL\\n user=\\\"postgres\\\",#username of postgreSQL\\n password=password,#password of that user in postgreSQL\\n host=\\\"127.0.0.1\\\",\\n port=\\\"5432\\\")\\n except psycopg2.OperationalError as exception:\\n messagebox.showerror(\\\"password\\\",\\\"PASSWORD IS INCORRECT. TRY AGAIN...\\\")\\n sys.exit()\",\n \"def connect_psycopg2():\\n server = yaml.load(open('config.yaml', 'r'))\\n\\n server = server['servers'][229]\\n\\n host = server['host']\\n database = server['database']\\n user = server['user']\\n password = server['password']\\n\\n conn = psycopg2.connect(\\n host=host, port=5432, database=database,\\n user=user, password=password)\\n return conn\",\n \"def connect(self):\\n # use our connection values to establish a connection\\n self.conn = psycopg2.connect(self.connectString)\\n # create a psycopg2 cursor that can execute queries\\n self.cursor = self.conn.cursor()\\n connectionStringEnding = self.databaseName + ' at ' + self.databaseHost\\n \\\"\\\"\\\"\\n if (self.cursor):\\n print('connection made to ' + connectionStringEnding)\\n else:\\n print('connection problem with ' + connectionStringEnding)\\\"\\\"\\\"\",\n \"def postgresql():\\n icanhaz.postgres.server()\\n\\n icanhaz.postgres.user('pguser', 'foo')\\n assert fabtools.postgres.user_exists('pguser')\\n\\n icanhaz.postgres.database('pgdb', 'pguser')\\n assert fabtools.postgres.database_exists('pgdb')\",\n \"def connect_db():\\n\\n # use nullpool because pooling breaks unit tests and we don't need the performance\\n return sqlalchemy.create_engine(\\n 'postgresql://' +\\n app.config[ 'DATABASE_USER' ] + ':' +\\n app.config[ 'DATABASE_PASSWORD' ] + '@' +\\n app.config[ 'DATABASE_HOST' ] + '/' +\\n app.config[ 'DATABASE' ],\\n poolclass = sqlalchemy.pool.NullPool\\n )\",\n \"def make_postgres(host, port, database, user, password):\\n if host is None:\\n return NoDb()\\n return psycopg2.connect(host=host, port=port, database=database, user=user, password=password)\",\n \"def connect(path=''):\\n conn = None\\n try:\\n # read connection parameters\\n params = utils.config(path)\\n\\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n\\n # create a cursor\\n cur = conn.cursor()\\n\\n # execute a statement\\n print('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n\\n # display the PostgreSQL database server version\\n db_version = cur.fetchone()\\n print(db_version)\\n\\n # close the communication with the PostgreSQL\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\\n print('Database connection closed.')\",\n \"def sqlconnect(host, database, user, password):\\r\\n # Assert valid connection\\r\\n conn = pg.connect(host = host, database=database, user = user, password = password)\\r\\n cur = conn.cursor()\\r\\n return cur\",\n \"def startConnection(self):\\n try:\\n self.conn = psycopg2.connect(\\\"dbname='library' user='postgres' host='localhost' password='Codechef'\\\")\\n # self.conn = psycopg2.connect(\\\"dbname='library' user='postgres' host='localhost' password='Codechef'\\\")\\n # self.conn = psycopg2.connect(\\\"dbname='db_b130974cs' user='postgres' host='localhost' password='Codechef'\\\")\\n except:\\n print \\\"I am unable to connect to the database\\\"\\n print \\\"connected to database...\\\"\\n self.schema = SchemaGraph(self.conn)\",\n \"def connect_db():\\n \\n con = None\\n try:\\n \\n # connect to the PostgreSQL server\\n \\n con = psycopg2.connect(host=config_esql.server,\\n database=config_esql.database,\\n user=config_esql.database,\\n password=config_esql.password)\\t\\n # create a cursor\\n return (con)\\n \\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n print ('Could not connect to DB. Exiting..')\\n sys.exit(2)\",\n \"def create_connection(self):\\r\\n dbname = \\\"dbname = \\\"+\\\"'\\\"+self.dbname+\\\"' \\\"\\r\\n user = \\\"user = \\\"+\\\"'\\\"+self.user+\\\"' \\\"\\r\\n host = \\\"host = \\\"+\\\"'\\\"+self.host+\\\"' \\\"\\r\\n password = \\\"password = \\\"+\\\"'\\\"+self.password+\\\"' \\\"\\r\\n connection_info = dbname + user + host + password\\r\\n try:\\r\\n conn = psycopg2.connect(connection_info)\\r\\n except psycopg2.Error:\\r\\n print(\\\"Unable to connect to DB\\\")\\r\\n return conn\",\n \"def connect(params_dic):\\n conn = None\\n try:\\n # connect to the PostgreSQL server\\n logger.info('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params_dic)\\n except (Exception, psycopg2.DatabaseError) as error:\\n logger.error(error)\\n return None\\n # sys.exit(1)\\n logger.info(\\\"Connection successful\\\")\\n return conn\",\n \"def create_connection():\\n config = ConfigParser.ConfigParser()\\n config.read(\\\"config.ini\\\")\\n localhost = config.get('postgresql','localhost')\\n database = config.get('postgresql','database')\\n username = config.get('postgresql','username')\\n password = config.get('postgresql','password')\\n port = config.get('postgresql', 'port')\\n conn_string = \\\"host='\\\" + localhost + \\\"' port='\\\" + port + \\\"' dbname='\\\" + database + \\\"' user='\\\" + username + \\\"' password='\\\" + password + \\\"'\\\"\\n print(\\\"Connecting to database...\\\")\\n conn = psycopg2.connect(conn_string)\\n print(\\\"Connection established!\\\")\\n return(conn)\",\n \"def connect(self):\\n try:\\n connect = psycopg2.connect(\\\"\\\"\\\"\\n dbname=ovdata_db \\n user=larsstegman \\n host=localhost \\n password=password\\n \\\"\\\"\\\")\\n return connect\\n except Exception as e:\\n return ConnectionError()\",\n \"def create_connection():\\r\\n\\r\\n conn = None\\r\\n try: \\r\\n # Connect to database\\r\\n conn = psycopg2.connect(user=data[\\\"user\\\"],\\r\\n password=data[\\\"pass\\\"],\\r\\n host=data[\\\"host\\\"],\\r\\n port=data[\\\"port\\\"],\\r\\n database=data[\\\"db\\\"]) \\r\\n return conn\\r\\n except (Exception, Error) as error:\\r\\n print(\\\"Error while connecting to PostgreSQL: \\\", error)\\r\\n return conn\",\n \"def createConnection(self):\\r\\n conn_string = \\\"host='{}' dbname='{}' user='{}' password='{}' port={}\\\".format(\\r\\n self.host, self.database, self.user, self.password, self.port)\\r\\n return psycopg2.connect(conn_string)\",\n \"def connect():\\n\\n conn = None\\n try:\\n # read connection parameters\\n params = config()\\n\\n # connect to the PostgreSQL server\\n print('Connection to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n\\n # create a cursor\\n cur = conn.cursor()\\n\\n print('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n\\n db_version = cur.fetchone()\\n print(db_version)\\n\\n # Close the connection\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print('Unable to connect!\\\\n{0}').format(error)\\n sys.exit(1)\\n \\n return conn\",\n \"def conn(self):\\n self.cnx = psycopg2.connect(**self.dbConfig) \\n self.cur = self.cnx.cursor()\",\n \"def connect():\\n conn = None\\n try:\\n print \\\"Connecting to ssm database...\\\"\\n conn = psycopg2.connect(host=\\\"\\\",\\n database=\\\"\\\",\\n user=\\\"\\\",\\n port=\\\"\\\")\\n except (Exception, psycopg2.DatabaseError) as error:\\n print error\\n return conn\",\n \"def connect():\\n try:\\n if not hasattr(connect, \\\"conn\\\"):\\n connect.conn = pg.connect(\\n user=DB_USER,\\n password=DB_PASSWORD,\\n host=DB_HOST,\\n port=DB_PORT,\\n database=DB_NAME)\\n connect.conn.set_client_encoding('UTF8')\\n elif connect.conn is None or connect.conn.closed != 0:\\n connect.conn = pg.connect(\\n user=DB_USER,\\n password=DB_PASSWORD,\\n host=DB_HOST,\\n port=DB_PORT,\\n database=DB_NAME)\\n connect.conn.set_client_encoding('UTF8')\\n return connect.conn\\n except pg.OperationalError as pgoe:\\n logging.error(pgoe.message)\\n raise\\n except Exception:\\n logging.error(\\\"Failed to connect to database.\\\")\\n raise\",\n \"def _postgresql(self):\\n logging.debug(\\\"Configured to use Postgresql for a database\\\")\\n try:\\n import pypostgresql\\n except ImportError:\\n print(WARN + \\\"You must install 'pypostgresql'\\\")\\n os._exit(1)\\n db_host, db_name, db_user, db_password = self._db_credentials()\\n postgres = \\\"postgresql+pypostgresql://%s:%s@%s/%s\\\" % (\\n db_user,\\n db_password,\\n db_host,\\n db_name,\\n )\\n if self._test_connection(postgres):\\n return postgres\\n else:\\n logging.fatal(\\\"Cannot connect to database with any available driver\\\")\\n os._exit(1)\",\n \"def createConnection(user, password, server, port, db):\\n Database.globalUser = user\\n try:\\n Database.connection = psycopg2.connect(user=user,\\n password=password,\\n host=server,\\n port=port,\\n database=db)\\n return Database.connection\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error conectando a PostgreSQL\\\", error)\\n return None\",\n \"def get_postgredb_connection(properties):\\n try:\\n return postgreconnect(host=properties[0],\\n database=properties[1],\\n user=properties[2],\\n password=properties[3])\\n except postgreError as error:\\n print(\\\"Error while connecting to PostgreSQL Database\\\", error)\",\n \"def doconn(desc):\\n debug(\\\"doconn({},{},{})\\\".format(desc['host'], desc['user'], desc['database']))\\n # debug(\\\"doconn({},{},{},{})\\\".format(desc['host'], desc['user'], desc['database'], desc['password']))\\n ret = psycopg2.connect(**desc)\\n ret.autocommit = True\\n return ret\",\n \"def connect_trades_db():\\n\\n conn = psycopg2.connect(os.environ.get('TRADES_DATABASE_URL'))\\n\\n return conn\",\n \"def dbConnect(database, user, password, host, autocommit = True):\\n trace(\\\"connecting to database %s as %s on host %s\\\" % (database, user, host))\\n con =psycopg2.connect(database = database, user = user, password = password, host = host)\\n con.autocommit = autocommit\\n return con\",\n \"def databaseConnect():\\n connection = None\\n try:\\n params = config()\\n connection = psycopg2.connect(**params)\\n cursor = connection.cursor()\\n\\n \\\"\\\"\\\"for query in createTables():\\n cursor.execute(query)\\\"\\\"\\\"\\n [cursor.execute(query) for query in createTables()]\\n\\n cursor.close()\\n connection.commit()\\n\\n except (Exception, psycopg2.DatabaseError) as errorDescription:\\n print('Error description:\\\\n', errorDescription)\\n finally:\\n if connection is not None:\\n connection.close()\",\n \"def database_connect():\\n # Read the config file\\n config = configparser.ConfigParser()\\n config.read('config.ini')\\n if 'database' not in config['DATABASE']:\\n config['DATABASE']['database'] = config['DATABASE']['user']\\n\\n # Create a connection to the database\\n connection = None\\n try:\\n # Parses the config file and connects using the connect string\\n connection = pg8000.connect(database=config['DATABASE']['database'],\\n user=config['DATABASE']['user'],\\n password=config['DATABASE']['password'],\\n host=config['DATABASE']['host'])\\n except pg8000.OperationalError as operation_error:\\n print(\\\"\\\"\\\"Error, you haven't updated your config.ini or you have a bad\\n connection, please try again. (Update your files first, then check\\n internet connection)\\n \\\"\\\"\\\")\\n print(operation_error)\\n return None\\n\\n # return the connection to use\\n return connection\",\n \"def connect_to_db(app):\\n\\n # Configure to use my PstgreSQL database\\n # ***come back to this to make sure db name is correct ***\\n app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql:///legislature'\\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\\n db.app = app\\n db.init_app(app)\",\n \"def connect(self):\\n if self.connection is not None:\\n logger.info(\\\" connection: %s \\\" % (self.connection is not None))\\n return self.connection\\n try:\\n self.connection = DataPostgres.connect(**self.options)\\n except Exception as e:\\n logger.critical(\\\"Unable to connect to DB: {0}\\\".format(e.message))\\n raise\\n\\n return self.connection\",\n \"def connect_db(user, password, db, host='localhost', port=5432):\\n url = 'postgresql://{}:{}@{}:{}/{}'\\n url = url.format(user, password, host, port, db)\\n con = sqlalchemy.create_engine(url, client_encoding='utf8')\\n meta = sqlalchemy.MetaData(bind=con, reflect=True)\\n return con, meta\",\n \"def connect_to_database():\\n engine = create_engine('postgresql://catalog:password@localhost/catalog')\\n Base.metadata.bind = engine\\n db_session = sessionmaker(bind=engine)\\n session = db_session()\\n return session\",\n \"def connect_transactions_db():\\n\\n conn = psycopg2.connect(os.environ.get('TRANSACTIONS_DATABASE_URL'))\\n\\n return conn\",\n \"def connect_to_db(app):\\n\\n\\t#Configure to use our Postgres databse\\n\\tapp.config['SQLALCHEMY_DATABASE_URI'] ='postgresql:///results' #double check this \\n\\tapp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\\n\\tdb.app = app\\n\\tdb.init_app(app)\",\n \"def make_db_conn():\\n\\n global DB_CONN\\n DB_CONN = psycopg2.connect(\\\"dbname=tornadowatch user=postgres host=localhost port=6432\\\")\\n DB_CONN.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\",\n \"def connect(db_config):\\n conn = None\\n try:\\n print('Connecting to db...')\\n url = \\\"dbname={} user={} password={} host={} port={}\\\".\\\\\\n format(db_config['dbname'], db_config['user'],\\n db_config['password'], db_config['host'],\\n db_config['port'])\\n conn = psycopg2.connect(url)\\n except(Exception, psycopg2.DatabaseError) as error:\\n print(\\\"Warning: Connection Error\\\", error)\\n finally:\\n if conn is not None:\\n print(\\\"Connected to db\\\")\\n db_connection['connection'] = conn\\n return conn\",\n \"def connect(db_name):\\n try:\\n db = psycopg2.connect(\\\"dbname={}\\\".format('news'))\\n c = db.cursor()\\n return db, c\\n except psycopg2.Error as e:\\n print(\\\"Unable to connect to database\\\")\",\n \"def connect(db_name='news'):\\n try:\\n db = psycopg2.connect(dbname=\\\"{}\\\".format(db_name))\\n cursor = db.cursor()\\n return db, cursor\\n except psycopg2.DatabaseError as e:\\n print('Error connecting to the database: {}'.format(e.pgerror))\",\n \"def connect():\\n conn = None\\n try:\\n # read connection parameters\\n params = config()\\n \\n # connect to the PostgreSQL server\\n print('Connecting to the PostgreSQL database...')\\n conn = psycopg2.connect(**params)\\n \\n # create a cursor\\n cur = conn.cursor()\\n \\n # execute a statement\\n print('PostgreSQL database version:')\\n cur.execute('SELECT version()')\\n \\n # display the PostgreSQL database server version\\n db_version = cur.fetchone()\\n print(db_version)\\n \\n return conn, cur\\n # close the communication with the PostgreSQL\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\\n print('Database connection closed.')\",\n \"def connect_db(self):\\n try:\\n self.connection = self.engine.connect()\\n except Exception:\\n self.print_std_error()\",\n \"def connect_sqlalchemy():\\n \\n path = os.path.dirname(os.path.abspath(__file__))\\n server = yaml.load(open(path + '/config.yaml', 'r'))\\n \\n server = server['servers'][229]\\n \\n host = server['host']\\n database = server['database']\\n user = server['user']\\n password = server['password']\\n\\n from sqlalchemy import create_engine\\n url = 'postgresql://{}:{}@{}/{}'\\n url = url.format(user, password, host, database)\\n return create_engine(url)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8593803","0.8553654","0.8398151","0.83853227","0.82916325","0.82193106","0.8194222","0.8186864","0.8180956","0.81787014","0.81350166","0.8125547","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8113675","0.8107166","0.80042225","0.79977673","0.7994506","0.79602164","0.79568505","0.7927523","0.7919216","0.79136384","0.79067004","0.7900443","0.78870225","0.7866989","0.7837346","0.78370243","0.7831925","0.78072274","0.77919155","0.77701205","0.7734326","0.77283275","0.7718047","0.7717827","0.7699559","0.7685814","0.76476115","0.7637042","0.7628264","0.7625028","0.761718","0.7611464","0.76015687","0.75795954","0.75769293","0.75506735","0.75503224","0.7538628","0.7537966","0.75359833","0.7534351","0.7530824","0.7513404","0.7511377","0.7503687","0.74977857","0.7477212","0.74663025","0.7465268","0.7458056","0.744715","0.74462426","0.73943794","0.7362448","0.7359454","0.7357114","0.73515207","0.73441595","0.7337867","0.73321486","0.73254955","0.7319186","0.7310226","0.728776","0.7284228","0.7279373","0.72764814","0.72732615","0.7264306","0.7243773","0.72269493","0.72151464","0.71900094","0.7177657","0.71738607","0.71679455","0.7162175","0.71610725"],"string":"[\n \"0.8593803\",\n \"0.8553654\",\n \"0.8398151\",\n \"0.83853227\",\n \"0.82916325\",\n \"0.82193106\",\n \"0.8194222\",\n \"0.8186864\",\n \"0.8180956\",\n \"0.81787014\",\n \"0.81350166\",\n \"0.8125547\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8113675\",\n \"0.8107166\",\n \"0.80042225\",\n \"0.79977673\",\n \"0.7994506\",\n \"0.79602164\",\n \"0.79568505\",\n \"0.7927523\",\n \"0.7919216\",\n \"0.79136384\",\n \"0.79067004\",\n \"0.7900443\",\n \"0.78870225\",\n \"0.7866989\",\n \"0.7837346\",\n \"0.78370243\",\n \"0.7831925\",\n \"0.78072274\",\n \"0.77919155\",\n \"0.77701205\",\n \"0.7734326\",\n \"0.77283275\",\n \"0.7718047\",\n \"0.7717827\",\n \"0.7699559\",\n \"0.7685814\",\n \"0.76476115\",\n \"0.7637042\",\n \"0.7628264\",\n \"0.7625028\",\n \"0.761718\",\n \"0.7611464\",\n \"0.76015687\",\n \"0.75795954\",\n \"0.75769293\",\n \"0.75506735\",\n \"0.75503224\",\n \"0.7538628\",\n \"0.7537966\",\n \"0.75359833\",\n \"0.7534351\",\n \"0.7530824\",\n \"0.7513404\",\n \"0.7511377\",\n \"0.7503687\",\n \"0.74977857\",\n \"0.7477212\",\n \"0.74663025\",\n \"0.7465268\",\n \"0.7458056\",\n \"0.744715\",\n \"0.74462426\",\n \"0.73943794\",\n \"0.7362448\",\n \"0.7359454\",\n \"0.7357114\",\n \"0.73515207\",\n \"0.73441595\",\n \"0.7337867\",\n \"0.73321486\",\n \"0.73254955\",\n \"0.7319186\",\n \"0.7310226\",\n \"0.728776\",\n \"0.7284228\",\n \"0.7279373\",\n \"0.72764814\",\n \"0.72732615\",\n \"0.7264306\",\n \"0.7243773\",\n \"0.72269493\",\n \"0.72151464\",\n \"0.71900094\",\n \"0.7177657\",\n \"0.71738607\",\n \"0.71679455\",\n \"0.7162175\",\n \"0.71610725\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80444777"},"document_rank":{"kind":"string","value":"24"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":757,"numItemsPerPage":100,"numTotalItems":95772,"offset":75700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjU4MTg0NywianRpIjoiMWRiMTQ5OGQtMDhjOS00YWIzLTlhMjgtYzViNDVjOGUwMmY1Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY1ODU0NDcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.5WjKCZIPwWUBV-9lxLodOYIwmspQY52WrFXY1laUGrjRMKFE2uxx5enj5xK_EdbpIGYFtkhPd0_jCaxH8BtKDA","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 |
|---|---|---|---|---|---|---|
Test for delete_resource_from_index_position method | def test_delete_resource_from_index_position(self):
s1 = System()
b1 = Books("1984", "George Orwell", "Harvill Secker", "1949", "0123456789123")
self.assertEqual(s1.delete_resource_from_index_position(0), print())
self.assertEqual(s1.delete_resource_from_index_position(-1), print())
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_removing_index(self):",
"def test_index_delete(self):\n a = self.test_index()\n a.delete()\n es = self.es\n es.refresh()\n r = es.search(query=StringQuery('zool'))\n eq_(r['hits']['total'], 0, \"We shouldn't get any hits.\")",
"def test_delete_document(index_w... | [
"0.716394",
"0.7033717",
"0.6886999",
"0.68802756",
"0.6583773",
"0.6499626",
"0.64575",
"0.6355071",
"0.6347227",
"0.63249075",
"0.63136363",
"0.6283639",
"0.6282689",
"0.6277738",
"0.62753356",
"0.62416536",
"0.6220821",
"0.6220821",
"0.6212967",
"0.6180838",
"0.61782694",
... | 0.82605475 | 0 |
Test for lending_process method | def test_lending_process(self):
s1 = System()
b1 = Books("1984", "George Orwell", "Harvill Secker", "1949", "0123456789123")
m1 = Members("Richard", "Blackmore", "14-04-1945", "Weston")
m2 = Members("Eric", "Eric Clapton", "30-03-1945", "Riplay")
#test what happens if book not in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_startProcess(self):\r\n self.pm.addProcess(\"foo\", [\"foo\"])\r\n self.pm.startProcess(\"foo\")\r\n self.assertIsInstance(self.pm.protocols[\"foo\"], LoggingProtocol)\r\n self.assertIn(\"foo\", self.pm.timeStarted.keys())",
"def process():\n pass",
"def test_start_proce... | [
"0.6173282",
"0.5930779",
"0.589129",
"0.58374673",
"0.5807327",
"0.57782507",
"0.5777092",
"0.5739012",
"0.57290083",
"0.56662744",
"0.56134325",
"0.55600566",
"0.551325",
"0.5463071",
"0.5449737",
"0.54214317",
"0.54202276",
"0.5412321",
"0.5412321",
"0.5412321",
"0.5399937... | 0.6112398 | 1 |
Test for return_book_process method | def return_book_process(self):
s1 = System()
b1 = Books("1984", "George Orwell", "Harvill Secker", "1949", "0123456789123")
m1 = Members("Richard", "Blackmore", "14-04-1945", "Weston")
s1.add_resource(b1)
s1.lending_process(b1, m1)
s1.remove_resource(b1)
#prove th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_book():\n return render_template('process_book.html')",
"def return_book(self, book_id, return_date):\r\n for book in LibraryDB.book_list:\r\n if book.book_id == book_id and book_id in [i.book_id for i in self.issued_books]:\r\n book.availability = True\r\n ... | [
"0.62140787",
"0.61643386",
"0.6148973",
"0.5966506",
"0.5959692",
"0.59526885",
"0.59435666",
"0.57925487",
"0.57628417",
"0.57617724",
"0.5734467",
"0.5713324",
"0.56746566",
"0.5623255",
"0.5604597",
"0.55610216",
"0.54859006",
"0.54459155",
"0.543411",
"0.5414501",
"0.538... | 0.78834885 | 0 |
Test for send_notification method | def send_notification(self):
s1 = System()
b1 = Books("1984", "George Orwell", "Harvill Secker", "1949", "0123456789123")
m1 = Members("Richard", "Blackmore", "14-04-1945", "Weston")
s1.send_notification("Please return book")
self.assertEqual(m1.get_notifications(), None)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_registered_with_notification(self):\n now = datetime.datetime.now()\n notification = reminders.Notification.objects.create(num_days=1,\n time_of_day=now)\n reminders.SentNotification.objects.create(notification=notification,\... | [
"0.73693633",
"0.736051",
"0.7148425",
"0.7107555",
"0.70434356",
"0.70280105",
"0.6939029",
"0.69161195",
"0.6906033",
"0.6807772",
"0.68013173",
"0.6771862",
"0.6753163",
"0.6710827",
"0.67098886",
"0.67033494",
"0.66510314",
"0.6616835",
"0.66149366",
"0.66149366",
"0.6571... | 0.7050926 | 4 |
handle ADVISE updates received from the API | def parse_fields(self, cxn, data):
trade_flag = False
quote_flag = False
pid = 'API_Symbol(%s)' % self.symbol
if data == None:
self.api.force_disconnect('LIVEQUOTE Advise has been terminated by API for %s' % pid)
return
self.update_rawdata(data)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_updates(self, update):\r\n self.__manage_pump()",
"def _update_from_rest_data(self) -> None:",
"async def _receive_updated_response(self, data):\n serialized_text_responses = await serialize_text_algo_api_response(data)\n await self.send_serialized_data(serialized_text_responses... | [
"0.60427433",
"0.5982816",
"0.5875664",
"0.584655",
"0.5830139",
"0.57291996",
"0.5651025",
"0.5650159",
"0.5627274",
"0.56108737",
"0.56108737",
"0.56008923",
"0.5590502",
"0.5588436",
"0.5569",
"0.55347794",
"0.5530559",
"0.55248404",
"0.55153596",
"0.55139554",
"0.5506361"... | 0.0 | -1 |
callable is stored and used to return results later | def __init__(self, api, id, label, callable, timeout=0):
self.api = api
self.id = id
self.label = label
self.callable = callable
self.started = time.time()
self.timeout = timeout or api.callback_timeout['DEFAULT']
self.api.debug(f"{self}.__init__(..., {self.id}, {... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getCallable():",
"def __call__(self):\n return self.fn()",
"def __call__(self, *args, **kw):\n return self.callable(*args, **kw)",
"def call(self):",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__(fun_name):",
"def ... | [
"0.75026315",
"0.68841547",
"0.6700387",
"0.66829556",
"0.66778564",
"0.66778564",
"0.66778564",
"0.66778564",
"0.66778564",
"0.65903527",
"0.6529461",
"0.6515489",
"0.6473615",
"0.6470478",
"0.63678133",
"0.6330051",
"0.63238966",
"0.6317023",
"0.6317023",
"0.63108605",
"0.6... | 0.0 | -1 |
complete callback by calling callable function with value of results | def complete(self, results):
self.api.debug(f"{self}.complete({repr(results)[:DEBUG_TRUNCATE_RESULTS]})")
self.elapsed = time.time() - self.started
if not self.done:
ret = self.format_results(results)
# TODO: fix sendString test
if self.callable.callback.__nam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def callback(self, fun: Callable[[], None] | None) -> None:",
"def callback(self, function: Optional[Callable[[int], None]]) -> None:",
"def run_callback(func, plus, result):\n data = result.value\n error = None if result.successful() else \"%s\" % result.exception\n try:\n ... | [
"0.7457204",
"0.70491445",
"0.68082875",
"0.67983425",
"0.6777269",
"0.6598992",
"0.6598992",
"0.65590227",
"0.64615554",
"0.6367073",
"0.62865394",
"0.6274837",
"0.62670946",
"0.62670946",
"0.6266958",
"0.61772156",
"0.60854393",
"0.60761684",
"0.60678756",
"0.6055987",
"0.6... | 0.61946976 | 15 |
handle input from rtgw | def gateway_receive(self, msg):
if self.debug_api_messages:
self.dump_input_message(msg)
try:
o = json.loads(msg)
except Exception as e:
return self.receive_exception(sys.exc_info()[0], e, msg)
msg_type = o['type']
msg_id = o['id']
m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_input(data: dict):",
"def _handleInput(self, paramInput):\n pass",
"def process_input(self,r,g,b):\n pass",
"def processInputs(self):",
"def server_do(self,input, connstream):\r\n pass",
"def scan_input(self):\n proc = subprocess.Popen([\"ssh\", \"-tt\", \"pi@127.0.0.1\... | [
"0.6189445",
"0.59328586",
"0.5913091",
"0.5756145",
"0.57243013",
"0.57033956",
"0.5689806",
"0.5636688",
"0.5594414",
"0.5576588",
"0.55494845",
"0.5536093",
"0.55195916",
"0.5503228",
"0.54935",
"0.5488789",
"0.5474541",
"0.54526436",
"0.54488224",
"0.5446376",
"0.5431604"... | 0.0 | -1 |
Upon connection to rtgw, start automatic queries | def setup_local_queries(self):
#what='BANK,BRANCH,CUSTOMER,DEPOSIT'
self.output("Sending initial Accounts query...")
what = '*'
self.rtx_request(
'ACCOUNT_GATEWAY', 'ORDER', 'ACCOUNT', what, '', 'accounts', self.handle_accounts, self.accountdata_callbacks,
self.ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self):\n self.conn.start()",
"def _run_query(self):",
"def run(self):\n print(\"[CONNEXION_MANAGER] start connecting\")\n while True:\n self.connexion_init()",
"def run(self):\n self.connect()",
"def start_task():\n get_results_from_message_queue()\n t... | [
"0.6515159",
"0.6490055",
"0.637998",
"0.63385695",
"0.62499195",
"0.6226698",
"0.6120617",
"0.611012",
"0.6016056",
"0.60100394",
"0.593291",
"0.58946085",
"0.5854323",
"0.5812457",
"0.58014137",
"0.57888764",
"0.5777909",
"0.5777844",
"0.5770826",
"0.5759801",
"0.57579243",... | 0.6350499 | 3 |
send a rendered order out to clients | def send_order_update(self, fields, mapped=False):
self.debug(f"{self} send_order_update({fields})")
symbol = fields['symbol']
if not fields.get('cusip'):
if mapped:
self.debug(f"{self} order update is still missing CUSIP after mapping, continuing...")
els... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_order(self, p_order, p_in_out, count):\n pass",
"def View_Inorder( self ):\r\n cb.order = 1\r\n self.system.Draw( )",
"def View_Postorder( self ):\r\n cb.order = 2\r\n self.system.Draw( )",
"def order_report():",
"def update_order():",
"def update_order():",
... | [
"0.6410005",
"0.60245323",
"0.5860148",
"0.5844451",
"0.58294475",
"0.58294475",
"0.57627046",
"0.5727182",
"0.5697262",
"0.5686734",
"0.55757695",
"0.54803777",
"0.54319847",
"0.54175246",
"0.54003465",
"0.5390121",
"0.5374137",
"0.5370205",
"0.532239",
"0.5300777",
"0.52997... | 0.0 | -1 |
send a rendered execution out to clients | def send_execution_update(self, fields, mapped=False):
self.debug(f"{self} send_execution_update({fields})")
symbol = fields['DISP_NAME']
if not fields.get('CUSIP'):
if mapped:
self.debug(f"{self} execution update is still missing CUSIP after mapping, continuing...")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render():\n html = request.get_data().decode('utf-8')\n sio.emit('render', html)\n return 'OK'",
"def execute():\n # print('Wow')\n result = gui.controller.main('execute')\n print(result)\n\n return render_template('results.html', data=json.dumps(result))",
"def run(self) -... | [
"0.67892045",
"0.67863506",
"0.667443",
"0.65264904",
"0.6506276",
"0.6436311",
"0.63117224",
"0.6292312",
"0.6266779",
"0.62642807",
"0.60074526",
"0.60074526",
"0.5999174",
"0.5973102",
"0.5965556",
"0.5928218",
"0.5914374",
"0.58973056",
"0.5872228",
"0.5870342",
"0.586916... | 0.0 | -1 |
Parse TQL ascii time field returning datetime.time | def parse_tql_time(self, data, pid, label):
field = self.parse_tql_field(data, pid, label)
if field:
hour, minute, second = [int(i) for i in field.split(':')[0:3]]
field = datetime.time(hour, minute, second)
return field | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_time(text_time):\n # return Observer.datetime_to_astropy_time(dt.datetime.strptime(text_time, '%d/%m/%Y %H:%M'))\n the_time = dt.datetime.strptime(text_time, '%d/%m/%Y %H:%M')\n return Time(the_time.strftime('%Y-%m-%d %H:%M'))\n #date = [int(i) for i in date.split('/')]",
"def _parse_time_str... | [
"0.70914274",
"0.70201325",
"0.68314403",
"0.67921424",
"0.6737764",
"0.66979635",
"0.6697224",
"0.6691148",
"0.6679622",
"0.6647432",
"0.6640266",
"0.6632124",
"0.6588161",
"0.6530218",
"0.6529041",
"0.65162116",
"0.6500483",
"0.64943856",
"0.64922947",
"0.64789206",
"0.6478... | 0.83321196 | 0 |
return API time corrected for local timezone | def localize_time(self, apitime):
return self.feedzone.localize(apitime).astimezone(self.localzone) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def timezone():\n \n pass",
"def local_time():\n return datetime.datetime.now().isoformat()[:len('2017-01-24T10:44:00')]",
"def _get_tz():\n return 'UTC'",
"def _get_datetime():\n pst_now = datetime.datetime.utcnow().astimezone(pytz.timezone(\"America/Los_Angeles\"))\n return pst_now.strftime(\"... | [
"0.7212565",
"0.7133227",
"0.69550765",
"0.67883587",
"0.67642075",
"0.66765994",
"0.6640911",
"0.6601219",
"0.6600101",
"0.6597976",
"0.6597636",
"0.6594265",
"0.6589398",
"0.6536743",
"0.64989436",
"0.6493774",
"0.64692587",
"0.6456862",
"0.644002",
"0.64021087",
"0.6392668... | 0.6696543 | 5 |
reverse localize_time to convert local timezone to API time | def unlocalize_time(self, apitime):
return self.localzone.localize(apitime).astimezone(self.feedzone) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def localize_time(self, apitime):\n return self.feedzone.localize(apitime).astimezone(self.localzone)",
"def localize_time_utc(non_utc_time):\n return pytz.utc.localize(non_utc_time)",
"def localize_time_utc(non_utc_time):\n return pytz.utc.localize(non_utc_time)",
"def localize(dt):\n if dt.... | [
"0.70825297",
"0.701795",
"0.701795",
"0.6822421",
"0.652934",
"0.652934",
"0.652934",
"0.64643",
"0.642147",
"0.64163226",
"0.6324556",
"0.626879",
"0.6254991",
"0.62439936",
"0.6230229",
"0.6203875",
"0.62010545",
"0.6193468",
"0.6165171",
"0.6119015",
"0.60487807",
"0.60... | 0.6317597 | 11 |
called when staged order ticket request has been submitted with 'poke' and Ack has returned | def ticket_submit_ack_callback(self, data):
self.output('staged order ticket submission acknowledged: %s' % repr(data)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def order_submit_ack_callback(self, data):\n self.output('order submission acknowleded: %s' % repr(data))",
"def ticket_submit_callback(self, data):\n self.output('staged order ticket submitted: %s' % repr(data))",
"def acknowledged(self):\n ...",
"def handleSent(): \r\n gl... | [
"0.65402454",
"0.6140557",
"0.6103491",
"0.6096946",
"0.6077967",
"0.6076535",
"0.6049912",
"0.5990767",
"0.59754926",
"0.5922344",
"0.5860195",
"0.5799573",
"0.57155365",
"0.57155365",
"0.5699258",
"0.56790817",
"0.5563556",
"0.55532837",
"0.55094004",
"0.5504198",
"0.550243... | 0.7298359 | 0 |
called when staged order ticket request has been submitted with 'poke' and OnOtherAck has returned | def ticket_submit_callback(self, data):
self.output('staged order ticket submitted: %s' % repr(data)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ticket_submit_ack_callback(self, data):\n self.output('staged order ticket submission acknowledged: %s' % repr(data))",
"def order_submit_ack_callback(self, data):\n self.output('order submission acknowleded: %s' % repr(data))",
"def acknowledged(self):\n ...",
"def ack(self, other, ... | [
"0.69958776",
"0.6328598",
"0.5991518",
"0.5967797",
"0.5919578",
"0.589803",
"0.5893169",
"0.58567333",
"0.5825594",
"0.5496221",
"0.5490138",
"0.541741",
"0.5410801",
"0.53815657",
"0.5380933",
"0.5376747",
"0.533026",
"0.5316723",
"0.52969855",
"0.52912843",
"0.52878857",
... | 0.6000672 | 2 |
called when order has been submitted with 'poke' and Ack has returned | def order_submit_ack_callback(self, data):
self.output('order submission acknowleded: %s' % repr(data)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ack(self):\n self.consumer.ack(self)",
"def ticket_submit_ack_callback(self, data):\n self.output('staged order ticket submission acknowledged: %s' % repr(data))",
"async def ack(self, offset: int):",
"def acknowledged(self):\n ...",
"def handleReceived():\r\n global receivedAck... | [
"0.6417594",
"0.63553673",
"0.6310921",
"0.6280499",
"0.6235027",
"0.6160638",
"0.615443",
"0.61338025",
"0.61163735",
"0.61023057",
"0.59978265",
"0.5971444",
"0.5940968",
"0.5938956",
"0.5914693",
"0.5914693",
"0.5908057",
"0.5868182",
"0.5846094",
"0.5846094",
"0.5846094",... | 0.70445883 | 0 |
called when order has been submitted with 'poke' and OnOtherAck has returned | def order_submit_callback(self, data):
self.output('order submitted: %s' % repr(data)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def order_submit_ack_callback(self, data):\n self.output('order submission acknowleded: %s' % repr(data))",
"def ack(self, other, city):\n pass",
"def on_order(self, order: OrderData):\n pass",
"def on_order(self, order: OrderData):\n pass",
"def on_order(self, order: OrderData)... | [
"0.68128896",
"0.64465505",
"0.63373786",
"0.63373786",
"0.63373786",
"0.6235354",
"0.60953945",
"0.60290235",
"0.60039335",
"0.5901038",
"0.58900285",
"0.58529925",
"0.5822434",
"0.57951164",
"0.5768062",
"0.5766398",
"0.57113135",
"0.5708668",
"0.56988627",
"0.5646987",
"0.... | 0.5715031 | 16 |
Return a copy of this session with different max_retries. | def with_retry(self, retry: Optional[urllib3.util.retry.Retry]) -> "Session":
return type(self)(
master=self._master,
user=self._user,
auth=self._auth,
cert=self._cert,
max_retries=retry,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_session_retry(self, retries=3, backoff_factor=0.2,\n status_forcelist=(404, 500, 502, 504),\n session=None):\n session = session or requests.Session()\n retry = Retry(total=retries, read=retries, connect=retries,\n backoff... | [
"0.6693097",
"0.6055224",
"0.6011351",
"0.5991151",
"0.5978382",
"0.5869661",
"0.5861507",
"0.5788364",
"0.5755023",
"0.57389003",
"0.5561054",
"0.5491818",
"0.54451287",
"0.5423037",
"0.54150796",
"0.53628075",
"0.5357361",
"0.53417206",
"0.5197484",
"0.5182304",
"0.51763517... | 0.6716732 | 0 |
The hashing function takes a string input and converts it into a hash using the SHA256 hashing technique present in the hashlib library in python. Arguments | def hashing(word) :
ans = hashlib.sha256(word.encode())
return ans.hexdigest() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def h(x):\n\n hasher = hashlib.sha256()\n hasher.update(x)\n return hasher.digest()",
"def get_256_hash_from_string(string):\n\n sha256 = hashlib.sha256()\n sha256.update(string.encode('utf-8'))\n\n return sha256.hexdigest()",
"def sha256_2_string(string_to_hash):\n\n # Solutio... | [
"0.7626421",
"0.74969774",
"0.7493603",
"0.73092085",
"0.7217957",
"0.71965355",
"0.7123386",
"0.71210426",
"0.71210426",
"0.7113178",
"0.70950216",
"0.7090701",
"0.70856744",
"0.7085671",
"0.7048058",
"0.70315",
"0.70186573",
"0.7007464",
"0.6985593",
"0.6978819",
"0.6970663... | 0.77725095 | 0 |
Add a measurement to the stored ones. | def add(self, measurement, index=None):
notification = ContainerChange(obj=self, name='measurements')
if index is None:
index = len(self.measurements)
self.measurements.append(measurement)
else:
self.measurements.insert(index, measurement)
notificatio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addMeasurement(self, measurement):\n if measurement != None:\n assert isinstance(measurement, VMMeasurement), \"Invalid measurement %s\" % (measurement)\n self.measruements.append(measurement)",
"def add_measurement(self):\n key, ok = QInputDialog.getText(self, 'Add measur... | [
"0.78313416",
"0.734542",
"0.7235791",
"0.7067416",
"0.6823846",
"0.68176687",
"0.6805293",
"0.6725209",
"0.66872317",
"0.6647406",
"0.6298246",
"0.6266476",
"0.6228618",
"0.6212711",
"0.6071609",
"0.6071609",
"0.6034524",
"0.60152614",
"0.5991599",
"0.5955154",
"0.58952105",... | 0.73238003 | 2 |
Remove a measurement or a list of measurement. | def remove(self, measurements):
if not isinstance(measurements, Iterable):
measurements = [measurements]
notification = ContainerChange(obj=self, name='measurements')
for measurement in measurements:
old = self.measurements.index(measurement)
del self.measure... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_measurement():\n meas_id = request.args.get('id', type=int)\n if meas_id is not None:\n db.session.query(Measurement).filter(Measurement.id == meas_id).delete()\n db.session.commit()\n\n return redirect('/measurements')",
"def remove_measurement(self):\n idx = self.measur... | [
"0.7006039",
"0.6825674",
"0.6718692",
"0.6366676",
"0.59642595",
"0.5941515",
"0.58450425",
"0.5829295",
"0.57420117",
"0.57420117",
"0.5720286",
"0.57030857",
"0.56745726",
"0.56653845",
"0.5613495",
"0.56089056",
"0.56086564",
"0.5605919",
"0.5605919",
"0.55991286",
"0.558... | 0.75901985 | 0 |
some attribute such as state distribution or the true value function are very costly to compute, so they are only evaluated, if really needed | def __getattr__(self, name):
if name == "mu":
self.mu = self.mdp.stationary_distribution(
seed=1000, iterations=100000, policy=self.target_policy)
return self.mu
elif name == "beh_mu":
self.beh_mu = self.mdp.stationary_distribution(
see... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, state):\n abstract",
"def critic_compute_vvalue(dict_states, val_func):\n\n values = val_func.predict(dict_states['states'])\n dict_states['values'] = values",
"def _optimise(self):\n pass",
"def Eval(state):\n\n# H1 = htest2(state)\n# return H1\n ... | [
"0.6278617",
"0.62057346",
"0.6162016",
"0.61589915",
"0.6142543",
"0.6142543",
"0.6127977",
"0.59733987",
"0.5966657",
"0.59584475",
"0.59096265",
"0.5894172",
"0.5893256",
"0.58858305",
"0.58858305",
"0.58840144",
"0.58717525",
"0.583482",
"0.5832933",
"0.5832933",
"0.58298... | 0.0 | -1 |
produce the feature representation of all states of mdp as a vertically stacked matrix, | def Phi(self):
if not hasattr(self, "Phi_"):
Phil = []
for s in self.mdp.states:
if hasattr(self.phi, "expectation"):
f = self.phi.expectation(s)
else:
f = self.phi(s)
Phil.append(f)
Phi =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def columns_state_to_matrix(state):\n m = []\n lits = []\n for key, values in state.items():\n if key != \"parity\":\n m.append(values)\n lits.append(key)\n m += [state[\"parity\"]]\n m = np.array(m).T.tolist()\n return m, lits",
"def getstate(self):\n out = ... | [
"0.61598355",
"0.60036",
"0.5909275",
"0.5774589",
"0.5735121",
"0.5707198",
"0.5678175",
"0.5677035",
"0.56657124",
"0.56510586",
"0.5636868",
"0.55877",
"0.55837166",
"0.55326474",
"0.55307454",
"0.5521124",
"0.5488227",
"0.5486122",
"0.54806095",
"0.5446708",
"0.5425378",
... | 0.0 | -1 |
the bellman operator T(V) = R + gamma P V details see Chapter 3 of Sutton, R. S., Maei, H. R., Precup, D., Bhatnagar, S., Silver, D., | def bellman_operator(self, V, policy="behavior"):
if policy == "behavior":
policy = self.behavior_policy
elif policy == "target":
policy = self.target_policy
if hasattr(self, "R"):
R = self.R
else:
R = self.mdp.P * self.mdp.r * policy.tab[:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RobustBellmanOp(P, Sigma, state, action, gamma):\n BV = 0\n # Here there is a strong assumption that\n # sum(p(s'|(s,a)) * R(s,a,s')) = R(s,a,s') = R(s,a)\n for t in P[state][action]:\n probability = t[0]\n nextstate = t[1]\n cost = t[2]\n done = t[3]\n # BV += ... | [
"0.6993826",
"0.6638062",
"0.6402498",
"0.63068825",
"0.63002235",
"0.6225984",
"0.6225984",
"0.6225984",
"0.6161945",
"0.61532336",
"0.61231595",
"0.60717344",
"0.60529256",
"0.5978915",
"0.5965468",
"0.5915182",
"0.5911414",
"0.58930737",
"0.5885514",
"0.58462185",
"0.58028... | 0.65719485 | 2 |
computes the KL Divergence between the behavioral and target policy while assuming that the steady state distribution is the state distribution of the behavioral policy! | def kl_policy(self):
r = .5 * (np.trace(np.dot(self.behavior_policy.precision, self.target_policy.noise))
- self.behavior_policy.dim_A - np.log(np.linalg.det(self.target_policy.noise) / np.linalg.det(self.behavior_policy.noise)))
dtheta = (self.behavior_policy.theta - self.target_poli... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_policy_network(policy_model, old_policy_model, A_hat, j, state_part_mean, state_part_std, config = PPO_config):\n \n # TODO: How to implement KL early stopping? \n \n learning_rate = np.amax([0.01, 1-j/config['J']]) * config['LR_policy']\n #PPO_loss = compute_surr_objective(A_hat, old_poli... | [
"0.659929",
"0.6538596",
"0.62946427",
"0.6181068",
"0.610246",
"0.6092874",
"0.60754335",
"0.60436636",
"0.6020627",
"0.60204685",
"0.59982336",
"0.59855235",
"0.596544",
"0.59235543",
"0.59141713",
"0.5912177",
"0.5882478",
"0.5874837",
"0.58348036",
"0.58177924",
"0.581293... | 0.64414257 | 2 |
some attribute such as state distribution or the true value function are very costly to compute, so they are only evaluated, if really needed | def __getattr__(self, name):
if name == "mu" or name == "mu_next" or name == "mu_r" or name == "mu_phi" or name == "mu_phi_next":
self.mu, self.mu_r, self.mu_next, self.mu_phi, self.mu_phi_next = mdp.samples_distribution(self.mdp, policy=self.target_policy,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, state):\n abstract",
"def critic_compute_vvalue(dict_states, val_func):\n\n values = val_func.predict(dict_states['states'])\n dict_states['values'] = values",
"def _optimise(self):\n pass",
"def Eval(state):\n\n# H1 = htest2(state)\n# return H1\n ... | [
"0.6278617",
"0.62057346",
"0.6162016",
"0.61589915",
"0.6142543",
"0.6142543",
"0.6127977",
"0.59733987",
"0.5966657",
"0.59584475",
"0.59096265",
"0.5894172",
"0.5893256",
"0.58858305",
"0.58858305",
"0.58840144",
"0.58717525",
"0.583482",
"0.5832933",
"0.5832933",
"0.58298... | 0.0 | -1 |
Mean Squared Bellman Error | def MSPBE_tar(self, theta):
V = np.array((theta * self.mu_phi_tar).sum(axis=1))
V2 = self.gamma * np.array((theta * self.mu_phi_next_tar).sum(axis=1))
return np.mean(np.array((V - np.dot(self.projection_operator(), V2 + self.mu_r_tar))) ** 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def mean_squared_error(self):\n print('Mean squared error regression loss: ' + str(mean_squared_error(self.model.dataset.get_y_test(),\n self.model.get_predicted())))",
"def calc... | [
"0.70723593",
"0.69170547",
"0.69018835",
"0.6720891",
"0.66452754",
"0.65128696",
"0.64996177",
"0.64721024",
"0.64610255",
"0.6454748",
"0.64024305",
"0.638498",
"0.6380651",
"0.63781095",
"0.63774097",
"0.63773036",
"0.6377058",
"0.63701975",
"0.6365091",
"0.6322976",
"0.6... | 0.0 | -1 |
Mean Squared Bellman Error | def MSPBE(self, theta):
V = np.array((theta * self.mu_phi).sum(axis=1))
V2 = self.gamma * np.array((theta * self.mu_phi_next).sum(axis=1))
return np.mean(np.array((V - np.dot(self.projection_operator(), V2 + self.mu_r))) ** 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def mean_squared_error(self):\n print('Mean squared error regression loss: ' + str(mean_squared_error(self.model.dataset.get_y_test(),\n self.model.get_predicted())))",
"def calc... | [
"0.70723593",
"0.69170547",
"0.69018835",
"0.6720891",
"0.66452754",
"0.65128696",
"0.64996177",
"0.64721024",
"0.64610255",
"0.6454748",
"0.64024305",
"0.638498",
"0.6380651",
"0.63781095",
"0.63774097",
"0.63773036",
"0.6377058",
"0.63701975",
"0.6365091",
"0.6322976",
"0.6... | 0.0 | -1 |
Mean Squared Bellman Error | def MSBE_tar(self, theta):
V = np.array((theta * self.mu_phi_tar).sum(axis=1))
V2 = self.gamma * np.array((theta * self.mu_phi_next_tar).sum(axis=1))
return np.mean((V - V2 - self.mu_r_tar) ** 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def mean_squared_error(self):\n print('Mean squared error regression loss: ' + str(mean_squared_error(self.model.dataset.get_y_test(),\n self.model.get_predicted())))",
"def calc... | [
"0.7074351",
"0.69184774",
"0.6903365",
"0.67231107",
"0.66480696",
"0.65150756",
"0.650225",
"0.6472207",
"0.64625514",
"0.6456857",
"0.64040864",
"0.6387255",
"0.6382737",
"0.63803643",
"0.63794786",
"0.63790506",
"0.6377615",
"0.6370702",
"0.63663834",
"0.6324409",
"0.6314... | 0.0 | -1 |
Mean Squared Bellman Error | def MSE(self, theta):
V = np.array((theta * self.mu_phi).sum(axis=1))
return np.mean((V - self.mu_accum_r) ** 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def mean_squared_error(self):\n print('Mean squared error regression loss: ' + str(mean_squared_error(self.model.dataset.get_y_test(),\n self.model.get_predicted())))",
"def calc... | [
"0.70734775",
"0.6917913",
"0.6903449",
"0.67222446",
"0.6645739",
"0.6514049",
"0.6500719",
"0.6472024",
"0.6461973",
"0.64560336",
"0.6403752",
"0.6385571",
"0.63817215",
"0.63791734",
"0.6379055",
"0.6377998",
"0.6377281",
"0.6370479",
"0.6365752",
"0.6324615",
"0.6314102"... | 0.0 | -1 |
Mean Squared Bellman Error | def MSBE(self, theta):
V = np.array((theta * self.mu_phi).sum(axis=1))
V2 = self.gamma * np.array((theta * self.mu_phi_next).sum(axis=1))
return np.mean((V - V2 - self.mu_r) ** 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def mean_squared_error(self):\n print('Mean squared error regression loss: ' + str(mean_squared_error(self.model.dataset.get_y_test(),\n self.model.get_predicted())))",
"def calc... | [
"0.70723593",
"0.69170547",
"0.69018835",
"0.6720891",
"0.66452754",
"0.65128696",
"0.64996177",
"0.64721024",
"0.64610255",
"0.6454748",
"0.64024305",
"0.638498",
"0.6380651",
"0.63781095",
"0.63774097",
"0.63773036",
"0.6377058",
"0.63701975",
"0.6365091",
"0.6322976",
"0.6... | 0.0 | -1 |
some attribute such as state distribution or the true value function are very costly to compute, so they are only evaluated, if really needed | def __getattr__(self, name):
if name == "V_true":
self.V_true = dynamic_prog.estimate_V_LQR(
self.mdp, lambda x, y: self.bellman_operator(
x, y, policy="target"),
gamma=self.gamma)
return self.V_true
elif name == "mu_phi_full":
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, state):\n abstract",
"def critic_compute_vvalue(dict_states, val_func):\n\n values = val_func.predict(dict_states['states'])\n dict_states['values'] = values",
"def _optimise(self):\n pass",
"def Eval(state):\n\n# H1 = htest2(state)\n# return H1\n ... | [
"0.627793",
"0.6207085",
"0.6162446",
"0.61590654",
"0.61424214",
"0.61424214",
"0.61269885",
"0.5973244",
"0.5966853",
"0.5959342",
"0.5910448",
"0.5893535",
"0.5892798",
"0.588487",
"0.5884815",
"0.5884815",
"0.5872024",
"0.5835179",
"0.58326495",
"0.58326495",
"0.5829513",... | 0.0 | -1 |
the bellman operator for the behavioral policy as a python function which takes the value function s^T P s + b represented as a numpy squared array P T(P,b) = R + theta_p^T Q theta_p + gamma (A + B theta_p)^T P (A + B theta_p), gamma (b + tr(P Sigma)) | def bellman_operator(self, P, b, policy="behavior"):
Q = np.matrix(self.mdp.Q)
R = np.matrix(self.mdp.R)
A = np.matrix(self.mdp.A)
B = np.matrix(self.mdp.B)
Sigma = np.matrix(np.diag(self.mdp.Sigma))
if policy == "behavior":
theta = np.matrix(self.behavior_po... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RobustBellmanOp(P, Sigma, state, action, gamma):\n BV = 0\n # Here there is a strong assumption that\n # sum(p(s'|(s,a)) * R(s,a,s')) = R(s,a,s') = R(s,a)\n for t in P[state][action]:\n probability = t[0]\n nextstate = t[1]\n cost = t[2]\n done = t[3]\n # BV += ... | [
"0.71001726",
"0.68565446",
"0.6523547",
"0.6306089",
"0.6291289",
"0.6230942",
"0.61771154",
"0.6148315",
"0.60413855",
"0.5933376",
"0.5930906",
"0.5928049",
"0.5900982",
"0.5886188",
"0.5881746",
"0.58785963",
"0.58734393",
"0.5857429",
"0.58492917",
"0.5840334",
"0.581764... | 0.7670253 | 0 |
Mean Squared Bellman Error | def MSBE(self, theta):
V = np.array((theta * self.mu_phi).sum(axis=1))
theta_trans = features.squared_tri(self.mdp.dim_S).param_forward(
*self.bellman_operator(*self.phi.param_back(theta)))
V2 = np.array((theta_trans * self.mu_phi_full).sum(axis=1))
return np.mean((V - V2) **... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def mean_squared_error(self):\n print('Mean squared error regression loss: ' + str(mean_squared_error(self.model.dataset.get_y_test(),\n self.model.get_predicted())))",
"def calc... | [
"0.70734775",
"0.6917913",
"0.6903449",
"0.67222446",
"0.6645739",
"0.6514049",
"0.6500719",
"0.6472024",
"0.6461973",
"0.64560336",
"0.6403752",
"0.6385571",
"0.63817215",
"0.63791734",
"0.6379055",
"0.6377998",
"0.6377281",
"0.6370479",
"0.6365752",
"0.6324615",
"0.6314102"... | 0.0 | -1 |
Mean Squared Projected Bellman Error | def MSPBE(self, theta):
V = np.matrix((theta * np.asarray(self.mu_phi)).sum(axis=1)).T
theta_trans = features.squared_tri(self.mdp.dim_S).param_forward(
*self.bellman_operator(*self.phi.param_back(theta)))
v = np.asarray(V - self.projection_operator(
) * np.matrix(self.mu_phi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MeanSqError(self):\r\n\t\treturn self.mse",
"def error(b, m, x_data):\n\n totalError = 0\n for i in range(0, len(x_data)):\n x = x_data[i]\n y = 2 * x_data[i] + 50 + 5 * np.random.random()\n\n totalError += (y - (m * x + b)) ** 2 # total error of gradient\n\n return total... | [
"0.6716116",
"0.6491055",
"0.64184654",
"0.63085115",
"0.626515",
"0.62538904",
"0.62059987",
"0.61504513",
"0.6075646",
"0.60743934",
"0.60544276",
"0.60458714",
"0.6041923",
"0.5958656",
"0.5950878",
"0.59448355",
"0.59426767",
"0.5940715",
"0.59404093",
"0.59390706",
"0.59... | 0.0 | -1 |
Return all softmasked regions as a BED3 format string. | def get_masked_regions(contig):
masked_regions = ""
seq = contig.seq
contig_end = len(seq)-1
masked = False
for i, n in enumerate(seq):
# mark the starting position of a softmasked region
if not masked and n.islower():
masked = True
start = i
# mark ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_regions_mask(self, input):",
"def dump(self, contents=False):\n print(self)\n for region in self.regions:\n print(region)\n hexdump(region.data, address=region.address)",
"def read_BEC_region_mask(grid):\n region_mask_file = '/home/ivan/Python/data/BEC_REGION_MASK... | [
"0.59224325",
"0.54149204",
"0.5379897",
"0.53791404",
"0.52565",
"0.5155345",
"0.5125964",
"0.50116074",
"0.5007976",
"0.50038373",
"0.50032556",
"0.49970403",
"0.49952328",
"0.49776202",
"0.494399",
"0.49382335",
"0.4912289",
"0.49092403",
"0.48832148",
"0.48810032",
"0.486... | 0.6836982 | 0 |
Convert a byte string to it's hex string representation e.g. for output. | def ByteToHex( byteStr ):
# Uses list comprehension which is a fractionally faster implementation than
# the alternative, more readable, implementation below
#
# hex = []
# for aChar in byteStr:
# hex.append( "%02X " % ord( aChar ) )
#
# return ''.join( hex ).stri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bytes_to_hex(s):\n\n return s.encode(\"hex\")",
"def hex(string):\n return string.encode('hex')",
"def ByteToHex( byteStr ):\r\n \r\n # Uses list comprehension which is a fractionally faster implementation than\r\n # the alternative, more readable, implementation below\r\n # \r\n # ... | [
"0.83955294",
"0.7957785",
"0.78720164",
"0.7845213",
"0.76932746",
"0.76224846",
"0.757223",
"0.75030285",
"0.74330866",
"0.7401707",
"0.73670447",
"0.73555",
"0.73397434",
"0.733254",
"0.7318627",
"0.7313053",
"0.7272483",
"0.72406524",
"0.72354645",
"0.723096",
"0.7204742"... | 0.7827128 | 4 |
Abstracts the common pattern of allowing both an object or an object's ID (UUID) as a parameter when dealing with relationships. | def getid(obj):
# Try to return the object's UUID first, if we have a UUID.
try:
if obj.uuid:
return obj.uuid
except AttributeError:
pass
try:
return obj.id
except AttributeError:
return obj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_model_relationship_id(\n thing: Union[\"ModelRelationship\", t.ModelRelationshipId, UUID, str]\n) -> t.ModelRelationshipId:\n if isinstance(thing, UUID):\n return t.ModelRelationshipId(thing)\n elif isinstance(thing, ModelRelationship):\n return thing.id\n return t.ModelRelationsh... | [
"0.6406559",
"0.6314725",
"0.61052245",
"0.60958296",
"0.6060378",
"0.5935831",
"0.58920884",
"0.5865537",
"0.5811203",
"0.58053535",
"0.5791913",
"0.5752252",
"0.5696992",
"0.5696992",
"0.5696992",
"0.5696992",
"0.5696992",
"0.5696992",
"0.5696992",
"0.5696992",
"0.5696992",... | 0.6084481 | 4 |
Builds a resource URL for the given kwargs. Given an example collection where `collection_key = 'entities'` and `key = 'entity'`, the following URL's could be generated. | def build_url(self, dict_args_in_out=None):
if dict_args_in_out is None:
dict_args_in_out = {}
url = dict_args_in_out.pop('base_url', None) or ''
url += '/%s' % self.collection_key
# do we have a specific entity?
entity_id = dict_args_in_out.pop('%s_id' % self.key, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_collection_url(self, **kwargs):\n\n url = self._generate_url(url_type='collection', **kwargs)\n return url",
"def _build_uri(self, **kwargs):\n target_uri, version = str(), None\n\n if kwargs.get('category') not in ['performance', 'common']:\n version = self._build_... | [
"0.65982926",
"0.64410657",
"0.6376219",
"0.5949595",
"0.59395957",
"0.5937871",
"0.5891844",
"0.5872246",
"0.5872246",
"0.58385074",
"0.58289504",
"0.5797399",
"0.5791687",
"0.57855177",
"0.57543904",
"0.5751335",
"0.57417595",
"0.573921",
"0.57283247",
"0.5718494",
"0.57029... | 0.7582599 | 0 |
????? Find a single item with attributes matching ``kwargs``. | def find(self, **kwargs):
url = self.build_url(dict_args_in_out=kwargs)
rl = self._list(
'%(url)s%(query)s' % {
'url': url,
'query': '?%s' % urllib.urlencode(kwargs) if kwargs else '',
},
self.collection_key)
num = len(rl)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(cls, **kwargs):\n key = [key for key in kwargs][0]\n objects = cls.get_all()\n if isinstance(objects, dict):\n return objects\n results = []\n for i in objects:\n if is_substring(kwargs[key], getattr(i, key)):\n results.append(i)\n ... | [
"0.64905286",
"0.6422516",
"0.63054526",
"0.6150706",
"0.6042665",
"0.60273176",
"0.60062563",
"0.5960116",
"0.59406686",
"0.5893654",
"0.58933294",
"0.58470035",
"0.5840514",
"0.58035016",
"0.5785908",
"0.57597953",
"0.57567614",
"0.5733581",
"0.57248026",
"0.57049215",
"0.5... | 0.6048774 | 4 |
Test user can get all sales | def test_get_all_sales(self):
self.register_user()
result = self.login_user()
access_token = json.loads(result.data.decode())['token']
response = self.client.get(self.sl_url,
headers=dict(Authorization="Bearer " + access_token))
result = json.loads(response.data.decode())
self.assertEqual(response.stat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_all_sale_records(self):\n reply = self.admin_add_product()\n\n resp = self.admin_create_user()\n reply = self.attendant_login()\n token = reply['token']\n sale = dict(products = [\n {\n \"prod_name\":\"NY_denims\", \n \"quanti... | [
"0.7474644",
"0.73433435",
"0.7226546",
"0.71686345",
"0.68853",
"0.68461",
"0.68375087",
"0.6793282",
"0.67523843",
"0.6588984",
"0.6573936",
"0.6551599",
"0.6486141",
"0.6473017",
"0.64615905",
"0.64156425",
"0.6399153",
"0.63895774",
"0.6347083",
"0.63440025",
"0.63045716"... | 0.8110322 | 0 |
test create a sale record | def test_post_sale_record(self):
self.register_user()
result = self.login_user()
access_token = json.loads(result.data.decode())['token']
res = self.client.post(self.p_url,
data=self.products_data,
headers=dict(Authorization="Bearer " + access_token))
result = json.loads(res.data.decode())
self.asser... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_sale_record(self):\n reply = self.admin_add_product()\n\n resp = self.admin_create_user()\n reply = self.attendant_login()\n token = reply['token']\n sale = dict(products = [\n {\n \"prod_name\":\"NY_denims\", \n \"quantity\":... | [
"0.78964686",
"0.7810856",
"0.7591687",
"0.7530914",
"0.7336629",
"0.70531434",
"0.70490855",
"0.70409393",
"0.6985339",
"0.692475",
"0.69237953",
"0.68040955",
"0.677054",
"0.6726252",
"0.6721466",
"0.67095804",
"0.6703448",
"0.6622187",
"0.66104954",
"0.65952396",
"0.657949... | 0.7876173 | 1 |
test get a specific sale_record by id | def test_get_one_sale_record(self):
self.register_user()
result = self.login_user()
access_token = json.loads(result.data.decode())['token']
response = self.client.post('/api/v1/sales',
data=self.sales_data,
headers=dict(Authorization="Bearer " + access_token))
self.assertEqual(response.status_code,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_specific_sale_record(self):\n \n self.register_admin_test_account()\n token = self.login_admin_test()\n\n response = self.app_test_client.get(\n '{}/saleorder'.format(self.base_url), json={\n 'sale_id': 1,\n 'name': \"Sample Bags\",\n ... | [
"0.8488579",
"0.734553",
"0.706829",
"0.69891703",
"0.68932825",
"0.6712079",
"0.66892236",
"0.6686362",
"0.66299886",
"0.6611321",
"0.6520579",
"0.6505532",
"0.64531493",
"0.6440169",
"0.63657",
"0.63465416",
"0.6296529",
"0.6288324",
"0.62848693",
"0.6208785",
"0.6182311",
... | 0.7370436 | 1 |
test a non existing sale_record cannot be retrieved | def test_get_non_existing_sale_record(self):
self.register_user()
result = self.login_user()
access_token = json.loads(result.data.decode())['token']
response = self.client.get('/api/v1/sale/100',
headers=dict(Authorization="Bearer " + access_token))
self.assertEqual(response.status_code, 404)
result =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_cannot_get_empty_sales(self):\n reply = self.admin_add_product()\n\n resp = self.admin_create_user()\n reply = self.attendant_login()\n token = reply['token']\n\n resp = self.client.get(\n '/api/v1/sales/1',\n headers={'Authorization': 'Bearer {}'.f... | [
"0.67065454",
"0.65563065",
"0.6445439",
"0.6376305",
"0.6364539",
"0.63549346",
"0.6344477",
"0.63023585",
"0.6260622",
"0.62462413",
"0.6177097",
"0.61122227",
"0.6104429",
"0.6096169",
"0.60852754",
"0.6059171",
"0.6040166",
"0.6034094",
"0.6025876",
"0.60226256",
"0.60205... | 0.83915794 | 0 |
test create a sale record with quantity as string | def test_post_sale_record_with_quantity_string(self):
self.register_user()
result = self.login_user()
access_token = json.loads(result.data.decode())['token']
res = self.client.post(self.sl_url,
data=self.string_sales,
headers=dict(Authorization="Bearer " + access_token))
result = json.loads(res.data.d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_add_sale_with_invalid_quantity(self):\n self.register_admin_test_account()\n token = self.login_admin_test()\n \n response = self.app_test_client.post('{}/saleorder'.format(\n self.base_url), json={'name': \"Hand Bag\", 'price': 1500, 'quantity': \"5\", 'totalamt': \... | [
"0.73696387",
"0.7272735",
"0.6952385",
"0.6769718",
"0.6723913",
"0.65521663",
"0.6526882",
"0.6447317",
"0.642388",
"0.6377979",
"0.6343794",
"0.63268614",
"0.628641",
"0.6271526",
"0.6246291",
"0.6237712",
"0.62305063",
"0.6228506",
"0.61977756",
"0.6193409",
"0.6190083",
... | 0.82792795 | 0 |
Reads input.yml file and returns the list for the fixture | def required_packages():
with open(r"required-packages.yml") as file:
inputs = yaml.load(file, Loader=yaml.FullLoader)
return inputs["required_packages"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def input_data(self):\n return read_yaml(self.file_path)",
"def read_input_files(input_file: str) -> list[Food]:\n with open(input_file) as input_fobj:\n foods = [Food.from_raw(line.strip()) for line in input_fobj]\n return foods",
"def _read_file(self, input_file):\n with io.open(in... | [
"0.69811755",
"0.64664984",
"0.6385069",
"0.6166269",
"0.6110681",
"0.59187686",
"0.5884266",
"0.5877623",
"0.584026",
"0.5834622",
"0.58186245",
"0.5811489",
"0.57915",
"0.57771146",
"0.5776398",
"0.57444304",
"0.5741574",
"0.57310414",
"0.572478",
"0.5699813",
"0.56599563",... | 0.0 | -1 |
Reads input.yml and returns a list of optional packages for the fixture | def optional_packages():
with open(r"tests/input.yml") as file:
inputs = yaml.load(file, Loader=yaml.FullLoader)
return inputs["optional_packages"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def python_packages():\n with open(r\"tests/input.yml\") as file:\n inputs = yaml.load(file, Loader=yaml.FullLoader)\n return inputs[\"python_packages\"]",
"def required_packages():\n with open(r\"required-packages.yml\") as file:\n inputs = yaml.load(file, Loader=yaml.FullLoader)\n ret... | [
"0.74960756",
"0.72168744",
"0.6479338",
"0.59237087",
"0.5857561",
"0.5720948",
"0.5557785",
"0.54705644",
"0.54513747",
"0.54507947",
"0.5422149",
"0.5409515",
"0.5387675",
"0.5322175",
"0.5320085",
"0.52968514",
"0.52756894",
"0.5262094",
"0.52328485",
"0.52328485",
"0.522... | 0.82169074 | 0 |
Reads input.yml and returns a list of python related packages | def python_packages():
with open(r"tests/input.yml") as file:
inputs = yaml.load(file, Loader=yaml.FullLoader)
return inputs["python_packages"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def required_packages():\n with open(r\"required-packages.yml\") as file:\n inputs = yaml.load(file, Loader=yaml.FullLoader)\n return inputs[\"required_packages\"]",
"def optional_packages():\n with open(r\"tests/input.yml\") as file:\n inputs = yaml.load(file, Loader=yaml.FullLoader)\n ... | [
"0.7422069",
"0.6971301",
"0.6811929",
"0.6220798",
"0.6169701",
"0.60719687",
"0.5946472",
"0.58931",
"0.58555245",
"0.5743362",
"0.57142967",
"0.5677042",
"0.56702876",
"0.5617658",
"0.5599167",
"0.5591488",
"0.5534712",
"0.5522331",
"0.5520315",
"0.55116594",
"0.55114037",... | 0.83976966 | 0 |
Generates a synthetic dataset of a single neuron with a jittered firing pattern. | def jittered_neuron(t=None, feature=None, n_trial=61, jitter=1.0, gain=0.0, noise=0.05, seed=1234):
# default time base
if t is None:
t = np.linspace(-5, 5, 150)
# default feature
if feature is None:
feature = lambda tau: np.exp(-(t-tau)**2)
# noise matrix
np.random.seed(seed)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_synthetic_noise_dataset(cfg):\n from colorednoise import powerlaw_psd_gaussian\n\n betas = np.linspace(cfg['data.mix_synthetic_noise.min_beta'],\n cfg['data.mix_synthetic_noise.max_beta'],\n num=cfg['data.mix_synthetic_noise.num_samples'])\n sample_... | [
"0.62634385",
"0.59594214",
"0.59539413",
"0.584299",
"0.5822103",
"0.57358575",
"0.57198215",
"0.5702747",
"0.5693655",
"0.5630784",
"0.5625543",
"0.5619661",
"0.561871",
"0.5601612",
"0.5601363",
"0.5598875",
"0.5587372",
"0.5576973",
"0.55743957",
"0.5572822",
"0.5543306",... | 0.5840544 | 4 |
Wraps the core BERT model as a keras.Model. | def get_bert_model(input_word_ids,
input_mask,
input_type_ids,
config=None,
name=None,
float_type=tf.float32,
share_parameter_across_layers=False):
bert_model_layer = BertModel(
config=config, float_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_model(self):\n\n\t\t# Input layers\n\t\tinput_bert_ids = Input(shape=(self.batch_max_sentences, self.bert_wrapper.max_seq_len), name='bert_input_ids', dtype='int32')\n\t\tinput_bert_att = Input(shape=(self.batch_max_sentences, self.bert_wrapper.max_seq_len), name='bert_attention_ids', dtype='int32')\n\t... | [
"0.72094166",
"0.71153945",
"0.6757933",
"0.6557551",
"0.6524431",
"0.65141064",
"0.6411738",
"0.6394894",
"0.6390862",
"0.6388574",
"0.63608235",
"0.6341187",
"0.63348085",
"0.63262266",
"0.6325279",
"0.6321138",
"0.6299965",
"0.6294272",
"0.62942296",
"0.6273413",
"0.627213... | 0.65128464 | 6 |
Create a new Job object. All jobs are created in state Not Submitted. | def __init__(self, pipeline, job_name, threads, stdout_path,
stderr_path, script_path, epilog_path, mem,
email_list, mail_options, env, depends_on, queue, walltime):
self.pipeline = pipeline
self.job_name = job_name
self.threads = threads
self.stdout_pat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self, validated_data):\n return Job.objects.create(**validated_data)",
"def create_job(self, context=None):\n return self._client.call_method(\n 'UserAndJobState.create_job',\n [], self._service_ver, context)",
"def job_create(self, sender, name=None):\n se... | [
"0.7540965",
"0.7418147",
"0.70385385",
"0.69179034",
"0.6895612",
"0.68949133",
"0.68898284",
"0.6832433",
"0.67487454",
"0.658722",
"0.6584338",
"0.6550805",
"0.64806885",
"0.6474937",
"0.64289117",
"0.63990796",
"0.63067824",
"0.630018",
"0.62902296",
"0.6271461",
"0.62333... | 0.0 | -1 |
Scans the database for jobs that are eligible to run; in other words, those with an empty dependency list and the status "Not Submitted". | def scan_for_runnable(limit=None):
logging.debug('Finding runnable jobs')
ready_jobs_query = Session.query(Job).filter(~Job.depends_on.any()). \
filter_by(status_id=Status.NOT_SUBMITTED)
if limit:
ready_jobs_query = ready_jobs_query.limit(limit)
ready_jobs = ready... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_waiting_jobs(self):\n return []",
"def run_pending(self):\n logger.debug(\"available jobs: \" + str(self.jobs))\n runnable_jobs = (job for job in self.jobs if job.should_run())\n logger.debug(\"runnable jobs: \" + str(self.jobs))\n for job in runnable_jobs:\n ... | [
"0.6436594",
"0.64285314",
"0.62542844",
"0.61889505",
"0.6012798",
"0.6004328",
"0.5882026",
"0.5847893",
"0.58347625",
"0.5828717",
"0.5812674",
"0.5812287",
"0.57511747",
"0.5739757",
"0.5721309",
"0.57154495",
"0.57145697",
"0.56779337",
"0.5662949",
"0.56397986",
"0.5629... | 0.6924647 | 0 |
Counts the frequency of double and triple letters in a string. | def count_twos_threes(line):
c = Counter(line).values()
return [2 in c, 3 in c] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def frequencyLetterDic(s):\n pass",
"def frequency(string):\n string=string.lower()\n string=[\"%s%s \" %(string[i],string[i+1]) for i in range(len(string)-1)]\n return Counter(string)",
"def get_letter_counts(str_):\n return dict(Counter(str_))",
"def tally_letters(string):\n output = dict... | [
"0.7622363",
"0.7536927",
"0.7222884",
"0.7205999",
"0.70403767",
"0.6936676",
"0.69063616",
"0.68783313",
"0.6791417",
"0.67859",
"0.66984016",
"0.6663252",
"0.6615753",
"0.6591209",
"0.6547494",
"0.65383995",
"0.6534882",
"0.6507763",
"0.6465973",
"0.6456511",
"0.64520377",... | 0.0 | -1 |
Tally up all the results of the count_tows_threes and multiply them together. | def calculate_checksum(lines):
twos = 0
threes = 0
for box in lines:
counts = count_twos_threes(box)
if counts[0]:
twos += 1
if counts[1]:
threes += 1
return twos * threes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_weighted_results():\n pass",
"def T(w):\n\tif w < 1: return 0 # Less than 1 penny has no possible combinations\n\tif counts.has_key(w): return counts[w] # If already in dict, return that sum\n\n\t# Solve by taking all possible combinations recursively\n\t\n\tval = 0\n\tfor coin in coins:\n\t\tva... | [
"0.5664931",
"0.5544347",
"0.52679944",
"0.5193733",
"0.51744133",
"0.5158381",
"0.5132905",
"0.5086527",
"0.5042575",
"0.5033788",
"0.50311965",
"0.5029944",
"0.502499",
"0.50198984",
"0.49910232",
"0.49615347",
"0.49170852",
"0.49108252",
"0.48988318",
"0.487201",
"0.484676... | 0.0 | -1 |
passed a list of post_ids returns the page content for those posts | def getmfpages(params):
url = 'https://www.metafilter.com/'
page = requests.get(url+params).content | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_contents(\n self, post_ids: List[str], datetime_filter_fn: Optional[Callable[[datetime], bool]] = None\n ) -> List[str]:\n contents = []\n url = f\"http://blog.naver.com/PostView.nhn\"\n params = {\"blogId\": self.naver_id}\n for post_id in post_ids:\n param... | [
"0.7112241",
"0.66041744",
"0.6226108",
"0.6030695",
"0.6012791",
"0.59088683",
"0.5856071",
"0.5844909",
"0.58421594",
"0.58397",
"0.5830324",
"0.5742852",
"0.5716763",
"0.57092553",
"0.5698524",
"0.5687093",
"0.56828797",
"0.56304216",
"0.5629964",
"0.5596571",
"0.55841935"... | 0.0 | -1 |
takes the input from user and gets it ready to extract. | def extractData(coords: str) -> (str, float, float, float, float):
aircraft_type = coords.split(":", 1)
x_coord, y_coord, heading, speed = aircraft_type[1].split(",")
return aircraft_type, float(x_coord), float(y_coord), float(heading), float(speed) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_input(user_input):\n return input(user_input)",
"def parse(self, input):\n pass",
"def _get_input(self):\n return self.__input",
"def _get_input(self):\n return self.__input",
"def _get_input(self):\n return self.__input",
"def _get_input(self):\n return self.__input",
"de... | [
"0.66469276",
"0.66302055",
"0.64704967",
"0.64704967",
"0.64704967",
"0.64704967",
"0.64704967",
"0.64704967",
"0.64704967",
"0.6442752",
"0.6415056",
"0.6387806",
"0.6328002",
"0.6300582",
"0.62887466",
"0.62386596",
"0.6186972",
"0.6157094",
"0.61116576",
"0.60960203",
"0.... | 0.0 | -1 |
Save dataframe on disk | def save_features(df, name, path=None):
if path is not None :
os.makedirs( f"{path}/{name}" , exist_ok=True)
if isinstance(df, pd.Series):
df0=df.to_frame()
else:
df0=df
log( f"{path}/{name}/features.parquet" )
log(df0, list(df0.columns))
df0.to_parque... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_df(data_frame, file_path):\r\n data_frame.to_csv(file_path)\r\n return None",
"def save(df, out_file):\n print('------------< save >------------')\n out_path = './data'\n makedirs(out_path, exist_ok=True)\n print(f'path: {out_path}/{out_file}')\n print(f'shape: {df.shape}')\n df.... | [
"0.8346727",
"0.78104883",
"0.7778965",
"0.77468526",
"0.7705605",
"0.77024066",
"0.7682654",
"0.7468174",
"0.73673975",
"0.73214656",
"0.7293688",
"0.72540563",
"0.7202353",
"0.71031183",
"0.71018416",
"0.70801187",
"0.7077303",
"0.70432234",
"0.7021891",
"0.6997206",
"0.699... | 0.7017662 | 19 |
Used for trainiing only Save params on disk | def preprocess(path_train_X="", path_train_y="", path_pipeline_export="", cols_group=None, n_sample=5000,
preprocess_pars={}, path_features_store=None):
##### column names for feature generation #####################################################
log(cols_group)
coly = cols_group... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_save_parameters(self):\n obj_points = self.get_object_points()\n cam_pos = self.get_camera_position()\n distortion = self.get_distortion_coeeficients()\n\n d = {\n 'object positions': obj_points,\n 'camera positions': cam_pos,\n 'distortion coe... | [
"0.7320969",
"0.7138416",
"0.70646757",
"0.7031888",
"0.70187485",
"0.6993781",
"0.68250567",
"0.6748856",
"0.6748856",
"0.6715416",
"0.66888726",
"0.66842914",
"0.66687477",
"0.66675746",
"0.6647949",
"0.66365165",
"0.66297126",
"0.661736",
"0.65698665",
"0.65528774",
"0.650... | 0.0 | -1 |
At Inference time, load model, params and preprocess data. Not saving the data, only output final dataframe | def preprocess_inference(df, path_pipeline="data/pipeline/pipe_01/", preprocess_pars={}, cols_group=None):
from util_feature import load, load_function_uri, load_dataset
#### Pipeline Execution ####################################################
pipe_default = [
{'uri' : 'source/prepro.py::pd_colnu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n df = prepro_last()\n X, y = train_build(df)\n fit_store(X, y)",
"def main():\n data = load_data()\n analyze_features(data['full_features'])\n model = train(data)\n\n with open('model.pickle', 'wb') as f:\n pickle.dump(model, f)\n evaluate(model, data)",
"def load_mo... | [
"0.64184946",
"0.6337976",
"0.6275767",
"0.62493265",
"0.62413377",
"0.6229349",
"0.6225312",
"0.62025195",
"0.6174834",
"0.61704147",
"0.61677074",
"0.61300826",
"0.6080634",
"0.6062665",
"0.60608184",
"0.6054831",
"0.6052776",
"0.60308474",
"0.60219646",
"0.6020345",
"0.599... | 0.6648521 | 0 |
Only 3 types of thresholding currently implemented | def perform_thresholding(f,M,type):
if type == "largest":
a = np.sort(np.ravel(abs(f)))[::-1] #sort a 1D copy of F in descending order
T = a[M]
y = f*(abs(f) > T)
elif type == "soft":
s = abs(f) - M
s = (s + abs(s))/2
y = np.sign(f)*s
elif type == "hard":
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def global_threshold(img, threshold_method):\n pass",
"def apply_thresholding(x):\n return x > threshold_otsu(x)",
"def thresh_setup():\n pass",
"def threshold(self,thresholdValue):\n # TO DO\n pass",
"def getThreshold(self): # real signature unknown; restored from __doc__\n p... | [
"0.7849862",
"0.7268157",
"0.7268108",
"0.72231966",
"0.7098394",
"0.698424",
"0.6918824",
"0.6797195",
"0.67815495",
"0.6730238",
"0.6698765",
"0.6675851",
"0.66437364",
"0.66252387",
"0.6544369",
"0.6448778",
"0.64323175",
"0.64106774",
"0.6361952",
"0.63429403",
"0.6325623... | 0.6608822 | 14 |
Helper method to load a LookML file from tests/resources and parse it. | def load(filename):
path = Path(__file__).parent / "resources" / filename
with path.open() as file:
return lkml.load(file) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_data_from_file(self, local_lookml_project_path: str) -> dict:\n logger.info(\n \"Parsing data from local LookML file {}\".format(\n self.lookml_file_name_and_path\n )\n )\n with open(\n utils.assemble_path(\n local_lookml_... | [
"0.66835463",
"0.6393878",
"0.6098357",
"0.56062335",
"0.5595313",
"0.5581819",
"0.55224305",
"0.5505772",
"0.54858464",
"0.5482919",
"0.54599816",
"0.5456783",
"0.54460776",
"0.54395825",
"0.54266834",
"0.5425838",
"0.54160774",
"0.5410144",
"0.5384232",
"0.53471184",
"0.533... | 0.63659114 | 2 |
Create a pumping rate well | def __init__(self, name="", description="", time_units="s", len_units="m",
pump_units="m3/s"):
# Set general info
self._type = 1 # pumping well id
self.parameters = {'full': True,
'rw': 1.,
'd': 0.,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(window_type=WindowType.DEFAULT, rate=10):\n pass",
"def create_pumper():\n return _Kalamazoo()",
"def run_loop_measurement(t=0.5, name='test', loops=4, pump_t=180, total_t=600, jump_x=10):\n\n incident_angles = [0.1, 0.4]\n waxs_arc = [20, 0]\n user = \"TP\"\n\n condition = (\n... | [
"0.6111014",
"0.59055704",
"0.5620826",
"0.5561464",
"0.5551125",
"0.5525205",
"0.547904",
"0.5449968",
"0.5444173",
"0.54401493",
"0.5416738",
"0.54025877",
"0.53928125",
"0.538614",
"0.536652",
"0.5296037",
"0.5283478",
"0.5277413",
"0.52544755",
"0.5250356",
"0.5236363",
... | 0.6467801 | 0 |
Add new observation well or piezometer object to the actual pumping well INPUTS x [int, float, list, tuple, ndarray] time vector y [int, float, list, tuple, ndarray] drawodwn vector wtype [int] type of data 0 observation well 1 piezometer name [string] well name that is used as label for plot description [string] well ... | def add_well(self, x=1, y=1, wtype=0, name="New well", description="Added well"):
if wtype == 0:
new_well = ObservationWell(name, description, time_units=self.time_units,
len_units=self.len_units)
elif wtype == 1:
new_well = Piezometer(name,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_figure(picks, curve, active_well): \n w = p.get_well(active_well) ##selects the correct welly.Well object\n picks_df = pd.read_json(picks)\n picks_selected = picks_df[picks_df['UWI'] == active_well.replace(' ', '-')]\n \n # regenerate figure with the new horizontal line\n fig = helper... | [
"0.5584504",
"0.53288746",
"0.52431315",
"0.5236649",
"0.5117322",
"0.5074837",
"0.5043187",
"0.50353146",
"0.5032513",
"0.4923973",
"0.48615775",
"0.48594245",
"0.48498887",
"0.4801942",
"0.47999173",
"0.4759228",
"0.4738073",
"0.47359067",
"0.47266603",
"0.47177938",
"0.471... | 0.78409934 | 0 |
Convert parameters, pumping rate, drawdown and data units given new units INPUTS time_units [string] new time units. If None, actual units are used length_units [string] new length units. If None, actual units are used pump_units [string] new pumping rate units. If None, actual units are used same [bool] if True, pump_... | def convert_units(self, time_units=None, len_units=None, pump_units=None,
same=False):
in_time = self.time_units
# Check new time units
if time_units is None:
time_units = in_time
flag = _units.validate_units(time_units)
if flag == -1:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_units(self, time_units=None, len_units=None):\n in_time = self.time_units\n # Check new time units\n if time_units is None:\n time_units = in_time\n flag = _units.validate_units(time_units)\n if flag == -1:\n raise ValueError('Bad time units inpu... | [
"0.73620564",
"0.6910942",
"0.62991405",
"0.6231261",
"0.5903703",
"0.57218415",
"0.57206535",
"0.5695423",
"0.56943077",
"0.5688793",
"0.56632334",
"0.5642992",
"0.5642229",
"0.560559",
"0.5601575",
"0.55584586",
"0.5550535",
"0.5549772",
"0.5539942",
"0.5536012",
"0.5520892... | 0.81115675 | 0 |
Converts the actual pumping rate units to the equivalent length and time units used in parameters and drawdown | def convert_same_units(self):
# Convert pumping rate data
pump_units = "%s3/%s" % (self.len_units, self.time_units)
flag = _units.validate_units(pump_units)
if flag == 2:
self.pumprate.convert_units(self.time_units, pump_units)
self.pump_units = pump_units | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_units(self,):\n self.UNITS = {'pressure':'Pa',}\n return",
"def update_units(self):\n unit_var_value = self.view.vars['unit'].get()\n if unit_var_value == 'm3ph':\n self.minran_u_label.config(text='m³/h')\n self.maxran_u_label.config(text='m³/h')\n ... | [
"0.68502927",
"0.6652245",
"0.6640804",
"0.65066785",
"0.64253104",
"0.6364545",
"0.6226802",
"0.6216705",
"0.6182806",
"0.61223763",
"0.5989723",
"0.5980712",
"0.5951391",
"0.5941575",
"0.5923968",
"0.5891878",
"0.58807355",
"0.58771986",
"0.5800373",
"0.5793957",
"0.5791693... | 0.6932813 | 0 |
Removes the well object from the associated wells list given the well name or index | def delete_well(self, key):
if type(key) is str:
idx = self.get_well_id(key)
elif type(key) is int:
idx = key
else:
raise TypeError('key must be a string or an integer.')
n = self.well_count()
if 0 <= idx <= n - 1:
raise ValueError(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_all_wells(self):\n self.wells = []",
"def remove(self, index):\n self._checkIndex(index)\n self._updateOwner(index, register=False)\n discard = self._items.pop(index)",
"def removePlayer(self, index):\n\n self.eloList.pop(index)\n self.idList.pop(index)",
... | [
"0.65468025",
"0.6206192",
"0.6196141",
"0.6068812",
"0.60405266",
"0.5954996",
"0.5951935",
"0.5905774",
"0.58772933",
"0.5833229",
"0.5827092",
"0.5823839",
"0.5822445",
"0.5816786",
"0.5801315",
"0.57647634",
"0.5764105",
"0.57499415",
"0.5734532",
"0.572572",
"0.57193094"... | 0.71419835 | 0 |
Removes all the associated observation wells or piezometers | def delete_all_wells(self):
self.wells = [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeIrisToOcc(self):\n\t\tshas = self._getShapes()\n\t\tfor sha in shas:\n\t\t\tif sha.a.iris_Occ.exists:\n\t\t\t\tsha.a.iris_Occ.delete()",
"def clean():\n filter_phase_data()\n combine_phase_data()\n remove_duplicates_phase_data()",
"def clear(self):\n for ob in self.obs:\n o... | [
"0.65370387",
"0.6513907",
"0.6458628",
"0.64248127",
"0.6260866",
"0.62452203",
"0.62053883",
"0.62028664",
"0.6195377",
"0.615413",
"0.60914683",
"0.6066282",
"0.60553503",
"0.6043595",
"0.6036875",
"0.60030484",
"0.59885705",
"0.59833914",
"0.59626335",
"0.59259456",
"0.59... | 0.75973433 | 0 |
Returns a list of plot options with the visible data to be plotted including pumping well, observation well or piezometer drawdown | def get_plot_options(self):
plot_options = []
# Get pumping rate plot options
op = self.pumprate.get_plot_options()
if op['visible']:
plot_options.append(op)
# Get associated data options
for i in range(self.well_count()):
well_options = self.wells... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_plot_options(self):\n plot_options = []\n # Get drawdown plot options\n op = self.drawdown.get_plot_options()\n if op['visible']:\n plot_options.append(op)\n # Get associated data options\n for i in range(self.data_count()):\n op = self.data[i... | [
"0.7651929",
"0.61596566",
"0.59600675",
"0.59015876",
"0.5859506",
"0.57540476",
"0.5697014",
"0.5660056",
"0.55900997",
"0.55315745",
"0.5520085",
"0.55183053",
"0.55124867",
"0.5510683",
"0.5503015",
"0.55007356",
"0.5492204",
"0.547329",
"0.5446899",
"0.5444196",
"0.54159... | 0.81560767 | 0 |
Returns the well id given a well name Only the first well with similar names is returned When well name is not found, 1 is returned | def get_well_id(self, name):
idx = -1
if type(name) is str:
wells_names = self.wells_list()
if name in wells_names:
idx = wells_names.index(name)
return(idx) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getwellid(infile, wellinfo):\r\n m = re.search(\"\\d\", getfilename(infile))\r\n s = re.search(\"\\s\", getfilename(infile))\r\n if m.start() > 3:\r\n wellname = getfilename(infile)[0:m.start()].strip().lower()\r\n else:\r\n wellname = getfilename(infile)[0:s.start()].strip().lower()\... | [
"0.7484223",
"0.6575883",
"0.62805164",
"0.6043915",
"0.60195637",
"0.5838901",
"0.5630826",
"0.55032206",
"0.549321",
"0.54633754",
"0.54303014",
"0.536089",
"0.53045803",
"0.5288866",
"0.5257819",
"0.52421564",
"0.5241637",
"0.5231975",
"0.5201168",
"0.5197424",
"0.51961",
... | 0.7903967 | 0 |
Returns well name using the data index as input If well index does not exist then None is returned | def get_well_name(self, idx):
name = None
if type(idx) is int:
n = self.well_count()
assert 0 <= idx <= n - 1, "Bad well index"
name = self.wells[idx].drawdown.name
return(name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_index_from_well(self, well):\n pass",
"def index_to_wellname(index, num_wells, direction=\"row\"):\n row, column = index_to_row_column(index, num_wells, direction)\n return coordinates_to_wellname((row, column))",
"def get_indexname(self,index):\n if index in self.indexname2index:\n... | [
"0.6899968",
"0.66426885",
"0.6603633",
"0.659633",
"0.63491505",
"0.60702014",
"0.60461676",
"0.591587",
"0.5881933",
"0.579355",
"0.57482564",
"0.5638517",
"0.55967",
"0.55727726",
"0.55493134",
"0.55107397",
"0.54778844",
"0.5453047",
"0.5377641",
"0.5374415",
"0.53504837"... | 0.75987595 | 0 |
Check if the well has a constant pumping rate (True) or if pumping rate varies in time (False) | def is_constant_rate(self):
n1 = self.pumprate.x.size
n2 = self.pumprate.y.size
if n1 == n2:
if n1 == 1:
return(True)
else:
return(False)
else:
raise ValueError('Pumping rate is incorrect, check the assigned values!') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def have_pump(self, pump):\n if pump > MAX_PUMPS:\n return False\n return bool(self.pump_array[pump])",
"def ready_to_measure(self):\n\n constant_tank_ready = self.tanks[CONSTANT_TANK].is_filled\n measuring_tank_ready = (\n self.valves[MEASURING_DRAIN_VALVE].is_c... | [
"0.62926847",
"0.62068665",
"0.6156856",
"0.6154851",
"0.6148754",
"0.6126147",
"0.6086376",
"0.60729843",
"0.60600513",
"0.60233563",
"0.6013671",
"0.59107727",
"0.5866539",
"0.5859206",
"0.5843414",
"0.58411956",
"0.57965446",
"0.57905734",
"0.57867235",
"0.577967",
"0.5741... | 0.6840193 | 0 |
Returns a dictionary with all the data contained by the pumping well (including observation wells). It could be used to save data into a .json file | def to_dict(self):
out_dict = _deepcopy(self.__dict__)
out_dict["pumprate"] = self.pumprate.to_dict()
out_wells = []
for i in range(self.well_count()):
out_wells.append(self.wells[i].to_dict())
out_dict["wells"] = out_wells
return(out_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDictWells(self):\n #Method begins here\n #nx=self.__grid['nx'] #From the geometry in grid\n ny=self.__grid['ny']\n nz=self.__grid['nz']\n minx=self.__grid['ox']\n miny=self.__grid['oy']\n minz=self.__grid['oz']\n rx=self.__grid['dx']\n ry=self... | [
"0.7113749",
"0.60998636",
"0.5969908",
"0.59269804",
"0.5899864",
"0.5834773",
"0.5746034",
"0.57153934",
"0.5714173",
"0.5697139",
"0.56901616",
"0.56225646",
"0.56192833",
"0.56154716",
"0.55966175",
"0.5588612",
"0.5565126",
"0.55407035",
"0.55356914",
"0.5494014",
"0.548... | 0.6850534 | 1 |
Returns a dictionary with drawdown data and well parameters needed for analysis models in chen | def to_model(self):
out_dict = _deepcopy(self.parameters)
out_dict["x"] = self.pumprate.x.copy()
out_dict["y"] = self.pumprate.y.copy()
out_dict["wtype"] = 1
return(out_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def asDict(self) -> dict:\n return {\n \"predominant_occlusion\": self.predominantOcclusion.value,\n \"estimations\": {\n \"chin\": self.chin,\n \"mouth\": self.mouth,\n \"clear\": self.clear,\n \"correct\": self.correct,\n ... | [
"0.587928",
"0.58295727",
"0.581102",
"0.5742519",
"0.5710721",
"0.5695244",
"0.56393194",
"0.56131953",
"0.5612707",
"0.56123364",
"0.56055796",
"0.55478233",
"0.5530112",
"0.5522836",
"0.5512796",
"0.5482065",
"0.54444814",
"0.54272425",
"0.5426051",
"0.54042035",
"0.539659... | 0.0 | -1 |
Updates the pumping well object using an input dictionary | def update(self, new_data):
if type(new_data) is not dict:
raise TypeError("Input parameter must be a dict")
# Update parameters
self._type = new_data.get("_type", self._type)
self.time_units = new_data.get("time_units", self.time_units)
self.len_units = new_data.get(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_dict(new,old):",
"def normal_update(self, game, elfDict):\n self.game = game # update game\n self.my_elves = [elf for elf in elfDict.values() if not elf.elf.already_acted] # update self.my_elves\n self.game = game # update self.game\n self.portals.portals_update(game) #... | [
"0.5895445",
"0.5812442",
"0.577191",
"0.5706581",
"0.56495655",
"0.5521227",
"0.5519335",
"0.5515376",
"0.55042815",
"0.5486805",
"0.54773784",
"0.53881663",
"0.5346425",
"0.5302062",
"0.528914",
"0.5283164",
"0.5267203",
"0.5243199",
"0.5229438",
"0.5223958",
"0.5216763",
... | 0.6680555 | 0 |
Verify well parameters and returns warnings according to possible errors | def validate_parameters(self):
flag = True
warnings = ""
# Check radius
r = self.parameters.get('rw', 0)
if type(r) not in [int, float]:
flag = False
warnings += "Well radius rw must be a float value\n"
else:
if r <= 0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Check(self, parameters):",
"def check_errors(self) -> None:",
"def _check_params(self):\n\t\tstrange_param_helper = False\n\t\tfor param in self.params:\n\t\t\n\t\t\t# It could be that the param encapsulates several values (e.g., \"FLUX_RADIUS(10)\")\n\t\t\t# So we have to dissect this\n\t\t\tmatch = re.co... | [
"0.7046296",
"0.6972314",
"0.69019",
"0.6827856",
"0.67996854",
"0.6766278",
"0.67596585",
"0.6748443",
"0.6729965",
"0.6717157",
"0.66976315",
"0.66686934",
"0.6619799",
"0.65809375",
"0.65628576",
"0.64479285",
"0.6446445",
"0.64453197",
"0.642674",
"0.6337774",
"0.6332849"... | 0.7049662 | 0 |
Returns the number of observation wells associate to pumping well | def well_count(self):
return(len(self.wells)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_wells(self):\n return self.info_wells['well'].nunique()",
"def num_injectors(self):\n injectors = self.info_wells.groupby('well_type').get_group('inj')\n return injectors['well'].nunique()",
"def num_producers(self):\n producers = self.info_wells.groupby('well_type').get_gr... | [
"0.8019491",
"0.7295038",
"0.7184108",
"0.6849336",
"0.6847076",
"0.64381284",
"0.6317566",
"0.6127201",
"0.60816175",
"0.6061327",
"0.6042718",
"0.5989033",
"0.5961877",
"0.59533936",
"0.5948019",
"0.59341747",
"0.5918273",
"0.5909013",
"0.5906111",
"0.5804282",
"0.5790544",... | 0.7780465 | 1 |
Returns a list with the well name given a type of well | def wells_list(self, wtype='all'):
list_names = []
for well_data in self.wells:
if wtype == 'all':
list_names.append(well_data.drawdown.name)
elif wtype == well_data._type - 2:
list_names.append(well_data.drawdown.name)
return(list_names) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_well_name(self, idx):\n name = None\n if type(idx) is int:\n n = self.well_count()\n assert 0 <= idx <= n - 1, \"Bad well index\"\n name = self.wells[idx].drawdown.name\n return(name)",
"def get_well_aliases(self):\n return self.info_wells['wel... | [
"0.6303364",
"0.62997895",
"0.60606045",
"0.60131824",
"0.60059685",
"0.5961164",
"0.5938659",
"0.58209205",
"0.5689827",
"0.5598569",
"0.55082047",
"0.54078776",
"0.53718215",
"0.5231179",
"0.5161112",
"0.51495016",
"0.51280046",
"0.5123762",
"0.5105599",
"0.50994503",
"0.50... | 0.76513284 | 0 |
Create an observation well object | def __init__(self, name="", description="", time_units="s", len_units="m"):
# Set general info
self._type = 2 # observation well id
self.time_units = time_units
self.len_units = len_units
self.parameters = {'full': True, # is full penetrating?
'r': ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_observation(self):",
"def create_observation(self):",
"def create_observation(obs):\n\n obs = {\n \"resourceType\": \"Bundle\",\n \"entry\": [\n {\n \"resource\": {\n \"resourceType\": \"Observation\",\n \"text\": \"Res... | [
"0.7262633",
"0.7262633",
"0.6304292",
"0.6092133",
"0.5869381",
"0.58260345",
"0.5790642",
"0.5662167",
"0.56451553",
"0.5643948",
"0.56339514",
"0.55029154",
"0.55016196",
"0.54881907",
"0.5431089",
"0.5425991",
"0.53834933",
"0.53741235",
"0.53203386",
"0.5315863",
"0.5306... | 0.5767391 | 7 |
Add new data object to the actual observation well or piezometer INPUTS x [int, float, list, tuple, ndarray] time vector y [int, float, list, tuple, ndarray] data vector dtype [int] type of data 1 drawdown 2 drawdown first derivative 3 drawdown second derivative name [string] data name that is used as label for plot de... | def add_data(self, x=1, y=1, dtype=1, name="New data", description="New data"):
assert 1 <= dtype <= 3, "Bad value for data type"
new_data = _Data(dtype, name=name, description=description)
new_data.set_data(x=x, y=y, xunits=self.time_units, yunits=self.len_units)
self.data.append(new_da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addData(self,data,x,y):\n self.nSamples+=1\n if self.nSamples == 1:\n self.indata.append(data)\n (self.ny,self.nx)=data.shape\n self.ny=int(data.shape[0])\n self.x=x\n self.y=y\n self.lx=self.x[-1:][0]\n self.ly=self.y[-... | [
"0.6510986",
"0.62951386",
"0.61421543",
"0.60370123",
"0.60024756",
"0.5935693",
"0.5730617",
"0.57196116",
"0.5718037",
"0.57020605",
"0.5682233",
"0.56609374",
"0.5654391",
"0.5629142",
"0.5609044",
"0.5552659",
"0.5545938",
"0.5531034",
"0.5513014",
"0.5501703",
"0.548691... | 0.7377167 | 0 |
Convert parameters, drawdown and data units given new units INPUTS time_units [string] new time units. If None, actual units are used length_units [string] new length units. If None, actual units are used | def convert_units(self, time_units=None, len_units=None):
in_time = self.time_units
# Check new time units
if time_units is None:
time_units = in_time
flag = _units.validate_units(time_units)
if flag == -1:
raise ValueError('Bad time units input {}'.format... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_units(self, time_units=None, len_units=None, pump_units=None,\n same=False):\n in_time = self.time_units\n # Check new time units\n if time_units is None:\n time_units = in_time\n flag = _units.validate_units(time_units)\n if flag == -1... | [
"0.7270952",
"0.67853004",
"0.6321186",
"0.62948203",
"0.6257909",
"0.62499386",
"0.6237745",
"0.609285",
"0.6059407",
"0.6052014",
"0.60473174",
"0.60461247",
"0.6036106",
"0.60240674",
"0.59835577",
"0.59373957",
"0.58589494",
"0.58529156",
"0.58520365",
"0.584814",
"0.5838... | 0.82226205 | 0 |
Removes the data object from the associated data list given the data name or id | def delete_data(self, key):
if type(key) is str:
idx = self.get_data_id(key)
elif type(key) is int:
idx = key
else:
raise TypeError('key must be a string or a integer.')
n = self.data_count()
if 0 <= idx <= n - 1:
raise ValueError('... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeData(self, data: ghidra.program.model.listing.Data) -> None:\n ...",
"def delete(self, data):\n self.data.remove(data)",
"def remove(self, data_id, idx):\n temp = self.database[data_id]\n del temp[idx]\n self.database[data_id] = temp",
"def _remove_link(self, name... | [
"0.7811379",
"0.7518123",
"0.723232",
"0.7106772",
"0.6698628",
"0.6691075",
"0.66770357",
"0.6628748",
"0.6618853",
"0.6594276",
"0.6585027",
"0.6566789",
"0.6539488",
"0.65005875",
"0.6422972",
"0.6422553",
"0.64187837",
"0.64134914",
"0.64002156",
"0.6397125",
"0.63957363"... | 0.63491714 | 22 |
Returns a list of plot options with the visible data to be plotted including observation well or piezometer drawdown | def get_plot_options(self):
plot_options = []
# Get drawdown plot options
op = self.drawdown.get_plot_options()
if op['visible']:
plot_options.append(op)
# Get associated data options
for i in range(self.data_count()):
op = self.data[i].get_plot_op... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_plot_options(self):\n plot_options = []\n # Get pumping rate plot options\n op = self.pumprate.get_plot_options()\n if op['visible']:\n plot_options.append(op)\n # Get associated data options\n for i in range(self.well_count()):\n well_options... | [
"0.7800204",
"0.60322195",
"0.59715027",
"0.59681576",
"0.58703583",
"0.58673334",
"0.57929796",
"0.5701164",
"0.55820847",
"0.5552981",
"0.5541041",
"0.5540643",
"0.55129695",
"0.5459508",
"0.54499143",
"0.544476",
"0.5441358",
"0.54261726",
"0.5417078",
"0.5395974",
"0.5373... | 0.77666795 | 1 |
Returns a list with the data name given a type of data | def data_list(self, dtype='all'):
list_names = []
for well_data in self.data:
if dtype == 'all':
list_names.append(well_data.name)
elif dtype == well_data.dtype:
list_names.append(well_data.name)
return(list_names) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDataTypes(self, name: unicode) -> List[ghidra.program.model.data.DataType]:\n ...",
"def get_text_data_list(self):\n return [self.name, str(self.type)]",
"def list_of_identifiers(data, type):\n identifiers = []\n for id_data in data:\n if id_data.get('type') == type:\n ... | [
"0.728314",
"0.6950621",
"0.67950463",
"0.64226145",
"0.6325345",
"0.6290614",
"0.6279772",
"0.6163454",
"0.61411387",
"0.6140158",
"0.6051041",
"0.6050611",
"0.603196",
"0.5979531",
"0.59499097",
"0.5932981",
"0.5897951",
"0.5894344",
"0.58363205",
"0.5806849",
"0.57793367",... | 0.7135787 | 1 |
Returns the number of data associated to the well or piezometer | def data_count(self):
return(len(self.data)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_data(self):\n try:\n ndata = len(self.x)\n logger.info(\"Number of data points: {0}\".format(ndata))\n except AttributeError:\n logger.error(\"Data object has not been defined\")\n ndata = 0\n return ndata",
"def get_number_of_measurement... | [
"0.71673447",
"0.67605793",
"0.6595559",
"0.6514471",
"0.6508083",
"0.6439929",
"0.63984495",
"0.63630575",
"0.6320137",
"0.6320137",
"0.6320137",
"0.6320137",
"0.63045174",
"0.6284571",
"0.62344813",
"0.61748326",
"0.6155655",
"0.61516064",
"0.6130794",
"0.61301106",
"0.6126... | 0.65397066 | 3 |
Returns the data object giving the data name or index | def get_data(self, key):
if type(key) is str:
idx = self.get_data_id(key)
elif type(key) is int:
idx = key
else:
raise TypeError('key must be a string or a integer.')
n = self.data_count()
if 0 > idx or idx > n - 1:
raise ValueError... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, index):\n\n if self._data_indices is not None:\n index = self._data_indices[index]\n data = self._dataset[index]\n return data",
"def __getitem__(self, name):\n if name in self.data: return self.data[name]",
"def get_at_index(self, index: int) -> obj... | [
"0.7211367",
"0.6997411",
"0.69773567",
"0.697084",
"0.6944017",
"0.6932011",
"0.6911341",
"0.6911341",
"0.6850977",
"0.68421745",
"0.67610013",
"0.6677398",
"0.6553262",
"0.6523296",
"0.65120715",
"0.64988226",
"0.6473112",
"0.64687705",
"0.64374113",
"0.6436611",
"0.6431836... | 0.6006657 | 54 |
Returns the data id in the list of data using the data name Only the first data with similar names is returned When data name is not found, 1 is returned | def get_data_id(self, name):
idx = -1
if type(name) is str:
data_names = self.data_list()
if name in data_names:
idx = data_names.index(name)
return(idx) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _data_id_from_label(self, label):\n for data_item in self.state.data_items:\n if data_item['name'] == label:\n return data_item['id']",
"def search(self, name):\n\n name = name.lower().strip()\n exact_names = get_close_matches(name, self.possible_names, n=1)\n ... | [
"0.6605792",
"0.6598897",
"0.6358529",
"0.6218891",
"0.6171888",
"0.5997316",
"0.59925854",
"0.5937047",
"0.5915923",
"0.5889121",
"0.58376426",
"0.5834831",
"0.57998013",
"0.57785743",
"0.5744238",
"0.569555",
"0.5694921",
"0.56620944",
"0.5656047",
"0.56512594",
"0.56259936... | 0.78273237 | 0 |
Returns data name using the data index as input If data idx does not exist then None is returned | def get_data_name(self, idx):
name = None
if type(idx) is int:
n = self.data_count()
assert 0 <= idx <= n - 1, "Bad data index"
name = self.data[idx].name
return(name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_indexed_component_data_name(component, index):\n if not component.is_indexed():\n return component.name\n elif index in component._data:\n ans = component._data[index].name\n else:\n for i in range(5):\n try:\n component._data[index] = component._Com... | [
"0.72503287",
"0.68155634",
"0.65541536",
"0.650533",
"0.6259094",
"0.62368953",
"0.60989296",
"0.60197777",
"0.59163827",
"0.5891898",
"0.5812209",
"0.57695496",
"0.57461333",
"0.5738705",
"0.5722598",
"0.57208043",
"0.56727195",
"0.56393695",
"0.5613669",
"0.56084156",
"0.5... | 0.8564489 | 0 |
Returns the data type given the data index | def get_data_type(self, idx):
return(self.data[idx].dtype) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_type():\n return DataTypeUtil.getDTypeForName(DataTypeUtil.getDtypeFromContext())",
"def data_type(self) -> int:\n return self.data[\"args\"][\"dataType\"]",
"def datatype(self):\n # datatype is type of first dataarg\n return self[self.dataargs()[0]].typename",
"def data_type... | [
"0.72349155",
"0.7209514",
"0.7037685",
"0.69469965",
"0.6839629",
"0.6827165",
"0.6827165",
"0.6827165",
"0.6680594",
"0.6609436",
"0.6554991",
"0.6540169",
"0.6508893",
"0.6493248",
"0.6450711",
"0.6441724",
"0.63943344",
"0.6353002",
"0.63170236",
"0.63162494",
"0.6316209"... | 0.8298103 | 0 |
Returns a dictionary with well parameters | def get_parameters(self):
return(_deepcopy(self.parameters)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_params(self):\n return {}",
"def parameters(self):\n return {\"W\": self.W,\n \"T\": self.T,\n \"P\": self.P,\n \"Wo\": self.Wo,\n \"To\": self.To,\n \"Po\": self.Po}",
"def get_params(self):\n return {\"d\": \"... | [
"0.7157849",
"0.71448296",
"0.70548433",
"0.69977343",
"0.69741374",
"0.6912348",
"0.6773086",
"0.6764622",
"0.67219996",
"0.6715262",
"0.6695996",
"0.66917676",
"0.6640885",
"0.6640533",
"0.6637852",
"0.66346395",
"0.65994066",
"0.65687644",
"0.6556268",
"0.65546185",
"0.652... | 0.0 | -1 |
Delete all the associate data | def reset_data(self):
self.data = [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_all(self):\n raise NotImplementedError()",
"def delete_all(self):\n with self.__lock:\n self.__data = dict()\n self.flush()",
"def deleteAll(self):\n self.deleteAttributeRange() #Default args = everything",
"def clear_data():\n logger.info(\"Delete Str... | [
"0.78988016",
"0.77220124",
"0.770756",
"0.7586882",
"0.7411554",
"0.73536557",
"0.7343204",
"0.7230482",
"0.71741986",
"0.71688086",
"0.714042",
"0.7107411",
"0.71067667",
"0.70971084",
"0.70971084",
"0.70941174",
"0.70929646",
"0.7079958",
"0.70230395",
"0.69778144",
"0.696... | 0.0 | -1 |
Replaces the well drawdown given the index of a drawdown data in data list | def set_as_drawdown(self, idx):
dtype = self.get_data_type(idx)
if dtype == "Pumping rate":
x, y = self.data[idx].get_data()
self.drawdown.set_data(x, y)
else:
raise TypeError('Selected data is not drawdown!') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_from_drawn(section: str, index: int):\r\n del drawn[section][index]",
"def activateChildWellIndex(self,index,value):\n activeIndices=self.activeChildWellIndices()\n if value and index not in activeIndices:\n activeIndices.append(index)\n activeIndices=sorted(acti... | [
"0.5802891",
"0.53335387",
"0.53131515",
"0.52770084",
"0.52692586",
"0.51205266",
"0.50659335",
"0.49722874",
"0.49578884",
"0.4950951",
"0.48292145",
"0.48174497",
"0.48127028",
"0.47716764",
"0.47540691",
"0.47284764",
"0.47264978",
"0.46799114",
"0.4676532",
"0.46628883",
... | 0.5534069 | 1 |
Set well or piezometer parameters | def set_parameters(self, full=None, r=None, l=None, d=None, z=None):
original = _deepcopy(self.parameters) # save in case of error
if type(full) is bool:
self.parameters["full"] = full
if type(r) in [int, float]:
self.parameters["r"] = float(r)
if self._type ==... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setParameters(self):\n\n # Set the parameters\n self.taux = 24.2\n self.mu = 0.23\n self.G = 33.75\n self.alpha_0 = 0.05\n self.delta = 0.0075\n self.p = 0.50\n self.I0 = 9500.0\n self.kparam = 0.55",
"def set_parameters(self,params):\n K3... | [
"0.66596335",
"0.652628",
"0.6494346",
"0.6468288",
"0.6423567",
"0.640161",
"0.63715726",
"0.63502043",
"0.6313164",
"0.6293706",
"0.6262655",
"0.62223566",
"0.61831945",
"0.61662924",
"0.61540055",
"0.613113",
"0.6102963",
"0.61024153",
"0.609081",
"0.60681343",
"0.60625523... | 0.6427534 | 4 |
Returns a list of dictionaries containing all the data in the well that can be used to storage the data as json format | def to_dict(self):
out_dict = _deepcopy(self.__dict__)
out_dict["drawdown"] = self.drawdown.to_dict()
out_data = []
for i in range(self.data_count()):
out_data.append(self.data[i].to_dict())
out_dict["data"] = out_data
return(out_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_datalist_fr_json(self):\n raw_data = json.load(open(self.saved_json_file, 'r'))\n for indivdual_set in raw_data['query']['results']['stats']:\n temp_dict_data = {}\n if type(indivdual_set) == str:\n #for single data\n continue # temp do no... | [
"0.7234665",
"0.714661",
"0.68842065",
"0.68358326",
"0.66206723",
"0.6602693",
"0.6447181",
"0.64074695",
"0.64045924",
"0.63781536",
"0.6338094",
"0.6336256",
"0.63291097",
"0.6313858",
"0.62933797",
"0.62647957",
"0.6225904",
"0.62003684",
"0.6192421",
"0.61876845",
"0.618... | 0.5839698 | 73 |
Returns a dictionary with drawdown data and well parameters needed for analysis models in chen | def to_model(self):
out_dict = _deepcopy(self.parameters)
out_dict["x"] = self.drawdown.x.copy()
out_dict["y"] = self.drawdown.y.copy()
if self._type == 2:
out_dict["wtype"] = 2
elif self._type == 3:
out_dict["wtype"] = 3
return(out_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def asDict(self) -> dict:\n return {\n \"predominant_occlusion\": self.predominantOcclusion.value,\n \"estimations\": {\n \"chin\": self.chin,\n \"mouth\": self.mouth,\n \"clear\": self.clear,\n \"correct\": self.correct,\n ... | [
"0.58797055",
"0.58303946",
"0.5813848",
"0.5742892",
"0.57119375",
"0.5697148",
"0.5641197",
"0.5615532",
"0.5615115",
"0.561501",
"0.55502295",
"0.5531489",
"0.55249536",
"0.5515042",
"0.5483852",
"0.54467213",
"0.54290205",
"0.54283315",
"0.54066515",
"0.5398945",
"0.53961... | 0.56081396 | 10 |
Updates the well or piezometer object using an input dictionary | def update(self, new_data):
if type(new_data) is not dict:
raise TypeError("Input parameter must be a dict")
# Update parameters
self._type = new_data.get("_type", self._type)
self.time_units = new_data.get("time_units", self.time_units)
self.len_units = new_data.get(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self,d:dict):\n for name,(value,n) in d.items():\n if n==0:\n continue\n self.meters[name].update(value,n)",
"def update(self,dict):\n for key in list(dict.keys()):\n print(\"Hey, I'm updating something\")\n self.d[key]=dict[key]... | [
"0.6955226",
"0.63827276",
"0.63701075",
"0.62050605",
"0.60651165",
"0.601024",
"0.5964989",
"0.5824813",
"0.5740105",
"0.5740105",
"0.571362",
"0.57038397",
"0.5703277",
"0.5690079",
"0.568872",
"0.5649773",
"0.5644877",
"0.5637672",
"0.5637672",
"0.5636201",
"0.56254166",
... | 0.61816335 | 4 |
Verify well parameters and returns warnings according to possible errors | def validate_parameters(self):
flag = True
warnings = ""
# Check radius
r = self.parameters.get('r', 0)
if type(r) not in [int, float]:
flag = False
warnings += "Radius r must be a float value\n"
else:
if r <= 0:
flag =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_parameters(self):\n\n flag = True\n warnings = \"\"\n # Check radius\n r = self.parameters.get('rw', 0)\n if type(r) not in [int, float]:\n flag = False\n warnings += \"Well radius rw must be a float value\\n\"\n else:\n if r <... | [
"0.7049662",
"0.7046296",
"0.6972314",
"0.69019",
"0.67996854",
"0.6766278",
"0.67596585",
"0.6748443",
"0.6729965",
"0.6717157",
"0.66976315",
"0.66686934",
"0.6619799",
"0.65809375",
"0.65628576",
"0.64479285",
"0.6446445",
"0.64453197",
"0.642674",
"0.6337774",
"0.6332849"... | 0.6827856 | 4 |
Create a piezometer object (works similar to Observation well) | def __init__(self, name="", description="", time_units="s", len_units="m"):
super(Piezometer, self).__init__()
# Set general info
self._type = 3 # piezometer id
self.parameters = {'full': True, # is full penetrating?
'r': 1., # distance until pumping well i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lick_piezo():\n lp_raw = read_npy_file('lickPiezo.raw.npy')\n lp_timestamps = read_npy_file('lickPiezo.timestamps.npy')\n lp_rate = get_rate(lp_timestamps)\n lick_piezo_ts = TimeSeries(\n name='lickPiezo',\n starting_time=lp_timestamps[0, 1],\n rate=lp_rate,\n data=np.ra... | [
"0.5920001",
"0.57205135",
"0.5660947",
"0.5660947",
"0.5660947",
"0.5660947",
"0.5660947",
"0.56580734",
"0.5646192",
"0.5608879",
"0.56043893",
"0.55835843",
"0.55835843",
"0.55835843",
"0.55835843",
"0.55835843",
"0.55835843",
"0.55835843",
"0.55545354",
"0.5550657",
"0.55... | 0.6613623 | 0 |
method to validate my input | def validate_empty(self):
result = ""
if(not re.search("[a-zA-Z0-9]", self.title) or not
re.search("^(\s|\S)*(\S)+(\s|\S)*$", self.body)):
result = "INCORRECT INPUT, YOU CAN'T SUBMIT EMPTY FIELD OR FIRST CHARACTER SHOULD BE ALPHA NUMERIC"
else:
result = True
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate():",
"def validate_input(self, *args):\n return",
"def isInputValid(self, input):\r\n pass",
"def check_validity(self):",
"def __validate():\n # TODO: implement",
"def validate(self):",
"def validate(self):",
"def _validate_input(self):\n self.data.validate()\n ... | [
"0.8256197",
"0.8026426",
"0.7914555",
"0.7372203",
"0.7364157",
"0.73189014",
"0.73189014",
"0.71831155",
"0.71817976",
"0.7078171",
"0.7075113",
"0.7054536",
"0.69421256",
"0.69395375",
"0.69082284",
"0.69082284",
"0.69082284",
"0.69082284",
"0.69082284",
"0.69082284",
"0.6... | 0.0 | -1 |
Connects to postgres db | def connect_to_postgres():
try:
conn = psycopg2.connect(database=os.environ['POSTGRES_DATABASE'], user=os.environ['POSTGRES_USER'],
password=os.environ['POSTGRES_PASSWORD'], host=os.environ['POSTGRES_HOST'])
return conn
except psycopg2.Error as err:
loggin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_to_postgres():\n postgres_creds = os.environ.get(\"POSTGRES_CREDS\")\n if not postgres_creds:\n postgres_creds = get_secret(\"POSTGRES_CREDS\")\n print(\"Using {} Postgres database credentials\".format(postgres_creds))\n postgres_url = os.environ.get(\"POSTGRES\")\n if not postgre... | [
"0.8593803",
"0.8553654",
"0.8398151",
"0.83853227",
"0.82916325",
"0.82193106",
"0.8194222",
"0.8186864",
"0.8180956",
"0.81787014",
"0.81350166",
"0.8125547",
"0.8113675",
"0.8113675",
"0.8113675",
"0.8113675",
"0.8113675",
"0.8113675",
"0.8113675",
"0.8113675",
"0.8113675"... | 0.80444777 | 24 |
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